Eng

This study evaluates the effectiveness of advanced statistical and geospatial methods for analyzing radon concentration distributions in indoor environments, using the district of San Martín de Porres, Lima, Peru, as a case study. Radon levels were monitored using LR-115 nuclear track detectors over three distinct measurement periods between 2015 and 2016, with 86 households participating. Detectors were randomly placed in various rooms within each household. Normality tests (Shapiro–Wilk, Anderson–Darling, and Kolmogorov–Smirnov) were applied to assess the fit of radon concentrations to a log-normal distribution. Additionally, analysis of variance (ANOVA) was used to evaluate the influence of environmental and structural factors on radon variability. Non-normally distributed data were normalized using a Box–Cox transformation to improve statistical assumptions, enabling subsequent geostatistical analyses. Geospatial interpolation methods, specifically Inverse Distance Weighting (IDW) and Kriging, were employed to map radon concentrations. The results revealed significant temporal variability in radon concentrations, with geometric means of 146.4 Bq·${\text{m}}^{-3}$, 162.3 Bq·${\text{m}}^{-3}$, and 150.8 Bq·${\text{m}}^{-3}$, respectively, across the three periods. Up to 9.5% of the monitored households recorded radon levels exceeding the safety threshold of 200 Bq·${\text{m}}^{-3}$. Among the interpolation methods, Kriging provided a more accurate spatial representation of radon concentration variability compared to IDW, allowing for the precise identification of high-risk areas. This study provides a framework for using advanced statistical and geospatial techniques in environmental risk assessment. Full article

Nowadays, the increase in sustainable investments, especially when it comes to energy efficiency in buildings, has been recognised as an important pillar towards reductions in energy consumption. In this context, there is a need for efficient and user-friendly digital tools that can support decision-making procedures for all involved parties in the energy efficiency value chain. The scope of this paper is to present a high-level architecture and system design of the energy efficiency marketplace developed within the framework of the ENERGATE project, an EU-funded initiative aiming to assist Building Owners, Project Implementors, and Financial Institutions to collaborate and execute energy-efficient building renovations. To this end, structured data through predefined information entries will be collected. This will facilitate the interactions between heterogenous stakeholders and contribute to the standardisation of processes. The paper focuses on the functional description of the system design of the ENERGATE platform by defining the architecture, components, modules, interfaces, and structured data, as well as highlighting the requirements of potential platform users and design principles to meet the necessary requirements while ensuring security, reliability, and effectiveness. Full article

The supercritical water gasification (SCWG) and carbon dioxide gasification of agro-industrial and urban waste residues—Coffee Husk, Eucalyptus Biochar, Energy Sugarcane, and Refuse-Derived Fuel (RDF)—were studied using TeS^® v.2 software, which employs a non-stoichiometric thermodynamic model to minimize Gibbs free energy and predict equilibrium compositions. The effects of temperature (873.15–1273.15 K), pressure (220–260 bar), biomass feed (18–69%), and gasifying agents on hydrogen and methane formation were analyzed. Higher temperatures and biomass feed percentages favored hydrogen production, while lower temperatures increased methane formation. At 1273.15 K, RDF showed the highest hydrogen yield in SCWG, rising from 0.43 to 1.42 mol, followed by Energy Sugarcane (0.39 to 1.23 mol), Coffee Husk (0.34 to 0.74 mol), and Eucalyptus Biochar (0.33 to 0.62 mol). In CO₂ gasification, hydrogen yields were lower but followed a similar trend. At 873.15 K, RDF also exhibited the highest methane increase in SCWG, from 0.14 to 0.91 mol, followed by Energy Sugarcane (0.12 to 0.65 mol), Coffee Husk (0.11 to 0.36 mol), and Eucalyptus Biochar (0.11 to 0.29 mol). Methane formation in CO₂ gasification was significantly lower, with RDF increasing from 0.0035 to 0.35 mol, followed by Energy Sugarcane (0.0024 to 0.24 mol), Coffee Husk (0.0002 to 0.058 mol), and Eucalyptus Biochar (0.0002 to 0.028 mol). On the other hand, a slight increase in hydrogen formation was observed as pressure decreased, while the opposite effect was observed for methane formation, with a small increase in its production as pressure increased. The impact of pressure change on the equilibrium compositions was not as significant as the effect observed by varying temperature; this behavior was observed in both gasification processes studied. Additionally, the behavior of the H₂/CO molar ratio for each biomass in the studied gasification processes was analyzed to assess the potential uses of the produced syngas. It was observed that the SCWG resulted in significantly higher H₂/CO molar ratios compared to CO₂ gasification. Full article

Today, the machining of heat-resistant alloys based on triple, quad, or penta equilibria high-entropy alloy systems of elements (ternary, quaternary, quinary iron-, titanium-, or nickel-rich alloys), including dual-phase by Gibb’s phase rule, steels of the austenite class, and nickel- and titanium-based alloys, are highly relevant for the airspace and aviation industry, especially for the production of gas turbine engines. Cutting tools in contact with those alloys should withstand intensive mechanical and thermal loads (tense state of 1.38·10⁸–1.54·10⁸ N/m², temperature up to 900–1200 °C). The most spread material for those tools is cutting ceramics based on oxides, nitrides of the transition and post-transition metals, and metalloids. This work considers the wear resistance of the cutting insert of silicon nitride with two unique development coatings — titanium–zirconium nitride coating (Ti,Zr)N and complex quad nitride coating with TiN content up to 70% (Ti,Al,Cr,Si)N with a thickness of 3.8–4.0 µm on which microtextures were produced by the assisted electric discharge machining with the electrode-tool of ø0.25 mm. The microtextures were three parallel microgrooves of R0.13^+0.02 mm at a depth of 0.025₋₀.₀₅. The operational life was increased by ~1.33 when the failure criterion in turning nickel alloy was 0.4 mm. Full article

The presented paper analyzes the impact of limestone dust accumulation on photovoltaic (PV) panel performance, focusing on the specific surrounding conditions near quarries. The results from the performed field measurements show that high concentrations of limestone dust accumulate significantly faster in these areas, and a hard layer is formed in the presence of moisture. This layer of dust is resistant to removal, even in moderate precipitation and winds with speeds between 6 and 9 m/s, making it a significant problem for the long-term performance of the systems. The analysis revealed that the lack of systematic cleaning of the panels leads to a drop in efficiency of over 20%, with this loss pointedly limiting the return on investment. This study highlights the need for innovative maintenance approaches, such as regular cleaning, use of special coatings and adapting designs to specific environmental conditions. This is essential for the development of strategies to manage, maintain and improve PV systems in areas with high levels of dust pollution. Full article

The perception of animal emotions is key to enhancing veterinary practice, human–animal interactions, and protecting domesticated species’ welfare. This study presents a unique emotion classification deep learning-based approach for pet animals. The actual and emotional status of dogs and cats have been classified using a modified EfficientNetB5 model. Utilizing a dataset of images classified into four different emotion categories—angry, sad, happy, and neutral—the model incorporates sophisticated feature extraction methods, such as Dense Residual Blocks and Squeeze-and-Excitation (SE) blocks, to improve the focus on important emotional indicators. The basis of the second strategy is EfficientNetB5, which is known for providing an optimal balance in terms of accuracy and processing capabilities. The model exhibited robust generalization abilities for the subtle identification of emotional states, achieving 98.2% accuracy in training and 91.24% during validation on a separate dataset. These encouraging outcomes support the model’s promise for real-time emotion detection applications and demonstrate its adaptability for wider application in ongoing pet monitoring systems. The dataset will be enlarged, model performance will be enhanced for more species, and real-time capabilities will be developed for real-world implementation. Full article

In this paper, a review of the advances in brushless synchronous motors is presented because there has been an increasing interest in advanced motor control and to address the weaknesses of conventional motor control. The traditional motor control strategies, for example, proportional plus integral controllers (PIs), are simple and easy to maintain. On the contrary, they require accurate tuning and are affected by motor parameter variations. To address these challenges and many others (power factors, torque ripple, current limit, voltage limit, speed limit), advanced control methods are required to enhance the performance of the motor drive control. The advanced control techniques include model predictive control, slide mode control, reinforcement learning, and fuzzy logic control. This paper provides a comprehensive review of advances in control methods and addresses the challenges and limitations associated with their practical application. Full article

The use of segmented stator and rotor designs in AC electric machine construction offers several significant advantages, including a high-copper fill factor, increased torque density, improved field-weakening performance, simplified manufacturing processes, and enhanced mechanical strength. Additionally, segmented designs allow for the incorporation of oriented steel—either partially or fully—which exhibits excellent magnetic properties in the rolling direction, resulting in more efficient machine performance. However, lamination segmentation also introduces challenges. Parasitic air gaps between segments and an increased number of cut edges in the assembled stack can alter the magnetic properties of the machine, potentially leading to degraded performance. Furthermore, the use of oriented steel remains complex, as its highly nonlinear magnetic properties vary depending on the direction of the magnetic flux. This paper reviews the widely adopted stator and rotor segmentation techniques available in the literature, discussing their potential benefits and limitations. It also covers key aspects such as popular manufacturing approaches, the impact of segmentation on machine performance, advanced finite-element analysis (FEA) techniques for numerical modeling, and experimental methods for evaluating the performance of segmented stator and rotor constructions in AC machines. By addressing these areas, this work provides a comprehensive resource for machine designers seeking to develop AC machines with segmented stators and rotors. Full article

In this work, electron beam welds between Cu and Al plates were formed using different power modes, namely 1800 W, 2400 W, and 3000 W. The structure, microhardness, and tensile strength of the raw materials and the weld seams were studied. The low power of the electron beam resulted in the improper penetration and insufficient depth of the weld seam. The low power resulted in high cooling rates, which hindered the nucleation of the copper and aluminum particles. A number of intermetallic compounds (IMCs) were formed, including the metastable Cu₉Al₄ one. An increase in the power of the electron beam reduced the cooling rate and increased the miscibility between the materials. This resulted in the formation of a mostly homogeneous structure comprising an αAl solid solution and dendritic eutectic CuAl₂ intermetallic compounds. A preferred crystallographic orientation of the aluminum phase was detected regarding the sample prepared using a power of 3000 W, forming a specific texture towards the {111} family of crystallographic planes, which is the closest-packed structure. This plane characterizes the highest chemical activity and the highest plasticity. As a result, this sample exhibited the best chemical bonding between the IMCs and the aluminum matrix and the best microhardness and tensile test values. Full article

Roads are critical components of infrastructure, and assessing their quality is essential to ensure the safe transport of people and goods, which in turn supports economic prosperity. Various factors, such as subsurface conditions, moisture content, and temperature, influence road performance and can degrade their efficiency as transportation networks. While surface road defects can often be identified through visual inspection, information about subsurface extensions, their impact on structural integrity, and potential risks remain concealed. This study aimed to perform a comparative analysis of dielectric permittivity (ε) using time-lapse Ground Penetrating Radar (GPR) measurements on pre- and post-renovated road sections. This study also sought to evaluate the effectiveness of this approach for road assessment and to employ forward modeling for a deeper understanding of road defects and their associated hazards. Results revealed that the pre-renovated road section exhibited significant fluctuations in dielectric values, ranging from 3.13 to 15.9. In contrast, the post-renovated section showed consistent values within a narrow range of 5 to 6.6. Different crack types were classified, and the mean ε for each visually identified crack type was calculated. Despite the higher frequency of transverse cracks compared to other defects, longitudinal cracks exhibited the highest mean dielectric value (~10.3), while alligator cracks had the lowest (~8.33). Numerical simulations facilitated accurate interpretation of the defects identified in the road section, providing insights into their nature and associated risks. The methodology used for crack classification and numerical simulation can be applied to other road sections globally, offering a standardized approach to road assessment and maintenance planning. Full article

Fault detection and classification in transmission lines are critical for maintaining the reliability and stability of electrical power systems. Quick and accurate fault detection allows for timely intervention, minimizing equipment damage, and reducing downtime. This study addresses the challenge of effective fault classification, particularly when dealing with smaller, more practical datasets. Initially, the study examined the performance of conventional machine learning algorithms on a comprehensive dataset of 7681 samples, demonstrating high accuracy owing to the inherent symmetry of sinusoidal voltage and current signals. However, the true efficacy of these algorithms was evaluated by minimizing the dataset to 231 training samples, with the remainder being used for testing. A novel Multi-Target Ensemble Classifier was developed to improve classification accuracy. The proposed algorithm achieved an impressive overall accuracy of 0.829165, outperforming traditional methods, including the K-Nearest Neighbors Classifier, support vector classification, random forest classifier, decision tree classifier, AdaBoost classifier, gradient boosting classifier, and Gaussian NB. This research highlights the importance of efficient fault classification techniques in power systems and proposes a superior solution in the form of a multitarget ensemble classifier. Full article

The paramount significance of temperature’s influence on rock engineering endeavors underscores its profound capacity to alter the physical and mechanical attributes of rocks. Among the most crucial techniques utilized to thermally induce damage and diminish the tensile resilience of rock materials are microwave irradiation and heat treatment. This research examines and compares the effects of these two modalities on the dynamic tensile characteristics of Fangshan granite (FG), including their implications under conditions of overload and dependencies on loading rate, utilizing the sophisticated Split Hopkinson Pressure Bar (SHPB) apparatus. In particular, the dynamic real tensile strength (RST) of Brazilian disc (BD) specimens was meticulously gauged and contrasted after subjecting them to microwave irradiation at a potent 6 kW for varying durations (1.5, 3.0, and 4.5 min) and heat treatment across distinct temperature thresholds (178 °C, 345 °C, and 473 °C). To enhance the precision of the measurements, an overload correction was implemented by affixing a strain gauge in close proximity to the core of the BD specimen. The conventional dynamic tensile strength exhibited a reduction of approximately 20 to 30% with the prolongation of microwave radiation time. Furthermore, an additional decrease in tensile strength was observed with the elevation of heat treatment temperatures, reaching a maximum reduction of up to 40%. This phenomenon can be attributed to the proliferation and expansion of microcracks within the rock matrix. It was noteworthy that the RTS, corrected for overloading effects, exhibited a comparable trend to the dynamic traditional tensile strength (TTS). Both were significantly correlated with the loading rate, with the dynamic tensile strength demonstrating an average decrease of approximately 25% when the loading rate was increased. Interpolation and fitting analyses were employed to investigate the effects of microwave radiation duration, heat treatment temperature, and loading rate on the dynamic tensile strength of FG samples. Furthermore, it was established that the overload ratio increased in conjunction with an increase in microwave radiation duration, heat treatment temperature, and loading rate, reaching a maximum value of 1.5. Full article

The formation of geotechnical structures on foundations composed of low-strength soils is associated with a number of risks and difficulties. Soils such as clay-salt slurries are characterized by low bearing capacity and a tendency to deform under load. In this study, a numerical simulation of the stability analysis of an embankment constructed on low-strength soils consisting of clay-salt slurries is carried out, and the study of the dependence of the stability and behavior of the embankment on the configuration of this foundation, without taking into account the embedment of rocks and with introduction of rocks into the geotechnical system, is considered. The results prove that the sloping configuration of low-strength soils greatly complicates the stability of the embankment. It is noted that the stability factor is significantly reduced under the influence of loads on low-strength soil, particularly when the geotechnical system has a configuration with slope angles of 5° and 10°, and, in addition, when rocks are embedded in low-strength soil if the underlying soil layer is a weak foundation. In view of this, the assessment of embankment stability on clay-salt slurries requires careful analysis due to a number of specific characteristics of these soils that create complex geotechnical conditions. Full article

Objectives: This study aims to evaluate the potential of lightweight concrete mixtures incorporating sustainable materials, such as nopal mucilage and aloe vera, to enhance thermal and structural performance while promoting eco-friendly construction practices. The objective is to analyze their effects on physical, mechanical, and thermal properties to optimize mixture design. Methods/Analysis: Six lightweight concrete mixtures were prepared using varying dosages of tuff, expanded clay, nopal mucilage, and aloe vera as lightweight and stabilizing agents. To assess their performance, a series of physical tests (bulk density, water absorption, and slump), mechanical tests (compressive strength), and thermal characterizations (conductivity, heat capacity, and resistivity) were conducted. Fractal analysis was employed to evaluate the structural complexity of the mixtures. Findings: The results revealed significant differences based on the materials used. Mixtures with aloe vera exhibited extreme water absorption (up to 11.472%) and varying consistency, from fluid (“spreads”) with tuff to workable with expanded clay. When combined with expanded clay, Nopal mucilage-based mixtures showed lower workability but higher compressive strengths (up to 11.447 MPa). Expanded clay increased bulk density and enhanced thermal efficiency, with mixtures incorporating aloe vera or nopal mucilage demonstrating high heat retention and structural complexity. The compressive strengths ranged from 7.343 MPa (aloe vera-tuff) to 12.207 MPa (water-tuff), highlighting the impact of stabilizing agents on mechanical performance. Novelty or Improvement: This study introduces a novel evaluation of lightweight concrete mixtures using nopal mucilage and aloe vera, focusing on their synergistic effects with lightweight aggregates such as tuff and expanded clay. The findings provide valuable insights into optimizing eco-friendly concrete mixtures with improved thermal retention, workability, and mechanical properties, offering a sustainable alternative for modern construction. Full article

Eggshells are an inorganic waste, and their accumulation rate is increasing globally, complicating waste management. However, the European Union defines eggshells as low-risk material that can be recycled and reused safely in other applications. Their chemical composition renders them an attractive precursor of calcium phosphate materials (CaPs). Because of their remarkable biocompatibility and capacity for natural degradation, CaPs are frequently employed in biomedical engineering applications. In this research, the wet precipitation method was employed for fabricating CaP powder. Initially, the eggshells were processed into CaCO₃ powder and then reacted with HCl to obtain CaCl₂ (aq). This reacted with Na₂HPO₄ to obtain a precipitate that was filtered and dried. The precipitate in powder form underwent X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) analysis to evaluate its microstructure, and elemental and phase composition. The results indicated that the recovered powder was brushite. Full article

This paper utilizes the digital integrated circuit testing model to compute the test yield curve of future wafers and explore the influence of test guardband (TGB) on quality and yield. With the passage of three years since the COVID-19 pandemic disrupted semiconductor production lines, the semiconductor manufacturing industry still faces chip shortages. Although initiatives such as the CHIPS and Science Act in the United States have helped stabilize chip supply chains, manufacturers still face inventory shortages and delayed deliveries. Moreover, the backwardness and inaccuracy of semiconductor test equipment have led to a decline in both test yield and wafer quality, resulting in reduced shipments. Therefore, to mitigate yield losses and enhance the test yield and shipment volume of semiconductor products, this paper proposes a diverse test method (DTM) to improve test outcomes through the alteration of the testing strategy and TGB adjustment. Furthermore, according to the wafer estimation table published in the IEEE International Roadmap for Devices and Systems (2023), the proposed DTM can effectively enhance the test yield of wafers and improve the testing capabilities of ATE testers (automatic test equipment). Consequently, suppliers can stabilize the chip supply chain and enhance their companies’ profits and reputation by improving chip test yield. Full article

This article presents a cost-effective camera network system that employs neural network-based object detection and stereo vision to assist a pan–tilt–zoom camera in imaging fast, erratically moving small aerial targets. Compared to traditional radar systems, this approach offers advantages in supporting real-time target differentiation and ease of deployment. Based on the principle of knowledge distillation, a novel data augmentation method is proposed to coordinate the latest open-source pre-trained large models in semantic segmentation, text generation, and image generation tasks to train a BicycleGAN for image enhancement. The resulting dataset is tested on various model structures and backbone sizes of two mainstream object detection frameworks, Ultralytics’ YOLO and MMDetection. Additionally, the algorithm implements and compares two popular object trackers, Bot-SORT and ByteTrack. The experimental proof-of-concept deploys the YOLOv8n model, which achieves an average precision of 82.2% and an inference time of 0.6 ms. Alternatively, the YOLO11x model maximises average precision at 86.7% while maintaining an inference time of 9.3 ms without bottlenecking subsequent processes. Stereo vision achieves accuracy within a median error of 90 mm following a drone flying over 1 m/s in an 8 m $\times$ 4 m area of interest. Stable single-object tracking with the PTZ camera is successful at 15 fps with an accuracy of 92.58%. Full article

Endocrine-disrupting compounds (EDCs) are emerging pollutants that can potentially accumulate in aquatic ecosystems at significant levels, with the potential to impact the health of both animals and humans. Many scientists have correlated human exposure to high concentrations of EDCs with critical physiological impacts, including infertility, thyroid imbalance, early sexual development, endometriosis, diabetes, and obesity. Several substances, such as heavy metals, belong to this family, ranging from natural to synthetic compounds, including pesticides, pharmaceuticals, and plastic-derived compounds. Domestic sewage represents a significant source of EDCs in the surrounding aquatic ecosystems. To this day, most rural and urban domestic wastewater in the municipality of Maricá is directly discharged into local aquatic environments without any treatment. The present study aimed to assess the potential contamination of the riverine and lagoonal environment in the municipality of Maricá. Water and sediment samples were collected seasonally at 18 sites along the Maricá watershed and the main lagoon, into which most of the watershed’s contributors flow. Water physico-chemical parameters (pH, reduction–oxidation potential—Eh, dissolved oxygen levels, salinity, turbidity, temperature, and fecal coliforms) were analyzed to characterize the urban influence on the aquatic environment. Sediment samples were also analyzed for grain size, total organic carbon percentage, potential bioavailable fraction of trace metals (Cd, Pb, Cu, Cr, Hg, Ni, Zn), and metalloid As. Finally, the sediment toxicity was assessed using yeast estrogen screen (YES) assays. The results obtained already demonstrate the presence of estrogenic effects and raise concerns about water quality. The current study indicates that, despite the absence of agricultural and industrial activities in the city of Maricá, EDCs are already present and have the potential to impact the local ecosystem, posing potential risks to human health. Full article

Decarbonization of hard-to-abate industrial sectors, namely the extractive industries, has become an imperative, and thus, processes such as carbon capture and utilization (CCU) have been explored thoroughly and seem to be a promising solution. Carbon dioxide (CO₂) catalytic hydrogenation employing green hydrogen (H₂) to produce synthetic methanol (MeOH) aims to utilize industrial-captured carbon. A thermodynamic and kinetic analysis of a pilot scale methanol synthesis reactor was conducted by modeling the process using Aspen Plus V12 software. The methanol synthesis model consists mainly of a multi-tubular packed-bed reactor with a thermal oil heat recovery system, a product separator, and an internal recycle loop for optimal efficiency. The reactor has a 5 kg h⁻¹ methanol production capacity, and its heat recovery system achieves an overall heat reduction of 64.1% and can retrieve 1.293 kWh per kg of methanol produced. The overall carbon conversion achieved is 80.6%. Valuable information concerning the design and profile of the reactor is provided in this study. Full article