Search Results (837)

LiDAR-based 3D object detection for autonomous driving must balance localization accuracy with real-time inference, while sparse point measurements make small-scale objects such as pedestrians and cyclists particularly challenging to represent at long range. This paper presents PES-PointPillars, an enhanced PointPillars detector with three coordinated design changes. First, pinwheel-shaped convolution (PConv) replaces selected backbone convolutions to expand horizontal and vertical receptive fields for sparse structural patterns. Second, an Improved Inter-Layer Feature Correlation (I-EFC) module uses soft gating and adaptive thresholding to fuse multi-level features through continuous, input-dependent weights. Third, a Smooth L1-NWD (SNWD) loss applies normalized Wasserstein distance to planar position and scale while retaining Smooth L1 regression for vertical position, height, and orientation. Using the parameter settings and configuration of the original PointPillars implementation, the locally executed PES-PointPillars experiment achieves Moderate 3D average precision values of 77.1% for cars, 46.7% for pedestrians, and 62.9% for cyclists at 68.3 FPS on the KITTI validation split. Relative to the source-reported PointPillars reference, the corresponding numerical differences are 2.1, 3.2, and 3.8 percentage points. The reported component-wise and staged ablations show category-dependent gains, with the complete model providing the strongest aggregate performance among the evaluated configurations. Full article

Gravity-matching navigation is one of the important means for the covert navigation of underwater vehicles. The production and application of gravity reference maps as core key technologies have a very significant impact on the accuracy of underwater navigation. Firstly, this paper adopts the factor analysis method to obtain the comprehensive results of nine characteristic parameters such as the standard deviation and roughness of the gravity reference map by setting a range sliding window. Secondly, the TERCOM algorithm is introduced to conduct simulation verification calculations within the sliding window. After comparing and verifying with the comprehensive results of the factor analysis characteristic parameters, the limitations of statistical methods in the evaluation of the adaptability of gravity reference maps are analyzed. Thirdly, intelligent processing methods such as the learning vector quantization neural network algorithm and the extreme learning machine are proposed. The characteristic parameters of some sliding window gravity reference maps and the simulation verification results of the TERCOM algorithm are used as training samples to predict the adaptability evaluation effect of underwater gravity navigation for other sliding windows. The results show that the prediction results are generally in good agreement with the simulation verification results of the TERCOM algorithm. Compared with the learning vector quantization neural network algorithm, the extreme learning machine algorithm exhibits superior performance in terms of classification accuracy and computational efficiency. Full article

Compressor surge poses a critical safety risk for aero-engines. However, conventional physics-driven detection methods—relying on single-domain features and fixed empirical thresholds—struggle to adapt across varying compressor configurations, wide operating ranges, and complex interference environments. This paper proposes a multi-feature fusion adaptive surge detection method that integrates time-domain amplitude, frequency-weighted power and slope features within a joint threshold criteria, enabling reliable and adaptive surge detection according to the statistical characteristics of the signal itself. A wavelet-based preprocessing strategy is established with the db4 wavelet and four-level decomposition identified as the optimal setting through systematic evaluation. A novel feature FWP is introduced herein, which applies frequency-dependent weighting to the power spectral density to suppress noise components while amplifying energy changes within surge-relevant bands, achieving 1.7 to 6.1 times greater magnitude variation near the surge point compared with total spectral power. The slope feature is further discovered to distinguish surge from transient interferences such as rapid valve throttling, fuel stepping and rapid acceleration. Among 100 samples, the three-feature joint detection strategy integrated with adaptive threshold criteria improves accuracy from 61% to 98%. A Bayesian optimization framework using Gaussian process surrogate models is developed for efficient cross-engine hyperparameter tuning, converging to optimal solutions within merely 11 to 13 iterations across two distinct compressors. Lastly, the method is implemented on an NI cRIO-based real-time platform and validated on two distinct ten-stage high-pressure compressors, covering surge tests across a wide speed range of 45% to 98%. Comparative tests against an industry-standard reference device demonstrate earlier warning lead times of 41 to 99 ms. The results confirm that the proposed method herein achieves high accuracy, strong robustness against operational interferences, and good cross-platform adaptability for practical application. Full article

Hyperspectral imagery (HSI) provides rich spectral information for land-cover classification, while Light Detection and Ranging (LiDAR) data provide complementary elevation and structural cues. Existing HSI–LiDAR fusion methods can achieve strong performance, but many rely on complex cross-modal interaction modules with substantial computational cost. This paper proposes the HSI-Primary Residual Adaptation with Consistency Regularization Network (SRAC-Net) for lightweight HSI–LiDAR classification. The method treats HSI as the primary spectral–spatial modality and introduces LiDAR features as an adapted residual correction. A learnable channel-wise residual scaling vector controls the contribution of the LiDAR residual in each feature channel. In addition, the HSI-primary branch is explicitly supervised and provides a stop-gradient reference distribution for consistency regularization of the fused prediction. Experiments on Houston2013, MUUFL, and Trento show that SRAC-Net achieves the highest mean OA, AA, and Kappa values among the evaluated internal baselines and selected representative fusion methods under the adopted protocol. The ablation results show that the complete configuration obtains the best mean performance among the evaluated variants. LiDAR perturbation experiments on Houston2013 further show smaller mean OA reductions than direct residual fusion under the tested Gaussian-noise, random-dropout, and block-occlusion settings. The method also maintains a compact parameter scale and low measured inference latency relative to several heavier multimodal architectures. These results suggest that HSI-primary residual adaptation with consistency regularization is an effective lightweight fusion alternative for the evaluated HSI–LiDAR classification settings. Full article

Industry–University–Research (IUR) networks are vital for knowledge sharing and collaborative innovation, yet existing network research largely ignores the maintenance cost of inter-organizational ties, which creates persistent theoretical tension between social capital theory (advocating dense connections) and structural hole theory (advocating sparse non-redundant ties). This study constructs a simulation model integrating barter knowledge exchange and multi-dimensional relational maintenance cost loss and systematically simulates the evolution of average knowledge stock (AKS) under regular, small-world and random network structure. The simulation results show that there exists a stable optimal node degree range of 20–40 for IUR actors, which is robust against changes in network scale, initial knowledge endowment and relational cost coefficients. Under moderate technological complexity, small-world networks realize the highest efficiency of knowledge accumulation; when technological complexity rises to a high level, regular networks with local agglomeration advantages become more efficient. This study supplements a cost-based analytical perspective to reconcile the contradiction between two core network theories and provides preliminary simulation evidence for the contingent design of IUR collaborative networks. From a practical perspective, the findings offer reference for adaptive governance of IUR alliances to balance relational costs and knowledge gains and further respond to the United Nations Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure). Limitations of this simulation-based analysis are clearly acknowledged in the discussion section. Full article

Climate change is expected to alter the climatic suitability of crops. This study evaluated the climatic suitability of maize cultivation in the TR21 Thrace Region of Türkiye using the EcoCrop model. Climatic suitability was assessed for a reference period (1950–2000) and projected for the 2050s using three CMIP5 global climate models (MPI_ESM_LR, HADGEM2_ES, and CNRM_CM5) under the RCP4.5 and RCP8.5 scenarios. The EcoCrop model implemented in DIVA-GIS was used to evaluate climatic suitability. All climate models projected increases in average annual temperature of 1.7–3.8 °C, whereas projected changes in average annual precipitation ranged from −92 to +46 mm. Despite variations in temperature and rainfall forecasts, the projections indicate that the area suitable for maize cultivation will increase; the HADGEM2_ES model produced the highest proportion of suitable areas. The suitability rate across all projections ranged from 54.7% to 96.8%. Projected climate change is likely to improve maize climatic suitability in TR21 by the 2050s. Because EcoCrop evaluates climatic suitability based solely on temperature and precipitation thresholds, the projected changes should be interpreted as a climatic envelope for maize rather than as a direct increase in future yield or productivity. These findings inform agricultural adaptation strategies and regional land-use planning under climate change. Full article

This study aimed to analyze the relationships between the electrical properties of fresh seed melon pulp and storage conditions, thereby providing a rapid electrical method for quality evaluation. The electrical parameters of fresh seed melon were measured using the parallel-plate electrode method at different storage temperatures of 4 °C, 8 °C, 12 °C, 16 °C, and 20 °C, and storage times of 0, 2, 4, 6, and 8 h. The relationships between electrical parameters and quality attributes at different frequencies were further investigated. An adaptive neuro-fuzzy inference system (ANFIS) model was established to predict the quality characteristics of fresh seed melon, with electrical parameters used as input variables and quality characteristics used as output variables. Eight membership function models were constructed and compared to select the optimal prediction model. The results showed that, with increasing frequency, the impedance (Z), capacitance (Cp), and resistance (Rp) of fresh seed melon decreased, while conductance (G) and reactance (X) increased at different storage temperatures. At different storage times, Z, quality factor (Q), Cp, and Rp decreased, while G and X increased with increasing frequency. The variation ranges of Z, Cp, Rp, G, and X gradually decreased at higher frequencies. Significant correlations between electrical parameters and quality characteristics were observed at the characteristic test frequency of 163.28 kHz. The ANFIS results showed that gauss2mf was the optimal model for predicting cohesiveness (Co, R² = 0.9491), pimf was the optimal model for predicting chewiness (Ch, R² = 0.9595), and gbellmf was the optimal model for predicting resilience (Re, R² = 0.9596). These results indicate that the combination of electrical properties and the ANFIS model has potential for evaluating the quality characteristics of fresh seed melon under the present experimental conditions. This study provides a theoretical basis for quality detection and storage preservation of fresh seed melon and offers a reference for the development of rapid electrical quality-evaluation techniques for seed melon pulp. Full article

Environmental concerns regarding the ecotoxicological effects of antifouling biocides have led to the development of products targeting biofilm-forming bacteria. However, the potential for sublethal biocide exposure to promote bacterial adaptation and increase the risk of resistance development poses a potential threat to marine ecosystems and human health, yet it remains insufficiently understood. Although this study focuses on conventional antifouling biocides, the findings are intended to inform the future development and evaluation of both conventional and environmentally friendly antifouling technologies by highlighting the importance of assessing resistance induction at sublethal concentrations. In this study, the effects of three representative antifouling biocides on marine bacterial growth and bacterial adhesion were investigated. Sea-Nine 211 (DCOIT), copper sulphate (CuSO₄), and tributyltin oxide (TBTO; included as a historical reference compound due to its environmental persistence) were tested at four concentrations (0.01, 0.1, 1.0, and 10 µg/mL) against six marine biofilm-forming bacteria: Vibrio proteolyticus, V. aestuarianus, V. harveyi, V. natriegens, Shewanella putrefaciens and Pseudoalteromonas elyakovii. The results showed that Sea-Nine 211 exhibited a strong antibacterial effect at 10 µg/mL against all tested species except V. harveyi, whereas at the lowest concentration it promoted bacterial adhesion in V. proteolyticus. In contrast, TBTO and CuSO₄ showed limited antibacterial activity and increased microbial adhesion at the three lowest concentrations tested. These findings demonstrate that antifouling biocides can induce distinct responses depending on the concentration, ranging from growth inhibition to enhanced bacterial adhesion. Given that reducing biocide release has been proposed as a strategy to mitigate environmental impacts, our results highlight two potential challenges: (i) reduced antifouling efficacy at sublethal concentrations and (ii) an increased risk of bacterial adaptation associated with enhanced adhesion. To support future monitoring and resistance risk assessment, we propose a conceptual Resistance Risk Index (RRI) framework that could contribute to the sustainable management of antifouling agents while accounting for local environmental conditions. Full article

Underground cables are critical infrastructure for electrical power and communication transmission, and their reliable operation is of paramount importance to urban public safety. Although Distributed Acoustic Sensing (DAS) enables wide-range, continuous, and real-time monitoring, traditional DAS signal processing methods suffer from poor intrusion discrimination and weak anti-interference capability. To address these limitations, we propose a dual-branch network based on multi-domain feature fusion, integrating a Multilayer Perceptron, a Long Short-Term Memory network (LSTM), and an attention mechanism. Vibration signals corresponding to four representative high-risk intrusion events were acquired through controlled field experiments, and a standardized, category-balanced dataset was constructed accordingly. Time-domain, frequency-domain and joint time-frequency features were extracted and mapped through a time-frequency weighting transformation to form one branch of the network, while the parallel branch employed an LSTM to capture long-range temporal dependencies. A multi-head attention mechanism enables deep adaptive fusion of two types of modal information and overcomes the limitations of conventional simple feature concatenation. Comparative experiments against KNN, 1D-CNN and LSTM baselines demonstrate that the proposed model achieves a test accuracy of 98.89%, outperforming all reference methods. Ablation studies further validate the necessity and effectiveness of each constituent module within the proposed architecture. The results indicate that this approach provides reliable support for DAS-based online monitoring of power cables against external damage. Full article

High-speed flow visualization provides imaging data containing quantitative information about cavitating-flow dynamics. Accurate determination of attached-cavity length is essential for characterizing cavitation regimes and validating mathematical models. In this study, an advanced image-processing algorithm for semi-automated analysis of cavitation patterns near hydrofoils is proposed. High-speed visualization data obtained for cavitating flow around a NACA0012 hydrofoil in a slit channel were used as input to the algorithm. The developed approach includes hydrofoil suppression, Otsu-based image binarization with threshold correction, filtering, and automated cavity-boundary detection. The initial search region for the cavity inception point is specified manually, whereas subsequent boundary tracking and cavity-length calculation are performed automatically. A dimensionless threshold correction coefficient was introduced to improve cavity identification, and its optimal range was determined. Additional geometric criteria were proposed to identify the cavity inception and closure locations and to separate attached cavities from detached vapor structures. The analysis showed that the optimal range of the threshold correction coefficient was $0.5$ < $th$ < $0.7$, while a geometric connectivity criterion based on a distance of 7 px between neighboring boundary pixels provided stable detection of the cavity closure location. The developed algorithm enables the determination of both instantaneous and time-averaged attached-cavity lengths, with a total estimated uncertainty not exceeding 3.5%. Comparison with previously published experimental and analytical data demonstrated good agreement and supported the reliability of the proposed approach. The method provides an explainable and training-free computer-vision pipeline that can potentially be adapted to other bluff-body geometries under comparable imaging and contrast conditions. It can also support automated annotation and the generation of reference datasets for the development and validation of future machine-learning methods for cavitation-flow analysis. Full article

Antimicrobial peptides (AMPs) and AMP-like fragments are important components of vertebrate innate immunity, but their genome-wide diversification in amphibious fishes remains unclear. Here, we performed integrated bioinformatics and comparative genomic analyses of three representative mudskippers, Boleophthalmus pectinirostris (Bp), Periophthalmus magnuspinnatus (Pma), and Periophthalmus modestus (Pmo), together with zebrafish and humans as the reference vertebrates. Through genomics comparisons, we identified 708 putative AMP-derived genes in the three mudskipper genomes. Compared with zebrafish and humans, mudskippers contained fewer numbers of AMP-derived genes, indicating lineage-associated differences in repertoire size although their evolutionary basis remains unresolved. Several AMP-derived genes showed a pattern of clustered chromosomal distribution, such as histone-associated clusters on the Chr12 and Chr14 of the Bp genome. Histone H2B-derived sequences were highly conserved among various vertebrates, but mudskippers showed a distinct alanine-to-glycine substitution at position 66. In addition, a Misgurin-like fragment was located within TNNT3a rather than as an independent AMP gene. Compared with pond loach Misgurin, the synthetic Bp Misgurin-like fragment showed no detectable antibacterial activity against six tested bacterial strains under the tested assay conditions and concentration range, while this fragment was absent from the human TNNT3 gene. These findings provide new insights into AMP-derived sequence diversity, chromosomal organization, and potential immune adaptation in amphibious mudskippers, and offer candidate resources for future functional validation in medical and aquaculture applications. Full article

Background/Objectives: The domestic yak (Bos grunniens) is a livestock species of major relevance in high-altitude environments and an important model for studying adaptation and reproductive isolation within Bovini. Despite its close phylogenetic relationship with cattle (Bos taurus), yak × cattle hybrids show a marked sex-biased fertility pattern, with fertile females and generally sterile F1 males, suggesting that subtle chromosomal or genomic differences may underlie post-zygotic reproductive barriers. In this study, we performed a cytogenetic characterization of eight adult yak bulls imported and reared in Central Italy using conventional and molecular cytogenetic approaches. Results: GTG-, RBG-, RBA- and CBA-banding confirmed the yak diploid number as 2n = 60 and the fundamental number as NF = 62, with banding patterns highly comparable to the standardized cattle karyotype. CBA-banding showed an X chromosome lacking evident constitutive heterochromatin and a Y chromosome with distal C-positive blocks. Chromosome instability was low, with 3.75% abnormal metaphases, mainly represented by chromatid and iso-chromatid breaks, while the mean sister chromatid exchange (SCE) rate was 5.19 ± 2.14 per cell. Sequential Ag-NOR/RBA staining localized nucleolar organizer regions (NORs) at the telomeres of autosomes 2, 3, 4, 11 and 25, as in cattle. Zoo-FISH using bovine chromosome paints for X, Y, 5 and 15 showed complete hybridization to the corresponding yak chromosomes, and BAC-FISH mapped the Y-linked ZFY and SRY genes to positions homologous to those reported in cattle. A comparative bioinformatics analysis of available yak genome assemblies confirmed the overall genome-wide correspondence with cattle, while revealing chromosome orientation issues and small local inconsistencies that may be relevant for comparative mapping and probe design. Conclusions: Overall, at the resolution tested, these findings support broad macrostructural conservation of yak and cattle karyotypes and provide cytogenetic reference data for yak populations reared outside of their traditional range. The persistence of F1 male sterility despite this large-scale chromosomal conservation suggests that fine-scale sex chromosome differences, particularly involving pseudoautosomal regions, recombination boundaries, or heterochromatin organization, may deserve targeted investigation. Full article

Deep networks dominate photovoltaic (PV) thermographic fault detection but need large annotated datasets and resist interpretation. We present a lightweight, interpretable infrared workflow combining support-vector-machine (SVM) module/background segmentation from four handcrafted features with an adaptive grid analysis labelling regions as nominal-intensity, high-intensity anomaly or low-intensity anomaly relative to a module-internal reference; the anomaly classes are inspection candidates, not confirmed faults. Evaluation used 21 close-range images of one 20 W module—recorded with the camera’s visible-light edge fusion active, so they are fused infrared/visible frames—and all 596 of a public five-sector UAV dataset. Segmentation against manual masks reached a mean intersection-over-union of 0.64; a feature ablation shows intensity statistics dominate, and an end-to-end Otsu pipeline gives almost the same high-intensity share (4.54% versus 4.50%): the SVM contributes reproducibility—removing the manual segmentation threshold, though not the empirical +48/−60 offsets—not accuracy. High-intensity regions concentrated in the module’s lower half, co-locating with a bus-bar defect known from hardware inspection—suggestive, not validated. The single-module, image-level close-range evaluation is optimistic, and the UAV shares, from a separately trained SVM, illustrate cross-domain application only. Segmentation runs at about 15 images per second on CPU. The method is a relative-intensity thermal screening workflow, not a validated defect-diagnosis or plant-health assessment method, and applies only where acquisition is controlled and the offsets are recalibrated for the target camera and palette. Full article

Brushless DC (BLDC) motors are now the dominant propulsion choice for electric vehicles (EVs) because of their high torque density, efficiency and reliability, but their nonlinear dynamics, electronic commutation, and wide load and speed range make fixed-gain control difficult. A single set of proportional–integral–derivative (PID) gains tuned at one operating point degrades when inertia, back-EMF, or load torque change. This paper presents a hybrid adaptive PID speed controller for a BLDC EV drive that couples an online PID auto-tuner that re-estimates the gains from a frequency response estimate of the plant, with a fast fixed-structure PID that supplies the rapid corrective action that the auto-tuner cannot provide during its estimation interval. The novelty of this work is this explicit two-element decomposition operating on a cascaded speed/voltage loop driven by Hall sensor feedback, which removes the need for an exact analytical feedback model while retaining the transparency of classical PID. A full analytical model of the BLDC machine and the closed-loop transfer functions is derived and implemented in MATLAB/Simulink. Across step references of 1000–1800 rpm and load steps to 10 N·m, and against a conventional fixed-gain PID and a Flower Pollination Algorithm (FPA)-tuned PID, the proposed controller holds overshoot below 1% at low-to-mid speed and a consistently lower torque ripple, while a 12.4% transient undershoot at 1800 rpm under sudden load identifies the present operating limit and a direction for future work. Full article

Conventional spoon-type seed-metering devices have fixed spoon-cavity geometry, which makes it difficult to achieve both stable sowing of small-sized seeds and precise seeding-rate regulation. To address this problem, a coordinated variable-volume adjustment method based on an adjustment ring-seed-picking spoon structure was proposed, and a variable-rate spoon-type millet seed-metering device was designed. Through circumferential rotation of the adjustment ring, the effective volume of the scooping spoon and the length of the transition groove were synchronously adjusted, enabling precise regulation of the number of seeds per hill without replacing components. The zoning characteristics of seed-population flow in the seed-metering chamber and the effects of operating speed on seed-filling mechanical behaviour were investigated through theoretical analysis and discrete element simulation. The seed-filling and seed-clearing processes were clarified, and key parameter ranges were determined. Through response surface methodology (RSM) experiments, the optimal parameter combination was determined as an operating speed of 3.43 km∙h⁻¹, a scooping-spoon inclination angle of 35.25° and a transition-groove inclination angle of 62.47°. Under these conditions, the qualified rate of seeds per hill was 93.11%, the average number of seeds per hill was 5.51 seeds and the coefficient of variation in seeds per hill was 23.71%. When the seeding-rate adjustment ring was adjusted within 0–10°, the average number of seeds per hill increased from 5.7 to 11.2 seeds, while the coefficient of variation decreased from 22.71% to 19.97%. The device can adapt to differences in grain shape among different millet varieties and to seeding-rate adjustment requirements under varying soil fertility conditions across different fields, achieving precise seeding-rate regulation and uniform seed metering. This study provides a reference for improving the variable-rate operating precision of precision hill-drop seeding devices for small-sized seeds. Full article