Appl. Sci., Volume 14, Issue 11 (June-1 2024) – 515 articles

A regionally developed and adapted dessert apple, ‘Wodarz’, was explored for its potential in apple cider production because of its consistent productivity when other apple cultivars have struggled with North Dakota’s climate. Due to the importance of yeast strain on the perceived quality of fermentation products, five commercial yeast strains, three wine yeasts (EC1118, Maurivin B, and 71B), and two cider yeasts (WLP775 and WY4766) were evaluated for their impact on the physicochemical properties, color, and sensory characteristics of ‘Wodarz’ cider. By assessing dynamic changes, such as spectral properties and sugar content, a comparison among yeasts was conducted across multiple dimensions. The lightness, chroma, and hue all showed variations throughout fermentation, though not across the final ciders. However, differences in the final color of the ciders were identified via ΔE calculations. Each yeast contributed different aromas and tastes to the final ciders. Among yeast strains, EC1118 had the strongest aroma intensity. Despite having subdued aroma intensity, 71B had strong acidity tastes and WLP775 had strong fruity tastes. Thus, our research suggests that yeast strains are an applicable factor in determining the final sensory attributes of local ‘Wodarz’ cider. This is the first report of fermentation outcomes using ‘Wodarz’ apples for cider. ‘Wodarz’ can be aromatically described using terms such as apple, honey, herbal, rose, and floral and fruit notes. The overall taste of ‘Wodarz’ cider is characterized by apple, honey, and rose notes followed by black pepper and grass. Full article

The United Nations Sustainable Development Goals (UN SDGs) ensure future human well-being. However, they face challenges due to the pressing need to reduce carbon emissions, with nearly 40% originating from the construction sector. With the current global environmental and energy crisis, there is a pressing need to address building carbon emissions and prioritise investments in passive strategies for improving indoor thermal comfort. Exploring fast-growing bio-based materials like bamboo, straw, hemp, and flax directly addresses these concerns, fostering environmental sustainability. Material selection in construction is crucial for advancing the SDGs, for example, promoting sustainable cities and communities (SDG11) and responsible consumption and production (SDG12). This paper proposes a comparative analysis of conventional and bio-based construction materials, focusing on their production stages through life cycle analysis. Tools such as Building Emissions Accounting for Materials (BEAM) and the Methodology for Relative Assessment of Sustainability (MARS) enable a detailed comparison. The results highlight the benefits of bio-based materials in storing carbon more rapidly and their lower environmental impact compared to conventional alternatives. Moreover, bio-based materials contribute to indoor moisture regulation and a healthier indoor environment, underscoring their potential to accelerate progress towards the UN SDGs through informed material choices in design practices. Full article

Background: The prevalence of falls among the older population underscores the imperative of comprehending human adaptations to gait perturbations. Dual-belt treadmills offer a controlled setting for such investigations. The purpose of this study was to examine the effect of the acceleration of one belt of the treadmill during three different phases of the gait cycle on kinematic and kinetic parameters and relate these changes to unperturbed gait. Methods: Twenty-one healthy young females walked on a treadmill in a virtual environment, in which five unexpected perturbations were applied to the left belt at the Initial Contact (IC), Mid Stance (MS), and Pre-Swing (PS) phase of the gait cycle. Data from the undisturbed gait and the first disturbance of each trial were extracted for analysis. Results: All perturbations significantly affected the gait pattern, mainly by decreasing the knee extension angle. The perturbation in the IC phase had the most significant effect, resulting in a 248.48% increase in knee flexion torque. The perturbation in the MS phase mainly affected plantar flexion torque, increasing it by 118.18%, while perturbation in the PS phase primarily increased the hip extension torque by 73.02%. Conclusions: The presence of perturbations in the IC and PS phases caused the most aggressive and significant changes in gait parameters. Full article

This research article concentrates on process conditions in addition to improving color selections in polymer compounders and developing more accurate simulation models. The feed rate (FR), temperature (T) and screw speed (SS) are three processing variables that the research investigates using general trends (GTs) and Box–Behnken design (BBD) response surface methodology. The identical set of processing settings was tweaked at three separate phases independently of one another. This study uses the experimental design to investigate process parameters’ optimization while holding all other parameters constant. This design was given the name GT. To develop this design and its statistical optimization, this study used the software of the design expert method. A regression model was run in this design, which displayed collective as well as individual effects of the parameters on color images. The values of tri-stimulus color with the best optimization had the smallest proper color variance (dE*). To obtain information on pigment characteristics, an SEM image analysis was conducted, which aids in improving future designs and overcoming manufacturing issues that affect color fluctuation properties and waste reduction for various chemical grades, both of which enhance environmentally friendly processes. Full article

Sports data analysis has significantly advanced and become an indispensable technology for planning strategy and enhancing competitiveness. In soccer, shot prediction has been realized on the basis of historical match situations, and its results contribute to the evaluation of plays and team tactics. However, traditional event prediction methods required tracking data acquired with expensive instrumentation and event stream data annotated by experts, and the benefits were limited to only some professional athletes. To tackle this problem, we propose a novel shot prediction method using soccer videos. Our method constructs a graph considering player relationships with audio and visual features as graph nodes. Specifically, by introducing players’ importance into the graph edge based on their field positions and team information, our method enables the utilization of knowledge that reflects the detailed match situation. Next, we extract latent features considering spatial–temporal interactions from the graph and predict event occurrences with uncertainty based on the probabilistic deep learning method. In comparison with several baseline methods and ablation studies using professional soccer match data, our method was confirmed to be effective as it demonstrated the highest average precision of 0.948, surpassing other methods. Full article

The valorization of wastes as an alternative or secondary raw material in various products and processes has been a solution for the implementation of sustainability, a safer environment, and the concept of circular economy in the efficient use and management of natural resources. To promote sustainability through a circular economy approach, this work tries to demonstrate the environmental gains that are obtained by bringing together, in an industrial symbiosis action, two large industrial sectors (the pulp and paper and the road pavement sectors) responsible for generating large amounts of wastes. A sustainability assessment, based on a life cycle and circular economy approach, is presented here, and discussed using a simple case study carried out on a real scale. Two wastes (dregs and grits) from the pulp and paper industry (PPI) were used to partially replace natural fine aggregates in the production of bituminous mixtures used on the top surface of road pavements. The impacts at a technical, environmental, economic, and social level were assessed and it was shown that this simple waste valorization action is not only positive for the final product from a technical point of view, but also for the environment, causing positive impacts on the different sustainability dimensions that were evaluated. Full article

This study presents a method based on active preference learning to overcome the challenges of designing reward functions for autonomous navigation. Results obtained from training with artificially designed reward functions may not accurately reflect human intentions. We focus on the limitations of traditional reward functions, which often fail to facilitate complex tasks in continuous state spaces. We propose the adoption of active preference learning to resolve these issues and to generate reward functions that align with human preferences. This approach leverages an individual’s subjective preferences to guide an agent’s learning process, enabling the creation of reward functions that reflect human desires. We utilize mutual information to generate informative queries and apply information gained to balance the agent’s uncertainty with the human’s response capacity, encouraging the agent to pose straightforward and informative questions. We further employ the No-U-Turn Sampler (NUTS) method to refine the belief model, which outperforms that constructed using the Metropolis algorithm. Subsequently, we retrain the agent using reward weights derived from active preference learning. As a result, our autonomous driving vehicle can navigate between random starting and ending points without dependence on high-precision maps or routing, relying solely on its forward vision. We validate our approach’s performance within the CARLA simulation environment. Our algorithm significantly improved the success rate of autonomous driving navigation tasks that originally failed due to artificially designed rewards, increasing it to approximately 60%. Experimental results show significant improvement over the baseline algorithm, providing a solid foundation for enhancing navigation capabilities in autonomous driving systems and advancing the field of autonomous driving intelligence. Full article

Artificial Intelligence (AI) is revolutionising many aspects of radiotherapy (RT), opening scenarios that were unimaginable just a few years ago. The aim of this study is to propose a Deep Leaning (DL) approach able to quickly generate synthetic Computed Tomography (CT) images from low-dose Cone Beam CT (CBCT) acquired on a modern linear accelerator integrating AI. Methods: A total of 53 patients treated in the pelvic region were enrolled and split into training (30), validation (9), and testing (14). A Generative Adversarial Network (GAN) was trained for 200 epochs. The image accuracy was evaluated by calculating the mean and mean absolute error (ME and ME) between sCT and CT. RT treatment plans were calculated on CT and sCT images, and dose accuracy was evaluated considering Dose Volume Histogram (DVH) and gamma analysis. Results: A total of 4507 images were selected for training. The MAE and ME values in the test set were 36 ± 6 HU and 7 ± 6 HU, respectively. Mean gamma passing rates for 1%/1 mm, 2%/2 mm, and 3%/3 mm tolerance criteria were respectively 93.5 ± 3.4%, 98.0 ± 1.3%, and 99.2 ± 0.7%, with no difference between curative and palliative cases. All the DVH parameters analysed were within 1 Gy of the difference between sCT and CT. Conclusion: This study demonstrated that sCT generation using the DL approach is feasible on low-dose CBCT images. The proposed approach can represent a valid tool to speed up the online adaptive procedure and remove CT simulation from the RT workflow. Full article

The correct assessment of the vertical jump height depends on an accurate and reliable measurement tool. This study aimed to determine the concurrent validity and reliability of the My Jump 2 app used for estimating the maximum height (MH) of the counter-movement jump (CMJ). Twenty-one male adults participated in this study. The MH of the CMJ was estimated based on the displacement of the jumper’s center of mass (force platform), the displacement time of the reflective marker placed on the jumper’s sacrum (smartphone, My Jump 2-DT) and the flight time of the jumper (smartphone, My Jump 2-FT). The assessment of the concurrent validity showed a poor agreement (ICC = 0.362; Bland–Altman bias = 12.4 cm) between the My Jump 2-FT and force platform (p < 0.001), and a good agreement (ICC = 0.858; Bland–Altman bias = −0.2 cm) between the My Jump 2-DT and force platform (p < 0.001). The ICC values for internal consistency (>0.9) indicated the excellent reliability of all measurement tools (p < 0.001). The findings revealed the high accuracy and good reliability of the My Jump 2 app for the new method of MH estimation for the CMJ, including the displacement time of the marker placed on the jumper’s sacrum. Full article

The aim of the study was to evaluate the growth, fatty acid profile and concentration of malondialdehyde of muscle tissue of meagre Argyrosomus regius fed diets with different lipid content. The long-term experiment was conducted in three feeding groups: A (CP = 52.0; CL = 21.0), B (CP = 56.0; CL = 18.0), C (CP = 48.0; CL = 16.0) with two replicates in marine net cages on Bisage Island, Adriatic Sea over 20 months. At the beginning of the experiment, fish were of equal weight (6.83 ± 1.03 g) and length (8.57 ± 0.49 cm) and were fed to satiation during the experiment. At the end of the experiment, the fish from each feeding group (n = 110) were measured and muscle tissue was collected (n = 60) and stored at −80 °C until analysis. The final weight and condition factor were significantly different (p < 0.05) between the groups. The highest ratio of crude fats and n-3/n-6-fatty acids was found in the muscle tissue of group A. Fish fed diet A also exhibited higher MDA levels compared to fish in the other feeding groups, indicating elevated levels of lipid peroxidation in muscle tissues. Experimental feeding group A showed better growth performance, a higher content of the beneficial fatty acids EPA and DHA and a more favorable n-3/n-6 ratio than feeding groups B and C. Continuously monitoring and adjusting feeding protocols in accordance with lipid content and fatty acid composition could maximize growth and health outcomes in meagre farming. Full article

Some evidence supports the influence of implant macro-design on primary stability. Additionally, tactile perception can be used to assess implant stability when placing the implant. This research aimed to quantify the primary and secondary stability of three implant systems with two different macro geometries (cylindrical and conical) determined based on the insertion torque and the implant stability quotient (ISQ) at the moment of implant placement as a function of implant-related factors (length, width, dental arch, and implant location in the arch), intraoperative factors (bone density determined subjectively by the clinician’s tactile perception), and patient-related factors (age, gender, and bone density determined objectively based on cone beam computed tomography (CBCT). Methods: 102 implants from three implant systems with two different macro geometries (conical and cylindrical) were placed in 53 patients. The insertion torque, the ISQ at the implant placement (ISQ0), and the bone quality according to the clinician’s tactile sensation were recorded on the day of the surgery. After a three-month healing period, the ISQ was re-evaluated (ISQ3). Results: The cylindrical implants exhibited significantly higher insertion torque and ISQ values at the moment of the surgery and after three months compared to the conical implants. The cylindrical implants also showed significantly lower indices of tactile evaluation of bone quality during the implant placement surgery. However, no differences were demonstrated in the bone density measured objectively using CBCT. (4) Conclusions: The cylindrical implants achieved the highest values for primary stability (Newtons × centimeter (Ncm) and ISQ) and secondary stability (ISQ after three months). The insertion torque was the variable that most influenced the ISQ on the day of the surgery. The implant location (incisors–canines, bicuspids–molars) and the implant macro geometry were the variables that most influenced the secondary stability (ISQ at three months). Full article

In order to solve the problem of traffic burst due to the increase in access points and user movement in an FTTR network, as well as to meet the demand for a high-performance network, it is necessary to rationally allocate network resources, and accurate traffic prediction is very important for dynamic bandwidth allocation in such a network. Therefore, this paper introduces a novel traffic prediction model, named CPO-BiTCN-BiLSTM-SA, which integrates the Crested Porcupine Optimizer (CPO), bidirectional temporal convolution (BiTCN), and bidirectional long short-term memory (BiLSTM) networks. BiTCN extends the traditional TCN by incorporating bidirectional data information, while BiLSTM enhances the network’s capability to learn from long sequences. Moreover, self-attention (SA) mechanisms are utilized to emphasize the crucial segments in the data. Subsequently, the BiTCN-BiLSTM-SA model is optimized by CPO to obtain the best network hyperparameters, and model training prediction is performed to achieve multi-step predictions based on single-step prediction. To evaluate the model’s generalization ability, two distinct datasets are employed for traffic prediction. Experimental findings demonstrate that the proposed model surpasses existing models in terms of the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination ($R^2$). In comparison with the traditional XGBoost model, the proposed model has an average reduction of 29.50%, 25.43%, and 25.00% in RMSE, MAE, and MAPE, respectively, with a 6.70% improvement in $R^2$. Full article

In agriculture, as precision farming increasingly employs robots to monitor crops, the use of weeding and harvesting robots is expanding the need for computer vision. Currently, most researchers and companies address these computer vision tasks with CNN-based deep learning. This technology requires large datasets of plant and weed images labeled by experts, as well as substantial computational resources. However, traditional feature-based approaches to computer vision can extract meaningful parameters and achieve comparably good classification results with only a tenth of the dataset size. This study presents these methods and seeks to determine the minimum number of training images required to achieve reliable classification. We tested the classification results with 5, 10, 20, 40, 80, and 160 images per weed type in a four-class classification system. We extracted shape features, distance transformation features, color histograms, and texture features. Each type of feature was tested individually and in various combinations to determine the best results. Using six types of classifiers, we achieved a 94.56% recall rate with 160 images per weed. Better results were obtained with more training images and a greater variety of features. Full article

Data acquisition based on network-on-chip (NoC) technology is a high-sampling-rate data acquisition scheme using low-sampling-rate analog–digital conversion (ADC) chips. It has the characteristics of multi-task parallel communication, being global asynchronous, local synchronous clock distribution, high throughput, low transmission latency, and strong scalability. High-speed data acquisition is realized through the combination of an on-chip network and time-interleaved data acquisition technology. In the time-interleaved sampling technique, the precision of clock synchronization directly affects the precision of sampling. Based on the proposed NOC data acquisition scheme, an improved White Rabbit clock synchronization protocol is applied to high-speed data acquisition to achieve high-precision synchronization of multi-channel time-interleaved sampling clocks. Firstly, the offset of the master clock and slave clock is determined by the PTP protocol, and the offset is corrected to achieve rough synchronization between the master clock and slave clock. Secondly, a digital dual-mixer time difference (DDMTD) is used to measure the phases of the master and slave clocks. After that, the phase of the slave clock is corrected through the dynamic phase-shift function of the clock’s phase-locked loop (PLL). Finally, according to the simulation results in Modelsim, the average absolute error of a TI-ADC sampling clock can be less than 20 ps. Full article

Whole-genome alignment (WGA) is a critical process in comparative genomics, facilitating the detection of genetic variants and aiding our understanding of evolution. This paper offers a detailed overview and categorization of WGA techniques, encompassing suffix tree-based, hash-based, anchor-based, and graph-based methods. It elaborates on the algorithmic properties of these tools, focusing on performance and methodological aspects. This paper underscores the latest progress in WGA, emphasizing the increasing capacity to manage the growing intricacy and volume of genomic data. However, the field still grapples with computational and biological hurdles affecting the precision and speed of WGA. We explore these challenges and potential future solutions. This paper aims to provide a comprehensive resource for researchers, deepening our understanding of WGA tools and their applications, constraints, and prospects. Full article

Spectral unmixing attempts to decompose a spectral ensemble into the constituent pure spectral signatures (called endmembers) along with the proportion of each endmember. This is essential for techniques like hyperspectral imaging (HSI) used in environment monitoring, geological exploration, etc. Several spectral unmixing approaches have been proposed, many of which are connected to hyperspectral imaging. However, most extant approaches assume highly diverse collections of mixtures and extremely low-loss spectroscopic measurements. Additionally, current non-Bayesian frameworks do not incorporate the uncertainty inherent in unmixing. We propose a probabilistic inference algorithm that explicitly incorporates noise and uncertainty, enabling us to unmix endmembers in collections of mixtures with limited diversity. We use a Bayesian mixture model to jointly extract endmember spectra and mixing parameters while explicitly modeling observation noise and the resulting inference uncertainties. We obtain approximate distributions over endmember coordinates for each set of observed spectra while remaining robust to inference biases from the lack of pure observations and the presence of non-isotropic Gaussian noise. As a direct impact of our methodology, access to reliable uncertainties on the unmixing solutions would enable robust solutions to noise, as well as informed decision-making for HSI applications and other unmixing problems. Full article

In Europe, it is estimated that 14% of existing buildings were built before 1919, whereas 26% were built before 1945. In Romania, about 31% of the buildings date from before 1961, contributing to the current stock of old buildings with historic and architectural value in the country. This paper illustrates the current state of buildings with historic and architectural value in Romania, alongside a case study of a representative administrative building in Câmpulung, Romania. The analysis of the Town Hall building in Câmpulung, Romania, demonstrates that potential energy savings of up to 47.53% can be achieved by implementing interventions such as upgrading windows, insulating the attic, and installing photovoltaic panels. The highest energy reduction is obtained by replacing the window glass with a value of 18.16% with attic insulation with a value of 16.1%. This paper also presents indoor measurements of temperature and humidity in different offices positioned in the north and the south. The study conducted on the south façade office revealed consistent temperatures ranging from 21.7 °C to 24.4 °C, with an average of 23.31 °C. However, the humidity levels fluctuated considerably, ranging from 17.1% to 39.1%, with an average of 26.89%. The sun-exposed section of the building saw relatively stable temperature conditions, but the varying humidity levels could have a detrimental impact on the quality of the indoor atmosphere and potentially decrease the effectiveness of the workforce. By contrast, the north façade office exhibited lower and more fluctuating temperatures, ranging from 19.8 °C to 23.6 °C, with an average of 21.74 °C. Additionally, it had higher and more stable humidity levels, ranging between 19.5% and 41.7%, with an average of 29.83%. A thermographic analysis was performed on the north façade of the Câmpulung Town Hall, utilizing thermal imaging technology to detect areas of heat loss, and thus identifying the energy inefficiency problems of the building’s exterior. The investigation found notable variations in temperature, especially around the windows, where temperatures could be as high as 14.1 °C, highlighting the insufficiency of the building’s antiquated timber-framed windows in preventing energy loss. Full article

Segmentation methods based on convolutional neural networks (CNN) have achieved remarkable results in the field of medical image segmentation due to their powerful representation capabilities. However, for brain-tumor segmentation, owing to the significant variations in shape, texture, and location, traditional convolutional neural networks (CNNs) with limited convolutional kernel-receptive fields struggle to model explicit long-range (global) dependencies, thereby restricting segmentation accuracy and making it difficult to accurately identify tumor boundaries in medical imaging. As a result, researchers have introduced the Swin Transformer, which has the capability to model long-distance dependencies, into the field of brain-tumor segmentation, offering unique advantages in the global modeling and semantic interaction of remote information. However, due to the high computational complexity of the Swin Transformer and its reliance on large-scale pretraining, it faces constraints when processing large-scale medical images. Therefore, this study addresses this issue by proposing a smaller network, consisting of a dual-encoder network, which also resolves the instability issue that arises in the training process of large-scale visual models with the Swin Transformer, where activation values of residual units accumulate layer by layer, leading to a significant increase in differences in activation amplitudes across layers and causing model instability. The results of the experimental validation using real data show that our dual-encoder network has achieved significant performance improvements, and it also demonstrates a strong appeal in reducing computational complexity. Full article

The circular hollow steel-reinforced concrete (HSRC) column consists of an inner circular hollow steel tube and outer circular hollow reinforced concrete (RC). This design provides several advantages, including being lightweight, having a wide sectional profile, and having a high flexural stiffness. This paper aims to investigate the behavior of the circular HSRC columns under axial compression through testing and finite element (FE) modeling. An FE model was established to simulate the circular HSRC columns under axial compression, which was validated against the test data. Additionally, the load distribution and the interface stress between the outer hollow RC and inner steel tube were analyzed. Subsequently, a systematic parametric analysis was conducted on the diameter (d) and thickness (t) of the steel tube; slenderness ratio (λ); strength of concrete (f_cu); yield strength of steel tube (f_sy), longitudinal rebar (f_ly), and stirrup (f_gy); as well as the stirrup spacing (s). The critical influencing factors of the circular HSRC columns under axial compression were identified. f_cu, λ, d, f_ly, and f_sy dramatically influence the bearing capacity, and the stiffness is notably affected by λ and f_cu. Finally, three simplified design methods were summarized and evaluated for calculating the bearing capacity of the circular HSRC columns under axial compression. Full article

This study aimed to conduct a kinetic analysis of the lateral and vertical jumps of elite male basketball players through a 12-week axial stability training program to improve sports performance. Thirty elite Taiwanese male basketball players were openly recruited and divided into experimental groups and control groups. The experimental group conducted the test twice a week, a 12-week (24-session) axial stability training program intervention in total, and the control group only received general basketball training. A double-track force plate was used to measure lateral and vertical jumps in order to understand their dynamic parameters. Finally, a difference analysis between the post-test of lateral and vertical jumps was conducted. The results show that the axial stability training program affected the activation of the abdominal and lower limb extensor muscles and had a stabilizing effect on the muscles of the experimental group. When the participants conducted a lateral jump, they were able to stand firm within 1 s and take off instantly. The θ value of the T-PRF ranged from 60.7° to 68.6°. The post-test of the participants’ vertical jump showed that the kurtosis of the RFD was steeper, the time required for the RFD was shorter, the GRF and the duration of passage increased, and the experimental group was better than the control group in all post-tests. By comparing the two types of jumps, it was found that they had the vertical force in common. The main differences were in the reaction force of the leg strength, the jump distance and height, and the take-off angle. Full article

Consumers are more interested in replacing commonly used chemical preservatives with natural substances. The effect of 5, 10, 20, 40, and 80 mg of thyme and lemongrass essential oils (THY and LMG, respectively) per 100 g of wheat flour was studied from the viewpoints of dough rheology, dough leavening progress, and the results of laboratory baking trial. Changes in dough viscoelastic properties were evaluated by the Mixolab rheometer and the company software. The higher the thyme oil portion, the higher the dough structure destruction by kneading and heat input in torque point C2, and consecutively shorter stability of dough consistency (up to one-half of the values recorded for the control); reversely, the LMG did not affect both features verifiably. In the 90 min leavening test, a dough weight loss was decelerated by both essential oils similarly. During the baking test, the average volume of wheat small breads as control was evaluated on level 167 mL (bread yield 451 mL/100 g flour). Independently of the dose of the THY or LMG, small bread volumes oscillated between 148–168 and 135–161 mL (average bread yields 442 and 443 mL/100 g flour, respectively). The shelf life of the products with a higher portion of essential oil was extended by up to 7 days. Full article

Unintentional electromagnetic (EM) emissions often include information about the data processed by electronic devices. Intrusion based on an unintentional EM emission leaves no evidence of an attacker’s activity, while the data owner is unaware that it has been lost. EM attacks can be performed without physically damaging a device that operates regularly. The most typical intrusion activities involve sensitive data exfiltration using various methods that do not require the physical connection of devices to the computer network or communication channels. This research examines EM emissions from computer monitors, wireless keyboards and mice, printers, scanners, conductors, piezoelectric sensors (PES), and radio frequency identification (RFID) devices. The telecommunication electronics material protected from emanating spurious transmissions (TEMPEST) profiling as a performance engineering of the EM footprint is discussed. This study also presents different TEMPEST standards and highlights their importance concerning unintentional EM radiation. Full article

Cone beam computed tomography (CBCT) has emerged as a crucial radiographic technique for orthodontic diagnosis and treatment planning, particularly for cases requiring the assessment of complex anatomical relationships. In the first part of the study, we examined metric characteristics measured on 3D cranial models of patients before and after surgery. In the second part, we conducted more complex data processing, analyzing a set of 12 cranial feature points using Procrustes analysis to quantify and visually represent surgical modifications. The third part involved comparing 3D facial surfaces using Iterative Closest Point (ICP) alignment and nearest point-to-point distances. Additionally, we tested point configurations in the facial soft tissues. The study included a group of orthodontic patients from whom CBCT data and 3D facial scans were obtained during treatment. The results demonstrated that each method could assess preoperative and postoperative changes to varying degrees. They also highlighted potential gender differences in surgical modifications that warrant further investigation and consideration during surgical planning. The aim of our study was to compare 3D visualizations of skull and facial models before and after surgery, to assess the degree of relative agreement or similarity, and to identify any morphological differences. Full article

Traditional iterative closest point (ICP) registration algorithms are sensitive to initial positions and easily fall into the trap of locally optimal solutions. To address this problem, a point cloud registration algorithm is put forward in this study based on adaptive neighborhood eigenvalue loading ratios. In the algorithm, the resolution of the point cloud is first calculated and used as an adaptive basis to determine the raster widths and radii of spherical neighborhoods in the raster filtering; then, the adaptive raster filtering is implemented to the point cloud for denoising, while the eigenvalue loading ratios of point neighborhoods are calculated to extract and match the contour feature points; subsequently, sample consensus initial alignment (SAC-IA) is used to carry out coarse registration; and finally, a fine registration is delivered with KD-tree-accelerated ICP. The experimental results of this study demonstrate that the feature points extracted with this method are highly representative while consuming only 35.6% of the time consumed by other feature point extraction algorithms. Additionally, in noisy and low-overlap scenarios, the registration error of this method can be controlled at a level of 0.1 mm, with the registration speed improved by 56% on average over that of other algorithms. Taken together, the method in this study cannot only ensure strong robustness in registration but can also deliver high registration accuracy and efficiency. Full article

Doing business in the age of information abundance indicates the importance of developing information management skills, enabling the individuals to be more productive but also more flexible to changes. In literature, innovation, as a driver of a firm’s performance, has been highlighted, while problem-solving capabilities are considered one of the key assumptions for the implementation of digital transformation strategy. The purpose of the study is to investigate the impact of digital citizenship and digital transformation enablers on innovation and problem-solving capabilities. The data collection procedure commenced by selecting a random sample of private companies from various industries A survey was carried out, and a total sample of 247 valid questionnaires were collected. The PLS-SEM method was used to test the relationships in the proposed model. The study found that some elements of digital citizenship are positively associated with digital transformation enablers. The results of the study confirmed that technology-based digital transformation enablers positively contribute to innovation and problem-solving capabilities. Also, technological facets of digital transformation enablers realize a mediating role in the relationship between digital citizenship and innovation, as well as with problem-solving capabilities. Full article

To solve the problems of brittleness, high cost, and the complicated construction process of traditional filling materials for filling abandoned roadways, various aspects of the physical and mechanical properties of the materials were studied using laboratory tests and were applied in coal mines. The research shows that the self-developed inorganic cementitious filling material has the advantages of being low cost, easy to cut and wash, and having good filling performance. A foaming agent is a direct factor in controlling the volume expansion of inorganic cementitious filling materials; the increase in the volume of slurry foaming with the addition of a foaming agent initially showed a large and then a small trend with a foaming agent dosage of 100 g. The increase in the volume of slurry foaming is the largest at 56.28%. The effect of the B material (calcium stearate+ J85 rapid-setting agent) on the foaming time and the number of foaming times of the slurry was significant. Foam stabilizers in the B material make the slurry particles uniformly distributed inside the slurry, while quick-setting agents control the initial and final setting time by affecting the slurry setting speed. The water/cement ratio directly affects the foaming times of the slurry but has little effect on the foaming time and setting speed. When the water/cement ratio is less than 1:2, the slurry foaming effect is poor, and the foaming volume remains unchanged. The strength of the material is significantly affected by the proportion of B material and the amount of blowing agent, and the compressive strength of materials with different compositions and ratios varies greatly. A whole set of systems of new inorganic gelatinized abandoned roadway filling materials was researched and applied in coal mines, achieving good results. Full article

This study investigated the effect of digital therapeutics on ROM, flexibility, dynamic balance, satisfaction, and adherence. A sample of 34 volunteers was randomly assigned into a Digital Therapeutic Group (DTG) (n = 17) and a Non-Digital Therapeutic Group (NDG) (n = 17). The groups performed four calf muscle stretches and two hamstring stretches for 4 weeks. Flexibility and balance were evaluated to assess the effectiveness of the exercise program. A survey was conducted to assess subjects’ satisfaction, and exercise performance record papers and video records were assessed for exercise adherence. The paired t-test was used to compare the two populations before and after the program. The independent t-test was used to compare the change scores between groups. In the pre- and post-comparison within each group, the DTG group showed significant differences in all items except the dorsiflexion range of motion (ROM) (p < 0.05). The left straight leg raise (SLR) was significantly different (p < 0.05), and there was a significant difference in exercise satisfaction and participation between the two groups (p < 0.05). DTG showed improved flexibility, dynamic balance, and higher exercise satisfaction and adherence than NDG. The application, providing lower extremity stretching, can improve dorsiflexion ROM, flexibility of the lower extremity, dynamic balance, exercise satisfaction, and exercise adherence. Full article

This study investigated the impact of tobacco exposure, specifically through heating and burning, on periodontopathogens in the subgingival microbiome among clinically healthy individuals and those diagnosed with periodontitis. The sample comprised 66 subjects (26–56 years, median 38 yrs; 64% females) classified as non-smokers, classic cigarette smokers, and tobacco heating system (THS) smokers (each N = 22). Full-mouth periodontal examination was performed, and 330 paper-point samples from periodontal pockets were collected. Next-generation sequencing of 16S rRNA genes was conducted to identify the composition of subgingival microbiome. Periodontitis prevalence among the groups was ranked as THS (41%) < non-smokers (44%) < cigarette smokers (68%), without statistically significant differences between the groups. The number of perio-pathogenic species was higher in subjects with periodontitis compared to those without (median 7 vs. 6 species; p = 0.005) but without significant differences between exposure groups: non-smokers (6) = smokers (6) < THS (6.5). When combining exposure and periodontal status, each smoker group had more perio-pathogenic species than non-smokers: non-smokers without periodontitis (5) < smokers without periodontitis (5.5) < THS without periodontitis (6); non-smokers with periodontitis (6.5) < THS with periodontitis (7) = smokers with periodontitis (7). Multiple linear regression indicated periodontitis as the sole predictor of perio-pathogenic species quantity, irrespective of the type of tobacco consumption, sex, age, or oral hygiene (R² = 0.163; p = 0.005). In conclusion, the quantity of perio-pathogenic species in the subgingival microbiome was more influenced by periodontitis than by exposure to tobacco smoke, regardless of whether it was heated or burned. Full article

Human pose estimation focuses on methods that allow us to assess ergonomic risk in the workplace and aims to prevent work-related musculoskeletal disorders (WMSDs). The recent increase in the use of Industry 4.0 technologies has allowed advances to be made in machine learning (ML) techniques for image processing to enable automated ergonomic risk assessment. In this context, this study aimed to develop a method of calculating joint angles from digital snapshots or videos using computer vision and ML techniques to achieve a more accurate evaluation of ergonomic risk. Starting with an ergonomic analysis, this study explored the use of a semi-supervised training method to detect the skeletons of workers and to estimate the positions and angles of their joints. A criticality index, based on RULA scores and fuzzy rules, is then calculated to evaluate possible corrective actions aimed at reducing WMSDs and improving production capacity using a collaborative robot that supports workers in carrying out critical operations. This method is tested in a real industrial case in which the manual assembly of electrical components is conducted, achieving a reduction in overall ergonomic stress of 13% and an increase in production capacity of 33% during a work shift. The proposed approach can overcome the limitations of recent developments based on computer vision or wearable sensors by performing an assessment with an objective and flexible approach to postural analysis development. Full article

An experiment was conducted on a prestressed concrete (PC) composite girder bridge with steel truss webs to investigate its flexural performance. The mechanical characteristics and failure modes of a PC composite girder bridge with steel truss webs was clarified. Finite element (FE) analysis was carried out, and the influence of the girder height-to-span ratio and eccentric loading effect on the flexural performance of a composite beam bridge with a steel truss web was discussed. The method for calculating the cracking bending moment, the bending moment at the rebar yield stage, and the ultimate bending moment of a PC composite girder with steel truss webs was proposed. Key findings include that, in both the elastic and cracking elastic stages, the strain of the bottom and top conforms to the plane-section assumption. Throughout the loading process, there was no occurrence of joint failure or local buckling failure in the steel truss webs; the composite girder ultimately fails due to excessive deformation, indicating that the overall mechanical performance of the composite beam is good. The deflection and stress in the mid-span section decrease with an increasing height-to-span ratio, and there are significant impacts of eccentric loading on deflection and stress. Compared with the results of the FE analysis and test, the calculation methods of the cracking moment, reinforcement yield moment, and ultimate moment of PC composite girders with steel truss webs presented in this paper have a high accuracy. Full article