Search Results (46,331)

This study investigates the application of Bayesian probability models in the diagnostic assessment of learning disabilities. The objective of this study was to determine whether specific conditions identified in expert reports could predict subsequent diagnoses. The sample consisted of 201 expert reports on children diagnosed with learning disabilities, which were analysed using qualitative content analysis, fuzzy set qualitative comparative analysis (fsQCA), and Bayesian conditional probability models. Variables such as vocabulary, working memory index, processing speed, and visuomotor coordination were examined as potential predictors. The analysis demonstrated that Bayesian networks captured conditional links, such as the strong association between working memory and perceptual inference, as well as an unexpected negative link between vocabulary and verbal comprehension. The study concludes that Bayesian networks provide a transparent and data-driven framework for pre-screening and risk assessment in special education settings. The limitations of this study include the absence of a control group and exclusive reliance on SNI cases. Future research should explore the integration of abductive reasoning into automated diagnostic software to enhance inclusivity and support decision-making. Full article

Robotization of production processes and the use of 3D vision systems are currently becoming more and more popular. It allows for more flexibility in the robotic process as well as expands the possibilities of process control, depending on changes in the parameters of the object, its pose, and changes in the process itself. Unfortunately, the use of standard solutions is limited to a relatively small space in which the robot’s vision system operates. The use of the latest solutions in the field of Artificial Intelligence (AI) and external vision systems, in combination with the closed structures of industrial robot control systems, provides advantages by enhancing the digital awareness of the environment of robotic systems. This article presents an example of solving the problem of low digital awareness of the environment of robotic systems resulting from the limited field of view of vision systems used in industrial robots, while maintaining high precision of the systems consisting of the combination of a 3D vision system using a stereovision camera and software with AI elements with the control system of an industrial robot from FANUC and an integrated Robot Vision (iRVision) system to maintain the positioning accuracy of the robot tool. Full article

This study investigates the determinants of service demand in the packaging and printing industry, identifying 19 key factors through expert evaluation. These factors were analyzed using the Importance–Satisfaction (I–S) Model to pinpoint areas requiring enhancement, with four elements classified within the improvement zone. Considering resource constraints, improvement priorities were established through a modified Risk Priority Number (RPN) framework derived from Failure Modes and Effects Analysis (FMEA), expressed as RPN = I × F × E. The highest-priority areas for improvement included product pricing, flexibility in meeting customer requirements, suppliers’ emergency response capabilities, and proactive communication regarding raw material price fluctuations. The findings indicate that consumers balance price against sustainability value, highlighting the necessity of setting prices that align with perceived value to sustain trust and meet expectations. Strengthening firms’ emergency response mechanisms and developing an online standard operating procedure (SOP) notification system for raw material price changes can enhance communication efficiency, increase transparency in pricing, and ultimately improve organizational competitiveness. Full article

The isomerization of geraniol using natural, acid-modified minerals such as vermiculite presents a promising approach aligned with the principles of green chemistry. Vermiculite, a naturally abundant layered silicate mineral, was subjected to the acid activation and thoroughly characterized using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). These methods allowed the evaluation of crystallinity, structural stability, and surface morphology, which are critical parameters in the heterogeneous catalysis. The catalytic performance of the modified vermiculite was examined in the transformation of geraniol under mild conditions. The study systematically investigated the influence of key process parameters—temperature, reaction time, and catalyst content—on the conversion of geraniol and products selectivities. Optimization using the response surface methodology (RSM), enabled the identification of conditions leading to high conversion of geraniol (up to 85%) and allowing us to obtain favorable selectivities toward linalool, thunbergol, and 6,11-dimethyl-2,6,10-dodecatrien-1-ol. The results indicate that the acid-treated vermiculite exhibits sufficient surface acidity to effectively catalyze isomerization and cyclization reactions, without requiring additional promoters or metal-based systems. Moreover, the use of RSM provided the efficient framework for optimization reaction conditions, reducing experimental workload, and enhancing process efficiency. This study demonstrates the viability of natural, low-cost minerals as environmentally friendly catalysts and supports their integration into sustainable and “green” chemical technologies. Full article

Early detection and diagnosis of plant diseases is critical for ensuring global food security and sustainable agricultural practices. This review comprehensively examines latest advancements in crop disease risk prediction, onset detection through imaging techniques, machine learning (ML), deep learning (DL), and edge computing technologies. Traditional disease detection methods, which rely on visual inspections, are time-consuming, and often inaccurate. While chemical analyses are accurate, they can be time consuming and leave less flexibility to promptly implement remedial actions. In contrast, modern techniques such as hyperspectral and multispectral imaging, thermal imaging, and fluorescence imaging, among others can provide non-invasive and highly accurate solutions for identifying plant diseases at early stages. The integration of ML and DL models, including convolutional neural networks (CNNs) and transfer learning, has significantly improved disease classification and severity assessment. Furthermore, edge computing and the Internet of Things (IoT) facilitate real-time disease monitoring by processing and communicating data directly in/from the field, reducing latency and reliance on in-house as well as centralized cloud computing. Despite these advancements, challenges remain in terms of multimodal dataset standardization, integration of individual technologies of sensing, data processing, communication, and decision-making to provide a complete end-to-end solution for practical implementations. In addition, robustness of such technologies in varying field conditions, and affordability has also not been reviewed. To this end, this review paper focuses on broad areas of sensing, computing, and communication systems to outline the transformative potential of end-to-end solutions for effective implementations towards crop disease management in modern agricultural systems. Foundation of this review also highlights critical potential for integrating AI-driven disease detection and predictive models capable of analyzing multimodal data of environmental factors such as temperature and humidity, as well as visible-range and thermal imagery information for early disease diagnosis and timely management. Future research should focus on developing autonomous end-to-end disease monitoring systems that incorporate these technologies, fostering comprehensive precision agriculture and sustainable crop production. Full article

This article reveals the nature, causes, and main stages of occurrence and development of endogenous fires in coal mines. It is emphasized that one of the key tasks of fire protection specialists is the most accurate determination of the stage of oxidation and self-heating of coal. A review of existing gas analysis methods for identifying the initial and subsequent stages of endogenous fire development is conducted. Particular attention is focused on the importance of obtaining prompt reliable information on the self-heating temperature of coal and the dynamics of its change in the early stages of the process. Since self-heating zones are usually inaccessible for direct instrumental control, the main source of information is the gas analysis of air samples. The authors present the results of research on the dependence of the indicator gas content on the coal self-heating temperature. Based on the Graham criterion, the stages of thermal development of the process are predicted. Correlation dependencies between temperature and integral parameters of indicator gas concentrations are developed, allowing for a sufficient degree of reliability in determining the stages of coal self-heating and spontaneous combustion. Based on the results of the work, methodological recommendations for the prevention and warning of endogenous fires in coal mines and opencasts are proposed. They are based on the most informative and accessible signs suitable for quantitative assessment. The implementation of these recommendations will improve the level of industrial safety and reduce the risks of fires and explosions during mining operations. Full article

In response to the growing safety challenges posed by large-scale and specialized freight transportation on China’s rapidly expanding highway network, this study investigates the operational characteristics of trucks in interchange diverging areas—a critical segment with elevated accident risks. Leveraging high-frequency trajectory data collected from 16 interchanges, we analyze speed profiles and acceleration behavior of heavy trucks across key sections: the diversion influence zone, preparation zone, transition segment, and deceleration lane. A key contribution of this work is the development of a continuous speed prediction model based on Partial Least Squares Regression, which integrates road geometric parameters and driving behavior features to estimate speeds at four critical cross-sections of the diverging process. Furthermore, we propose a comprehensive safety evaluation framework incorporating three novel indicators: longitudinal speed consistency, lateral stability, and deceleration comfort. The model demonstrates strong performance, with all mean absolute percentage errors below 10% during validation using data from four independent interchanges. Comparative analysis with existing safety standards confirms the practical applicability and accuracy of the proposed methodology. This research offers three major contributions: (1) a systematic approach for processing large-scale trajectory data and predicting truck speeds in diverging areas; (2) a safety assessment framework tailored for geometric design consistency evaluation; and (3) empirical support for optimizing traffic safety facilities in interchange design and operation. The findings address a significant gap in current highway design guidelines and provide actionable insights for enhancing safety in truck-dominated transportation environments. Full article

Ecosystem services provide the scientific foundation and optimization objectives for constructing ecological security patterns, and their spatial characteristics directly affect planning decisions such as ecological source identification and corridor layout. However, current methods for constructing ecological security patterns rely excessively on static spatial optimization of landscape structure and ecological processes, while overlooking the dynamic variations in ecosystem service values under climate change. Taking Yunnan Province as a case study, this paper calculates ecosystem service values, analyzes their spatiotemporal variations, and based on ecosystem service value hotspots, applies the MSPA model and circuit theory to identify ecological sources, corridors, pinch points, barrier areas, and improvement areas. On this basis, we construct and optimize the ecological security pattern of Yunnan Province and propose ecological protection strategies. The results show that: (1) From 2000 to 2030, ecosystem service values in Yunnan exhibit significant spatiotemporal heterogeneity. From 2000 to 2020, they first declined and then increased, with aquatic ecosystems contributing the most. Under future climate scenarios, ecosystem service values continue to increase, with the greatest growth under the SSP2-4.5 scenario. The spatial pattern is characterized by higher values in the central region and lower values in the eastern and western areas. (2) In 2020, 56 ecological sources were identified; under the SSP1-1.9 scenario, 61 were identified, while 57 were identified under both SSP2-4.5 and SSP5-8.5 scenarios. These sources are mainly distributed in northwestern Yunnan and the Nujiang and Lancang River basins, presenting a “more in the west, fewer in the east” pattern. (3) In 2020, 132 ecological corridors and 74 pinch points were identified. By 2030, under SSP1-1.9, there are 149 corridors and 84 pinch points; under SSP2-4.5, 135 corridors and 55 pinch points; and under SSP5-8.5, 134 corridors and 60 pinch points. (4) By integrating results across multiple scenarios, an ecological security pattern characterized as “three screens, two zones, six corridors, and multiple points” is constructed. Based on regional ecological background characteristics, differentiated strategies for ecological security protection of territorial space are proposed. This study provides a scientific reference for the synergistic optimization of ecosystem services and ecological security patterns under climate change. Full article

Northern Taiwan is a tectonically and volcanically active region shaped by plate convergence, active faulting, and subsurface hydrological processes. To investigate surface deformation across this complex setting, we applied Persistent Scatterer InSAR (PSInSAR) to Sentinel-1A imagery acquired from 2017 to 2022. Using data from ascending and descending tracks, and removing GNSS-derived northward motion, we decomposed line-of-sight velocities into vertical and eastward components. The resulting deformation fields, validated by dense precision leveling and continuous GNSS observations, reveal consistent but minor (less than 1 cm/year) land subsidence in the Taipei Basin, spatially variable uplift near the Tatun Volcano Group, and a previously vaguely documented uplift zone in northeastern Taoyuan. InSAR-derived eastward motion is consistent with expected kinematics along the southern Shanchiao Fault and supports broader patterns of clockwise tectonic rotation near Keelung. Our InSAR results show the effectiveness of PSInSAR in resolving multidirectional surface motion and exemplifies the value of integrating satellite-based and ground-based geodetic data for fault assessment, hydrologic monitoring, and geohazard evaluation in northern Taiwan. Full article

Bone drilling is a widely encountered process in orthopedic surgeries and keyhole neuro surgeries. We are developing a sensor-integrated smart end-effector for drilling for robotic surgical applications. In manual surgeries, surgeons assess tool wear based on experience and force perception. In this work, we propose a machine-learning (ML)-based tool condition monitoring system based on multi-sensor data to preempt excessive tool wear during drilling in robotic surgery. Real-time data is acquired from the six-component force sensor of a collaborative arm along with the data from the temperature and multi-axis vibration sensor mounted on the bone specimen being drilled upon. Raw data from the sensors may have noises and outliers. Signal processing in the time- and frequency-domain are used for denoising as well as to obtain additional features to be derived from the raw sensory data. This paper addresses the challenging problem of identification of the most suitable ML algorithm and the most suitable features to be used as inputs to the algorithm. While dozens of features and innumerable machine learning and deep learning models are available, this paper addresses the problem of selecting the most relevant features, the most relevant AI models, and the optimal hyperparameters to be used in the AI model to provide accurate prediction on the tool condition. A unique framework is proposed for classifying tool wear that combines machine learning-based modeling with multi-sensor data. From the raw sensory data that contains only a handful of features, a number of additional features are derived using frequency-domain techniques and statistical measures. Using feature engineering, we arrived at a total of 60 features from time-domain, frequency-domain, and interaction-based metrics. Such additional features help in improving its predictive capabilities but make the training and prediction complicated and time-consuming. Using a sequence of techniques such as variance thresholding, correlation filtering, ANOVA F-test, and SHAP analysis, the number of features was reduced from 60 to the 4 features that will be most effective in real-time tool condition prediction. In contrast to previous studies that only examine a small number of machine learning models, our approach systematically evaluates a wide range of machine learning and deep learning architectures. The performances of 47 classical ML models and 6 deep learning (DL) architectures were analyzed using the set of the four features identified as most suitable. The Extra Trees Classifier (an ML model) and the one-dimensional Convolutional Neural Network (1D CNN) exhibited the best prediction accuracy among the models studied. Using real-time data, these models monitored the drilling tool condition in real-time to classify the tool wear into three categories of slight, moderate, and severe. Full article

The rise of digital platforms has transformed heritage interpretation from a single official narrative to multi-stakeholder participation. This study investigates how such platforms mediate the formation of a sense of place at the Kulangsu World Heritage Site (WHS). Data were collected from official narrative texts and user-generated content (UGC) on Dianping and Ctrip, and analyzed using high-frequency word statistics and semantic network analysis. The results reveal a clear divergence between official narratives, which emphasize Outstanding Universal Value (OUV), and tourist perceptions, which focus on visual landmarks and “check-in” practices shaped by the “digital gaze.” Moreover, the sense of place is shown to be a dynamic process, co-constructed through pre-visit expectations, on-site experiences, and post-visit reflections. The findings also highlight a transformation in tourists’ roles, shifting from passive cultural consumers to active participants in the co-construction of heritage values, with digital platforms serving as critical mediators. Theoretically, the study advances digital heritage scholarship by clarifying the mechanism of the digital gaze and the dynamic nature of sense of place. Practically, it underscores the importance of integrating official narratives with UGC to strengthen OUV communication, foster broader public engagement, and support the sustainable development of WHSs. Full article

Background and objectives: Pain is a prevalent and often underestimated issue in children with cerebral palsy (CP). When left untreated, pain can result in secondary complications such as reduced mobility and mental health challenges, which negatively impact social activity, participation, and overall quality of life. This review explores the complex mechanisms underlying pain in CP, highlights contributing factors, and places particular emphasis on diagnostic challenges and multimodal pain management strategies. Methods: Three scientific databases and, additionally, guideline repositories (2015–2025) were searched, yielding 1335 records. Following a two-step deduplication process, 850 unique items remained. Eighty-five full texts were assessed, of which 49 studies were included. These comprised one randomised controlled trial, 16 non-randomised studies, 12 systematic reviews, 8 non-systematic reviews, and 12 guidelines or consensus statements. Methodological quality was appraised with AMSTAR-2 where applicable, and Oxford levels of evidence were assigned to all studies. Results: Study quality was variable: 25% were systematic reviews, with only one randomised controlled trial. This literature identifies overlapping nociceptive, neuropathic, and nociplastic mechanisms of pain development. Classification remains inconsistent, though the International Classification of Diseases provides a useful framework. Only five assessment tools have been validated for this population. Interventions were reported in 45% of studies, predominantly pharmacological (27%) and physiotherapeutic (23%). Evidence gaps remain substantial. Conclusions: This review highlights the complexity of pain in children and adolescents with cerebral palsy and the need for a biopsychosocial approach to assessment and management. Evidence supports individualised, multimodal strategies integrating physical therapies, contextual supports, and, where appropriate, medical or surgical interventions. Clinical implementation remains inconsistent due to limited high-quality evidence, inadequate assessment tools, and poor interdisciplinary integration. Full article

A core challenge in multimodal emotion recognition lies in the precise capture of the inherent multimodal interactive nature of human emotions. Addressing the limitation of existing methods, which often process visual signals (facial expressions) and physiological signals (EEG, ECG,EOG, and GSR) in isolation and thus fail to exploit their complementary strengths effectively, this paper presents a new multimodal emotion recognition framework called the Gated Biological Visual Network (GBV-Net). This framework enhances emotion recognition accuracy through deep synergistic fusion of facial expressions and physiological signals. GBV-Net integrates three core modules: (1) a facial feature extractor based on a modified ConvNeXt V2 architecture incorporating lightweight Transformers, specifically designed to capture subtle spatio-temporal dynamics in facial expressions; (2) a hybrid physiological feature extractor combining 1D convolutions, Temporal Convolutional Networks (TCNs), and convolutional self-attention mechanisms, adept at modeling local patterns and long-range temporal dependencies in physiological signals; and (3) an enhanced gated attention fusion module capable of adaptively learning inter-modal weights to achieve dynamic, synergistic integration at the feature level. A thorough investigation of the publicly accessible DEAP and MAHNOB-HCI datasets reveals that GBV-Net surpasses contemporary methods. Specifically, on the DEAP dataset, the model attained classification accuracies of 95.10% for Valence and 95.65% for Arousal, with F1-scores of 95.52% and 96.35%, respectively. On MAHNOB-HCI, the accuracies achieved were 97.28% for Valence and 97.73% for Arousal, with F1-scores of 97.50% and 97.74%, respectively. These experimental findings substantiate that GBV-Net effectively captures deep-level interactive information between multimodal signals, thereby improving emotion recognition accuracy. Full article

Sustainable management of built heritage is a complex process made more difficult by the need to maintain significance and comply with any associated protections. Historic Building Information Modelling (HBIM), an information management and modelling process, has the potential to assist with this. This paper investigates how a pre-existing Historic Building Information Modelling (HBIM) model can be used to assist the energy-efficient interventions at the historic Rowheath Pavilion in Bournville, England. Two potential methods (HBIM and building energy modelling integration and HBIM and thermal image integration) are evaluated against their outputs and the available resources. Subsequently, the paper presents a case study wherein a thermal image survey was undertaken at Rowheath Pavilion and the resulting images were integrated with the pre-existing HBIM model. The apparent thermal performance of the pavilion was qualitatively evaluated. The described method was easy to apply and repeat. The integration of thermal images combined with the visualisation capabilities of HBIM resulted in the identification of energy inefficiencies and allowed the Rowheath staff to implement immediate small-scale changes to improve the sustainability of the pavilion. Full article

Introduction: Ambiguous loss is a profound yet underexplored phenomenon in the lives of aging migrants. Older adults who have experienced migration often face disruptions to their sense of belonging, identity, and continuity across borders. These losses are compounded by aging, health challenges, and social isolation. Despite its significance, ambiguous loss among aging migrants has not been conceptually analyzed in depth, limiting the development of culturally responsive care practices. Aim: This concept analysis aimed to identify the defining attributes of ambiguous loss among aging migrants and to develop a conceptual definition that enhances our understanding of the phenomenon and informs future research and practice. Method: Walker and Avant’s eight-step concept analysis framework was applied to examine the concept of ambiguous loss in the context of aging migrants. A systematic keyword search was conducted across four databases (CINAHL, Medline, SCOPUS, PsycINFO), Google Scholar, and relevant gray literature, covering the years of 2010–2024. Covidence software supported the screening process. From 367 records identified, 146 underwent full-text review, and 74 met inclusion criteria. The analysis drew on literature synthesis, case exemplars, antecedents, consequences, and empirical referents. This review followed PRISMA (2020) reporting guidelines. Results: Four defining attributes of ambiguous loss among aging migrants were identified: (a) physical, social, and emotional loss; (b) displacement and loss of homeland; (c) erosion of social identity and agency; and (d) cultural and transnational bereavement. A conceptual definition emerged, describing ambiguous loss as a multifaceted experience of disconnection, intensified by aging, illness, economic hardship, and social isolation. The analysis also highlighted antecedents such as forced migration and health decline, as well as consequences including diminished well-being, resilience challenges, and barriers to integration. Conclusions: Ambiguous loss among aging migrants is a complex construct encompassing intertwined physical, social, and cultural dimensions of loss. This conceptual clarity provides a foundation for developing culturally responsive care models that promote adaptation, resilience, and social inclusion among older migrants. Full article