Search Results (107,162)

PROTACs represent a cutting-edge approach to the discovery of innovative cancer therapies. By exploiting the cell’s ubiquitin–proteasome system, PROTACs induce the selective degradation of target proteins, providing a distinct advantage over conventional inhibitors that only block protein function. This review delves into the design principles and mechanisms underlying PROTACs, emphasizing their potential to circumvent drug resistance and achieve prolonged therapeutic effects. Recent advancements have focused on enhancing the specificity, bioavailability, and overall efficacy of PROTACs, demonstrating promising results in preclinical cancer models. The ability of PROTACs to target previously “undruggable” proteins opens new avenues for cancer treatment, offering the potential for more effective and personalized therapeutic strategies. As research continues to evolve, the optimization and clinical translation of PROTACs could declare a new era in oncology, revolutionizing the landscape of cancer drug discovery and personalized medicine. Full article

This study investigates the operations in automated container terminals, in which scheduling decisions need to be generated automatically. Therefore, an integrated optimization model is established considering the two-way transportation process of containers between the berth and the yard with the assignment and scheduling of berths, quay cranes, and automated guided vehicles (AGVs). In particular, a buffer zone is added at the interface between the berth and the  GV routing area. The mathematical model is divided into two stages, in which the first stage deals  with the arrangement of vessels and quay cranes, while the second stage assigns container transport tasks to AGVs. Furthermore, a coordinated mechanism with an elite solution pool is introduced to coordinate the seaside scheduling and AGV transportation decisions. To solve the first stage, an adaptive large neighborhood search (ALNS) enhanced by reinforcement learning is proposed. The reinforcement learning strategy updates the selection rule for destroy-and-repair operator pairs. For the second stage, the AGV transportation environment is modeled as a directed graph, encoding the road network, the AGV interaction relationships, and the task states using a graph neural network (GNN). The AGVs are divided into several groups, mainly according to the initial locations, and each group serves nearby tasks. Therefore, multi-agent proximal policy optimization (MAPPO) is introduced with the GNN to arrange the AGV tasks. The proposed models and algorithms are evaluated through computational experiments with different problem scales, demonstrating better performance compared with the selected baseline methods under the tested settings. Full article

This research investigates the problem of robust control of a nonlinear, fast-dynamics, high-precision planar manipulator with a limited workspace. Starting from a mathematical model formulated in terms of Euler-Lagrange dynamics, the proposed approach considers, given the limitations of the physical system, that some states are unavailable for direct measurement, and the system is perturbed by external disturbances. Consequently, a reduced-order state observer and an estimator with time-varying gains are proposed. Using these estimates in the control stage, four types of control laws are synthesized. Two are derived from super-twisting control theory (STC), the third is an integer-order PID (IOPID) controller, and the fourth is based on a fractional-order PID (FOPID) approach with optimization-based tuning. The first three strategies require accurate information about the mathematical model of the system. All the considered control strategies address external perturbations to ensure the robustness of the system. Several tests have been conducted to validate the overall estimation and control scheme. Based on the dynamic evolution of the manipulator tip and the analyzed performance metrics, the numerical results show that all the proposed controllers provide suitable solutions to robust tracking problems. Full article

Digital-intelligent integration, defined as the integration of data factors and artificial intelligence technologies, serves as a core driver of the high-quality development of enterprises. Its relationship with enterprise innovation capacity and the underlying influencing mechanisms have attracted extensive academic attention. Based on panel data of Chinese A-share listed enterprises from 2000 to 2023, this study employs fixed-effects models, mediation analysis, robustness tests, heterogeneity analysis, and machine learning methods to examine the relationship between digital-intelligent integration and enterprise innovation capacity and explore its underlying mechanisms. The empirical results show that (1) digital-intelligent integration is significantly and positively associated with enterprise innovation capacity, and this core finding remains robust after alternative measures are employed; (2) mediation analysis indicates that digital-intelligent integration is positively associated with R&D intensity, which, in turn, is associated with higher enterprise innovation capacity, providing evidence consistent with the proposed mediating mechanism; (3) heterogeneity analysis indicates that the innovation-promoting association of digital-intelligent integration is significantly stronger among enterprises with lower managerial shareholding ratios, enterprises located in regions with weaker intellectual property protection, and state-owned enterprises. This study provides empirical evidence and practical implications for governments to optimize digital development policies and for enterprises to accelerate the deep integration of digital and intelligent technologies. Full article

This study presents a starch-modified calcined-ZnAl layered double-hydroxide (S-C-ZnAl-LDH) nanocomposite as a multifunctional nanofiller for poly(vinylidene fluoride) (PVDF) efficiently performing, simultaneously, ultrafiltration membrane filtration and efficient adsorbent for the recovery of Acid Blue dye-contaminated water. The synergistic effects of starch modification and thermal activation on nanofiller structure, interfacial compatibility, and membrane performance were systematically investigated through a comparison with pristine ZnAl-LDH, calcined ZnAl-LDH, starch-modified ZnAl-LDH, and calcined starch-modified ZnAl-LDH. SEM, TEM, and XRD analyses confirmed the formation of hierarchical layered nanosheet architectures with a uniform dispersion of crystalline ZnAl domains within a partially amorphous starch matrix, promoting enhanced polymer–nanofiller interfacial interactions Adsorption performance was influenced by solution pH, initial dye concentration, and temperature. Nonlinear kinetic analysis showed that the PFO model described the kinetic data better. However, the overall kinetic modeling findings suggest that Acid Blue 92 adsorption is governed by a combination of physicochemical interactions, suggesting a complex adsorption mechanism was involved. The starch-modified nanocomposite exhibited excellent regeneration stability, retaining approximately 88–90% of its adsorption capacity after five adsorption–desorption cycles. More importantly, the incorporation of S-C-ZnAl-LDH into PVDF membranes significantly enhanced membrane functionality, increasing water flux and permeance by 42.9% and 25%, respectively, while improving Acid Blue rejection by 35.7% to approximately 98%. These improvements are attributed to enhanced membrane hydrophilicity, optimized nanofiller dispersion, and favorable polymer–filler interfacial interactions that facilitate water transport while maintaining high separation efficiency. This work demonstrates an effective strategy for integrating renewable bio-based modifiers with layered nanomaterials to engineer advanced polymeric films exhibiting enhanced permeability, selectivity, durability, and reusability, providing a sustainable platform for multifunctional membrane technologies in water purification and environmental protection. Full article

Feed shortage and water scarcity critically constrain livestock production in semiarid regions, yet the interactive mechanism of growth by which planting density and mowing shape the trade-off between biomass yield and nutritional quality remain poorly quantified for rainfed sweet sorghum (Sorghum bicolor L. Moench). The Loess Plateau is located in the north-central part of China. It belongs to the semiarid continental monsoon climate and is mainly rainfed by agriculture. A two-year field experiment (2023–2024) was conducted in this region to investigate how four planting densities (50,000, 70,000, 90,000 and 110,000 plants·ha⁻¹) and two mowing regimes regulate plant morphology, root development and comprehensive nutritional traits. Compared with a mowing treatment, a non-mowing treatment significantly increased dry matter yield from 8.54 t·ha⁻¹ to 21.59 t·ha⁻¹ (an increase of 152.7%) in 2023 and from 10.32 t·ha⁻¹ to 20.91 t·ha⁻¹ (an increase of 102.6%) in 2024. Increasing planting density elevated population biomass, with the 90,000 plants·ha⁻¹ treatment optimally balancing individual growth and interplant resource competition. Mowing reduced stem diameter, leaf area index, and root biomass, thereby lowering fiber concentration but suppressing total dry matter accumulation. Structural equation modeling (GFI = 0.937) quantified this dual effect: mowing exerted a strong negative direct effect on dry matter yield (path coefficient = −4.211, explaining 94.9% of the yield variance) but indirectly improved nutritional quality by thinning stems to optimize leaf allocation. Integrated random forest and radar chart multi-index evaluation identified non-mowing at 90,000 plants·ha⁻¹ as the optimal cultivation regime (comprehensive score Y = 0.917), which coordinated population biomass and nutritional performance while delivering a 23.3% higher net profit than conventional mowing treatments. This study revealed the physiological trade-off path between biomass productivity of sorghum and its quality under drought stress under rainfed conditions on the Loess Plateau and provides a quantitative, replicable evaluation framework to optimize agronomic management for sweet sorghum and analogous C4 crops in global semiarid rainfed ecosystems, offering practical strategies for sustainable forage production. Full article

Accurate road graph extraction from satellite imagery is essential for large-scale mapping and geospatial analysis. Recent one-shot graph extraction frameworks based on foundation models have achieved promising performance, but their effectiveness decreases in complex environments where road structures are partially obscured by vegetation, buildings, shadows, and other surface conditions. These occlusion-induced disturbances lead to incomplete connectivity and degraded topology reconstruction, particularly under out-of-domain scenarios. This study proposes an occlusion-aware refinement framework to improve the robustness of satellite image road graph extraction while maintaining the original backbone architecture. The proposed framework introduces three complementary strategies: Synthetic Occlusion Augmentation for explicit occlusion-aware representation learning, an Occlusion-Adaptive Extended-Line strategy with Hard-Mining Topology Optimization for improved connectivity reasoning, and an Occlusion-Adaptive Node-Guided Resampling mechanism for reliable graph node localization. Experiments conducted on the Global-Scale road graph extraction benchmark demonstrate that the proposed method consistently improves topology reconstruction performance. Compared with the reproduced SAM-Road++ baseline, the proposed framework improves TOPO F1 from 61.81 to 62.56 on the in-domain split and from 46.93 to 51.51 on the out-of-domain split. Furthermore, the ID-OOD performance gap is reduced from 14.88 to 11.05, indicating enhanced robustness under unseen geographic conditions. The results demonstrate that explicitly modeling occlusion as a structured factor can effectively improve the generalization capability of satellite road graph extraction systems. Full article

The structural integrity of precast concrete composite beams heavily relies on the new-to-old concrete interface. Existing numerical models and traditional analytical methods struggle to capture the complex, multi-variable interfacial degradation mechanisms. This study proposes an integrated framework combining high-fidelity 3D nonlinear finite element (FE) simulations with explainable machine learning (XML). A mixed interface constitutive model, seamlessly coupling surface-based cohesive behavior with residual Coulomb friction, was established and validated to accurately replicate the full-range progressive damage and frictional slip. Using an orthogonal experimental design, a 53-sample database was generated to evaluate key design variables, including material strengths and interfacial roughness. An eXtreme Gradient Boosting (XGBoost)-based surrogate model successfully mapped the nonlinear relationships between these features and core flexural indicators, achieving an R² exceeding 0.965. The SHapley Additive exPlanations (SHAP) framework was subsequently introduced to decode the algorithmic black box, providing transparent traceability of feature importance. Results reveal that cast-in-place concrete strength dominates initial flexural stiffness, whereas tensile reinforcement dictates yield and ultimate capacities. Crucially, interfacial roughness governs the post-peak response, enhancing energy dissipation by over 400%. This data-driven strategy validates that rationally matching material strengths with optimal interfacial friction maximizes the comprehensive flexural potential of composite members. Full article

Amid global urban transition from sprawling expansion to stock-oriented regeneration, exploring the spatiotemporal heterogeneity and mechanisms of urban spatial vitality in old urban areas is crucial for advancing sustainable urban renewal and enhancing human well-being. This study builds a built environment index system, taking the old urban areas of Wuwei City, China, as a case study, and employs the MGWR model to analyze spatial vitality and its drivers. Results show: (1) Diverse functional elements exhibit significant spatiotemporal variations, with basic living facilities having stable impacts, while cultural and catering facilities show morning local agglomeration and nighttime region-wide driving effects, respectively. (2) Regarding accessibility, the road network is a key spatial factor. It shows a positive statistical association with regional vitality during the day, but may correlate with traffic and environmental stress at night. This indicates a potential trade-off between commercial activity and residential comfort across different time periods. (3) Spatial quality elements show strong spatial stability. Specifically, floor area ratio and open space ratio exhibit positive statistical associations with vitality, while building density and height tend to show negative correlations. This may reflect structural bottlenecks imposed by high-density development on sustainable living spaces. (4) Strategies like micro-functional layouts, day-night differentiated traffic networks, and spatial chassis optimization are proposed to provide quantitative evidence for sustainable stock-oriented regeneration, balancing heritage conservation with modern urban vitality. Full article

Compressive light field (CLF) imaging relies on the interplay between sensing and sparse representation, where the coherence between the measurement matrix and the reconstruction dictionary fundamentally determines reconstruction performance. Existing approaches generally optimize either the sensing mask or the reconstruction dictionary separately, leading to limited compatibility between acquisition and reconstruction. To address this issue, this paper proposes a mutual coherence-driven joint optimization framework for computational light field imaging. Specifically, an optimized sensing mask is first designed by minimizing its mutual coherence with a predefined dictionary, producing a measurement matrix better suited for compressive acquisition. A dictionary optimization model is then developed for fixed-mask imaging by jointly considering reconstruction fidelity and measurement consistency under a mutual coherence constraint. The resulting alternating optimization framework effectively improves the compatibility between the sensing process and sparse representation while preserving the practical imaging configuration. Unlike generic sensing-matrix design, the proposed formulations explicitly account for the repeated and spatially shifted mask structure induced by CLF image formation. Experiments on five Stanford light field scenes at sampling ratios of 1/4 and 1/9 show that the optimized mask improves the mean PSNR over the uniform-random mask by 1.21 dB and 2.21 dB, respectively, while the optimized dictionary improves the mean PSNR over K-SVD by 0.70 dB and 0.83 dB, respectively. These results demonstrate the effectiveness of adapting either the coded mask or the reconstruction dictionary to the CLF measurement model. Full article

Transferring unmanned surface vehicle (USV) algorithms from simulation to physical vessels is constrained by heterogeneous hardware interfaces, degraded wireless links, and ambiguous boundaries between human and autonomous control. This study designs and implements a ship–shore cooperative experimental platform comprising a shore control station, a portable control terminal, and an onboard system. The platform integrates multimodal sensing, private-radio and 4G/5G communication, an independent short-range remote-control (RC) path, and hardware arbitration. Small, safety-relevant commands are transmitted redundantly over the heterogeneous links using a shared application protocol with sequence-based deduplication. A two-dimensional control-authority model separates the authorized control source (shore, portable terminal, or short-range RC) from the active onboard behavior (path following, local collision avoidance, or safety protection) to organize fail-safe degradation and control transfer. Geometric light detection and ranging (LiDAR) obstacle detection and optimal reciprocal collision avoidance provide a representative onboard avoidance workflow. Full-scale vessel tests closed the loop from mission dispatch and command parsing to actuation and status feedback and demonstrated autonomous navigation, human takeover, and local collision avoidance. Across four water environments, the platform recorded 5.39 h of multimodal data over 18.26 km of valid trajectories. The results establish a physical testbed for ship–shore cooperative control, algorithm transfer, and multimodal data acquisition. Full article

High penetration of renewable energy imposes greater demands on the scheduling flexibility of demand-side resources in virtual power plant (VPP) dispatch. Nevertheless, heterogeneous resources exhibit remarkable differences in response reliability, and electric vehicles (EVs) in particular show distinct execution performance between orderly charging and vehicle-to-grid (V2G) modes. To tackle this issue, this paper proposes a two-stage optimal scheduling strategy for multi-agent VPPs incorporating scheduling success probability. A quantitative model for the effective dispatch contribution coefficient is constructed from two dimensions, i.e., relative capacity weight and dispatch execution reliability, with differentiated parameters tailored for EV charging and V2G modes. The two-stage leader–follower game problem is decoupled via backward induction, and the optimal dispatch price is rigorously derived through Karush–Kuhn–Tucker conditions. A 24 h case study covering wind power, photovoltaics, energy storage, EVs, and air-conditioning loads validates the proposed method. Results indicate that the strategy boosts total VPP revenue by 7.43% compared with independent operation, lifts the renewable energy accommodation rate from 88.3% to 94.6%, and reduces the average operating cost by 19 CNY/MWh. Through dual-mode differentiated scheduling, EVs achieve 5.10% revenue growth and serve as a key flexible resource for VPP economic operation. Full article

The vehicle routing problem is one of the most often studied optimization problems. In this study, an improved artificial bee colony (ABC) algorithm is proposed which is structured specifically to address the capacitated vehicle routing problem (CVRP), a significant challenge in combinatorial optimization. The proposed algorithm integrates several novel features to improve its performance on CVRP instances. These include a unique initialization strategy that spreads non-repeated customers during initialization, an innovative dual group strategy in the employed bee phase, and an improved scout bee phase with perturbation techniques. Additionally, the proposed algorithm effectively handles infeasible solutions using a novel penalty function formula. The Chebyshev Minkowski’s distance is utilized for route evaluation, enhancing spatial relationship representation over traditional Euclidean distances. Moreover, the classical CVRP model is extended by introducing new variables and constraints to capture changing demand at each customer location within a route. The algorithm’s efficiency was extensively evaluated using benchmark data sets comprising 73 instances from data sets A, B, and P sourced from the VRP instances library site. This rigorous testing enables comprehensive assessments and meaningful comparisons with other algorithms. Overall, the proposed ABC algorithm offers a promising solution for addressing CVRP challenges, providing advancements in solution quality, robustness, and adaptability. Finally, the optimal results are compared in terms of their statistical significance using Friedman and Wilcoxon rank tests with three well-known optimizer algorithms in the literature. Full article

The textile industry is considered a high-risk sector due to its labor-intensive nature and the numerous hazards present in the production area. Furthermore, musculoskeletal disorders resulting from repetitive movements, improper posture, and excessive physical workload observed in textile workers are among the most significant occupational health problems. In this study, ergonomic risks in the dyeing, weaving, and sewing departments of an upholstery fabric manufacturing factory were evaluated using the Rapid Entire Body Assessment (REBA) method. Based on the REBA scores, cycle times, and repetition frequencies, an REBA-weighted ergonomic workload index was calculated for each task. Based on the obtained data, separate mathematical optimization models were developed for each department to minimize the maximum individual REBA-weighted ergonomic workload index under different predefined staffing scenarios. For each scenario, the model optimized the allocation of task repetitions among the available operators, and alternative staffing configurations were evaluated based on ergonomic workload distribution and workforce requirements. The results showed that the proposed approach enabled a more balanced distribution of ergonomic workload among operators, particularly in departments characterized by high repetition frequencies. Based on the evaluated scenarios, configurations of six operators for dyeing, three for weaving, and five for sewing were selected as the proposed staffing configurations. These findings indicate that ergonomic improvement in textile production should not rely solely on posture correction but should also consider workload distribution, staffing levels, and organizational improvements. Overall, the proposed approach provides a decision-support framework for evaluating staffing scenarios and optimizing task allocation to improve ergonomic workload balance in textile production environments. Full article

Building thermal performance has become a major concern as global warming intensifies and building energy demand is expected to rise by 30% by 2030. Bio-based construction materials offer a promising solution by improving thermal comfort and reducing operational energy use. This study presents a computational framework for optimising bio-based masonry block design through the integration of numerical simulation and data-driven modelling. A parametric thermal model was developed in COMSOL Multiphysics to simulate steady-state heat transfer through hollow masonry blocks with varying cavity geometries and material properties. A surrogate model-based approach was then used to generate a dataset of simulated block configurations, from which key thermal performance indicators, particularly heat flux, were extracted. These outputs were used to train a machine learning surrogate model capable of accurately predicting thermal performance across a wide design space without repeated finite-element simulations. The proposed workflow achieved an average heat-flux mismatch of 0.23%, reduced computational cost, and improved prediction accuracy by approximately 7%. In addition, the surrogate-assisted optimisation identified block geometries with substantially improved thermal performance compared with conventional reference configurations. The proposed methodology provides a scalable digital design framework for the development and evaluation of bio-based masonry materials. Future work will incorporate real-time sensor networks and Internet of Things (IoT) systems for the continuous monitoring and validation of masonry wall thermal performance. Full article