Search Results (1,968)

Artificial intelligence has become crucial in sports, leveraging its analytical capabilities to enhance the understanding and prediction of complex events. Machine learning algorithms in sports, especially basketball, are transforming performance analysis by identifying patterns and trends invisible to traditional methods. This technology provides in-depth insights into individual and team performance, enabling precise evaluation of strategies and tactics. Consequently, the detailed analysis of every aspect of a team’s routine can significantly elevate the level of competition in the sport. This study investigates a range of machine learning models, including Logistic Regression (LR), Ridge Regression Classifier (RR), Random Forest (RF), Naive Bayes (NB), K-Nearest Neighbors (KNNs), Support Vector Machine (SVM), Stacking Classifier (STACK), Bagging Classifier (BAG), Multi-Layer Perceptron (MLP), AdaBoost (AB), and XGBoost (XGB), as well as deep learning architectures such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), to compare their effectiveness in predicting game outcomes in the NBA and WNBA leagues. The results show highly acceptable prediction accuracies of 65.50% for the NBA and 67.48% for the WNBA. This study allows us to understand the impact that artificial intelligence can have on the world of basketball and its current state in relation to previous studies. It can provide valuable insights for coaches, performance analysts, team managers, and sports strategists by using machine learning and deep learning models to predict NBA and WNBA outcomes, enabling informed decisions and enhancing competitive performance. Full article

Excessive energy consumption of communication protocols in IoT/IIoT systems constitutes one of the key constraints for the operational longevity of remote sensor nodes, where radio transmission often incurs higher energy costs than data acquisition or local computation. Previous studies have remained fragmented, typically focusing on selected technologies or specific layers of the communication stack, which has hindered the development of comparable quantitative metrics across protocols. The aim of this study is to design and validate a unified evaluation framework enabling consistent assessment of both wired and wireless protocols in terms of energy efficiency, reliability, and maintenance costs. The proposed approach employs three complementary research methods: laboratory measurements on physical hardware, profiling of SBC devices, and simulations conducted in the COOJA/Powertrace environment. A Unified Comparative Method was developed, incorporating bilinear interpolation and weighted normalization, with its robustness confirmed by a Spearman rank correlation coefficient exceeding 0.9. The analysis demonstrates that MQTT-SN and CoAP (non-confirmable mode) exhibit the highest energy efficiency, whereas HTTP/3 and AMQP incur the greatest energy overhead. Results are consolidated in the ICoPEP matrix, which links protocol characteristics to four representative RS-IoT scenarios: unmanned aerial vehicles (UAVs), ocean buoys, meteorological stations, and urban sensor networks. The framework provides well-grounded engineering guidelines that may extend node lifetime by up to 35% through the adoption of lightweight protocol stacks and optimized sampling intervals. The principal contribution of this work is the development of a reproducible, technology-agnostic tool for comparative assessment of IoT/IIoT communication protocols. The proposed framework addresses a significant research gap in the literature and establishes a foundation for further research into the design of highly energy-efficient and reliable IoT/IIoT infrastructures, supporting scalable and long-term deployments in diverse application environments. Full article

As a critical component of rail travel, the train communication network (TCN) is an integrated central platform that is used to realize the train control, condition monitoring, and data transmission, whose failure will disrupt the symmetry of TCN topology and endanger the security of rail trains. To enhance the reliability of TCN, an intelligent fault diagnosis method is proposed based on active learning (AL) and a stacked consistent autoencoder (SCAE), which is capable of building a competitive classifier with a limited amount of labeled training samples. SCAE can learn better feature presentations from electrical multifunction vehicle bus (MVB) signals by reconstructing the same raw input data layer by layer in the unsupervised feature learning phase. In the supervised fine-tuning phase, a deep AL-based fault diagnosis framework is proposed, and a dynamic fusion AL method is presented. The most valuable unlabeled samples are selected for labeling and training by considering uncertainty and similarity simultaneously, and the fusion weight is dynamically adjusted at the different training stages. A TCN experimental platform is constructed, and experimental results show that the proposed method achieves better performance under three different metrics with fewer labeled samples compared to the state-of-the-art methods; it is also symmetrically valid in class-imbalanced data. Full article

An AA6061-7075 composite with a heterogeneous structure was fabricated via the additive friction stir deposition (AFSD) method, and in situ processing data were monitored during the manufacturing process. The results show that the cross-section of the composite subjected to AFSD exhibits a lower degree of plastic deformation behavior compared to the surface and side of the composite, owing to serious heat accumulation during the layer-by-layer stacking process. The denser, heterogeneous structure, consisting of finer (softer) and coarser (harder) grains, which correspond to AA6061 and AA7075, was formed according to transmission electron microscopy (TEM) analysis. Furthermore, the obtained composite subjected to AFSD in this work presents outstanding mechanical properties compared to other as-fabricated AA6061/AA7075 depositions acquired by other additive manufacturing methods along the horizontal building direction, with the ultimate tensile strength (266 MPa) being 89% of that of AA6061-T6 and the elongation 1.1 times that of AA7075-T6. The findings provide useful guidelines for the in situ preparation of Al-based composites and offer ideas for manufacturing high-strength heterostructures for large-scale practical engineering applications. Full article

This paper presents a resilient and energy-efficient framework for jamming mitigation in cluster-based wireless sensor networks (WSNs), addressing a critical vulnerability in hostile or interference-prone environments. The proposed approa ch integrates dynamic cluster reorganization, adaptive MAC-layer behavior, and multipath routing strategies to restore communication capabilities and sustain network functionality under jamming conditions. The framework is evaluated across heterogeneous topologies using Zigbee and Bluetooth Low Energy (BLE); both stacks were validated in a physical testbed with matched jammer and traffic conditions, while simulation was used solely to tune parameters and support sensitivity analyses. Results demonstrate significant improvements in Packet Delivery Ratio, end-to-end delay, energy consumption, and retransmission rate, with BLE showing particularly high resilience when combined with the mitigation mechanism. Furthermore, a comparative analysis of routing protocols including AODV, GAF, and LEACH reveals that hierarchical protocols achieve superior performance when integrated with the proposed method. This framework has broader applicability in mission-critical IoT domains, including environmental monitoring, industrial automation, and healthcare systems. The findings confirm that the framework offers a scalable and protocol-agnostic defense mechanism, with potential applicability in mission-critical and interference-sensitive IoT deployments. Full article

Given the high casualty rate on expressways, this study aimed to accurately predict traffic accident rates and the key factors influencing them. Taking an expressway in Southern China as the research object, we constructed a two-layer stacking model integrating neural networks and tree models, based on accident data, traffic flow data, and road segment characteristic data. Six base models were integrated for prediction, and the Shapley Additive exPlanations (SHAP) method was used to analyze influencing factors. Results showed that the proposed model achieved the best performance, with a root mean square error (RMSE) of 11.05 and a mean absolute error (MAE) of 6.12, and its performance was significantly superior to that of other models (p < 0.05). Results from hyperparameter optimization and 5-fold cross-validation indicated that the proposed model had an RMSE of 8.91 ± 2.03, which was better than that of other models. Among all input factors, the proportion of tunnel length to total length and the variance of bridge width had the most significant impact on the expressway traffic accident rate, while the average width of tunnels had the lowest impact. This study realizes accurate prediction using widely available data and clarifies key factor mechanisms, providing support for expressway safety management and early risk warnings. Full article

Abdominal fat is a key indicator of chicken meat quality. Excessive deposition not only reduces meat quality but also decreases feed conversion efficiency, making the breeding of low-abdominal-fat strains economically important. Genomic selection (GS) uses information from genome-wide association studies (GWASs) and high-throughput sequencing data. It estimates genomic breeding values (GEBVs) from genotypes, which enables early and precise selection. Given that abdominal fat is a polygenic trait controlled by numerous small-effect loci, this study combined population genetic analyses with machine learning (ML)-based feature selection. Relevant single-nucleotide polymorphisms (SNPs) were first identified using a combined GWAS and linkage disequilibrium (LD) approach, followed by a two-stage feature selection process—Lasso for dimensionality reduction and recursive feature elimination (RFE) for refinement—to generate the model input set. We evaluated multiple machine learning models for predicting genomic estimated breeding values (GEBVs). The results showed that linear models and certain nonlinear models achieved higher accuracy and were well suited as base learners for ensemble methods. Building on these findings, we developed a Dynamic Adaptive Weighted Stacking Ensemble Learning Framework (DAWSELF), which applies dynamic weighting and voting to heterogeneous base learners and integrates them layer by layer, with Ridge serving as the meta-learner. In three independent validation populations, DAWSELF consistently outperformed individual models and conventional stacking frameworks in prediction accuracy. This work establishes an efficient GEBV prediction framework for complex traits such as chicken abdominal fat and provides a reusable SNP feature selection strategy, offering practical value for enhancing the precision of poultry breeding and improving product quality. Full article

With the rapid development of intelligent connected vehicle technology, false data injection (FDI) attacks have become a major challenge in the Internet of Vehicles (IoV). While deep learning methods can effectively identify such attacks, the dynamic, distributed architecture of the IoV and limited computing resources hinder both privacy protection and lightweight computation. To address this, we propose FedIFD, a federated learning (FL)-based detection method for false data injection attacks. The lightweight threat detection model utilizes basic safety messages (BSM) for local incremental training, and the Q-FedCG algorithm compresses gradients for global aggregation. Original features are reshaped using a time window. To ensure temporal and spatial consistency, a sliding average strategy aligns samples before spatial feature extraction. A dual-branch architecture enables parallel extraction of spatiotemporal features: a three-layer stacked Bidirectional Long Short-Term Memory (BiLSTM) captures temporal dependencies, and a lightweight Transformer models spatial relationships. A dynamic feature fusion weight matrix calculates attention scores for adaptive feature weighting. Finally, a differentiated pooling strategy is applied to emphasize critical features. Experiments on the VeReMi dataset show that the accuracy reaches 97.8%. Full article

As 3D stacking technologies advance, low-temperature hybrid Cu bonding has become essential for fine-pitch integration. This study focuses on evaluating Pd nanolayers deposited by electroless plating (ELP) on Cu surfaces and compares them to electroplated (EP) Pd to assess their suitability for hybrid bonding. Pd nanolayers (5~7 nm) were deposited on Cu films, and their surface morphology, crystallinity, and chemical composition were characterized using AFM, TEM, GIXRD, and XPS. EP-Pd layers exhibited lower roughness and larger grain size, acting as effective Cu diffusion barriers. In contrast, ELP-Pd layers showed small grains, higher surface roughness, and partial Cu diffusion and oxidation. At 200 °C, both Pd layers enabled bonding, but ELP-Pd samples achieved more uniform and continuous interfaces with thinner copper oxide layers. Shear testing revealed that ELP-Pd samples exhibited higher average bonding strength (20.58 MPa) and lower variability compared to EP-Pd (16.47 MPa). The improved bonding performance of ELP-Pd is attributed to its grain-boundary-driven diffusion and uniform interface formation. These findings highlight the potential of electroless Pd as a passivation layer for low-temperature hybrid Cu bonding and underscore the importance of optimizing pre-bonding surface treatments for improved bonding quality. Full article

This work presents a novel Gallium Nitride (GaN) high-electron-mobility transistor (HEMT)-based ultraviolet (UV) photodetector architecture that integrates advanced material and structural design strategies to enhance detection performance and stability under room-temperature operation. This study is conducted as a fully numerical simulation using the Silvaco Atlas platform, providing detailed electrothermal and optoelectronic analysis of the proposed device. The device is constructed on a high-thermal-conductivity silicon carbide (SiC) substrate and incorporates an n-GaN buffer, an indium nitride (InN) channel layer for improved electron mobility and two-dimensional electron gas (2DEG) confinement, and a dual-passivation scheme combining silicon nitride (SiN) and hexagonal boron nitride (h-BN). A p-GaN layer is embedded between the passivation interfaces to deplete the 2DEG in dark conditions. In the device architecture, the metal contacts consist of a 2 nm Nickel (Ni) adhesion layer followed by Gold (Au), employed as source and drain electrodes, while a recessed gate embedded within the substrate ensures improved electric field control and effective noise suppression. Numerical simulations demonstrate that the integration of a hexagonal boron nitride (h-BN) interlayer within the dual passivation stack effectively suppresses the gate leakage current from the typical literature values of the order of ${10}^{-8}$ A to approximately ${10}^{-10}$ A, highlighting its critical role in enhancing interfacial insulation. In addition, consistent with previous reports, the use of a SiC substrate offers significantly improved thermal management over sapphire, enabling more stable operation under UV illumination. The device demonstrates strong photoresponse under 360 nm ultraviolet (UV) illumination, a high photo-to-dark current ratio (PDCR) found at approximately ${10}^6$, and tunable performance via structural optimization of p-GaN width between 0.40 μm and 1.60 μm, doping concentration from $5\times {10}^{16}$ ${\mathrm{cm}}^{-3}$ to $5\times {10}^{18}$ ${\mathrm{cm}}^{-3}$, and embedding depth between 0.060 μm and 0.068 μm. The results underscore the proposed structure’s notable effectiveness in passivation quality, suppression of gate leakage, and thermal management, collectively establishing it as a robust and reliable platform for next-generation UV photodetectors operating under harsh environmental conditions. Full article

The photoelectrochemical oxidation method was utilized to directly grow a gate oxide layer and simultaneously create gate-recessed regions for fabricating GaN-based depletion-mode metal-oxide-semiconductor high-electron mobility transistors (D-mode MOSHEMTs). The LiNbO₃ gate ferroelectric layer and stacked gate oxide layers of LiNbO₃/HfO₂/Al₂O₃ were respectively deposited on the created gate-recessed regions using the photoelectrochemical etching method to fabricate the GaN-based enhancement mode MOSHEMTs (E-mode MOSHEMTs). GaN-based complementary integrated circuits were realized by monolithically integrating the D-mode MOSHEMTs and the E-mode MOSHEMTs. The performances of the inverter circuit manufactured using the integrated GaN-based complementary MOSHEMTs were measured and analyzed. Full article

Perovskite solar cells (PSCs) have emerged as promising photovoltaic technologies owing to their high power conversion efficiency (PCE) and material versatility. Conventional optimization of PSC architectures largely depends on iterative experimental approaches, which are often labor-intensive and time-consuming. In this study, a data-driven modeling strategy is introduced to accelerate the design of efficient and mechanically robust PSCs. Seven supervised regression models were evaluated for predicting key photovoltaic parameters, including PCE, short-circuit current density (Jsc), open-circuit voltage (Voc), and fill factor (FF). Among these, a stacking ensemble framework exhibited superior predictive accuracy, achieving an R² of 0.8577 and a root mean square error of 2.084 for PCE prediction. Model interpretability was ensured through Shapley Additive exPlanations(SHAP) analysis, which identified precursor solvent composition, A-site cation ratio, and hole-transport-layer additives as the most influential parameters. Guided by these insights, ten device configurations were fabricated, achieving a maximum PCE of 24.9%, in close agreement with model forecasts. Furthermore, multiscale mechanical assessments, including bending, compression, impact resistance, peeling adhesion, and nanoindentation tests, were conducted to evaluate structural reliability. The optimized device demonstrated enhanced interfacial stability and fracture resistance, validating the proposed predictive–experimental framework. This work establishes a comprehensive approach for performance-oriented and reliability-driven PSC design, providing a foundation for scalable and durable photovoltaic technologies. Full article

The SiGe/Si multilayer is a critical component for fabricating stacked Si channel structures for next-generation three-dimensional (3D) logic and 3D dynamic random-access memory (3D-DRAM) devices. Achieving these structures necessitates highly selective SiGe etching. Herein, CF₄/O₂ and CF₄/N₂ gas chemistries were employed to elucidate and enhance the selective etching mechanism. To clarify the contribution of radicals to the etching process, a nonconducting plate (roof) was placed just above the samples in the plasma chamber to block ion bombardment on the sample surface. The CF₄/N₂ gas chemistries demonstrated superior etch selectivity and profile performance compared with the CF₄/O₂ gas chemistries. When etching was performed using CF₄/O₂ chemistry, the SiGe etch rate decreased compared to that obtained with pure CF₄. This reduction is attributed to surface oxidation induced by O₂, which suppressed the etch rate. By minimizing the ion collisions on the samples with the roof, higher selectivity, and a better etch profile were obtained even in the CF₄/N₂ gas chemistries. Under high-N₂-flow conditions, X-ray photoelectron spectroscopy revealed increased surface concentrations of GeF_x species and confirmed the presence of Si–N bond, which inhibited Si etching by fluorine radicals. A higher concentration of GeF_x species enhanced SiGe layer etching, whereas Si–N bonds inhibited etching on the Si layer. The passivation of the Si layer and the promotion of adhesion of etching species such as F on the SiGe layer are crucial for highly selective etching in addition to etching with pure radicals. This study provides valuable insights into the mechanisms governing selective SiGe etching, offering practical guidance for optimizing fabrication processes of next-generation Si channel and complementary field-effect transistor (CFET) devices. Full article

To enhance the RF power properties of CMOS-compatible gold-free GaN devices, this work introduces a kind of GaN-on-Si HEMT with a low parasitic regrown ohmic contact technology. Attributed to the highly doped n⁺ InGaN regrown layer and smooth morphology of gold-free ohmic stacks, the lowest ohmic contact resistance (R_c) was presented as 0.072 Ω·mm. More importantly, low RF loss and low total dislocation density (TDD) of the Si-based GaN epitaxy were achieved by a designed two-step-graded (TSG) transition structure for the use of scaling-down devices in high-frequency applications. Finally, the fabricated GaN HEMTs on the Si substrate presented a maximum drain current (I_drain) of 1206 mA/mm, a peak transconductance (G_m) of 391 mS/mm, and a breakdown voltage (V_BR) of 169 V. The outstanding material and DC performances strongly encourage a maximum output power density (P_out) of 10.2 W/mm at 8 GHz and drain voltage (V_drain) of 50 V in active pulse mode, which, to our best knowledge, updates the highest power level for gold-free GaN devices on Si substrates. The power results reflect the reliable potential of low parasitic regrown ohmic contact technology for future large-scale CMOS-integrated circuits in RF applications. Full article

The stacking arrangement of layered covalent organic frameworks (LCOFs) critically influences their structure and function. We present a fully ab initio-based workflow to characterize stacking disorder in COF-1, combining simulated powder X-ray diffraction (PXRD) with stacking energy landscape analysis. By comparing PXRD patterns of idealized eclipsed, inclined, serrated, and staggered stacking with experiment, we rule out periodic high-symmetry motifs. A comprehensive “PXRD reference guide” links specific diffraction features to slip directions and magnitudes, providing a blueprint for the interpretation of experimental data of slipped structures. Quantum-mechanical potential energy surfaces reveal multiple symmetry-equivalent minima separated by small barriers. This makes diverse slip configurations thermally accessible and large-scale stacking disorder inevitable. Nevertheless, as staggered configurations are found to be energetically disfavored, open pore channels prevail despite the disorder. From the energy landscapes, we construct static disordered models using Boltzmann-weighted probabilities, where also the question is addressed, which energies should be used for actually calculating the Boltzmann weights. Simulated PXRD patterns from these models excellently reproduce experimental peak positions, shapes, and stacking distances, suggesting the dominance of disordered stacking not only in COF-1. Full article