Search Results (9,746)

An efficient and stereoselective method for the synthesis of chiral biheterocyclic N-Boc-protected pyrrolidine derivatives bearing indole, pyrazole, and indazole moieties is presented. In this approach, nucleophilic substitution of heterocyclic carboxylates with enantiomerically pure N-Boc-3-methanesulfonyloxypyrrolidines is used to afford N-(pyrrolidin-3-yl) derivatives in high yields with an inverted configuration. The methodology accommodates a range of substrates, enabling structural diversity. Reactions with pyrazole- and indazolecarboxylates generate regioisomeric products. Representative peptide-coupling reactions further demonstrated the synthetic utility of the synthesized chiral biheterocyclic pyrrolidine derivatives containing protected amino and carboxyl functionalities as building blocks for peptide synthesis. Halogenated indole and pyrazole derivatives underwent further functionalization via palladium-catalyzed cross-coupling to introduce aryl, heteroaryl, and alkynyl substituents. All of the N-Boc-substituted biheterocycle–pyrrolidine carboxylates displayed NMR spectra with two sets of signals, notable signal broadening, or both. These effects are due to the dynamic equilibrium between two conformers in a deuterated solvent and were studied in depth. The structures and stereochemistry of the synthesized compounds were confirmed by means of chiral HPLC, single-crystal X-ray diffraction, and advanced NMR analyses. This synthetic strategy provides access to chiral heterocyclic amino acid-like building blocks for peptide synthesis and medicinal chemistry. Full article

Terrestrial water storage (TWS) is a key parameter for understanding regional water cycles and climate change. To address the low spatial resolution and temporal gaps of Gravity Recovery and Climate Experiment (GRACE) and its successor satellites (GRACE Follow-On) data, as well as the uneven spatial distribution of Global Navigation Satellite System (GNSS) stations, this study integrates GNSS vertical deformation with GRACE/GFO Mascon data to jointly invert and conduct an in-depth analysis of TWS changes and hydrological drought characteristics in three central Chinese provinces (Hubei, Hunan, and Jiangxi) from January 2011 to June 2023. For missing parts of GRACE and GNSS data, different methods were effectively employed to fill the gaps. The optimal weighting factors were then determined using the Akaike Bayesian Information Criterion (ABIC), leading to the inversion of TWS variations. Combined with hydrometeorological data (precipitation, evapotranspiration, and runoff), drought monitoring was further conducted. The results indicate that joint inversion effectively integrates the high-frequency spatial signals of GNSS with the large-scale smoothing features of GRACE. The spatial distribution of the annual TWS amplitude obtained from different methods (GRACE, GNSS-Green, GNSS-Slepian, and Joint) showed high consistency, generally exhibiting a pattern of lower values in the northwest and higher values in the southeast. Using the TWS derived from joint inversion, a drought index (Joint-DSI) was constructed, successfully identifying and tracking seven major drought events in the study area. Among these, the drought from April 2017 to November 2018 lasted the longest (20 months), while the event from August 2022 to June 2023 was the most severe, with a peak deficit of 142.303 km³. This study demonstrates that the joint inversion method can effectively overcome the spatiotemporal limitations of single observation techniques, providing a high-precision, high-resolution, and reliable geodetic approach for regional water resource management and extreme drought monitoring. Full article

Keyword spotting (KWS) is the always-on front end of voice interfaces; running it directly on microcontrollers, rather than streaming audio to the cloud, is essential for low-latency, privacy-preserving, and energy-efficient operation, yet it must reconcile high accuracy with severe memory, compute, and energy limits. Existing small-footprint KWS solutions are developed and benchmarked almost exclusively on English and are seldom validated on physical hardware or under quantization for other languages; their transferability to typologically different, consonant-rich languages such as Polish therefore remains largely unverified. We present a KWS pipeline for Polish, deployed and benchmarked on the RP2350 microcontroller (Raspberry Pi Pico 2, Raspberry Pi Ltd., Cambridge, Great Britain).We propose three compact architectures from the convolutional (CNN), convolutional recurrent (CRNN), and depthwise separable neural network (DS-CNN) families and benchmark them against the state-of-the-art BC-ResNet on a 25-keyword Polish vocabulary using MFCC features. All models are evaluated in full precision (Float32) and after eight-bit integer (INT8) post-training quantization, with inference latency measured directly on the target hardware. BC-ResNet attains the highest full-precision accuracy (97.81%) but is the most fragile under quantization, whereas the proposed CRNN is the most accurate quantized model (94.28%), the DS-CNN the smallest (46.15 KB), and the CNN the fastest (107.5 ms); all quantized models meet a one-second real-time budget. We further show that the accuracy ranking inverts after quantization, that memory savings are highly architecture-dependent, and that phonetically similar Polish words are the dominant source of error. These results offer practical guidance for deploying small-footprint KWS in Polish and other underrepresented languages. Full article

China’s forest ecological security (FES) system is currently undergoing a transformative phase characterized by the synergy between quantitative expansion and qualitative improvement. Against the backdrop of advancing ecological civilization, China has particular difficulties juggling economic expansion with ecological preservation. Improving forestry green total factor productivity (FGTFP) is regarded as a crucial pathway to achieving the dual objectives of “sustaining forestry growth without deforestation and ensuring ecological security without compromising output.” This study employs panel data from 30 Chinese provinces spanning 2004 to 2018. The entropy weight method and super-efficiency SBM-GML index model are utilized to quantitatively measure FES and FGTFP, respectively. This empirically examines the underlying mechanisms and threshold effects governing their relationship. The results indicate that FGTFP significantly improves FES, with urbanization exerting a positive moderating effect. Heterogeneity analysis reveals that FES dividends derived from FGTFP are more pronounced in southern collective forest regions and economically developed areas. Further threshold testing uncovers distinct patterns: once economic agglomeration surpasses specific thresholds, FGTFP triggers a stepwise surge in its positive effect on FES. While crossing its respective threshold, the tertiary industry share exhibits an inverted U-shaped trajectory. This research elucidates the stage-specific patterns of China’s green forestry transition, offering differentiated policy insights for fostering high-quality forestry development and strengthening the national ecological security barrier. Full article

Scanning magnetometry based on diamond nitrogen-vacancy (NV) centers enables quantitative magnetic imaging with nanoscale spatial resolution. However, large-area scanning is often limited by geometric distortion arising from the nonlinear hysteresis of piezoelectric positioners, leading to non-uniform pixel spacing and reduced image fidelity. Here, we present a software-based polynomial compensation method that corrects piezoelectric hysteresis without requiring additional hardware or closed-loop position control. By calibrating and analytically inverting the forward and backward hysteresis responses, compensated driving voltages are generated and directly applied during scanning. The method is experimentally validated using a current-carrying patterned gold device and magnetic domains in a hard-disk sample, demonstrating significantly improved geometric accuracy and the removal of scanning artifacts in both topographic and magnetic images. This simple and cost-effective approach provides an effective solution for improving large-area NV scanning magnetometry and other scanning probe imaging techniques based on open-loop piezoelectric positioners. Full article

When flexible job shop scheduling faces concurrent disturbances such as machine failures and rush orders, worker-centric constraints emphasized under Industry 5.0 must also be satisfied. Existing deep reinforcement learning methods for the Dynamic Flexible Job Shop Scheduling Problem (DFJSP) seldom treat worker workload balance as an explicit constraint, and most depend on static penalty coefficients that are difficult to tune across different scenarios. In this paper, we suggest ALP-PPO, an adaptive Lagrangian penalty-enhanced proximal policy optimization algorithm, for real-time rescheduling under concurrent machine breakdowns and rush orders. We formulate the scheduling environment as a constrained Markov decision process. Worker skill heterogeneity, fatigue accumulation and workload equity are modeled as coupled constraints alongside classical scheduling objectives. By decoupling operation sequencing, machine allocation and worker assignment into coordinated sub-decisions, a hierarchical action space is constructed. Dual Lagrangian multipliers for workload balance and fatigue are updated adaptively during training, so that manual penalty tuning is no longer required. An event-triggered mechanism selects between right-shift and full rescheduling on the basis of a disruption severity index. We employ weighted-sum scalarization of makespan, energy consumption and workload variance during training, and Pareto solution sets are obtained by systematically varying the weight vectors across independent training runs. On extended Brandimarte benchmarks augmented with worker and dynamic event parameters, ALP-PPO delivers superior scheduling performance across makespan, energy consumption and workload variance when compared with Double DQN, Dueling DQN, standard PPO, NSGA-II and MOEA/D, as measured by Hypervolume (HV) and Inverted Generational Distance (IGD) indicators. Ablation studies indicate that the adaptive Lagrangian mechanism reduces constraint violations by more than 40% relative to fixed-penalty alternatives while keeping the primary objectives competitive. An analysis of computational efficiency shows that ALP-PPO completes online inference in under 20 ms per decision step, making real-time rescheduling practically feasible. Generalization experiments on previously unseen instances further validate the transferability of the learned policy. These findings support human-centric intelligent scheduling in Industry 5.0 manufacturing. Full article

Domain shift between microscopy imaging domains poses a significant challenge for deploying deep learning-based cell detection models across different experimental setups. Manual annotation of new microscopy datasets remains resource-intensive and time-consuming. This study presents a detection-oriented Cycle-Consistent Generative Adversarial Network (CycleGAN)-based annotation transfer framework for adapting a labeled B16BL6 source domain to a HeLa target domain. Annotated B16BL6 melanoma microscopy images are translated into the visual appearance of HeLa microscopy data while retaining their original bounding-box annotations, enabling YOLOv8x detector training without full manual annotation of the target domain. Three YOLOv8x configurations were compared: a source-only bright B16BL6 baseline, an intensity-inverted dark B16BL6 baseline, and the proposed CycleGAN-translated B16BL6 → HeLa configuration. Performance was evaluated using standard object detection metrics on a manually annotated 100-frame HeLa target-domain subset, together with complementary unsupervised proxy metrics on the full unlabeled HeLa dataset. The CycleGAN-trained detector achieved the highest supervised target-domain performance, with a precision of 0.244, recall of 0.353, F1-score of 0.288, and mAP@0.50 of 0.186, compared with mAP@0.50 values of 0.027 and 0.009 for the bright and dark baselines, respectively. It also achieved the highest exploratory composite reliability score on the full HeLa sequence. These findings demonstrate that source-to-target image translation improves detector generalization under the investigated B16BL6 → HeLa phase-contrast microscopy domain shift, thereby reducing the need for extensive manual target-domain annotation. Full article

Reliable photovoltaic (PV) operation requires monitoring strategies that can detect performance degradation before it develops into persistent efficiency loss. This study proposes an interpretable data-driven framework for condition monitoring and early warning of low-efficiency events using only inverter-based electrical measurements. The novelty of this study lies in its focus on detecting low-efficiency operating conditions from inverter electrical data, rather than merely classifying individual PV fault types. Thirty-minute operational data from a 110 kW grid-connected PV plant in Kastamonu, Türkiye, covering January 2023–December 2025, were analyzed. Phase currents, phase voltages, total active power, and DC power were transformed into electrical health indicators, including mean current, mean voltage, current and voltage variability, phase imbalance index, and conversion efficiency. Correlation and imbalance analyses showed highly synchronized three-phase operation, with a mean phase imbalance index of 0.004865. Conversion efficiency remained stable, with an instantaneous mean of 0.964. Generalized Additive Model results explained 66.4% of efficiency variability and identified mean current as the dominant nonlinear determinant, while phase imbalance acted as a secondary but significant factor. A Random Forest classifier achieved 96.34% accuracy, 3.87% out-of-bag error, and 53.4% recall for rare low-efficiency events. Decision-tree rules indicated high risk when mean current fell below 9.4 A and very low risk above 12 A. The framework provides a practical, sensor-minimal, and interpretable approach for PV performance monitoring and proactive maintenance. Full article

Reliable perception of young apple fruits in natural orchards is a prerequisite for automated thinning and intelligent orchard management, yet remains difficult in real field conditions due to small fruit size, dense distribution, branch–leaf occlusion, background similarity, and severe illumination degradation at night. This study presents FruitDet, a lightweight multi-module detector designed for robust day–night young apple fruit detection in complex orchard environments. A field dataset was established in a high-density apple orchard in Aksu, Xinjiang, covering daylight and low-light night-time scenes with diverse occlusion, scale, and illumination variations. To improve detection robustness without sacrificing computational efficiency, FruitDet combines three complementary mechanisms: an inverted-bottleneck-based multi-scale feature enhancement module for preserving small-fruit details, a channel–spatial attention module for suppressing foliage and illumination interference, and a lightweight Transformer-based context module for modeling long-range dependencies between fruits and surrounding orchard structures. In daytime scenes, FruitDet achieved 91.904% precision, 77.557% recall, 83.254% mAP50, and 66.427% mAP50–95; in night-time scenes, it maintained 90.107% precision, 75.135% recall, 80.544% mAP50, and 64.719% mAP50–95. Compared with mainstream detectors including YOLOv5n, YOLOv8n, YOLO11n, YOLO26n, Faster R-CNN, RT-DETR, and RT-DETRv2, FruitDet consistently delivered higher accuracy across lighting conditions. Ablation, visualization, public-dataset testing, and edge-deployment experiments verified that the proposed modules jointly improve small-object representation, background discrimination, low-light robustness, and real-time applicability. With 2.960 M parameters, 3.726 G FLOPs, and approximately 180 FPS, FruitDet offers a practical and efficient visual perception approach for Young fruit monitoring was conducted under both daytime and night-time orchard conditions covered in this study. All-weather orchard monitoring and robotic young-fruit thinning. The shareable data are available Full article

Increasing high-impact, low-probability (HILP) disruptions requires a paradigm shift in emergency power for critical infrastructure (CI), moving away from traditional cost-driven assessments toward physical resilience. Existing multi-criteria decision frameworks for microgrid technology selection typically treat survivability as one criterion among several, with economic performance retained as an equal or dominant factor. This study addresses this gap by inverting that hierarchy: it develops a resilience-oriented, two-stage screening framework that prequalifies energy technologies (including CHP and CCHP) for CI facing prolonged outages based exclusively on survivability criteria, deferring economic optimization to later design stages. The methodology prioritizes islanding readiness, black-start capability, fuel autonomy, multi-vectorr energy coverage, implementation feasibility, and operational safety. A hospital serves as the reference CI due to its rigorous demand for simultaneous electricity, heat, cooling, and process loads. The framework applies a Stage I Go/No-Go boundary filter followed by a Stage II weighted scoring matrix, evaluating a broad technology basket encompassing gas, biogas, and biomass CHP, CCHP with absorption cooling, hybrid CHP/BESSs, RESs + BESSs, and diesel generators. Rather than providing a definitive techno-economic ranking, this study contributes a transparent, replicable, front-end engineering tool that can be generalized to other critical infrastructure classes. The results define boundary conditions for prequalifying multi-vector energy architectures, establishing a foundation for future FEED-stage modeling and dynamic simulation of CI microgrids. Full article

In practical photovoltaic grid-tied four-leg inverter systems (PV-GTFLIs), changes in radiation, temperature, and grid voltage magnitude cause significant disturbances, including DC-link voltage disturbance and power unbalance, which can impact the system’s dynamic responses, control performance, grid power quality, efficiency, and reliability. To deal with these problems, this article proposes an improved active disturbance rejection control (ADRC) methodology for optimizing DC-link voltage regulation in PV-GTFLIs. The proposed ADRC approach incorporates a nonlinear high-gain observer (NHGO) within the external DC-link voltage control loop to estimate and mitigate disturbances. The suggested ADRC approach adopts the NHGO instead of the traditional extended state observer due to its superior characteristics, which include excellent dynamic responses, rapid and accurate disturbance estimation and rejection, and enhanced resilience against measurement noise. Thus, it enhances the stability of the DC bus voltage, enhances the system’s dynamic responses, improves its steady-state performance, increases its ability to reject voltage disturbances, and improves the reliability of the PV-GTFLI, as well as reducing the cost and size. The effectiveness of the proposed ADRC approach based on the NHGO is confirmed through software-in-the-loop real-time validation tests, including changes in irradiation and PV cell temperature, sag in grid voltage amplitude, and internal uncertainties, using the OPAL real-time digital simulator. Full article

In distribution networks with a high resistance-to-reactance (R/X) ratio, grid-forming (GFM) inverters can adopt reverse droop control (P-V, Q-f) to achieve the decoupling of active and reactive power. When grid voltage sags trigger the overcurrent protection of the inverter, the interaction between the circular current limiter and the embedded virtual impedance leads to highly complex nonlinear large-signal dynamics. Reverse droop control drives the system power angle via reactive power, which renders the conventional transient stability analysis method based on the P-δ curve invalid, making it difficult to analyze the transient stability of converters based on reverse droop control. To address these issues, this paper first establishes an equivalent circuit model of a GFM inverter considering the circular current limiter and virtual impedance. Second, a transient stability analysis method based on the Q-δ curve is proposed, and the conditions for the existence of the system’s transient equilibrium point (TEP) and the influence of virtual impedance parameters on it are analytically derived. Subsequently, a parameter tuning strategy based on Bayesian optimization (BO) is proposed. Finally, simulation results verify the accuracy of the proposed theory. Full article

Large-gradient mining subsidence is difficult to reconstruct completely using small baseline subset interferometric synthetic aperture radar (SBAS-InSAR), because decorrelation and phase-unwrapping errors underestimate central subsidence, whereas the probability integral method (PIM) is sensitive to parameter inversion accuracy. This study proposes a basin-reconstruction framework combining PIM parameter inversion based on an improved rime optimization algorithm (RIME) with SBAS-InSAR residual fusion. Sobol initialization, a nonlinear adaptive search factor, and a stagnation-triggered perturbation improve RIME convergence and inversion accuracy. The inverted PIM field provides a physically constrained baseline, while normalized subsidence intensity regulates the SBAS-InSAR–PIM residual contribution for local correction. For a thick-coal-seam working face in northern Shaanxi, the improved RIME achieved a root-mean-square error (RMSE) of 66.7 ± 1.3 mm, equivalent to approximately 1.9% of the maximum measured dip-direction subsidence, together with a coefficient of determination (R²) of 0.990 ± 0.001. Its area under the convergence curve was 87.99% and 44.96% lower than those of particle swarm optimization (PSO) and the original RIME, respectively. PIM predicted a maximum subsidence of 3400 mm, whereas SBAS-InSAR detected 190 mm. With an optimal weight-shape parameter of 2.9, the fused result achieved an RMSE of 43 mm and a mean absolute error (MAE) of 25 mm, reducing the PIM-only errors by 33.85% and 51.92%, respectively. Full article

With the electrification of urban bus fleets, the sustainability of public transport operation increasingly depends on resource-efficient energy management at bus depots. Under time-of-use electricity pricing and limited depot resources, shifting many charging tasks to low-price periods may increase depot-level peak load and charger competition, thereby affecting vehicle time windows and departure state-of-charge (SOC) requirements. This study formulates a multi-objective nighttime centralized charging scheduling model that minimizes an economic objective while controlling depot-level peak load. The model combines staged charging characteristics with an interval-overlap-based load calculation method to link SOC evolution, time-window occupancy, and depot load formation. For the resulting constrained discrete scheduling task, a problem-specific SPEA2-based solution framework, named DCS-SPEA2, is developed with vehicle-block encoding, repair-coupled decoding, and archive-based Pareto search. A case study using Shanghai bus route 71 shows that the compromise solution achieves a 100% minimum departure SOC compliance rate, reduces the depot-level peak load to 562.74 kW, and decreases the economic objective by 17.97% and 16.30% compared with first-come-first-served charging and valley-price-priority charging, respectively. Compared with standard SPEA2, DCS-SPEA2 increases the average hypervolume from 0.5783 to 0.6620 and reduces the average inverted generational distance from 0.2505 to 0.1993. These results indicate that coordinated charging scheduling can improve resource use efficiency and operational reliability at electric bus depots, thereby supporting sustainable public transport operation under limited depot resources. Full article

Past studies have confirmed that managerial attention significantly shapes firm strategy yet have overlooked the heterogeneity of managerial attention. Thus, this study delineates the types of top management team (TMT) digital attention, examines the effects of TMT digital attention on firm export performance, and analyzes the moderating effects of foreign ownership and internationalization speed on this relationship. Based on panel data from publicly listed Chinese firms spanning 2013 to 2023, this study conducts empirical regression analyses using a two-way fixed-effects model to verify the aforementioned relationship. Grounded in the attention-based view (ABV), this study analyzes the total time and effort that TMTs dedicate to digital transformation across three dimensions: sustainability, breadth, and intensity. The results show that the sustainability of TMT digital attention positively impacts export performance, while the breadth of TMT digital attention limits export growth. Additionally, an inverted U-shaped relationship exists between the intensity of TMT digital attention and export performance. This research enriches and extends the study of TMT digital attention to international strategy, providing practical insights for companies seeking to improve their export performance. Full article