Search Results (7,095)

High-technology supply chains are increasingly concentrated in advanced economies, limiting the industrial upgrading potential of emerging regions. At the same time, growing sustainability pressures require the integration of circular economy principles into production systems. However, existing research rarely integrates supply chain localization, circular value creation, and regional capability within a unified framework. This study addresses the following research question: how can circular supply chain design be structurally integrated into high-technology localization strategies to support sustainable industrial development in emerging economies? To answer this question, the study develops an integrative conceptual framework through the synthesis of localization theory, circular supply chain design, and capability accumulation literature. The framework is structured around three interdependent structural dimensions (SDs): (1) core technological supply chain processes, (2) circular value creation mechanisms, and (3) regional capability accumulation pathways. The framework embeds circular mechanisms—such as modularity, repairability, remanufacturing, and lifecycle management—within the supply chain architecture, enabling the transition from linear acquisition models to lifecycle-oriented systems. It provides an analytical basis for understanding circular localization and offers practical insights for policymakers and firms seeking to develop sustainable high-technology supply chains in emerging economies. This contribution advances the integration of circular economy and localization strategies and supports sustainable industrial transformation. Full article

Lipid nanodiscs are synthetic nanoparticles capable of solubilizing lipophilic drugs and have been shown to improve the potency of the antifungal Amphotericin B (AmphB) against various fungal pathogens. In this study, the SpyCatcher–SpyTag covalent labeling system was used to couple AmphB-loaded Apolipoprotein A1 (ApoA1) lipid nanodiscs to the receptor domain of Dectin-1, which binds to β-1,3/1,6 glucans present in many fungal cell walls. Denaturing protein gel electrophoresis demonstrated that ApoA1-SpyTag003 lipid nanodiscs could be covalently labeled with SpyCatcher003-Dectin-1-superfolder GFP (sfGFP). In microtiter growth assays with Saccharomyces cerevisiae, Dectin-1 AmphB nanodiscs displayed an IC₅₀ 1.5-fold lower than uncoupled AmphB nanodiscs and 2.8-fold lower than AmphB-only controls. Nanodiscs without AmphB and SpyCatcher003-Dectin-1-sfGFP themselves did not inhibit yeast growth. Fluorescence microscopy showed that SpyCatcher003-Dectin-1-sfGFP binds to yeast cell walls and accumulated at hot spots, matching the budding scar enrichment pattern previously described for other Dectin-1 fusion proteins. Together these results indicate that Dectin-1 fusions can target AmphB-loaded lipid nanodiscs to fungal cell walls and improve drug delivery. The results here establish the use of a modular SpyCatcher–SpyTag coupling system for targeting drug-loaded lipid nanodiscs to different cells or tissues, thereby increasing drug retention at infection sites, increasing drug potency, and reducing harmful side-effects. Full article

Hyperconnected IoT ecosystems have become crucial for organizational operations; yet, existing governance structures remain fragmented, are technology-centric, and not well-equipped to manage the risks, compliance pressures, and resilience needs of IoT. This paper presents an integrated, theory-based information security governance model that is tailored for IoT-driven organizations. A conceptual synthesis is performed through integrating five theoretical anchors: governance theory, socio-technical systems theory, risk governance theory, institutional/compliance theory, and resilience/adaptive capacity theory. These theoretical lenses are used to derive essential governance constructs and to develop a modular architecture tailored to IoT security needs. The model’s validity is grounded in theoretical integration rather than empirical testing, consistent with the nature of conceptual research. The integrated model provides six interdependent governance dimensions: strategic governance, operational governance, technical oversight, compliance alignment, risk governance, and resilience/adaptation, anchored by an ecosystem coordination layer. It provides structured decision rights, continuous risk monitoring, regulatory legitimacy, and native adaptive capabilities toward dynamic cyber-physical threats. This research addresses a known gap in the literature on IoT governance by providing an integrated, theoretically validated governance model that systematically connects the rationale and operational mechanisms of governance for resilient, future-proof IoT adoption. The model is further operationalized through a five-level maturity structure, enabling organizations to assess and progressively enhance governance capabilities. Full article

Brain–machine interfaces (BMIs) aim to decode motor intentions from neural activity to enable direct control of external devices. However, most existing decoders rely on monolithic architectures that fail to capture the distinct neural representations of different joint movement directions, limiting their generalizability. In this work, we propose a Single-Direction CNN-LSTM decoder inspired by motor cortex encoding mechanisms, which separately models extension and flexion dynamics through parallel CNN-LSTM branches. Each branch extracts spatial–temporal features from neural spike data and predicts directional joint variables, which are then combined by subtraction to yield the net angular velocity and torque of upper-limb joints. Using invasive recordings from a macaque during a 2D center-out reaching task, we demonstrate that our decoder achieves comparable performance to a conventional CNN-LSTM when trained on all tasks, while significantly outperforming both CNN-LSTM and linear regression baselines in cross-target generalization scenarios. Moreover, the model can capture physiologically meaningful co-contraction patterns, providing richer insights into motor control. These results suggest that incorporating neuroscience-inspired modular decoding into deep neural architectures enhances robustness and adaptability across tasks, offering a promising pathway for BMI applications in prosthetics and rehabilitation. Full article

Fuel cell technologies are increasingly investigated as alternatives to conventional auxiliary diesel generators in order to enhance shipboard energy efficiency and reduce greenhouse gas emissions. This study presents a unified and uncertainty-driven system-level assessment of solid oxide fuel cell (SOFC) and proton exchange membrane fuel cell (PEMFC) systems operating as auxiliary power sources on a 200 m bulk carrier. Both technologies are evaluated under identical vessel characteristics, operating profiles, auxiliary load levels (360–600 kW), and cost assumptions, and are benchmarked directly against a conventional three–diesel-generator configuration. A modular numerical framework is developed to model propulsion–auxiliary interactions for ship speeds between 10 and 14 knots. SOFC systems are assessed using grey, bio-derived, and green natural gas pathways, while PEMFC systems are examined under grey, blue, and green hydrogen supply routes. Performance indicators include annual fuel consumption, carbon dioxide (CO₂) emission reduction, net present value (NPV), internal rate of return (IRR), payback period (PBP), and marginal abatement cost (MAC). Economic uncertainty is explicitly embedded in the framework through Monte Carlo simulation, where fuel prices (±20%) and capital costs are sampled across defined ranges, generating probabilistic distributions rather than single deterministic estimates. This uncertainty-centred approach enables assessment of robustness, downside risk, and probability of profitability. Results show that replacing a single operating 600 kW diesel generator with fuel cell systems reduces auxiliary fuel energy demand by 25–35% for SOFC and approximately 15–25% for PEMFC relative to the diesel benchmark. Annual CO₂ reductions range from 1.1 to 1.3 kt for SOFC systems and 1.8–2.8 kt for PEMFC configurations. Under grey fuel pathways, median NPVs reach approximately 2–4.5 M$ for SOFC and 9–17 M$ for PEMFC as load increases, with IRRs exceeding 15% and 30%, respectively. Transitional pathways exhibit narrower margins, while renewable pathways remain more sensitive to fuel price variability. The findings demonstrate that fuel pathway cost dominates lifecycle outcomes under uncertainty and that hydrogen-based PEMFC systems exhibit the strongest economic resilience within the examined market ranges. The framework provides structured, uncertainty-aware decision support and establishes a foundation for integration into model-based systems engineering (MBSE) environments for early stage ship energy system design. Full article

The safe and precise regulation of therapeutic gene expression remains a major challenge for mammalian synthetic biology and cell-based therapies. Many existing inducible systems rely on non-mammalian regulatory components or ligands with limited clinical compatibility. Designer receptors exclusively activated by designer drugs (DREADDs) offer a human G protein-coupled receptor (GPCR)-based framework for pharmacological control of intracellular signaling, yet their application as clinically relevant gene-regulation platforms remains underexplored. Here, we report a clozapine-responsive gene switch that couples a designer GPCR to signaling-dependent transcriptional control. By linking clozapine-activated receptors to cyclic adenosine monophosphate (cAMP)- or calcium-responsive synthetic promoters, receptor activation is converted into robust transgene expression across a broad dynamic range, with sensitivity to sub-nanomolar to low-nanomolar clozapine concentrations. In vivo, alginate-encapsulated reporter cells implanted in C57BL/6J mice responded to systemic or local clozapine administration with efficient secretion of a reporter protein, achieving robust induction at low daily doses (0.3 mg/kg) following either oral administration or local delivery. Together, these results establish a human GPCR-based clozapine-responsive gene switch that integrates regulation by a clinically used small molecule with modular transcriptional outputs, providing an additional approach for pharmacologically controllable gene expression in mammalian cells and in vivo. Full article

Accurate, timely, and resource-efficient decision-making is critical for sustainable precision agriculture. This paper proposes an agentic AI-based Internet of Things (IoT) framework that enables coordinated, closed-loop perception–decision–action processes across heterogeneous sensing and actuation components. The framework models agricultural systems as distributed collections of goal-driven agents responsible for multimodal sensing, uncertainty-aware reasoning, and adaptive decision-making. To provide a structured foundation, the proposed architecture is formalized within a Multi-Agent Partially Observable Markov Decision Process (MPOMDP) perspective, enabling systematic treatment of coordination, uncertainty, and decision policies. The framework integrates multimodal information sources, including vision-based perception and environmental sensing, and defines mechanisms for their fusion and use in system-level decision-making. A proof-of-concept instantiation is presented using publicly available datasets, combining visual perception models and tabular reasoning models within the proposed agentic workflow. The experiments are designed to demonstrate the feasibility, modularity, and coordination capabilities of the framework, rather than to benchmark predictive performance or provide field-validated evaluation. The results illustrate how multimodal information can be integrated to support adaptive and resource-aware decision processes. Finally, the paper discusses key challenges and outlines directions for future work, including real-world deployment, integration with physical actuation systems, and validation under operational conditions. Full article

To address the challenges of unstable target localization and poor multi-module coordination in automated green pepper harvesting—caused by occlusions from branches and leaves, as well as varying lighting conditions—this paper presents the design and implementation of a modular robotic picking system. At the perception level, the system integrates a YOLOv8 detector with a RealSense D435i camera to identify and locate the calyx–ectocarp junctions of green peppers. An integrated multi-target tracking and filtering framework is proposed, which fuses multi-feature association, trajectory smoothing and coordinate denoising strategies to suppress depth noise and trajectory jitter, thereby enhancing the stability and accuracy of 3D localization. At the control and execution level, a depth-first picking sequence strategy with ID freeze-state management is implemented within a multithreaded software–hardware co-design architecture. This approach avoids task conflicts and duplicate operations while supporting continuous multi-fruit harvesting. Field experiments under natural outdoor lighting and varying occlusion levels demonstrate that the proposed system achieves recognition rates of 91.57% and 80.29% and harvesting success rates of 82.85% and 77.68% for non-occluded and lightly occluded fruits, respectively. The average picking cycle per pepper fruit is 9.8 s. This system provides an effective technical solution for addressing stability control challenges in the automated harvesting process of green peppers. Full article

This paper presents a comparative modeling and experimental validation study for a modular four-wheel omnidirectional mobile robot, focusing on two locomotion architectures implemented on the same platform: four omni wheels (90° rollers) and four Mecanum wheels (45° rollers). Both configurations were evaluated under identical benchmark conditions on a 1 m × 1 m square path (4 m total path length), using the same nominal 12 V supply and the same test duration, in order to ensure a fair and reproducible cross-architecture comparison. A MATLAB/Simulink–Simscape dynamic model was developed for both architectures, while experimental validation was performed using Hall-effect current sensors integrated into the drive modules. Based on the measured and simulated motor currents, a 12 V-based electrical input-power estimate was evaluated at both motor and robot level. For the considered benchmark, the four-Mecanum configuration exhibited a lower measured input-power estimate than the four-omni configuration (17.88 W vs. 25.75 W), corresponding to an approximate reduction of 30.6% under the adopted assumptions. At robot level, the deviation between simulated and measured total input-power estimate was 3.70% for the four-omni architecture and 21.42% for the four-Mecanum architecture, indicating higher predictive agreement for the omni-wheel model in its present form. The comparative analysis also suggests that wheel–ground interaction and roller geometry influence not only the measured current demand but also the level of agreement between simulation and experiment. Although the present study is limited to a single standardized benchmark and nominal-voltage conditions, it provides a controlled basis for comparing the two locomotion solutions and for identifying directions for further model refinement. The findings should therefore be interpreted as benchmark-specific comparative results offering practical guidance for locomotion architecture selection and for future refinement of friction-aware omnidirectional robot models. Full article

The Dedekind $\eta$-function plays an important role in number theory, particularly in the study of modular forms, q-series, and partition identities. In this paper, we investigate several level-12 $\eta$-function identities and examine their combinatorial implications. These identities are obtained from algebraic transformations of known expansions involving mock theta functions, which were originally introduced by Srinivasa Ramanujan. By employing classical q-series techniques and modular transformations, we derive identities that reveal interesting relationships among $\eta$-functions. We further interpret these identities combinatorially to establish correspondences between specific classes of colored partitions with prescribed color restrictions. These results provide new insights into the structure of colored partition functions and highlight the interplay between mock theta functions, Dedekind $\eta$-function identities, and combinatorial partition theory. Our findings contribute to a deeper understanding of the connections between modular forms and colored partitions and suggest further directions for research in number theory and combinatorics. Full article

Background/Objectives: Craniofacial malformations such as orofacial clefts affect ~1 in 700 births; 40–60% lack clear genetic etiology, and many exhibit asymmetry and variable expressivity unexplained by classical Sonic Hedgehog (SHH) morphogen gradient models. We investigated whether integrated molecular modules linking morphogen signaling with metabolic stress responses may better account for craniofacial developmental outcomes. Methods: Sequential UniProt gene set integration identified 186 candidate craniofacial regulators. STRING network analysis revealed modular architecture. Molecular docking profiled 17 compounds against SMO, CK1δ, PINK1, and TIE2 (control). Pathway reconstruction integrated the SHH–CK1δ–HIF1A–HEY1–PINK1 axis with in-silico-predicted CK1δ phosphorylation sites on SMO (S615, T593, S751), HIF1A (Ser247), and GLI1/2/3 transcription factors. A developmental decision tree mapped affinity profiles to node-specific phenotype hypotheses. Results: CK1δ and PINK1 emerged as candidate nodes coupling morphogen signaling with mitochondrial quality control. Cross-docking showed preferential binding to developmental kinases (CK1δ: −8.34 kcal/mol; PINK1: −8.80 kcal/mol) versus TIE2 control (−6.76 kcal/mol; p < 0.001). Pathway reconstruction suggested that CK1δ-mediated Ser247 phosphorylation of HIF1A disrupts ARNT dimerization, redirecting HIF1A toward ARNT-independent HEY1 induction and consequent PINK1 suppression. Based on computed profiles, node-specific associations were proposed as computational hypotheses: SMO perturbation → midline defects; CK1δ → facial asymmetry/clefting; PINK1 → mandibular hypoplasia. Multi-target compounds (e.g., purmorphamine, taladegib) generated composite phenotype predictions consistent with clinical complexity. Conclusions: This strictly in silico study identifies candidate integrated morphogenic modules whose multi-node perturbation may underlie anatomically specific craniofacial malformation patterns. Node–phenotype associations are prioritized computational hypotheses requiring experimental validation; if confirmed, the framework could inform developmental toxicity assessment, therapeutic design, and reclassification of idiopathic craniofacial anomalies. Full article

High-quality training data are essential for the performance and generalization of artificial intelligence systems, particularly in dynamic environments such as adaptive stream processing for disaster response. However, constructing large and representative datasets remains costly and time-consuming, especially in domains where real data are scarce or difficult to obtain. Large Language Models (LLMs) provide powerful capabilities for synthetic text generation, yet the quality of generated data strongly depends on the design of input prompts. Prompt engineering is therefore critical, but it remains largely manual and difficult to scale, particularly in black-box settings where model internals are inaccessible. This work introduces EVOLMD-MO, a multi-objective evolutionary framework for automated prompt learning aimed at generating high-quality synthetic text datasets using black-box LLMs. The proposed approach formulates prompt optimization as a multi-objective search problem in which candidate prompts evolve through genetic operators guided by two complementary objectives: semantic fidelity to reference data and generative diversity of the produced samples. To support scalable optimization, the framework integrates a modular multi-agent architecture that decouples prompt evolution, LLM interaction, and evaluation mechanisms. The evolutionary process is implemented using the NSGA-II algorithm, enabling the discovery of diverse Pareto-optimal prompts that balance semantic preservation and diversity. Experimental evaluation using large-scale disaster-related social media data demonstrates that the proposed approach consistently improves prompt quality across generations while maintaining a stable trade-off between fidelity and diversity. Compared with a single-objective baseline, EVOLMD-MO explores a significantly broader semantic search space and produces more diverse yet semantically coherent synthetic datasets. These results indicate that multi-objective evolutionary prompt learning constitutes a promising strategy for black-box LLM-driven data generation, with potential applicability to adaptive data analytics and real-time decision-support systems in highly dynamic environments, pending broader validation across domains and models. Full article

The cascaded H-bridge static synchronous compensator (STATCOM) has been widely employed in medium- and high-voltage reactive power compensation applications due to its high modularity, fast response speed, and direct grid connection capability. However, the DC-link voltage exhibits an inherent double-frequency ripple, which poses a serious challenge to power quality. Therefore, numerous Active Power Decoupling (APD) techniques have been proposed. However, existing schemes still exhibit certain limitations: independent APD topologies are associated with higher costs, whereas single bridge-arm multiplexed APD topologies are confronted with issues such as elevated DC-side voltage and increased current stress on the multiplexed arm. Consequently, comprehensive optimization is difficult to achieve in terms of the number of power devices, decoupling accuracy, level of capacitor multiplexing, and device stress. To address the above issues, this paper proposes a DC split capacitor (DC-SC)-based dual bridge-arm multiplexed cascaded H-bridge STATCOM with active power decoupling capability, along with its corresponding control method. By constructing a fundamental-frequency common-mode voltage on the decoupling capacitor, this method effectively suppresses the double-frequency ripple in the DC-side voltage and reduces the current stress on the switching devices. The simulation and experimental results have verified the correctness and effectiveness of the proposed topological structure and control method. Full article

Evacuation planning is increasingly challenged by compound hazards in which interacting threats degrade infrastructure, influence human behavior, and destabilize transportation systems. Although agent-based models and dynamic traffic simulations have advanced substantially, much of the evacuation literature remains hazard-specific, case-bound, or difficult to transfer across regions. In parallel, transportation resilience research shows that multi-hazard effects are often non-additive and that cascading infrastructure failures can amplify disruption beyond directly affected areas, raising important sustainability concerns related to community safety, infrastructure continuity, social equity, and long-term planning capacity. These realities motivate the development of evacuation modeling frameworks that are modular, adaptable, and capable of representing co-evolving behavioral and network processes under compound hazard conditions. This review synthesizes advances in evacuation agent-based modeling, dynamic traffic assignment, hazard-induced network degradation, and compound disaster research to propose an adaptable compound-hazard evacuation framework integrating three interdependent layers: hazard processes, transportation network dynamics, and agent decision-making. The proposed framework is organized around four principles: (1) modular hazard representation, (2) decoupling behavioral decision logic from hazard physics, (3) dynamic network state evolution, and (4) neighborhood-scale performance metrics. To support sustainable and equitable local planning, the framework prioritizes spatially resolved outputs, including neighborhood clearance time, isolation probability, accessibility loss, and shelter demand imbalance. By emphasizing modularity, configurability, and policy-relevant metrics, this review connects methodological advances in evacuation modeling to the broader sustainability goals of resilient infrastructure systems, inclusive disaster risk reduction, and locally informed emergency planning. Full article

This survey article offers a snapshot view of the present state of fixed point theory within modular spaces, highlighting fundamental principles and their applications. The discussion primarily revolves around operators and their semigroups that satisfy pointwise asymptotic nonexpansive and contractive conditions in the modular sense, and the results can also be applied directly to Banach spaces. Utilizing the framework of regular and super-regular modular spaces, our research generalizes several established results concerning fixed points of nonlinear operators, applicable to both Banach spaces and modular function spaces. The study seeks to identify and discuss current challenges, knowledge gaps, and unresolved questions, providing insights into the potential of future research opportunities. Full article