Search Results (12,038)

A principled, model-agnostic framework for structural feature extraction in time series is presented, grounded in topological data analysis (TDA). The motivation stems from two gaps identified in the literature: First, compact and interpretable representations that summarise the global geometric organisation of trajectories across scales remain scarce. Second, a unified, task-agnostic protocol for evaluating structure preservation against established non-topological families is still missing. To address these gaps, time-delay embeddings are employed to reconstruct phase space, sliding windows are used to generate local point clouds, and Vietoris–Rips persistent homology (up to dimension two) is computed. The resulting persistence diagrams are summarised with three transparent descriptors—persistence entropy, maximum persistence amplitude, and feature counts—and concatenated across delays and window sizes to yield a multiscale representation designed to complement temporal and spectral features while remaining computationally tractable. A unified experimental design is specified in which heterogeneous, regularly sampled financial series are preprocessed on native calendars and contrasted with competitive baselines spanning lagged, calendar-driven, difference/change, STL-based, delay-embedding PCA, price-based statistical, signature (FRUITS), and network-derived (NetF) features. Structure preservation is assessed through complementary criteria that probe spectral similarity, variance-scaled reconstruction fidelity, and the conservation of distributional shape (location, scale, asymmetry, tails). The study is positioned as an evaluation of representations, rather than a forecasting benchmark, emphasising interpretability, comparability, and methodological transparency while outlining avenues for adaptive hyperparameter selection and alternative filtrations. Full article

Autonomous ships require reliable and economical navigation; however, their performance is hindered when satellite-based positioning signals become unavailable. In such global navigation satellite system (GNSS)-denied conditions, a backup navigation system integrating a strapdown inertial navigation system (SINS), Doppler velocity logger (DVL), and a compass (SINS/DVL/COMPASS) can provide essential state information, but the accuracy and fault tolerance of such systems are constrained by weak observability of position/heading errors and strong dependence on DVL measurements. This study proposes a fault-tolerant optimization method based on perceptual information compensation. First, radar imagery and electronic chart data are fused at the feature level using a weighted wavelet strategy to enhance the environmental feature saliency for shoreline extraction. Second, characteristic coastline inflection points are detected and tracked using a dual-curvature and distance-constrained procedure, generating external position observations via radar–chart matching. These observations are incorporated into the SINS/DVL/COMPASS framework to improve its state observability and robustness. Simulation results show that under nominal conditions, perceptual compensation mitigates error divergence and promotes the convergence of position errors, improving the positioning stability. In terms of robustness, the proposed method delivered more stable state-error behavior than the baseline under DVL speed faults of +2 m/s, −2 m/s, and +2 m/s injected at 301–330, 701–730, and 1101–1130 s, respectively. Quantitatively, the 3σ bounds of velocity and position-related errors are reduced under fault conditions, indicating improved fault tolerance and suitability for short-term nearshore autonomous navigation during GNSS outages. Full article

An archaeological exposure near Hachum, featuring a ditch profile interpreted as part of a Neolithic earthwork, was characterized using DNA analyses of bacterial 16S rRNA from soil samples. The NGS data from 13 sampling points at different positions and depths within the trench profile were compared with regard to the percentage distribution of phyla and the frequency of occurrence of individual bacterial types (genera or operational taxonomic units, OTUs). Characteristic differences between parts of the trench profile became apparent based on correlations of OTU abundances as well as the occurrence of specific types. In particular, a high similarity in bacterial communities was observed among samples from intermediate trench depths, while a markedly different composition was found in the area of the central trench bottom. These findings indicate that the trench must have remained open for a certain period of time and was later filled relatively homogeneously. The results showed that the middle and lower parts of the ditch fill could be clearly distinguished from each other and from the surrounding area based on the composition of soil bacterial DNA. Genera detected predominantly in the lower part of the ditch suggest that, after the ditch was completed, organic matter, animal dung, and possibly even human feces were accumulated at the bottom. The investigations demonstrate that analyses of soil bacterial communities can provide valuable insights into the history and function of a Neolithic earthwork and, more generally, represent an important additional source of information for interpreting archaeological contexts that are devoid of or poor in finds. Full article

This research develops a decision support system for prioritizing Battery Energy Storage System (BESS) installations at existing closed-circuit television (CCTV) camera locations experiencing power interruptions in Latkrabang subdistrict. The methodology integrates nine validated features: outage frequency, downtime duration, maximum outage duration, Net Present Value (NPV), combined ROI, outage impact score, annual BESS cost, combined risk score, and UPS installation cost, derived from historical power outage records (2020–2023) and engineering economics calculations. An unsupervised K-means clustering algorithm, validated through silhouette analysis and the elbow method, categorizes installations into five risk levels, namely critical, very high, high, medium, and low, addressing the absence of predefined ground truth labels. Subsequently, Support Vector Machine (SVM) with hyperparameter optimization classifies priority installations using stratified train-test splitting (80:20). The model was initially developed and validated using 82 CCTV cameras from Lamphla Tiew subdistrict (the pilot area). The validated model was then successfully applied to 101 CCTV cameras in Latkrabang subdistrict (the target area), identifying 27 critical installation points requiring immediate BESS deployment. The weighted recommendation system balances data-driven clustering with scoring: NPV (35%), outage impact (25%), combined ROI (20%), maximum outage duration (10%), and BESS cost efficiency (10%). Full article

Background/Objectives: Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra (SN). Because current therapeutics have limited efficacy once PD is fully developed, it is crucial to start disease-modifying interventions during the prodromal stage of PD. In the present study, we aimed to evaluate whether intranasally delivered human umbilical cord mesenchymal stem cells (hUC-MSCs) have an efficacy in the rotenone-induced prodromal PD-like phenotype mouse model. Methods: To produce the prodromal PD mouse model, C57BL/6 mice were treated with intraperitoneal (i.p.) rotenone for 1 or 2 weeks. hUC-MSCs or PBS were delivered intranasally for 1 or 2 weeks with rotenone injection. We subsequently performed behavioral assessments to evaluate motor and non-motor features, followed by pathological analyses of the mouse brains. Results: Intranasal administration of hUC-MSCs restored motor performance and protected dopaminergic neurons in the SN of mice treated with rotenone for 2 weeks. In the 1-week rotenone mice, hUC-MSCs treatment ameliorated depressive-like behaviors and attenuated olfactory dysfunction. Furthermore, intranasal hUC-MSC treatment suppressed the accumulation of protein aggregates in the brains of mice, which is associated with enhanced autophagic function, as indicated by increased LC3B and normalization of LAMP2A protein expression. Conclusions: Our data demonstrate that intranasal administration of hUC-MSCs improves non-motor symptoms at early time points and attenuates progression to nigrostriatal loss and motor deficits in the rotenone-induced PD mouse model. These findings support the potential of a non-invasive, prodromal-stage intervention to modulate early pathological progression in PD. Full article

Neutral atomic hydrogen (H I) regulates galaxy growth and quenching, but direct 21 cm measurements remain observationally expensive and affected by selection biases. We develop Bayesian neural networks (BNNs)—a type of neural model that returns both a prediction and an associated uncertainty—to infer the H I mass, ${log}_{10}\left(M_{\mathrm{HI}}\right)$, from widely available optical properties (e.g., stellar mass, apparent magnitudes, and diagnostic colors) and simple structural parameters. For continuity with the photometric gas fraction (PGF) literature, we also report the gas-to-stellar-mass ratio, ${log}_{10}(G/S)$, where explicitly noted. Our dataset is a reproducible cross-match of SDSS DR12, the MPA–JHU value-added catalogs, and the 100% ALFALFA release, resulting in 31,501 galaxies after quality controls. To ensure fair evaluation, we adopt fixed train/validation/test partitions and an additional sky-holdout region to probe domain shift, i.e., how well the model extrapolates to sky regions that were not used for training. We also audit features to avoid information leakage and benchmark the BNNs against deterministic models, including a feed-forward neural network baseline and gradient-boosted trees (GBTs, a standard tree-based ensemble method in machine learning). Performance is assessed using mean absolute error (MAE), root-mean-square error (RMSE), and probabilistic diagnostics such as the negative log-likelihood (NLL, a loss that rewards models that assign high probability to the observed H I masses), reliability diagrams (plots comparing predicted probabilities to observed frequencies), and empirical 68%/95% coverage. The Bayesian models achieve point accuracy comparable to the deterministic baselines while additionally providing calibrated prediction intervals that adapt to stellar mass, surface density, and color. This enables galaxy-by-galaxy uncertainty estimation and prioritization for 21 cm follow-up that explicitly accounts for predicted uncertainties (“risk-aware” target selection). Overall, the results demonstrate that uncertainty-aware machine-learning methods offer a scalable and reproducible route to inferring galactic H I content from widely available optical data. Full article

This is a review article that covers the history of the development of the equation of motion for solid particles in fluids, starting with the early work, before the Navier–Stokes equations were developed. Particular emphasis is placed on the development of the transient equation of motion, which features the history (or memory) term and the added mass (virtual mass) term. The salient features of the equation and the methods of their derivation are pointed out. Creeping, non-inertia flows as well as advective flows are surveyed, with particular emphasis on their effects on the functional form of the history term. Modifications to the hydrodynamic force due to possible interface slip are also examined. The review also deals with the inclusion of the weaker lateral (lift) forces and the inclusion of the effects of Brownian movement, which gives rise to thermophoresis—an important source of nanoparticle movement and surface deposition. The drag on irregularly shaped particles—another important feature of nanoparticles—is also examined. The review concludes with a short section on significant unknown issues and work that may be carried out in the near future for the theoretical and computational development of the subject. Full article

Time series anomaly detection plays a vital role in the supervision of complex systems, including spacecraft operations, industrial production lines, and Internet of Things infrastructures. However, the existing methods face two key challenges: (1) fixed-threshold frequency filters fail to adapt to non-stationary noise, often leading to the loss of critical anomaly signals; and (2) deep models struggle to balance local feature extraction and global temporal dependency, resulting in limited robustness and generalization. To address these problems, we propose the Spectral-Convolutional Anomaly Transformer (SCAT), a unified framework integrating spectral domain adaptive filtering and spatio-temporal gated learning. Specifically, the Spectral Energy Gating Unit (SEGU) dynamically suppresses noise through learnable frequency masking, while Spatio-Temporal Gated Fusion (ST-Gate) combines multi-scale causal convolution and ConvGRU to harmonize local and long-term patterns. A joint optimization strategy further enhances the discrimination between normal and anomalous sequences. Our experiments on five public benchmarks (SMAP, MSL, PSM, SMD, SWaT) showed that SCAT attained an average improvement of 2.46 percentage points on the F1-score relative to leading baseline approaches, demonstrating strong adaptability and robustness in complex noisy environments. Full article

Tea bud recognition and localization constitute a fundamental step toward enabling fine-grained tea plantation management and intelligent harvesting, offering substantial value in improving the picking quality of premium tea materials, reducing labor dependency, and accelerating the development of smart tea agriculture. However, most existing methods for detecting tea buds are built upon Artificial Neural Networks (ANNs) and rely extensively on floating-point computation, making them difficult to deploy efficiently on energy-constrained edge platforms. To address this challenge, this paper proposes an energy-efficient tea bud detection model, GAE-SpikeYOLO, which improves upon the Spiking Neural Networks (SNNs) detection framework SpikeYOLO. Firstly, Gated Attention Coding (GAC) is introduced into the input encoding stage to generate spike streams with richer spatiotemporal dynamics, strengthening shallow feature saliency while suppressing redundant background spikes. Secondly, the model incorporates the Temporal-Channel-Spatial Attention (TCSA) module into the neck network to enhance deep semantic attention on tea bud regions and effectively suppress high-level feature responses unrelated to the target. Lastly, the proposed model adopts the EIoU loss function to further improve bounding box regression accuracy. The detection capability of the model is systematically validated on a tea bud object detection dataset collected in natural tea garden environments. Experimental results show that the proposed GAE-SpikeYOLO achieves a Precision (P) of 83.0%, a Recall (R) of 72.1%, a mAP@0.5 of 81.0%, and a mAP@[0.5:0.95] of 60.4%, with an inference energy consumption of only 49.4 mJ. Compared with the original SpikeYOLO, the proposed model improves P, R, mAP@0.5, and mAP@[0.5:0.95] by 1.4%, 1.6%, 2.0%, and 3.3%, respectively, while achieving a relative reduction of 24.3% in inference energy consumption. The results indicate that GAE-SpikeYOLO provides an efficient and readily deployable solution for tea bud detection and other agricultural vision tasks in energy-limited scenarios. Full article

Additive manufacturing (AM) is a significant contributor to Industry 4.0. However, one considerable challenge is usually encountered by AM due to the bed size limitations of 3D printers, which prevent them from being adopted. An appropriate post-joining technique should be employed to address this issue properly. This study investigates the influence of key friction stir butt welding (FSBW) factors (FSBWFs), such as tool rotational speed (TRS), tool traverse speed (TTS), and pin profile (PP), on the weldability of 3D-printed PLA–Chromium (PC) composites (3PPCC). A filament containing 10% by weight of chromium reinforced in PLA was used to prepare samples. The material extrusion additive manufacturing process (MEX) was employed to prepare the 3D-printed PCC. A Taguchi-based design of experiments (DOE) (L9 orthogonal array) was employed to systematically assess weld hardness (WH), weld temperature (WT), weld strength (WS), and weld efficiency. As far as the 3D-printed samples were concerned, two distinct infill patterns (linear and tri-hexagonal) were also examined to evaluate their effect on joint performance; however, all other 3D printing factors were kept constant. Experimentally validated findings revealed that weld efficiency varied significantly with PP and infill pattern, with the square PP and tri-hexagonal infill pattern yielding the highest weld efficiency, i.e., 108%, with the corresponding highest WS of 30 MPa. The conical PP resulted in reduced WS. Hardness analysis demonstrated that tri-hexagonal infill patterns exhibited superior hardness retention, i.e., 46.1%, as compared to that of linear infill patterns, i.e., 34%. The highest WTs observed with conical PP were 132 °C and 118 °C for both linear and tri-hexagonal infill patterns, which were far below the melting point of PLA. The lowest WT was evaluated to be 65 °C with a tri-hexagonal infill, which is approximately equal to the glass transition temperature of PLA. Microscopic analysis using a coordinate measuring machine (CMM) indicated that optimal weld zones featured minimal void formation, directly contributing to improved weld performance. In addition, scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) were also performed on four deliberately selected samples to examine the microstructural features and elemental distribution in the weld zones, providing deeper insight into the correlation between morphology, chemical composition, and weld performance. Full article

Wearable robots can improve human walking economy; however, their effectiveness depends on user adaptation to assistance. This study introduces a framework for real-time estimation of user adaptation that relies only on wearable sensor data during operation. Metabolic measurements were used solely to establish the ground truth adaptation curves for model training and validation but are not required for real-time inference. Five healthy adults completed six days of treadmill walking while wearing a soft hip exosuit that provided hip extension assistance. Thigh-mounted inertial measurement units recorded step timing and hip-angle trajectories, from which three variability-based features (step-frequency variability, maximum hip-flexion variability, and maximum hip-extension variability) were extracted. A Long Short-Term Memory (LSTM) model used these gait-variability inputs to estimate each user’s adaptation level relative to a metabolic cost benchmark obtained from respiratory gas analysis. Across sessions, the metabolic cost decreased by 9.0 ± 5.6% from Day 1 to Day 6 (p < 0.01) with a mean time constant of 202 ± 78 min, In contrast, the variability in step frequency, maximum hip flexion, and maximum hip extension decreased by 66.4 ± 6.8%, 37.9 ± 24.2%, and 42.8 ± 10.6%, respectively, indicating that these reductions were users’ progressive adaptation to the exosuit’s assistance. Under leave-one-subject-out (LOSO) evaluation across five participants, 59.2% of the model predictions fell within ±10 percentage points of the metabolic cost–based adaptation curve. These results suggest that simple kinematic variability measured with wearable sensors can track user adaptation and support practical approaches to real-time monitoring. Such capability can facilitate adaptive control and training protocols that personalize exosuit assistance. Full article

Withcontinuous advancements in remote sensing technology, object detection in remote sensing images has emerged as a critical research direction in maritime surveillance, port management, and national defense. Among these applications, ship detection is a key task. Due to the fact that ships in images typically exhibit arbitrary rotations, multi-scale distributions, and complex backgrounds, conventional detection methods based on horizontal or rotated bounding boxes often fail to adequately capture the fine-grained information of the targets, thereby compromising detection accuracy. This paper proposes the Dense Representative Points-Guided Rotated-Ship Detection (DenseRRSD) method. The proposed approach represents ship objects using dense representative points (RepPoints) to effectively capture local semantic information, thereby avoiding the background noise issues associated with traditional rectangular bounding box representations. To further enhance detection accuracy, an edge region sampling strategy is devised to uniformly sample RepPoints from critical ship parts, and a Weighted Residual Feature Pyramid Network (WRFPN) is introduced to efficiently fuse the multi-scale features through residual connections and learnable weights. In addition, a Weighted Chamfer Loss (WCL) combined with a staged localization loss strategy is employed to progressively refine localization from coarse to fine stages. Experimental results on both the HRSC2016 dataset and the newly constructed DOTA-SHIP dataset demonstrate that DenseRRSD achieves state-of-the-art detection accuracy, with mean Average Precision (mAP) scores of 91.2% and 83.2%, respectively, significantly outperforming existing methods. These results verify the effectiveness and robustness of the proposed approach in rotated-ship detection under diverse conditions. Full article

Additive manufacturing enables lightweight sandwich structures with complex cellular cores, but the selection of material and process settings typically involves trade-offs among mechanical performance, cost, and sustainability. This study proposes an integrated multi-criteria decision-making framework to identify the most suitable configuration for Fused Filament Fabrication (FFF) sandwich structures featuring a gyroid triply periodic minimal surface (TPMS) core. Eight alternatives are evaluated by combining two materials (PLA and PLA–Flax biocomposite) with two extrusion temperatures (200 °C and 220 °C) and two infill densities (20% and 30%). Mechanical performance is represented by flexural strength obtained from three-point bending tests reported in a previously published experimental campaign, while economic and environmental indicators are quantified through material cost and printing energy consumption, respectively. Criteria weights are derived using the Analytic Hierarchy Process (AHP) based on expert judgment and consistency-ratio verification, and the alternatives are ranked using the TOPSIS method. The results highlight a clear dominance of PLA-based configurations under the adopted weighting scheme, with PLA printed at 200 °C and 20% infill emerging as the best compromise solution. PLA–Flax options are penalized by higher material cost, higher printing-process energy demand, and lower flexural strength in the investigated conditions. The proposed AHP–TOPSIS workflow supports transparent, data-driven selection of AM process–material combinations for gyroid sandwich structures, and it can be readily extended by including additional sustainability metrics (e.g., CO₂-equivalent) and application-specific constraints. A sensitivity analysis under alternative weighting scenarios further confirms the robustness of the obtained ranking. Full article

Accurate path planning was the prerequisite for autonomous navigation of agricultural vehicles. An Unmanned Aerial Vehicle (UAV)-based coverage path planning was developed in this research for automating guidance of agricultural vehicles and reducing the operator maneuver in the creation of navigation maps. High-resolution orthophoto maps of the field were constructed by using low-altitude UAV photogrammetry to obtain spatial information. Travel paths and working paths were automatically generated from anchor points selected by the operator under the image coordinate domain. The navigation path for unmanned agricultural vehicles was generated by Mercator projection-based conversion for the anchor pixel coordinates into latitude and longitude geographic coordinates. A Graphical User Interface (GUI) was developed for path generation, visualization, and performance evaluation, through which the proposed path planning method was implemented for autonomous agricultural vehicle navigation. Calculation accuracy tests demonstrated the mean planar coordinate error was 2.23 cm and the maximum error was 3.37 cm for path planning. Field tests showed that lateral navigation errors remained within ±5.5 cm for the unmanned high-clearance sprayer, which indicated that the developed UAV-based coverage path planning method was feasible and featured high accuracy. It provided an effective solution for achieving fully autonomous agricultural vehicle operations. Full article

Network intrusion detection has challenges that fundamentally differ from language and vision tasks typically addressed by Transformer models. In particular, network traffic features lack inherent ordering, datasets are extremely class-imbalanced (with benign traffic often exceeding 80%), and reported accuracies in the literature vary widely (57–95%) without systematic explanation. To address these challenges, we propose a controlled experimental study that isolates and quantifies the impact of tokenization strategies on Transformer-based intrusion detection systems. Specifically, we introduce and compare three tokenization approaches—feature-wise tokenization (78 tokens) based on CICIDS2017, a sample-wise single-token baseline, and an optimized sample-wise tokenization—under identical training and evaluation protocols on a highly imbalanced intrusion detection dataset. We demonstrate that tokenization choice alone accounts for an accuracy gap of 37.43 percentage points, improving performance from 57.09% to 94.52% (100 K data). Furthermore, we show that architectural mechanisms for handling class imbalance—namely Batch Normalization and capped loss weights—yield an additional 15.05% improvement, making them approximately 21× more effective than increasing the training data by 50%. We achieve a macro-average AUC of 0.98, improve minority-class recall by 7–12%, and maintain strong discrimination even for classes with as few as four samples (AUC 0.9811). These results highlight tokenization and imbalance-aware architectural design as primary drivers of performance in Transformer-based intrusion detection and contribute practical guidance for deploying such models in modern network infrastructures, including IoT and cloud environments where extreme class imbalance is inherent. This study also presents practical implementation scheme recommending sample-wise tokenization, constrained class weighting, and Batch Normalization after embedding and classification layers to improve stability and performance in highly unstable table-based IDS problems. Full article