Editor’s Choice Articles

The fabrication of robots and their embedded systems is challenging due to the complexity of the interacting components. The integration of additive manufacturing (AM) to robotics has made advancements in robotics manufacturing through sophisticated and state-of-the-art AM technologies and materials. With the emergence of 3D printing, 3D printing materials are also being considered and engineered for specific applications. This study reviews different 3D printing materials for 3D printing embedded robotics. Materials such as polyethylene glycol diacrylate (PEGDA), acrylonitrile butadiene styrene (ABS), flexible photopolymers, silicone, and elastomer-based materials were found to be the most used 3D printing materials due to their suitability for robotic applications. This review paper revealed that the key areas requiring more research are material formulations for improved mechanical properties, cost, and the inclusion of materials for specific applications. Future perspectives are also provided. Full article

The durability of a platinum catalyst in a polymer electrolyte membrane fuel cell is studied at various operating conditions with respect to the different electric potential difference (called voltage) applied in accelerated stress tests. The electrochemical reactions of Pt ion dissolution and Pt oxide coverage of the catalyst lead to the degradation of platinum described by a one-dimensional Holby–Morgan model. The theoretical study of the underlying reaction–diffusion system with the nonlinear reactions is presented by numerical simulations which allow to predict a lifetime of the catalyst under applied voltage cycling. The computer simulation investigates how the Pt mass loss depends on the voltage slope and the upper potential level in cycles. Full article

A robotic technology consisting of an industrial robot mounted on an autonomous rover used to tap slash pine trees and collect their oleoresin for processing is introduced, and the technological challenges related to the robotic operations are discussed in detail. Unlike the case of industrial automated manufacturing systems where the relative position between the tool and workpiece can be controlled within a few hundredths of a millimeter accuracy, when used in highly unstructured environments characteristic to forestry or agriculture, the positioning accuracy between the industrial robot and the target on which it operates can be much lower than the accuracy required for the operation of the industrial robot. The paper focuses on presenting the robotic operations necessary for drilling three converging boreholes in the pine tree, spraying the boreholes with chemicals, inserting a plastic tube with pre-attached collection bag in one borehole and inserting two plugs in other two boreholes. The challenges related to performing these robotic operations in conditions of large variations in the actual shape of the pine tree trunk and variations in the relative position between the robot and the pine tree after the autonomous vehicle positions itself in front of the tree are presented. The technical solutions used to address these challenges are also described. The strategies used to programmatically adjust the robot toolpath based on detection of the borehole entry points and on the measurement of the insertion force are presented. Full article

To realize precision agriculture at multiple locations in the field, a low-cost measurement system should be developed for easy collection of hydrometeorological data, such as temperature, moisture, and light. In this study, a compact and low-cost hydrometeorological measurement system with a simplified wire code, which is customizable according to the purpose of observation, was built using a circuit board that connects Arduino to the sensors, which was then implemented and analyzed. The developed system measures air and soil temperatures, soil water content, and photosynthetic photon flux density using a sensor connected to Arduino Uno and saves the continuous, high-temporal-resolution output to an SD card. The results obtained from continuous measurement showed that the data collected using this system was significantly better than those collected using commercially available equipment. Anyone can easily measure the weather environments by using this fully open, highly versatile, portable, and user-friendly system. This system can contribute to the growth and expansion of precision agriculture, field management, development of crop models, and laborsaving. It can also provide a global solution to ongoing agricultural issues and improve the efficiency of farming operations, particularly in developing and low-income countries. Full article

Over the last decade, manufacturing processes have undergone significant change. Most factory activities have been transformed through a set of features built into a smart manufacturing framework. The tools brought to bear by the fourth industrial revolution are critical enablers of such change and progress. This review article describes the series of industrial revolutions and explores traditional manufacturing before presenting various enabling technologies. Insights are offered regarding traditional manufacturing lines where some enabling technologies have been included. The manufacturing supply chain is envisaged as enhancing the enabling technologies of Industry 4.0 through their integration. A systematic literature review is undertaken to evaluate each enabling technology and the manufacturing supply chain and to provide some theoretical synthesis. Similarly, obstacles are listed that must be overcome before a complete shift to smart manufacturing is possible. A brief discussion maps out how the fourth industrial revolution has led to novel manufacturing technologies. Likewise, a review of the fifth industrial revolution is given, and the justification for this development is presented. Full article

Consideration of heat treatment in simulations of structural components and its impact on predictions of behaviour during operation is analysed here. An automotive machine element with a complex geometry and dynamic load is analysed rather than a standard laboratory specimen under controlled conditions. The heat treatment analysis of a starter clutch barrel has been performed in DANTE followed by a structural analysis in ANSYS 2019 R3 during operation simulating a load cycle due to the start of an internal combustion engine. The heat treatment simulation consisted of carburisation, quenching and tempering. First, the carbon content and its distribution have been simulated. Next, the hardness of the starter clutch barrel and its distribution have been analysed with respect to the carbon distribution and hardness-dependent material properties of the AISI/SAE 4142 steel. Finally, the stress field after the heat treatment and during the operation of the starter clutch barrel has been thoroughly evaluated and compared to the simulation without the consideration of the heat treatment. Results of the simulation show that the heat treatment introduces favourable compressive stresses at the critical location of the starter clutch barrel and reduces the effective amplitude of the equivalent stress during the operation. Furthermore, the results of the simulation prove that heat treatment should be considered already during the early stages of the R & D process as it can have a decisive effect on the operational behaviour of the structural component. Moreover, a non-consideration of the heat treatment can lead into erroneous conclusions regarding the suitability of machine elements. Full article

In recent times, an alternative synthesis pathway involving severe plastic deformation for Mg-based materials has been explored involving the generation of turnings according to a set of machining parameters and cold compaction into billets followed by hot extrusion. This is known as the turning induced deformation (TID) method and has shown potential to alter the properties of resulting Mg-based materials for the better, not to mention economic benefits arising from this processing method. This work summarizes exploratory efforts involving this method for synthesis of Mg-based materials. The TID method resulted in overall superior properties compared to conventional processing methods, while two distinct parameters (high depth of cut and low cutting speed) were found to have significant positive influence on the final material properties, and as such are considered to be suitable basis on which further exploratory work in this field may be conducted. Full article

Three-dimensional (3D) printing in soil science is relatively rare but offers promising directions for research. Having 3D-printed soil samples will help academics and researchers conduct experiments in a reproducible and participatory research network and gain a better understanding of the studied soil parameters. One of the most important challenges in utilizing 3D printing techniques for soil modeling is the manufacturing of a soil structure. Until now, the most widespread method for printing porous soil structures is based on scanning a real sample via X-ray tomography. The aim of this paper is to design a porous soil structure based on mathematical models rather than on samples themselves. This can allow soil scientists to design and parameterize their samples according to their desired experiments. An open-source toolchain is developed using a Lua script, in the IceSL slicer, with graphical user interface to enable researchers to create and configure their digital soil models, called monoliths, without using meshing algorithms or STL files which reduce the resolution of the model. Examples of monoliths are 3D-printed in polylactic acid using fused filament fabrication technology with a layer thickness of 0.20, 0.12, and 0.08 mm. The images generated from the digital model slicing are analyzed using open-source ImageJ software to obtain information about internal geometrical shape, porosity, tortuosity, grain size distribution, and hydraulic conductivities. The results show that the developed script enables designing reproducible numerical models that imitate soil structures with defined pore and grain sizes in a range between coarse sand (from 1 mm diameter) to fine gravel (up to 12 mm diameter). Full article

Myoelectric signals can be used to control prostheses or exoskeletons as well as robots, i.e., devices assisting the user or replacing a missing part of the body. A typical application of myoelectric prostheses is the human hand. Here, the development of a low-cost myoelectric thumb is described, which can either be used as an additional finger or as prosthesis. Combining 3D printing with inexpensive sensors, electrodes, and electronics, the recent project offers the possibility to produce an individualized myoelectric thumb at significantly lower costs than commercial myoelectric prostheses. Alternatively, a second thumb may be supportive for people with special manual tasks. These possibilities are discussed together with disadvantages of a second thumb and drawbacks of the low-cost solution in terms of mechanical properties and wearing comfort. The study shows that a low-cost customized myoelectric thumb can be produced in this way, but further research on controlling the thumb as well as improving motorization are necessarily to make it fully usable for daily tasks. Full article

The pressure pain threshold (PPT) is a useful tool for evaluating mechanical sensitivity in individuals suffering from various musculoskeletal disorders. The aim of this study is to investigate PPT at the heel area in order to assist in the design of orthotic shoes for sufferers of heel pain due to a calcaneal spur. The size and location of the calcaneal spur was determined by x-ray images, with PPT data measured around the spur at five points by using algometer FDIX 25. The pain test experiment was conducted by pressing each point to obtain the pain minimum compressive pressure (PMCP) and its location. The information of shoe size, spur location and dimensions, and the PMCP location for each individual is used to obtain the exact point location for applying a softer material to the shoe in-sole, in order to reduce heel pain. The results are significant as it can be used by designers to design appropriate shoe in-soles for individuals suffering from heel pain. Full article

Additive manufacturing, commonly known as 3D printing, is an advancement over traditional formative manufacturing methods. It can increase efficiency in manufacturing operations highlighting advantages such as rapid prototyping, reduction of waste, reduction of manufacturing time and cost, and increased flexibility in a production setting. The additive manufacturing (AM) process consists of five steps: (1) preparation of 3D models for printing (designing the part/object), (2) conversion to STL file, (3) slicing and setting of 3D printing parameters, (4) actual printing, and (5) finishing/post-processing methods. Very often, the 3D printed part is sufficient by itself without further post-printing processing. However, many applications still require some forms of post-processing, especially those for industrial applications. This review focuses on the importance of different finishing/post-processing methods for 3D-printed polymers. Different 3D printing technologies and materials are considered in presenting the authors’ perspective. The advantages and disadvantages of using these methods are also discussed together with the cost and time in doing the post-processing activities. Lastly, this review also includes discussions on the enhancement of properties such as electrical, mechanical, and chemical, and other characteristics such as geometrical precision, durability, surface properties, and aesthetic value with post-printing processing. Future perspectives is also provided towards the end of this review. Full article

Electrical impedance tomography is a low-cost, safe, and high temporal resolution medical imaging modality which finds extensive application in real-time thoracic impedance imaging. Thoracic impedance changes can reveal important information about the physiological condition of patients’ lungs. In this way, electrical impedance tomography can be a valuable tool for monitoring patients. However, this technique is very sensitive to measurement noise or possible minor signal errors, coming from either the hardware, the electrodes, or even particular biological signals. Thus, the design of a good performance electrical impedance tomography hardware setup which properly interacts with the tissue examined is both an essential and a challenging concept. In this paper, we adopt an extensive simulation approach, which combines the system’s analogue and digital hardware, along with equivalent circuits of 3D finite element models that represent thoracic cavities. Each thoracic finite element model is created in MATLAB based on existing CT images, while the tissues’ conductivity and permittivity values for a selected frequency are acquired from a database using Python. The model is transferred to a multiport RLC network, embedded in the system’s hardware which is simulated at LT SPICE. The voltage output data are transferred to MATLAB where the electrical impedance tomography signal sampling and digital processing is also simulated. Finally, image reconstructions are performed in MATLAB, using the EIDORS library tool and considering the signal noise levels and different electrode and signal sampling configurations (ADC bits, sampling frequency, number of taps). Full article

Congenital heart disease, the most frequent malformation at birth, is usually not fatal but leads to multiple hospitalisations and outpatient visits, with negative impact on the quality of life and psychological profile not only of children but also of their families. In this paper, we describe the entire architecture of a system for remotely monitoring paediatric/neonatal patients with congenital heart disease, with the final aim of improving quality of life of the whole family and reducing hospital admissions. The interesting vital parameters for the disease are ECG, heart rate, oxygen saturation, body temperature and body weight. They are collected at home using some biomedical sensors specifically selected and calibrated for the paediatric field. These data are then sent to the smart hub, which proceeds with the synchronisation to the remote e-Health care center. Here, the doctors can log and evaluate the patient’s parameters. Preliminary results underline the sensor suitability for children and infants and good usability and data management of the smart-hub technology (E@syCare). In the clinical trial, some patients from the U.O.C. Paediatric and Adult Congenital Cardiology- Monasterio Foundation are enrolled. They receive a home monitoring kit according to the group they belong to. The trial aims to evaluate the effects of the system on quality of life. Psychological data are collected through questionnaires filled in by parents/caregivers in self-administration via the gateway at the beginning and at the end of the study. Results highlight an overall improvement in well-being and sleep quality, with a consequent reduction in anxious and stressful situations during daily life thanks to telemonitoring. At the same time, users reported a good level of usability, ease of data transmission and management of the devices. Full article

Heart disease, one of the main reasons behind the high mortality rate around the world, requires a sophisticated and expensive diagnosis process. In the recent past, much literature has demonstrated machine learning approaches as an opportunity to efficiently diagnose heart disease patients. However, challenges associated with datasets such as missing data, inconsistent data, and mixed data (containing inconsistent missing data both as numerical and categorical) are often obstacles in medical diagnosis. This inconsistency led to a higher probability of misprediction and a misled result. Data preprocessing steps like feature reduction, data conversion, and data scaling are employed to form a standard dataset—such measures play a crucial role in reducing inaccuracy in final prediction. This paper aims to evaluate eleven machine learning (ML) algorithms—Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Naive Bayes (NB), Support Vector Machine (SVM), XGBoost (XGB), Random Forest Classifier (RF), Gradient Boost (GB), AdaBoost (AB), Extra Tree Classifier (ET)—and six different data scaling methods—Normalization (NR), Standscale (SS), MinMax (MM), MaxAbs (MA), Robust Scaler (RS), and Quantile Transformer (QT) on a dataset comprising of information of patients with heart disease. The result shows that CART, along with RS or QT, outperforms all other ML algorithms with 100% accuracy, 100% precision, 99% recall, and 100% F1 score. The study outcomes demonstrate that the model’s performance varies depending on the data scaling method. Full article

Speech perception in an adverse background/noisy environment is a complex and challenging human process, which is made even more complicated in foreign-accented language for bilingual and monolingual individuals. Listeners who have difficulties in hearing are affected most by such a situation. Despite considerable efforts, the increase in speech intelligibility in noise remains elusive. Considering this opportunity, this study investigates Bengali–English bilinguals and native American English monolinguals’ behavioral patterns on foreign-accented English language considering bubble noise, gaussian or white noise, and quiet sound level. Twelve regular hearing participants (Six Bengali–English bilinguals and Six Native American English monolinguals) joined in this study. Statistical computation shows that speech with different noise has a significant effect (p = 0.009) on listening for both bilingual and monolingual under different sound levels (e.g., 55 dB, 65 dB, and 75 dB). Here, six different machine learning approaches (Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-nearest neighbors (KNN), Naïve Bayes (NB), Classification and regression trees (CART), and Support vector machine (SVM)) are tested and evaluated to differentiate between bilingual and monolingual individuals from their behavioral patterns in both noisy and quiet environments. Results show that most optimal performances were observed using LDA by successfully differentiating between bilingual and monolingual 60% of the time. A deep neural network-based model is proposed to improve this measure further and achieved an accuracy of nearly 100% in successfully differentiating between bilingual and monolingual individuals. Full article

Effectiveness, efficiency, scalability, autonomy, engagement, flexibility, adaptiveness, personalization, conversationality, reflectiveness, innovation, and self-organization are some of the fundamental features of smart environments. Smart environments are considered a good learning practice for formal and informal education; however, it is important to point out the pedagogical approaches on which they are based. Smart learning environments (SLEs) underline the flexibility of eclectic pedagogy that places students at the center of any educational process and takes into account the diversity in classrooms. Thus, SLEs incorporate pedagogical principles derived from (1) traditional learning theories, e.g., behaviorism and constructivism, (2) contemporary pedagogical philosophy, e.g., differentiated teaching and universal design for learning, (3) theories that provide specific instructions for educational design, e.g., cognitive theory of multimedia learning and gamification of learning. The innovative concept of transmedia learning is an eclectic pedagogical approach, which in addition to learning principles, blends all available media so far. WUIM is a transmedia program for training independent living skills aimed primarily at children with learning disabilities, which emerged from the composition of pedagogical theories, traditional educational materials and cutting-edge technologies such as augmented and virtual reality, and art-based production methodologies. This paper outlines the development of WUIM, from the prototyping presented at the 4th International Conference in Creative Writing (2019) to the Alpha and Beta stages, including user and expert evaluations. Full article

Solid waste is one of the social and environmental challenges that urban areas are facing. The study assesses the state of solid waste in Addis Ababa during 2016–2020 to provide implications for achieving green architecture concepts through better management of solid waste and its economic contribution. The study uses secondary and primary data. Quantitative and qualitative data are analyzed through descriptive statistics and context analysis, respectively. The result reveals that most solid waste is generated from households, followed by commercial centers, street sweeping, industries/factories, hotels, and hospitals, respectively. From 2016 to 2020, an average of 80.28% of solid waste is collected, whereas 19.72% of the waste is not collected. There are little or no efforts made to segregate solid waste at the source. The generated waste is disposed of in the Reppi open landfill. Together with Ethiopian electric power (EEP) and the City Government of Addis Ababa, waste has been converted to energy since 2019. The study suggests minimizing waste from its source by reducing generation, composting, reusing, recycling, waste-to-energy strategy, and well-designed buildings to achieve the concept of green architecture in Addis Ababa through better solid waste management. Full article

Diseases have adverse effects on crop production and yield loss. Various diseases such as leaf rust, stem rust, and strip rust can affect yield quality and quantity for a studied area. In addition, manual wheat disease identification and interpretation is time-consuming and cumbersome. Currently, decisions related to plants mainly rely on the level of expertise in the domain. To resolve these challenges and to identify wheat disease as early as possible, we implemented different deep learning models such as Inceptionv3, Resnet50, and VGG16/19. This research was conducted in collaboration with Bishoftu Agricultural Research Institute, Ethiopia. Our main objective was to automate plant-disease identification using advanced deep learning approaches and image data. For the experiment, RGB image data were collected from the Bishoftu area. From the experimental results, the VGG19 model classified wheat disease with 99.38% accuracy. Full article

The fast emergence of IoT devices and its accompanying big and complex data has necessitated a shift from the traditional networking architecture to software-defined networks (SDNs) in recent times. Routing optimization and DDoS protection in the network has become a necessity for mobile network operators in maintaining a good QoS and QoE for customers. Inspired by the recent advancement in Machine Learning and Deep Reinforcement Learning (DRL), we propose a novel MADDPG integrated Multiagent framework in SDN for efficient multipath routing optimization and malicious DDoS traffic detection and prevention in the network. The two MARL agents cooperate within the same environment to accomplish network optimization task within a shorter time. The state, action, and reward of the proposed framework were further modelled mathematically using the Markov Decision Process (MDP) and later integrated into the MADDPG algorithm. We compared the proposed MADDPG-based framework to DDPG for network metrics: delay, jitter, packet loss rate, bandwidth usage, and intrusion detection. The results show a significant improvement in network metrics with the two agents. Full article

The localization of the nodes in wireless sensor networks is essential in establishing effective communication among different devices connected, within the Internet of Things. This paper proposes a novel method to accurately determine the position and distance of the wireless sensors linked in a local network. The method utilizes the signal strength received at the target node to identify its location in the localized grid system. The Most Valuable Player Algorithm is used to solve the localization problem. Initially, the algorithm is implemented on four test cases with a varying number of sensor nodes to display its robustness under different network occupancies. Afterward, the study is extended to incorporate actual readings from both indoor and outdoor environments. The results display higher accuracy in the localization of unknown sensor nodes than previously reported. Full article

Today, tool life in high pressure die casting (HPDC) is of growing interest. A common agreement is that die life is primarily decided by the thermal load and temperature gradients in the die materials. Conformal cooling with the growth of additive manufacturing has raised interest as a means of extending die life. In the current paper, conformal cooling channels’ performance and effect on the thermal cycle in high-pressure die casting and rheocasting are investigated for conventional HPDC and semisolid processing. It was found that conformal cooling aids die temperature reduction, and the use of die spray may be reduced and support the die-life extension. For the die filling, the increased temperature was possibly counterproductive. Instead, it was found that the main focus for conformal cooling should be focused to manage temperature around the in-let bushing and possibly the runner system. Due to the possible higher inlet pressures for semisolid casting, particular benefits could be seen. Full article

Polyethersulfone (PES) films are widely employed in the construction of membranes where there is a desire to make the surface more hydrophilic. Therefore, UV photo-oxidation was studied in order to oxidize the surface of PES and increase hydrophilicity. UV photo-oxidation using low pressure mercury lamps emitting both 253.7 and 184.9 nm radiation were compared with only 253.7 nm photons. The modified surfaces were characterized using X-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), and water contact angle (WCA) measurements. Both sets of lamps gave similar results, showing an increase of the oxygen concentration up to a saturation level of ca. 29 at.% and a decrease in the WCA, i.e., an increase in hydrophilicity, down to ca. 40°. XPS detected a decrease of sp² C-C aromatic group bonding and an increase in the formation of C-O, C=O, O=C-O, O=C-OH, O-(C=O)-O, and sulphonate and sulphate moieties. Since little change in surface roughness was observed by AFM, the oxidation of the surface caused the increase in hydrophilicity. Full article

Augmented Reality (AR) is worldwide recognized as one of the leading technologies of the 21st century and one of the pillars of the new industrial revolution envisaged by the Industry 4.0 international program. Several papers describe, in detail, specific applications of Augmented Reality developed to test its potentiality in a variety of fields. However, there is a lack of sources detailing the current limits of this technology in the event of its introduction in a real working environment where everyday tasks could be carried out by operators using an AR-based approach. A literature analysis to detect AR strength and weakness has been carried out, and a set of case studies has been implemented by authors to find the limits of current AR technologies in industrial applications outside the laboratory-protected environment. The outcome of this paper is that, even though Augmented Reality is a well-consolidated computer graphic technique in research applications, several improvements both from a software and hardware point of view should be introduced before its introduction in industrial operations. The originality of this paper lies in the detection of guidelines to improve the Augmented Reality potentialities in factories and industries. Full article

Facial recognition (FR) in unconstrained weather is still challenging and surprisingly ignored by many researchers and practitioners over the past few decades. Therefore, this paper aims to evaluate the performance of three existing popular facial recognition methods considering different weather conditions. As a result, a new face dataset (Lamar University database (LUDB)) was developed that contains face images captured under various weather conditions such as foggy, cloudy, rainy, and sunny. Three very popular FR methods—Eigenface (EF), Fisherface (FF), and Local binary pattern histogram (LBPH)—were evaluated considering two other face datasets, AT&T and 5_Celebrity, along with LUDB in term of accuracy, precision, recall, and F1 score with 95% confidence interval (CI). Computational results show a significant difference among the three FR techniques in terms of overall time complexity and accuracy. LBPH outperforms the other two FR algorithms on both LUDB and 5_Celebrity datasets by achieving 40% and 95% accuracy, respectively. On the other hand, with minimum execution time of 1.37, 1.37, and 1.44 s per image on AT&T,5_Celebrity, and LUDB, respectively, Fisherface achieved the best result. Full article

The Internet of Things (IoT) in healthcare has gained significant attention in recent years. This study demonstrates an adaptation of IoT in healthcare by illustrating a method of respiration rate measurement from a platform that simulates breathing. Respiration rate is a crucial physiological measure in monitoring critically ill patients. The devised approach, with further development, may be suitable for integration into neonatal intensive care units (NICUs) to measure infants’ respiration rate. A potential advantage of this method is that it monitors respiration using a wireless non-contact method and could add benefits such as preservation of skin integrity. The paper aimed to assess the accuracy of an IoT-integrated ultrasound (US)-based method for measuring respiration rate. Chest movement due to respiration was simulated by a platform with a controllable moving surface. The magnitude and frequency of the movements were accurately controlled by a signal generator. The surface movements were tracked using US as a reliable and cost-effective technology. ESP8266 NodeMCU was used to wirelessly record the US signal and ThingSpeak and Matlab^© were used to analyze and visualize the data in the cloud. A close relationship between the measured rate of the simulated respiration and the actual frequency was observed. The study demonstrated a possible adaption of IoT for respiration rate measurement, however further work will be needed to ensure security and reliability of data handling before use of the system in medical environments. Full article

Shape-memory polymers (SMPs) can be deformed, cooled down, keeping their new shape for a long time, and recovered into their original shape after being heated above the glass or melting temperature again. Some SMPs, such as poly(lactic acid) (PLA), can be 3D printed, enabling a combination of 3D-printed shapes and 2D-printed, 3D-deformed ones. While deformation at high temperatures can be used, e.g., to fit orthoses to patients, SMPs used in protective equipment, bumpers, etc., are deformed at low temperatures, possibly causing irreversible breaks. Here, we compare different typical infill patterns, offered by common slicing software, with self-designed infill structures. Three-point bending tests were performed until maximum deflection as well as until the maximum force was reached, and then the samples were recovered in a warm water bath and tested again. The results show a severe influence of the infill pattern as well as the printing orientation on the amount of broken bonds and thus the mechanical properties after up to ten test/recovery cycles. Full article

The evolving global landscape for electrical distribution and use created a need area for energy storage systems (ESS), making them among the fastest growing electrical power system products. A key element in any energy storage system is the capability to monitor, control, and optimize performance of an individual or multiple battery modules in an energy storage system and the ability to control the disconnection of the module(s) from the system in the event of abnormal conditions. This management scheme is known as “battery management system (BMS)”, which is one of the essential units in electrical equipment. BMS reacts with external events, as well with as an internal event. It is used to improve the battery performance with proper safety measures within a system. Therefore, a safe BMS is the prerequisite for operating an electrical system. This report analyzes the details of BMS for electric transportation and large-scale (stationary) energy storage. The analysis includes different aspects of BMS covering testing, component, functionalities, topology, operation, architecture, and BMS safety aspects. Additionally, current related standards and codes related to BMS are also reviewed. The report investigates BMS safety aspects, battery technology, regulation needs, and offer recommendations. It further studies current gaps in respect to the safety requirements and performance requirements of BMS by focusing mainly on the electric transportation and stationary application. The report further provides a framework for developing a new standard on BMS, especially on BMS safety and operational risk. In conclusion, four main areas of (1) BMS construction, (2) Operation Parameters, (3) BMS Integration, and (4) Installation for improvement of BMS safety and performance are identified, and detailed recommendations were provided for each area. It is recommended that a technical review of the BMS be performed for transportation electrification and large-scale (stationary) applications. A comprehensive evaluation of the components, architectures, and safety risks applicable to BMS operation is also presented. Full article

Additive manufacturing has evolved over the past decades into a technology that provides freedom of design through the ability to produce complex-shaped solid structures, reducing the operational time and material volumes in manufacturing significantly. However, the surface of parts manufactured by the additive method remains now extremely rough. The current trend of expanding the industrial application of additive manufacturing is researching surface roughness and finishing. Moreover, the limited choice of materials suitable for additive manufacturing does not satisfy the diverse design requirements, necessitating additional coatings deposition. Requirements for surface treatment and coating deposition technology depend on the intended use of the parts, their material, and technology. In most cases, they cannot be determined based on existing knowledge and experience. It determines the scientific relevance of the analytical research and development of scientific and technological principles of finishing parts obtained by laser additive manufacturing and functional coating deposition. There is a scientific novelty of analytical research that proposes gas-discharge plasma processing for finishing laser additive manufactured parts and technological principles development including three processing stages—explosive ablation, polishing with a concentrated beam of fast neutral argon atoms, and coating deposition—for the first time. Full article

The performance of a free-space optical (FSO) communications link suffers from the deleterious effects of weather conditions and atmospheric turbulence. In order to better estimate the reliability and availability of an FSO link, a suitable distribution needs to be employed. The accuracy of this model depends strongly on the atmospheric turbulence strength which causes the scintillation effect. To this end, a variety of probability density functions were utilized to model the optical channel according to the strength of the refractive index structure parameter. Although many theoretical models have shown satisfactory performance, in reality they can significantly differ. This work employs an information theoretic method, namely the so-called Jensen–Shannon divergence, a symmetrization of the Kullback–Leibler divergence, to measure the similarity between different probability distributions. In doing so, a large experimental dataset of received signal strength measurements from a real FSO link is utilized. Additionally, the Pearson family of continuous probability distributions is also employed to determine the best fit according to the mean, standard deviation, skewness and kurtosis of the modeled data. Full article

Today’s smart devices come equipped with powerful hard- and software-enabling professional use cases. The latest hardware by Apple utilizes LiDAR and TrueDepth, which offer the capability of 3D scanning. Devices equipped with these camera systems allow manufacturers to obtain 3D data from their customers at low costs, which potentially enables time-efficient mass customization and product differentiation strategies. However, the utilization is limited by the scanning accuracy. To determine the potential application of LiDAR and TrueDepth as a 3D scanning solution, in this paper an evaluation was performed. For this purpose, different Lego bricks were scanned with the technologies and an industrial 3D scanner. The results were compared according to shape and position tolerances. Even though the industrial 3D scanner consistently delivered more accurate results, the accuracy of the smart device technologies may already be sufficient, depending on the application. Full article

I present a novel machine learning approach to predict sex in the bioarchaeological record. Eighteen cranial interlandmark distances and five maxillary dental metric distances were recorded from n = 420 human skeletons from the necropolises at Alfedena (600–400 BCE) and Campovalano (750–200 BCE and 9–11th Centuries CE) in central Italy. A generalized low rank model (GLRM) was used to impute missing data and Area under the Curve—Receiver Operating Characteristic (AUC-ROC) with 20-fold stratified cross-validation was used to evaluate predictive performance of eight machine learning algorithms on different subsets of the data. Additional perspectives such as this one show strong potential for sex prediction in bioarchaeological and forensic anthropological contexts. Furthermore, GLRMs have the potential to handle missing data in ways previously unexplored in the discipline. Although results of this study look promising (highest AUC-ROC = 0.9722 for predicting binary male/female sex), the main limitation is that the sexes of the individuals included were not known but were estimated using standard macroscopic bioarchaeological methods. However, future research should apply this machine learning approach to known-sex reference samples in order to better understand its value, along with the more general contributions that machine learning can make to the reconstruction of past human lifeways. Full article

In the last years, the Internet of Things (IoT) has emerged as a key application context in the design and evolution of technologies in the transition toward a 5G ecosystem. More and more IoT technologies have entered the market and represent important enablers in the deployment of networks of interconnected devices. As network and spatial device densities grow, energy efficiency and consumption are becoming an important aspect in analyzing the performance and suitability of different technologies. In this framework, this survey presents an extensive review of IoT technologies, including both Low-Power Short-Area Networks (LPSANs) and Low-Power Wide-Area Networks (LPWANs), from the perspective of energy efficiency and power consumption. Existing consumption models and energy efficiency mechanisms are categorized, analyzed and discussed, in order to highlight the main trends proposed in literature and standards toward achieving energy-efficient IoT networks. Current limitations and open challenges are also discussed, aiming at highlighting new possible research directions. Full article

The paper presents a new analytical model for thin structural adhesives in glued tube-to-tube butt joints. The aim of this work is to provide an interface condition that allows for a suitable replacement of the adhesive layer in numerical simulations. The proposed model is a nonlinear and rate-dependent imperfect interface law that is able to accurately describe brittle and ductile stress–strain behaviors of adhesive layers under combined tensile–torsion loads. A first comparison with experimental data that were available in the literature provided promising results in terms of the reproducibility of the stress–strain behavior for pure tensile and torsional loads (the relative errors were less than $6\%$) and in terms of failure strains for combined tensile–torsion loads (the relative errors were less than $14\%$). Two main novelties are highlighted: (i) Unlike the classic spring-like interface models, this model accounts for both stress and displacement jumps, so it is suitable for soft and hard adhesive layers; (ii) unlike classic cohesive zone models, which are phenomenological, this model explicitly accounts for material and damage properties of the adhesive layer. Full article

Hybrid models combine physics-based models and data-driven models. This combination is a useful technique to detect fault and predict the current degradation of equipment. This paper proposes a physics-based model, which will be part of a hybrid model, for a heating, ventilation, and air conditioning system installed in the passenger vehicle of a train. The physics-based model is divided into four main parts: heating subsystems, cooling subsystems, ventilation subsystems, and cabin thermal networking subsystems. These subsystems are developed when considering the sensors that are located in the real system, so the model can be linked via the acquired sensor data and virtual sensor data to improve the detectability of failure modes. Thus, the physics-based model can be synchronized with the real system to provide better simulation results. The paper also considers diagnostics and prognostics performance. First, it looks at the current situation of the maintenance strategy for the heating, ventilation, air conditioning system, and the number of failure modes that the maintenance team can detect. Second, it determines the expected improvement using hybrid modelling to maintain the system. This improvement is based on the capabilities of detecting new failure modes. The paper concludes by suggesting the future capabilities of hybrid models. Full article

This work proposes new interface conditions between the layers of a three-dimensional composite structure in the framework of coupled thermoelasticity. More precisely, the mechanical behavior of two linear isotropic thermoelastic solids, bonded together by a thin layer, constituted of a linear isotropic thermoelastic material, is studied by means of an asymptotic analysis. After defining a small parameter $\epsilon$, which tends to zero, associated with the thickness and constitutive coefficients of the intermediate layer, two different limit models and their associated limit problems, the so-called soft and hard thermoelastic interface models, are characterized. The asymptotic expansion method is reviewed by taking into account the effect of higher-order terms and defining a generalized thermoelastic interface law which comprises the above aforementioned models, as presented previously. A numerical example is presented to show the efficiency of the proposed methodology, based on a finite element approach developed previously. Full article

Any severe motor disability is a condition that limits the ability to interact with the environment, even the domestic one, caused by the loss of control over one’s mobility. This work presents RoboEYE, a power wheelchair designed to allow users to move easily and autonomously within their homes. To achieve this goal, an innovative, cost-effective and user-friendly control system was designed, in which a non-invasive eye tracker, a monitor, and a 3D camera represent some of the core elements. RoboEYE integrates functionalities from the mobile robotics field into a standard power wheelchair, with the main advantage of providing the user with two driving options and comfortable navigation. The most intuitive and direct modality foresees the continuous control of frontal and angular wheelchair velocities by gazing at different areas of the monitor. The second, semi-autonomous modality allows navigation toward a selected point in the environment by just pointing and activating the wished destination while the system autonomously plans and follows the trajectory that brings the wheelchair to that point. The purpose of this work was to develop the control structure and driving interface designs of the aforementioned driving modalities taking into account also uncertainties in gaze detection and other sources of uncertainty related to the components to ensure user safety. Furthermore, the driving modalities, in particular the semi-autonomous one, were modeled and qualified through numerical simulations and experimental verification by testing volunteers, who are regular users of standard electric wheelchairs, to verify the efficiency, reliability and safety of the proposed system for domestic use. RoboEYE resulted suitable for environments with narrow passages wider than 1 m, which is comparable with a standard domestic door and due to its properties with large commercialization potential. Full article

Software-Defined Networking (SDN) is a new paradigm that revolutionizes the idea of a software-driven network through the separation of control and data planes. It addresses the problems of traditional network architecture. Nevertheless, this brilliant architecture is exposed to several security threats, e.g., the distributed denial of service (DDoS) attack, which is hard to contain in such software-based networks. The concept of a centralized controller in SDN makes it a single point of attack as well as a single point of failure. In this paper, deep learning-based models, long-short term memory (LSTM) and convolutional neural network (CNN), are investigated. It illustrates their possibility and efficiency in being used in detecting and mitigating DDoS attack. The paper focuses on TCP, UDP, and ICMP flood attacks that target the controller. The performance of the models was evaluated based on the accuracy, recall, and true negative rate. We compared the performance of the deep learning models with classical machine learning models. We further provide details on the time taken to detect and mitigate the attack. Our results show that RNN LSTM is a viable deep learning algorithm that can be applied in the detection and mitigation of DDoS in the SDN controller. Our proposed model produced an accuracy of 89.63%, which outperformed linear-based models such as SVM (86.85%) and Naive Bayes (82.61%). Although KNN, which is a linear-based model, outperformed our proposed model (achieving an accuracy of 99.4%), our proposed model provides a good trade-off between precision and recall, which makes it suitable for DDoS classification. In addition, it was realized that the split ratio of the training and testing datasets can give different results in the performance of a deep learning algorithm used in a specific work. The model achieved the best performance when a split of 70/30 was used in comparison to 80/20 and 60/40 split ratios. Full article

The development of micromechanical models to predict the effective properties of multiphase composites is important for the design and optimization of new materials, as well as to improve our understanding about the structure–properties relationship. In this work, the two-scale asymptotic homogenization method (AHM) is implemented to calculate the out-of-plane effective complex-value properties of periodic three-phase elastic fiber-reinforced composites (FRCs) with parallelogram unit cells. Matrix and inclusions materials have complex-valued properties. Closed analytical expressions for the local problems and the out-of-plane shear effective coefficients are given. The solution of the homogenized local problems is found using potential theory. Numerical results are reported and comparisons with data reported in the literature are shown. Good agreements are obtained. In addition, the effects of fiber volume fractions and spatial fiber distribution on the complex effective elastic properties are analyzed. An analysis of the shear effective properties enhancement is also studied for three-phase FRCs. Full article

Vehicles on the road are rising in extensive numbers, particularly in proportion to the industrial revolution and growing economy. The significant use of vehicles has increased the probability of traffic rules violation, causing unexpected accidents, and triggering traffic crimes. In order to overcome these problems, an intelligent traffic monitoring system is required. The intelligent system can play a vital role in traffic control through the number plate detection of the vehicles. In this research work, a system is developed for detecting and recognizing of vehicle number plates using a convolutional neural network (CNN), a deep learning technique. This system comprises of two parts: number plate detection and number plate recognition. In the detection part, a vehicle’s image is captured through a digital camera. Then the system segments the number plate region from the image frame. After extracting the number plate region, a super resolution method is applied to convert the low-resolution image into a high-resolution image. The super resolution technique is used with the convolutional layer of CNN to reconstruct the pixel quality of the input image. Each character of the number plate is segmented using a bounding box method. In the recognition part, features are extracted and classified using the CNN technique. The novelty of this research is the development of an intelligent system employing CNN to recognize number plates, which have less resolution, and are written in the Bengali language. Full article

In recent years, with the current advancements in Robotics and Artificial Intelligence (AI), robots have the potential to support the field of healthcare. Robotic systems are often introduced in the care of the elderly, children, and persons with disabilities, in hospitals, in rehabilitation and walking assistance, and other healthcare situations. In this survey paper, the recent advances in robotic technology applied in the healthcare domain are discussed. The paper provides detailed information about state-of-the-art research in care, hospital, assistive, rehabilitation, and walking assisting robots. The paper also discusses the open challenges healthcare robots face to be integrated into our society. Full article

In the present work, the radiation properties of a graphene plasmonic patch antenna are investigated and enhanced in terms of efficiency, utilizing circuit-matching techniques. Initially, the reflection coefficient of graphene surface waves due to discontinuities is studied, while the power flow towards free-space is numerically extracted. This analysis indicates that the radiated power is increased for higher values of the chemical potential, although the surface wave is weakly confined and prone to degradation due to interference. For this reason, a graphene sheet that supports strongly confined surface waves is terminated via a matching layer, in order to enhance the radiating power. In particular, the matching layer consists of an appropriately selected larger chemical potential value, in order to minimize the reflection coefficient and boost the radiation performance. The numerical investigation of this setup validates the upgraded performance, since the radiating power is significantly increased. Then, a realistic setup that includes a graphene patch antenna is examined numerically, proving the augmentation of the radiation efficiency when the matching layer is utilized. Finally, the latter is designed with a graded increment in the chemical potential, and the computational analysis highlights the significant enhancement of the graphene plasmonic antenna gain towards the desired direction. Consequently, a more reliable framework for wireless communications between plasmonic devices at THz frequencies is established, which corresponds to the practical significance of the proposed methodology for improved radiation efficiency. All numerical results are extracted by means of an efficient modification of the Finite-Difference Time-Domain (FDTD) scheme, which models graphene accurately. Full article

Bug prediction aims at finding source code elements in a software system that are likely to contain defects. Being aware of the most error-prone parts of the program, one can efficiently allocate the limited amount of testing and code review resources. Therefore, bug prediction can support software maintenance and evolution to a great extent. In this paper, we propose a function level JavaScript bug prediction model based on static source code metrics with the addition of a hybrid (static and dynamic) code analysis based metric of the number of incoming and outgoing function calls (HNII and HNOI). Our motivation for this is that JavaScript is a highly dynamic scripting language for which static code analysis might be very imprecise; therefore, using a purely static source code features for bug prediction might not be enough. Based on a study where we extracted 824 buggy and 1943 non-buggy functions from the publicly available BugsJS dataset for the ESLint JavaScript project, we can confirm the positive impact of hybrid code metrics on the prediction performance of the ML models. Depending on the ML algorithm, applied hyper-parameters, and target measures we consider, hybrid invocation metrics bring a 2–10% increase in model performances (i.e., precision, recall, F-measure). Interestingly, replacing static NOI and NII metrics with their hybrid counterparts HNOI and HNII in itself improves model performances; however, using them all together yields the best results. Full article

Self-supervised learning has gained popularity because of its ability to avoid the cost of annotating large-scale datasets. It is capable of adopting self-defined pseudolabels as supervision and use the learned representations for several downstream tasks. Specifically, contrastive learning has recently become a dominant component in self-supervised learning for computer vision, natural language processing (NLP), and other domains. It aims at embedding augmented versions of the same sample close to each other while trying to push away embeddings from different samples. This paper provides an extensive review of self-supervised methods that follow the contrastive approach. The work explains commonly used pretext tasks in a contrastive learning setup, followed by different architectures that have been proposed so far. Next, we present a performance comparison of different methods for multiple downstream tasks such as image classification, object detection, and action recognition. Finally, we conclude with the limitations of the current methods and the need for further techniques and future directions to make meaningful progress. Full article

This work presents a unified approach to the analysis of contact problems with various interface laws that model the processes involved in contact between a deformable body and a rigid or reactive foundation. These laws are then used in the formulation of a general static frictional contact problem with unilateral constraints for elastic materials, which is governed by three parameters. A weak formulation of the problem is derived, which is in the form of an elliptic variational inequality, and the Tykhonov well-posedness of the problem is established, under appropriate assumptions on the data and parameters, with respect to a special Tykhonov triple. The proof is based on arguments on coercivity, compactness, and lower-semicontinuity. This abstract result leads to different convergence results, which establish the continuous dependence of the weak solution on the data and the parameters. Moreover, these results elucidate the links among the weak solutions of the different models. Finally, the corresponding mechanical interpretations of the conditions and the results are provided. The novelty in this work is the application of the Tykhonov well-posedness concept, which allows a unified and elegant framework for this class of static contact problems. Full article

A mathematical model employing the concept of energy-equivalent inhomogeneity is applied to analyze short cylindrical fiber composites with interfaces described by the Steigmann–Ogden material surface model. Real inhomogeneity consists of a cylindrical fiber of finite length, and its surface possessing different properties is replaced by a homogeneous, energy-equivalent cylinder. The properties of the energy-equivalent fiber, incorporating properties of the original fiber and its interface, are determined on the basis of Hill’s energy equivalence principle. Closed-form expressions for components of the stiffness tensor of equivalent fiber have been developed and, in the limit, shown to compare well with the results available in the literature for infinite fibers with the Steigmann–Ogden interface model. Dependence of those components on the radius, length of the cylindrical fiber, and surface parameters is included in these expressions. The effective stiffness tensor of the short-fiber composites with so-defined equivalent cylindrical fibers can be determined by any homogenization method developed without accounting for interface. Full article

Recently, the use of extended reality (XR) systems has been on the rise, to tackle various domains such as training, education, safety, etc. With the recent advances in augmented reality (AR), virtual reality (VR) and mixed reality (MR) technologies and ease of availability of high-end, commercially available hardware, the manufacturing industry has seen a rise in the use of advanced XR technologies to train its workforce. While several research publications exist on applications of XR in manufacturing training, a comprehensive review of recent works and applications is lacking to present a clear progress in using such advance technologies. To this end, we present a review of the current state-of-the-art of use of XR technologies in training personnel in the field of manufacturing. First, we put forth the need of XR in manufacturing. We then present several key application domains where XR is being currently applied, notably in maintenance training and in performing assembly task. We also reviewed the applications of XR in other vocational domains and how they can be leveraged in the manufacturing industry. We finally present some current barriers to XR adoption in manufacturing training and highlight the current limitations that should be considered when looking to develop and apply practical applications of XR. Full article

Customer Experience (CX) is monitored through market research surveys, based on metrics like the Net Promoter Score (NPS) and the customer satisfaction for certain experience attributes (e.g., call center, website, billing, service quality, tariff plan). The objective of companies is to maximize NPS through the improvement of the most important CX attributes. However, statistical analysis suggests that there is a lack of clear and accurate association between NPS and the CX attributes’ scores. In this paper, we address the aforementioned deficiency using a novel classification approach, which was developed based on logistic regression and tested with several state-of-the-art machine learning (ML) algorithms. The proposed method was applied on an extended data set from the telecommunication sector and the results were quite promising, showing a significant improvement in most statistical metrics. Full article

The aim of this study was to perform a content analysis of electronic activity monitors that also evaluates utility features, code behavior change techniques included in the monitoring systems, and align the results with intervention functions of the Behaviour Change Wheel program planning model to facilitate informed device selection. Devices were coded for the implemented behavior change techniques and device features. Three trained coders each wore a monitor for at least 1 week from December 2019–April 2020. Apple Watch Nike, Fitbit Versa 2, Fitbit Charge 3, Fitbit Ionic—Adidas Edition, Garmin Vivomove HR, Garmin Vivosmart 4, Amazfit Bip, Galaxy Watch Active, and Withings Steel HR were reviewed. The monitors all paired with a phone/tablet, tracked exercise sessions, and were wrist-worn. On average, the monitors implemented 27 behavior change techniques each. Fitbit devices implemented the most behavior change techniques, including techniques related to the intervention functions: education, enablement, environmental restructuring, coercion, incentivization, modeling, and persuasion. Garmin devices implemented the second highest number of behavior change techniques, including techniques related to enablement, environmental restructuring, and training. Researchers can use these results to guide selection of electronic activity monitors based on their research needs. Full article

Falls are the main cause of susceptibility to severe injuries in many humans, especially for older adults aged 65 and over. Typically, falls are being unnoticed and interpreted as a mere inevitable accident. Various wearable fall warning devices have been created recently for older people. However, most of these devices are dependent on local data processing. Various algorithms are used in wearable sensors to track a real-time fall effectively, which focuses on fall detection via fuzzy-as-a-service based on IEEE 1855–2016, Java Fuzzy Markup Language (FML) and service-oriented architecture. Moreover, several approaches are used to detect a fall using machine learning techniques via human movement positional data to avert any accidents. For fuzzy logic web services, analysis is performed using wearable accelerometer and gyroscope sensors, whereas in machine learning techniques, k-NN, decision tree, random forest and extreme gradient boost are used to differentiate between a fall and non-fall. This study aims to carry out a comparative analysis of real-time fall detection using fuzzy logic web services and machine learning techniques and aims to determine which one is better for real-time fall detection. Research findings exhibit that the proposed fuzzy-as-a-service could easily differentiate between fall and non-fall occurrences in a real-time environment with an accuracy, sensitivity and specificity of 90%, 88.89% and 91.67%, respectively, while the random forest algorithm of machine learning achieved 99.19%, 98.53% and 99.63%, respectively. Full article

The paper is devoted to the research of the effect of ultrasonic postprocessing—specifically, the effects of ultrasonic cavitation-abrasive finishing, ultrasonic plastic deformation, and vibration tumbling on surface quality, wear resistance, and the ability of real aircraft parts with complex geometries and with sizes less than and more than 100 mm to work in exploitation conditions. The parts were produced by laser powder bed fusion from two types of anticorrosion steels of austenitic and martensitic grades—20Kh13 (DIN 1.4021, X20Cr13, AISI 420) and 12Kh18N9T (DIN 1.4541, X10CrNiTi18-10, AISI 321). The finishing technologies based on mechanical action—plastic deformation, abrasive wear, and complex mechanolysis showed an effect on reducing the submicron surface roughness, removing the trapped powder granules from the manufactured functional surfaces and their wear resistance. The tests were completed by proving resistance of the produced parts to exploitation conditions—vibration fatigue and corrosion in salt fog. The roughness arithmetic mean deviation R_a was improved by 50–52% after cavitation-abrasive finishing, by 28–30% after ultrasonic plastic deformation, and by 65–70% after vibratory tumbling. The effect on wear resistance is correlated with the improved roughness. The effect of used techniques on resistance to abrasive wear was explained and grounded. Full article