Search Results (8,227)

This research introduces an innovative passive vibration control methodology employing acoustic black hole (ABH) structures to mitigate vibration transmission in electric automotive steering machines—a prevalent issue adversely affecting driving comfort and vehicle safety. Leveraging the inherent bending wave manipulation properties of ABH configurations, we conceive an integrated vibration suppression framework synergizing advanced computational modeling with intelligent optimization algorithms. A high-fidelity finite element (FEM) model integrating ABH-attached steering machine system was developed and subjected to experimental validation via rigorous modal testing. To address computational challenges in design optimization, a hybrid modeling strategy integrating parametric design (using Latin Hypercube Sampling, LHS) with Kriging surrogate modeling is proposed. Systematic parameterization of ABH geometry and damping layer dimensions generated 40 training datasets and 12 validation datasets. Surrogate model verification confirms the model’s precise mapping of vibration characteristics across the design space. Subsequent multi-objective genetic algorithm optimization targeting RMS velocity suppression achieved substantial vibration attenuation (29.2%) compared to baseline parameters. The developed methodology provides automotive researchers and engineers with an efficient suitable design tool for vibration-sensitive automotive component design. Full article

With the expansion of offshore oil and gas resources to deepwater areas, the problem of the vortex-induced vibration of marine risers, as a key structure connecting offshore platforms and subsea wellheads, has become increasingly prominent. At present, there are few reviews on the vortex-induced vibration of flexible risers. This review provides a detailed discussion of vortex-induced vibration in marine risers. This review begins with the engineering background. It then systematically analyzes the key factors that influence VIV response. These factors include the riser’s structural parameters, such as aspect ratio and mass ratio. They also include the external fluid environment. Next, this review evaluates current VIV suppression strategies by analyzing specific experimental results. It compares the effectiveness and trade-offs of passive techniques. It also examines the potential and limitations of active methods, which often use smart materials, like piezoelectrics. This study highlights the major challenges in VIV research today. These challenges relate to prediction accuracy and suppression efficiency. Key problems include model uncertainty at high Reynolds numbers and the practical implementation of suppression devices in engineering systems. Finally, this paper presents an outlook on the future directions. It concludes that an intelligent, full-lifecycle integrity management system is the best path forward. Full article

Background/Objectives: Hot-melt extrusion (HME) has gained prominence for the manufacture of sustained-release oral dosage forms, yet the application of wax-based matrices and their resilience to alcohol-induced dose dumping (AIDD) remains underexplored. This study aimed to develop and characterise wax-based sustained-release felodipine formulations, with a particular focus on excipient functionality and robustness against AIDD. Methods: Felodipine sustained-release formulations were prepared via HME using Syncrowax HGLC as a thermally processable wax matrix. Microcrystalline cellulose (MCC) and lactose monohydrate were incorporated as functional fillers and processing aids. The influence of wax content and filler type on mechanical properties, wettability, and drug release behaviour was systematically evaluated. Ethanol susceptibility testing was conducted under simulated co-ingestion conditions (4%, 20%, and 40% v/v ethanol) to assess AIDD risk. Results: MCC-containing tablets demonstrated superior sustained-release characteristics over 24 h, showing better wettability and disintegration. In contrast, tablets formulated with lactose monohydrate remained structurally intact during dissolution, overly restricting drug release. This limitation was effectively addressed through granulation, where reduced particle size significantly improved surface accessibility, with 0.5–1 mm granules achieving a satisfactory release profile. Ethanol susceptibility testing revealed divergent behaviours between the two filler systems. Unexpectedly, MCC-containing tablets showed suppressed drug release in ethanolic media, likely resulting from inhibitory effect of ethanol on filler swelling and disintegration. Conversely, formulations containing lactose monohydrate retained their release performance in up to 20% v/v ethanol, with only high concentrations (40% v/v) compromising matrix drug-retaining functionality and leading to remarkably increased drug release. Conclusions: This study highlights the pivotal role of excipient type and constitutional ratios in engineering wax-based sustained-release formulations. It further contributes to the understanding of AIDD risk through in vitro assessment and offers a rational design strategy for robust, alcohol-resistant oral delivery systems for felodipine. Full article

The objective of this research was to conduct a comprehensive review of rainbow trout (Oncorhynchus mykiss) culture in recirculating aquaculture systems (RAS), identify knowledge gaps, and propose strategies oriented towards intelligent and sustainable aquaculture. A systematic review and bibliometric analysis of 387 articles published between 1941 and 2025 in the Scopus database was carried out. Since 2011, there has been a sustained growth in scientific production, with the United States, Denmark, Finland, and Germany standing out as the main contributors. The journals with the highest number of publications were Aquacultural Engineering, Aquaculture, and Aquaculture Research. The conceptual analysis revealed the following three thematic clusters: experimental studies on physiology and metabolism; research focused on nutrition, growth, and yield; and technological developments for water treatment in RAS. This evolution reflects a transition from basic approaches to applied technologies oriented towards sustainability. There was also evidence of a thematic transition toward molecular tools such as proteomics, transcriptomics, and real-time PCR. However, there is still limited integration of smart technologies such as the IoT. It is recommended to incorporate self-calibrating multi-parametric sensors, machine learning models, and autonomous systems for environmental regulation in real time. Full article

Near-field acoustic holography (NAH) provides an effective way to achieve wide-band, high-resolution visualization measurement of the sound insulation performance of building components. However, based on Green’s function, the microphone array’s inherent amplitude and phase mismatch errors will exponentially amplify the sound field inversion process, significantly reducing the measurement accuracy. To systematically evaluate this problem, this study combines numerical simulation with actual measurements in a soundproof room that complies with the ISO 10140 standard, quantitatively analyzes the influence of array system errors on NAH reconstructed sound insulation and acoustic images, and proposes an error correction strategy based on channel transfer function normalization. The research results show that when the array amplitude and phase mismatch mean values are controlled within 5% and 5°, respectively, the deviation of the weighted sound insulation measured by NAH can be controlled within 1 dB, and the error in the key frequency band of building sound insulation (200–1.6k Hz) does not exceed 1.5 dB; when the mismatch mean value increases to 10% and 10°, the deviation of the weighted sound insulation can reach 2 dB, and the error in the high-frequency band (≥1.6k Hz) significantly increases to more than 2.0 dB. The sound image shows noticeable spatial distortion in the frequency band above 250 Hz. After applying the proposed correction method, the NAH measurement results of the domestic microphone array are highly consistent with the weighted sound insulation measured by the standard method, and the measurement difference in the key frequency band is less than 1.0 dB, which significantly improves the reliability and applicability of low-cost equipment in engineering applications. In addition, the study reveals the inherent mechanism of differential amplification of system errors in the propagating wave and evanescent wave channels. It provides quantitative thresholds and operational guidance for instrument selection, array calibration, and error compensation of NAH technology in building sound insulation detection. Full article

Viruses use a range of sophisticated strategies to evade detection by cytotoxic T-lymphocytes (CTLs) within host cells. Beyond elaborating dedicated viral proteins that disrupt the MHC class I antigen-presentation machinery, some viruses possess intrinsic, cis-acting genome-encoded elements that interfere with antigen processing and display. These protein features, including G-quadruplex motifs, repetitive peptide sequences, and rare-codon usage, counterintuitively limit production of proteins critical to virus survival, particularly during latency. By slowing viral protein synthesis, these features reduce antigen production and proteosomal degradation, ultimately limiting the generation of peptides for MHC I presentation. These built-in evasion tactics enable viruses to remain “invisible” to CTLs during latency. While these primary sequence intrinsic immune evasion (PSI) mechanisms are well-described in select herpesviruses, emerging evidence suggests that they may also play a critical role in RNA viruses. How these proteins are made, rather than what they functionally target, determines their immune evasion properties. Understanding PSI mechanisms could rationally inform the design of engineered viral antigens with altered or removed evasion elements to restore antigen CTL priming and activation. Such vaccine strategies have the potential to enhance immune recognition, improve clearance of chronically infected cells, and contribute to the treatment of persistent viral infections and virus-associated cancers. Full article

Concrete structures increasingly face dynamic loading conditions, such as seismic events, vehicular traffic, and environmental vibrations, necessitating enhanced energy dissipation capabilities. The damping ratio, a critical parameter quantifying a material’s ability to dissipate vibrational energy, is typically low in conventional concrete, prompting extensive research into strategies for improvement. This review comprehensively explores the impact of advanced concrete types—such as Engineered Cementitious Composites (ECCs), Ultra-High-Performance Concrete (UHPC), High-Performance Concrete (HPC), and polymer concrete—on enhancing the damping behavior. Additionally, key mix design innovations, including fiber reinforcement, rubber powder incorporation, and aggregate modification, are evaluated for their roles in increasing energy dissipation. External factors, particularly curing conditions, are also discussed for their influence on the damping performance. The findings consolidate experimental and theoretical insights into how material composition, mix design, and external treatments interact to optimize dynamic resilience. To guide future research, this paper identifies critical gaps including the need for multi-scale numerical simulation frameworks, standardized damping test protocols, and long-term performance evaluation under realistic service conditions. Advancing work in material innovation, optimized mix design, and controlled curing environments will be essential for developing next-generation concretes with superior vibration control, durability, and sustainability. These insights provide a strategic foundation for applications in seismic-prone and vibration-sensitive infrastructure. Full article

Marine biomass represents a valuable yet underexploited resource for the development of high-value biomaterials. Recent advances have highlighted the significant potential of marine-derived polysaccharides, proteins, and peptides in biomedical applications, most notably in drug delivery and wound healing. This review provides a comprehensive synthesis of current research on the extraction, processing and pharmaceutical valorization of these biopolymers, with a focus on their structural and functional properties that allow these materials to be engineered into nanocarriers, hydrogels, scaffolds, and smart composites. Key fabrication strategies such as ionic gelation, desolvation, and 3D bioprinting are critically examined for their role in drug encapsulation, release modulation, and scaffold design for regenerative therapies. The review also covers preclinical validation, scale-up challenges, and relevant regulatory frameworks, offering a practical roadmap from sustainable sourcing to clinical application. Special attention is given to emerging technologies, including stimuli-responsive biomaterials and biosensor-integrated wound dressings, as well as to the ethical and environmental implications of marine biopolymer sourcing. By integrating materials science, pharmaceutical technology and regulatory insight, this review aims to provide a multidisciplinary perspective for researchers and industrial stakeholders seeking sustainable and multifunctional pharmaceutical platforms for precision medicine and regenerative therapeutics. Full article

Analyzing and leveraging data effectively has been an advantageous strategy in the management workflows of many contemporary organizations. In business and finance, data-informed decision workflows are nowadays essential for enabling development and growth. However, there is yet a theoretical or quantitative approach for analyzing the cost–benefit of the processes in such workflows, e.g., in determining the trade-offs between machine- and human-centric processes and quantifying biases. The aim of this work is to translate an information-theoretic concept and measure for cost–benefit analysis to a methodology that is relevant to the analysis of hybrid decision workflows in business and finance. We propose to combine an information-theoretic approach (i.e., information-theoretic cost–benefit analysis) and an engineering approach (e.g., workflow decomposition), which enables us to utilize information-theoretic measures to estimate the cost–benefit of individual processes quantitatively. We provide three case studies to demonstrate the feasibility of the proposed methodology, including (i) the use of a statistical and computational algorithm, (ii) incomplete information and humans’ soft knowledge, and (iii) cognitive biases in a committee meeting. While this is an early application of information-theoretic cost–benefit analysis to business and financial workflows, it is a significant step towards the development of a systematic, quantitative, and computer-assisted approach for optimizing data-informed decision workflows. Full article

This paper presents an interpretable and modular framework for energy management in smart buildings based on fuzzy logic and the IEEE Std 1855-2016. The proposed system builds upon the JFML-IoT library, enabling the integration and execution of fuzzy rule-based systems on resource-constrained IoT devices using a lightweight and extensible architecture. Unlike conventional data-driven controllers, this approach emphasizes semantic transparency, expert-driven control logic, and compliance with fuzzy markup standards. The system is designed to enhance both operational efficiency and user comfort through transparent and explainable decision-making. A four-layer architecture structures the system into Perception, Communication, Processing, and Application layers, supporting real-time decisions based on environmental data. The fuzzy logic rules are defined collaboratively with domain experts and encoded in Fuzzy Markup Language to ensure interoperability and formalization of expert knowledge. While adherence to IEEE Std 1855-2016 facilitates system integration and standardization, the scientific contribution lies in the deployment of an interpretable, IoT-based control system validated in real conditions. A case study is conducted in a realistic indoor environment, using temperature, humidity, illuminance, occupancy, and CO${}_2$ sensors, along with HVAC and lighting actuators. The results demonstrate that the fuzzy inference engine generates context-aware control actions aligned with expert expectations. The proposed framework also opens possibilities for incorporating user-specific preferences and adaptive comfort strategies in future developments. Full article

River embankments are critical flood defense structures, stretching for thousands of kilometers across alluvial plains. They often originated as natural levees resulting from overbank flows and were later enlarged using locally available soils yet rarely designed according to modern engineering standards. Substantially under-characterized, their performance to extreme events provides an invaluable opportunity to highlight their vulnerability and then to improve monitoring, management, and reinforcement strategies. In May 2023, two extreme meteorological events hit the Emilia-Romagna region in rapid succession, causing numerous breaches along river embankments and therefore widespread flooding of cities and territories. These were followed by two additional intense events in September and October 2024, marking an unprecedented frequency of extreme precipitation episodes in the history of the region. This study presents the methodology adopted to create a regional database of 66 major breaches and damages that occurred during May 2023 extensive floods. The database integrates multi-source information, including field surveys; remote sensing data; and eyewitness documentation collected before, during, and after the events. Preliminary interpretation enabled the identification of the most likely failure mechanisms—primarily external erosion, internal erosion, and slope instability—often acting in combination. The database, unprecedented in Italy and with few parallels worldwide, also supported a statistical analysis of breach widths in relation to failure mechanisms, crucial for improving flood hazard models, which often rely on generalized assumptions about breach development. By offering insights into the real-scale behavior of a regional river defense system, the dataset provides an important tool to support river embankments risk assessment and future resilience strategies. Full article

The rise in antibiotic-resistant bacteria has made the management of bacterial diseases increasingly challenging. As a result, bacteriophages have gained attention as a promising alternative to antibiotics for combating bacterial pathogens. However, the usage of phages as biocontrol agents faces many challenges, including environmental stability, delivery efficiency, host specificity, and potential bacterial resistance. Advancements in genetic engineering and nanotechnology have been explored to enhance the stability, efficacy, and adaptability of phage-based treatments. In this review, we discuss the key barriers to the effective implementation of phage therapy and highlight innovative strategies to overcome these challenges. By addressing these limitations, this review aims to provide insights into optimizing phage-based approaches for widespread therapeutic and biocontrol applications. Full article

High rates of childhood vaccination defaulting remain a significant barrier to achieving full vaccination coverage in sub-Saharan Africa, contributing to preventable morbidity and mortality. This study evaluated the utility of machine learning algorithms for predicting childhood vaccination defaulters in Ghana, addressing the limitations of traditional statistical methods when handling complex, high-dimensional health data. Using a merged dataset from two malaria vaccine pilot surveys, we engineered novel temporal features, including vaccination timing windows and birth seasonality. Six algorithms, namely logistic regression, support vector machine, random forest, gradient boosting machine, extreme gradient boosting, and artificial neural networks, were compared. Models were trained and validated on both original and synthetically balanced and augmented data. The results showed higher performance across the ensemble tree classifiers. The random forest and extreme gradient boosting models reported the highest F1 scores (0.92) and AUCs (0.95) on augmented unseen data. The key predictors identified include timely receipt of birth and week six vaccines, the child’s age, household wealth index, and maternal education. The findings demonstrate that robust machine learning frameworks, combined with temporal and contextual feature engineering, can improve defaulter risk prediction accuracy. Integrating such models into routine immunization programs could enable data-driven targeting of high-risk groups, supporting policymakers in strategies to close vaccination coverage gaps. Full article

The transition towards a power system characterized by a reduced presence of synchronous generators (SGs) and an increased reliance on inverter-based resources (IBRs), including wind, solar photovoltaics (PV), and battery storage, presents new operational challenges, particularly when these sources exceed 50–60% of the system’s demand. While current grid-following (GFL) IBRs, which are equipped with fast and rigid control systems, continue to dominate the inverter landscape, there has been a notable surge in research focused on grid-forming (GFM) inverters in recent years. This study conducts a comparative analysis of the practicality and control methodologies of GFM inverters relative to traditional GFL inverters from a system planning perspective. A comprehensive framework aimed at assisting system developers and consulting engineers in the grid-integration of wide-scale renewable energy sources (RESs), incorporating strategies for the deployment of inverters, capacitor banks, and harmonic filters, is proposed in this paper. The discussion includes an examination of the reactive power capabilities of the plant’s inverters and the provision of additional reactive power to ensure compliance with grid interconnection standards. Furthermore, the paper outlines a practical approach to assess the necessity for enhanced filtering measures to mitigate potential resonant conditions and achieve harmonic compliance at the installation site. The objective of this work is to offer useful guidelines and insights for the effective addition of RES into contemporary power systems. Full article

Tailoring culture media and supplementation strategies to the specific requirements of a target product is essential for enhancing microbial production efficiency. This work addresses an unexplored aspect of K. phaffii cultivation: optimizing culture media for metabolite production from xylose, diverging from the conventional focus on recombinant protein expression and the use of glycerol or methanol as primary substrates. Ethylene glycol biosynthesis in an engineered K. phaffii strain was improved by evaluating media and nutrient supplementation. Among the seven evaluated formulations, FM22 and d’Anjou were the most effective, with inositol and thiamine dichloride playing key roles in enhancing production. Salt concentrations in both media were optimized using Central Composite Design (CCD), reducing complexity while increasing yields. Ethylene glycol production increased by 54% in FM22 and 21% in d’Anjou, accompanied by a threefold and 26% reduction in the total salt content, respectively. The vitamin solution was streamlined from seven to two components, each at half the standard concentration. Trace element solutions were reduced to 25% of the original volume without compromising productivity. These findings underscore the dual benefit of culture medium optimization: improved ethylene glycol yields and simplified formulations, establishing a foundation for the development of more efficient and cost-effective bioprocesses using K. phaffii. Full article