Search Results (13,890)

Continuous blood pressure (BP) monitoring provides valuable insight into the body’s dynamic cardiovascular regulation across various physiological states such as physical activity, emotional stress, postural changes, and sleep. Continuous BP monitoring captures different variations in systolic and diastolic pressures, reflecting autonomic nervous system activity, vascular compliance, and circadian rhythms. This enables early identification of abnormal BP trends and allows for timely diagnosis and interventions to reduce the risk of cardiovascular diseases (CVDs) such as hypertension, stroke, heart failure, and chronic kidney disease as well as chronic stress or anxiety disorders. To facilitate continuous BP monitoring, we propose an AI-powered estimation framework. The proposed framework first uses an expert-driven feature engineering approach that systematically extracts physiological features from photoplethysmogram (PPG)-based arterial pulse waveforms (APWs). Extracted features include pulse rate, ascending/descending times, pulse width, slopes, intensity variations, and waveform areas. These features are fused with demographic data (age, gender, height, weight, BMI) to enhance model robustness and accuracy across diverse populations. The framework utilizes a Tab-Transformer to learn rich feature embeddings, which are then processed through an ensemble machine learning framework consisting of CatBoost, XGBoost, and LightGBM. Evaluated on a dataset of 1000 subjects, the model achieves Mean Absolute Errors (MAE) of 3.87 mmHg (SBP) and 2.50 mmHg (DBP), meeting British Hypertension Society (BHS) Grade A and Association for the Advancement of Medical Instrumentation (AAMI) standards. The proposed architecture advances non-invasive, AI-driven solutions for dynamic cardiovascular health monitoring. 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

Acoustic tweezers, as advanced micro/nano manipulation tools, play a pivotal role in biomedical engineering, microfluidics, and precision manufacturing. However, piezoelectric-based acoustic tweezers face performance limitations due to multi-physical coupling effects during microfabrication. This study proposes a novel approach using injection molding with embedded electronics (IMEs) technology to fabricate piezoelectric micro-ultrasonic transducers with micron-scale precision, addressing the critical issue of acoustic node displacement caused by thermal–mechanical coupling in injection molding—a problem that impairs wave transmission efficiency and operational stability. To optimize the IME process parameters, a hybrid multi-objective optimization framework integrating NSGA-II and MOPSO is developed, aiming to simultaneously minimize acoustic node displacement, volumetric shrinkage, and residual stress distribution. Key process variables—packing pressure (80–120 MPa), melt temperature (230–280 °C), and packing time (15–30 s)—are analyzed via finite element modeling (FEM) and validated through in situ tie bar elongation measurements. The results show a 27.3% reduction in node displacement amplitude and a 19.6% improvement in wave transmission uniformity compared to conventional methods. This methodology enhances acoustic tweezers’ operational stability and provides a generalizable framework for multi-physics optimization in MEMS manufacturing, laying a foundation for next-generation applications in single-cell manipulation, lab-on-a-chip systems, and nanomaterial assembly. Full article

Land cover-based models that rely on purpose-specific thematic details are common in environmental fields such as hydrology, water quality, and ecology. Global remotely sensed land cover from the Dynamic World dataset on Google Earth Engine has trees and built area classes, but enables modelers to create more thematically detailed classifications based on pixel class probabilities from their convolutional neural network (CNN) classifier. However, more information is needed about how these probabilities relate to actual heterogeneity within a land cover class. We used Dynamic World CNN class probabilities to subclassify temperate and tropical forest types from the trees class in the Eastern United States and Costa Rica, as well as developed area intensities from the built class. Subclassifications were evaluated against reference data and in a watershed they were not trained for. The results on dominant temperate forest type user’s accuracy (i.e., of all the pixels classified as a specific land cover type, how many are actually that type) ranged from 43% to 76%, while producer’s accuracy (i.e., of all the actual pixels of a specific land cover type, how many were correctly classified) ranged from 50% to 70%. In the untrained watershed, the overall accuracy was 85% for temperate forest types and 52% for developed areas, demonstrating reliability in classifying forest and developed land cover types. This approach creates opportunities to access up-to-date land cover information with greater thematic detail. Full article

The safety and longevity of engineering structures depend on precise and timely monitoring, especially during load testing inspections. Conventional displacement measurement methods—such as LVDT sensors, GNSS, RTS, and levels—each present benefits and limitations in terms of accuracy, applicability, and practicality. Photogrammetry has emerged as a promising alternative, offering non-contact measurement, cost-effectiveness, and adaptability in challenging environments. This study investigates the potential of photogrammetric methods for determining structural displacements during load testing in real-world conditions where such approaches remain underutilized. Two photogrammetric techniques were tested: (1) a single-image homography-based approach, and (2) a multi-image bundle block adjustment (BBA) approach using both UAV and tripod-mounted imaging platforms. Displacement results from both methods were compared against reference measurements obtained by traditional LVDT sensors and robotic total station. The study evaluates the influence of different camera systems, image acquisition techniques, and processing methods on the overall measurement accuracy. The findings suggest that the photogrammetric method, especially when optimized, can provide reliable displacement data with sub-millimeter accuracy, highlighting their potential as a viable alternative or complement to established geodetic and sensor-based approaches in structural testing. Full article

Membrane fouling remains a major challenge in full-scale membrane bioreactor (MBR) systems, reducing operational efficiency and increasing maintenance needs. This study introduces a predictive and analytic framework for membrane fouling by integrating artificial intelligence (AI)-driven feature engineering and explainable AI (XAI) using real-world data from an MBR treating food processing wastewater. The framework refines the target parameter to specific flux (flux/transmembrane pressure (TMP)), incorporates chemical oxygen demand (COD) removal efficiency to reflect biological performance, and applies a moving average function to capture temporal fouling dynamics. Among tested models, CatBoost achieved the highest predictive accuracy (R² = 0.8374), outperforming traditional statistical and other machine learning models. XAI analysis identified the food-to-microorganism (F/M) ratio and mixed liquor suspended solids (MLSSs) as the most influential variables affecting fouling. This robust and interpretable approach enables proactive fouling prediction and supports informed decision making in practical MBR operations, even with limited data. The methodology establishes a foundation for future integration with real-time monitoring and adaptive control, contributing to more sustainable and efficient membrane-based wastewater treatment operations. However, this study is based on data from a single full-scale MBR treating food processing wastewater and lacks severe fouling or cleaning events, so further validation with diverse datasets is needed to confirm broader applicability. Full article

During the last few years, the requirements for highly efficient, sustainable, and versatile materials in modern biomedicine, aircraft and aerospace industries, automotive production, and electronic and electrical engineering applications have increased. This has led to the development of new and innovative methods for material modification and optimization. This can be achieved in many different ways, but one such approach is the application of surface thin films. They can be conductive (metallic), semi-conductive (metal-ceramic), or isolating (polymeric). Special emphasis is placed on applying semi-conductive thin films due to their unique properties, be it electrical, chemical, mechanical, or other. The particular thin films of interest are composite ones of the type of transition metal oxide (TMO) and transition metal nitride (TMN), due to their widespread configurations and applications. Regardless of the countless number of studies regarding the application of such films in the aforementioned industrial fields, some further possible investigations are necessary to find optimal solutions for modern problems in this topic. One such problem is the possibility of characterization of the applied thin films, not via textbook approaches, but through a simple, modern solution using their electrical properties. This can be achieved on the basis of measuring the films’ electrical impedance, since all different semi-conductive materials have different impedance values. However, this is a huge practical work that necessitates the collection of a large pool of data and needs to be based on well-established methods for both characterization and formation of the films. A thorough review on the topic of applying thin films using physical vapor deposition techniques (PVD) in the field of different modern applications, and the current results of such investigations are presented. Furthermore, current research regarding the possible methods for applying such films, and the specifics behind them, need to be summarized. Due to this, in the present work, the specifics of applying thin films using PVD methods and their expected structure and properties were evaluated. Special emphasis was paid to the electrical impedance spectroscopy (EIS) method, which is typically used for the investigation and characterization of electrical systems. This method has increased in popularity over the last few years, and its applicability in the characterization of electrical systems that include thin films formed using PVD methods was proven many times over. However, a still lingering question is the applicability of this method for backwards engineering of thin films. Currently, the EIS method is used in combination with traditional techniques such as X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), energy-dispersive X-ray spectroscopy (EDX), and others. There is, however, a potential to predict the structure and properties of thin films using purely a combination of EIS measurements and complex theoretical models. The current progress in the development of the EIS measurement method was described in the present work, and the trend is such that new theoretical models and new practical testing knowledge was obtained that help implement the method in the field of thin films characterization. Regardless of this progress, much more future work was found to be necessary, in particular, practical measurements (real data) of a large variety of films, in order to build the composition–structure–properties relationship. Full article

This paper provides research that shows that the scale and quantification of the degree of fracturing in a rock mass should and can be considered when estimating geological strength index (GSI) ratings for rock mass strength and deformability estimates. In support of this notion, a brief review is provided to demonstrate why it is imperative that scale is considered when using GSI in engineering design. The impact of scale and scale effects on the engineering response of a rock mass typically requires a definition of fracture intensity relative to the volume or size of rock mass under consideration and the relative scale of the project being built. In this research three volume scales are considered: the volume of a structural domain, a representative elemental REV, and unit volume. A theoretical framework is established that links these three volume scales together, how they are estimated, and how they relate to parameters used to estimate engineering behaviour. Analysis of data from several examples and case histories for real rock masses is presented that compares and validates the use of a new and innovative but practical method (a sphere of unit volume) to estimate fracture intensity parameters VFC or P30 (fractures/m³) and P32 (fracture area—m²/m³) that is included on the vertical axis of the volumetric V-GSI chart. The research demonstrates that the unit volume approach to calculating VFC and P32 used in the V-GSI system compares well with other methods of estimating these two parameters (e.g., discrete fracture network (DFN) modelling). The research also demonstrates the reliability of the VFC-correlated rating scale included on the vertical axis of the V-GSI chart for use in estimating first-order strength and deformability estimates for rock masses. This quantification does not negate or detract from geological logic implicit in the original graphical GSI chart. Full article

A new, simple machine was developed to address a long-standing challenge in biomedical and mechanical engineering: how to enhance the primary stability and long-term integration of screws and implants in low-density or heterogeneous materials, such as bone or composite substrates. Traditional screws often rely solely on external threading for fixation, leading to limited cohesion, poor integration, or early loosening under cyclic loading. In response to this problem, we designed and built a novel device that leverages a unique mechanical principle to simultaneously perforate, collect, and compact the substrate material during insertion. This mechanism results in an internal material interlock, enhancing cohesion and stability. Drawing upon principles from physics, chemistry, engineering, and biology, we evaluated its biomechanical behavior in synthetic bone analogs. The maximum insertion (MIT) and removal torques (MRT) were measured on synthetic osteoporotic bones using a digital torquemeter, and the values were compared directly. Experimental results demonstrated that removal torque (mean of 21.2 Ncm) consistently exceeded insertion torque (mean of 20.2 Ncm), indicating effective material interlocking and cohesive stabilization. This paper reviews the relevant literature, presents new data, and discusses potential applications in civil infrastructure, aerospace, and energy systems where substrate cohesion is critical. The findings suggest that this new simple machine offers a transformative approach to improving fixation and integration across multiple domains. 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

In the Kelameili volcanic gas reservoir, primary hydraulic fracturing treatments in some wells take place on a limited scale, resulting in a rapid decline in production post stimulation and necessitating re-fracturing operations. However, prolonged production has led to a significant evolution in the in situ stress field, which complicates the design of re-fracturing parameters. To address this, this study adopts an integrated geology–engineering approach to develop a formation-specific geomechanical model, using rock mechanical test results and well-log inversion to reconstruct the reservoir’s initial stress field. The dynamic stress field simulations and re-fracturing parameter optimization were performed for Block Dixi-14. The results show that stress superposition effects induced by multiple fracturing stages and injection–production cycles have significantly altered the current in situ stress distribution. For Well K6, the optimized re-fracturing parameters comprised a pump rate of 12 m³/min, total fluid volume of 1200 m³, prepad fluid ratio of 50–60%, and proppant volume of 75 m³, and the daily gas production increased by 56% correspondingly, demonstrating the effectiveness of the optimized re-fracturing design. This study not only provides a more realistic simulation framework for fracturing volcanic rock gas reservoirs but also offers a scientific basis for fracture design optimization and enhanced gas recovery. The geology–engineering integrated methodology enables the accurate prediction and assessment of dynamic stress field evolution during fracturing, thereby guiding field operations. Full article

This present study proposes different machine learning-based predictors for the assessment of the residual compressive strength of Self-Compacting Concrete (SCC) subjected to high temperatures. The investigation is based on several literature algorithmic approaches based on Artificial Neural Networks with distinct training algorithms (Bayesian Regularization, Levenberg–Marquardt, Scaled Conjugate Gradient, and Resilient Backpropagation), Support Vector Regression, and Random Forest methods. A training database of 150 experimental data points is derived from a careful literature review, incorporating temperature (20–800 °C), geometric ratio (height/diameter), and corresponding compressive strength values. A statistical analysis revealed complex non-linear relationships between variables, with strong negative correlation between temperature and strength and heteroscedastic data distribution, justifying the selection of advanced machine learning techniques. Feature engineering improved model performance through the incorporation of quadratic terms, interaction variables, and cyclic transformations. The Resilient Backpropagation algorithm demonstrated superior performance with the lowest prediction errors, followed by Bayesian Regularization. Support Vector Regression achieved competitive accuracy despite its simpler architecture. Experimental validation using specimens tested up to 800 °C showed a good reliability of the developed systems, with prediction errors ranging from 0.33% to 23.35% across different temperature ranges. 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

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