Advancing Open Science

The growing adoption of renewable energy conversion systems and smart infrastructures has increased the demand for accurate monitoring solutions to ensure system performance and reliability, as well as seamless integration with cloud-based platforms. Voltage and current sensing are central to this task; however, sensor selection often involves a trade-off between cost and measurement precision. Rather than comparing technologies as equivalent options, this study investigates the practical impact of using low-cost versus high-precision sensors in electrical power generation monitoring. The evaluation includes representative low-cost sensors and high-precision alternatives based on instrumentation amplifiers and a closed-loop Hall-effect transducer. All sensors were characterized under controlled laboratory conditions and analyzed using statistical indicators, including MAE, RMSE, MAPE, and R². Results show that high-precision sensors achieved R² > 0.97 and MAPE < 4%, whereas low-cost sensors showed R² as low as 0.73 and errors exceeding 10% under dynamic irradiance conditions. Low-cost sensors present deviations of 5–8% in RMS measurement, while high-precision sensors maintain error below 1%.

The aim of the study was to investigate the effect of disinfection with penicillin and/or streptomycin, added to ethanol mist, on the surface properties of both model and historical leather materials from the collections of the Auschwitz-Birkenau State Museum (A-BSM) in Oświęcim, Poland. The experimental conditions involved application of 90% ethanol mist alone or with penicillin, streptomycin or a mixture of both antibiotics using an airbrush. Changes in the morphology, structure and chemical properties of the sample surfaces compared to non-exposed samples were evaluated using Scanning Electron Microscopy (SEM), confocal microscopy (CM) and X-ray Photoelectron Spectroscopy (XPS). Microscopic studies demonstrated that exposure to penicillin or the antibiotic mixture caused subtle smoothing and flattening of tested leathers and a significant reduction in contamination of biological and mineral origin. Decreases in fluorescence intensity and fluorescent layer thickness were also observed, which, according to the XPS results, may be caused by the removal of a large amount of surface deposits or the reveal of deeper leather layers that were previously covered with inorganic particles. Therefore, it can be concluded that the developed method of applying antibiotics in ethanol mist does not have any significant negative effect on the surface of model and historical leather.

Surplus out-of-spec Al powders, typically discarded, remain an underused resource. Their reuse via alternative consolidation routes is a sustainable path for AlSi10Mg alloy recycling, but studies on the feasibility of such routes remain scarce. This study proposes a novel route combining powder compaction (under 50 kN and 80 kN loads) and remelting/solidification via suction casting to assess the feasibility of producing dense parts with enhanced properties. Microstructure, mechanical properties (compression and Vickers microhardness), and tribological performance (ball-crater wear under dry and abrasive conditions) were evaluated. The proposed route produced dense AlSi10Mg parts with low porosity levels (≤0.2%) and refined dendritic microstructures (spacing between 2.4 and 4.6 µm). Increased cooling rates promoted microstructural refinement, while higher compaction loads improved densification. The refined microstructure samples achieved compressive strengths above 500 MPa. Remarkably, microstructural refinement led to significantly increased hardness, with values reaching ≥100 HV. The samples compacted at 50 kN and subjected to the highest cooling rate exhibited the lowest dry wear rate (2.3 × 10⁻⁴ mm³/N·m), comparable to additively manufactured AlSi10Mg (AM) samples, confirming the efficiency of this recycling route. The dry wear rates ranged from 2.3 to 3.9 × 10⁻⁴ mm³/N·m, reinforcing the inverse correlation between hardness and dry wear performance. Although abrasive wear resulted in a material loss approximately 3 times higher than dry wear, it preserved the same microstructural dependence: finer, harder, and denser samples exhibited better wear resistance.

Heavy metal contamination in agricultural soils is a critical global concern, threatening ecosystem safety and food security. The wheat–corn rotation system, vital for food production in regions like Northern China, is particularly vulnerable. However, comprehensive studies investigating vertical migration, future dynamics under climate change, and predictive modeling of heavy metals within this system are still limited. This study combined field sampling of soil profiles (0–200 cm) with geochemical modeling (the PROFILE and SSCL models) and machine learning techniques (Multiple Regression, Neural Networks, and Random Forest). Key findings revealed that atmospheric deposition was the primary input source for most heavy metals, contributing 49.50–93.27%. The release rates (Rm) of heavy metals were significantly higher during the corn season than the wheat season and are projected to increase by 1.2–1.5 times under the RCP4.5 climate scenario. Vertical distribution analysis showed a significant accumulation of heavy metals in the middle soil layer (20–120 cm), with Arsenic (As) and Cadmium (Cd) exhibiting the strongest migration potential, posing a threat to groundwater. The Random Forest model demonstrated superior performance (R² > 0.95) in predicting heavy metal behavior, identifying Fed and soil TOC as the dominant controlling factors. This study provides a unique and significant contribution by integrating geochemical fate modeling with climate projections and advanced machine learning to offer a predictive, multi-faceted risk assessment framework, thereby supplying a scientific basis for targeted pollution control and sustainable soil management in wheat–corn rotation systems under a changing climate.

In this paper, we report on the characterization of a silicon carbide static induction transistor (SiC SIT) for potential use in sensor interface circuits that operate at frequencies up to 100 MHz and temperatures up to 400 °C. Measurements were performed to generate current–voltage curves, capacitive transistor characteristics, and high-frequency scattering parameters at temperatures between 25 and 400 °C. The measured data were used to extrapolate the transconductance, g_m, as a function of temperature and to develop a small signal model. Circuit simulation tools were used to generate scattering parameters, which were compared to the measured values. At 400 °C, the maximum difference between the measured and simulated scattering parameters for frequencies from 20 to 100 MHz were all less than 0.1 dB, indicating strong agreement between the model and measurement results. The average transition frequency, f_t, calculated from measured parameters was 197.8 MHz, which compares favorably to the simulated value from the model (200 MHz). This is also the first paper to report the characterization of a SiC SIT at temperatures above 100 °C. The high-temperature model is the first of its kind for a silicon carbide static induction transistor and the findings reported herein provide a platform to stimulate further development for sensor interface circuits that require transistors that operate at both high frequency and high temperature.

Reducing greenhouse gas (GHG) emissions and fuel consumption remains a critical objective in courier fleet management. Dynamic routing, which continuously updates delivery routes in response to real-time conditions, offers a promising solution. However, its implementation is hindered by challenges in real-time data analytics and intelligent decision-making. This study addresses two underexplored, yet impactful, variables in dynamic fleet routing: (1) the changing weight of delivery trucks due to unloading at each stop and (2) traffic conditions on local roads, where most deliveries occur. We propose a machine learning-driven smart rerouting system that integrates real-time data analytics into a dynamic routing optimization framework focused on minimizing fuel consumption. Our approach consists of two key components. First, trucks are equipped to collect continuous real-time data on vehicle weight, which are analyzed using frequency domain techniques, and traffic conditions, which are interpreted via neural networks. Second, these data inform an optimization model that explicitly captures the relationship between fuel consumption, emissions, vehicle weight, and traffic dynamics. This model surpasses conventional capacitated vehicle routing approaches by embedding real-time reasoning into route planning. Extensive simulation studies demonstrate that the proposed system significantly reduces both GHG emissions and fuel consumption compared to traditional routing models, highlighting its potential for sustainable and cost-effective fleet operations.

The convergence of youth migration and the nuclearization of families has altered conventional living arrangements in India, indicating a sharp rise in the number of families in which older adults live alone due to the outmigration of their adult children. This study aims to explore the perceptions of left-behind older adults regarding long-distance care practices by their adult children and to describe the practical and functional care deficits that lead to vulnerability and unmet mental health care in migrant households. Twenty older adults above 65 years of age living alone or with a spouse for at least one year due to the out-migration of their adult children were selected purposively. The analysis revealed that distance from migrant children makes older adults feel anxious, miss their family togetherness, and experience increased loneliness and care gaps in later years, contributing to a multifaceted causality of vulnerability while aging alone. Narratives of distance care are often shaped by the bidirectional flow of care across generations through virtual and in-person means, where emotional and functional deprivations continue to challenge the quality of informal distant care among left-behind older adults. Mental health promotion among community-dwelling older adults is crucial for sustaining their functional capacity, thereby delaying psychological morbidities during aging.

Background/Objectives: Speech produced by individuals with hearing loss differs notably from that of normal-hearing (NH) individuals. Although cochlear implants (CIs) provide sufficient auditory input to support speech acquisition and control, there remains considerable variability in speech intelligibility among CI users. As a result, speech produced by CI talkers often exhibits distinct acoustic characteristics compared to that of NH individuals. Methods: Speech data were obtained from eight cochlear-implant (CI) and eight normal-hearing (NH) talkers, while electroencephalogram (EEG) responses were recorded from 11 NH listeners exposed to the same speech stimuli. Support Vector Machine (SVM) classifiers employing 3-fold cross-validation were evaluated using classification accuracy as the performance metric. This study evaluated the efficacy of Support Vector Machine (SVM) algorithms using four kernel functions (Linear, Polynomial, Gaussian, and Radial Basis Function) to classify speech produced by NH and CI talkers. Six acoustic features—Log Energy, Zero-Crossing Rate (ZCR), Pitch, Linear Predictive Coefficients (LPC), Mel-Frequency Cepstral Coefficients (MFCCs), and Perceptual Linear Predictive Cepstral Coefficients (PLP-CC)—were extracted. These same features were also extracted from electroencephalogram (EEG) recordings of NH listeners who were exposed to the speech stimuli. The EEG analysis leveraged the assumption of quasi-stationarity over short time windows. Results: Classification of speech signals using SVMs yielded the highest accuracies of 100% and 94% for the Energy and MFCC features, respectively, using Gaussian and RBF kernels. EEG responses to speech achieved classification accuracies exceeding 70% for ZCR and Pitch features using the same kernels. Other features such as LPC and PLP-CC yielded moderate to low classification performance. Conclusions: The results indicate that both speech-derived and EEG-derived features can effectively differentiate between CI and NH talkers. Among the tested kernels, Gaussian and RBF provided superior performance, particularly when using Energy and MFCC features. These findings support the application of SVMs for multimodal classification in hearing research, with potential applications in improving CI speech processing and auditory rehabilitation.