Search Results (1,070)

Non-absorbable surgical meshes are key biomedical materials used for tissue reinforcement, designed for durability, biocompatibility, and mechanical stability in clinical applications. The mechanical properties of these meshes, such as tensile strength, elasticity, and porosity, are crucial for their long-term performance and integration with host tissue. In the context of Industry 4.0/5.0, emphasis is placed on integrating intelligent technologies, such as real-time data acquisition and advanced computational modeling, to improve the design and production of surgical meshes. Computational models simulate the mechanical behavior of meshes under physiological conditions, enabling precise optimization of their material properties and design. In this article, we propose potential artificial intelligence (AI)-based approaches for future research, such as machine learning (ML), for analyzing large datasets from computational and experimental studies to identify optimal mesh configurations. The direction of tensile loading significantly influences the mechanical response of the mesh. Transversely stretched specimens demonstrated higher maximum failure forces and greater fatigue resistance than longitudinally stretched specimens, both in sutured and unsutured conditions. Suturing the mesh to biological tissue significantly reduced its mechanical strength and stiffness, demonstrating a weakening effect at the mesh-tissue interface. Cyclic loading revealed a gradual decrease in strength in all specimens, suggesting fatigue, but transversely stretched meshes maintained higher forces for >1000 cycles than longitudinally stretched meshes. The observed differences in mechanical behavior can be attributed to the anisotropic mesh structure and mechanical suturing effects, which introduce stress concentrations and structural discontinuities. These results emphasize the importance of considering both directionality and surgical technique when selecting and implementing mesh implants. Both AI-based models achieved scores above 80%, demonstrating their clinical utility and the potential for development toward prediction accuracy above 85–90% in clinical settings. Future research should incorporate AI-based computational models to improve predictive capabilities, ultimately leading to the development of more effective, patient-specific surgical meshes. Full article

Chemically vapor-deposited silicon carbide (CVD-SiC) is a high-performance material that possesses excellent mechanical, chemical, and electrical properties, making it highly promising for components in the semiconductor, aerospace, and automotive industries. However, its inherent hardness and brittleness present significant challenges to precision machining, thereby hindering the commercialization of reliable, high-precision parts. Therefore, the application of CVD-SiC in fields that require ultra-precision shaping and nanometric surface finishing necessitates the exploration of machining methods specifically tailored to the material’s unique characteristics. This paper presents a comprehensive review of CVD-SiC machining—from traditional mechanical approaches to advanced hybrid and high-energy techniques—aimed at overcoming machining limitations from its material properties and achieving high-efficiency and nanometric-quality machining. The study discusses various grinding tools designed for superior surface finishing and efficient material removal, as well as machining techniques that utilize micro-scale removal mechanisms for ductile regime machining. Looking ahead, the integration of AI-based process optimization with enhanced machining methods is expected to fully exploit the superior properties of CVD-SiC and broaden its industrial application as a high-performance material. Full article

The pharmaceutical industry’s pursuit of enhanced product quality, regulatory compliance, and operational efficiency has catalyzed the integration of Artificial Intelligence (AI) into Ongoing Process Verification (OPV) frameworks. This comprehensive review examines the synergistic application of Human-in-the-Loop (HITL) AI systems within OPV, contextualized by the evolving regulatory landscape, particularly the newly introduced Annex 22 of the European Union Good Manufacturing Practices (EU-GMP). The review delineates the sector’s strategic shift from traditional validation models toward dynamic, data-driven approaches that leverage AI for real-time monitoring, predictive analytics, and proactive process control. Central to this transformation is the HITL paradigm, which ensures that human expertise remains embedded in critical decision-making loops, thereby safeguarding patient safety, product quality, data integrity, and ethical responsibility. Annex 22 explicitly mandates deterministic behavior, traceability, and explainability for AI models used in GMP-critical applications, excluding adaptive and probabilistic systems from such contexts. The document also reinforces the necessity of multidisciplinary governance, rigorous validation protocols, and risk-based oversight throughout the AI lifecycle. This paper synthesizes current industry practices, regulatory expectations, and technological capabilities, offering a structured framework for compliant AI deployment in OPV. By aligning AI implementation with Annex 22 principles and existing GMP frameworks (e.g., Annex 11 and ICH Q9), the pharmaceutical sector can harness AI’s transformative potential while maintaining robust regulatory compliance. The review concludes with actionable recommendations for integrating HITL AI into OPV strategies, fostering a resilient, transparent, ethical, and future-ready manufacturing ecosystem. Full article

The rapid evolution of platform technologies is transforming industries, interoperability, and innovation. Despite numerous studies on individual technologies, no prior review unifies AI, IoT, blockchain, and 5G with cross-sector standards, governance, and technical enablers to provide a comprehensive view of platform convergence. This narrative review synthesizes conceptual and technical literature from 2015–2025, focusing on how converging platform technologies interact across sectors. The review organizes findings by technological enablers, cross-domain integration mechanisms, sector-specific applications, and emergent trends, highlighting systemic synergies and challenges. The study demonstrates that AI, IoT, blockchain, cloud-edge architectures, and advanced communication networks collectively enable interoperable, secure, and adaptive ecosystems. Key enablers include standardized protocols, edge–cloud orchestration, and cross-platform data sharing, while challenges involve cybersecurity, regulatory compliance, and scalability. Sectoral examples span healthcare, finance, manufacturing, smart cities, and autonomous systems. Platform convergence offers transformative potential for sustainable and intelligent systems. Critical research gaps remain in unified architectures, privacy-preserving AI and blockchain mechanisms, and dynamic orchestration of heterogeneous systems. Emerging technologies such as quantum computing and federated learning are poised to further strengthen collaborative ecosystems. This review provides actionable insights for researchers, policymakers, and industry leaders aiming to harness platform convergence for innovation and sustainable development. Full article

This paper presents a new image captioning system which contains facial expression recognition as a way to provide better emotional and contextual comprehension of the captions generated. A combination of affective cues and visual features is made, which enables semantically full and emotionally conscious descriptions. Experiments were carried out on two created datasets, FlickrFace11k and COCOFace15k, with standard benchmarks such as BLEU, METEOR, ROUGE-L, CIDEr, and SPICE to analyze their effectiveness. The suggested model produced better results in all metrics as compared to baselines, like Show-Attend-Tell and Up-Down, remaining consistently better on all the scores. Remarkably, it has reached gains of 2.5 points on CIDEr and 1.0 on SPICE, which means a closer correlation to the prompt captions made by people. A 5-fold cross-validation confirmed the model’s robustness, with minimal standard deviation across folds (<±0.2). Qualitative results further demonstrated its ability to capture fine-grained emotional expressions often missed by conventional models. These findings underscore the model’s potential in affective computing, assistive technologies, and human-centric AI applications. The pipeline is designed for on-prem/edge deployment with lightweight interfaces to IoT middleware (MQTT/OPC UA), enabling smart-factory integration. These characteristics align the method with Industry 4.0 sensor networks and human-centric analytics. Full article

As a key medium in industry, lubricating oil plays a significant role in reducing friction, cooling sealing and transmitting power, which directly affects equipment life and energy efficiency. Traditional mineral-based lubricating oils rely on non-renewable petroleum, and they have high energy consumption and poor biodegradability (<30%) during the production process. They can easily cause lasting pollution after leakage and have a high carbon footprint throughout their life cycle, making it difficult to meet the “double carbon” goal. Bio-based lubricating oil uses renewable resources such as cottonseed oil and waste grease as raw materials. This material offers three significant advantages: sustainable sourcing, environmental friendliness, and adjustable performance. Its biodegradation rate is over 80%, and it reduces carbon emissions by 50–90%. Moreover, we can control its properties through processes like hydrogenation, isomerization, and transesterification to ensure it complies with ISO 6743 and other relevant standards. However, natural oils and fats have regular molecular structure, high freezing point (usually > −10 °C), and easy precipitation of wax crystals at low temperature, which restricts their industrial application. In recent years, a series of modification studies have been carried out around “pour point depression-viscosity preservation”. Catalytic isomerization can reduce the freezing point to −42 °C while maintaining a high viscosity index. Epoxidation–ring-opening modification introduces branched chains or ether bonds, taking into account low-temperature fluidity and oxidation stability. The deep dewaxing-isomerization dewaxing process improves the base oil yield, and the freezing point drops by 30 °C. The synergistic addition of polymer pour point depressant and nanomaterials can further reduce the freezing point by 10–15 °C and improve the cryogenic pumping performance. The life cycle assessment shows that using the “zero crude oil” route of waste oil and green hydrogen, the carbon emission per ton of lubricating oil is only 0.32 t, and the cost gradually approaches the level of imported synthetic esters. In the future, with the help of biorefinery integration, enzyme catalytic modification and AI molecular design, it is expected to realize high-performance, low-cost, near-zero-carbon lubrication solutions and promote the green transformation of industry. Full article

This article introduces the Olfactory Attribution Circle (OAC), a conceptual tool for integrating olfaction, color and semantic attributes in the design of sensory atmospheres. Developed through a multi-method strategy, the research combined a literature review, semi-structured interviews with academic and industry sources, a case study of Every Human (Algorithmic Perfumery), and AI-assisted exploration. The review revealed a lack of tools operationalizing olfactory design within the built environment. Interviews provided practice-based insights on inclusion, intensity calibration, and feasibility, while the case study demonstrated the potential and limitations of AI-driven personalization. AI was employed to generate mappings between 60 essences, semantic attributes, and chromatic codes, refined through authorial curation. Results highlight systematic crossmodal correspondences between scents, linguistic attributes, and chromatic values, underscoring the importance of crossmodal congruence in designing coherent sensory experiences. The OAC enables congruent, human-centered olfactory design, though cultural variability and semantic ambiguity limit universal application. The study positions the OAC as both a methodological contribution and a foundation for future empirical testing across diverse cultural contexts. Full article

The transition from Industry 4.0 to Industry 5.0 emphasizes the need for ethical, transparent, and human-centric artificial intelligence systems. In this context, ensuring the authenticity of digital information has become crucial for maintaining societal trust. This study addresses the challenge of detecting manipulated multimedia content, including synthetic images, videos, and audio generated by artificial intelligence, commonly known as Deepfakes. We analyze and compare general-purpose and Deepfake-specific detection methods to assess their effectiveness in real-world scenarios. This work introduces a refined reference model that integrates both application-oriented and methodological criteria, grouping tools into Blind Forensic, Handcrafted Machine Learning, Deep Learning-based methods, and Toolkits. This structured taxonomy provides a clearer comparative framework than existing works, which typically classify detectors using only one of these dimensions. To ensure reproducible evaluation, all experiments were performed using the SAFL dataset, which consolidates real and synthetic multimedia content generated with publicly available tools under a unified protocol. Among the tested tools, Forensically achieved the highest accuracy in image forgery detection 86.9%, while Autopsy reached 69.5% among Deepfake-specific image detectors. In video analysis, Forensically obtained 98.6% accuracy, whereas Deepware Scanner achieved 91.2% as the most effective Deepfake-focused tool. These results highlight that general-purpose methods remain robust for images, while specialized detectors perform competitively in videos. Overall, the proposed model and dataset establish a consistent foundation for advancing hybrid detection strategies aligned with the ethical and transparent AI principles envisioned in Industry 5.0. Full article

Semiconductor wafer fabrication is one of the most complex and demanding processes in industry. The process involves numerous sequential steps, including photolithography, deposition, etching, and chemical–mechanical polishing (CMP). At advanced process nodes below 5 nanometers, even angstrom-level deviations in parameters such as oxide thickness or critical dimension (CD) can lead to yield degradation or device failure. Traditional single-factor experimental methods are insufficient to capture the inherent multivariate interactions within plasma, thermal, and chemical processes. This review introduces the application of Design of Experiments (DOE) in wafer fabrication and demonstrates that it provides a statistically rigorous framework for addressing these challenges. It enables the simultaneous analysis of multiple variables, quantifying main effects and interactions, and developing predictive models with fewer runs. DOE can accelerate process development, reduce wafer consumption, enhance process robustness, and support applications in processes such as photolithography, CMP, and deposition. Beyond process optimization, DOE, combined with virtual metrology, machine learning, and digital twin technologies, provides a balanced dataset for predictive analytics and real-time control. Its functions encompass proactive monitoring, adaptive formulation optimization, and eco-efficient manufacturing aligned with sustainability goals. As wafer fabs adopt AI-assisted, simulation-driven environments, experimental design remains the foundation for knowledge-intensive, data-driven decision-making. This ensures continuous improvement in yield, manufacturability, and competitiveness in future semiconductor miniaturization processes. Full article

The collection and annotation of large-scale image datasets remains a significant challenge in training vision-based AI models, especially in domains such as industrial automation. In industrial settings, this limitation is especially critical for quality inspection tasks within Flexible Manufacturing Systems and Batch-Size-of-One production, where high variability in components restricts the availability of relevant datasets. This study presents a pipeline for generating photorealistic synthetic images to support automated visual inspection. Rendered images derived from geometric models of manufactured parts are enhanced using a Cycle-Consistent Adversarial Network (CycleGAN), which transfers pixel-level features from real camera images. The pipeline is applied in two scenarios: (1) domain transfer between similar objects for data augmentation, and (2) domain transfer between dissimilar objects to synthesize images before physical production. The generated images are evaluated using mean Average Precision (mAP) and the Turing test, respectively. The pipeline is further validated in two industrial setups: object detection for a pick-and-place task using a Niryo robot, and anomaly detection in products manufactured by a FESTO machine. The successful implementation of the pipeline demonstrates its potential to generate effective training data for vision-based AI in industrial applications and highlights the importance of enhancing domain quality in industrial synthetic data workflows. Full article

Permissionless blockchains have evolved beyond cryptocurrency into foundations for Web3 applications, decentralized finance (DeFi), and digital asset ownership, yet this rapid expansion has intensified privacy vulnerabilities. This study provides a comprehensive review of recent trends, emerging privacy threats, and mitigation strategies in permissionless blockchain ecosystems. We examine six developments reshaping the landscape: meme coin proliferation on high-throughput networks, real-world asset tokenization linking on-chain activity to regulated identities, perpetual derivatives exposing trading strategies, institutional adoption concentrating holdings under regulatory oversight, prediction markets creating permanent records of beliefs, and blockchain–AI integration enabling both privacy-preserving analytics and advanced deanonymization. Through this work and forensic analysis of documented incidents, we analyze seven critical privacy threats grounded in verifiable 2024–2025 transaction data: dust attacks, private key management failures, transaction linking, remote procedure call exposure, maximal extractable value extraction, signature hijacking, and smart contract vulnerabilities. Blockchain exploits reached $2.36 billion in 2024 and $2.47 billion in the first half of 2025, with over 80% attributed to compromised private keys and signature vulnerabilities. We evaluate privacy-enhancing technologies, including zero-knowledge proofs, ring signatures, and stealth addresses, identifying the gap between academic proposals and production deployment. We further propose a Secure Development Lifecycle framework incorporating measurable security controls validated against incident data. This work bridges the disconnect between privacy research and industrial practice by synthesizing current trends, providing insights, documenting real-world threats with forensic evidence, and providing actionable insights for both researchers advancing privacy-preserving techniques and developers building secure blockchain applications. Full article

The universal need for sustainable and renewable energy sources has accelerated the shift towards bioenergy as a valuable option to fossil fuels. However, a significant challenge remains in the underutilisation of biomass resources and the environmental pollution caused by improper biomass disposal methods. Biochar, a by-product of biomass pyrolysis rich with carbon, serves as a means to convert underused biomass into valuable energy and a tool for environmental remediation. Biochar can be integrated into a biorefinery for improved bioelectricity and biogas production, but there are challenges with regard to its production scalability, quality control, and standardisation. This article provides a comprehensive review of the prospective processes useful in the valorisation of biomass into biochar for bioenergy, co-firing potential with fossil fuels, and in waste biomass transformation. This article also provides insight into business development and policy-making by bioentrepreneurs, bioengineers, and the government, as it identifies grey opportunities for bioenergy production and improvement. The prospect of AI technology in improving the production, quality, and yield of biochar, by identifying the most efficient parameters and conditions, as well as optimising the application of biochar in various industries, is also highlighted. The transition to biofuels in aviation, a step towards a future in the industry that is more sustainable, is also suggested in this review. Full article

Generative AI is bringing revolutionary changes to architectural design. From data-driven and sustainable perspectives, this study introduces scientific data analysis methods to explore the specific application scenarios and effectiveness of generative AI in the early, middle, and late stages of architectural project design, while also examining its potential value in the field of sustainability. The research first synthesizes industry viewpoints through online data analysis. Secondly, it selects three typical practical architectural projects of different scales and types in which the author participated in comparative testing, recording the time, operational processes, and outputs required for schemes generated by the “traditional creative workflow” vs. the “AI-assisted workflow” at various stages. A multi-dimensional evaluation is conducted combining subjective questionnaires and objective performance simulation data. This study finds that generative AI can significantly enhance design efficiency and scheme diversity and guide the construction of sustainability dimensions, but challenges exist in quality control and technology integration. This research will provide an empirical framework and data benchmarks for architectural practitioners, clarifying a new design path of “data-driven–human–machine collaboration–sustainable optimization”, which holds significant reference value for promoting the transformation of the construction industry towards high efficiency and low carbon. Full article

Industry 5.0 represents a paradigm shift toward human–AI collaboration in manufacturing, incorporating unprecedented volumes of robots, Internet of Things (IoT) devices, Augmented/Virtual Reality (AR/VR) systems, and smart devices. This extensive interconnectivity introduces significant cybersecurity vulnerabilities. While AI has proven effective for cybersecurity applications, including intrusion detection, malware identification, and phishing prevention, cybersecurity professionals have shown reluctance toward adopting black-box machine learning solutions due to their opacity. This hesitation has accelerated the development of explainable artificial intelligence (XAI) techniques that provide transparency into AI decision-making processes. This systematic review examines XAI-based intrusion detection systems (IDSs) for Industry 5.0 environments. We analyze how explainability impacts cybersecurity through the critical lens of adversarial XAI (Adv-XIDS) approaches. Our comprehensive analysis of 135 studies investigates XAI’s influence on both advanced deep learning and traditional shallow architectures for intrusion detection. We identify key challenges, opportunities, and research directions for implementing trustworthy XAI-based cybersecurity solutions in high-stakes Industry 5.0 applications. This rigorous analysis establishes a foundational framework to guide future research in this rapidly evolving domain. Full article

The rapid advancement of AI, especially large language models (LLMs), brings opportunities and challenges to industries. In construction, LLMs can enhance project coordination, support decision-making and reduce workload, but adoption is limited by hallucination, data security and domain complexity. This study investigates the current state of LLM adoption in China’s construction industry through a four-step approach, including a comprehensive literature review to identify potential drivers and barriers, questionnaire design and data collection for empirical analysis, and the application of the Entropy Weight Method (EWM) to quantify and rank the relative importance of each factor. The findings reveal that the top drivers originate at the company level, including strategic partnerships, internal research teams, and staff training—highlighting the central role of organizational readiness in enabling LLM integration. Conversely, the most critical barriers are embedded in the construction domain itself, including knowledge gaps, workflow integration, and data heterogeneity, which reflect structural limitations in the sector. Although LLM implementation remains in its early stages, survey responses show widespread optimism among stakeholders regarding its future potential. The study proposes several actionable strategies for both construction firms and policymakers to facilitate effective LLM adoption. Moreover, the identified drivers and barriers are not exclusive to construction but are also relevant to other digitally transforming sectors—such as manufacturing, healthcare, and energy—offering broader implications for AI adoption in complex, project-based environments. Full article