Search Results (10,797)

Aeolian sand bodies unrelated either to coastal barrier systems of Holocene or earlier age or to modern beaches have been identified along the central New South Wales coast of southeast Australia. Some of these deposits cap headlands or are located above high sea-cliffs. Others lie below modern sea-levels, whilst one substantial dune field extends 12 km inland. Many of these have previously been interpreted as Early Holocene cliff-top dunes, the product of the migration of beach sands up aeolian sand ramps at the foot of the sea-cliffs of the region and onto the cliff tops. The rising sea-levels of the Middle Holocene eroded the ramps and cut off the supply of sand to the dunes, allowing them to stabilise. But re-investigation shows that these dune fields accumulated at times of low Quaternary sea-levels, with a particle-size distribution suggestive of an inland rather than a coastal origin. We therefore propose an alternative model for the accumulation of these features. At times of low sea-level, sediments exposed on the inner shelf were reworked onto the adjacent coast by onshore winds, where they accumulated in locations unconnected to the modern or the earlier Holocene coastal aeolian sedimentary regime. This model challenges the conventional story that the dominant glacial maximum winds across southeastern Australia were from the west (and thus offshore). This pattern of sediment accumulation and its associated wind regime may have been more stable (continuing for over 30 000 years) and more long-lived (repeated through at least the last two glacial cycles) than has previously been believed. Although the cliff-top dune model has been widely applied, we question its suitability in its type location and suggest a more cautious approach to its application elsewhere. We argue that the products of the landward aeolian reworking of sediment exposed on the continental shelf have been overlooked, despite their potential for the preservation of long-term environmental records. Full article

Suspended ceiling systems are among the most seismically vulnerable non-structural components in buildings, posing significant life-safety risks and economic losses, yet understanding their full-field kinematic behavior under seismic loading remains a major experimental challenge. Conventional contact sensors offer limited spatial coverage and can alter the dynamic properties of lightweight panels due to mass loading. In contrast, non-contact optical alternatives are rarely feasible in shake-table environments due to restricted viewing angles, extensive areal coverage requirements, and the risk of equipment damage from falling panels. This study proposes an end-to-end three-dimensional displacement measurement framework for large-scale shake-table testing of suspended ceiling systems, employing consumer-grade cameras with purpose-built tools that cover the complete experimental workflow, including motion-based video trimming, semi-automated calibration, a robust multi-stage image-tracking pipeline that maintains trajectory continuity under extreme inter-frame displacements, and a ceiling system motion visualization and analysis tool. The framework was validated through a full-scale shake-table experiment continuously tracking 324 spatial nodes across 81 ceiling panels, achieving an RMSE below 3 mm in all spatial directions and exact peak-frequency agreement in 9 out of 10 test cases. A parallel processing architecture reduced total processing time from over 27 h to under 10 min without GPU acceleration, and six-degree-of-freedom rigid-body analysis resolved the complete panel failure sequence from constrained oscillation through multi-axis rotation to gravitational free fall, a level of kinematic detail unattainable with conventional instrumentation. This framework establishes a practical, scalable foundation for full-field seismic performance assessment of non-structural systems where conventional instrumentation is physically or logistically infeasible. Full article

Emerging-market equity exchanges require regime forecasting systems that are continuous in time, robust to heavy-tailed distributions, and optimised against false alarms. No existing method addresses all three simultaneously, and no prior study has reported a crisis false-alarm rate on JSE equities. We propose S-NODE-ANF-RRC: a stochastic neural ODE within an Adaptive Neuro-Fuzzy Risk-Regime Clustering architecture, integrated by a Milstein scheme with Lyapunov-regularised dual-loss training. The system is evaluated as a one-step-ahead probabilistic forecaster ($h=1$ trading day) on 2696 daily observations across 17 JSE securities (March 2015–March 2026). Gaussian mixture clustering on raw features (kurtosis 54.8) inflates ARI by 1.3×; log-transformation corrects this artefact. Two operational profiles emerge: the N-ODE-ANF-RRC achieves the lowest cost (10,350 bp, 65.1% below GMM) and longest lead time (0.71 days); the S-NODE-ANF-RRC achieves the lowest false alarm rate among probabilistic architectures (FAR = 0.051), with a 42.0% cost reduction versus GMM (McNemar $p=0.027$, power $1-\beta =0.73$; bootstrap CI [5250, 19,600] bp excludes zero). Ablation confirms drift, diffusion, and dual-loss as the minimum viable daily-frequency configuration. Full article

The health system is now more complex, uncertain, interdependent, and dynamically interconnected than ever, making traditional planning decisions based on static, reductionist models increasingly impracticable. Systems analytics approaches such as system dynamics, agent-based modeling, and network analysis are often deployed in isolation and fail to capture cross-level interactions and emergent system behavior. This study proposes an integrated multi-layer systems analytics framework that combines these analytical paradigms within a unified architecture to support adaptive health systems planning under uncertainty. The proposed framework introduces an Adaptive Policy Intelligence Layer (APIL), which enables continuous feedback-driven policy adaptation through dynamic monitoring, scenario evaluation, and real-time adjustment mechanisms. The model is evaluated under multiple simulation scenarios, including baseline conditions, demand shocks, resource constraints, and digital transformation environments. The findings provide strong quantitative and analytical evidence of improved system performance and resilience. More specifically, the digital transformation scenario achieved the lowest mean system pressure (0.128) and the highest resilience index (0.887), while the demand shock scenario produced the highest peak system pressure (0.306). The results demonstrate enhanced system resilience, more efficient resource deployment, and superior policy responsiveness compared with traditional single-method approaches. The originality of this study lies in integrating multi-level systems analytics with adaptive policy intelligence into a unified, feedback-driven decision-support framework for resilient health systems governance. The study contributes to systems analytics literature by advancing a synergistic and adaptive modeling paradigm capable of supporting policymakers in navigating complex and unstable healthcare environments. Full article

Stereo matching has become an increasingly important component of modern autonomous systems. Developing deep learning-based stereo-matching models that deliver high accuracy while operating in real time continues to be a major challenge in computer vision. In the domain of cost volume-based stereo matching, accurate disparity estimation depends heavily on large-scale cost volumes. However, such large volumes store substantial redundant information and also require computationally intensive aggregation units for processing and regression, making real-time performance unattainable. Conversely, small-scale cost volumes followed by lightweight aggregation units provide a promising route for real-time performance, but lack sufficient information to ensure highly accurate disparity estimation. To address this challenge, we propose the Enhanced Shuffle Mixer (ESM) to mitigate information loss associated with small-scale cost volumes. ESM restores critical details by integrating primary features into the disparity upsampling unit. It quickly extracts features from the initial disparity estimation and fuses them with image features. These features are mixed by shuffling and layer splitting, then refined through a compact feature-guided hourglass network to recover more detailed scene geometry. The ESM focuses on local contextual connectivity with a large receptive field and low computational cost, leading to improved disparity estimation accuracy while maintaining real-time performance under the evaluated settings. The compact version of ESMStereo achieves an inference speed of 116 FPS on RTX 4070S and 91 FPS on the AGX Orin. Full article

Bisphenol A type epoxy resin has the problem of relatively high brittleness after curing. Although traditional polysiloxane toughening methods can improve toughness, they often come at the expense of strength. In this paper, methylphenyl dimethoxysilane (MPS) was used as a monomer to synthesize end-hydroxyl poly(methylphenyl)siloxane (PMPS), which was then used to modify E51 epoxy resin. The structure and reaction degree were characterized by infrared spectroscopy, proton nuclear magnetic resonance spectroscopy, matrix-assisted laser desorption/ionization time-of-flight/time-of-flight mass spectrometry and viscosity tests. The mechanical test results show that when the PMPS content is 20 wt%, the tensile, flexural, compressive and impact strengths of the modified resin increase by 31.26%, 26.16%, 18.53% and 98.66%, respectively, compared with the unmodified resin, and the tensile and flexural elastic moduli increase by 38.36% and 32.25%, respectively. The fracture toughness increases by 60.29%, indicating that the strength, stiffness and toughness of the material have all been improved. Dynamic mechanical analysis shows that the glass transition temperature and crosslinking density of the system gradually decrease with increasing PMPS content. Thermogravimetric analysis shows that the introduction of PMPS increases the char yield and decreases the maximum thermal decomposition rate, thereby enhancing the thermal stability of the system. Microscopic morphology analysis by optical microscopy, scanning electron microscopy and atomic force microscopy shows that the system has good compatibility, and the internal different modulus phases are distributed in a network-like manner, forming a uniform co-continuous or bicontinuous phase structure. This structure effectively promotes stress transfer and energy dissipation, alleviates local stress concentration, and thus comprehensively improves the mechanical properties of the resin system. Full article

Indoor Air Quality (IAQ) monitoring, especially in multi-zone smart buildings, is typically limited by the high computational and energy requirements of continuous sensor processing, which makes event-driven methods desirable for efficiency. Energy proportionality, in this context, refers to a system whose computational cost scales with the significance of detected environmental changes rather than with the fixed sampling rate. This paper presents a spike-driven neuromorphic sensing framework for decentralized IAQ monitoring that combines adaptive Kalman filter preprocessing, dynamic threshold-based asynchronous spike encoding, and a Leaky Integrate-and-Fire neural network with Spike-Timing-Dependent Plasticity (STDP) learning. Multiple-parameter IAQ data including PM₁, PM_2.5, PM₁₀, CO₂, CO, TVOCs, and O₃ were sampled from nine functionally differing zones of an educational building in Metro Manila, Philippines. The neuromorphic model yielded a mean Sparse Firing Ratio of 10.94%, a Mean Response Time of 10.62 timesteps, and an energy efficiency proxy score of 9.28. Neuron population scaling and parameter robustness analyses revealed that the four neurons per parameter were enough to saturate the performance, and FLOP-based estimation indicated an 8.9-fold computational reduction (approximately 89% fewer FLOPs) compared to LSTM inference. In addition, the revised Performance Efficiency Index and composite efficiency score corroborated the stable and energy-proportional nature of behavior in all zones. These results illustrate that spike-based neuromorphic computation is an energy-efficient and scalable way for decentralized smart-building IAQ monitoring, though hardware-level validation on dedicated neuromorphic processors remains necessary for absolute power saving verification. Multi-seed validation (five seeds) with expanded baselines including GRU, Temporal CNN, XGBoost, and Logistic Regression confirmed the robustness and repeatability of reported metrics. Full article

Real-time assessment of reading comprehension remains a challenge in educational research. Traditional evaluation methods, such as questionnaires, provide delayed and retrospective measures and therefore do not capture the dynamic nature of comprehension during reading. This exploratory study investigates whether beta-band electroencephalography (EEG) activity can be used to estimate EEG-derived indicators related to reading comprehension during academic reading. The study included 40 university students who read a conceptually demanding scientific text while EEG signals were continuously recorded. Beta-band activity (13–30 Hz) was extracted from six cognition-related channels and segmented into non-overlapping 2 s windows. Principal component analysis (PCA) was applied for dimensionality reduction, followed by kernel ridge regression (KRR) for prediction. At the window level, the proposed KRR–PCA framework achieved a mean absolute error (MAE) of 5.797, a root mean square error (RMSE) of 7.783, an MAE-based accuracy of 94.2%, and an explained variance of R² = 0.275 on a held-out test set. At the participant level, aggregated predictions showed a significant correlation with questionnaire-based comprehension scores (r = 0.59), indicating that EEG-derived features captured meaningful inter-individual differences. The framework also generated time-resolved prediction profiles that reflected fluctuations in EEG-derived comprehension estimates during reading. These findings suggest that beta-band EEG contains information related to reading comprehension and may support the development of future EEG-based educational monitoring systems. Further validation using larger cohorts and time-resolved comprehension measures is needed to confirm the practical applicability of the approach. Full article

This work addresses the design and implementation of an automated system for the handling and transportation of parts, integrating speed sensors, an optimized PID controller, an HMI interface, and an industrial robotic system. The speed sensors, powered by 5 V DC, enable continuous measurement of the conveyor belt’s speed and direction of rotation, providing the feedback signal required for the control loop. The core element of the system is the implementation of a PID controller applied to a direct current motor responsible for driving the conveyor belt. This controller regulates the motor speed by analyzing the error between the reference speed and the measured speed, using proportional, integral, and derivative actions to improve system stability, reduce steady-state error, and minimize oscillations. The application of PID control makes it possible to achieve an appropriate dynamic response, ensuring accuracy and reliability in the transportation process. System monitoring and operation are carried out through a human–machine interface (HMI) developed in LOGO Web Editor, which communicates with the PLC (LOGO V8) to visualize and control the status of the conveyor belt, sensors, and control elements in real time. This interface facilitates interaction between the operator and the system, allowing both virtual and physical operation. In addition, RAPID programming is used to control the IRB 14000 industrial robot, enabling the reading of PLC signals and the execution of coordinated trajectories between both arms. The operating sequence includes picking up a part with the left arm, placing it on the conveyor belt, and, after detection by sensors and PLC control, subsequent manipulation by the right arm to a specific point. Finally, both arms return to their original position, ensuring synchronized and collision-free operation. Lastly, this work integrates scientific knowledge related to the modeling, analysis, and control of dynamic systems, particularly in the implementation of closed-loop PID control optimized using genetic algorithms. This control is applied directly to an embedded system through the use of an Arduino board as the processing and control platform. Likewise, technological knowledge associated with industrial automation, PLC programming, HMI development, and industrial robotics is incorporated. The convergence of these scientific and technological approaches results in a comprehensive and compelling project that demonstrates the practical application of theoretical concepts in a functional automated system representative of real industrial environments. Full article

Background: Advances in monitoring systems featuring wearable sensors, computer vision, and artificial intelligence (AI) have been increasingly used in sports science and rehabilitation practices as a means of movement pattern analysis, injury prevention, and training optimization. These technologies are becoming essential components of athlete-performance analysis and rehabilitation-monitoring systems designed to support biomechanical assessment, athlete development, and movement-quality evaluation. Athlete-performance analysis and rehabilitation monitoring increasingly rely on intelligent multimodal sensing systems capable of continuously evaluating movement quality, biomechanical patterns, training execution, and recovery progress. Human activity recognition (HAR) serves as a key enabling technology for these applications by providing automated assessment of human movement using wearable and vision-based sensing modalities. Therefore, the purpose of this study was to develop and evaluate an attention-based multimodal framework that integrates wearable inertial sensing and RGB video analysis for robust athlete-performance assessment and rehabilitation monitoring through accurate recognition of human movement patterns. Methods: Athlete-performance analysis and rehabilitation monitoring combining inertial sensor data and RGB-based visual information was introduced. Inertial signals were segmented with adaptive windowing, whereas silhouette refinement was performed to analyze motion structures from visual inputs in support of athlete-performance analysis and rehabilitation monitoring. Temporal, spatial, and motion features such as trajectory, orientation, and skeleton-based space-time representations were calculated from multimodal inputs. The proposed framework was designed to capture complex movement dynamics associated with rehabilitation exercises and sports-related motion patterns across heterogeneous sensing environments. Extracted features were then combined and optimized with a multimodal feature fusion approach, while the Ranger optimization algorithm was utilized during the process. An attention-based deep learning classifier was implemented to classify movement activities. Results: The results showed that the proposed framework reached accuracy scores of 88.40% and 87.96% on the VIDIMU dataset and the UTD-MHAD dataset respectively. Recognition performance across both inertial and vision-based modalities provided greater robustness than single-modality solutions. The integration of wearable sensing and computer vision modalities further improved the ability of the framework to analyze complex movement behaviors under varying execution conditions and environmental variations. Conclusion: The proposed multimodal framework provides a foundation for intelligent athlete-performance and rehabilitation-monitoring systems by integrating wearable sensing, computer vision, and attention-based artificial intelligence for robust movement analysis. The findings highlight its potential to support biomechanical assessment, movement-quality evaluation, training-performance monitoring, rehabilitation tracking, and injury-risk management in modern sports and healthcare environments. Full article

Excessive concentrations of carbon dioxide (CO₂) in the atmosphere lead to adverse environmental effects. Biologically assisted processes that rely on organisms such as microalgae (i.e., Chlorella vulgaris) are common in capturing CO₂ from the atmosphere. Microalgae are rich in proteins, vitamins, minerals, and omega-3 fatty acids. Thus, microalgae production serves both health and environmental sectors. Varying light intensity and temperature are shown to influence algae growth. To quantify algae production under different light intensity and temperature conditions, and monitoring or scaling-up of biological reactors, reliable mathematical models are required. In this work, mathematical models that incorporate light intensity and temperature effects on algae growth in batch and continuous bioreactors are developed. Based on the modeling, the growth rate is maximum at T_opt = 25 °C, reaching the value of μ_max = 0.14 day⁻¹. The growth rate exponentially increases until light intensity (I) reaches around 150 ${\displaystyle \frac{\mathsf{\mu }\mathrm{m}\mathrm{o}\mathrm{l}}{{\mathrm{m}}^2\mathrm{s}}}$, which is approximately the optimal light intensity for Chlorella vulgaris. The effect of T on growth rate is found to be more sensitive than light intensity (I) in both batch and continuous reactor systems. When there are too many parameters in models, uncertainties exist and parameter estimation and model predictions become cumbersome. For these reasons analytical solutions to the models are presented in simplified forms and these models are more practical and easier to implement. The novelty of the work is also the presentation of the models in analytical forms. Analytical solutions to the two reactor models (batch and continuous) will help quantify biomass production as a function of time under the varying light intensity and temperature conditions encountered. Full article

Walking is not merely locomotion but a window into the nervous system, integrating cortical, subcortical, cerebellar, spinal, and peripheral networks into a unified motor behavior. Across neurological diseases—including Parkinson’s disease, atypical parkinsonism, cerebellar ataxias, stroke, multiple sclerosis, neuropathies, neuromuscular disorders, and functional gait syndromes—gait disturbances are among the most disabling clinical features, contributing to falls, loss of independence, institutionalization, and premature mortality. Traditional bedside observation remains indispensable, but it lacks the sensitivity and reproducibility needed to capture subtle, episodic, or prodromal abnormalities. Over the past decade, advances in wearable sensors, marker-based and markerless motion capture, pressure-sensitive walkways, force plates, artificial intelligence, and machine learning have positioned digital mobility outcomes as promising, ecologically valid biomarkers of neurological function. These measures can support differential diagnosis, provide prognostic information on falls and survival, and serve as sensitive endpoints in therapeutic trials. They may also detect early abnormalities, such as increased stride-to-stride variability or prolonged double-support time, before overt clinical deterioration becomes evident. Clinical applications are increasingly evident across disorders, including distinguishing Parkinson’s disease from atypical parkinsonism, quantifying treatment response in normal-pressure hydrocephalus, tracking progression in ataxia and multiple sclerosis, predicting functional decline in motor neuron disease, and guiding rehabilitation after stroke. Integration with neuroimaging, electrophysiology, and molecular biomarkers is beginning to reveal the circuits underlying variability, instability, and freezing, positioning gait as a systems-level marker of neural integrity. Nevertheless, methodological heterogeneity, limited disease-specific validation, insufficient longitudinal data, and lack of consensus on clinically meaningful parameters continue to constrain translation. Cognitive, affective, and environmental influences also remain insufficiently represented in digital frameworks, while equity, accessibility, algorithmic bias, and privacy require careful ethical governance. Reconceptualizing gait as a “sixth vital sign” reframes mobility as a multidimensional biomarker of neural and systemic health. With harmonized protocols, robust validation, multimodal integration, and appropriate ethical frameworks, gait analysis could become a cornerstone of precision neurology. Full article

Severe Combined Immunodeficiency (SCID), Spinal Muscular Atrophy (SMA), and X-Linked Agammaglobulinemia (XLA) are rare but life-threatening genetic disorders in infants that can lead to severe infections, progressive neuromuscular degeneration, or severe immune dysfunction associated with significant morbidity and mortality if not diagnosed early. Advances in newborn screening (NBS) technologies have enabled pre-symptomatic detection of these conditions, allowing early initiation of life-saving interventions such as hematopoietic stem cell transplantation, gene therapy, and immunoglobulin replacement therapy. However, the absence of a standardized national clinical pathway linking screening, confirmatory testing, and specialist referral in Malaysia continues to contribute to delayed diagnosis and suboptimal patient outcomes. This review examines and synthesizes current evidence on the clinical pathways for early diagnosis and management of SCID, SMA, and XLA, with particular emphasis on diagnostic workflows, screening technologies, and healthcare system challenges within the Malaysian context. The review examines disease epidemiology, consequences of delayed diagnosis, and the role of expanded NBS under the Screening for Health, Intervention, Nurturing of Every Child (SHINE) program in improving early diagnosis and management. In addition, the paper outlines the current NBS landscape, the use of multiplex real-time polymerase chain reaction (PCR) assays for simultaneous detection of T-cell receptor excision circles (TREC), kappa-deleting recombination excision circles (KREC), and survival motor neuron 1 (SMN1) gene deletion of exon 7 from dried blood spot (DBS) samples. A structured diagnostic framework incorporating screening interpretation, confirmatory testing, and urgency-based referral pathways is also proposed. By addressing current operational barriers and coordinating laboratory referral systems, expanding NBS programs could significantly improve early diagnosis and long-term outcomes for infants affected by SCID, SMA, and XLA in Malaysia. Full article

Background/Objectives: The accuracy and reliability of automated clinical analyzers are fundamental to patient safety and effective medical decision-making. Traditional quality control (QC) methods, which rely on periodic manual calibration and reactive maintenance, are inherently limited by high latency and labor costs; furthermore, they fail to provide continuous, real-time monitoring. This paper introduces a novel hybrid ensemble learning framework for the automated quality inspection of medical devices through the analysis of time-series reaction curves. Methods: Our system integrates three heterogeneous anomaly detection paradigms: an Enhanced Dynamic Time Warping (DTW) detector for robust non-linear pattern matching, a Shape Template Matching (STM) detector that mimics expert clinical logic by analyzing morphological features in a normalized shape space, and a specialized Time-series Variational Autoencoder (TimeVAE) for deep representation learning. The outputs of these detectors are fused using a weighted ensemble strategy, which is specifically designed to prioritize the minimization of false negatives—a critical requirement in medical diagnostics. Results: We evaluate our framework on a comprehensive, multi-center real-world dataset comprising seven distinct biochemical assays. Experimental results demonstrate that our proposed method achieves superior performance, attaining a $0\%$ false negative rate on CRE and DBIL assays and outperforming all baseline methods on the other five datasets. An ablation study confirms the model’s robustness even with limited training data, and a comparative analysis against eight state-of-the-art baseline methods further validates the effectiveness of our domain-optimized ensemble approach. Conclusions: The system provides a robust, interpretable, and highly automated solution for transitioning from reactive maintenance to proactive, real-time quality assurance in clinical laboratories. Full article

Time-domain dynamic full-field optical coherence tomography (D-FFOCT) is a powerful label-free imaging modality that enables functional visualization of cellular activity in living tissues with subcellular resolution. However, its sensitivity remains a major limitation for imaging highly scattering three-dimensional (3D) biological models such as retinal organoids, where incoherent background and inefficient optical flux distribution reduce dynamic contrast and limit imaging depth. In this work, we introduce a ratio-free optical configuration for time-domain D-FFOCT that enables continuous tuning of the sample-to-reference field ratio while minimizing photon losses and suppressing parasitic reflections. This polarization-based architecture allows optimal redistribution of optical flux according to sample scattering conditions and improves sensitivity under both power-limited and dose-limited conditions. Compared with conventional non-polarizing beam splitter configurations, the proposed approach provides a $\sqrt{2}$-fold (3 dB) sensitivity improvement through optical optimization alone. In addition, we investigate for the first time the use of partial-field illumination (PFI) in time-domain D-FFOCT to reduce incoherent background arising from multiple scattering. In retinal organoids imaged at 120 $\mathsf{\mu }$m depth, PFI yields up to a 14.5-fold (23.2 dB) increase in dynamic signal sensitivity, while preserving functional contrast. When combined, ratio-free detection and PFI provide a cumulative sensitivity improvement of 20.5-fold (26.2 dB). These gains enable improved cellular-scale visualization in retinal organoids, including cell-resolved imaging within rosette regions, as well as improved detection of intracellular dynamics in Müller glial cell cultures. This work establishes a practical framework for sensitivity optimization in D-FFOCT and expands its potential for functional imaging, disease modeling, and live-cell monitoring in complex biological systems. Full article