Search Results (4,055)

Existing research on e-commerce product reviews has primarily focused on analysing consumers’ opinions, emotions, sentiments and associated star ratings. Whilst these approaches provide insights into consumers’ perceptions of products, they offer limited understanding of how products are used in real-world contexts. Therefore, they do little to enhance the e-commerce experience by helping consumers make more informed purchasing decisions based on products’ intended uses without requiring them to read numerous reviews during the decision-making process. To address this problem, this paper investigates the feasibility of automatically identifying and classifying product usage activities from e-commerce reviews. A methodology combining natural language processing, manual activity-level annotation and deep learning-based text classification was developed and evaluated. An initial dataset of 60,000 Amazon product reviews was manually labelled according to six activity classes: run, walk, hike, swim, climb and unknown. Following quality inspection and data cleaning, a final dataset of 50,843 reviews was used for model training and evaluation. Multiple classification approaches were assessed, including CNN, LSTM, hybrid LSTM-CNN architectures and transformer-based models (DistilBERT and DistilBERT-CNN). Experimental evaluation was conducted using multiple random seeds to ensure robustness and reproducibility. The results indicate that activity classification from e-commerce reviews is a challenging task due to ambiguity and overlapping usage descriptions, with all evaluated models achieving comparable performance on the full dataset. Among the evaluated models, the hybrid LSTM-CNN-GloVe architecture achieved the highest performance on the keyword-filtered dataset, whilst the DistilBERT-CNN model also demonstrated strong results. The findings demonstrate the feasibility of extracting activity-oriented information from product reviews and highlight activity classification as a distinct and under-explored natural language processing task that complements traditional sentiment analysis. The proposed methodology provides a foundation for improving product discovery and supporting usage-oriented search and recommendation systems in e-commerce environments. Full article

The accuracy of spectro-temporal features for Makhaarij al-Huroof and Sifaat distinguishes between the ten canonical Qiraát recitation styles of the Holy Quran. However, real-world room reverberations blur formant contours and corrupt inter-word energies, thus making Qiraat discrimination difficult. The current dereverberation methods were designed to work under ordinary speech conditions and are not capable of preserving phonetic qualities for domain-specific purposes. This paper introduces a four-step, phase-consistent signal-processing approach prioritizing phonetic preservation over direct reverberation suppression. The four steps are: (1) adaptive noise-floor attenuation; (2) soft-voice activity detection using power-law boundary decay; (3) application-specific spectral contour adjustment from clean Quranic reference audio; and (4) Griffin–Lim algorithm-based phase correction. A total of 48 real-world room recordings were utilized for the evaluation of this approach based on Energy Ratio (ER), Spectral Contrast (SC), and Spectral Contour Stability (SCS)—measures specific to the Quran audio domain—alongside conventional speech-quality metrics. The proposed approach yielded the highest scores in three of seven metrics, namely SC (+40.11), SCS (+822.94), and PESQ (+1.251), alongside the second-highest Energy Ratio (+19.58 dB), while being superior to Spectral Subtraction, Wiener Filtering, and WPE Dereverberation approaches. Moreover, the perceptual enhancement was verified in a synthetic controlled experiment where the proposed approach scored an improved PESQ metric (+2.495; SNR −1.874 dB). The results illustrate the fact that an optimization for general-purpose metrics does not necessarily ensure phonetic preservation required for specific classification. Full article

Background: Children growing up in alcohol-affected families are exposed to chronic stress, adverse childhood experiences (ACEs), emotional insecurity, and environmental instability, all of which may influence neurodevelopmental processes. Numerous EEG markers have been proposed as indicators of attentional regulation, emotional functioning, and stress responsivity; however, their relative diagnostic and practical value remains unclear. The aim of the present study was to verify whether commonly reported EEG markers remain valid indicators of neurofunctional difficulties in children from alcohol-affected families, to establish their hierarchy of importance, and to determine how identified neurofunctional profiles may inform the sequencing of educational interventions and the development of individualized support plans used by educators and social workers. Methods: The study included children aged 6–10 years from alcohol-affected families (n = 20) and a control group from non-dysfunctional family environments (n = 25). Resting-state EEG recordings were conducted under eyes-open and eyes-closed conditions, with analyses focused on the eyes-open condition. Quantitative EEG (qEEG) indices included global, frontal, prefrontal, and midline Theta–Beta Ratio (TBR), frontal alpha asymmetry (FAA), temporal beta stress and parietal beta2 tension. EEG preprocessing was performed using EEGLAB and included artifact rejection, filtering, epoch segmentation, and spectral power analysis. Group differences were analyzed using Welch’s t-tests with Benjamini–Hochberg correction for multiple comparisons. Results: The analyzed EEG markers differed in their ability to distinguish children from alcohol-affected families and controls. The strongest effects were observed for Theta–Beta Ratio (TBR) measures, particularly in frontal and prefrontal regions, indicating impairments in attention regulation, executive functioning, and self-control. Elevated temporal beta stress and parietal beta2 tension reflected increased physiological arousal and chronic stress. In contrast, frontal alpha asymmetry (FAA), commonly associated with depressive emotional processing, was not significant after correction for multiple comparisons. The obtained findings enabled the establishment of a hierarchy of neurofunctional markers, with attentional and executive-function indicators demonstrating greater importance than markers related to depressive symptomatology. Conclusions: The EEG profile of children from alcohol-affected families is characterized primarily by chronic stress, heightened physiological activation, and impaired attention regulation rather than by neurophysiological patterns associated with depression. The results suggest that educational difficulties in this group may stem mainly from deficits in attention control, inhibitory processes, and cognitive flexibility. Consequently, educational interventions should prioritize learning strategies, attentional training, and self-regulated learning skills. The identified hierarchy of EEG markers may also support the development of individualized educational plans and social-support programs, including participation in structured extracurricular activities and interventions aimed at strengthening executive and learning-related competencies. However, given the pilot nature of the present study and the relatively small sample size, these findings should be considered preliminary. Replication in larger, more diverse, and independent cohorts is necessary to confirm the stability, reliability, and generalizability of the identified neurofunctional profile and the proposed hierarchy of qEEG markers before they can be recommended for broader educational and social applications. Full article

Static, one-time authentication mechanisms such as passwords and PINs are increasingly inadequate for protecting mobile devices throughout an active session. Behavioral biometric continuous authentication (BBCA) addresses this gap by passively monitoring user-specific interaction patterns—keystroke dynamics, touch and swipe gestures, gait, and motion—to verify identity on an ongoing basis. This systematic review synthesizes 80 studies selected via a PRISMA-compliant protocol from IEEE Xplore, ACM Digital Library, Scopus, ScienceDirect, Web of Science, and SpringerLink (2017–2025). We examine behavioral and multimodal biometric modalities, machine learning approaches ranging from classical classifiers to deep sequence and transformer architectures, and their integration with intelligent personal agents, wearable devices, and IoT/edge infrastructures. Security analyses cover spoofing, adversarial and generative attacks, mimicry, and model-level threats including membership inference and reconstruction. Privacy-preserving mechanisms—cancelable biometrics, Bloom filter encodings, zero-knowledge proof protocols, federated learning, and blockchain-based identity management—are evaluated against practical trade-offs in energy consumption and latency on resource-constrained devices. Key research gaps are identified: the absence of standardized adversarial benchmarks, lack of end-to-end pipeline evaluations under simultaneous adversarial and privacy threat models, and limited user-centered studies on consent and acceptance of privacy-preserving mechanisms under frameworks such as GDPR. Recommended future directions combine adaptive multimodal fusion, privacy-preserving cryptography, energy-aware modality selection, and interdisciplinary human-centered evaluation to advance practical, resilient continuous authentication for mobile and assistant-enriched environments. Full article

2-phenylbenzimidazole-5-sulfonic acid (PBSA) is a commonly used organic ultraviolet (UV) filter frequently found in aquatic environments, raising substantial ecological health concerns. While some toxic effects of PBSA on aquatic organisms have been reported, its intergenerational developmental and neurotoxic risks remain poorly understood. In this study, we established a zebrafish life-cycle exposure model to explore the intergenerational toxicity of environmentally relevant concentrations of PBSA (0.2, 2, and 20 μg/L). Offspring were categorized into three exposure groups: parental exposure only (F0+/F1−), parental exposure with continuous F1 exposure (F0+/F1+), and only F1 exposure without parental treatment (F0−/F1+). Our findings demonstrate the transfer of PBSA from parental gonads to F1 embryos. Parental lifetime exposure significantly inhibited somitogenesis and increased mortality and malformation rates in the F1 generation, with the most pronounced developmental damage observed in the F0+/F1+ group. Whole-mount immunohistochemistry revealed that PBSA notably reduced motor neuron axon length in F1 larvae, accompanied by downregulation of developmental-related genes including gap43, mbp, and shha. Mechanistically, the F0+/F1− group exhibited a marked increase in MDA levels. Excessive ROS accumulation and reduced CAT activity were specifically observed in the F0+/F1+ group, whereas the F0+/F1− and F0−/F1+ groups showed significantly elevated CAT activity, jointly driving developmental and neurotoxic changes. In silico predictions indicated low acute aquatic toxicity of PBSA, contradicting its observed intergenerational risks. Our findings demonstrate that parental lifetime PBSA exposure can induce significant intergenerational neurodevelopmental toxicity in zebrafish offspring by disrupting oxidative balance and neural gene expression, with continuous offspring exposure further aggravating the adverse effects. This research underscores that traditional single-generation toxicity assessments underestimate the ecological dangers of UV filters. It also offers new insights into the environmental risk evaluation of PBSA and similar emerging contaminants. Full article

Hybrid systems integrating grid-following (GFL) and grid-forming (GFM) inverters are increasingly deployed in renewable-energy-dominated power systems. However, impedance coupling between the two inverter types may induce low-frequency oscillations and high-frequency resonances, particularly under weak-grid conditions and varying power injections. This paper clarifies the stability mechanism of a hybrid GFL/GFM inverter system and develops a frequency-segmented active damping strategy. Small-signal impedance models are first derived for the GFL inverter, the GFM inverter, and the overall hybrid system, incorporating the control loops, digital delay, LC filters, interconnection branch impedances, and external grid impedance. Impedance decomposition, Bode plots, and Nyquist criteria are then employed to quantify the influence of power operating points and grid strength on system stability. The results indicate that increasing the GFL inverter output power weakens the stability margins in both low- and high-frequency ranges, whereas variations in the GFM inverter output power provide only limited impedance reshaping in the targeted oscillation bands. On this basis, a low-frequency damping loop is designed on the GFM inverter side, while a high-frequency damping loop based on capacitor-current feedback is implemented on the GFL inverter side. Simulation results confirm that the proposed strategy suppresses low-frequency oscillations and high-frequency harmonic components, maintains stable operation in the hybrid system under high GFL power injection, and reduces the THD of the PCC current from 14.52% to 0.62%. Full article

Industrial Internet of Things (IIoT) networks use on-demand sensing and wireless power transfer (WPT) for self-sustainable operation. Existing scheduling frameworks are fundamentally limited because they react only after energy levels decline. Consequently, IoT nodes enter charging mode only when their residual energy falls below a threshold, causing energy outages, increased latency, and missed sensing tasks while preventing proactive WPT resource allocation. This paper proposes a Digital Twin (DT)-enabled proactive scheduling framework that transforms IIoT scheduling from reactive to proactive. The key innovation is a closed-loop virtual–real integration in which a DT layer, co-located with the control centre, maintains a Kalman filter predictor to forecast node energy over an H-slot horizon, enabling scheduling decisions before energy shortages occur. Physical layer security (PLS) constraints and DT-based anomaly detection protect against eavesdropping, energy depletion, and false data injection attacks. A multi-objective formulation jointly optimises sensing utility and WPT efficiency while accounting for DT synchronisation overhead and uplink bandwidth consumption. The resulting multi-slot Binary Integer Linear Programmes (BILP) are solved using branch-and-bound with a reliability branching rule, and a fast greedy heuristic is also developed. Simulation results over 50 Monte Carlo iterations show that the proposed framework reduces energy outage events by approximately 70% compared with the reactive baseline, activates less than 50% of available sensing nodes, and schedules less than 60% of energy transmitters for WPT. Ablation studies confirm that DT prediction is the primary contributor to the outage reduction. DT-based anomaly detection achieves a false alarm rate below 3% while maintaining a detection rate above 95%. The proposed framework improves the sustainability, efficiency, and security of IIoT networks with practical computational overhead, making it well suited for Industry 5.0 deployments. Full article

Active suspension systems with road preview capability rely on accurate LiDAR elevation measurements; however, offline calibration cannot compensate for dynamic measurement errors caused by vibration, temperature drift, and road impacts. This paper proposes a hierarchical control strategy to address this issue. A virtual road reference is generated via least-squares plane fitting, enabling the vehicle to conform to the macroscopic terrain while filtering out local irregularities. An adaptive nonlinear extended state observer is then designed to automatically adjust its gain based on road conditions, achieving simultaneous noise suppression and fast tracking of time-varying errors. An explicit dynamic mapping between actuator acceleration and vehicle attitude is also established, providing a theoretical bound on the closed-loop tracking error. Simulation results on various road profiles demonstrate the effectiveness of the proposed strategy in numerical studies. Full article

Apple leaf disease detection in orchards faces a unique challenge: lesion regions (foreground) are semantically important but visually less salient (weak texture, blurred boundaries), while background distractors (soil, weeds) are visually salient but semantically irrelevant. Traditional models, reliant on visual saliency, are usually misled by strong background textures. Unlike humans who first use leaf shape to locate possible lesions and then examine local textures, these models lack this semantic-guided strategy, leading to inaccurate detection. To address this problem, we propose a Semantic-Decoupled Dual Attention Network (SD-DAN). The core idea is to achieve target-distractor separation via semantic decoupling and then foreground enhancement and background suppression via dual attention. The network consists of two core modules. First, a frequency-enhanced preprocessing module adaptively fuses low- and high-frequency information to highlight lesion details and suppress redundant low-frequency information. Second, an attention-based foreground-background decoupling and enhancement module explicitly separates foreground and background with a learnable threshold, and applies a dual-attention mechanism: spatial-frequency enhancement to the foreground (active focusing) and spatial-frequency suppression to the background (active filtering), to improve discriminative features of apple disease. Experiments on the Apple Leaf Disease Dataset 9 (ALDD9) and the Apple Leaf Diseases Dataset demonstrate that SD-DAN achieves strong performance and consistently outperforms mainstream detectors across nine common apple disease detection tasks. Full article

This paper presents an electronically tunable first-order universal filter capable of operating in multiple analog modes, realized through a compact architecture built around multiple-input operational transconductance amplifiers (MI-OTAs). By leveraging the MI-OTA’s ability to accommodate several input signals within a single transconductance stage—allowing direct arithmetic operations such as summation and subtraction—the proposed design minimizes the number of active elements traditionally required for mixed-mode filtering. Consequently, both inverting and non-inverting forms of low-pass, high-pass, and all-pass responses can be generated in voltage mode, current mode, transadmittance mode, and transimpedance mode, enabling a total of 24 distinct first-order transfer functions using one unified circuit topology. The pole for all responses can be conveniently adjusted by electronically tuning the OTA transconductance. The multiple-input capability is realized using a multi-input MOS technique, while subthreshold-biased bulk-driven transistors allow the circuit to function from a 0.5 V supply with an extended input voltage range and ensure ultra-low power dissipation. The filter was designed and evaluated in Cadence Virtuoso using a 65 nm TSMC CMOS process. Under a 7 nA bias current, the low-pass configuration achieves a consumption of 87.5 nW and a dynamic range of 44.7 dB. Additionally, experimental verification was performed using the commercial LM13700 OTA, confirming the correct operation and practicality of the proposed approach. Full article

The increasing industrialization of construction requires design strategies that reconcile productive repeatability with architectural customization, functional differentiation and material reproducibility. This study proposes a manufacturing-oriented parametric design framework for customizable surface components in industrialized construction. Geometry is treated as an active design variable capable of supporting hypotheses related to drainage, texture, porosity, friction, light filtering and material efficiency. The methodology links biomimetic abstraction, parametric modelling and material transfer through indirect additive manufacturing, mould fabrication and mineral-based replication. Rather than validating a specific functional performance, the study experimentally assesses a mould-based replication workflow in which customization is defined as controlled variation within a constrained design space. A modular component with a stable perimeter and parametrizable textured surface was developed, followed by the production of an additively manufactured master, a negative mould and replicas made from technical plaster and fine mineral mortar as construction-grade surrogate materials. These materials enabled the evaluation of mould filling, demoulding, defect transfer and surface-detail preservation before introducing ceramic drying and firing. The results confirm the feasibility of the proposed workflow and identify manufacturability, demoulding, dimensional deviation, material selection and application-specific validation as critical design constraints. Full article

Material screening, including oxide catalyst discovery, remains constrained by costly synthesis-and-test cycles, making sample-efficient experiment selection a central challenge. Active learning can accelerate this process, but standard acquisition rules mainly optimize predictive utility and offer limited support for clarifying the causal structure behind material-screening targets. CIAL is a causal-structure-aware acquisition framework. It combines an intervention-inspired graph-structure term with expected improvement, using an adaptive schedule that begins with structure clarification and shifts toward prediction-oriented exploitation. On synthetic benchmarks with known ground-truth causal graphs, CIAL reduces final-iteration structural Hamming distance by 76.2% relative to the standard EI baseline; significance is assessed using two-sided Welch’s t-tests on final-iteration metrics across five random seeds, with $p=0.0036$ for SHD and $p<0.001$ for F1. For real-data screening, CIAL is evaluated on a Materials Project transition metal oxide benchmark built from filtered oxide entries with formation-energy labels and generic bulk/compositional descriptors. Formation energy is an upstream stability-relevant property, not a direct catalytic activity label. On the main MP benchmark, CIAL is competitive with EI; its clearest advantage over EI appears under NOTEARS-based cross-split validation, but random sampling achieves the highest inferred-reference graph agreement in several real-data settings. An exploratory OC20 catalyst dataset check follows the same ordering but remains statistically inconclusive. Ablation results show that neither the causal nor the predictive component alone recovers the full benefit of the combined policy. CIAL improves graph recovery on controlled synthetic benchmarks; the real-data evidence is mixed and protocol-dependent, establishing CIAL as a proof-of-concept structure-aware acquisition method rather than a validated catalyst-discovery framework. Full article

Background. Eye-controlled interfaces can support communication and selection tasks when physical interaction is difficult, but conventional multi-point calibration may be burdensome for users who fatigue quickly or have severe motor limitations. This study operationally evaluates a low-burden single-point adaptive gaze-to-cursor correction pipeline for a dwell-based selection interface. Methods. The pipeline combines central single-point offset estimation, robust median/median absolute deviation (MAD) sample handling, recorded-stream jump rejection, exponential moving average filtering, dwell-based selection, and post-selection offset adaptation. It was evaluated using application-level gaze-to-cursor coordinates from 43 participants across 129 sessions. The primary endpoint was target-proximity Root Mean Square (RMS) during automatically logged dwell-active selection periods. Because these periods were defined by the pipeline’s online state, the endpoint was algorithm-conditioned. Whole-quiz RMS was analyzed as a secondary full-trajectory descriptor rather than as independent validation. Results. Averaged across three repeated measurements, target-proximity RMS during dwell-active periods decreased from 193.40 pixels (px) (approximately 3.15°) for the recorded uncorrected coordinates to 56.02 px (0.91°) for the complete pipeline output. The mean reduction was 137.38 px (95% confidence interval (CI), 116.59–158.17 px; Cohen’s d_z = 2.03), and all 43 participants showed a reduction. Conclusions. The results support the practical feasibility of the complete correction pipeline for target localization in the evaluated dwell-based interface. They do not independently validate native eye-tracker accuracy or establish superiority over conventional multi-point calibration. Full article

This paper investigates the task-space position tracking problem of a redundant manipulator under multiple disturbances. Different from conventional joint-space tracking schemes, the considered task only constrains the end-effector position, leaving redundant degrees of freedom to be exploited for secondary optimization. However, conventional computed torque control is sensitive to dynamic-model mismatch, while integer-order equivalent input disturbance compensation has limited flexibility in balancing disturbance tracking and noise attenuation. To address these limitations, a composite control framework is proposed by integrating task-space position error regulation, null-space redundancy optimization, and a fractional-order equivalent input disturbance compensation (FEIDC) strategy. The task-space controller generates the desired acceleration, which is mapped to the joint acceleration command through a damped pseudoinverse Jacobian, and a null-space term is incorporated to optimize secondary criteria. For the feedback-linearized joint dynamics, the proposed FEIDC introduces a fractional-order filter into the equivalent input disturbance estimation channel, providing an additional order parameter for shaping disturbance attenuation and noise sensitivity. Simulation validation on a UR5e manipulator compares the effectiveness of the proposed method with sliding mode control (SMC), active disturbance rejection control (ADRC) and integer-order equivalent input disturbance compensation strategy (IEIDC). In comparisons with SMC, ADRC, and IEIDC, the proposed FEIDC achieves the lowest joint and Cartesian RMSEs, namely, $7.2863\times {10}^{-4}$ rad and $3.8035\times {10}^{-4}$ m, respectively. Full article

This study investigates the design, fabrication, and manufacturing/mechanical characterization of 3D-printed polylactic acid (PLA) cylindrical filter supports developed as substrates for subsequent hydrochar functionalization. Filters with an outer diameter of 30 mm, a height of 20 mm, and a wall thickness of 3 mm were manufactured by material extrusion additive manufacturing and internally filled with gyroid architectures generated in Bambu Studio at three density levels, namely 10%, 15%, and 20%. The printed filters were first evaluated in terms of weight and compressive response. The main focus of the work was the manufacturing consistency and mechanical response of the gyroid supports, while hydrochar deposition was considered as an initial functionalization screening step. The results showed that increasing gyroid density led to higher maximum compressive stress, while mass-normalized analysis revealed a trade-off between absolute mechanical resistance and material efficiency. Surface-treatment screening trials were then carried out on flat PLA specimens to evaluate whether algae-derived hydrochar could be retained on PLA after alkaline activation. Visual and SEM observations showed partial and heterogeneous hydrochar-related surface coverage, with localized agglomerates and partial masking of the original printing lines, but without the formation of a homogeneous coating. EDX analysis of selected agglomerates revealed C and O together with Na, Cl, K, and Ca, supporting the presence of hydrochar-related/mineral-containing deposits on the treated PLA surface, although K may also be associated with residual species from the KOH activation step. FTIR analysis did not reveal clear hydrochar-related spectral features, indicating that FTIR alone was not sufficient to demonstrate effective homogeneous surface functionalization and supporting the interpretation of heterogeneous surface retention. Overall, the study provides a first manufacturing-oriented basis for PLA gyroid filter supports intended for hydrochar deposition and highlights the need for improved surface activation strategies before subsequent functional validation. Full article