Search Results (1,384)

Micro/nanorobotics is emerging as a promising biomedical technology because of its precision, minimal invasiveness, multifunctionality, and potential to mitigate systemic adverse effects. At these ultraminiaturized scales, unique physical constraints necessitate design principles and actuation strategies distinct from those of conventional robotic systems, making material choice, structural design, propulsion mechanisms, and fabrication methods central to overall performance. In this review, we examine recent trends in micro/nanorobot development, with particular emphasis on the advantages of employing hydrogels and the current technical limitations associated with their use. Magnetic, chemical, acoustic, optical, and biohybrid propulsion strategies are comparatively analyzed, together with the material requirements and biological compatibility associated with each approach. Representative applications in drug delivery, tissue regeneration, and cancer therapy are further discussed to highlight the broad medical potential of these systems. Finally, remaining challenges related to material limitations, actuation efficiency, biocompatibility, and manufacturing scalability are identified, and future directions toward clinical translation and practical deployment are outlined. Overall, this review provides an integrated perspective on how hydrogel properties, actuation physics, fabrication strategies, and translational considerations collectively shape the development of more adaptive, biocompatible, and clinically relevant microrobotic systems. Full article

Against the backdrop of the ongoing advancement of the “dual carbon” goals and the carbon emission trading system, green innovation in small and medium-sized manufacturing enterprises faces multiple practical constraints, including financing constraints, technological commercialization risk, and market recognition costs. To examine the mechanism through which government regulation affects firms’ green innovation behavior, this study develops a four-party evolutionary game model involving government, small and medium-sized manufacturing enterprises, consumers, and investment institutions, and analyzes the strategic interactions and dynamic evolution of these actors. The results show that regulatory intensity, consumer green preference, and financial support from investment institutions all exert significant effects on green innovation decisions in small and medium-sized manufacturing enterprises. Whether firms choose substantive green innovation depends primarily on such key factors as financing uncertainty, technological commercialization risk, the intensity of government penalties, and the level of policy incentives. Further stability analysis and numerical simulations indicate that stronger administrative penalties significantly increase the likelihood that firms adopt substantive green innovation and also promote green consumption among consumers. This effect becomes more pronounced when financing uncertainty declines. At the same time, stronger policy incentives for green investment enhance the willingness of investment institutions to participate in green projects, and this effect is further reinforced when technological commercialization risk is reduced. The findings suggest that green innovation in small and medium-sized manufacturing enterprises is characterized by strong multi-actor interdependence. Its evolutionary outcome is shaped not only by regulatory pressure, but also by green financial support, the conditions for technological commercialization, and market demand. Accordingly, sustained green innovation in small and medium-sized manufacturing enterprises requires coordinated efforts to improve regulatory arrangements, strengthen green finance support systems, reduce the cost of technological commercialization, and cultivate green consumer markets. Full article

Advanced pultrusion technology for composite materials is an automated forming process that uses pre-impregnated materials as raw materials and is oriented towards the manufacturing of continuous components. It is particularly suitable for the continuous manufacturing of ultra-long components with uniform cross-sections and has a promising application prospect in the field of aviation composite materials. However, during the preforming stage, the pre-impregnated materials are prone to strain concentration and uneven thickness under the constraint of the mold surface, and in severe cases, there is a tendency to form wrinkles. Moreover, the severity of these defects is further influenced by the process parameters. In response to the above problems, this paper proposes a mold surface optimization method based on the finite element model with the goal of three-dimensional strain homogenization, which controls the thickness direction and in-plane strain within 5%, effectively improving the material deformation coordination. Furthermore, the influence law of preforming temperature, traction speed and tension on preforming quality was systematically analyzed through experimental research. It was found that the influence of each process parameter on appearance quality, thickness uniformity and internal quality all showed a trend of “improvement first and then deterioration”, thus obtaining a relatively better combination of process parameters for preforming quality. The results of this study provide methodological and technical support for the research on advanced pultrusion preforming processes of complex cross-section components. Full article

Energy-intensive processes such as flexographic printing, extrusion coating, slitting, compressed air generation, and chilled water production make liquid carton packaging manufacturing a major electricity consumer, increasing the need for cost-effective and sustainable energy solutions. This study evaluates the real-world performance of a 679 kWp grid-tied solar photovoltaic (PV) system integrated at the 11 kV level in a liquid carton packaging factory in Nairobi, Kenya, operating under regulatory export control constraints that require full on-site consumption of PV generation. Using measured operational data from energy monitoring platforms, including Sunny Portal, 1.31.8 Schneider EcoStruxure, and Sphera Cloud 8.17.2, system performance was assessed in accordance with IEC 61724-1, focusing on final yield, capacity utilization factor, grid offset contribution, and carbon emissions reduction. The results show that the system generated 617 MWh over the assessment period, corresponding to an average daily final yield of 2.49 kWh/kWp·day and a capacity utilization factor of 10.38%. On-site PV generation supplied approximately 17% of the plant’s annual electricity demand and avoided about 277.7 t CO₂ emissions. Performance benchmarking against comparable installations in Kenya, Morocco, Malaysia, Senegal, and Uzbekistan indicates that the lower observed yield is primarily driven by curtailment and industrial load-matching limitations rather than inadequate solar resource or component inefficiency. The findings demonstrate that meaningful electricity cost savings and emissions reductions can be achieved in energy-intensive manufacturing environments despite export restrictions while highlighting the importance of improved load alignment and data-driven operational strategies to enhance PV utilization. Full article

Total factor productivity (TFP) acts as the core micro-foundation for enterprises to enhance resource allocation efficiency, thereby fundamentally boosting their sustainable development capability and long-term sustainability performance. Based on differentiated exposure to the R&D additional deduction policy (the R&D policy), this paper explores TFP disparities and heterogeneous responses among intelligent manufacturing enterprises, together with potential mechanisms. The results indicate that enterprises with access to the R&D policy present higher TFP levels on average and show noticeable differences in TFP performance relative to non-affected enterprises. Mechanism tests suggest that the R&D policy is associated with relieved financing constraints, strengthened R&D willingness, and optimized allocation of R&D resources, which may jointly correlate with the variation in enterprise TFP. Further heterogeneous analysis demonstrates that such disparities in TFP performance are more pronounced in enterprises with high labor intensity, low capital intensity, slow industrial technology iteration, eastern regional distribution, and large scale. This paper clarifies the differential performance characteristics and potential influencing pathways of enterprise TFP under the context of the R&D policy, and provides empirical evidence and practical references originating from China for relevant policy research in other countries and regions. Full article

Artificial intelligence is increasingly embedded in cyber–physical systems that coordinate sensing, computation, communication, and control across critical and semi-critical physical environments. Within the European Union, however, its adoption is shaped not only by technological maturity and economic value, but also by an increasingly dense regulatory landscape governing data processing, cybersecurity, product security, accountability, traceability, interoperability, and safety-relevant deployment. A PRISMA ScR-informed scoping review is used to examine how European Union regulation influences artificial intelligence adoption across four representative domains: energy and smart grids, smart buildings, mobility and transport systems, and industrial and manufacturing environments. The analysis draws on primary legal sources, the peer-reviewed literature, and policy and standards-related materials, and is structured around three dimensions: regulatory barriers, technological and architectural challenges, and cross-sector implications for governance, innovation, and competitiveness. The results show that regulation functions simultaneously as a constraint and an enabling condition. It increases compliance burden, raises integration complexity, and slows deployment in higher risk settings, while promoting trustworthy artificial intelligence, stronger cybersecurity, lifecycle governance, clearer accountability, and more interoperable digital infrastructures. The central finding is that regulation is not external to artificial intelligence adoption in cyber–physical systems, but actively shapes the design space within which such systems can be developed, integrated, validated, and scaled. Future progress therefore depends on regulation-aware systems engineering, stronger implementation guidance, and cross-sector reference architectures capable of aligning legal compliance with technical architecture and operational value creation. Full article

As Industry 4.0 transforms manufacturing operations, maintenance organizations face a group decision-making problem: how to consolidate diverse expert judgments into a defensible, transparent ranking of the competencies that maintenance personnel most need. This paper applies an integrated Delphi–AHP framework—with explicit notation, operators, and diagnostics—to prioritize maintenance competencies in advanced-manufacturing settings. The Delphi stage consolidates expert-generated items under median–interquartile-range consensus and round-to-round stability rules, while the Analytic Hierarchy Process (AHP) transforms validated pairwise comparisons into ratio-scale priority weights through geometric-mean Aggregation of Individual Judgments (AIJ) and eigenvector derivation. Consistency screening (CI/CR), inter-rater agreement (Kendall’s W), and perturbation-based sensitivity analysis accompany the resulting weight vector. A bounded AI-assisted consistency-check step supports terminology harmonization during Delphi statement consolidation, subject to explicit human-validation constraints. A panel of fifteen industry experts participated in the study; five competency dimensions and twenty-nine indicators were retained through three Delphi rounds. AHP weighting identified Basic Knowledge and Skills as the highest-priority dimension, followed by Safety and Regulation Awareness and Problem-Solving Ability. Aggregated pairwise comparison matrices, local and global weights, and sensitivity results are reported to support reproducibility. The study contributes a rigorously specified application of combined Delphi–AHP to a domain—Industry 4.0 maintenance asset management—where multi-criteria decision analysis has seen limited formal application, and closes common specification gaps in published Delphi–AHP implementations. Full article

Extracellular vesicles (EVs) hold significant potential in cardiovascular diagnosis and treatment. However, their clinical applications are limited by challenges such as isolation efficiency, subpopulation heterogeneity, analytical standardization, and manufacturing scalability. Artificial intelligence (AI) and machine learning (ML) offer a computational framework to address these constraints through data-driven platform engineering. This review examines AI-assisted strategies in three interconnected EV platform pillars in cardiovascular medicine. These include: (i) isolation and processing platforms where ML algorithms optimize microfluidic separation and improve signal accuracy; (ii) analytical and diagnostic platforms where deep learning supports single vesicle phenotyping, multi-omics biomarker engineering, and biosensor interpretation; and (iii) therapeutic and manufacturing platforms where AI guides cargo loading, biodistribution estimation, and process control. We also assess key translational challenges, including MISEV2023 compliance, dataset bias, reproducibility, and regulatory alignment. This review positions artificial intelligence as the fundamental layer of the EV bioengineering process, providing a structured framework for advancing EV-based cardiovascular platforms from laboratory research to clinical application. Full article

The substrate plays a critical yet often underappreciated role in determining the performance, stability and manufacturability of photovoltaic devices. While conventional glass and polymer films have enabled the rapid development of solar technologies, emerging applications such as building-integrated photovoltaics, wearable systems and large-area conformal devices demand the use of non-conventional substrates, including ceramics, metals, paper, textiles and elastomeric materials. This review provides a comprehensive analysis of the current state of the art of non-conventional substrates for photovoltaic technologies, with particular emphasis on the interplay between material properties, surface chemistry and deposition processes. These substrates introduce distinct mechanical, thermal and interfacial constraints that fundamentally alter thin-film growth, defect formation and device reliability. Key challenges such as porosity, roughness, thermal transport limitations and outgassing are discussed in relation to nucleation, film continuity and interfacial stability. The role of substrate-dependent effects in both chemical and physical deposition techniques is critically examined, highlighting cases where conventional processing approaches are insufficient. Representative device demonstrations are analyzed to illustrate how substrate selection influences performance and integration strategies across different photovoltaic platforms. Finally, common limitations and emerging opportunities are identified, emphasizing the need for the co-design of substrates, materials and processing routes. This work establishes a unified framework to guide the development of next-generation photovoltaic devices on unconventional substrates. Full article

In the context of mass customization and mixed-model production systems, Master Production Scheduling (MPS), which determines production start dates, plays a critical role. However, in such environments, MPS faces a dual challenge: ensuring due-date adherence under multiple capacity constraints while also reducing operational instability caused by uneven day-to-day consumption of critical components, referred to as Replenishment and Industrial Characteristics (RICs). This paper proposes a new mathematical model for MPS with a Smoothing Mechanism for RICs (MPS-SM). This bi-objective formulation extends a baseline due-date-driven model with an explicit production smoothing/leveling (also known as Heijunka) term, minimizing deviations of RIC usage from weekly ideal levels. By embedding smoothing directly into MPS, the approach provides a pre-leveling effect that can reduce (or ideally eliminate) downstream complexity, specifically related to schedule modifications required in a separate smoothing stage. To reflect changing scheduling priorities, smoothing is weighted through an innovative context-aware non-linear weekly function that assigns lower importance near execution and greater importance farther into the horizon. The models are evaluated in a rolling-horizon simulation-optimization framework using data from a real-world truck manufacturer. Several experiments over 300 discrete-event simulated days show that MPS-SM consistently reduces RIC variability while inducing a controlled increase in lateness penalties. Full article

This paper examines the integration of three-dimensional (3D) printing and robotic fabrication in contemporary architectural design, with a focus on overcoming the technical limitations that constrain large-scale adoption. While additive manufacturing enables the production of complex geometries and customized structures, its standalone application remains limited by fixed build volumes, planar deposition, lack of tensile reinforcement, open-loop process control, and single-process extrusion. To address these constraints, the paper proposes a functional integration framework that systematically maps robotic fabrication capabilities onto these five critical limitations. Evidence from recent studies demonstrates that such integration has already led to measurable advances, including up to a 90-fold increase in printable volume through mobile robotic systems, robotically fabricated reinforcement systems (e.g., Mesh Mold) achieving post-crack behavior comparable to conventional reinforced concrete, and the implementation of closed-loop sensor-based process control to enhance interlayer bonding. Despite these achievements, interdisciplinary collaboration across architecture, structural engineering, materials science, and robotics remains largely fragmented and is predominantly confined to academic and pilot-scale projects, such as the ETH Zurich DFAB House. Regulatory progress is also limited, with only isolated code-compliant implementations under frameworks such as ICC-ES AC509 and ISO/ASTM 52939. Persistent barriers including high capital costs, loss of information in BIM-to-fabrication workflows, anisotropic material behavior, and the absence of long-term durability standards continue to restrict widespread adoption. These findings suggest that advancing robotic additive manufacturing in architecture requires not only technological innovation but also coordinated cross-disciplinary integration, standardized testing protocols, and harmonized regulatory frameworks. Full article

Securing IoT networks presents fundamental challenges rooted in hardware constraints: firmware is often non-upgradeable and every security boundary is fixed at manufacture. Machine learning-based intrusion detection offers a scalable response, yet nearly all published systems assume clean training data and clean inference conditions. Production IoT environments satisfy neither assumption. Sensors degrade, packets drop, and adversaries deliberately corrupt telemetry streams to evade detection. The framework described here is built around that reality. The proposed framework is distinguished from prior work by four design decisions. First, three encoding branches, a residual DNN, a 1D-CNN, and a BiLSTM, are run in parallel and are fused by concatenation, each capturing structural patterns in tabular traffic data that the others miss. Second, a dual-view consistency loss trains the model under simultaneous feature masking and Gaussian noise, penalizing prediction divergence between two independently corrupted views of the same sample. Third, we introduce entropy-weighted attention: rather than fixed learned weights, per-feature importance is adjusted dynamically from information entropy measured across training batches, giving higher-entropy features stronger influence because they carry more discriminative variation. Fourth, branch-dropout regularization randomly silences entire branches during training, forcing each to develop independently useful representations instead of co-adapting. Class imbalance is handled through severity-aware loss weighting which scales contributions by the operational cost of missing each attack category, not purely by inverse frequency. On UNSW-NB15, the full model achieves 99.99% accuracy, 100% precision, 99.97% recall, and a false-negative rate of 2.65 × 10⁻⁴—the lowest across all compared architectures. Full article

Background: Rehabilitation of edentulous mandibles in a post-oncologic setting remains a major clinical challenge. In such situations, placement of conventional endosseous implants may be compromised by severe bone deficiency, a history of peri-implant infection, and constraints related to reconstructive soft tissues. Customized titanium subperiosteal implants, made possible by three-dimensional imaging, computer-aided design, and additive manufacturing, represent a potential alternative when conventional options are unfavorable. This case report describes a full-arch fixed rehabilitation of an edentulous mandible in a patient previously treated for squamous cell carcinoma of the floor of the mouth. Methods: A patient-specific titanium additively manufactured subperiosteal jaw implant (AMSJI) made of biocompatible titanium was designed using a digital planning workflow. Implant placement was performed in a single surgical session under general anesthesia, with fixation using osteosynthesis screws. A screw-retained full-arch provisional prosthesis was delivered intraoperatively, allowing immediate loading with adjustments aimed at avoiding compression of the healing soft tissues. Results: The patient achieved satisfactory functional and esthetic rehabilitation. Postoperative follow-up showed overall favorable mucosal tolerance; an early, limited peri-abutment mucosal dehiscence was observed and managed with suturing under local anesthesia, without compromising implant stability. Conclusions: This case highlights the clinical interest of patient-specific titanium subperiosteal implants as a fixed rehabilitation option in post-oncologic patients with major osseous and mucosal constraints and a history of reconstructive procedures. The combination of accurate digital planning and custom-made manufacturing may avoid the need for extensive bone grafting. However, these findings should be interpreted with caution due to the short-term follow-up and the inherent limitations of a single-case report, which limit the level of evidence and generalizability. Full article

Metal additive manufacturing (AM) has progressed from prototyping toward industrial deployment, yet adoption remains uneven because many initiatives are still driven by isolated process demonstrations rather than system-level manufacturing strategy. This framework review proposes a gated decision workflow for integrating metal AM into industrial systems by coupling process-family selection and route definition, Design for Additive Manufacturing (DfAM) and sustainability considerations. The paper consolidates a comparative matrix of six metal AM process families for early down-selection, introduces a minimal evidence checklist linking each decision gate to required artifacts, and contextualizes the workflow through representative part archetypes. The framework is further supported by practical guidance on process-specific DfAM constraints, including support strategy, residual stress, and surface integrity in powder bed fusion; shrinkage-driven design in sinter-based routes; and machining allowances in repair and hybrid manufacturing. Rather than positioning metal AM as a universal substitute for conventional manufacturing, this work defines it as a complementary, strategy-dependent enabler whose sustainability benefits depend on system-level integration and application context. Full article

Sustainable manufacturing requires schedules that balance environmental responsibility with delivery reliability. This paper studies the Carbon-Aware Flexible Job Shop Scheduling Problem with Tardiness Penalty (CAFJSP-T), where total carbon emissions and total tardiness penalty are the primary objectives. We propose a Policy-based Rough Optimization with a Large Neighborhood Search (Pro-LNS) framework integrating Proximal Policy Optimization (PPO) and adaptive Large Neighborhood Search (LNS). PPO constructs a feasible schedule by selecting operation-machine assignments from job-readiness, machine-availability, earliest-completion, and critical-path features. This policy-generated schedule provides a structurally informed incumbent, enabling LNS to avoid unguided search and focus destroy-and-repair refinement on high-impact operations. Both phases use the same normalized scalarized carbon-tardiness objective, which guides PPO rewards and LNS removal, reinsertion, and acceptance while preserving precedence, eligibility, and capacity constraints. Experiments on small, medium, and large workcenter benchmarks show strong due-date performance and controlled carbon emissions. Under equal objective weighting, Pro-LNS achieves a median optimality gap of 6.12% relative to the exact formulation, with all instances within 14%, while requiring 4.08 s on average and at most 10.51 s. Comparisons with PPO-only, Advantage Actor-Critic (A2C), Soft Actor-Critic (SAC), and Genetic Algorithm (GA) schedulers show that Pro-LNS attains the best weighted scalarized objective across representative instance-weight settings. Friedman and Holm-corrected Wilcoxon tests confirm significant improvements over all competitors, with average weighted-objective gains of 4.90%, 7.25%, 8.81%, and 9.51% over PPO-only, A2C, SAC, and GA, respectively. These results demonstrate that Pro-LNS is an effective and computationally practical hybrid approach for carbon-aware, tardiness-sensitive flexible job shop scheduling. Full article