Search Results (9,945)

Based on the mechanism of thermally modulated reflected light, visible light images combined with machine learning methods can be used to estimate the surface temperature of metal equipment at ambient temperature under sunlight conditions. However, the surface conditions of on-site equipment and camera imaging parameters vary greatly across different scenarios, leading to poor generalization of models trained solely on laboratory image databases. To address this, it is necessary to update the original laboratory database by incorporating on-site images and retrain the model accordingly; on the other hand, since most of the on-site equipment is working normally, there are few images capturing fault-induced high temperatures. Even if the method of updating and retraining on-site images is used, the data imbalance in the image database can still cause significant measurement errors in these high-temperature images. This study studies image database update schemes to address both multi-scenario and data imbalance problems and demonstrates that retraining with as little as 5% scenario-specific images or 1% high-temperature images significantly improves temperature prediction accuracy, which was validated through on-site experiments at a substation. By comparing four machine learning algorithms (random forest regression, gradient boosted regression trees, decision trees, and k-nearest neighbors), this study reveals that RFR yields the best performance. These findings enhance the practical applicability of visible light image-based temperature measurement models in engineering contexts. Full article

Predictive maintenance (PdM) and predictive quality monitoring (PQM) increasingly rely on data-driven condition monitoring using vibration and related signals. However, real-world deployment often faces domain drift across machines, operating regimes, and sensing conditions, while only a few labeled target samples are available. This combination of distribution shift and label scarcity creates a substantial deployment gap for models trained in a single setting. This paper proposes a physics-informed few-shot learning (PI-FSL) domain adaptation framework that is among the first to combine episodic metric learning with soft physics-consistency regularization to improve cross-domain generalization. The framework integrates CWT-based time–frequency encoding, relation-based episodic classification, physics-consistency constraints at representation and signal levels, and PSD-guided episodic sampling within a unified adaptation pipeline. We evaluated PI-FSL under explicit few-shot transfer scenarios on tool-wear and bearing-condition-monitoring datasets. On the Bosch benchmark, PI-FSL achieved an F1 = 0.960 (balanced accuracy = 0.961) for cross-machine transfer and an F1 = 0.907 (balanced accuracy = 0.901) under a combined machine-operation shift. A cross-dataset evaluation across tool-wear and multiple bearing-fault benchmarks under a unified two-way five-shot protocol further demonstrated a competitive and transferable performance. PI-FSL achieved the best average macro-F1 and a balanced accuracy, with the largest margin on PU bearing transfer (macro-F1, 0.663 vs. 0.590; balanced accuracy, 0.710 vs. 0.634). The ablation results showed that few-shot fine-tuning is the main contributor, while physics regularization provides an additional stabilizing gain under transfer. These findings support PI-FSL as a practical episodic framework for robust cross-domain condition monitoring across heterogeneous industrial datasets under realistic drift and limited labels. Full article

Background/Objectives: Relative Fat Mass (RFM) is an anthropometric index estimating whole-body fat percentage. Though RFM is analyzed in scientific articles in various contexts, the research on the association between RFM and cardiorespiratory fitness (CRF) level is extremely limited. The aim of this study was to investigate the prognostic value of the relative fat mass pediatric index (RFMp) in predicting CRF results over a two-year period among school-age children, in comparison with alternative indices. Methods: Based on data comprising student measurements collected previously, in the years 2017–2019, a multiple regression analysis was conducted. Predictive models for CRF were constructed over a two-year period, separately for each of the eight indicators associated with obesity assessment. The models were prepared for boys and for girls separately. Results: over 40% of girls and boys have a BMI above the norm. In the case of both girls and boys, RFMp turned out to be the best CRF predictor over a two-year period. It proved to be better in terms of its predictive power than body mass index (BMI), body fat percentage (%BF), waist circumference (WC), waist-to-height ratio (WHtR), waist-to-hip ratio (WHR), tri-ponderal mass index (TMI) and waist-BMI ratio. Conclusions: RFMp demonstrated a certain advantage in terms of predictive ability compared to alternative indicators. This indicates its potential for implementation in the general pediatric population and clinical practice for the prediction of CRF. However, this needs to be confirmed in further studies involving a larger and more diverse population. Full article

The growing demand for environmentally responsible lubricants motivates the use of bio-based base stocks and benign solid additives. This study assesses the tribological performance of two Amazonian vegetable oils, Carapa guianensis (andiroba) and Copaifera spp. (copaiba resin) and a paraffinic mineral oil (PNL30) formulated with different zinc oxide (ZnO) particles, namely nanocrystals and microcrystals, at 0.01, 0.05, and 0.10 wt.%. Reciprocating sliding tests, coupled with 3D profilometry, viscosity, and sedimentation analyses, were used to link dispersion stability with friction and wear responses. A preliminary stability screening constrained the practical loading window to ≤0.10 wt.% for reproducible suspensions. Performance depended on the interplay between particle type and base-oil chemistry. Andiroba exhibited the most pronounced gains, with ZnO microcrystals near 0.05 wt.% delivering the best friction outcomes and the largest wear reductions (up to ~35%). In copaiba resin oil, nanocrystals produced small, sometimes non-significant improvements, whereas microcrystals tended to worsen wear consistent with abrasive third-body effects in a less polar matrix. In PNL30, the overall benefits were modest: nanocrystal additions generally increased wear, whereas microcrystals particularly at the highest loading 0.10 wt.% achieved a 36.4% reduction in SWR, representing a measurable and statistically significant improvement in wear resistance. These results highlight that eco-efficient lubricant design should co-optimize particle characteristics and dosage with base-oil polarity and film-forming tendencies, prioritizing dispersion stability alongside tribological targets. Full article

Smart city decision-making increasingly relies on heterogeneous urban data sources. Dense traffic sensor streams provide continuous quantitative measurements, while citizen-generated textual reports offer event-driven contextual information. However, integrating these modalities remains challenging due to temporal misalignment, textual sparsity, and semantic noise. This paper investigates multi-modal learning for traffic congestion severity prediction through an experimental integration of open traffic sensor data (METR-LA: Los Angeles, USA) and citizen-generated textual reports (NYC 311: New York City, USA). Congestion severity is formulated as a four-class classification task derived from traffic speed measurements. We propose an end-to-end framework that combines: (i) sensor time-series encoding using a GRU-based temporal encoder, (ii) textual representation learning using a BERT-based encoder, (iii) a symmetric time-window alignment strategy (±Δ) to associate irregular reports with sensor time steps, and (iv) multiple fusion architectures, including early fusion, late fusion, and a cross-attention module for cross-modal interaction modeling. Experiments on publicly available datasets show that multi-modal early fusion achieves the best overall performance (Accuracy = 0.8283, Macro-F1 = 0.8231) compared to uni-modal baselines. In the studied cross-city setting with sparse and weakly aligned textual signals, the proposed cross-attention fusion does not outperform the strong sensor-only baseline, suggesting that the sensor modality dominates when cross-modal signal strength is limited. These results highlight both the potential and the practical constraints of multi-modal fusion in heterogeneous smart-city environments, emphasizing the importance of alignment design, modality relevance, and transparent experimental validation. Full article

Phenomenological crystal plasticity (CP) models are widely used in Integrated Computational Materials Engineering (ICME) to link microstructural features with engineering-scale mechanical behaviour. Their practical use, however, is limited by the high computational cost of physics-based simulations and the labour-intensive nature of parameter calibration, challenges that are amplified in additively manufactured materials with location-dependent properties. To address these obstacles, we first developed deep neural network (DNN) surrogate models of physics simulations to predict the stress–strain response of an additively manufactured AlSi10Mg alloy. Twenty-five experimentally derived scenarios (five microstructures × five sets of grain orientations) were used for training 25 separate DNNs, with datasets for validated material behaviour generated using the Düsseldorf Advanced Material Simulation Kit (DAMASK) platform and a Fast Fourier Transform (FFT)-based solver. Once trained, the DNNs produced stress–strain curves almost instantaneously, enabling an exhaustive grid-search exploration of a vast parameter space. Our approach yielded significant efficiency gains, which were comprehensively quantified. The best-fit CP parameters obtained through this approach are expected to be more accurate than those derived from conventional trial-and-error calibration, which is restricted to a limited number of candidate values. In addition, the minimum number of CP-FFT simulations required to train the DNNs with sufficient accuracy was identified, reducing the need for costly physics simulations in future studies. The proposed framework enhances the practical utility of CP models for simulation-informed materials engineering and optimisation and is broadly applicable to parameter identification in phenomenological models of other domains. Full article

This study aimed to develop and validate a machine learning-based model for predicting 30-day mortality in cardiac surgery patients and to implement a functional, clinician-oriented web application that enables the real-time use of the model. A retrospective cohort of 325 cardiac surgery patients was analysed using supervised machine learning. After preprocessing and clinical feature selection, several models were trained and evaluated through cross-validation. XGBoost achieved the best results, with an AUC-ROC of 0.968, recall of 0.800, and Brier score of 0.058. To facilitate clinical usability, a web-based application was developed using StreamLit, enabling clinicians to input patient data and predict mortality in real time. The application includes SHAP-based explainability for each prediction, thereby ensuring model transparency. Preliminary feedback from clinicians indicated that the tool was intuitive and informative and showed potential for preoperative risk assessment. The integration of a robust ML (machine learning) model with a functional clinical application offers a practical tool for supporting decision-making in cardiac surgery. This combined approach enhances both accuracy and accessibility, which are key to real-world impacts. Future work will involve multicentre validation and user-centred refinement. Full article

The demand for high-quality sulfur that is used in medicine, chemistry, and other industries is growing. The technological processes for extracting sulfur from harmful acid gases in oil refining are characterized by complex, nonlinear, and fuzzy relationships between input and output parameters, complicating the development of their models. Therefore, solving the problems of modeling and optimizing sulfur production processes under uncertainty, as they occur in sulfur recovery units (SRUs), is a highly relevant scientific and practical task. To address these issues, we propose a method for synthesizing a neuro-fuzzy model for assessing the integrated quality and volume of sulfur, enabling the development of a highly adequate model under fuzzy conditions. The developed hybrid model, based on the proposed method, is trained on historical data and adapts its fuzzy rules, enabling the modeling of complex nonlinear, fuzzy relationships between the input and output parameters of sulfur production processes. An ANFIS architecture for a neuro-fuzzy model for assessing the quality and volume of sulfur from the reactor outlet of the Atyrau refinery SRU was developed. A fuzzy Pareto optimization method was proposed, which, based on the developed neuro-fuzzy model, enables vector optimization of sulfur production processes, taking into account the constraints, and determines a Pareto-optimal solution in a fuzzy environment. The best solution selected by the decision-maker from the Pareto set, depending on the current situation, ensures a balance between the sulfur volume and its integrated quality. As a result of multi-criteria optimization of sulfur production processes at the Atyrau refinery SRU based on the proposed methods, the volume of high-quality sulfur increased by 7.39%, hydrogen by 10.71%, and energy consumption decreased by 80 kW/h, demonstrating the effectiveness of the proposed methods. Full article

Phishing websites pose a major cybersecurity threat, exploiting unsuspecting users and causing significant financial and organisational harm. Traditional machine learning approaches for phishing detection often require extensive feature engineering, continuous retraining, and costly infrastructure maintenance. At the same time, proprietary large language models (LLMs) have demonstrated strong performance in phishing-related classification tasks, but their operational costs and reliance on external providers limit their practical adoption in many business environments. This paper presents a detection pipeline for malicious websites and investigates the feasibility of Small Language Models (SLMs) using raw HTML code and URLs. A key advantage of these models is that they can be deployed on local infrastructure, providing organisations with greater control over data and operations. We systematically evaluate 15 commonly used SLMs, ranging from 1 billion to 70 billion parameters, benchmarking their classification accuracy, computational requirements, and cost-efficiency. Our results highlight the trade-offs between detection performance and resource consumption. While SLMs underperform compared to state-of-the-art proprietary LLMs, the gap is moderate: the best SLM achieves an F1-score of 0.893 (Llama3.3:70B), compared to 0.929 for GPT-5.2, indicating that open-source models can provide a viable and scalable alternative to external LLM services. Full article

With the increasing complexity of optimization problems, existing methods are often inadequate for addressing these challenges, creating a pressing need for more versatile and robust approaches capable of solving a wide range of optimization problems. Meta-heuristic algorithms have become powerful tools in this regard, owing to their flexibility, ease of implementation, and suitability for high-dimensional and complex problems. This paper introduces the Higher-order Thinking Skills Optimizer (HTSO), a novel meta-heuristic algorithm inspired by Higher-order Thinking Skills (HOTS) from educational theory. HTSO simulates the four key aspects of HOTS: creativity, problem-solving, critical thinking, and decision-making. Creativity reflects the intrinsic human drive for knowledge, prompting exploration of unknown domains. When faced with difficulties, individuals focus on gathering information to solve problems. However, due to the inconsistent quality of information, critical thinking is essential for effectively assessing it. In HTSO, creativity and problem-solving serve as the exploration and exploitation mechanisms, respectively. Crucially, critical thinking functions as a metacognitive controller that evaluates the quality of solutions and dynamically guides the selection and adaptation of creativity strategies, thereby ensuring an effective balance between exploration and exploitation. Moreover, HTSO is designed as a user-friendly algorithm with minimal parameter tuning requirements, and its key parameter demonstrates strong robustness across diverse problem types and dimensions, which enhances its practical applicability. Extensive experiments were conducted across three CEC benchmark sets with multiple dimensions (CEC-2017: 30, 50, 100 dimensions; CEC-2020: 10, 15, 20 dimensions; CEC-2022: 10, 20 dimensions), comparing HTSO with 21 other algorithms, including several CEC champion algorithms. The results demonstrate that HTSO outperforms all comparative algorithms on most test functions, indicating high effectiveness and robustness. Furthermore, HTSO was compared with 14 algorithms on 12 real-world constrained engineering optimization problems. Finally, HTSO and 14 other algorithms were applied to unmanned aerial vehicle 3D path planning in seven different complex mountainous scenarios. HTSO also achieved the best performance across all tested real-world engineering problems and UAV path planning scenarios, consistently outperforming the comparative algorithms. These results demonstrate the effectiveness and potential of HTSO in addressing real-world optimization challenges. Full article

Farmers worldwide use agrochemicals and biological inputs to fertilize fields, manage pests and diseases, and promote plant growth. However, there is still limited field-based evidence on the extent to which biological inputs function as substitutes, incremental complements, or alternatives to agrochemicals in key farming practices. This study presents preliminary results on the use of synthetic and biological inputs for the most common practices employed by large soybean farmers in central Brazil. We combined literature review, regulatory data on registered biological products, and in-person interviews with farmers and market experts. Our results show that, in most practices, biological products are adopted alongside the continued use of synthetic inputs, in some cases reducing the frequency or dosage of chemical applications. Inoculants based on nitrogen-fixing bacteria already substitute mineral nitrogen fertilization in soybean, while biosolubilizers and plant activators are used incrementally to enhance the efficiency of chemical fertilizers. Bioinsecticides and biofungicides are predominantly employed as alternatives within spray programs, especially in preventive or early interventions, thereby reducing the number of conventional pesticide sprays. Bionematicides emerge as the main biological tools used as substitutes for synthetic nematicides in preventive treatments, whereas biological herbicides are not yet available on the market. Field evidence presented in this study showed that farmers adopt biological products in diverse ways, including as substitutes, incremental, or alternatives to chemical products, depending on the technologies available. These findings provide a more nuanced understanding than the common views that, on one hand, biological inputs simply complement rather than substitute chemical products, and on the other, that biological solutions can fully substitute synthetic products. As environmental and economic implications, we conclude that biological inputs can underpin trajectories towards more regenerative management in large-scale soybean systems, even when synthetic inputs remain part of the production matrix. Full article

This review paper discusses the technological advancements and innovative strategies of heating, ventilation, and air conditioning (HVAC) systems for buildings. Buildings are a major contributor to energy consumption and greenhouse gas (GHG) emissions, representing about 35% of global final energy use and 26% of energy-related GHG emissions. In Canada, the building sector accounts for roughly 31% of energy demand and 18% of total GHG emissions, with HVAC systems responsible for 40–50% of this energy use. The current challenges, emerging trends, and future prospects for HVAC and related technologies are systematically reviewed to promote sustainability, affordability, and resilience in buildings. The literature scanning begins with an overview of the prevailing energy scenario in buildings, HVAC technologies, and other regulatory and policies. The paper thoroughly examines the critical role of HVAC systems in reducing energy consumption, minimizing environmental impact, improving building affordability and enhancing occupant health and productivity. It discusses emergent technological opportunities, energy efficiency measures, sensors, smart controllers, Internet of Things (IoT) and AI-based technologies. The paper highlights the barriers to adopting new technologies and strategies. It provides an evolving topography of HVAC technologies, their current state and emerging directions to tackle environmental challenges, including net zero energy and zero carbon building goals. The review suggests that while there are promising advancements in HVAC technology, further research and practical demonstrations of innovative solutions are necessary to maintain the momentum in building modernization efforts. Full article

(1) Background: In response to the educational challenges brought on by the COVID-19 pandemic, APPE preceptors implemented the Intention/Reflection (I/R) practice as a structured engagement tool. I/R is designed to promote engagement, motivation, metacognitive growth, and self-awareness among student pharmacists with the goal of enhancing learning experiences in diverse APPE settings. This project aimed to assess the impact of I/R strategies on student pharmacist engagement during APPEs in the post-pandemic landscape, with the overarching goal of identifying and advancing best practices in experiential pharmacy education. (2) Methods: This retrospective qualitative study included 20 student pharmacists from two U.S. colleges who participated in APPE elective rotations featuring I/R activities. Student pharmacists’ responses to five structured I/R prompts were collected and thematically analyzed by two independent researchers using qualitative data analysis software. (3) Results: Four themes were identified in the I/R responses: two themes each from the intention and reflection responses. The intention themes “Embracing Discomfort as a Catalyst for Confidence, Engagement, and Leadership Growth” and “Purposeful Precision: Growing into Adaptive Leadership” both illustrate the students’ journeys as they develop greater confidence and resilience in overcoming challenges. The reflection themes “Reflection as a Catalyst for Professional Learning and Engagement” and “Reflection as a Tool for Focused Growth and Self-Awareness” synthesized the evolution of the student pharmacist and forward thinking for future career. (4) Conclusion: Overall, participants perceived the I/R practice as transformative, citing benefits such as sustained learning, increased confidence, and continued professional development. These findings suggest that integrating I/R into experiential pharmacy education can significantly enhance student engagement and contribute to best practices for post-pandemic pharmacy training. Full article

Large flange shafts are the core load-bearing and connecting components of high-end equipment, and their welding multi-physical fields directly affect the quality and service safety of the components. Traditional experiments and finite element methods suffer from long cycles and low efficiency, which can hardly meet the demand for rapid prediction. Aiming at the fast and accurate prediction of welding temperature, deformation and residual stress, this study combines thermal–mechanical coupled finite element simulation with machine learning to construct and compare a variety of prediction models. A dataset is built based on simulation data from 100 groups of process parameters. Overfitting is reduced through strategies including early stopping and dropout, and models such as MLP, RF, RBF-SVR, TabNet, XGBoost, and FT-Transformer are established and verified through 10-fold cross-validation. The results show that the MLP model performs best in the prediction of temperature, deformation and residual stress, and is in good agreement with the simulation values. The prediction errors of the peak temperature of the weld and base metal are below 5%, and the errors of deformation and residual stress are controlled within 10%. The average error of peak residual stress is about 6 MPa, and the deviation of most samples is less than 5 MPa. The RF model ranks second in accuracy, with an average error of about 6.5 MPa for peak residual stress, showing a satisfactory interpretability and engineering applicability. RBF-SVR and TabNet can meet basic prediction requirements. Under the small-sample condition in this work, XGBoost and FT-Transformer present relatively large errors and a weak generalization ability, making it difficult to achieve high-precision prediction. The MLP model established in this paper can effectively reproduce the evolution of welding multi-physical fields and supports the rapid prediction and process optimization of large flange shaft welding. The generalization ability and practical performance of the model can be further improved by expanding the dataset and experimental verification in the future. Full article

Objectives: To identify, evaluate, and synthesize best evidence-based practices for the comprehensive care and management of cutaneous lesions in Pemphigus patients, encompassing assessment, prevention, topical care, health education, and follow-up. This aims to provide evidence-based guidance for clinical practice. Methods: Guided by the “6S” evidence model, a systematic search was performed across multiple databases, guideline repositories, and professional organization websites. The literature published from the inception of each database up to 25 February 2025 was considered. Two researchers with training in evidence-based methods independently assessed the quality of included literature, extracted data, and synthesized the evidence. Results: A total of 14 publications were included, consisting of 1 clinical decision tool, 6 guidelines, 6 expert consensus documents, and 1 systematic review. From these, 24 evidence recommendations were summarized, organized into five key areas: management principles, skin assessment, lesion care, health education, and recurrence and follow-up. Conclusions: This review integrates current best evidence on skin lesion management in Pemphigus into a structured set of recommendations. The findings offer practical, evidence-based guidance for clinical practice and can support the development of standardized care protocols to improve patient outcomes. Full article