Search Results (170)

Lane detection is a fundamental perception task for autonomous driving and intelligent transportation systems. Although existing methods have achieved promising performance, many of them mainly focus on individual lane instances and insufficiently exploit the structural relationships among lanes, such as relative ordering, geometric continuity, and spatial parallelism. This limitation may lead to broken lanes, ordering errors, and geometric inconsistencies in complex road scenarios. To address these issues, we propose TPDNet, a topology-aware lane detection framework that incorporates structural reasoning into the detection pipeline at three complementary levels. First, a Topology-aware Perception Reasoner (TPR) is introduced at the feature level to capture relational dependencies among lane features and enhance the representation of global road topology. Second, a Topology-Decoupled Head (TDH) is designed at the prediction level to decouple geometric regression from lane classification, thereby reducing task interference and improving prediction stability. Third, a Topology Consistency Loss (TCL) is formulated as a complementary supervision term to encourage smoothness and ordering consistency in predicted lanes. Extensive experiments on three public benchmarks demonstrate the effectiveness of the proposed method. On CULane, TPDNet achieves an F1@50 of 81.46 with ResNet101 and remains competitive with the strongest compared methods, while showing improved robustness in challenging scenarios such as Curve and Dazzle light. On TuSimple, TPDNet obtains an F1 score of 98.01 among the compared methods, while maintaining competitive accuracy. On CurveLanes, TPDNet achieves an mF1 of 58.74, exceeding the strongest baseline by 4.98 points. These results suggest that topology-aware reasoning can improve the generalization capability of lane detection and help produce more structurally coherent lane predictions under diverse road conditions. Full article

This study evaluated the effects of replacing 25% of wheat straw with dried carob (Ceratonia siliqua) leaves and twigs, either untreated or treated with 5% sodium hydroxide (NaOH), on growth performance, nutrient digestibility, carcass traits, meat quality, blood metabolites, and rumen microbial populations in Assaf lambs. Twenty-four male lambs (2.5 months old; 29 ± 0.5 kg) were randomly assigned to three dietary treatments (n = 8): a control diet containing wheat straw as the sole roughage source, supplemented with a concentrate feed, a diet with 25% untreated carob leaves and twigs (UCL), and a diet with 25% NaOH-treated carob leaves and twigs (TCL). Following a 14-day adaptation period, lambs were fed the corresponding experimental diet for 14 weeks. Carob inclusion improved growth performance, with UCL lambs showing the highest average daily gain (214 g/d) compared with TCL (201 g/d) and control (160 g/d), resulting in improved feed conversion ratio (9.02 vs. 5.68 and 5.63, respectively) (p < 0.001). Blood urea nitrogen was reduced (p < 0.001) in UCL lambs (26.8 vs. 38.5 mg/dL in control), suggesting improved nitrogen retention. Digestibility responses differed between treatments (p < 0.001), as TCL increased dry matter digestibility to 72.6% compared with 65.4% (UCL) and 63.6% (control), indicating enhanced nutrient utilization following NaOH treatment. Both UCL and TCL increased (p < 0.001) carcass weights (up to 24.7 vs. 21.0 kg in control), while TCL achieved the highest dressing percentage (46.6% vs. 43.4%). Meat quality traits were generally unaffected in terms of color (lightness, redness, and yellowness) and water-holding capacity; however, shear force decreased from 33.6 N (control) to 30.0 N (TCL), indicating improved tenderness. Carob inclusion modified meat composition by increasing (p < 0.001) lipid content (12.0–12.2 vs. 9.6%) and improving fatty acid profile, with reduced saturated fatty acids (53.4–56.5 vs. 61.4%) and increased α-linolenic acid (2.04 vs. 1.58%), leading to a lower n-6/n-3 ratio (5.54–5.61 vs. 6.45). Rumen fermentation was also affected (p < 0.001), as carob diets increased total bacterial populations and reduced protozoal counts, suggesting shifts toward more efficient microbial activity. In conclusion, replacing 25% of wheat straw with carob leaves improved growth performance and feed efficiency, with untreated carob primarily enhancing nitrogen utilization and treated carob improving fiber digestibility and carcass yield. These findings support the use of carob by-products as a viable alternative feed resource, although responses depend on processing method and targeted production outcomes. Full article

Temperature is a physical quantity that represents the degree of heat or cold of an object and has significant application value across various fields. Traditional contact temperature measurement technologies, such as thermocouples and infrared thermometers, suffer from limitations like poor environmental adaptability and low spatial resolution, which makes it difficult to meet the temperature measurement requirements for micro-/nano-devices and extreme environments. In recent years, non-contact optical temperature measurement technology based on the luminescence characteristics of rare earth ions has garnered widespread attention due to its high sensitivity, strong interference resistance, and good environmental adaptability. In addition to inorganic luminescent materials, lanthanide-based molecular and coordination-complex thermometers have also become an important branch of this field; however, this paper focuses on inorganic rare earth luminescence thermometry. This paper provides a systematic review of the mechanisms of temperature measurement using rare earth ion luminescence, including single-energy-level luminescence intensity measurement and luminescence intensity ratio measurement based on thermally coupled levels (TCLs) and non-thermally coupled levels (NTCLs). It analyzes the principles of various technologies, performance parameters (such as absolute sensitivity S_a, relative sensitivity S_r, and temperature resolution δT), and their application progress in fields such as biomedical imaging, high-temperature aerospace environments, and the integration of micro-/nano-devices. Special attention is paid to emerging research directions, including Stark sublevel engineering for enhanced sensitivity, negative thermal expansion (NTE) host design for anti-thermal quenching, multi-modal collaborative thermometry, and artificial intelligence (AI)-assisted material design and data processing. The article also discusses the challenges currently faced by the technology, such as high-temperature fluorescence quenching and signal interference, and looks forward to future development directions, including artificial intelligence-assisted material design and multi-modal cooperative temperature measurement, aiming to provide a reference for the research and application of rare earth luminescence temperature sensing technology. Full article

Existing deep-learning-based motor imagery (MI) electroencephalogram (EEG) decoding methods face challenges in generalizing across sessions and providing channel-level physiological interpretability. These limitations hinder the practical application of MI-EEG systems. Accordingly, DO-PI-EATCNet (Dream-Optimization-Enhanced, Physics-Inspired, Efficient-Attention Temporal Channel Network) is proposed to improve generalization and interpretability in MI-EEG classification. Unlike models that simply combine multiple components, DO-PI-EATCNet assigns distinct roles to feature representation, temporal channel modeling, temporal regularization, and channel compactness. Latent-Projected Attention (LPA) enhances spatiotemporal discriminability by aligning attention in a low-dimensional latent space, and Temporal Channel Cascaded Collaborative Attention (TCCA) refines dependencies between time and channels. Fractional-Order Difference Temporal Consistency Loss (FD-TCL) is introduced as a neurodynamics-inspired temporal regularizer to reduce high-frequency fluctuations in prediction sequences and improve within-subject cross-session prediction stability. The Multi-Population Dream Optimization Algorithm (MPDOA) is used for channel selection to obtain a compact EEG channel subset and reduce computational load, although it introduces a slight accuracy decrease compared with the uncompressed full model. Under a within-subject cross-session protocol on the BCI Competition IV-2a four-class MI dataset, the final compact model achieves an average accuracy of 84.4% and Cohen’s κ of 0.790, outperforming the reimplemented baselines. Compared with the uncompressed LPA-TCCA-FD-TCL variant, MPDOA slightly decreases accuracy from 84.9% to 84.4%, but reduces EEG channels from 22 to about 15 and decreases MACs by 27%. Scalp topographies and selected-channel visualizations provide qualitative support for channel-level anatomical plausibility, as the selected electrodes are mainly located over expected sensorimotor-related regions, while t-SNE offers a descriptive visualization of the learned feature distributions. Full article

Integrating sensors with wireless communication capabilities into smart wireless sensing devices allows us to form a wireless sensing network. This network works in conjunction with monitors to display and control parameters at different locations or in the environment. By deploying a wireless sensing network, the system can interact with the user by sending notifications when necessary, based on the environmental conditions and user activities detected by the wireless sensors, and make corresponding adjustments to or control the environment. The advancement and widespread adoption of the internet have enabled the development of this technology. Wireless sensors are widely used in product positioning and environmental monitoring management, making the management of complex products more accurate. The Monitor and Control System (MCS), which combines network cameras and wireless sensors with neural network technology and fuzzy control systems, improves the existing positioning method and enhances positioning accuracy. Product management, which comprises comprehensive digital services and is facing serious staff shortages, has turned to digital payment to reduce labor costs. This experiment was simulated using Network Simulator 2 (NS2). In the sensing system part, the application of a ZigBee network and its status were explored, and interference was analyzed. Information on network interference simulations and their impact on normal services was compiled for network management purposes. Using NS2 network simulation, this study utilizes ZigBee with different neuron nodes and different training times to find the best network model, compares various queuing mechanisms and functions as a network interference intrusion detection system, and explores its node defense capabilities in cases of interference. Node Density: Node density is typically determined by the number of nodes in the simulation area and the size of the scene. Low Density: Sparse node distribution, prone to network partitioning, is suitable for testing latency-tolerant networks (DTNs) or route discovery capabilities. High Density: It entails dense node distribution, severe signal interference, and packet collisions. It is suitable for testing MAC layer collision prevention mechanisms (such as CSMA/CA) and the scalability of outing protocols. Configuration Method: the “set Dest” tool is used in a Tcl script to generate a mobile scene file, defining the number of nodes, range (X, Y), and time to be more significant in product management. Full article

Background: In 2021, the UK National Screening Committee commissioned an evaluation of newborn bloodspot screening for severe combined immunodeficiency (SCID) in England. This paper describes the experiences of parents who received an SCID or non-SCID T-cell lymphopenia (non-SCID TCL) result for their baby during the evaluation. Methods: A qualitative exploratory design was employed using semi-structured interviews with 12 parents (n = 5 who had received an SCID outcome and n = 7 who had received a non-SCID TCL following SCID NBS). Results: The impact on parents whose baby was diagnosed with SCID was complex, reflecting the experience of receiving a presymptomatic diagnosis. Parents of babies who had been diagnosed with a non-SCID TCL viewed their baby’s result in terms of risk; while their baby might still have a serious immunological condition, it was not considered to be as serious as SCID. All parents reported that they valued their participation in the SCID screening evaluation. Conclusions: Support for families following a positive screening result for SCID needs to be considered. This includes tailored psychosocial support, given their experiences will not be the same as those of parents of non-screened babies with SCID. Full article

An automated framework for the parametric FE model generation and sizing of composite aircraft wings suitable for early-stage studies is presented. Implemented in Python and HyperMesh TCL, the tool controls both outer-geometry parameters, such as span, taper ratio, and twist, and internal-structural layout parameters, such as spar locations, rib spacing, and stringer layouts, and generates analysis-ready 2D composite GFEM models with material assignment and layups for size optimization. To demonstrate the workflow, a Design of Experiments (DoE) is performed on a representative transport wing internal structural layout, while keeping the outer geometry fixed. For each DoE point, OptiStruct performs gradient-based composite-size optimization to minimize structural mass, subject to composite strength (max strain), buckling, and metallic no-yielding constraints. A staged multi-run strategy is implemented to mitigate the effects of local minima. DoE results show a strong correlation and a non-monotonic effect of stringer number, an increase in mass as the front spar moves aft, and a comparatively weaker effect of the number of aluminum ribs. As a preliminary baseline, a Random Forest surrogate trained on the DoE predicts the wing structural mass with reasonable accuracy (RMSE $=0.081$), motivating the future implementation of Gaussian process models with uncertainty modeling. The framework accelerates early-stage structural design exploration and is amenable to surrogate-based optimization. Full article

Precise manipulation of two-phase flow in micro-confined electrowetting pixels is limited by contact angle hysteresis (CAH). To elucidate this non-equilibrium process, we establish a high-fidelity electrohydrodynamic (EHD) phase-field simulation framework. The model rigorously couples Navier–Stokes equations with molecular kinetic theory (MKT) to characterize energy dissipation at the three-phase contact line (TCL) and further integrates charge transport kinetics. Numerical results reveal CAH is driven by physical pinning and interfacial charge trapping, with the latter dominating interfacial retreat and causing significant residual displacement. Furthermore, analysis shows alternating current (AC) waveforms mitigate charge accumulation and promote depinning via micro-oscillations, minimizing the hysteresis loop compared to direct current (DC) waveforms. Additionally, an overdrive strategy utilizing a suprathreshold Maxwell stress pulse rapidly overcomes static friction. This strategy significantly improves transient dynamics, substantially reducing the time to reach 90% of the steady-state target from 19.6 ms (under standard DC waveform driving) to 7.4 ms. This work provides a comprehensive theoretical basis and design criteria for optimizing active driving strategies in optofluidic and digital microfluidic systems. Full article

The deep integration of the Internet of Things (IoT) and blockchain technology enables emerging applications in multi-party collaboration and trusted data sharing. However, the scalability constraints of blockchain networks remain a major bottleneck when handling high-frequency interactions in IoT–blockchain systems. Sharding addresses this challenge by partitioning the blockchain network into parallel sub-networks. Nevertheless, it introduces significant coordination overhead for cross-shard transactions. Among mitigation strategies, Broker-based mechanisms (e.g., BrokerChain) have attracted increasing attention for their efficiency in handling cross-shard communication by reducing verification overhead and communication latency. Despite these advantages, existing research typically treats the Broker group as a fixed configuration, neglecting the impact of Broker selection on system performance. To bridge this gap, this paper proposes the Accumulative Activity–Temporal Liveness Broker Selection Strategy (AT-BSS) to optimize cross-shard transaction processing in sharded IoT–blockchains. Specifically, we formally characterize the Accumulative Activity and Temporal Liveness of accounts in the account–transaction network and use these two metrics to identify accounts that maximize transaction-aggregation efficiency. We implement AT-BSS on the BlockEmulator platform and evaluate it against two baselines, namely, ABChain and BrokerChain. Under different settings of the number of Brokers (BrokerNum), number of shards (ShardNum), transaction arrival rate (InjectSpeed), and maximum block size (MaxBlockSize), AT-BSS consistently outperforms both baselines in terms of Transactions Per Second (TPS), Transaction Confirmation Latency (TCL), and Cross-shard Transaction Ratio (CTX). Compared with ABChain, AT-BSS achieves up to 15.5% higher TPS and reduces TCL and CTX by up to 80.2% and 28.7%, respectively. AT-BSS yields more pronounced results over BrokerChain, with TPS improvements of up to 229% and reductions of up to 97.7% in TCL and 80.5% in CTX. Full article

The deltoid ligament (DL) is the primary stabilizer of the medial ankle; however, a limited understanding of the functional roles of its various bundles hinders rational surgical decision-making. This study aims to investigate the roles of individual DL bundles in maintaining ankle stability and articular contact pressure and thus seeks to guide decisions on whether reconstruction is required for specific injuries. A validated finite element foot model was used to simulate isolated and multiple deficiencies in the DL bundle. The articular displacements, rotations, and peak talar cartilage contact pressure were evaluated under anterior drawer force and under internal–external rotation, eversion, and plantarflexion–dorsiflexion moments. Compared with the intact model, anterior tibiotalar ligament (ATTL) deficiency resulted in the greatest anterior drawer displacement (increase: 29%). Talonavicular ligament (TNL) deficiency caused the largest internal–external rotation and plantarflexion (increases in external rotation: 69%; in internal rotation: 10%; in plantarflexion: 32%). Tibiocalcaneal ligament (TCL) deficiency caused the largest eversion (increase: 93%). Deep posterior tibiotalar ligament (dPTTL) deficiency caused the largest dorsiflexion (increase: 68%). The maximum talar cartilage contact pressure occurred in the TNL-deficient model under the plantarflexion condition. In conclusion, individual DL bundles exhibit specific functions in terms of controlling multidirectional ankle stability—the ATTL, TNL, TCL, and dPTTL are the primary stabilizers for anterior translation, rotation/plantarflexion, eversion, and dorsiflexion, respectively. These findings provide a biomechanical rationale for personalized surgical strategies. When comprehensive DL reconstruction is not feasible, clinicians can prioritize the reconstruction of specific bundles according to the patient’s instability severity and functional demands across degrees of freedom. Full article

Graphene (GRA) and graphene oxide (GO) have drawn significant attention in materials science, chemistry, and nanotechnology because of their tunable physicochemical properties and wide range of potential uses in biomedical and environmental applications. Building reliable, large-scale molecular models of GRA and GO is essential for molecular simulations of wetting, adsorption, and catalytic behavior. However, current methods often struggle to generate large, chemically consistent sheets at high oxidation levels. In addition, the resulting structures are frequently incompatible across different simulation packages. This work introduces a step-by-step protocol with custom Tool Command Language (Tcl) and modified Python version 3.12 scripts for building large-scale, AMBER-compatible GO structures with oxidation levels from 0% to 68%. The workflow applies a systematic surface modification strategy combined with post-processing and atom-type assignment routines to ensure chemical accuracy and force field consistency. The dataset includes fifteen MOL2 format files of 20 × 20 nm² GO sheets, ranging from pristine to highly oxidized surfaces, each validated through oxidation-ratio analysis and structural integrity checks. Together, the dataset and protocol provide a design of scalable and chemically reliable GO molecular models for molecular dynamics simulations. Full article

In order to meet the demand for new optical temperature-sensing materials with high sensitivity and a wide application temperature range, Yb³⁺/Er³⁺: CaYAlO₄ phosphor with excellent physical and chemical stability and thermal conductivity was studied for the first time. Yb³⁺/Er³⁺: CaYAlO₄ phosphors have been synthesized by the high-temperature solid-state method. Under 980 nm excitation, three characteristic emission bands peaking at 528, 549 and 665 nm were observed which are attributed to the transitions ²H_11/2, ⁴S_3/2 and ⁴F_9/2 to ⁴I_15/2, respectively. The temperature-sensing behaviors of the phosphor were investigated using the luminescence intensity ratio technique based on both the TCL (²H_11/2/⁴S_3/2) and NTCL (⁴F_9/2/⁴S_3/2, ²H_11/2/⁴F_9/2) model over a wide temperature range of 163–700 K. The maximum relative sensitivities of TCLs (²H_11/2/⁴S_3/2), NTCLs (⁴F_9/2/⁴S_3/2) and NTCLs (²H_11/2/⁴F_9/2) were 3.69% K⁻¹, 0.443% K⁻¹ and 3.86% K⁻¹ at 163 K, 275 K and 163 K, while the maximum absolute sensitivities were 4.04 × 10⁻³ K⁻¹, 15.2 × 10⁻³ K⁻¹ and 7.81 × 10⁻⁴ K⁻¹ at 499 K, 499 K and 247 K, respectively. Results suggest that Yb³⁺/Er³⁺: CaYAlO₄ phosphor is a promising temperature-measuring material with advanced optical sensing capabilities over a wide temperature range. Full article

Suction anchors are widely used in marine engineering because of their easy installation, cost-effectiveness, and excellent load-bearing capacity. However, existing research on their bearing capacity has primarily focused on homogeneous soils, which fails to adequately reflect the actual bearing capacity of layered seabed soils. Therefore, this study conducted a series of numerical simulations to investigate the pullout bearing capacity of suction anchors subjected to inclined loads in upper-stiff–lower-soft layered clay. By considering the clay strength (S_um/kD) and soil layer thickness ratio (T_h/L, T_c/L), this study systematically explores the influence of the optimal centerline loading depth (Z_cl,opt), uniaxial ultimate bearing capacity (H_ult and V_ult), and the VH failure envelope of suction anchors. The results indicate that the layer thickness ratio T_h/L of lightly overconsolidated clay (LOC) is the key factor influencing the Z_cl,opt and ultimate bearing capacity H_ult and V_ult. An increase in T_h/L significantly enhances the pullout resistance of suction anchors, which primarily results from the combined enhancement effect of lateral friction resistance and end resistance at the anchor–soil interface. The layered clay has a distinct influence on the horizontal and vertical bearing capacities of suction anchors. Based on the results of parameter analysis, a conservative analytical expression for the lower bound of the VH failure envelope curve is further proposed. The research conclusions provide a theoretical basis and engineering practice guidance for the optimized design and safety assessment of suction anchors in layered soil. Full article

Background/Objectives: The Vesical Imaging-Reporting and Data System (VI-RADS) provides high diagnostic accuracy for muscle-invasive bladder cancer; however, its prognostic value remains limited. We propose serum cytokeratin 19 fragment (CYFRA 21-1) and tumor contact length (TCL) as complementary prognostic factors. We aimed to construct a composite risk score integrating VI-RADS, CYFRA 21-1, and TCL for prognostic stratification. Methods: We retrospectively analyzed data from 101 patients with bladder cancer (BC) who underwent transurethral resection of bladder tumor (TURBT), magnetic resonance imaging, and postoperative serum CYFRA 21-1 measurement. For each factor, cut-off values were determined using receiver operating characteristic (ROC) analysis; meeting each threshold contributed one point (score range, 0–3). Overall survival (OS) was assessed using Kaplan–Meier and Cox regression analyses. Results: ROC analysis identified cut-offs of VI-RADS ≥ 3 (area under the curve [AUC] 0.779), TCL ≥ 40 mm (AUC 0.817), and CYFRA 21-1 ≥ 2.1 ng/mL (AUC 0.875). Based on these, patients were stratified into low- (0–1, n = 81), intermediate- (2, n = 12), and high-risk (3, n = 8) groups with 3-year OS rates of 95.1%, 75.0%, and 25.0%, respectively (p < 0.001). In univariate Cox regression, all factors significantly predicted poor OS: VI-RADS ≥ 3 (hazard ratio [HR], 6.51; p = 0.015), TCL ≥ 40 mm (HR, 8.36; p < 0.001), and CYFRA 21-1 ≥ 2.1 ng/mL (HR, 14.02; p < 0.001). In multivariate analysis, only CYFRA 21-1 remained independently significant (HR, 11.80; p < 0.001). Conclusions: A composite risk score combining VI-RADS, TCL, and CYFRA 21-1 effectively stratified patients with BC into distinct groups using minimally invasive, peri-TURBT assessments. Prospective multicenter validation is warranted. Full article

With the in-depth construction of the new power system, the importance of demand-side resources is becoming more and more prominent. The virtual power plant (VPP) has become a powerful means to explore the potential value of distributed resources. However, the differentiated resources between different VPPs are not reasonably deployed, and the problem of realizing the sharing of resources and the distribution of revenues among multi-VPP needs to be urgently solved. A cooperative operation optimization strategy for multi-VPP to participate in the energy and reserve capacity markets is proposed, and the potential risks associated with uncertainty in distributed generators (DGs) output are quantitatively assessed using conditional value-at-risk (CVaR). Firstly, due to the good adjustable performance of electric vehicles (EVs) and thermostatically controlled loads (TCLs), their virtual energy storage (VES) models are established to participate in VPP scheduling. Secondly, based on the asymmetric Nash negotiation theory, a P2P trading method between VPPs in a multi-marketed environment is proposed, which is decomposed into a virtual power plant alliance (VPPA) benefit maximization subproblem and a cooperative revenue distribution subproblem. The alternating direction multiplier method is chosen to solve the model, which protects the privacy of each subject. Simulation results show that the proposed multi-VPP cooperative operation optimization strategy can effectively quantify the uncertainty risk, maximize the alliance benefit, and reasonably allocate the cooperative benefit based on the contribution size of each VPP. Full article