In this paper, a hydraulic breaker system is suggested with optimized impact forces and with active control to improve the system’s energy efficiency. While performing operations such as demolition, rock pulverization, and ground hammering, the consistent breaking force causes energy dissipation due to the various strengths of the bedrock. Moreover, if the ground condition is not monitored, this may lead to catastrophic damage to the whole system. Moreover, frequent part changes can result in fatal malfunction. Therefore, a novel rock breaker is needed that is able to predict target properties of the rock in order to perform at the optimal impact force. The characteristics of rock forecasts obtained by a proximity sensor can determine the depth at which the piston stroke will reach the object. Moreover, a cascade control system for multiple levels of impact points, included operating and monitoring modules, is developed by applying ICT convergence through a wireless communication system. Consequently, adequate results were obtained from the applied field test on the feasibility of the suggested breaking system, thus confirming the possibility of applying this system in conventional heavy industries. Full article

Cities demand vast amounts of energy for their everyday operation, resulting in significant degradation of energy in the form of heat in the urban environment. This leads to high cooling requirements in cities, while also presenting the opportunity to reuse such waste heat in order to provide low-carbon heating for buildings and processes. Among the many potential energy sources that could be exploited in urban areas, underground railway tunnels are particularly attractive, as the operation of the trains produce considerable amounts of heat throughout the year. This paper reviews how secondary energy sources in urban areas can be integrated into heating and cooling networks, with emphasis on underground rail tunnels. This involves investigating potential urban waste heat sources and the existing state-of-the-art technologies that could be applied to efficiently recover this secondary energy, as well as analyzing how district heating and cooling networks have been a key mechanism to allow for a smooth transition from current fossil fuel based to future low-carbon energy sources. Full article

According to the international technical standards, higher lighting levels (luminance and illuminance levels) are expected in trafficked and central roads (where restrictive minimum lighting requirements are necessary) and lower lighting levels are expected in peripheral and less trafficked roads. Starting from this assumption, in this paper, the authors analyse the correlations between spatial properties (expressed by spatial indicators, for example, the integration index and the choice index) and lighting levels (expressed by lighting parameters, for example luminance and illuminance) upon roads of an urban context. The analysis has been applied to the case study of the medium sized town of Pontedera (central Italy). From the obtained results, it has been possible to observe how the correlations between integration index and luminance and illuminance values are significant in the case of roads equipped with lighting systems able to satisfy the lighting requirements established by the regulations. The presence of the discussed correlations lays the foundation for a change in the lighting design approach on urban scale, being able to set lighting requirements on the basis of space syntax results without the use of traditional methods of road classifications involving traffic volume estimations. Full article

Pattern projection-based 3D measurement systems are widely used for contactless, non-destructive optical 3D shape measurements. In addition, many robot-operated automation tasks require real-time reconstruction of accurate 3D data. In previous works, we have demonstrated 3D scanning based on statistical pattern projection-aided stereo matching between two cameras. One major advantage of this technology is that the actually projected patterns do not have to be known a priori in the reconstruction software. This allows much simpler projector designs and enables high-speed projection. However, to find corresponding pixels between cameras, it is necessary to search the best match amongst all pixels within the geometrically possible image area (that is, within a range on the corresponding epipolar line). The well-established method for this search is to compare each candidate pixel by temporal normalized cross correlation of the brightness value sequences of both pixels. This is computationally expensive and interdicts fast real-time applications on inexpensive computer hardware. We show two variants of our algorithm “Binary Correspondence Search” (BICOS), which solve this task in significantly reduced calculation time. In practice, our algorithm is much faster than traditional, purely cross-correlation-based search while maintaining a similar level of accuracy. Full article

Early detection and diagnosis of wind turbine faults is critical for applying a possible maintenance and control strategy to avoid catastrophic incidents. This paper presents a novel method to estimate the parameter of faults in a wind turbine. In this work, the estimation of fault parameters is reformulated as the state estimation problem by augmenting the parameters as an additional state. The novelty of the proposed method lies in the use of an adaptive fuzzy fading algorithm for the adaptive Kalman filter so that the convergence property during the estimation of fault parameter can be improved. The performance of the proposed method is evaluated through a set of numerical simulations with both linear and non-linear models. Full article

Early detection and effective treatment of myocardial infarction can prevent the deterioration of ischemic heart disease and greatly reduce the possibility of sudden death. On the basis of standard 12-lead electrocardiogram (ECG) records, this paper proposes a bidirectional, long short-term memory (Bi-LSTM) network with a heartbeat-attention mechanism to effectively and automatically detect myocardial infarction (MI). First, we divide the standard 12-lead ECG records into sliding windows with the same number of heartbeats. Subsequently, we do not use any labels of heartbeats to train the Bi-LSTM network and the heartbeat-attention mechanism is applied to automatically weight the difference between unlabeled heartbeats. Finally, our method is validated by patients’ complete ECG records and real labels in the Physikalisch-Technische Bundesanstalt (PTB) diagnostic ECG database. When compared with the same network without the heartbeat-attention mechanism or other existing methods, our method achieves comparable or better performance. The accuracy, sensitivity, and specificity reach 94.77%, 95.58%, and 90.48%, respectively. Full article

In a software tracking system, the bug assignment problem refers to the activities that developers perform during software maintenance to fix bugs. As many bugs are submitted on a daily basis, the number of developers required is quite large, and it therefore becomes difficult to assign the right developers to resolve issues with specific bugs. Inappropriate dispatches results in delayed processing of bug reports. In this paper, we propose an algorithm called ABC-DR to solve the bug assignment problem. The ABC-DR algorithm is a two-part composite approach that includes analysis between bug reports (i.e., B-based analysis) and analysis between developers and bug reports (i.e., D-based analysis). For analysis between bug reports, we use the multi-label k-nearest neighbor (ML-KNN) algorithm to find similar bug reports when compared with the new bug reports, and the developers who analyze similar bug reports recommend developers for the new bug report. For analysis between developers and bug reports, we find developer rankings similar to the new bug report by calculating the relevance scores between developers and similar bug reports. We use the artificial bee colony (ABC) algorithm to calculate the weight of each part. We evaluated the proposed algorithms on three datasets—GCC, Mozilla, and NetBeans—comparing ABC-DR with DevRec, DREX, and Bugzie. The experimental results show that the proposed ABC-DR algorithm achieves the highest improvement of 51.2% and 53.56% over DevRec for recall@5 and recall@10 in the NetBeans dataset. Full article

As a major device for reducing vibration and protecting passengers, the low-frequency vibration control performance of commercial vehicle seating systems has become an attractive research topic in recent years. This article reviews the recent developments in active seat suspensions for vehicles. The features of active seat suspension actuators and the related control algorithms are described and discussed in detail. In addition, the vibration control and reduction performance of active seat suspension systems are also reviewed. The article also discusses the prospects of the application of machine learning, including artificial neural network (ANN) control algorithms, in the development of active seat suspension systems for vibration control. Full article

The complexity and dynamic of the manufacturing environment are growing due to the changes of manufacturing demand from mass production to mass customization that require variable product types, small lot sizes, and a short lead-time to market. Currently, the automatic manufacturing systems are suitable for mass production. To cope with the changes of the manufacturing environment, the paper proposes the model and technologies for developing a smart cyber-physical manufacturing system (Smart-CPMS). The transformation of the actual manufacturing systems to the Smart-CPMS is considered as the next generation of manufacturing development in Industry 4.0. The Smart-CPMS has advanced characteristics inspired from biology such as self-organization, self-diagnosis, and self-healing. These characteristics ensure that the Smart-CPMS is able to adapt with continuously changing manufacturing requirements. The model of Smart-CPMS is inherited from the organization of living systems in biology and nature. Consequently, in the Smart-CPMS, each resource on the shop floor such as machines, robots, transporters, and so on, is an autonomous entity, namely a cyber-physical system (CPS) which is equipped with cognitive capabilities such as perception, reasoning, learning, and cooperation. The Smart-CPMS adapts to the changes of manufacturing environment by the interaction among CPSs without external intervention. The CPS implementation uses the cognitive agent technology. Internet of things (IoT) with wireless networks, radio frequency identification (RFID), and sensor networks are used as information and communication technology (ICT) infrastructure for carrying out the Smart-CPMS. Full article

A two-phase closed thermosyphon is an efficient heat transfer element. The heat transfer process of this type of thermosyphon includes conduction and convective heat transfer accompanied by phase changes. Variations in the inclination angle of a thermosyphon affect the steady-state heat transfer performance of the device. Therefore, the inclination angle is an important factor affecting the performance of a thermosyphon. In this paper, an equation for the actual heating area variations with respect to the inclination angle is deduced, and a model for the areal thermal resistance of a thermosyphon is proposed by analyzing the main influence mechanisms of the inclination angle on the heat transfer process. The experimental results show that the areal thermal resistance, which accounts for the effect of the actual heating area, does not change with respect to the inclination angle and exhibits a linear relationship with the heat transfer rate. The thermal resistance equation is fit according to the experimental data when the inclination angle of the thermosyphon is vertically oriented (90°), and the predicted values of the thermosyphon’s thermal resistance are obtained when the thermosyphon is inclined. The deviations between the experimental data and predicted values are less than ±0.05. Therefore, the theoretical equation can accurately predict the thermosyphon’s thermal resistance at different inclination angles. Full article

A rocket uses fuel and oxidizers to generate propulsion by combustion and ejection, and is used for space exploration aircrafts, weapons, and satellite launches. In particular, the nozzle generating thrust of solid-propellant rockets is exposed to a high-temperature and high-pressure environment with erosion occurring from the combustion gas. When erosion occurs on the nozzle throat of such a rocket, it has a great impact on the flight performance such as reaching distance and flight speed. Many studies have been conducted to characterize erosion based on the thermochemical erosion model, since it has become important to choose nozzle materials suitable for such environments having robustness against combustion gasses of high temperature and high pressure. However, there is a limit to fully analyze the erosion characteristics only by the thermochemical erosion model. In this paper, we thus consider the mechanical erosion model with the thermochemical model for better understanding of erosion characteristics and investigate the thermochemical and mechanical erosion characteristics of nozzle throat heat-resistant materials made of graphite and carbon–carbon composites; the main factors affecting erosion are discussed by comparing the results of the experimental and theoretical models. Full article

Machine learning (ML) is the technology that allows a computer system to learn from the environment, through re-iterative processes, and improve itself from experience. Recently, machine learning has gained massive attention across numerous fields, and is making it easy to model data extremely well, without the importance of using strong assumptions about the modeled system. The rise of machine learning has proven to better describe data as a result of providing both engineering solutions and an important benchmark. Therefore, in this current research work, we applied three different machine learning algorithms, which were, the Random Forest (RF), Support Vector Machines (SVM), and Artificial Neural Network (ANN) to predict kyphosis disease based on a biomedical data. At the initial stage of the experiments, we performed 5- and 10-Fold Cross-Validation using Logistic Regression as a baseline model to compare with our ML models without performing grid search. We then evaluated the models and compared their performances based on 5- and 10-Fold Cross-Validation after running grid search algorithms on the ML models. Among the Support Vector Machines, we experimented with the three kernels (Linear, Radial Basis Function (RBF), Polynomial). We observed overall accuracies of the models between 79%–85%, and 77%–86% based on the 5- and 10-Fold Cross-Validation, after running grid search respectively. Based on the 5- and 10-Fold Cross-Validation as evaluation metrics, the RF, SVM-RBF, and ANN models achieved accuracies more than 80%. The RF, SVM-RBF and ANN models outperformed the baseline model based on the 10-Fold Cross-Validation with grid search. Overall, in terms of accuracies, the ANN model outperformed all the other ML models, achieving 85.19% and 86.42% based on the 5- and 10-Fold Cross-Validation. We proposed that RF, SVM-RBF and ANN models should be used to detect and predict kyphosis disease after a patient had undergone surgery or operation. We suggest that machine learning should be adopted and used as an essential and critical tool across the maximum spectrum of answering biomedical questions. Full article

The objectives of the study were to examine occupational exposure to phthalates of hairdressing apprentices from Slovakia (n = 74, 89.2% females; 10.8% males), outcomes related to body composition and pulmonary functions. We used high-performance liquid chromatography and tandem mass spectrometry to the quantified urinary concentration of phthalates. Pulmonary function test (PFT), anthropometric measurements, and questionnaire were also conducted. We observed a decrease of % of predicted values of forced vital capacity (FVC% of PV) related exposure to mono(2-ethyl-5-oxohexyl) phthalate (MEOHP; p = 0.054) and sum of bis(2-ethylhexyl) phthalate metabolites (∑DEHP; p = 0.037), and a decrease of % of predicted values of vital capacity (VC% of PV) related to exposure to MEOHP, ∑DEHP (p = 0.008), and mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP; p = 0.014) in females. We detected associations between forced vital capacity (FVC) with weight (p = 0.002) and fat-free mass index (FFMI, p = 0.010). Vital capacity (VC) and VC% of PV increased with weight, body mass index (BMI), waist circumference (WC), hip circumference (HC), waist-hip ratio (WHR), the waist-height ratio (WHtR), fat mass index (FMI) and FFMI in females (p ≤ 0.014). Results of multivariate regression between PFT and anthropometric parameters adjusted to phthalates indicated exposure to MnBP and MEHP, changing body structure (BMI and FMI), subsequently affecting values of FEV₁/FVC. Full article

This study presents the development of a number of finite element (FE) models of the pelvis using different continuum and structural modelling approaches. Four FE models were developed using different modelling approaches: continuum isotropic, continuum orthotropic, hybrid isotropic and hybrid orthotropic. The models were subjected to an iterative adaptation process based on the Mechanostat principle. Each model was adapted to a number of common daily living activities (walking, stair ascent, stair descent, sit-to-stand and stand-to-sit) by applying onto it joint and muscle loads derived using a musculoskeletal modelling framework. The resulting models, along with a structural model previously developed by the authors, were compared visually in terms of bone architecture, and their response to a single load case was compared to a continuum FE model derived from computed tomography (CT) imaging data. The main findings of this study were that the continuum orthotropic model was the closest to the CT derived model in terms of load response albeit having less total bone volume, suggesting that the role of material directionality in influencing the maximum orthotropic Young’s modulus should be included in continuum bone adaptation models. In addition, the hybrid models, where trabecular and cortical bone were distinguished, had similar outcomes, suggesting that the approach to modelling trabecular bone is less influential when the cortex is modelled separately. Full article

In this paper, a study on the impact of changing the space-based Automatic Identification System (AIS) monopole antenna orientation on its message reception performance in orbit has been conducted. The study has been carried out by maneuvering the attitude of LAPAN-A2, an equatorial orbiting microsatellite with AIS antenna fixedly mounted on the satellite’s body, into the desired orientation. Based on the analysis of the datasets collected during the maneuver, the orientation of AIS monopole antenna 45° toward its flight direction increases the overall detection performance of the AIS message, including class A ship to 208.80% and also class B ships to 175.93%. This orientation also increases the detection of AIS messages in ocean areas having low detection probability due to AIS signal collision. The result of this research could become a reference in order to specify AIS antenna position and orientation in a small satellite carrying a space-based AIS system for maritime surveillance & monitoring purposes. Full article