Search Results (14)

This study reviews key aspects of utilizing superconductors in wastewater treatment. It analyzes the interplay between magnetic fields and treatment processes, with a particular focus on the application of superconductors. The potential of MgB₂ superconductors is evaluated based on their inherent properties, alongside an exploration of the challenges and future opportunities associated with their potential implementation. A comprehensive literature review demonstrates the efficacy of magnetic fields in eliminating or drastically removing heavy metals, especially from industrial wastewater streams, through magnetic separation techniques. This review compares the efficiency of magnetic separation to conventional treatment methods, highlighting its potentials. Critical factors such as magnetization in wastewater, magnetic gradients, and magnetic memory are identified and discussed as crucial elements in optimizing magnetic separation processes. Furthermore, the study draws upon extensive research to investigate the technical considerations associated with magnetic wastewater treatment, ultimately evaluating the role of superconductors, particularly MgB₂, in advancing this technology. The feasibility and future prospects of MgB₂ superconductors within the context of wastewater treatment are also explored. Full article

Introduction: Sepsis has a high mortality rate, especially in low-income countries. Improving outcomes depends on the early recognition of patients at risk of death. Therefore, rapid and applicable prediction scores are needed in emergency triage. Objective: This study assessed the effectiveness of the qSOFA, NEWS, and IEWS scales in predicting in-hospital mortality during emergency triage. Additionally, we analyzed the efficacy of the IEWS_L, which integrates the IEWS with arterial lactate levels measured upon admission to the emergency department. Method: This retrospective study included patients who consulted the emergency department with suspected sepsis, where various scoring systems were evaluated for their effectiveness. We evaluated the diagnostic capacity of the tests by measuring the specificity, sensitivity, positive and negative predictive values, as well as the areas under the curve (AUC) of each score to predict mortality. Results: The study included 383 patients who had visited the emergency department. The overall mortality rate was 20.6%, and the mortality rate, precisely due to septic shock, was 35.2%. The AUC values for predicting in-hospital deaths due to sepsis were as follows: qSOFA: 0.68 (95% CI: 0.62–0.74); NEWS: 0.71 (95% CI: 0.64–0.77); IEWS: 0.74 (95% CI: 0.68–0.80); IEWS_L: 0.81 (95% CI: 0.76–0.86). Conclusions: In the emergency department, the IEWS scale demonstrated the best ability to accurately predict in-hospital mortality from sepsis when compared to the qSOFA and NEWS scale. Additionally, incorporating the serum lactate level into the IEWS scale significantly enhances its capacity to predict mortality. Full article

Conventional waste monitoring relies heavily on manual inspection, while most detection models are trained on close-range, simplified datasets, limiting their applicability for real-world surveillance. Even with surveillance imagery, challenges such as cluttered backgrounds, scale variation, and small object sizes often lead to missed detections and reduced robustness. To address these challenges, this study introduces EcoDetect-YOLOv2, a lightweight and high-efficiency object detection model developed using the Intricate Environment Waste Exposure Detection (IEWED) dataset. Building upon the YOLOv8s architecture, EcoDetect-YOLOv2 incorporates a small object detection P2 detection layer to enhance sensitivity to small objects. The integration of an efficient multi-scale attention (EMA) mechanism prior to the P2 head further improves the model’s capacity to detect small-scale targets, while bolstering robustness against cluttered backgrounds and environmental noise, as well as generalizability across scale variations. In the feature fusion stage, a Dynamic Upsampling Module (Dysample) replaces traditional nearest-neighbor upsampling to yield higher-quality feature maps, thereby facilitating improved discrimination of overlapping and degraded waste particles. To reduce computational overhead and inference latency without sacrificing detection accuracy, Ghost Convolution (GhostConv) replaces conventional convolution layers within the neck. Based on this, a GhostResBottleneck structure is proposed, along with a novel ResGhostCSP module—designed via a one-shot aggregation strategy—to replace the original C2f module. Experiments conducted on the IEWED dataset, which features multi-object, multi-class, and highly complex real-world scenes, demonstrate that EcoDetect-YOLOv2 outperforms the baseline YOLOv8s by 1.0%, 4.6%, 4.8%, and 3.1% in precision, recall, mAP₅₀, and mAP_50:95, respectively, while reducing the parameter count by 19.3%. These results highlight the model’s effectiveness in real-time, multi-object waste detection, providing a scalable and efficient tool for automated urban and digital governance. Full article

Scoping review is an iterative knowledge synthesis methodology concerned with broad questions about the nature of a research subject. The increasingly large number of published documents in scholarly domains poses challenges in conducting scoping reviews. Despite attempts to address these challenges, the specific step of sensemaking in the context of scoping reviews is seldom addressed. We address sensemaking of a curated document collection by developing a VIsual analytiCs sysTem for scOping RevIew of dOcUment Sets (VICTORIOUS). Using known methods within the machine learning community, we propose and develop six modules within VICTORIOUS: Map, Summary, Skim, SemJump, BiblioNetwork, and Compare. To demonstrate the utility of VICTORIOUS, we describe three usage scenarios. We conclude by a qualitative comparison of VICTORIOUS and other available systems. While existing systems leave their users with singular information items regarding a document set and gaining an aggregated assessment in a scoping review is often a challenge, VICTORIOUS shows promise for making sense of documents in a scoping review process. Full article

The power system and the water system are two important infrastructures of human society that are closely related and interdependent. However, the reliability problems of the power system and water system are becoming more and more prominent. To better reveal the impact of the complex coupling relationship between the power system and the water system on the reliability of the Integrated Electricity–Water System (IEWS), this paper investigates a reliability evaluation method of the IEWS based on multi-state models of equipment in the water system. Firstly, a multi-state reliability model is established based on the failure mechanism of equipment in the water system, such as pipes and Reverse Osmosis (RO) desalination plants. Secondly, combined with the multi-state model of equipment in the water system and the Markov chain Monte Carlo (MCMC) method, the IEWS reliability evaluation method is established. Finally, two IEWSs with different scales are simulated to verify the validity and adaptability of the proposed model. Full article

The COVID-19 epidemic, which can be compared to the economic catastrophe of World War II, slowed down business activities and had a significant impact on all aspects of business operations. Fuzzy scales are popular MCDM (multi-criteria decision-making) methods in modeling COVID-19 problems owing to the multi-dimensionality and complexity of health and socio-economic systems. This study aims to examine 104 works that used MCDM approaches with fuzzy scales in various COVID-19 pandemic issues and were published in top peer-reviewed journals indexed in Web of Science and Scopus. This study presents a systematic review of (1) the prevalence of fuzzy scales in scientific research for multiple criteria decision-making during COVID-19; (2) bibliometric analysis was used to identify the most important articles, authors, journals, themes, and countries; and (3) the impact of fuzzy scales on spreading established fields of research in new directions was iew of Fuzzy Scales for Multiple Criteriaconsidered. Furthermore, it addresses pertinent filed criticism, validating certain claims and dispelling others. Finally, the present study result helps regulators, academic scholars, and policy-makers to understand the current perspective and trends on multiple criteria decision-making with fuzzy scales during COVID-19 and understand the relevant areas that require further investigation. Full article

Tea is one of the most popular drinks in the world. The rapid and accurate recognition of tea diseases is of great significance for taking targeted preventive measures. In this paper, an information entropy masked vision transformation (IEM-ViT) model was proposed for the rapid and accurate recognition of tea diseases. The information entropy weighting (IEW) method was used to calculate the IE of each segment of the image, so that the model could learn the maximum amount of knowledge and information more quickly and accurately. An asymmetric encoder–decoder architecture was used in the masked autoencoder (MAE), where the encoder operated on only a subset of visible patches and the decoder recovered the labeled masked patches, reconstructing the missing pixels for parameter sharing and data augmentation. The experimental results showed that the proposed IEM-ViT had an accuracy of 93.78% for recognizing the seven types of tea diseases. In comparison to the currently common image recognition algorithms including the ResNet18, VGG16, and VGG19, the recognition accuracy was improved by nearly 20%. Additionally, in comparison to the other six published tea disease recognition methods, the proposed IEM-ViT model could recognize more types of tea diseases and the accuracy was improved simultaneously. Full article

Marine current turbines (MCTs) may exhibit reduced energy production and structural instability due to attachments, such as biofouling and plankton. Semantic segmentation (SS) is utilized to recognize these attachments, enabling on-demand maintenance towards optimizing power generation efficiency and minimizing maintenance costs. However, the degree of motion blur might vary according to the MCT rotational speed. The SS methods are not robust against such variations, and the recognition accuracy could be significantly reduced. In order to alleviate this problem, the SS method is proposed based on image entropy weighted spatio-temporal fusion (IEWSTF). The method has two features: (1) A spatio-temporal fusion (STF) mechanism is proposed to learn spatio-temporal (ST) features in adjacent frames while conducting feature fusion, thus reducing the impact of motion blur on feature extraction. (2) An image entropy weighting (IEW) mechanism is proposed to adjust the fusion weights adaptively for better fusion effects. The experimental results demonstrate that the proposed method achieves superior recognition performance with MCT datasets with various rotational speeds and is more robust to rotational speed variations than other methods. Full article

Nowadays, climate change not only leads to riverine floods and flash floods but also to inland excess water (IEW) inundations and drought due to extreme hydrological processes. The Carpathian Basin is extremely affected by fast-changing weather conditions during the year. IEW (sometimes referred to as water logging) is formed when, due to limited runoff, infiltration, and evaporation, surplus water remains on the surface or in places where groundwater flowing to lower areas appears on the surface by leaking through porous soil. In this study, eight different machine learning approaches were applied to derive IEW inundations on three different dates in 2021 (23 February, 7 March, 20 March). Index-based approaches are simple and provide relatively good results, but they need to be adapted to specific circumstances for each area and date. With an overall accuracy of 0.98, a Kappa of 0.65, and a QADI score of 0.020, the deep learning method Convolutional Neural Network (CNN) gave the best results, compared to the more traditional machine learning approaches Maximum Likelihood (ML), Random Forest (RF), Support Vector Machine (SVM) and artificial neural network (ANN) that were evaluated. The CNN-based IEW maps can be used in operational inland excess water control by water management authorities. Full article

Groundwater, the main drinking water source in the West Bank, is highly vulnerable to pollution given the karstic nature of the aquifer. This study was aimed at screening the quality of groundwater used for water supply, in terms of physicochemical and microbiological properties, and heavy metals concentrations. Attention was given to groundwater chemistry, using piper and Durov diagrams, to assess potent impact of pollution on groundwater. Twenty-nine groundwater samples from selected wells, representing the different groundwater fields in the West Bank, were collected and analyzed. The results revealed that the concentration of the ions and parameters affecting the aesthetic and health related water quality, such as Cl⁻, Na⁺, NH₄⁺_, TDS, and NO₃⁻, and selected (semi) metals, including Cr, Cu, Fe, Mn, Pb, Cd, and As, are within the limits recommended for drinking water. The dominant cations and anions were in the order of Ca²⁺ > Na⁺ > Mg²⁺ > K⁺ > NH₄⁺ and HCO₃⁻ > Cl⁻ > NO₃⁻ > SO₄²⁻, respectively. The total average groundwater hardness is approximately 2.1 mmol/L and can be attributed to calcium (approximately 60%) and magnesium. The major ground water types in the West Bank were fresh water (Ca-Mg-HCO₃), fresh water mixed with another water type (Ca-Mg-Na-HCO₃ or Ca-Mg-HCO₃-Cl), and extreme water type (Na-Ca-Mg-HCO₃-Cl or Na-Ca-HCO₃-Cl) showing high TDS, Cl⁻ and Na⁺. Signs of pollution, namely elevated levels of nitrate and ammonium, were, however, observed even in some deep wells (>600 m), despite the thick cover of soil, tapping the Lower Ceneomanian confined aquifer. Full article

The impact elastic wave method (IEW) has been applied to evaluate the thickness and internal defects of the target structure based on the dominant frequency of the response wave that is formed by the repeated reflections in the thickness direction. However, it is difficult to evaluate the size and position of the defect by IEW if the size and depth are relatively small and deep, respectively, and further, it is known that the technique is inapplicable if the target is not a plate-like structures. Therefore, the authors propose a new technique that uses Difference value as a new evaluation index to overcome the limitations of the conventional methods. Difference value shows the change of the response waveform in the time domain; it is computed by using a response waveform of the structures in sound condition as a reference. In this paper, the practicality of the Difference value is investigated by performing experiments using concrete specimens. The results of the experiments demonstrate that Difference value changes by the influence of internal defects, and Difference value evaluates the location of the relatively small defect that is difficult to evaluate by the conventional technique. Full article

Drought has significant impacts on the agricultural productivity and well-being of Pacific Island communities. In this study, a user-centred integrated early warning system (I-EWS) for drought was investigated for Papua New Guinea (PNG). The I-EWS combines satellite products (Standardised Precipitation Index and Vegetation Health Index) with seasonal probabilistic forecasting outputs (chance of exceeding median rainfall). Internationally accepted drought thresholds for each of these inputs are conditionally combined to trigger three drought early warning stages—”DROUGHT WATCH”, “DROUGHT ALERT” and “DROUGHT EMERGENCY”. The developed I-EWS for drought was used to examine the evolution of a strong El Niño-induced drought event in 2015 as well as a weaker La Niña-induced dry period in 2020. Examining the evolution of drought early warnings at a provincial level, it was found that tailored warning lead times of 3–5 months could have been possible for several impacted PNG provinces. These lead times would enable increasingly proactive drought responses with the potential for prioritised allocation of funds at a provincial level. The methodology utilised within this study uses inputs that are openly and freely available globally which indicates promising potential for adaptation of the developed user-centred I-EWS in other Pacific Island Countries that are vulnerable to drought. Full article

Emergency events in the industrial sector have been increasingly reported during the past decade. However, studies that focus on emergency evacuation to improve industrial safety are still scarce. Existing evacuation-related studies also lack a perspective of fire assembly point’s analysis. In this research, location of assembly points is analyzed using the multi-criteria decision analysis (MCDA) technique based on the integrated information entropy weight (IEW) and techniques for order preference by similarity to ideal solution (TOPSIS) to support the fire evacuation plan. Next, we propose a novel simulation model that integrates fire dynamics simulation coupled with agent-based evacuation simulation to evaluate the impact of smoke and visibility from fire on evacuee behavior. Factors related to agent and building characteristics are examined for fire perception of evacuees, evacuees with physical disabilities, escape door width, fire location, and occupancy density. Then, the proposed model is applied to a case study of a home appliance factory in Chachoengsao, Thailand. Finally, results for the total evacuation time and the number of remaining occupants are statistically examined to suggest proper evacuation planning. Full article

Changing climate is expected to cause more extreme weather patterns in many parts of the world. In the Carpathian Basin, it is expected that the frequency of intensive precipitation will increase causing inland excess water (IEW) in parts of the plains more frequently, while currently the phenomenon already causes great damage. This research presents and validates a new methodology to determine the extent of these floods using a combination of passive and active remote sensing data. The method can be used to monitor IEW over large areas in a fully automated way based on freely available Sentinel-1 and Sentinel-2 remote sensing imagery. The method is validated for two IEW periods in 2016 and 2018 using high-resolution optical satellite data and aerial photographs. Compared to earlier remote sensing data-based methods, our method can be applied under unfavorite weather conditions, does not need human interaction and gives accurate results for inundations larger than 1000 m². The overall accuracy of the classification exceeds 99%; however, smaller IEW patches are underestimated due to the spatial resolution of the input data. Knowledge on the location and duration of the inundations helps to take operational measures against the water but is also required to determine the possibilities for storage of water for dry periods. The frequent monitoring of the floods supports sustainable water management in the area better than the methods currently employed. Full article