Search Results (3,645)

To mitigate performance degradation caused by variations in host-platform ground configurations, this paper presents a compact wideband printed antenna for Wi-Fi 6E and Wi-Fi 7 applications. Occupying an ultra-miniature footprint of only 18 × 15 mm², the proposed antenna consists of an asymmetric T-shaped monopole (ATM), an open stub, and a meandered loop ground-line structure. The meandered ground line operates as a modified planar sleeve balun to reduce PCB-induced common-mode current propagation and improve robustness against representative host–ground variations. By controlling the electromagnetic coupling between the ATM radiator and the meandered ground loop, broadband impedance matching is achieved without additional matching circuits. The proposed antenna covers three operating bands: 2.4 to 2.5 GHz, 5.15 to 5.85 GHz, and 5.925 to 7.125 GHz. Measurement results demonstrate stable resonant frequencies and wide impedance bandwidths across the investigated host–ground configurations, with measured radiation efficiencies up to 88% at the investigated frequencies. The measured radiation patterns exhibit quasi-omnidirectional characteristics at 2.45 GHz, while multilobe behavior becomes more pronounced at 5.5 and 6.5 GHz due to higher-order modes associated with the meandered ground loop. A quantitative coverage-efficiency or minimum-realized-gain analysis is beyond the scope of this study. Although demonstrated using a USB wireless adapter, the proposed operating mechanism is associated with the antenna structure rather than the USB connector itself. Full article

Electroencephalography (EEG) provides a non-invasive and cost-effective tool for supporting the assessment of attention-deficit/hyperactivity disorder (ADHD). However, the nonstationary nature of EEG produces substantial variability among signal windows recorded from the same participant, which can obscure diagnostic structure and lead to inconsistent predictions. To address this problem, we propose the Contextualized Transfer Entropy Network (CTE-Net), an end-to-end deep-learning architecture that combines global content-based contextualization with nonlinear and directed EEG connectivity estimation. CTE-Net first employs a Transformer encoder to contextualize the multichannel representations within each EEG window. The resulting signals are processed using channel-wise nonlinear temporal filters and Takens delay-coordinate embeddings. A differentiable matrix-based Transfer Entropy module, formulated using Rényi’s $\alpha$-entropy and a rational quadratic kernel, then estimates directed predictive information dependencies between all ordered electrode pairs. The resulting connectivity coefficients are used for ADHD-versus-control classification. The model was evaluated on a publicly available pediatric EEG dataset comprising 120 participants, equally divided between ADHD and control groups, using five fixed subject-wise folds and ten random training repetitions. At the window level, CTE-Net achieved an accuracy of $80.9\pm 1.7\%$, precision of $82.7\pm 2.1\%$, and sensitivity of $84.2\pm 2.3\%$. At the participant level, it achieved an accuracy of $83.4\%$ (95% CI: $78.2$–$88.2$) and an ROC-AUC of $90.2\%$ (95% CI: $85.1$–$94.6$), demonstrating competitive and comparatively balanced classification performance. Beyond classification performance, the directed Transfer Entropy representation exhibited the lowest within-subject dispersion among the analyzed representation stages, with a median reduction of $38.35\%$ relative to raw EEG. This reduction remained consistent across different PCA dimensionalities and distance definitions. These single-dataset findings support CTE-Net as a compact and interpretable methodological framework for representing directed EEG interactions while attenuating window-specific variability within individual participants. Full article

The widespread adoption of autonomous vehicle technologies requires accurate predictions of coordinated multi-agent motion. While predicting such motion under arbitrary control mechanisms is generally intractable, this paper focuses on certain classes of optimal control where the system dynamics reduce to Lie-Poisson equations. In this context, the goal of the paper is to learn the dynamics solely from data, without prior knowledge of the control Hamiltonian or the inter-agent interaction laws. The main achievement of this paper is the introduction of Control Optimal Lie-Poisson Neural Networks (CO-LPNets), which are built from a composition of Poisson maps. By design, CO-LPNets preserve the system’s Casimir invariants to machine precision. The paper also demonstrates the completeness of these neural networks and highlights their representational efficiency. CO-LPNets are applied to systems of interacting particles on the $\mathrm{SO}\left(3\right)$ and $\mathrm{SE}\left(3\right)$ Lie groups, modeling coupled rigid body rotations and the spatial navigation of unmanned vehicles, respectively. Numerical evaluations confirm that CO-LPNets accurately learn the global phase-space dynamics from sparse data, faithfully reproducing trajectories over hundreds of time steps. Furthermore, the paper demonstrates the robustness of the architecture against observational noise. Requiring minimal training data (∼200 points per dimension) and highly compact architectures (∼1000 parameters), CO-LPNets offer a highly efficient, structure-preserving solution well-suited for practical edge deployment in autonomous systems. Full article

Quantitative motion analysis is important for understanding the characteristics of climbing movement and for providing objective feedback in sport climbing. Although optical motion capture systems can measure three-dimensional body motion with high accuracy, their application to climbing is limited by the need for markers, multiple cameras, and a controlled measurement space, as well as by occlusion caused by the wall, holds, and body segments. A markerless measurement method using a compact sensor configuration would therefore be advantageous for analyzing climbing motion in more practical environments. This study presents a markerless three-dimensional motion measurement method for sport climbing using a single RGB-D camera and iterative closest point (ICP)-based body-part model fitting. The proposed method first separates the human region from the RGB image using image segmentation and then detects two-dimensional body keypoints using OpenPose. The detected keypoints are projected onto the depth image to reconstruct an initial three-dimensional posture. To refine the reconstructed posture, predefined body-part models are fitted to the segmented human point cloud using the ICP algorithm. The joint points included in the fitted body-part models are then used as the refined three-dimensional joint coordinates. Rather than relying solely on the accuracy of the image-based pose estimator, the proposed method introduces a model-based correction process that compensates for uncertainty in the initial keypoint-based reconstruction. This design aims to reduce the influence of background objects, unstable depth measurements, and incorrect or missing two-dimensional keypoints, which are common difficulties in climbing environments. The proposed method was quantitatively evaluated using five successfully completed trials from two participants under a low-occlusion condition and eight successfully completed trials from four participants under an occlusion-prone condition. The proposed method reduced the average three-dimensional RMSE across six representative body points relative to the initial RGB-D reconstruction under both conditions, with a more pronounced reduction under the occlusion-prone condition. The results indicate that ICP-based body-part model fitting is a useful correction step for single-camera RGB-D motion measurement in sport climbing. Full article

TinyStressNet is a compact neural classifier for three-class academic stress estimation (low, medium, high) from three physiological features (galvanic skin response, heart rate, and skin temperature) designed for low-cost educational sensing. We evaluate it on 1000 interval-level observations from five engineering students collected over three months, with labels derived from repeated 6-item short-form of the State-Trait Anxiety Inventory (STAI-6) self-reports aligned with contemporaneous physiological readings. For subject-independent evidence, we use leave-one-subject-out (LOSO) evaluation as the primary protocol, complemented by a per-user chronological split for temporal robustness. We study augmentation through the lens of approximate invariance to bounded, label-preserving nuisance transformations, comparing no augmentation, Gaussian jitter, a radial-rescaling family, and a bounded affine (BA) family against strong classical baselines (Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and Support Vector Machines (SVMs), logistic and ordered-logit models). Under LOSO, the best TinyStressNet configuration reaches 0.899 accuracy and 0.898 weighted F1; within users, it reaches 0.912 accuracy. The model uses 3139 parameters and 2800 Multiply-Accumulate operations (MACs), and post-training int8 export preserves predictions with almost the same accuracy (0.992 float–int8 agreement). Strong classical baselines remain competitive on this low-dimensional representation, which we read as evidence about the geometry of the three-feature space rather than as a limitation of the model. Repeating the full evaluation under ten seeds shows that the augmentation families are not separable on this representation: subject-independent weighted F1 spans 0.894 to 0.900 across the four conditions with a seed-to-seed standard deviation of about 0.005, and no augmentation is not measurably worse than the best family. The BA family is the most consistent condition, the strongest in five of ten seeds and never the weakest, which we report as a stability observation rather than a performance gain. TinyStressNet is presented as a compact, quantization-ready reference model and a controlled testbed for invariance-aware augmentation in low-dimensional physiological sensing. Full article

The stream of onions entering a self-propelled onion collector varies with field conditions, feeding density, and the transient lifting behavior of the onion–soil mass, whereas the collection conveyor is conventionally operated at a fixed speed, wasting hydraulic energy. This study proposes and evaluates, through field-calibrated simulation, a vision-based feedforward conveyor speed control framework that couples reel-synchronous onion counting with variable-displacement pump control. To mitigate the periodic occlusion caused by the compact dual-conveyor structure, a frame-selection method synchronized with the detected conveyor position was implemented, and a YOLOv8n detector was trained and evaluated on 314 images extracted from 32 indoor and field source videos partitioned at the source-video level. On the independent test subset, the model achieved precision, recall, and mAP@0.5 of 0.952, 0.944, and 0.959, and the proposed counting method maintained count recovery ratios above 95% across all engine speeds in 45 independent field trials, outperforming fixed-period sampling and tracking-based baselines by 6.6 and 3.8 percentage points, respectively. An AMESim model of the fixed-displacement hydraulic system was calibrated and shown to be consistent with field measurements and was then used to evaluate the variable-displacement configuration. Net conveyor-related fuel savings (engine no-load consumption subtracted) were estimated at 14.3–18.7% under the field-identified constant transmission efficiency, with conservative estimates of 11.5–16.9% when partial-displacement efficiency losses were accounted for through an anchored loss model. These results demonstrate the feasibility of vision-based feedforward conveyor speed control and its potential for energy savings in bulb-crop collection. Full article

Soil release appearance grading evaluates residual stains after standardized laundering, but visual assessment is subjective and adjacent half grades are difficult to distinguish. This study proposes a lightweight Tri-Semantic Injection Network (TSI-Net) for nine-grade assessment. From one red–green–blue (RGB) image, a fixed CIE L*a*b* (CIELAB) branch constructs a mean-background image B, a pixel-wise color-difference image D, and a stain-appearance image S. The trainable backbone progressively injects S, D, and B and predicts grades from 1.0 to 5.0 at 0.5-grade intervals. Gaussian soft targets represent the ordering of these grades. The dataset contains 325 images, and TSI-Net has 1,403,336 trainable parameters. In a 33-image evaluation, Gaussian-trained TSI-Net achieved exact-grade and within-half-grade accuracies of 87.88% and 96.97%, compared with 84.85% and 93.94% for one-hot training. Both training methods achieved 100.00% accuracy within one grade. Gaussian training therefore showed a 3.03-percentage-point advantage for each of the two stricter metrics in this comparison. This method requires no manual stain segmentation and provides a compact framework for standardized fabric appearance assessment under controlled acquisition conditions. Full article

The transition toward sustainable energy systems requires reliable, efficient, and computationally practical control strategies for renewable energy-based microgrids. Grid-connected DC microgrids provide an effective platform for integrating distributed renewable energy resources, while their sustainable operation requires robust regulation under load variations, nonlinear loads, and input disturbances. This study proposes a lightweight Polynomial Regression Controller (PRC) for voltage regulation in grid-connected DC microgrids. The proposed data-driven controller uses a second-order polynomial model to estimate the converter duty cycle from input voltage, voltage error, and load current. The model is trained offline using independently generated operating trajectories and evaluated under previously unseen operating conditions. The results demonstrate accurate DC bus voltage regulation and robust operation under linear, constant power, motor load, and grid-connected conditions. The PRC maintains the DC bus voltage close to its 50 V reference, with steady-state errors of 0.002–0.008% and a settling time of 0.001 s under fast transient responses. The proposed approach combines nonlinear mapping capability with a compact computational structure, supporting practical implementation on resource-constrained platforms. Overall, the proposed PRC contributes to reliable renewable energy integration, resilient microgrid operation, and the development of sustainable, efficient, and scalable smart energy systems. Full article

Thermal energy storage (TES) technologies are essential for renewable energy integration, industrial waste heat recovery, grid flexibility, and improved energy efficiency. Despite advances in sensible heat thermal energy storage (SHTES), latent heat thermal energy storage (LHTES), and thermochemical energy storage (TCES), practical deployment remains constrained by inadequate heat transfer rates, which limit charging and discharging processes, reduce storage utilization, and increase system size and cost. Conventional heat-transfer enhancement approaches, including fins, embedded heat exchangers, conductive additives, porous structures, and flow intensification techniques often introduce trade-offs related to manufacturability, complexity, durability, and energy consumption. Additive manufacturing (AM) has emerged as a promising approach for overcoming these limitations by enabling precise control of internal geometry, porosity, surface-area-to-volume ratio, and fluid pathways. Through the fabrication of architected structures, lattice networks, triply periodic minimal surface (TPMS) geometries, and multifunctional heat-transfer architectures, AM enables geometry-driven optimization of thermal performance that is difficult to achieve using conventional manufacturing methods. These capabilities support the development of compact TES systems with enhanced heat transfer, improved thermal uniformity, and increased energy utilization. This review examines additive manufacturing technologies relevant to TES applications, including powder bed fusion, directed energy deposition, material extrusion, vat photopolymerization, and binder jetting. The relationships among manufacturing processes, material selection, and thermal performance are discussed across SHTES, LHTES, and TCES systems. Particular emphasis is placed on AM-enabled heat-transfer enhancement strategies, phase change material (PCM)-integrated structures, architected thermal networks, embedded heat exchangers, and computational design methodologies such as topology optimization. Current challenges involving material compatibility, scalability, cost, and long-term durability are also evaluated. The review highlights how additive manufacturing is transforming TES design from a material-centered paradigm toward geometry-enabled thermal engineering, creating new opportunities for next-generation energy storage systems. Full article

This study evaluates four granular sub-bases for pavement applications under Colombian conditions. A natural granular sub-base with a nominal maximum aggregate size of approximately 38 mm (i.e., GSB38) served as the control. Three modified mixtures comprised (i) 90% GSB38 with 5% Recycled Concrete Aggregate (RCA) and 5% Crumb Rubber (CR), (ii) 80% GSB38 with 10% RCA and 10% CR, and (iii) 70% GSB38 with 15% RCA and 15% CR. Thus, these formulations represented total recycled contents of 10%, 20%, and 30%, respectively, calculated on a dry-mass basis. Particle size distribution, standard Proctor compaction, Los Angeles abrasion, and four-day soaked California Bearing Ratio (CBR) tests were performed, and results were evaluated against Colombian specifications. An attributional cradle-to-gate Life-Cycle Assessment (LCA) with a one-tonne functional unit used SimaPro 9.4, Ecoinvent 3.9, and TRACI 2.1. All formulations complied with gradation, compaction, and abrasion requirements. As recycled content increased, optimum moisture content rose from 7.695% to 9.001%, maximum dry density fell from 2.388 to 2.072 g/cm³, and abrasion loss rose from 42.0% to 45.5%. Design CBR responded nonmonotonically, with design CBR values of 44.4%, 24.9%, 59.3%, and 90.1% for formulations with 0, 10, 20, and 30% total recycled material content, respectively. The 10% formulation failed the minimum CBR criterion, whereas the other mixtures met the high-traffic requirement. All recycled formulations reduced all considered environmental indicators; for example, the 30% recycled-content mixture decreased ozone depletion by 20.199% and global warming potential by 3.501%. Overall, the sub-base formulations with 20% and 30% of total recycled content showed strong technical and environmental potential, although replicated testing and field validation remain necessary. Full article

This study presents a sustainable approach for developing multifunctional hyaluronic acid–TMSPMA hydrogel films incorporated with banana stem-derived carbon dots (BCDs) via gamma radiation. The solvent-free radiation process enables sterile synthesis, homogeneous crosslinking, and uniform BCD distribution. FTIR and XPS confirmed oxygen- and nitrogen-rich functional groups on BCDs; TGA demonstrated excellent thermal stability. The nanocomposite films (HT series) exhibited improved structural integrity, enhanced thermal resistance, and smooth compact morphology. Cytocompatibility verified non-toxicity and biocompatibility. Surface and barrier analyses indicated increased hydrophilicity and reduced oxygen/moisture permeability. The films showed strong antioxidant performance exceeding 60% (DPPH) and 70% (ABTS) radical scavenging for HT3, and a controlled, diffusion-mediated BCD release. In banana packaging, the films delayed ethylene and CO₂ evolution, prolonging shelf life and reducing greenhouse gas emissions. Overall, this work introduces a green and scalable route for producing BCD-reinforced hydrogel films combining antioxidant functionality, biocompatibility, and biodegradability for next-generation active food packaging. Full article

Long-term transgene silencing remains a major challenge in lentiviral gene delivery, particularly in pluripotent stem cells undergoing lineage-specific differentiation. Universal chromatin opening elements (UCOEs) have emerged as effective regulatory elements for protecting transgene expression against epigenetic silencing; however, their relatively large-size limits vector design flexibility. In the present study, we evaluated the anti-silencing activity of three next-generation UCOE constructs (1.7 kb, 1.2 kb, and a newly developed minimal 0.5 kb fragment) during long-term culture and neuronal differentiation of human induced pluripotent stem cells (iPSCs). UCOE fragments were cloned into self-inactivating lentiviral vectors and validated by restriction enzyme analysis and agarose gel electrophoresis. Lentiviral particles produced in HEK293T cells were used to transduce human iPSCs, followed by neuronal differentiation. Transgene expression was monitored for up to 60 days using fluorescence microscopy, confocal immunofluorescence, flow cytometry, and vector copy number analysis by quantitative PCR. All UCOE-containing vectors demonstrated significantly improved transgene stability compared with the UCOE-less control throughout both the undifferentiated and differentiated stages. The UCOE-containing constructs maintained substantially greater expression stability than the UCOE-less control, with the minimal 0.5 kb UCOE showing long-term performance comparable to the larger UCOE constructs. These findings demonstrate that considerable UCOE minimization can be achieved without markedly compromising anti-silencing activity, providing a potential vector-design advantage by reducing the regulatory cassette footprint and increasing available vector capacity. The compact 0.5 kb UCOE therefore represents a promising regulatory element for sustained transgene expression in stem cell engineering, disease modeling, regenerative medicine, and future gene therapy applications. However, the present study did not directly assess genomic safety, insertional effects, genotoxicity, or cellular transformation, and dedicated preclinical studies will be required to determine the long-term safety and in vivo performance of this compact UCOE configuration. Full article

Global oil and gas exploration and development are gradually expanding into deep and ultra-deep formations. Deep limestone exists in a long-term multi-field coupled environment featuring high temperature, high in situ stress and high pore pressure, which brings great challenges to reservoir stimulation and wellbore stability. To investigate the effects of confining pressure and pore pressure on limestone under high temperatures, triaxial compression tests were conducted on limestone at various temperatures (25~150 °C) using the GCTS RTR-2000 rock mechanics testing system. This paper investigates the evolution laws of strength and deformation parameters of limestone under varied temperature, confining pressure and pore pressure. The results indicate that: (1) Within the 25~150 °C range, the peak strength and elastic modulus of limestone exhibit a “decrease-then-increase” trend, with a strength rebound occurring at 150 °C driven by the “thermal expansion and compaction” effect. (2) Under a pore pressure of 50 MPa, temperature and confining pressure exert a significant coupled control effect on the mechanical properties of the rock, characterized by a critical confining pressure threshold of approximately 100–110 MPa. Below this threshold, high temperature acts as a weakening factor, whereas above it, high temperature acts as a strengthening factor and induces intense brittle failure under high pressure. (3) In the pore pressure coupling tests, the rock undergoes ductile failure as confining pressure increases at normal/room temperature, while a temperature of 100 °C strengthens the rock under high confining pressure. (4) Energy evolution analysis reveals that within the 75~125 °C range, the thermal pressurization of pore water and local thermal stresses induce massive microcracks, causing the dissipated energy to surge sharply to nearly 80%. The research findings provide a theoretical basis for wellbore stability analysis and fracturing parameter optimization in deep carbonate reservoirs. Full article

Remote electroencephalography (EEG) systems require compact representations that remain useful when communication noise corrupts the transmitted message. We evaluated whether task-oriented residual refinement of filter bank common spatial pattern (FBCSP) features compressed by principal component analysis (PCA) improves four-class motor imagery (MI) decoding without increasing the transmitted dimension. The BNCI2014-001 dataset was assessed in nine subjects using bidirectional subject-specific cross-session evaluation. All methods transmitted $K\in \{16,32,64\}$ power-normalized real values through additive white Gaussian noise (AWGN) at seven signal-to-noise ratios (SNRs) and a noise-free reference. Balanced accuracy was averaged over 20 paired noise realizations per noisy condition, and paired subject-level differences were evaluated with exact joint sign-flip max-$\left|t\right|$ inference. At $K=32$, the proposed method achieved 41.10%, 49.46%, and 56.15% balanced accuracy at $-10$, $-5$, and 0 dB, compared with 38.63%, 46.28%, and 53.26% for conventional FBCSP–PCA transmission. Ten of the 24 semantic-versus-conventional comparisons were significant after family-wise max-$\left|t\right|$ correction, including all nine comparisons at $-10$, $-5$, and 0 dB. Receiver-only controls closely reproduced conventional performance at all three message dimensions, whereas alternative loss weights, uniform-SNR training and selection, and removal of the 0 dB/noise-free reference-condition guard retained positive low-SNR gains. Overall, baseline-preserving task-oriented refinement improved MI decision robustness under severe AWGN without increasing the number of transmitted values. Full article

The Internet of Vehicles (IoV) relies on Basic Safety Messages (BSMs) for cooperative awareness, yet these broadcasts remain vulnerable to falsification, replay, flooding, Sybil-based, and motion-manipulation attacks. This paper proposes a temporal-window-aware physics-informed edge-oriented Intrusion Detection System (IDS) for 20-class IoV misbehavior detection under two simulation-based BSM observability regimes: ideal noise-free kinematics and realistic noise-inclusive observables reconstructed using the sensor-error components supplied separately by VeReMi Extension. Accordingly, “realistic” denotes a noise-inclusive simulation condition rather than real-world validation. From VeReMi Extension streams, the framework derives a compact 20-feature representation capturing kinematics, timing, replay cues, pseudonym dynamics, position-consistency residuals, zero-pattern behavior, and long-horizon motion indicators. These features are normalized with a training-only robust scaler, organized into sender-specific temporal windows, and classified using a lightweight three-layer stacked Long Short-Term Memory (LSTM) with residual temporal pooling. Four implementation variants are evaluated: dense Keras, default-optimized TensorFlow Lite, pruning-only Keras, and pruning-plus-compression TensorFlow Lite. Temporal sensitivity identifies $T=40$ as the best robustness–latency compromise under the realistic noise-inclusive regime. At $T=40$, the final pruned-and-compressed TensorFlow Lite model achieves $99.60\%$ accuracy and $99.09\%$ macro-F1 under ideal observability, and $99.38\%$ accuracy and $98.67\%$ macro-F1 under realistic noise-inclusive observability, with an $88.38$ KB footprint and $0.1283$ ms controlled-runtime latency. Large-scale Central Processing Unit (CPU) benchmarks on $\mathrm{150,000}$ noise-inclusive test sequences provide a platform-dependent runtime reference, with the pruned TensorFlow Lite model reaching $99.14\%$ accuracy, $98.18\%$ macro-F1, and $3.544$ ms average latency on a multi-core Intel Xeon CPU. To complement this high-throughput evaluation, edge-deployment potential is profiled using the official C++ TensorFlow Lite benchmark tool. When evaluated using a single CPU thread without batching, the final artifact achieves an unbatched per-sequence latency of $1.356$ ms, corresponding to less than $1.4\%$ of the standard 100 ms BSM generation interval. An architecture-width ablation identifies the $64/32/32$ recurrent stack as the performance–resource knee point: expanding it to $128/64/64$ improves validation macro-F1 by only $0.0019$ percentage points while increasing TensorFlow Lite footprint and latency by factors of $2.46$ and $2.32$, respectively. A training-time architecture-preserving feature-family ablation confirms that engineered descriptors are essential: raw kinematics alone reduce noise-inclusive macro-F1 from $98.67\%$ to $67.49\%$, with pseudonym dynamics and position-consistency cues producing the largest individual degradations. Full article