Search Results (18,682)

Background/Objectives: Low-pressure hydrocephalus (LPH) is an underappreciated pathology in which patients present with ventriculomegaly and intracranial pressures (ICPs) below normal, making traditional pressure-driven cerebrospinal fluid diversion treatment strategies challenging. LPH has been observed in patients following traumatic brain injury (TBI), often within the neurocritical care unit (NCCU). However, its presentation and associated patient characteristics remain unclear. Thus, we sought to compile patients from our institution and the literature to characterize LPH after TBI. Methods: Eleven patients were identified from our institutional chart review. Additionally, we performed a scoping review using PRISMA guidelines, which yielded eight publications, resulting in a total of 46 patients with LPH after TBI. Results: The median age was 34 years (IQR 23 to 50.75), with 86.96% male. When used, ventriculoperitoneal shunt (VPS) valves were programmed to a median pressure setting of 45 mmH₂O (IQR 22 to 45). The median Glascow Outcome Scale-Extended (GOSE) score at 2–6 months post-shunt was 3 (IQR 2.25 to 4), indicating lower-severe disability and a 16.28% mortality. Conclusions: To our knowledge, this is the largest curation of TBI patients with LPH to date. From this pooled cohort, we observed that patients affected by LPH after TBI are often young to middle-aged and undergo multiple neurosurgical procedures prior to LPH diagnosis. Outcomes were poor with significant long-term disability and mortality. Clinicians should consider LPH diagnosis in TBI patients with hydrocephalus, especially if they have undergone prior neurosurgical procedures. Additionally, providers should recognize that these patients may potentially require lower opening pressures or certain pharmacological interventions compared to those with traditional post-traumatic hydrocephalus. Improved recognition of LPH after TBI can advance the understanding of the underlying mechanisms and inform treatment strategies. Full article

A physical–mathematical model was previously developed to estimate dust emissions from granular surfaces exposed to wind erosion. The model is based on the main physical mechanisms governing wind-driven dust emissions, whereby the release of fine particles is controlled by saltation and the associated sandblasting process. A probabilistic Monte Carlo approach is used to simulate saltator impacts on the erodible surface and estimate Particulate Matter (PM) emissions from the mass of elementary particles released during each collision. While the original study provided only a qualitative assessment, the present work introduces computational refinements and presents the first quantitative evaluation of the model in terms of both numerical performance and the physical consistency of the predicted PM emission behaviour. The algorithm was modified by introducing a fixed number of simulated impacts, thereby reducing computational cost. The revised model was applied to lead and zinc sulphide concentrates from an industrial plant in Sardinia (Italy) to evaluate the effects of the proposed model improvements. The assessment focused on (i) the sensitivity of the simulated emissions to the number of simulated impacts and (ii) the ability of the revised model to reproduce the PM emission magnitude, sandblasting efficiency, and their dependence on wind friction velocity. The results show that reducing the number of simulated impacts from 10,000 to 500 resulted in a median relative difference of 3.35% in the simulated PM emissions compared with the highest-sampling configuration investigated, while reducing the computational time by approximately 95%. Moreover, the model reproduces emission magnitudes and key emission parameters generally consistent with those reported in the literature for materials with similar physical properties. Overall, the revised model provides an efficient, physically based tool for estimating PM emissions from industrial granular materials. Full article

Remote-sensing small-object detection offers significant advantages and holds great importance in monitoring and measurement fields such as scene perception and environmental monitoring. Although deep neural networks have advanced the development of remote-sensing object detection, challenges remain, including large-scale variations and the difficulty of detecting dense, minute objects. To address these challenges, we propose a dynamic multiscale adaptive mechanism-driven remote-sensing object detection network (DMAM). First, to overcome the inherent limitations of traditional convolutional fixed sampling positions and uniform parameter distributions, we introduce adaptive kernel convolution (AKConv). By dynamically adjusting sampling positions and optimizing parameter distributions, AKConv enables more flexible and efficient feature extraction. Second, to effectively leverage prior spatial knowledge for expanding the receptive field, we propose a dynamic multiscale context adaptation (DMCA) module. This module implements a content-aware dynamic gating mechanism, which adaptively allocates weights between local details and global context by analyzing image content at each spatial location. By integrating local features with global information, it enhances scene comprehension, thereby improving detection accuracy and robustness. Finally, a shape-aware metric function (Shape-IoU) is introduced, which incorporates a shape-adaptive weighting mechanism to dynamically adjust the penalty weights for different geometric factors based on the target’s own shape characteristics, thereby achieving more precise bounding box regression. Results on three public datasets show that the proposed method demonstrates robust performance compared to state-of-the-art detection networks. Specifically, DMAM achieves an average accuracy of 97.7% on the RSOD dataset, 92.5% on the NWPU VHR-10 dataset, and 88.0% on the DIOR dataset. Full article

Future streamflow projections are critical for water management. In this study, we coupled the Geomorphology-Based Ecohydrological Model (GBEHM) with the Physics-aware Hybrid Learning and eXtreme Gradient Boosting models to provide a preliminary assessment of streamflow variations in the Minjiang River basin (MRB) over the period of 2020–2099 under the emission scenarios of five CMIP6 models, using data from the 2010s as the baseline. We considered both climatic and anthropogenic influences, assuming that the current anthropogenic disturbances and river network configuration will remain unchanged. The performance of the GBEHM is acceptable, with error metrics exceeding 0.80 and 0.60 before and after the impoundment of the Zipingpu Reservoir, respectively. The cascade of data-driven models demonstrates good performance, with error metrics exceeding 0.90 over the whole simulation period. Under the influence of climate change, the decadal mean streamflow at Zipingpu station will decrease by 2.73–12.16% before 2069 and increase thereafter, while at Gaochang station, it will generally increase by 1.44–13.67% after 2020. Moreover, the decadal mean streamflow at Pengshan station will increase by 13.22–36.41% over the coming decades. However, the combined effects of anthropogenic disturbances and climate change will significantly decrease future streamflow by 61.91–112.16 m³/s on average, corresponding to a reduction of 14.08–25.51% from the baseline. We also suggest strategies to mitigate future water risks and enhance basin management in the MRB. Full article

Bio-based alternatives to conventional rigid foams have proven to be good substitutes owing to their enhanced sustainability and competitive performance. However, because their manufacturing processes are complex and destructive testing is often impractical, this study investigates whether microstructural features can be correlated with mechanical properties in lignin-containing rigid polyurethane (PU) foams using machine learning approaches. Various types and percentages of lignin-based polyols were investigated as partial replacements for polyol, including LHO, DCA, DCA-D, LHO-O, Kraft lignin (KL), and LHO-MD, at polyol replacement levels ranging from 12.5% to 50%, together with a control formulation. Scanning electron microscopy (SEM) images and corresponding mechanical compression data were used to train a custom state-of-the-art dual-head convolutional neural network (CNN) targeting the specific prediction of density, specific compression modulus, specific yield stress, and specific compression strength. The CNN was optimized with a weighted multi-output loss function, achieving strong predictive performance with R² values ranging from 0.850 to 0.91 and correlation coefficients above 0.92, while maintaining mean absolute error percentages below ≈9%. This proves the trained network’s capability to predict and capture morphological features governing load-bearing responses. On the other hand, Grad-CAM visualization revealed that the network focused its predictions on physically meaningful microstructural regions such as cell walls and strut junctions, which confirms that the proposed network can be classified as an interpretable, non-destructive, and data-driven framework for predicting and understanding bio-based PU foams’ mechanical behavior, hence reducing the inconvenience caused by time-consuming manufacturing and destructive testing. Full article

Groundwater is an essential source of irrigation water in Mediterranean agricultural regions, where seasonal crop water requirements and recurrent drought increase pressure on shallow aquifers. Forecasting groundwater depth can help irrigators and water managers anticipate changes in pumping conditions and identify periods of increased abstraction risk. This study presents a data-driven framework for forecasting groundwater depth in the Tagus Nitrate Vulnerable Zone, central Portugal, an intensively cultivated region substantially dependent on shallow alluvial groundwater. The framework combines an autoregressive model with exogenous inputs and extreme gradient boosting and applies a leakage-safe rolling-origin validation strategy. Groundwater depth was modelled independently at each monitoring well using monthly observations, accounting for data gaps and uneven record lengths. Performance was assessed over forecast horizons of up to 12 months using error metrics calculated only from observed values. Feature-importance analysis showed the dominant role of groundwater persistence and seasonality, together with site-dependent contributions from precipitation, reference evapotranspiration, and river discharge. Forecast errors increased gradually with lead time but remained below 1 m for 14 of 17 wells. The framework provides forward-looking information that can complement irrigation-demand assessment and groundwater monitoring, supporting the anticipation of changing pumping conditions and the identification of areas requiring closer abstraction management. Full article

Unmanned vehicles require reliable embedded systems that can acquire, process, and transmit operational data in real time to support monitoring, navigation, and decision-making. This paper presents a modular Logic Computer architecture designed for telemetry and system supervision. The proposal integrates embedded hardware to provide sensor acquisition, wireless telemetry transmission, and fault reporting within a lightweight, scalable architecture. Experimental results demonstrate the proposed framework’s effectiveness and suitability for unmanned vehicle applications. Furthermore, the results provide insight into data transmission analysis in terms of technical aspects such as latency, jitter, throughput, and packet loss within a low-power-consumption framework. Based on these metrics, a comparison with existing solutions is presented, highlighting differences, drawbacks, and advantages. Tests conducted in the field also provide environmental and geographic positioning data results to demonstrate performance. Consequently, the presented prototype offers strong scalability and performance advantages over current systems, driven by its open-source architecture and high-bandwidth data transmission. Full article

E-commerce website performance—encompassing both technical delivery quality and information quality—is associated with customer retention: platforms exhibiting poor technical metrics (session friction, high cart abandonment) or poor information quality (low engagement, weak content relevance) tend to show elevated churn. This study operationalizes website performance prediction by mapping behavioural and transactional signals to a Technical Metrics sub-vector $T_i$ and an Information Quality sub-vector $Q_i$ (theoretically assigned proxy indicators), constructing a Website Performance Influence Score (WPIS), and training an XGBoost prediction engine that achieves $92.1\pm 0.3\%$ accuracy across five stratified train–test splits, with all preprocessing parameters fit exclusively on each split’s training fold. Using a publicly available E-Commerce Customer Behavior dataset of 50,000 records, the model attains F1-scores of 0.95 (low-risk) and 0.86 (high-risk) and an AUC-ROC of 0.93 for the churned class. Feature importance, cross-validated against SHAP attributions, identifies customer service interactions, customer lifetime value, discount usage rate, and cart abandonment rate as the dominant performance-failure signals, with the engineered Information Quality Degradation Index (IQD${}_i$) and Technical Performance Failure Score (TPF${}_i$) ranking sixth and ninth by gain. A Strategic MIS Decision Support Layer stratifies customers into risk tiers and maps each tier to illustrative, scenario-based intervention strategies. These results support the hypothesis that ensemble learning captures non-linear interactions between technical and information-quality dimensions, offering a reproducible, theoretically grounded foundation for data-driven MIS decision-making in digital commerce. Full article

Sixth-generation (6G) communication technologies are projected to enable exceptionally low-latency services, massive device connectivity, edge intelligence, and real-time processing of more complicated data. However, meeting these requirements using conventional technology is challenging because continued transistor scaling is associated with increased leakage current, power density, heat generation, and fabrication complexity. Emerging nanoscale computing technologies, particularly quantum-dot cellular automata (QCA), provide a promising alternative by enabling binary information processing through electronic interactions between neighboring cells rather than conventional current-driven switching. In this study, several QCA-based arithmetic and logic circuits are designed, including a fault-tolerant full adder (FA), 4-bit and 8-bit ripple-carry adders (RCA), and a binary-coded decimal (BCD) adder architecture. The proposed BCD adder performs binary addition and activates a decimal correction stage whenever the intermediate result exceeds nine or produces a carry-out. The circuit structures are enhanced to reduce cell count, occupied area, propagation delay, and energy dissipation while maintaining reliable signal transmission. The results demonstrate the potential of the proposed QCA arithmetic circuits as compact and energy-efficient computational components for future 6G edge devices, nanoscale processors, and communication systems. Full article

The rapid development of artificial intelligence (AI) has created new opportunities for improving the performance, robustness, and efficiency of electrical machine drive systems. Among AI-based approaches, fuzzy logic control (FLC) has attracted considerable attention because of its ability to handle nonlinear dynamics, parameter uncertainties, and external disturbances without relying on an accurate mathematical model. This review systematically examines FLC-based control strategies for electrical machine drives, with particular emphasis on induction motors, switched reluctance motors, permanent-magnet synchronous motors, synchronous reluctance motors, and brushless DC motors. The review follows the PRISMA 2020 framework, and the selected studies are analyzed according to machine type, FLC architecture, control strategy, optimization method, implementation platform, and validation approach. The reviewed evidence indicates that FLC-based strategies can improve dynamic response, tracking accuracy, robustness, and torque regulation under the specific conditions reported in the literature. Hybrid approaches combining FLC with field-oriented control, direct torque control, sliding-mode control, model predictive control, neural networks, ANFIS, and optimization algorithms provide additional opportunities for adaptation and parameter tuning. However, the reported performance is strongly dependent on machine topology, controller architecture, tuning methodology, computational requirements, and validation platform. The review also identifies important limitations, including the lack of standardized benchmarking, computational complexity, dependence on expert knowledge, and limited HIL and experimental validation of several advanced approaches. Emerging directions include Type-2 and higher-order fuzzy systems, neuro-fuzzy and hybrid AI controllers, data-driven optimization, digital-twin-assisted control, edge computing, and hardware-oriented implementation. The objective of this review is to provide a structured and critical synthesis of the existing evidence, clarify the evolution and practical applicability of AI-driven FLC approaches, and identify research priorities for reliable, computationally efficient, and experimentally validated electrical machine control. Full article

Intelligent systems deployed in smart cities, smart grids, environmental monitoring, and other data-driven applications increasingly depend on reliable multivariate time-series forecasting. Recent deep forecasting models have achieved strong performance on various benchmarks, but their final predictions are often generated through a fixed forecast-generation process. This one-size-fits-all approach may be suboptimal because input windows exhibit different values, trends, and periodic patterns. We propose gap-aware residual selection (GARS), a plug-in correction module for time-series forecasting that is attached to a base forecasting model and adjusts its initial forecast rather than replacing the model. From the observed input window and the initial forecast alone, GARS constructs deterministic reference forecasts, uses their differences from the initial forecast as gap-aware residual information, and forms three component forecasts: the initial forecast, a gap-aware residual component, and a direct residual component. GARS combines these components with soft weights over segments of the forecast horizon, and future target values are used only for training and evaluation. Experiments on five multivariate benchmark datasets related to solar energy, weather, electricity consumption, traffic, and exchange rates, using four representative forecasting models trained on the complete chronological training splits, show that GARS reduces the normalized-scale mean squared error by an average of $6.6\%$ across the 80 evaluated settings. This comparison is between GARS trained jointly with each base model and the same base models trained without it. The mean absolute error is not consistently improved, and the difference between the two metrics is associated with a redistribution of error across the test samples. Under the same protocol, a direct conditional mixture without the gap signal performs at least as well as GARS on average and a parameter-matched control also improves on the base models, and thus the gain cannot be attributed to the gap-based construction. On two datasets not used elsewhere in this study, the same configuration increased the mean squared error, and thus the improvements are not established beyond the evaluated benchmarks. Full article

As a class of low-cost and broad-spectrum antibiotics in wide aquaculture use, residues of tetracyclines (TCs) are imperiling aquatic organisms and public health worldwide, leading to a high demand for TC removal from the aquaculture environment. In addition, considerable consumption of thick-shell mussels generates massive mussel shell waste, and their treatment has been an economic and environmental issue. Herein, a low-cost and high-efficiency mussel shells-based material was successfully prepared for TC removal by thermal modification of shell waste of Mytilus coruscus. Morphological and structural characterizations revealed the phase transformation of calcium carbonate to calcium oxide. The shell-based material calcined at 1050 °C (MC1050) yielded a Langmuir-fitted maximum removal capacity (q_m) of 68.0 mg/g towards oxytetracycline (OTC). Thermodynamic calculations indicate that OTC removal by MC1050 is a spontaneous and endothermic process. OTC removal by MC1050 is likely achieved via multiple co-existing pathways, where Ca-OTC co-precipitation driven by CaO-phase hydration and resultant high-pH conditions dominates, accompanied by auxiliary surface-interaction contributions. Furthermore, MC1050 achieves removal efficiencies exceeding 96% for the ten target TCs spiked at 20 μg/L in real-world freshwater and marine aquaculture effluent matrices. Overall, the developed shell-based material shows promising performance for wastewater treatment and facilitates the resource utilization of waste mussel shells. Full article

In networked control systems, power line communication technique is used to transfer data over existing energy cables. A soft fault degrades the integrity of the signal without impacting the system behaviour. This work develops a transferometry-based method for detecting, localising, and estimating the severity of two simultaneous soft faults in such cables. A soft fault is modelled as a series impedance, and the transmission coefficient (TC) is computed from the ABCD cascade model of the cable. We prove that, under unmatched terminations, the time-domain TC exhibits a five-pulse signature whose peak positions and amplitudes map directly to the two fault positions and their individual severities. A residual signal constructed from this signature yields closed-form estimators for the fault positions and their combined severities; individual fault severities require a bounded nonlinear least-square fit, valid for approximately symmetric, known terminations. We further show that the method extends to n simultaneous soft faults under a combined soft-fault condition, with the $(2n+1)$-pulse pattern verified in simulation for $n\in \{1, 2, 3, 4\}$. A Monte Carlo study using correct localisation probability as the detection criterion establishes a practical SNR threshold of 25 dB; fault-separation resolvability shows intermittent, sidelobe-driven degradation rather than a single threshold. Simulations on a measured 24 AWG cable, extrapolated beyond its characterised band, confirm reliable two-fault diagnosis under additive noise, with reliable multi-fault performance demonstrated for $n=1,2$, presented as a numerical proof of concept on this extrapolated cable model rather than a characterisation confirmed by measurement over the full simulated band. Full article

The use of hazardous chemicals in construction materials raises concerns regarding their release into the environment and their potential impact on ecosystems and human health. This study investigated the flows of tris(2-chloro-1-methylethyl) phosphate (TCPP), diuron, and bis(2-(perfluorohexyl)ethyl) phosphate (6:2 diPAP) from buildings to environmental compartments and evaluated their toxicological relevance through environmental occurrence and exposure pathways. A quantitative substance flow analysis was performed for TCPP, while conceptual flow models were developed for diuron and 6:2 diPAP. TCPP flow analysis showed that approximately 70,804 t/year accumulated in products, primarily rigid polyurethane insulation materials, alongside continuous environmental releases into indoor dust, soils, and aquatic systems. It resulted in annual accumulation of nearly 444 t of TCPP in soils and 64 t in aquatic systems. Diuron was identified as a source of aquatic exposure through rainfall-driven wash-off, whereas 6:2 diPAP exhibited indoor and outdoor emission pathways and transformation into persistent perfluoroalkyl carboxylic acids. Field measurements of Interreg NonHazCity3 project confirmed the occurrence of organophosphate esters, biocides, and PFAS in indoor dust, stormwater, and wastewater. The results demonstrate that buildings act as continuous sources of toxicologically relevant substances, pointing to the necessity of upstream source control, the prevention of regrettable chemical substitution, and enhanced transparency in construction materials to mitigate long-term environmental and health risks. Full article

Emergency Medical Services (EMS) personnel require exceptional spatial awareness and rapid decision-making within the confined environment of an ambulance. While Virtual Reality (VR) offers a safe alternative to traditional training, the lack of high-fidelity, regionally accurate, and VR-optimized 3D ambulance models limits its application. This study presents a scenario-driven methodology for developing a VR-ready 3D ambulance environment prototype tailored for Emergency Medical Technician (EMT) training. Utilizing reality-capture techniques, terrestrial laser scanning was performed to accurately document the interior of a standard Hungarian ambulance simulator. The resulting point cloud underwent systematic processing, manual retopology, PBR shading, and the implementation of a custom dual-rigging animation system to optimize complex mechanical movements—such as stretcher operations—for standalone VR platforms. The workflow successfully reduced the vertex count to 25,373 while maintaining millimeter-level spatial fidelity. Technical evaluation confirmed that geometrical, functional, and material objectives were fulfilled, whereas pedagogical implementation remains incomplete. Structural accuracy and animation readiness were verified through preliminary inspection within Blender’s VR viewport inspector. However, interactive game-engine integration remains future work, and educational effectiveness has not yet been tested with EMT learners. Overall, this workflow delivers a 3D asset foundation that establishes the necessary technical basis for subsequent software implementation and clinical evaluation. Full article