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This paper develops a procedure for assessing both supply chain processes and strategic criteria in the context of platform-based supply chains, addressing the problem that organizations often invest in digital platforms without a clear understanding of how process effectiveness, process dysfunction, and strategic platform priorities jointly influence implementation success. The main research objective is to evaluate how effective and dysfunctional supply chain processes, together with prioritized strategic platform criteria, shape performance, productivity, and resilience outcomes in platform-based supply chain integration. The paper further discusses how identified dysfunctional processes and prioritized strategic criteria relate to operational vulnerabilities and resilience-building measures. The research adopts a multi-criteria decision-making (MCDM) approach to address the challenges of digital transformation and platform integration. An exploratory study was conducted applying the analytic hierarchy process (AHP) to evaluate functional and dysfunctional processes, complemented by the best worst method (BWM) to prioritize critical strategic criteria. The combined assessment highlights effective and dysfunctional processes while also identifying the most influential factors driving platform-based adoption and their potential implications for operational vulnerability and resilience. The results demonstrate how platform integration contributes to performance improvement, process alignment, and productivity gains across supply chain operations. The study contributes to both theory and practice by integrating MCDM techniques to support structured decision-making, enhancing responsiveness, resilience, and alignment with platform-oriented strategies. The primary contribution lies in providing a dual-level framework that enables supply chain managers to diagnose weaknesses, leverage strengths, and strategically guide the transition toward platform-based supply chain operations, with a measurable impact on organizational performance and productivity development. Full article

Therapeutic peptides have emerged as promising tools in oncology due to their high specificity, favorable safety profile, and capacity to target molecular hallmarks of cancer. Their clinical translation, however, remains limited by poor stability, rapid proteolytic degradation, and inefficient biodistribution. Iron oxide nanoparticles (IONPs) offer a compelling solution to these challenges. Owing to their biocompatibility, magnetic properties, and ability to serve as both drug carriers and imaging agents, IONPs have become a versatile platform for precision nanomedicine. The integration of peptides with IONPs has generated a new class of hybrid systems that combine the biological accuracy of peptide ligands with the multifunctionality of magnetic nanomaterials. Peptide functionalization enables selective tumor targeting and deeper tissue penetration, while the IONP core supports controlled delivery, MRI-based tracking, and activation of therapeutic mechanisms such as magnetic hyperthermia. These hybrids also influence the tumor microenvironment (TME), facilitating stromal remodeling and improved drug accessibility. Importantly, the iron-driven redox chemistry inherent to IONPs can trigger regulated cell death pathways, including ferroptosis and autophagy, inhibiting opportunities to overcome resistance in aggressive or refractory tumors. As advances in peptide engineering, nanotechnology, and artificial intelligence accelerate design and optimization, peptide–IONP conjugates are poised for translational progress. Their combined targeting precision, imaging capability, and therapeutic versatility position them as promising candidates for next-generation cancer theranostics. Full article

As China’s urbanization rate continues to rise, the scale of high-rise residences also grows, emerging as one of the main sources of building energy consumption and carbon emissions. It is therefore crucial to conduct energy-efficient design tailored to local climate and resource endowments during the schematic design phase. At the same time, consideration should also be given to its impact on economic efficiency and environmental comfort, so as to achieve synergistic optimization of energy, carbon emissions, and economic and environmental performance. This paper focuses on typical high-rise residences in three cities across China’s northwestern region, each with distinct climatic conditions and solar energy resources. The optimization objectives include building energy consumption intensity (BEI), useful daylight illuminance (UDI), life cycle carbon emissions (LCCO₂), and life cycle cost (LCC). The optimization variables include 13 design parameters: building orientation, window–wall ratio, horizontal overhang sun visor length, bedroom width and depth, insulation layer thickness of the non-transparent building envelope, and window type. First, a parametric model of a high-rise residence was created on the Rhino–Grasshopper platform. Through LHS sample extraction, performance simulation, and calculation, a sample dataset was generated that included objective values and design parameter values. Secondly, an SVM prediction model was constructed based on the sample data, which was used as the fitness function of MOPSO to construct a multi-objective optimization model for high-rise residences in different cities. Through iterative operations, the Pareto optimal solution set was obtained, followed by an analysis of the optimization potential of objective performances and the sensitivity of design parameters across different cities. Furthermore, the TOPSIS multi-attribute decision-making method was adopted to screen optimal design patterns for high-rise residences that meet different requirements. After verifying the objective balance of the comprehensive optimal design patterns, the influence of climate differences on objective values and design parameter values was explored, and parametric models of the final design schemes were generated. The results indicate that differences in climatic conditions and solar energy resources can affect the optimal objective values and design variable settings for typical high-rise residences. This paper proposes a building optimization design framework that integrates parametric design, machine learning, and multi-objective optimization, and that explores the impact of climate differences on optimization results, providing a reference for determining design parameters for climate-adaptive high-rise residences. Full article

To address challenges such as small target size, high density, severe occlusion, complex background interference, and edge device computational constraints, a lightweight model, GAFNet, is proposed based on YOLO11n, optimized for rice pest detection in field environments. To improve feature perception, we propose the Global Attention Fusion and Spatial Pyramid Pooling (GAM-SPP) module, which captures global context and aggregates multi-scale features. Building on this, we introduce the C3-Efficient Feature Selection Attention (C3-EFSA) module, which refines feature representation by combining depthwise separable convolutions (DWConv) with lightweight channel attention to enhance background discrimination. The model’s detection head, Enhanced Ghost Detect (EGDetect), integrates Enhanced Ghost Convolution (EGConv), Squeeze-and-Excitation (SE), and Sigmoid-Weighted Linear Unit (SiLU) activation, which reduces redundancy. Additionally, we propose the Focal-Enhanced Complete-IoU (FECIoU) loss function, incorporating stability and hard-sample weighting for improved localization. Compared to YOLO11n, GAFNet improves Precision, Recall, and mean Average Precision (mAP) by 3.5%, 4.2%, and 1.6%, respectively, while reducing parameters and computation by 5% and 21%. GAFNet can deploy on edge devices, providing farmers with instant pest alerts. Further, GAFNet is evaluated on the AgroPest-12 dataset, demonstrating enhanced generalization and robustness across diverse pest detection scenarios. Overall, GAFNet provides an efficient, reliable, and sustainable solution for early pest detection, precision pesticide application, and eco-friendly pest control, advancing the future of smart agriculture. Full article

During gastrointestinal digestion, dietary proteins are hydrolyzed into peptides and free amino acids that modulate enteroendocrine function and satiety-related hormone secretion along the gut–brain axis, thereby contributing to obesity prevention. We investigated whey protein concentrate (WPC) as a source of bioactive peptides and evaluated the effects of its digests on cholecystokinin (CCK) secretion in STC-1 enteroendocrine cells by integrating the standardized INFOGEST in vitro digestion protocol, peptidomics (LC–MS/MS), and in silico bioactivity prediction. In STC-1 cells, the <3 kDa intestinal peptide fraction exhibited the strongest CCK stimulation, positioning these low-molecular-weight peptides as promising bioactive components for satiety modulation and metabolic health applications. Peptidomic analysis of this fraction identified short sequences derived primarily from β-lactoglobulin (β-La) and α-lactalbumin (α-La), enriched in hydrophobic and aromatic residues, including neuropeptide-like sequences containing the Glu–Asn–Ser–Ala–Glu–Pro–Glu (ENSAEPE) motif of β-La f(108–114). In silico bioactivity profiling with MultiPep predicted antihypertensive, angiotensin-converting enzyme (ACE)–inhibitory, antidiabetic, dipeptidyl peptidase-IV (DPP-IV)–inhibitory, antioxidant, antibacterial, and neuropeptide-like activities. Overall, digestion of WPC released low-molecular-weight peptides and amino acids that enhanced CCK secretion in vitro; these findings support their potential use in nutritional strategies to enhance satiety, modulate appetite and energy intake, and improving cardiometabolic health. Full article

The cheese microbiome is a complex ecosystem strongly influenced by both technological practices and the processing environment. Moving beyond traditional cultured-based methods, the integration of shotgun metagenomics into cheese microbiology has enabled in-depth resolution of microbial communities at the species and strain levels. The aim of the present study was to review recent applications of shotgun metagenomics in cheese research, underscoring its role in tracking microbial dynamics during production and in discovering genes of technological importance. In addition, the review highlights how shotgun metagenomics enables the identification of key metabolic pathways, including amino acid catabolism, lipid metabolism, and citrate degradation, among others, which are central to flavor formation and ripening. Results of the discussed literature demonstrate how microbial composition, functional traits, and overall quality of cheese are determined by factors such as raw materials, the cheesemaking environment, and artisanal practices. Moreover, it highlights the analytical potentials of shotgun metagenomics, including metagenome-assembled genomes (MAGs) reconstruction, characterization of various genes contributing to flavor-related biosynthetic pathways, bacteriocin production, antimicrobial resistance, and virulence, as well as the identification of phages and CRISPR-Cas systems. These insights obtained are crucial for ensuring product’s authenticity, enabling traceability, and improving the assessment of safety and quality. Despite shotgun metagenomics’ advantages, there are still analytical restrictions concerning data handling and interpretation, which need to be addressed by importing standardization steps and moving towards integrating multi-omics approaches. Such strategies will lead to more accurate and reproducible results across studies and improved resolution of active ecosystems. Ultimately, shotgun metagenomics has shifted the field from descriptive surveys to a more detailed understanding of the underlying mechanisms shaping the overall quality and safety of cheese, thus bringing innovation in modern dairy microbiology. Full article

As one of the major sources of global carbon emissions, the manufacturing industry urgently requires green transformation. The utilization of renewable energy in production workshop offers a promising route toward zero-carbon manufacturing. However, renewable energy fluctuations and dynamic workshop events make efficient scheduling increasingly challenging. This paper introduces a low-carbon and energy-efficient dynamic flexible job shop scheduling problem oriented towards renewable energy integration, and develops a multi-agent deep reinforcement learning framework for dynamic and intelligent production scheduling. Inspired by the Proximal Policy Optimization (PPO) algorithm, a routing agent and a sequencing agent are designed for machine assignment and job sequencing, respectively. Customized state representations and reward functions are also designed to enhance learning performance and scheduling efficiency. Simulation results demonstrate that the proposed method achieves superior performance in multi-objective optimization, effectively balancing production efficiency, energy consumption, and carbon emission reduction across various job shop scheduling scenarios. Full article

This paper explores simulation application functions for the computer monitoring system of a hydro–wind–solar integrated control center, focusing on five core areas: platform management, operational training, performance optimization, exception handling, and emergency drills. Against the “dual carbon” backdrop, multi-energy complementary system simulation faces key challenges including multi-energy coupling, real-time response, and cybersecurity protection. Research shows that integrating digital twin, heterogeneous computing, and artificial intelligence technologies markedly improve simulation accuracy and intelligent decision-making. Dispatch strategies have shifted from single-energy optimization to system-level coordination, while cybersecurity frameworks now provide comprehensive safeguards covering algorithms, data, systems, user behavior, and architecture. Intelligent operation and maintenance with fault diagnosis—powered by big data and deep learning—enables equipment condition prediction, and emergency drill platforms boost response capacity via 3D visualization and scriptless modeling. Current hurdles include absent multi-energy modeling standards, poor extreme-condition adaptability, and inadequate knowledge transfer mechanisms. Future research should prioritize hybrid physical–data-driven approaches, multi-dimensional robust scheduling, federated learning-based diagnostics, and integrated digital twin, edge computing, and decentralized ledger technologies. These advances will drive simulation platforms toward greater intelligence, interoperability, and reliability, laying the technical foundation for unified hydro–wind–solar control centers. Full article

Polymer composites have opened a novel innovation phase in additive manufacturing (AM), and now lightweight, high-strength, and geometrical advanced components with tailored functionalities can be produced. The present study introduces advances in polymer composite materials and their integration into AM processes, particularly in rapidly growing industries such as aerospace, automotive, biomedical, and electronics. The embedding of cutting-edge reinforcement materials, such as nanoparticles, carbon fibers, and natural fibers, into polymer matrices enhances mechanical, thermal, electrical, and multifunctional properties. These material developments are combined with advanced fabrication techniques, including multi-material printing, in situ curing, and functionally graded manufacturing, which achieves accurate regulation of microstructures and properties. Furthermore, high-impact innovations such as smart polymer composites with self-healing or stimuli-responsive behaviors, the growing shift toward sustainable, bio-based composite alternatives, are driving progress. Despite significant advances, challenges remain in interfacial bonding, printability, process repeatability, and long-term durability. This review offers a comprehensive overview of current advancements and outlines future directions in polymer composite–based AM. Full article

Private Set Intersection (PSI) is a fundamental cryptographic primitive in privacy-preserving computation and has been widely applied in federated learning, secure data sharing, and privacy-aware data analytics. However, most existing PSI protocols rely on RSA or standard elliptic curve cryptography, which limits their applicability in scenarios requiring domestic cryptographic standards and often leads to high computational and communication overhead when processing large-scale datasets. In this paper, we propose a novel PSI protocol based on the Chinese commercial cryptographic standard SM2, referred to as SM2-OT-PSI. The proposed scheme constructs an oblivious transfer-based Oblivious Pseudorandom Function (OPRF) using SM2 public-key cryptography and the SM3 hash function, enabling efficient multi-point OPRF evaluation under the semi-honest adversary model. A formal security analysis demonstrates that the protocol satisfies privacy and correctness guarantees assuming the hardness of the Elliptic Curve Discrete Logarithm Problem. To further improve practical performance, we design a software–hardware co-design architecture that offloads SM2 scalar multiplication and SM3 hashing operations to a domestic reconfigurable cryptographic accelerator (RSP S20G). Experimental results show that, for datasets with up to millions of elements, the presented protocol significantly outperforms several representative PSI schemes in terms of execution time and communication efficiency, especially in medium and high-bandwidth network environments. The proposed SM2-OT-PSI protocol provides a practical and efficient solution for large-scale privacy-preserving set intersection under national cryptographic standards, making it suitable for deployment in real-world secure computing systems. Full article

With the rapid development of urban rail transit, train operation control is required to meet increasingly stringent demands in terms of energy consumption, comfort, punctuality, and precise stopping. The optimization and tracking control of speed profiles are two critical issues in ensuring the performance of automatic train operation systems. However, conventional model predictive control (MPC) methods are highly dependent on parameter settings and show limited adaptability, while heuristic optimization approaches such as the whale optimization algorithm (WOA) often suffer from premature convergence and insufficient robustness. To overcome these limitations, this study proposes an optimized model predictive controller using the multi-objective whale optimization algorithm (MPC-MOWOA) for urban rail train tracking control. In the improved optimization algorithm, a nonlinear convergence mechanism and the Tchebycheff decomposition method are introduced to enhance convergence accuracy and population diversity, which enables effective optimization of the initial parameters of the MPC. During real-time operation, the MPC is further enhanced by integrating a fuzzy satisfaction function that adaptively adjusts the softening factor. In addition, the control coefficients are corrected online according to the speed error and its rate of change, thereby improving adaptability of the control system. Taking the section from Lvshun New Port to Tieshan Town on Dalian Metro Line 12 as the study case, the proposed control algorithm was deployed on a TMS320F28335 embedded processor platform, and hardware-in-the-loop simulation experiments (HILSEs) were conducted under the same simulation environment, a unified train dynamic model, consistent operating conditions, and an identical evaluation index system. The results indicate that, compared with the Fuzzy-PID control method, the proposed control strategy reduces the integral of time-weighted absolute error nearly by 39.6% and decreases energy consumption nearly by 5.9%, while punctuality, stopping accuracy, and comfort are improved nearly by 33.2%, 12.4%, and 7.1%, respectively. These results not only verify the superior performance of the proposed MPC-MOWOA, but also demonstrate its capability for real-time implementation on embedded processors, thereby overcoming the limitations of purely MATLAB-based offline simulations and exhibiting strong potential for practical engineering applications in urban rail transit. Full article

With the growing prominence of skin photodamage caused by ultraviolet (UV) radiation, the development of efficient and safe natural photoprotectants has become a major research focus. Ginsenoside Re (G-Re), a primary active component of ginseng (Panax ginseng C. A. Mey.), has attracted much attention due to its significant antioxidant and anti-inflammatory activities; however, its systemic role and mechanism in protecting against photodamage remain unclear. In this study, a UVB-induced rat photodamage model was established to evaluate the protective effect of ginsenoside Re through histopathological staining, biochemical assay, and immunohistochemical analysis. Furthermore, an integrated transcriptomic and metabolomic approach was applied to elucidate the molecular mechanism of G-Re protection and to establish the association between the photodamage phenotype, metabolic pathways, and gene functions. Following their identification via integrated multi-omics analysis, the key targets were subjected to verification via Western blotting. The results showed that G-Re could effectively alleviate UVB-induced pathological injury and reduce the level of oxidative stress and inflammatory factors, which could reverse regulate the abnormal expression of 265 differential genes and 30 metabolites. The glutathione metabolism pathway was proven as a key pathway mediating the protective effects of ginsenoside Re against skin photodamage via integrated analysis, WB verification, and molecular docking. The current study indicated that G-Re could be a promising natural sunscreen additive in cosmetical products. Full article

The rate of acidification (pH = 6.1) at an appropriate degree is responsible for supplying suitable aroma components and functional properties to cheese. This study aimed to evaluate variations in physicochemical, functional properties, and consumer acceptance in yak mozzarella cheese produced using different starter cultures or lactic acid during ripening. The results showed that consumers preferred ripened yak M cheese, made with mesophilic multi-strain starter, which received the highest scores for aroma (6.8) and flavor (5.9). The average levels of most major volatile organic compounds were relatively higher in ripened M cheese. Furthermore, the degree of proteolysis increased continuously during the 42 d ripening period. The contents of pH 4.6-soluble nitrogen and 12% trichloroacetic acid-soluble nitrogen in cheeses produced with starter cultures reached 11% and 8%, significantly higher than those of directly acidified L cheese. Specifically, greater protein degradation corresponded to lower hardness and stretchability, hardness of T and M cheeses decreased from 226.67 ± 2.23 g and 232.87 ± 3.66 g to 202.36 ± 2.63 g and 197.09 ± 2.33 g, respectively, while their stretchability declined from 52.1 ± 1.6 cm and 49.3 ± 1.7 cm to 34.5 ± 1.2 cm and 37.6 ± 2.4 cm. However, yield and moisture content of T and M cheeses were significantly lower than those of L cheese. Overall, this study provides valuable insights for optimizing the production and quality of yak mozzarella cheese. Full article

The adaptive event-triggered fault-tolerant control problem for bidirectional consensus of multi-agent systems (MASs) subject to sensor faults and external disturbances is investigated. A hierarchical algorithm is first introduced to eliminate the dependence on Laplacian matrix information, thereby reducing computational complexity. Subsequently, a disturbance observer (DO) and a compensation signal were constructed to accommodate external disturbances, filtering errors, and approximation errors introduced by the radial basis function neural network (RBFNN). Compared with the absence of a disturbance observer, the tracking performance was improved by $15.2\%$. In addition, a switching event-triggered mechanism is considered, in which the advantages of fixed-time triggering and relative triggering are integrated to balance communication frequency and tracking performance. Finally, the boundedness of all signals under the proposed fault-tolerant control (FTC) scheme is established. It has been clearly demonstrated by the simulation results that the proposed mechanism achieves a $39.8\%$ reduction in triggering frequency relative to the FT scheme, while simultaneously yielding a $5.0\%$ enhancement in tracking performance compared with the RT scheme, thereby highlighting its superior efficiency and effectiveness. Full article

Traditional fusion methods for integrating multi-source gravity data rely on predefined mathematical models that inadequately capture complex nonlinear relationships, particularly at wavelengths shorter than 10 km. We developed a convolutional neural network incorporating differential marine geodetic data (DMGD-CNN) to enhance marine gravity anomaly recovery from HY-2A satellite altimetry. The DMGD-CNN framework encodes spatial gradient information by computing differences between target points and their surrounding neighborhoods, enabling the model to explicitly capture local gravity field variations. This approach transforms absolute parameter values into spatial gradient representations, functioning as a spatial high-pass filter that enhances local gradient information critical for short-wavelength gravity signal recovery while reducing the influence of long-wavelength components. Through systematic ablation studies with eight parameter configurations, we demonstrate that incorporating first- and second-order seabed topography derivatives significantly enhances model performance, reducing the root mean square error (RMSE) from 2.26 mGal to 0.93 mGal, with further reduction to 0.85 mGal achieved by the differential learning strategy. Comprehensive benchmarking against international gravity models (SIO V32.1, DTU17, and SDUST2022) demonstrates that DMGD-CNN achieves 2–10% accuracy improvement over direct CNN predictions in complex topographic regions. Power spectral density analysis reveals enhanced predictive capabilities at wavelengths below 10 km for the direct CNN approach, with DMGD-CNN achieving further precision enhancement at wavelengths below 5 km. Cross-validation with independent shipborne surveys confirms the method’s robustness, showing 47–63% RMSE reduction in shallow water regions (<2000 m depth) compared to HY-2A altimeter-derived results. These findings demonstrate that deep learning with differential marine geodetic features substantially improves marine gravity field modeling accuracy, particularly for capturing fine-scale gravitational features in challenging environments. Full article