Search Results (375)

Recent studies show that titanium (Ti)-based alloys combine established mechanical strength, corrosion resistance, and biocompatibility with emerging electrical and electrochemical properties relevant to bioelectronics. The main goal of the present manuscript is to give a wide-ranging overview on the use of Ti-alloys in electronics and biomedicine, focusing on a comprehensive analysis and synthesis of the existing literature to identify gaps and future directions. Concurrently, the identification of possible correlations between the effects of the manufacturing process, alloying elements, and other degrees of freedom influencing the material characteristics are put in evidence, aiming to establish a global view on efficient interdisciplinary efforts to realize high-added-value smart devices useful in the field of biomedicine, such as, for example, implantable apparatuses. This review mostly summarizes advances in surface modification approaches—including anodization, conductive coatings, and nanostructuring that improve conductivity while maintaining biological compatibility. Trends in applications demonstrate how these alloys support smart implants, biosensors, and neural interfaces by enabling reliable signal transmission and long-term integration with tissue. Key challenges remain in balancing electrical performance with biological response and in scaling laboratory modifications for clinical use. Perspectives for future work include optimizing alloy composition, refining surface treatments, and developing multifunctional designs that integrate mechanical, biological, and electronic requirements. Together, these directions highlight the potential of titanium alloys to serve as foundational materials for next-generation bioelectronic medical technologies. Full article

The real-time phase picking, detection, and location of seismic events is a crucial challenge for monitoring in densely populated volcanic areas. In such contexts, low-magnitude events may escape traditional detection methods due to high levels of anthropogenic noise, which often masks weak seismic signals. This study presents the implementation of a near-real-time automatic event detector with a seismic phase recognizer, pick associator, and localiser. The system is based on PhaseNet, a well-established deep neural network recognized for its effectiveness in seismology. The main innovation introduced in this work lies in the direct application of this method to real-time data streams. This integration allows for the enhanced identification and cataloguing of low-magnitude seismic events that would otherwise remain unobserved. The adoption of the system in a real-time operational context not only increases monitoring sensitivity and responsiveness but also contributes to a more detailed and comprehensive understanding of seismic activity in critical volcanic areas, providing essential data for risk assessment and prevention. Full article

Affective Computing and emotion recognition hold significant importance in healthcare, identity verification, human–computer interaction, and related fields. Accurate identification of emotion is crucial for applications in medicine, education, psychology, and military domains. Electroencephalographic (EEG) signals have gained widespread application in emotion recognition due to their inherent characteristics of being non-concealable and directly reflecting brain activity. In recent years, with the establishment of open datasets and advancements in deep learning, an increasing number of researchers have integrated EEG with deep learning methods for emotion recognition studies. This review summarizes commonly used deep learning models in EEG-based emotion recognition along with their applications in this field, including the design of different network architectures, optimization strategies, and model designs based on EEG signal features. We also discuss limitations from the perspectives of commonality–individuality (C-I) and suggest improvements. The review outlines future research directions and provided a minimal C-I framework to assess models. Through this review, we aim to provide researchers in this field with a comprehensive reference and approach to balance universality and personalization to promote the development of deep learning-based EEG emotion recognition methods. Full article

Flywheel energy storage systems play an important role in frequency regulation and power quality control within modern power grids, yet the fault signals generated by defects in their rolling bearings are typically indistinct, making direct diagnosis difficult. Raw noisy signals often yield unsatisfactory diagnostic performance when directly processed by neural networks. Although MOMEDA (Multipoint Optimal Minimum Entropy Deconvolution Adjusted) can effectively extract impulsive fault components, its performance is highly dependent on the selected fault period and filter length. To address these issues, this paper proposes an improved fault diagnosis method that integrates MOMEDA-based periodic extraction with a neural network classifier. The Artificial Fish Swarm Algorithm (AFSA) is employed to adaptively determine the key parameters of MOMEDA using multi-point kurtosis as the optimization objective, and the optimized parameters are used to enhance impulsive fault features. The filtered signals are then converted into image representations and fed into a ResNet-18 network (a compact 18-layer deep convolutional neural network from the residual network family) to achieve intelligent identification and classification of bearing faults. Experimental results demonstrate that the proposed method can effectively extract and diagnose bearing fault signals. Full article

This review presents a critical and comparative analysis of carbon-based electrochemical sensing platforms for the determination of heavy metal ions in water, with emphasis on Pb²⁺, Cd²⁺, and Hg²⁺. The growing discharge of industrial and mining effluents has led to persistent contamination of aquatic environments by toxic metals, creating an urgent need for sensitive, rapid, and field-deployable analytical technologies. Carbon-based nanomaterials, including graphene, carbon nanotubes (CNTs), and MXene, have emerged as key functional components in modern electrochemical sensors due to their high electrical conductivity, large surface area, and tunable surface chemistry. Based on reported studies, typical detection limits for Pb²⁺ and Cd²⁺ using differential pulse voltammetry (DPV) on glassy carbon and thin-film electrodes are in the range of 0.4–1.2 µg/L. For integrated thin-film sensing systems, limits of detection of 0.8–1.2 µg/L are commonly achieved. MXene-based platforms further enhance sensitivity and enable Hg²⁺ detection with linear response ranges typically between 1 and 5 µg/L, accompanied by clear electrochemical or optical signals. Beyond conventional electrochemical detection, this review specifically highlights self-sustaining visual sensors based on MXene integrated with enzyme-driven bioelectrochemical systems, such as glucose oxidase (GOD) and Prussian blue (PB) assembled on ITO substrates. These systems convert chemical energy into measurable colorimetric signals without external power sources, enabling direct visual identification of Hg²⁺ ions. Under optimized conditions (e.g., 5 mg/mL GOD and 5 mM glucose), stable and distinguishable color responses are achieved for rapid on-site monitoring. Overall, this review not only summarizes current performance benchmarks of carbon-based sensors but also identifies key challenges, including long-term stability, selectivity under multi-ion interference, and large-scale device integration, while outlining future directions toward portable multisensor water-quality monitoring systems. Full article

This paper presents a systematic literature review (SLR) of research on strategic positioning of companies and the measurement of greenwashing in sustainability reporting. Its main aim is to synthesize and organize the existing literature, identify key research gaps, and outline directions for future studies. Drawing on a rigorous content analysis of 88 studies, we delineate strategic typologies of greenwashing in sustainability reporting and examine content-analysis-based measurement approaches used to detect it. Our SLR shows that most strategic typologies draw on theories such as legitimacy theory, impression management theory, signaling theory, and stakeholder theory. Several studies adopt a four-quadrant matrix with varying conceptual dimensions, while others classify strategic responses to institutional pressures along a passive–active continuum. However, the evidence suggests that to assume that companies uniformly pursue sustainability reporting strategies is a major oversimplification. The findings also indicate that the literature proposes a variety of innovative, content-analysis-based approaches aimed at capturing divergences between communicative claims and organizational realities—most notably, discrepancies between disclosure and measurable performance, and between symbolic and substantive sustainability actions, as well as the identification of selective or manipulative communication practices that may signal greenwashing. Analytical techniques commonly focus on linguistic and visual cues in sustainability reports, including tone (sentiment and narrative framing), readability (both traditional readability indices and machine learning–based textual complexity measures), and visual content (selective emphasis, imagery framing, and graphic distortions). We also synthesize studies that document empirically verified instances of greenwashing and contrast them with research that, in our view, relies on overly simplified or untested assumptions. Based on this SLR, we identify central theoretical and methodological priorities for advancing the study of greenwashing in sustainability reporting and propose a research agenda to guide future research. Full article

Bridges serve as vital connectors in the transportation network and infrastructure. Given their significance, it is crucial to continuously monitor bridge conditions to ensure the efficient operation of transportation systems. With advancements in sensing technologies, transportation infrastructure assessment has evolved through the integration of structural health monitoring (SHM) methodologies. Traditionally, bridge monitoring has relied on direct sensor instrumentation; however, this method encounters practical obstacles, including traffic disruptions and limited sensor availability. In contrast, indirect bridge health monitoring (iBHM) utilizes data from moving traffic on the bridge itself. This innovative approach eliminates the need for embedded instrumentation, as sensors on vehicles traverse the bridge, capturing the dynamic characteristics of the bridge. In this paper, system identification methods are explored to analyze the acceleration data gathered using a bicycle-mounted sensor traversing the bridge. To explore the feasibility of this micromobility-based approach, bridge responses are measured under varying traversing conditions combined with dynamic rider–bicycle–bridge interaction for comprehensive evaluation. The proposed method involves a hybrid approach combining Wavelet Packet Transform (WPT) with Synchro-extracting Transform (SET), which are employed to analyze the time–frequency characteristics of the acceleration signals of bike-based iBHM. The results indicate that the combination of WPT-SET demonstrates superior robustness and accuracy in isolating dominant nonstationary frequencies. The performance of the proposed method is compared with another prominent signal processing algorithm known as Time-Varying Filtering Empirical Mode Decomposition (TVF-EMD). Ultimately, this study underscores the potential of bicycles as low-cost, mobile sensing platforms for iBHM that are otherwise inaccessible to motorized vehicles. Full article

Early detection of bubble generation from tube arrays in systems such as fast reactor steam generators, Pressurized Water Reactor (PWR) cores, and Liquefied Natural Gas (LNG) regasification units is critical for safety. While various methods have been proposed, they face challenges such as high spatial resolution requirements, rapid response times, and varying strengths and weaknesses, suggesting the need for a combined approach. This study integrates ultrasonic testing (UT) with Machine Learning (ML) to identify the presence, location, and direction of bubbles within a complex tube array that cause signal attenuation. A Convolutional Neural Network (CNN) successfully achieved 100% identification accuracy. Furthermore, a method was developed that uses an autoencoder as a feature extractor, combined with a One-Class Support Vector Machine (SVM) and k-means. This approach achieved high accuracy and a correct decision basis. It also demonstrated strong generalization, successfully detecting anomalies without requiring labels for anomalous data, enabling robust bubble identification. Full article

Ferroptosis is a novel iron-sensitive subtype of regulated cell death (RCD), persisting under extreme lipid peroxidation and iron/redox imbalances. Unlike apoptosis, necroptosis, and pyroptosis, ferroptosis is a signaling-driven process mediated through iron metabolism imbalance, polyunsaturated fatty acid (PUFA) exceeding oxidation, and defects in its protective systems like Xc-/GSH/GPx4. Specifically, this review establishes that iron-driven ferroptosis is a central underlying pathomechanistic factor in a broad range of human diseases. Significantly, whether its modulation is therapeutic, it is entirely conditional on the specific disease context. Thus, its induction can provide a promising antidote for destructive cancer cells when conjoined with immuno-therapies to boost anticancer immunity. Conversely, iron-mediated ferroptosis suppression is a key factor in countering destructive changes in a whole range of degenerative and acute injuries. Current therapeutic approaches include iron chelators, lipid oxidation inhibitors, GPx4 activators, natural and active compounds, and novel drug delivery systems. However, against all odds and despite its intense therapeutic promise, its translation into a practical medicinal strategy faces many difficulties. Thus, a therapeutic agent specifically focused on its modulation is still lacking. The availability of selective biologic markers is a concern. The challenges in the direct pathologic identification of ferroptosis in a complex in vivo systemic scenario remain. Current avenues for its future development are pathogen infections, the discovery of novel regulating factors, and novel approaches to personalized medicine centered on its organ-level in vivo signatures. Full article

Sepsis is a life-threatening condition driven by a dysregulated immune response, leading to systemic inflammation and multi-organ failure. Among the key molecular regulators, S100A8/A9 has emerged as a critical damage-associated molecular pattern (DAMP) protein, amplifying pro-inflammatory signaling via the Toll-like receptor 4 (TLR4) and receptor for advanced glycation end products (RAGE) pathways. Elevated S100A8/A9 levels correlate with disease severity, making it a promising biomarker and therapeutic target. To unravel the role of S100A8/A9 in sepsis, we integrate scRNA-seq and RNA-seq approaches. scRNA-seq enables cell-type-specific resolution of immune responses, uncovering cellular heterogeneity, state transitions, and inflammatory pathways at the single-cell level. In contrast, RNA-seq provides a comprehensive view of global transcriptomic alterations, allowing robust statistical analysis of differentially expressed genes. The integration of both approaches enables precise deconvolution of immune cell contributions, validation of cell-specific markers, and identification of potential therapeutic targets. Our findings highlight the S100A8/A9-driven inflammatory cascade, its impact on immune cell interactions, and its potential as a diagnostic and prognostic biomarker in sepsis. Eight protein targets resulted from the integrative transcriptomics studies (corresponding to S100A8, S100A9, S100A6, NAMPT, FTH1, B2M, KLF6 and SRGN) have been used to predict interaction affinities with 2958 ChEMBL approved drugs, by using a pre-trained AI models (PLAPT) in order to point directions on drug repurposing in sepsis. The strongest predicted interactions have been confirmed with molecular docking and molecular dynamics analysis. This study underscores the power of combining high-throughput transcriptomics to advance our understanding of sepsis pathophysiology and develop precision medicine strategies. Full article

Electromechanical systems are inherently hybrid in nature, combining electrical and mechanical processes, and their increasing complexity requires the development of universal and computationally efficient mathematical models. In this study, we propose a macromodeling approach that represents the electromechanical system as a “black box,” in which internal physical processes are disregarded and the system behavior is defined solely by the relationship between input and output signals. The identification of such macromodels is reduced to solving a nonlinear optimization problem. To address this challenge, the directed cone method is applied, which searches for the global minimum of the objective function through stochastic movement across the hyperplane defined by the optimization problem. Several algorithmic improvements of the directed cone method are investigated, including step-size adaptation, simultaneous adaptation of step size and hypercone opening angle, and a tunneling procedure. Their effectiveness is evaluated using the construction of a macromodel of a single-phase asynchronous motor as a case study. Performance was assessed according to computational complexity (measured as the number of objective function evaluations until convergence), relative modeling accuracy, and the dynamics of progression toward the global minimum. The experimental results show that the tunneling-based algorithm provides the highest modeling accuracy with the lowest computational cost, whereas the step-size-only adaptation was found to be the least effective. The proposed approach demonstrates the feasibility of constructing accurate macromodels of electromechanical systems that can be integrated into computer-aided modeling environments such as MATLAB/Simulink R2023b. Future work will focus on extending the approach to a broader class of electromechanical systems and developing hybrid algorithms to enhance robustness with respect to model nonlinearity. Full article

Accurate identification of rear light signals in preceding vehicles is pivotal for Advanced Driver Assistance Systems (ADAS), enabling early detection of driver intentions and thereby improving road safety. In this work, we present a novel approach that leverages a meta-learning-enhanced YOLOv8 model to detect left and right turn indicators, as well as brake signals. Traditional radar and LiDAR provide robust geometry, range, and motion cues that can indirectly suggest driver intent (e.g., deceleration or lane drift). However, they do not directly interpret color-coded rear signals, which limits early intent recognition from the taillights. We therefore focus on a camera-based approach that complements ranging sensors by decoding color and spatial patterns in rear lights. This approach to detecting vehicle signals poses additional challenges due to factors such as high reflectivity and the subtle visual differences between directional indicators. We address these by training a YOLOv8 model with a meta-learning strategy, thus enhancing its capability to learn from minimal data and rapidly adapt to new scenarios. Furthermore, we developed a post-processing layer that classifies signals by the geometric properties of detected objects, employing mathematical principles such as distance, area calculation, and Intersection over Union (IoU) metrics. Our approach increases adaptability and performance compared to traditional deep learning techniques, supporting the conclusion that integrating meta-learning into real-time object detection frameworks provides a scalable and robust solution for intelligent vehicle perception, significantly enhancing situational awareness and road safety through reliable prediction of vehicular behavior. Full article

Drosophila melanogaster (D. melanogaster) has been widely used in biology, including classical genetics, for almost a century. With the entire D. melanogaster genome sequenced and the existence of transgenic and mutant individuals, the species offers opportunities for targeted gene expression and manipulation. Genes involved in the regulation of the animal’s cardiac development include genes associated with the ancient regulatory networks that direct the formation of the cardiac form. However, additional loci can also affect cardiac development, including genes associated with cellular metabolism and protein homeostasis; signaling pathways necessary for the establishment of body segmentation and polarity; homeotic genes involved in the establishment of the animal body plan; and finally, genes encoding chromatin modification enzymes. Conservation in the genetic networks governing cardiac development between D. melanogaster and mammalian vertebrates, coupled with the absence of genetic redundancy in D. melanogaster, allows for the study and evaluation of mutations that could potentially disrupt cardiac development in the former. In this manner, phenotypes in D. melanogaster can be compared with phenotypes present in vertebrate animal models and human patients; this, in turn, allows for comparisons of gene function to be made across different species and for identification of candidate genes with a potential effect on cardiac development. These genes can then be further tested in vertebrate models with possible clinical implications. It is thus the purpose of this comprehensive literature review to summarize and categorize studies evaluating the results of genetic mutations on D. melanogaster cardiac development, as well as uncover any associations between D. melanogaster and similar phenotypes in vertebrates and humans due to effects on the corresponding gene orthologs. Full article

Obtaining accurate information on stratigraphic conditions and drilling status is necessary to ensure the safety of the drilling process and to guarantee the production of oil and gas. Sensing while drilling and intelligent monitoring technology, which employ multiple sensors and involve the use of intelligent algorithms, can be used to collect downhole information in situ to ensure safe, reliable, and efficient drilling and mining operations. These approaches are characterized by effective sensing and comprehensive utilization of drilling information through the integration of multi-sensor signals and intelligent algorithms, a core component of machine learning. The article summarizes the current research status of domestic and international sensing while drilling and intelligent monitoring technology using systematically collected relevant information. Specifically, first, the drilling-sensing methods used for in situ acquisition of downhole information, including fiber-optic sensing, electronic-nose sensing, drilling engineering-parameter sensing, drilling mud-parameter sensing, drilling acoustic logging, drilling electromagnetic wave logging, and drilling seismic logging, are described. Next, the basic composition and development direction of each sensing technology are analyzed. Subsequently, the application of intelligent monitoring technology based on machine learning in various aspects of drilling- and mining-status identification, including bit wear monitoring, stuck drill real-time monitoring, well surge real-time monitoring, and real-time monitoring of oil and gas output, is introduced. Finally, the potential applications of sensing while drilling and intelligent monitoring technology in deep-earth, deep-sea, and deep-space contexts are discussed, and the challenges, constraints, and development trends are summarized. Full article

Heat exchangers are widely used process equipment in industrial sectors, making the study of their temperature dynamics particularly appealing due to the nonlinearities involved. Model-free approaches enable the use of input and output data to generate specific and accurate estimations for each proposed system. In this work, a model-free identification strategy is proposed using a convolutional neural network to estimate the system’s behavior. Notably, the model does not rely on direct temperature measurements; instead, temperature is inferred from other system signals such as reference, flow, and control inputs. This data-driven approach offers greater specificity and adaptability, often outperforming manufacturer-provided coefficients whose performance may vary from design expectations. The results yielded an $R^2$ index of 0.9951 under nominal conditions and 0.9936 when the system was subjected to disturbances. Full article