Search Results (16,417)

The aim of this work is to develop a beneficial methodology for improving the ecological and economic efficiency of chemico-technological systems (CTS). The problem is formulated as a control with a vector objective function that includes economic and environmental components. A practical approach to enhancing the environmental and economic efficiency of CTS is presented. Some approaches to accident prevention including the application of a problem-oriented dynamic model are introduced. Extended Ecological–Technological Portfolios have been developed. These Portfolios represent simplified visual models aiming to increase the environmental and economic efficiency of the CTS. Portfolios allow for the identification of dependencies between technological faults and ecological criteria and enable the investigation of the impact of the concrete chemico-technological process on the environment. Based on the Portfolios, decisions can be made for improving the economic–ecological efficiency of CTS and the prevention of accidents. Ecological–Technological Matrices, which provide a generalized characterization of technological breakdowns, have been developed. A strategy for adjusting technological constraints, using Matrices and vector criteria, has been proposed. Portfolios and Matrices can be applied in data preparation to solve certain artificial intelligence tasks for increasing the environmental and economic efficiency of potentially hazardous CTS. Some examples are given, presenting the industrial control of ammonia synthesis, methane conversion, and chemical absorption of CO₂. Full article

A photonic frequency discrimination and phase identification system based on an on-chip dual Mach–Zehnder modulator (MZM) is proposed. By utilizing the power cancellation (PCD) condition, the system achieves high-precision frequency discrimination and phase identification of multi-frequency radio frequency (RF) signals. The system adopts an on-chip dual-MZM architecture, effectively reducing phase interference in signal transmission caused by environmental factors. This is achieved through precise bias control and the adjustment of the local oscillator (LO) signal’s optical path delay using a tunable optical delay line (TODL), ensuring that the dual MZM operates in the phase inversion condition. When the LO frequency matches that of an RF signal, a significant power attenuation is observed at the system output. The phase of the RF signal is extracted from the corresponding PCD. Experimental results demonstrate that the system achieves a bandwidth of 30 GHz, a frequency resolution of 700 kHz, and a frequency resolution error of less than 498 kHz, with a phase identification range from 0° to 65°. With high integration, the system demonstrates excellent accuracy in multi-frequency signal measurement and phase identification, offering a reliable solution for complex RF scenarios. Full article

The sustainable management of water resources and the development of climate-resilient infrastructure depend on the precise identification of water bodies in satellite imagery. This paper presents a novel deep learning architecture that integrates a convolutional block attention module (CBAM) into a modified EfficientNet–UNet backbone. This integration allows the model to prioritize informative features and spatial areas. The model robustness is ensured through a rigorous training regimen featuring five-fold cross-validation, dynamic test-time augmentation, and optimization with the Lovász loss function. The final model achieved the following values on the independent test set: precision = 90.67%, sensitivity = 86.96%, specificity = 96.18%, accuracy = 93.42%, Dice score = 88.78%, and IoU = 79.82%. These results demonstrate improvement over conventional segmentation pipelines, highlighting the effectiveness of attention mechanisms in extracting complex water-body patterns and boundaries. The key contributions of this paper include the following: (i) adaptation of CBAM within a UNet-style architecture tailored for remote sensing water-body extraction; (ii) a rigorous ablation study detailing the incremental impact of decoder complexity, attention integration, and loss function choice; and (iii) validation of a high-fidelity, computationally efficient model ready for deployment in large-scale water-resource and ecosystem-monitoring systems. Our findings show that attention-guided segmentation networks provide a robust pathway toward high-fidelity and sustainable water-body mapping. Full article

In response to the requirements for groundwater pollution risk identification and zoning-based management in typical coastal industrial agglomeration areas, this study takes the coastal industrial zone of the Jidong Plain as the research area and. It develops an integrated evaluation framework for groundwater pollution prevention zoning. The framework is quantitatively centered on pollution source load assessment and groundwater vulnerability analysis, and applies the Analytic Hierarchy Process (AHP) solely as an interpretative and decision-support tool. In this study, the Linear Weighted Function (LWF) method and the DRASTIC model are employed to quantitatively characterize pollution source load intensity (PI) and groundwater system vulnerability (DI), respectively. By constructing a prevention and control index (R) in the form of the product of pollution source load and groundwater vulnerability, the framework achieves an integrated representation of pollution input intensity and the carrying capacity of the groundwater system. The AHP is not directly involved in indicator weighting or zoning calculations; instead, it is applied as a post hoc analytical approach to identify the relative importance of different evaluation factors in groundwater pollution prevention zoning, thereby supporting the interpretation of the zoning results and management priority setting. The results indicate that the overall pollution source load in the study area is relatively low, with low-to-moderately low load zones accounting for 68.7% of the area. In comparison, high-load zones account for only 1.43% and are mainly concentrated in the southeastern coastal industrial belt. Shallow groundwater generally exhibits high vulnerability, with highly vulnerable zones covering 86.56% of the area and predominantly distributed in the northeastern Quaternary unconsolidated sedimentary regions. Based on the prevention and control index (R), the study area is classified into prevention zones and remediation zones. Prevention zones account for 94.47% of the total area, whereas remediation zones account for 5.53%. High-risk areas are mainly concentrated in coastal industrial belts and highly vulnerable cultivated areas. The results demonstrate that the proposed integrated evaluation framework effectively couples pollution source load and groundwater vulnerability, and, on the basis of the finalized zoning results, enhances the interpretability and management specificity of the zoning outcomes through post hoc decision-support analysis, thereby providing a scientific basis and methodological reference for groundwater pollution prevention zoning and differentiated management in coastal industrial regions. Full article

Background/Objectives: Safe delivery and correct identification of newborns are critical aspects of healthcare systems globally. The accreditation of healthcare and standards regulation significantly promotes the adoption of modern technologies to address risks related to infant abduction and misidentification. The effectiveness and extent of these mandates vary across settings and countries. Therefore, this study aims to map and explore modern technologies used for safe newborn delivery and correct identification aligned with healthcare accreditation and regulatory frameworks. Methods: This review adheres to the Preferred Reporting Items for Systematic Review and Meta-Analysis extension for scoping reviews (PRISMA-ScR) guidelines. The Problem, Intervention, Comparison, and Outcome (PICO) framework was employed to facilitate the development of the research question. This study examined studies reporting technologies such as radio frequency identification (RFID), biometric identification, and real-time monitoring across healthcare settings for infant protection through the Normalization Process Theory (NPT). Among three databases and search engines (PubMed, Google Scholar, and Web of Science). The risk of bias for each study was assessed using the AACODS Checklist, SQUIRE 2.0 Checklist, TIDieR Checklist, and JBI tools. Results: Out of 8753 records, only 27 reports were eligible to be included in this review. The most frequently reported technologies were RFID systems (11 studies, 37.9%) and biometric systems such as footprint and facial recognition (6 studies, 20.7%). Despite strong technological potential, many healthcare institutions struggled with the adoption of infant protection technologies. Accreditation systems among the high-resource settings actively mandate advanced technologies and support the integration of staff training and simulation drills. Comparably, middle- and low-income regions usually face challenges related to regulatory enforcement, infrastructure, staff readiness, and limited adoption of modern technologies. Conclusions: Accreditation and standards development are critical catalysts for the adoption of modern infant protection technology. Standards must be comprehensible, adaptable, and supported by investment in human resources and infrastructure. Future regulation must focus on strengthening enforcement, continuous quality improvement, and capacity building to achieve sustainable protection across the world. Full article

To address the performance degradation of traditional adaptive filtering algorithms in environments with correlated input signals and non-Gaussian noise, this paper proposes a complex-valued affine projection algorithm based on the $\alpha$-Sigmoid cost function ($\alpha$-CSAP). The algorithm leverages the nonlinear characteristics of the $\alpha$-Sigmoid function and implicitly achieves variable step-size updates by introducing a normalization factor, which effectively suppresses impulsive noise interference and avoids matrix inversion, thereby reducing computational complexity. Theoretical analysis derives the steady-state mean square deviation (MSD) expression for the algorithm. Simulation results demonstrate that the proposed $\alpha$-CSAP algorithm exhibits superior performance compared to traditional complex adaptive filtering algorithms in both system identification and beamforming application scenarios. Full article

Effective and sustainable livestock management within Mediterranean ecosystems depends heavily on accurate and timely monitoring of sward composition. Traditionally, this task has relied on human observers who must traverse large and often rugged areas to identify the distribution of grasses, legumes, shrubs, and other plant groups. However, this approach is not only labor-intensive and slow but also susceptible to substantial human error, especially when observations must be repeated frequently or carried out under difficult field conditions. In the present study, an alternative method that integrates wearable cameras with modern computer-vision techniques to automatically recognize pasture plant species through an edge device present in farm premises was investigated. Additionally, the feasibility of achieving reliable classification performance on resource-constrained edge devices was evaluated. To this end, five widely used pre-trained convolutional neural networks were compared against a lightweight custom model developed entirely from scratch. The results demonstrated that ResNet50 delivered the strongest classification accuracy, achieving a Matthews Correlation Coefficient (MCC) of 0.992. Nonetheless, the custom lightweight model proved to be a practical compromise for real-world field use, reaching an MCC of 0.893 while requiring only 6.24 MB of storage. The inference performance on Raspberry Pi 4, Raspberry Pi 5, and Jetson Orin Nano platforms was also evaluated, revealing that the Selective Search stage remains a major computational limitation for achieving real-time operation. The results obtained confirm the possibility of implementing a plant identification system in agricultural facilities without the need to transfer images to a cloud-based application. Full article

The increasing integration of hybrid AC/DC networks in modern power systems introduces new challenges in fault detection and grounding scheme design, necessitating advanced techniques for stable and reliable operation. This paper investigates fault detection and grounding schemes in hybrid AC/DC networks using a machine learning (ML) approach to enhance accuracy, speed, and adaptability. Traditional methods often struggle with the dynamic and complex nature of hybrid systems, leading to delayed or incorrect fault identification. To address this, we propose a data-driven ML framework that leverages features such as voltage, current, and frequency characteristics for real-time detection and classification of faults. Additionally, the effectiveness of various grounding schemes is analyzed under different fault conditions to ensure system stability and safety. Simulation results on a hybrid AC/DC test network demonstrate the superior performance of the proposed ML-based fault detection method compared to conventional techniques, achieving high precision, recall, and robustness against noise and varying operating conditions. The findings highlight the potential of ML in improving fault management and grounding strategy optimization for future hybrid power grids. Full article

Human epidermal growth factor receptor 2 (HER2) is a molecular target of interest in prostate cancer due to its association with poor prognosis and its potential role in androgen receptor signaling. However, earlier clinical trials investigating HER2-targeted therapies, including antibodies and small molecules, have shown limited efficacy. More recent studies using the HER2 antibody-drug conjugate (ADC) trastuzumab deruxtecan (T-DXd) suggest potential therapeutic benefit in prostate cancer. However, its effective utilization requires a HER2 IHC scoring system that accurately represents HER2 expression patterns unique to prostate cancer, which is currently not established. We have developed a modified HER2 IHC scoring system that, unlike the breast and gastrointestinal tumor HER2 IHC grading scales, considers the distinct spatiotemporal expression of HER2 in prostate tumors. In this commentary, we discussed two patients with metastatic prostate cancer who were classified as HER2 IHC 3+ using our prostate cancer-specific scoring system and who demonstrated meaningful clinical responses and responded to treatment with T-DXd. We further review the historical evolution of HER2 testing in prostate cancer, as well as factors that may have contributed to the failure of previous clinical trials targeting HER2 in prostate tumors. Our aim is to highlight the need for developing a standardized HER2 IHC grading model in prostate cancer, which could improve the predictive value of HER2 IHC expression, enabling a more accurate identification of patients likely to benefit from HER2-targeted ADCs. Full article

Background/Objectives: The study primarily examined whether an IoT-based medication assistance system enhances ART adherence relative to standard care, and secondarily evaluated device feasibility and error patterns over time. Methods: This prospective study was conducted between June 2022 and October 2023 at a tertiary hospital in South Korea. Adults (≥19 years) living with HIV and prescribed ART were included; those with comorbid hepatitis B or C were excluded. People living with HIV who agreed to use the IoT-based InPHRPILL system (Sofnet Inc., Seoul, Republic of Korea) were assigned to the intervention group, whereas those who declined were assigned to the control group. Viral suppression, CD4⁺ cell counts, and adherence rates were measured. Additional analyses evaluated 12-month longitudinal adherence using pill-count data in both groups, and device-measured adherence and device-associated error rates in the intervention group. Results: Thirty-five participants (12 in the intervention group and 23 in the control group) were included. The intervention group demonstrated marginally shorter durations since HIV diagnosis and ART initiation at study enrollment, as well as slightly higher baseline HIV-RNA levels; however, these differences did not reach statistical significance. The median pill-counting and IoT device adherence rates were 100% and 87.4%, respectively (median deviation error rate = 4.4%). Poisson regression revealed significantly reduced error rates over time (β = −0.06493, p < 0.01), suggesting improved device use proficiency. Conclusions: IoT-based medication assistance systems may provide objective, real-time monitoring of ART adherence and facilitate identification of discrepancies between clinical evaluations and actual adherence patterns. Larger studies targeting individuals with suboptimal adherence are warranted to determine whether such systems can enhance adherence outcomes. Full article

With the increasing deployment of grid-forming static var generators (GFM-SVG) in modern power systems, the reliability of the valve hall that houses the core power modules has become a critical concern. To overcome the limitations of conventional wired monitoring systems—complex cabling, poor scalability, and incomplete state perception—this paper proposes and implements a multi-source fusion wireless data acquisition system specifically designed for GFM-SVG valve halls. The system integrates acoustic, visual, and infrared sensing nodes into a wireless sensor network (WSN) to cooperatively capture thermoacoustic visual multi-physics information of key components. A dual-mode communication scheme, using Wireless Fidelity (Wi-Fi) as the primary link and Fourth-Generation Mobile Communication Network (4G) as a backup channel, is adopted together with data encryption, automatic reconnection, and retransmission-checking mechanisms to ensure reliable operation in strong electromagnetic interference environments. The main innovation lies in a multi-source information fusion algorithm based on an improved Dempster–Shafer (D–S) evidence theory, which is combined with the object detection capability of the You Only Look Once, Version 8 (YOLOv8) model to effectively handle the uncertainty and conflict of heterogeneous data sources. This enables accurate identification and early warning of multiple types of faults, including local overheating, abnormal acoustic signatures, and coolant leakage. Experimental results demonstrate that the proposed system achieves a fault-diagnosis accuracy of 98.5%, significantly outperforming single-sensor approaches, and thus provides an efficient and intelligent operation-and-maintenance solution for ensuring the safe and stable operation of GFM-SVG equipment. Full article

This study aims to explore how digital transformation (DT) accelerates the achievement of net zero (NZ) goals within Taiwan’s green energy industry. By employing quality function deployment as the analytical framework and multiple-attribute decision making for systematic evaluation, a practical and integrative model is developed to identify key DT technologies. The results reveal that establishing a comprehensive carbon footprint management system is the most essential NZ strategy, while Radio-Frequency Identification emerges as the most influential DT enabler supporting sustainability, emission reduction, and industrial transformation toward a smart and low-carbon economy. Full article

The preservation of cultural heritage within museum environments requires systematic control and monitoring of indoor microclimatic conditions. Over the past four decades, scientific evidence has established the critical role of environmental parameters, including air temperature, relative humidity, light, and airborne pollutants, in the preventive conservation of artifacts. International standards and national guidelines mandate continuous, non-invasive monitoring protocols that integrate conservation requirements with the architectural and operational constraints of historic buildings. Effective implementation necessitates a multidisciplinary approach balancing artifact preservation, human comfort, and building energy efficiency. Recent international recommendations further promote adaptive approaches wherein microclimate thresholds are calibrated to site-specific “historical climate” conditions, derived from minimum one-year baseline datasets. While essential for long-term conservation management, the design and implementation of such monitoring systems present significant technical and logistical challenges. This study presents a replicable methodological approach wherein preliminary surveys and three short-term monitoring campaigns (duration: 2 to 5 weeks) supported design, sensor selection, and spatial deployment and will allow the validation of a long-term continuous monitoring infrastructure (at least one year). These preliminary investigations enabled the following: (1) identification of priority environmental parameters; (2) optimization of sensor placement relative to exhibition layouts and maintenance protocols; and (3) preliminary assessment of microclimate risks in naturally ventilated spaces in the absence of HVAC systems. Full article

Electroencephalography-based motor imagery (EEG-MI) classification is a cornerstone of Brain–Computer Interface (BCI) systems, enabling the identification of motor intentions by decoding neural patterns within EEG signals. However, conventional methods, predominantly reliant on convolutional neural networks (CNNs), are proficient at extracting local temporal features but struggle to capture long-range dependencies and global contextual information. To address this limitation, we propose a Triple-path Heterogeneous Feature Collaboration Network (TPHFC-Net), which synergistically integrates three distinct temporal modeling pathways: a multi-scale Temporal Convolutional Network (TCN) to capture fine-grained local dynamics, a Transformer branch to model global dependencies via multi-head self-attention, and a Long Short-Term Memory (LSTM) network to track sequential state evolution. These heterogeneous features are subsequently fused adaptively by a dynamic gating mechanism. In addition, the model’s robustness and discriminative power are further augmented by a lightweight front-end denoising diffusion model for enhanced noisy feature representation and a back-end prototype attention mechanism to bolster the inter-class separability of non-stationary EEG features. Extensive experiments on the BCI Competition IV-2a and IV-2b datasets validate the superiority of the proposed model, achieving mean classification accuracies of 82.45% and 89.49%, respectively, on the subject-dependent MI task and significantly outperforming existing mainstream baselines. Full article

Pre-eclampsia is a Hypertensive Disorder of Pregnancy (HDP) characterized by hypertension and proteinuria, affecting 2–8% of pregnancies worldwide and constituting a major public health concern. Genes of the renin–angiotensin system have been investigated as potential causative factors, but inconclusive results have been obtained. The objective of this pilot study is to evaluate the possible contribution of alleles, genotypes or haplotypes of two single-nucleotide polymorphisms (SNPs) T174M (rs4762) and M235T (rs699) in AGT gene to pre-eclampsia in the Mexican population. We analyzed the association by performing PCR-RFLP with DNA extracted from whole blood samples of Mexican women with pre-eclampsia or normotensive pregnancy and the general population (GP). Our results showed a significant difference in the rate of heterozygosity for the T174M polymorphism between cases and controls. In addition, this polymorphism together with homozygosity for the M235T polymorphism may represent a possible genetic marker associated with pre-eclampsia. The T-C haplotype (174M–M235) was more common in patients with pre-eclampsia (non-significant difference p = 0.0503). The identification of genetic risk markers may support the early detection of pre-eclampsia and strengthen peripartum maternal health strategies within a global health framework aimed at reducing maternal mortality. Full article