Search Results (1,325)

The emergence of Flexible Manufacturing Systems (FMS) presents new challenges in Industrial IoT (IIoT) environments. Unlike traditional real-time systems, FMS must accommodate task set variability driven by human–machine interaction. As such variations can lead to abrupt resource overload or idleness, a dynamic scheduling mechanism is required. Although prior studies have explored dynamic scheduling, they often relax deadlines for lower-criticality tasks, which is not well suited to IIoT systems with strict deadline constraints. In this paper, instead of treating dynamic scheduling as a prediction problem, we model it as deterministic planning in response to explicit, observable user input. To this end, we precompute feasible resource plans for anticipated task set variations through offline optimization and switch to the appropriate plan at runtime. During this process, our approach jointly optimizes processor speeds, memory allocations, and edge/cloud offloading decisions, which are mutually interdependent. Simulation results show that the proposed framework achieves up to 73.1% energy savings compared to a baseline system, 100% deadline compliance for real-time production tasks, and low-latency responsiveness for user-interaction tasks. We anticipate that the proposed framework will contribute to the design of efficient, adaptive, and sustainable manufacturing systems. Full article

Despite significant efforts to promote web accessibility through the adoption of various standards and tools, the web remains inaccessible to many users. One of the main barriers is the limited knowledge of accessibility issues among website designers. This gap in expertise results in the development of websites that fail to meet accessibility standards, hindering access for people with diverse abilities and needs. In response to this challenge, this paper presents the ACG WebAcc prototype, which enables the automatic generation of accessible HTML code using a model-driven development (MDD) approach. The tool takes as input a Unified Modeling Language (UML) model, with a specific profile, and incorporates predefined Object Constraint Language (OCL) rules to ensure compliance with accessibility guidelines. By automating this process, ACG WebAcc reduces the need for extensive knowledge of accessibility standards, making it easier for designers to create accessible websites. Full article

With the widespread use of Internet technologies, the protection of personal data to ensure cyber security and privacy compliance has become a major challenge for not only enterprises but also governments. Traditional Data Loss Prevention (DLP) systems primarily focus on preventing internal data from leaking out. However, there is a lack of effective solutions to proactively detect sensitive images already exposed on the Internet. To address these challenges, we developed an automatic sensitive image search system to identify data leaks on the Internet. Our system utilizes artificial intelligence (AI) to automatically search, detect and flag potential privacy-leaking data that contains personal data. We first analyze the sensitive data, such as identification (ID) cards, driver’s licenses, passports, and financial statements, to obtain the standardized document layouts and their personal data elements. Then, our system uses generative AI (GenAI) to generate an example image of user-defined sensitive data. This example image is applied to search target images from the Internet. As there may be lots of target images, we propose AI-driven methods to effectively suppress meaningless matches and then analyze the meaningful images to identify sensitive content. At last, remediation recommendations for cyber security and privacy compliance are then provided by large language models (LLMs). To demonstrate the real value of our system, we have shown some examples of searching data leaks on the Internet. Our system does discover some significant issues. Full article

Three-dimensional printing, particularly Fused Deposition Modeling (FDM), has revolutionized dermatological drug delivery by offering the ability to create personalized and precise drug formulations. This technology enables the design of customized drug delivery systems using a variety of polymers, such as Polylactic Acid (PLA), Polyvinyl Alcohol (PVA), Polyethylene Glycol (PEG), and Polycaprolactone (PCL), each with unique properties that enhance drug release, patient compliance, and treatment efficacy. This review analyzes these polymers in terms of their advantages, limitations, and suitability for dermatological applications. The ability to tailor these materials offers significant potential in overcoming treatment regimens. Additionally, the customization of three-dimensional-printed drug delivery systems provides a platform for creating patient-specific solutions that are more effective and adaptable to individual needs. Despite challenges such as moisture sensitivity and mechanical brittleness, the potential of FDM technology to improve dermatological treatments remains promising. The future of three-dimensional printing in dermatology lies in the integration of optimized materials and advanced printing techniques, which could further enhance patient-specific care and broaden the clinical applicability of these technologies in the pharmaceutical and biomedical sectors. By addressing these limitations and expanding material choices, FDM-based drug delivery systems have the potential to revolutionize the management of dermatological conditions, offering improved therapeutic outcomes and quality of life for patients. Full article

The advancement of topology optimization (TO) and additive manufacturing (AM) has significantly enhanced structural design flexibility and the potential for lightweight structures. However, challenges such as intermediate density, mesh dependency, checkerboard patterns, and local extrema in TO can lead to suboptimal performance. Moreover, existing AM technologies confront geometric constraints that limit their application. This study investigates minimum compliance as the objective function and volume as the constraint, employing the solid isotropic material with penalization method, density filtering, and the method of moving asymptotes. It examines how factors like mesh type, mesh size, volume fraction, material properties, initial density, filter radius, and penalty factor influence the TO results for a metallic gooseneck chain. The findings suggest that material properties primarily affect numerical variations along the TO path, with minimal impact on structural configuration. For both hexahedral and tetrahedral mesh types, a recommended mesh size is identified where the results show less than a 1% difference across varying mesh sizes. An initial density of 0.5 is advised, with a filter radius of approximately 2.3 to 2.5 times the average unit edge length for hexahedral meshes and 1.3 to 1.5 times for tetrahedral meshes. The suggested penalty factor ranges of 3–4 for hexahedral meshes and 2.5–3.5 for tetrahedral meshes. The optimal geometric reconstruction model achieves weight reductions of 23.46% and 22.22% compared to the original model while satisfying static loading requirements. This work contributes significantly to the integration of TO and AM in engineering, laying a robust foundation for future design endeavors. Full article

Global demands for sustainable energy and advanced therapeutics necessitate innovative interdisciplinary solutions. Integrated biorefining emerges as a strategic response, enabling the co-production of biofuels and pharmaceutical compounds through biomass valorization. This integrated model holds promise in enhancing resource utilization efficiency while ensuring economic viability. Our critical review methodically evaluates seven pivotal methodologies: seven key strategies: microbial metabolites, synthetic biology platforms, biorefinery waste extraction, nanocatalysts, computer-aided design, extremophiles, and plant secondary metabolites. Through systematic integration of these approaches, we reveal pivotal synergies and potential technological innovations that can propel multi-product biorefinery systems. Persistent challenges, particularly in reconciling complex metabolic flux balancing with regulatory compliance requirements, are analyzed. Nevertheless, advancements in systems biology, next-generation bioprocess engineering, and artificial intelligence-enhanced computational modeling present viable pathways for overcoming these obstacles. This comprehensive analysis substantiates the transformative capacity of integrated biorefining in establishing a circular bioeconomy framework, while underscoring the imperative of transdisciplinary cooperation to address existing technical and policy constraints. Full article

The valorization of agri-food by-products is a critical pathway toward building sustainable food systems, reducing waste, and advancing the circular economy. This review aims to identify recent advances, key challenges, and future perspectives in this field. We conducted a critical and systematic synthesis of 159 peer-reviewed studies (2019–2025) selected based on quality and thematic relevance from leading international databases. The analysis focuses on emerging technologies such as ultrasound-assisted extraction, microencapsulation, spray drying, lyophilization, deep eutectic solvents, and colloidal systems, emphasizing their efficiency in recovering bioactive compounds from agro-industrial by-products. Significant challenges include industrial scalability, economic feasibility, regulatory compliance, and consumer acceptance. This paper also discusses current applications in functional foods and nutraceuticals, outlining promising directions for the sector. Although challenges remain, the findings offer valuable insights for researchers, industry, and policymakers aiming to foster sustainable innovation and implement strategies aligned with circular economy principles. Full article

The petrochemical industry faces significant safety challenges, necessitating stringent protocols and advanced monitoring systems. Traditional methods rely on manual inspections and fixed sensors, often reacting to hazards only after they occur. Multimodal AI, integrating visual, sensor, and textual data, offers a transformative solution for real-time, proactive safety management. This paper evaluates AI models—Gemini 1.5 Pro, OPENAI GPT-4, and Copilot—in detecting workplace hazards, ensuring compliance with Process Safety Management (PSM) and DuPont safety frameworks. The study highlights the models’ potential in improving safety outcomes, reducing human error, and supporting continuous, data-driven risk management in petrochemical plants. This paper is the first of its kind to use the latest multimodal tech to identify the safety hazard; a similar model could be deployed in other manufacturing industries, especially the oil and gas (both upstream and downstream) industry, fertilizer industries, and production facilities. Full article

Background/Objectives: Semi-active prosthetic feet present a promising solution that enhances adaptability while maintaining modest size, weight, and cost. We propose a semi-active Two-Keel Variable-Stiffness Foot (VSF-2K), the first prosthetic foot where both the hindfoot and forefoot stiffness can be independently and actively modulated. We present a model-based analysis of the effects of different VSF-2K settings on prosthesis characteristics and gait metrics. Methods: The study introduces a simulation model for the VSF-2K: (1) one sub-model to optimize the design of the keels of VSF-2K to maximize compliance, (2) another sub-model to simulate the stance phase of walking with different stiffness setting pairs and ankle alignment angles (dorsiflexion/plantarflexion), and (3) a third sub-model to simulate the keel stiffness of the hindfoot and forefoot keels comparably to typical mechanical testing. We quantitatively analyze how the VSF-2K’s hindfoot and forefoot stiffness settings and ankle alignments affect gait metrics: Roll-over Shape (ROS), Effective Foot Length Ratio (EFLR), and Dynamic Mean Ankle Moment Arm (DMAMA). We also introduce an Equally Spaced Resampling Algorithm (ESRA) to address the unequal-weight issue in the least-squares circle fit of the Roll-over Shape. Results: We show that the optimal-designed VSF-2K successfully achieves controlled stiffness that approximates the stiffness range observed in prior studies of commercial prostheses. Our findings suggest that stiffness modulation significantly affects gait metrics, and it can mimic or counteract ankle angle adjustments, enabling adaptation to sloped terrain. We show that DMAMA is the most promising metric for use as a control parameter in semi-active or variable-stiffness prosthetic feet. We identify the limitations in ROS and EFLR, including their nonmonotonic relationship with hindfoot/forefoot stiffness, insensitivity to hindfoot stiffness, and inconsistent trends across ankle alignments. We also validate that the angular stiffness of a two-independent-keel prosthetic foot can be predicted using either keel stiffness from our model or from a standardized test. Conclusions: These findings show that semi-active variation of hindfoot and forefoot stiffness based on single-stride metrics such as DMAMA is a promising control approach to enabling prostheses to adapt to a variety of terrain and alignment challenges. Full article

The increased use of smartphones in daily life challenges researchers regarding the quality of light emitted by screens. This study aims to analyze displays’ qualitative and quantitative light parameters from various smartphone models available on the market over the last decade. Advanced photometric and colorimetric measurements using complex instrumentation were performed. It covered the color gamut, channel linearity response, refresh rate, flickering, spatial radiation distribution, luminance, uniformity, and static contrast. The analysis showed that, despite advances in smartphone display technology, differences in visible radiation parameters between older and newer models are surprisingly marginal. However, improvements were observed in newer models in terms of viewing angles and compliance with the sRGB standard. Tested built-in blue light reduction filters were ineffective. It only slightly reduces light between 380 nm and 480 nm. In contrast, much higher decreases in this spectral range were achieved for dedicated applications. However, it lowered radiant power density across the visible spectrum, significantly decreasing the displays’ correlated color temperature. Enabling the power-saving mode caused the deterioration of parameters such as refresh rate, but the flicker depth remained constant. Static contrast for most tested devices was also at the same level. The findings confirm the need for further studies on display technology development that supports user well-being while minimizing its harmful effects. Full article

The growing demand for therapeutic monoclonal antibodies (mAbs) has heightened the need for efficient and scalable purification strategies. A major challenge in downstream processing is the removal of antibody aggregates, which can compromise drug safety, efficacy, and regulatory compliance. This review explores the use of multimodal chromatography for aggregate separation, providing an in-depth analysis of commercially available resins and emerging adsorbent prototypes. It also examines the mechanisms of aggregate formation during bioprocessing. A comparative evaluation of conventional single-mode chromatography techniques—affinity, ion exchange, and hydrophobic interaction—is presented alongside multimodal chromatography, which integrates ion-exchange, hydrophobic, and other non-covalent interactions for enhanced aggregate clearance and process flexibility. The review primarily assesses commercial multimodal resins in terms of aggregate removal efficiency, binding capacity, and scalability. Additionally, advancements in prototype resins and multimodal membranes are discussed. Finally, the advantages, limitations, and future directions of multimodal chromatography in mAb aggregate removal are outlined. As purification demands continue to evolve, multimodal chromatography is poised to play an increasingly critical role in achieving the high purity standards required for therapeutic antibodies. Full article

AI data governance is a crucial framework for ensuring that data are utilized in the lifecycle of large language model (LLM) activity, from the development process to the end-to-end testing process, model validation, secure deployment, and operations. This requires the data to be managed responsibly, confidentially, securely, and ethically. The main objective of data governance is to implement a robust and intelligent data governance framework for LLMs, which tends to impact data quality management, the fine-tuning of model performance, biases, data privacy laws, security protocols, ethical AI practices, and regulatory compliance processes in LLMs. Effective data governance steps are important for minimizing data breach activity, enhancing data security, ensuring compliance and regulations, mitigating bias, and establishing clear policies and guidelines. This paper covers the foundation of AI data governance, key components, types of data governance, best practices, case studies, challenges, and future directions of data governance in LLMs. Additionally, we conduct a comprehensive detailed analysis of data governance and how efficient the integration of AI data governance must be for LLMs to gain a trustable approach for the end user. Finally, we provide deeper insights into the comprehensive exploration of the relevance of the data governance framework to the current landscape of LLMs in the healthcare, pharmaceutical, finance, supply chain management, and cybersecurity sectors and address the essential roles to take advantage of the approach of data governance frameworks and their effectiveness and limitations. Full article

Decision-making is a fundamental challenge in science and engineering, mainly when subjective factors influence the process. This paper introduces a decision support model based on the Analytic Hierarchy Process (AHP) that was specifically adapted for binary decisions and we term Binary AHP. The model facilitates structured decision-making when evaluating two opposing alternatives, such as yes/no scenarios. To demonstrate its applicability, we applied the Binary AHP model to a real-world case in the Brazilian public sector, where agencies must determine whether a technological solution qualifies as an Information and Communication Technology (ICT) solution. This classification is crucial since it directly impacts procurement policies and regulatory compliance. Our results show that Binary AHP enhanced the decision consistency, transparency, and reproducibility, and reduced the subjective discrepancies between the evaluators. Additionally, by inverting the priority vectors, the model allowed for a comparative analysis of both decision alternatives, thus offering more profound insights into the classification process. This study highlights the flexibility of AHP-based decision support methodologies and proposes a structured approach to refining binary decision frameworks in complex, multi-criteria environments. Full article

The global transition toward low-carbon energy systems necessitates fundamental innovations in demand-side flexibility, particularly in industrial load regulation. This study presents a systematic review and critical analysis of 90 key research works (2015–2025) to establish a comprehensive framework for industrial load flexibility enhancement. We rigorously examined the tripartite interdependencies among the following: (1) Multi-energy flow physical coupling, addressing temporal-scale disparities in electricity-thermal-gas coordination under renewable penetration; (2) Uncertainty quantification, integrating data-driven and physics-informed modeling for robust decision-making; (3) Market mechanism synergy, analyzing demand response, carbon-P2P hybrid markets, and regulatory policy impacts. Our analysis reveals three fundamental challenges: the accuracy-stability trade-off in cross-timescale optimization, the policy-model disconnect in carbon-aware scheduling, and the computational complexity barrier for real-time industrial applications. The paper further proposes a roadmap for next-generation industrial load regulation systems, emphasizing co-optimization of technical feasibility, economic viability, and policy compliance. These findings advance both academic research and practical implementations for carbon-neutral power systems. Full article

Liposome-based drug delivery systems have revolutionized modern pharmaceutics, offering unparalleled versatility and precision in therapeutic delivery. These lipid vesicles, capable of encapsulating hydrophilic, hydrophobic, and amphiphilic drugs, have demonstrated significant potential in addressing pharmacokinetic challenges such as poor solubility, systemic toxicity, and rapid clearance. This review provides a comprehensive exploration of the evolution of liposomes from laboratory models to clinically approved therapeutics, highlighting their structural adaptability, functional tunability, and transformative impact on modern medicine. We discuss pivotal laboratory-scale preparation techniques, including thin-film hydration, ethanol injection, and reverse-phase evaporation, along with their inherent advantages and limitations. The challenges of transitioning to industrial-scale production are examined, with emphasis on achieving batch-to-batch consistency, scalability, regulatory compliance, and cost-effectiveness. Innovative strategies, such as the incorporation of microfluidic systems and advanced process optimization, are explored to address these hurdles. The clinical success of Food and Drug Administration (FDA)-approved liposomal formulations such as Doxil^® and AmBisome^® underscores their efficacy in treating conditions ranging from cancer to fungal infections. Furthermore, this review delves into emerging trends, including stimuli-responsive and hybrid liposomes, as well as their integration with nanotechnology for enhanced therapeutic precision. As liposomes continue to expand their role in gene therapy, theranostics, and personalized medicine, this review highlights their potential to redefine pharmaceutical applications. Despite existing challenges, ongoing advancements in formulation techniques and scalability underscore the bright future of liposome-based therapeutics in addressing unmet medical needs. Full article