Search Results (37,160)

Time-Weighted Dynamic Time Warping (TWDTW), adapted from speech recognition, is used in agricultural remote sensing to model crop growth, particularly under limited ground sample conditions. However, most related studies rely on full-season or empirically selected features, overlooking the systematic optimization of features at each observation time to improve TWDTW’s performance. This often introduces a large amount of redundant information that is irrelevant to crop discrimination and increases computational complexity. Therefore, this study focused on maize as the target crop and systematically conducted mapping experiments using Sentinel-1/2 images to evaluate the potential of integrating TWDTW with optimally selected multi-source time series features. The optimal multi-source time series features for distinguishing maize from non-maize were determined using a two-step Jeffries Matusita (JM) distance-based global search strategy (i.e., twelve spectral bands, Normalized Difference Vegetation Index, Enhanced Vegetation Index, and the two microwave backscatter coefficients collected during the maize jointing to tasseling stages). Then, based on the full-season and optimal multi-source time series features, we compared TWDTW with two widely used temporal machine learning models in agricultural remote sensing community. The results showed that TWDTW outperformed traditional supervised temporal machine learning models. In particular, compared with TWDTW driven by the full-season optimal multi-source features, TWDTW using the optimal multi-source time series features improved user accuracy by 0.43% and 2.30%, and producer accuracy by 7.51% and 2.99% for the years 2020 and 2021, respectively. Additionally, it reduced computational costs to only 25% of those driven by the full-season scheme. Finally, maize maps of Yangling District from 2020 to 2023 were produced by optimal multi-source time series features-based TWDTW. Their overall accuracies remained consistently above 90% across the four years, and the average relative error between the maize area extracted from remote sensing images and that reported in the statistical yearbook was only 6.61%. This study provided guidance for improving the performance of TWDTW in large-scale crop mapping tasks, which is particularly important under conditions of limited sample availability. Full article

This study explores advanced methods for analyzing the two-parameter alpha-power exponential (APE) distribution using data from a novel generalized progressive hybrid censoring scheme. The APE model is inherently asymmetric, exhibiting positive skewness across all valid parameter values due to its right-skewed exponential base, with the alpha-power transformation amplifying or dampening this skewness depending on the power parameter. The proposed censoring design offers new insights into modeling lifetime data that exhibit non-monotonic hazard behaviors. It enhances testing efficiency by simultaneously imposing fixed-time constraints and ensuring a minimum number of failures, thereby improving inference quality over traditional censoring methods. We derive maximum likelihood and Bayesian estimates for the APE distribution parameters and key reliability measures, such as the reliability and hazard rate functions. Bayesian analysis is performed using independent gamma priors under a symmetric squared error loss, implemented via the Metropolis–Hastings algorithm. Interval estimation is addressed using two normality-based asymptotic confidence intervals and two credible intervals obtained through a simulated Markov Chain Monte Carlo procedure. Monte Carlo simulations across various censoring scenarios demonstrate the stable and superior precision of the proposed methods. Optimal censoring patterns are identified based on the observed Fisher information and its inverse. Two real-world case studies—breast cancer remission times and global oil reserve data—illustrate the practical utility of the APE model within the proposed censoring framework. These applications underscore the model’s capability to effectively analyze diverse reliability phenomena, bridging theoretical innovation with empirical relevance in lifetime data analysis. Full article

Chatter vibration in machining operations has been identified as one of the major obstacles to improving surface quality and productivity. Therefore, efficiently and accurately predicting stable cutting regions is becoming increasingly important, especially in high-speed milling processes. In this study, on the basis of a predictor–corrector scheme, the following three error correction methods are developed for milling stability analysis: the Correction Hamming–Milne-based method (CHM), the Correction Adams–Milne-based method (CAM) and the Predictor–Corrector Hamming–Adams–Milne-based method (PCHAM). Firstly, we employ the periodic delay differential equations (DDEs), which are usually adopted to describe mathematical models of milling dynamics, and the time period of the coefficient matrix is divided into two unequal subintervals based on an analysis of the vibration modes. Then, the Hamming method and the fourth-order implicit Adams–Moulton method are separately utilized to predict the state term, and the Milne method is adopted to correct the state term. Based on local truncation error, combining the Hamming and Milne methods creates a CHM that can more precisely approximate the state term. Similarly, combining the fourth-order implicit Adams–Moulton method and the Milne method creates a CAM that can more accurately approximate the state term. More importantly, the CHM and the CAM are employed together to acquire the state transition matrix. Thereafter, the effectiveness and applicability of the three error correction methods are verified by comparing them with three existing methods. The results demonstrate that the three error correction methods achieve higher prediction accuracy without sacrificing computational efficiency. Compared with the 2nd SDM, the calculation times of the CHM, CAM and PCHAM are reduced by around 56%, 56% and 58%, respectively. Finally, verification experiments are carried out using a CNC machine (EMV650) to further validate the reliability of the proposed methods, where ten groups of cutting tests illustrate that the stability lobes predicted by the three error correction methods exhibit better agreement with the experimental results. Full article

To address suboptimal throughput performance in conventional intelligent apple harvesting systems predominantly employing single manipulators, a dual-arm harvesting robot prototype was engineered. Leveraging the AUBO-i5 manipulator framework and kinematic characteristics, a coordinated workspace arrangement was established. Subsequently, the dual-manipulator harvesting platform was fabricated. A dynamic task allocation methodology and intelligent fruit sequencing approach were formulated, grounded in U-tube optimization principles. This framework achieved parallel operation ratios between 82.1% and 99%, with combined trajectory lengths spanning 9.24–11.90 m. Building upon established apple harvesting knowledge, a sequencing strategy incorporating dynamic manipulator zoning was developed. Validation was conducted through V-REP kinematic simulations where end-effector poses were continuously tracked, confirming zero limb interference during coordinated motion. Field assessments yielded parallel operation rates of 85.7–93.3%, total harvest durations of 17.8–22.3 s, and inter-manipulator path differentials of 267–541 mm. Throughout testing, collision-free operation was maintained while successfully harvesting all target fruits according to planned sequences. These outcomes validate the efficacy of U-tube-based dynamic zoning and sequencing methodologies for dual-manipulator fruit harvesting in intelligent orchard applications. Full article

The digital economy is a key driver of industrial upgrading and regional growth. Focusing on Gansu Province—an under-represented, less-developed region in northwest China—this study constructs a multidimensional digital economy index (DEI) for 2009–2023 under a unified normalisation and weighting scheme. Two complementary MCDA approaches—entropy-weighted TOPSIS and SESP-SPOTIS—are implemented on the same 0–1 normalised indicators. Robustness is assessed using COMSAM sensitivity analysis and is benchmarked against a PCA reference. The empirical analysis then estimates log-elasticity models linking modern logistics production (MLP) and the DEI to the provincial GDP and sectoral value added, with inferences based on White heteroskedasticity–robust standard errors and bootstrap confidence intervals. Results show a steady rise in the DEI with a temporary dip in 2021 and recovery thereafter. MLP is positively and significantly associated with GDP and value added in the primary, secondary, and tertiary sectors. The DEI is positively and significantly associated with GDP, the primary sector, and the tertiary sector, but its effect is not statistically significant for the secondary sector, indicating a manufacturing digitalisation gap relative to services. Cross-method agreement and narrow sensitivity bands support the stability of these findings. Policy implications include continued investment in digital infrastructure and accessibility, targeted acceleration of manufacturing digitalisation, and the development of a “digital agriculture–smart logistics–green development” pathway to foster high-quality, sustainable regional growth. Full article

In space applications, the required levels of performance and reliability drive up hardware costs. Reducing the efforts related to device development and validation may help balance the budget. A versatile transmitter for space telemetry is implemented here that may help in this respect. Such a device can switch across different linear and continuous phase modulation schemes just by modifying its parameters, while maintaining the same hardware structure. Results from an extensive campaign of experimental test measurements of the device are reported. A GNURadio-implemented receiver is developed to test performance of the actual transmitter by considering all the main blocks of the receiver chain and computing the bit error rate (BER) at the receiver. After testing different configurations, results confirm that the BER of the improved one-filter modulated signal is lower than the BER obtained using only the first Laurent decomposition component. Full article

The growing digitization of healthcare has amplified concerns about the privacy and security of medical images, as conventional encryption methods often fail to provide sufficient protection. To address this gap, we propose a privacy-enhancing image encryption algorithm that integrates SHA-256 hashing, block-wise processing (16 × 16 with zero-padding), DNA encoding with XOR operations, and logistic map-driven key generation into a unified framework. This synergistic design balances efficiency and robustness by embedding data integrity verification, ensuring high sensitivity to initial conditions, and achieving strong diffusion through dynamic DNA rules. Experimental results confirm that the scheme achieves high NPCR (0.997), UACI (0.289), entropy (7.995), and PSNR (27.89 dB), outperforming comparable approaches while maintaining scalability to large image formats and robustness under compression (JPEG quality factors 90 and 70). These findings demonstrate that the proposed method offers an efficient and resilient solution for securing medical images, ensuring confidentiality, integrity, and practical applicability in real-world healthcare environments. Full article

Many trauma-focused psychotherapies for posttraumatic stress disorder (PTSD) focus on the most distressing trauma. However, military personnel are often exposed to multiple traumatic experiences. This study aimed to evaluate and categorize the top three traumatic experiences identified by United States (U.S.) military service members seeking treatment for PTSD and compare frequency of trauma types by demographic/military characteristics. Active duty service members and veterans (N = 110) with PTSD identified and ranked their top three most distressing experiences. Behavioral health professionals classified experiences according to one categorical and four dichotomous classification schemes. The categorical scheme included life threat to self, life threat to others, aftermath of violence, traumatic loss, moral injury by self, and moral injury by others. The Life Threat to Self classification represented the largest portion of categorical experiences (43%). Most experiences were dichotomously classified as military-related (86%), combat-related (70%), non-sexual (91%), and trainability (versus futility; 71%). Women were more likely to report sexual traumatic experiences and less likely to report military- and combat-related experiences. Military occupational specialty, number of deployments, time in military, active duty status, and marital status were also associated with different classification rates. There was noteworthy variability in types of experience across top three traumas, especially among certain subpopulations. Full article

In the context where the surrounding rock of deep coal mine roadways is in a complex mechanical environment of “three highs and one disturbance”, mining disturbances are prone to cause instability and damage to the roadways, and the severe deformation of the south wing main roadway caused by mining disturbances in the 2404 working face of a certain mine in the Jiaoping Mining Area restricts safe production. In order to reduce the deformation and damage of the south wing main roadway affected by long-term dynamic pressure, this study proposes a determination method of key rock strata for top cutting pressure relief and the pressure-relief method along the stress transmission path of the south wing main roadway. It completes the design and field test of the hydraulic fracturing scheme for the hard roof of the 2404 transportation roadway, and evaluates the pressure-relief effect through means such as pressure curves, mine pressure manifestation laws, and borehole observation. The results show that hydraulic fracturing significantly weakens the strength of the roof rock strata, forms through cracks between the pressure-relief holes, reduces the average working resistance of the support by 18% after fracturing, and reduces the average pressure step distance of the roof by 34%. During the mining process, the stress variation range of the coal pillar is small, and there is no obvious deformation or damage to the surrounding rock and support structure of the south wing main roadway. It effectively cuts off the stress transmission path of the hard roof and controls the deformation of the roadway, providing technical support for the control of surrounding rock in deep dynamic pressure roadways. Full article

Globally, both natural and human-induced activities are accelerating biodiversity loss and land degradation, posing a significant threat to food security. Implementing sustainable biodiversity management in the agriculture sector provides a solution by enabling long-term food production, along with preserving environmental health. In this context, biodiversity finance emerges as a valuable tool to strengthen the agricultural sector and achieving Sustainable Development Goals (SDGs). In the Indian context, Madhya Pradesh stands out for its agricultural relevance but faces challenges between productivity and conservation, posing significant challenges and a threat to the state’s long-term sustainability. This study applies to the Biodiversity Finance Initiative (BIOFIN) framework to assess government investments in sustainable agriculture and agricultural biodiversity conservation from 2016 to 2022. Of the INR 21,197.55 crore (~USD 2.46 billion) allocated, approximately INR 4202.03 crore (19.8%) (~USD 0.49 billion) directly supported biodiversity-related agricultural initiatives. While sustainability-focused schemes saw a sharp rise in investment from 26.4% to 87.55%, allocations for conservation awareness declined. No financing gap was identified for achieving targets under the Madhya Pradesh Biodiversity Strategy and Action Plan (2018–2030). The study proposes a BIOFIN aligned investment strategy emphasizing landscape-level management, payment for ecosystem services, and institutional coordination to ensure long-term agricultural sustainability. Full article

A three-dimensional load regression system based on fiber Bragg grating strain sensor is proposed to meet the load monitoring requirements of the main landing gear of an aircraft during take-off and landing. The FBG sensors, featuring a strain resolution of 1 με and a strain sensitivity of 1.18 pm/με, were selected to ensure precise strain acquisition. Through three-dimensional modeling and static simulation of the main landing gear, the strain response trend of the structure under this load state is obtained as a reference for sensor placement. On this basis, the sensor networking scheme is designed, and the ground static load of the main landing gear is calibrated. The strain–load regression matrix model for the measured main landing gear is constructed through the collected strain data, and the reliability of its prediction is verified. The results show that the system can effectively monitor the structural load, and the error between the back-calculated regression load and the applied load is within 4%. Full article

To enhance spatial resource complementarity and cross-entity coordination among multi-regional integrated energy systems (MRIESs), an optimized operation strategy is developed based on a bilevel Stackelberg game framework. In this framework, the integrated energy system operator (IESO) and MRIES act as the leader and followers, respectively. Guided by an integrated demand response (IDR) mechanism and a collaborative green certificate and carbon emission trading (GC–CET) scheme, energy prices and consumption strategies are optimized through iterative game interactions. Inter-regional electricity transaction prices and volumes are modeled as coupling variables. The solution is obtained using a hybrid algorithm combining particle swarm optimization (PSO) with mixed-integer programming (MIP). Simulation results indicate that the proposed strategy effectively enhances energy complementarity and optimizes consumption structures across regions. It also balances the interests of the IESO and MRIES, reducing operating costs by 9.97%, 27.7%, and 4.87% in the respective regions. Moreover, in the case study, renewable energy utilization rates in different regions—including an urban residential zone, a renewable-rich suburban area, and an industrial zone—are improved significantly, with Region 2 increasing from 95.06% and Region 3 from 77.47% to full consumption (100%), contributing to notable reductions in carbon emissions. Full article

Aquatic environments are potential reservoirs for the persistence and spread of pathogenic bacteria. This study investigated the prevalence of Salmonella spp. in stream environments and their relationship with clinical isolates in Republic of Korea. A total of 4582 water samples were collected from 94 streams. We identified these isolates using MALDI–TOF MS and the Kauffmann–White scheme. Polymerase chain reaction and sequencing were performed to identify the resistance genes. Whole genome sequencing analysis and pulsed-field gel electrophoresis (PFGE) were performed to investigate genetic relatedness. In total, 110 Salmonella isolates showing 23 serotypes were isolated from the streams. S. Typhimurium (20.9%) was the most common, followed by S. Livingstone (17.3%), S. Infantis (10.9%), S. Othmarschen (6.4%), S. I. 4,[5],12:i:- (5.5%), and S. Thompson (5.5%). PFGE patterns of eight serotypes were identical or closely related to the stream and clinical strains. The sequence types of S. Mbandaka and S. Livingstone isolates from streams were identical to those of the clinical specimens as ST413 and ST543, respectively. Salmonella strains are highly prevalent in streams and are closely related to the isolates obtained from patients. Therefore, the continuous monitoring of stream environments is required to control the spread of Salmonella. Full article

Hydrological modeling is essential for the sustainable management of watershed systems. Physically based models like MOHID-Land simulate soil water dynamics using Richards’ equation, parameterized through the van Genuchten–Mualem (VGM) model. Although the sensitivity of individual VGM parameters—residual water content (θr), saturated water content (θs), pore size distribution (n), inverse of air entry pressure (α), and saturated hydraulic conductivity (Ksat)—is well documented, their combined effects remain underexplored. This study assessed both isolated and joint impacts of these parameters through a deterministic ±10% perturbation scheme, resulting in 31 unique parameter combinations. Model performance was evaluated using the Nash–Sutcliffe Efficiency (NSE) and Percent Bias (PBIAS). Full-parameter interaction achieved the best results (NSE = 0.50, PBIAS = 25.32), compared to the uncalibrated baseline (NSE = 0.01, PBIAS = 34.06). The pair θs and n emerged as the most influential. Adding secondary parameters to this core pair yielded only marginal performance gains, while removing them from the full set caused similarly marginal declines. These findings reveal a hierarchical sensitivity structure, emphasizing θs and n as key targets for calibration. Prioritizing this pair enables a more efficient soil calibration process, preserving model accuracy while reducing computational cost by limiting parameter space exploration. Full article

To address the challenges of dynamic coupling interference and time-frequency feature degradation in current approaches to Ultra-High-Frequency Radio-Frequency Identification (UHF RFID) behavior recognition, this study proposes a novel behavior recognition method integrating multi-feature analysis with a dual-path residual network. The proposed method mitigates interference by using phase difference methods to eliminate signal errors and cross-correlation, as well as adaptive equalization algorithms to decouple interfering signals. To identify the target tags participating in behavioral interactions, we construct a three-dimensional feature space and apply an improved weighted isolated forest algorithm to detect active tags during interactions. Subsequently, Doppler shift analysis extracts behavioral features, and multiscale wavelet-packet decomposition generates time-frequency representations. The dual-path residual network then fuses global and local features from these time-frequency representations for behavioral classification, thereby identifying interaction behaviors such as ‘taking away’, ‘putting back’, and ‘hesitation’ (characterized by picking up, then putting back). Experimental results demonstrate that the proposed scheme achieves behavioral recognition accuracy of 94% in complex scenarios, significantly enhancing the overall robustness of interaction behavior recognition. Full article