Abstract: This article presents a computational model for transmission and generation expansion planning that incorporates the effects of a new booster inter-area Virtual Transmission lines model, achieved through investments in energy storage systems within the transmission network areas. This approach enables the evaluation of potential reductions and deferrals in transmission line investments, including those involving inter-area trunk lines. Furthermore, the model captures flexibility from TSO-DSO interconnections to examine their influence on overall system expansion choices. A review of state-of-the-art flexibility indicators supports the selection of a metric that effectively quantifies resources for mitigating short-term operational variabilities; this metric is integrated into the model's unit commitment module, incorporating generator ramping and flexibility constraints. Flexibility is supplied to the AC transmission network via connected distribution systems at transmission nodes, with required flexibility levels derived from expansion planning performed by the associated DSOs. The model's core objective is to minimize total system costs, encompassing operations, investments in transmission and generation, and flexibility provisions. To handle uncertainties in demand and variable renewable energy generation, a data-driven distributionally robust optimization (DDDRO) method is employed. The framework utilizes a two-level architecture based on the column-and-constraint generation algorithm and duality-free decomposition, augmented by a third level to embed unit commitment and generator ramping constraints. Validation through case studies on the IEEE RTS-GMLC network illustrates the model's efficacy, highlighting the benefits of the proposed contributions in achieving cost savings, enhanced transmission and generation efficiency, and flexibility metrics.

Abstract: This article presents PrevOccupAI-HAR, a new publicly available dataset designed to advance smartphone-based human activity recognition (HAR) in office environments. PrevOccupAI-HAR comprises two sub-datasets: (1) a model development dataset collected under controlled conditions, featuring 20 subjects performing nine sub-activities associated to three main activity classes (sitting, standing, and walking), and (2) a real-world dataset captured in an unconstrained office setting captured from 13 subjects carrying out their daily office work for six hours continuously. Three machine learning models, namely k-nearest neighbors (KNN), support vector machine (SVM), and random forest, were trained on the model development dataset to classify the three main classes independently of sub-activity variation. The models achieved accuracies of 90.94 %, 92.33 %, and 93.02 % for the KNN, SVM, and Random Forest, respectively, on the development dataset. When deployed on the real-world dataset, the models attained mean accuracies of 69.32 %, 79.43 %, and 77.81 %, reflecting performance degradations between 21.62 % and 12.90 %. Analysis of sequential predictions revealed frequent short-duration misclassifications, predominantly between sitting and standing, resulting in unstable model outputs. The findings highlight key challenges in transitioning HAR models from controlled to real-world contexts and point to future research directions involving temporal deep learning architectures or post-processing methods to enhance prediction consistency.

Abstract: The chili pepper (Capsicum annuum) is among the most widely consumed vegetables worldwide, valued for its sensory and nutritional properties. Still, it is highly vulnera-ble to deterioration due to its elevated moisture content. Effective preservation strate-gies, such as the addition of salt combined with drying, are therefore crucial to main-taining quality and extending shelf life. This study employed a modified Reaction En-gineering Approach (REA) to model the drying kinetics and temperature behavior of chili paste under continuous and intermittent conductive hydro-drying conditions. Thirty experiments were conducted, considering various salt concentrations (0, 7.5 y 15 g salt/100 g paste) , water temperatures in the hydro-dryer, and heating intermit-tency through on/off cycles. The modified REA model accurately predicted both mois-ture and temperature profiles, with determination coefficients of 0.9463 and 0.8820, respectively. In addition to direct validation with the complete dataset, cross-validation between cayenne and jalapeño varieties demonstrated the ability of the model to generalize across different formulations and structural characteristics. These results confirm the robustness of the proposed framework and its suitability as a predictive tool for heterogeneous food matrices. Overall, the model provides a reliable platform for analyzing, designing, optimizing, and controlling hydro-drying processes in semi-solid foods, supporting the development of more efficient and sustainable preservation strategies.

Abstract: Facial expressions are crucial in conveying emotions and for engaging in social interactions. The facial musculature activations and their pattern of movements under emotions are similar in all humans; hence, facial expressions are considered a behavioral phenotype. Facial features related to the expression of various emotions change under different health impairments, including in cognitive decline and pain experience. Hence, evaluating these facial expression deviations in comparison to healthy baseline conditions can help in the early detection of health impairments. Recent advances in machine learning and computer vision have introduced a multitude of tools for extracting human facial features and researchers have explored the application of these tools in early screening and detection of different health conditions. Advances in these studies can specially help in telemedicine applications and in remote patient monitoring, and potentially reduce the current excessive demand on the healthcare system. In addition, once developed, these technologies can assist healthcare professionals in emergency room triage, early diagnosis, and treatments. The aim of the present review is to discuss the available tools that can objectively measure facial features and to record the studies that use these tools in various health assessments.

Abstract: The paper presents an example of a wearable system for caring for the elderly. It focuses on the relationships between the individual components of this system from a macro ergonomics point of view. The protection of older people has been identified as a standard of well-being that, in highly developed countries, is evolving from a passive social security model to an active, holistic paradigm. This new paradigm is aimed not only at prolonging life, but also at ensuring its highest quality, dignity and full social integration. This new standard goes far beyond pensions and basic healthcare, becoming a measure of a country's humanitarian maturity and social advancement. The design of such telecare systems should be user-oriented in accordance with the principles of universal design. Defining the relationship between humans and work elements is crucial. Its purpose is to ensure hygiene, safety and comfort at work, while maintaining high production efficiency.

Abstract: Over the centuries, many birds have gone extinct, and many are currently endangered due to anthropogenic activities, inability of some birds to compete for food and the negative effects of climate change. To promote biodiversity of rare birds requires deliberate human efforts to create ecosystems that conserve them and enhance their survival. This work implemented a design-driven solution to an identified problem of squirrel feeding on bird seeds. Thus, it reports the design, development, prototyping and testing of a squirrel-proof birdfeeder capable of selectively preventing squirrels but allowing birds to feed from it. The design comprised of a compression spring and two concentric cylinders. Finite Element Analysis and Failure Mode and Effect Analysis were used to optimise the structural design and functionality of the bird feeder. Testing of the bird feeder showed that birds successfully fed from it, whilst squirrels could not access the feeds due to the mass differential mechanism based on Hooke’s law. Camera-recoded interactions showed that when a squirrel exerted its weight anywhere on the surface of the feeder, the spring compressed to displace the outside surface downwards to close-off the feeding holes and prevented a squirrel from accessing the bird seed. The prototype is a reliable solution to the problem of squirrels consuming bird seeds at home and in the parks.

Abstract: Image datasets characterized by high intra-image structural heterogeneity pose significant challenges for supervised classification, particularly when local patterns contribute unevenly to image-level decisions. In such scenarios, direct image-level learning may obscure relevant local variability and introduce bias in both training and evaluation. This study proposes a statistically guided, patch-based computational pipeline for the automatic classification of elementary morphological patterns, with application to bioelectrographic imaging data. The pipeline is progressively refined through explicit statistical diagnostics, including image-level data splitting to prevent data leakage, class imbalance handling, and decision threshold calibration based on validation performance. To further control structural bias across images, a continuous image-level descriptor, denoted as \textit{pct\_point\_true}, is introduced to quantify the proportion of point-like structures and support dataset stratification and stability analysis. Experimental results demonstrate consistent and robust patch-level performance, together with coherent behavior under complementary image-level aggregation analysis. Rather than emphasizing architectural novelty, the study prioritizes methodological rigor and evaluation validity, providing a transferable framework for patch-based analysis of structurally heterogeneous image datasets in applied computer vision contexts.

Abstract: Structural vibration is a significant problem created by industrial machinery (i.e., compressors, motors, and generators) that can negatively affect the performance of equipment as well as the overall integrity of buildings or structures. Although various vibration isolation technologies are available for reducing the structural vibrations produced by machinery, most of these methods have inherent limitations because of a lack of sufficient damping at lower frequencies relative to that observed higher frequency ranges. The purpose of this paper is to evaluate the use of advanced vibration isolation technologies using re-entrant auxetic structures that are characterized by their negative Poisson ratios. Through a comprehensive evaluation of 92 published articles within the areas of auxetic unit cell design and topology optimization, the mechanics of materials related to negative Poisson ratios, energy absorption mechanisms, vibration reduction in sandwich structures, and dynamic analyses of frame and plate systems, this review presents the current state-of-the-art re-entrant auxetic structures that can be employed as vibration isolation technologies for machine foundations. The analysis reveals that compared with standard structures, re-entrant geometry-based structures exhibit high levels of energy absorption (up to a 767% increase over the standard designs), along with superior vibration isolation characteristics. A hybrid approach utilizing combinations of geometric modification, multimaterial fabrication, and foam filling is identified as the most promising method for optimizing the relationship between stiffness and damping capacity. Additionally, advancements in additive manufacturing have made it possible to fabricate complex auxetic geometries that were previously unfeasible via traditional processes. In addition to identifying significant research gaps, such as scaling up to large macroscale steel implementations, this paper presents general design guidelines for future vibration isolation systems for industrial machinery.

Abstract: The main objective of this study is to automatically detect real-time snow-related road-surface conditions utilizing existing webcams along interstate freeways. Blowing snow is considered one of the most critical road surface conditions, causing vertigo and adversely affecting vehicle performance. A comprehensive image reduction process was performed to extract two distinct reference datasets. The first dataset comprised two image categories: blowing snow and no blowing snow, while the second dataset consisted of five categories: blowing snow, dry, slushy, snow-patched, and snow-covered. Six pre-trained convolutional neural networks (CNN) were utilized for road-surface condition classification: AlexNet, SqueezeNet, ShuffleNet, ResNet18, GoogleNet, and ResNet50. In Dataset 1, it was concluded that AlexNet is a superior model with respect to training time and 97.56% overall detection accuracy. Regardless of differences in training time, ResNet50 achieved the highest overall accuracy of 97.88%, as well as the highest recall and F1-score. In Dataset 2, the ResNet18 model achieved an optimal overall detection accuracy of 96.10%, while the AlexNet model demonstrated the shortest training time and an overall detection accuracy of 95.88%. In addition, a comprehensive comparison was conducted between pre-trained CNNs and traditional machine learning models, with the former displaying significantly superior detection performance. Analysis of the confusion matrices revealed that AlexNet performed the best in detecting blowing snow events. The proposed models could automatically provide real-time accurate and consistent surface condition information.

Abstract: This paper introduces a hybrid framework for assessing the technical feasibility and system readiness of emerging multimodal mobility concepts, with a focus on the Pods4Rail project. The methodology combines qualitative and quantitative approaches to address the inherent uncertainty of early design phases. The qualitative component evaluates Technology Readiness Levels (TRLs) for individual subsystems using expert judgment and visual heat maps, identifying critical challenges in automation, digitalization, and sustainability. The quantitative approach distinguishes between the probabilistic model—representing the uncertainties in TRL and IRL—the problem of propagating these uncertainties to estimate the System Readiness Level (SRL), and the algorithm used to solve this problem, which in this case is Monte Carlo simulation. This framework enables SRL estimation under uncertainty, where explicit quantification of uncertainties is essential for sound decision-making. Results indicate that Pods4Rail project currently falls between SRL 1 and SRL 2, corresponding to concept refinement and technology development stages. While subsystems such as the Transport Unit and Rail Carrier Unit exhibit higher maturity, planning and logistics remain less developed. By combining interpretative insights with statistical rigor, this framework provides a comprehensive readiness assessment and supports informed decision-making for future integration and risk management. The proposed approach is transferable to other innovative mobility systems facing similar challenges in early development stages.

Abstract: Core body temperature (CBT) is a fundamental physiological parameter tightly regulated by thermoregulatory mechanisms and is critically important for heat stress assessment, clinical management, and circadian rhythm research. Although invasive measurements such as pulmonary artery, esophageal, and rectal temperatures provide high accuracy, their practical use is limited by invasiveness, discomfort, and restricted feasibility for continuous monitoring in daily-life or field environments. Consequently, extensive efforts have been devoted to developing non-invasive CBT measurement and estimation techniques. This review provides an application-oriented synthesis of invasive reference methods and representative non-invasive approaches, including in-ear sensors, infrared thermography, ingestible telemetric sensors, heat-flux-based techniques, and model-based estimation using wearable physiological signals. For each approach, measurement principles, accuracy, invasiveness, usability, and application domains are comparatively examined, with particular emphasis on trade-offs between measurement fidelity and real-world implementability. Rather than ranking methods by absolute performance, this review highlights their relative positioning across clinical, occupational, and daily-life contexts. While no single non-invasive technique can universally replace invasive gold standards, recent advances in wearable sensing, heat-flux modeling, and multimodal estimation demonstrate growing potential for practical CBT monitoring. Overall, the findings suggest that future CBT assessment will increasingly rely on hybrid and context-aware systems that integrate complementary methods to enable reliable monitoring under real-world conditions.

Abstract: Direction-of-arrival (DOA) estimation is a fundamental problem in array signal processing with applications spanning radar, sonar, and wireless communications. Traditional subspace methods like MUSIC assume white Gaussian noise and often fail to exploit the noncircular property of many communication signals. This paper presents a tractable expectation-maximization (EM) algorithm that jointly estimates DOAs and the spatially colored noise covariance matrix while exploiting signal noncircularity through an extended observation model. We derive closed-form expressions for the E-step and M-step, establish convergence properties, and provide comprehensive performance analysis. Experimental results demonstrate that the proposed method achieves superior resolution and accuracy compared to conventional MUSIC and noncircular MUSIC, particularly in scenarios with strong spatial noise correlation. Monte Carlo simulations show RMSE improvements of up to 60% over standard methods at low SNR conditions. The algorithm successfully resolves sources separated by as little as 2 degrees with 100% detection rate, significantly outperforming existing techniques.

Abstract: In this work, we report a dual-mode ferroelectrically programmable on-chip antenna. The antenna is built on a silicon wafer using Complementary Metal-Oxide-Semiconductor (CMOS) processes and exhibits two programmable resonant modes: one in the super high frequency (SHF) range and one in the extremely high frequency (EHF) range. The SHF mode resonates at 8.5 GHz and exhibits ultrawideband (UWB) behavior, while the EHF mode resonates at 36.6 GHz. Both resonance frequencies can be tuned in a non-volatile fashion by controlling the ferroelectric polarization state of a Hafnium Zirconium Oxide (HZO) varactor monolithically integrated into the feed line. This programmability arises from the ferroelectric switching of the embedded HZO film, which results in a non-volatile variation of its permittivity upon application of a voltage pulse. Ferroelectric switching occurs at approximately ±3 V and induces maximum resonance frequency shifts of 381 MHz for the SHF mode and 3 GHz for the EHF mode, corresponding to fractional frequency changes of 4.5% and 8.3%, respectively. Unlike previously reported ferroelectrically tunable antennas, our reported antenna combines full integration, CMOS compatibility, higher operating frequency, compact footprint, and non-volatile programmability.

Abstract: The increasing demand for high-quality dragon fruit in the European market requires efficient quality assessment methods. This study explores a non-destructive image analysis approach for classifying ripe dragon fruits based on fruit ripeness and weight. A low-cost system equipped with visible and ultraviolet lighting was employed to capture images of 60 ripe dragon fruits over a storage period, extracting parameters such as visible and ultraviolet perimeter, maximum and minimum diameter and area, and RGB color coordinates. In a first step, the main characterization magnitudes were confirmed. A ripening index was calculated based on soluble solid content and acidity. Then, a cluster analysis was used to segregate the fruits into three quality characteristics based on the ripening index and weight. In a second step, a step-by-step discriminant analysis was conducted to classify the fruits into the three quality categories (based on the laboratory measured weight, soluble solid content and total acidity) using the non-destructive magnitudes extracted from the image analysis. The proposed classification system achieved an accuracy of nearly 85 \% of well classified dragon fruits, effectively segregating dragon fruits into the three established categories. urthermore, the established model could select the very high-quality dragon fruit (riper and larger fruits) with 93 \% of correctly dentified products.Compared to conventional destructive methods, this non-destructive approach offers a promising, cost-effective, and reliable solution for quality assessment. The findings highlight the potential for integrating smart technologies into fruit classification processes, during automatic harvest and postharvest operations, ultimately improving efficiency, reducing labor costs, and enhancing product consistency in the dragon fruit industry.

Abstract: Malaysia's 2012 amendment to the Uniform Building By-Laws introduced mandatory water efficiency requirements for new construction, yet the extensive inventory of public buildings constructed before this regulatory milestone remains largely uncharacterized in terms of water consumption patterns and efficiency potential. This study develops a comprehensive assessment framework specifically designed for evaluating water supply and demand in four critical public building types, namely government offices, hospitals, police stations, and mosques, constructed before the UBBL 2012 amendment. Through systematic analysis of international water benchmarking literature and synthesis of building-specific consumption patterns, an integrated assessment methodology is proposed combining water auditing protocols, high-resolution metering strategies, cluster-based benchmarking approaches, and building-type-specific performance indicators. Literature synthesis reveals substantial variability in public building water consumption internationally, with hospitals demonstrating consumption ranging from 103 to 458 cubic meters per bed per year, government offices showing documented savings potential of 31 to 82 percent through systematic monitoring programs, and mosques achieving approximately 45.5 percent fresh water savings through greywater reuse from ablution facilities. However, police stations represent a critical research gap with zero documented consumption studies in the available literature. The proposed framework establishes building-type-specific indicators, standardized data collection protocols, and benchmarking clusters to enable systematic assessment and prioritization of retrofitting interventions for Malaysia's pre-2012 public building stock.

Abstract: In the age of the Internet of Things (IoT), IoT devices scattered across various locations gather and store data in a decentralized manner to improve computational efficiency. Nevertheless, within IoT networks, factors such as fragile devices, challenging deployment conditions, and unreliable data transmission are raising the likelihood of data gaps, potentially having a substantial impact on the subsequent data processing resulting in failure of the system. Conventional imputation approach relies on using historical trend or sensor fusion techniques to combine information from different sensors to fill in the gaps in where information is missing. Historical trend struggles to capture new or emerging patterns, whereas using sensor fusion, even though it shows promising results, relies on information from multiple sensors from same target environment, making it vulnerable to single-point failures. This article presents an alternative strategy: using sensor-based fusion, but in this case, multiple sensors gather data from different targets independently. The architecture intelligently looks and gathers the sensor information from other location/target (multiple locations), sensing the same environmental information, learns the distribution and correlation and employ algorithm to generate synthetic data for imputing missing information. The study conducted experiments by fusing weather station data from various US locations and comparing the effectiveness of this approach to conventional methods. Further, the proposed synthetic data generation approach outperformed other algorithms when applied to the fused weather station dataset. This innovative approach mitigates the risk of single-point failures and offers a more robust solution for dealing with missing data in IoT networks.

Abstract: This paper develops a generative AI decision-support and optimisation framework for advancing sustainability and resilience in industrial logistics. The framework combines data aggregation, generative scenario creation, simulation-based evaluation, and multi-objective optimisation to support evidence-based management under tightening European Union sustainability regulations. Building upon the decision-aid lineage of the International Journal of Production Research, it integrates policy variables such as the Carbon Border Adjustment Mechanism (CBAM), the EU Emissions Trading System for maritime transport, FuelEU Maritime, the Digital Product Passport (DPP), and the Corporate Sustainability Reporting Directive (CSRD) directly into logistics-planning equations. Recent studies on digital twins and adaptive optimisation (Longo et al., 2023; Flores-García et al., 2025) highlight the need for AI systems that translate these policies into dynamic cost and carbon trade-offs. The proposed model responds to this need by coupling generative scenario synthesis with traceable optimisation and governance controls consistent with the EU AI Act (European Commission, 2025). An illustrative case from the mining-rope industry demonstrates how global sourcing and transport routes in European, South African, and Chinese configurations can be simulated within the generative environment to evaluate comparative cost, emission, and compliance profiles. Both SME-light and enterprise implementations achieved reduced analysis time and improved transparency of carbon-related decisions. The study contributes a replicable methodology that transforms generative AI from a creative text tool into a quantifiable governance instrument, linking strategic foresight with operational resilience in sustainable logistics networks.

Abstract: The development and deployment of robust technical solutions for sustainability improvement have become increasingly critical in response to growing environmental and social pressures, while maintaining economic viability, particularly in industrial systems that require multi-stage technology implementation. Identifying such solutions requires the systematic treatment of significant uncertainties that affect sustainability-related decision making. Among these, epistemic uncertainty, arising from incomplete or imperfect knowledge, is inherently subjective and, in principle, reducible. Fuzzy set theory provides an effective and well-established framework for representing and managing epistemic uncertainty in sustainability analysis. In this work, a fuzzy decision-making framework is proposed to support multi-stage technology development and deployment for dynamic sustainability performance improvement in industrial systems. The framework integrates comprehensive sustainability assessment with fuzzy representations of epistemic uncertainty to enable consistent comparison of alternative strategies at each implementation stage. It identifies the most appropriate strategy at each stage while ensuring alignment with long-term sustainability objectives. The proposed approach functions as a decision-support tool for guiding adaptive, stage-wise technology deployment under uncertainty. A case study of a nickel electroplating system is presented to demonstrate the applicability and effectiveness of the methodology.

Abstract: Sustainability has become a strategic priority for Higher Education Institutions (HEIs), particularly in the context of the Sustainable Development Goals, where university research plays a key role in addressing environmental, social, economic, and institutional challenges. However, the evaluation of sustainability-oriented research models remains limited by fragmented indicators, descriptive approaches, and the absence of robust, data-driven assessment frameworks. This study proposes a comprehensive framework for assessing the sustainability orientation of university research models, integrating validated measurement instruments with advanced analytical and predictive techniques to support evidence-based decision-making in higher education governance. The framework is based on a multidimensional instrument comprising 26 indicators across environmental, social, economic, and institutional dimensions, developed through expert judgment using the Delphi method and statistically validated by Confirmatory Factor Analysis (CFA). The instrument was applied to 260 researchers from four public HEIs located in the Colombia–Ecuador border region, and perceived performance was contrasted with actual institutional indicators, revealing significant non-linear discrepancies. To address this complexity, an artificial neural network model was developed to estimate real sustainability performance based on survey data, achieving a predictive accuracy of 90.92%. Beyond institutional diagnosis, the proposed framework functions as a decision-support tool that enables HEIs to identify critical gaps, prioritize interventions, and guide continuous improvement strategies in research management. Due to its methodological rigor, scalability, and transferability, the framework can be adapted to different higher education contexts, contributing to the advancement of sustainability assessment methods and governance practices in universities.

Abstract: Drone-view mixed reality (MR) in the Architecture, Engineering, and Construction (AEC) sector faces significant self-localization challenges in low-texture environments, such as bare concrete sites. This study proposes an adaptive sensor fusion framework integrating thermal and visible light (RGB) imagery to enhance tracking robustness for diverse site applications. We introduce the Effective Inlier Count (Neff) as a lightweight gating mechanism to evaluate the spatial quality of feature points and dynamically weight sensor modalities in real-time. By employing a 20 ×16 grid-based spatial filtering algorithm, the system effectively suppresses the influence of geometric burstiness without significant computational overhead on server-side processing. Validation experiments across various real-world scenarios demonstrate that the proposed method maintains high geometric registration accuracy where traditional RGB-only methods fail. In texture-less and specular conditions, the system consistently maintained an average Intersection over Union (IoU) above 0.72, while the baseline suffered from complete tracking loss or significant drift. These results confirm that thermal-RGB integration ensures operational availability and improves long-term stability by mitigating modality-specific noise. This approach offers a reliable solution for various drone-based AEC tasks, particularly in GPS-denied or adverse environments.