Search Results (647)

The BN-350 is the first industrial fast neutron reactor in the history of nuclear energy. It is currently undergoing decommissioning. One of the key challenges of decommissioning is managing the solid radioactive waste that has accumulated throughout the reactor’s operational life. At the moment, the accumulated solid radioactive waste is stored in a storage facility within the BN-350 reactor complex. An analysis showed that more than ~7262 tons with 5.17 × 10¹⁴ Bq activity of various types of solid radioactive waste have been accumulated over the reactor operation. They are mainly represented by materials with low activity. At the same time, the main share of activity is comprised of highly active waste with a total mass of ~170 tons and an activity of 4.73 × 10¹⁴ Bq. A solid radioactive waste management strategy has been developed. It includes all stages from collection and classification to transportation and long-term storage. Modern technologies now offer new possibilities. Some radioactive waste can be processed and reused in other economic sectors. In particular, recycling metals and alloys can reduce the volume of solid radioactive waste. It can also return valuable materials to industrial use. Full article

Abandoned coal mine lands, particularly waste coal piles that predate the Surface Mining Control and Reclamation Act (SMCRA) of 1977, pose significant environmental and safety risks. Unlike sites mined after SMCRA—where operators are legally mandated to conduct reclamation—there is no legal obligation for companies or individuals to restore lands disturbed before the law’s enactment. As a result, these historical sites remain largely unmanaged and understudied. This study develops a satellite imagery-based analytical workflow to identify and monitor such historical waste coal piles. Using supervised classification of Sentinel-2 imagery with four machine learning models, we identified waste coal piles in both active mining areas and regions disturbed prior to SMCRA. Among the models tested, Random Forest achieved the highest accuracy for classifying waste coal, with a precision of 86% and a recall of 77%. A subsequent time-series analysis revealed that historical waste coal piles have undergone gradual but consistent vegetation recovery since 1986, indicating a natural reclamation process. These areas showed minimal changes in disturbance magnitude, suggesting the absence of significant disturbing events. In contrast, active mining regions showed substantial disturbance consistent with ongoing operations. The combined classification and change detection approach successfully distinguished historical waste coal piles from those in active mining regions, with a precision of 78% and recall of 100%. These findings highlight the potential of remote sensing and temporal analysis to support the identification and assessment of historical waste coal piles. The proposed approach can help prioritize reclamation efforts and inform policy decisions addressing the long-term environmental impacts of historical coal mining. Full article

Lubricating oils are utilised in equipment and machinery to reduce friction and enhance material utilisation. The utilisation of oil leads to an increase in its thickness and density over time. Current methods for assessing oil life are slow, expensive, and complex, and often only applicable in laboratory settings and unsuitable for real-time or field use. This leads to unexpected equipment failures, unnecessary oil changes, and economic and environmental losses. A comprehensive review of the extant literature revealed no studies and no national or international patents on neural network algorithm-based oil life modelling and classification using green sensors. In order to address this research gap, this study, for the first time in the literature, provides a green conductivity sensor with high-accuracy prediction of oil life by integrating real-time field measurements and artificial neural networks. This design is based on analysing resistance change using a relatively low-cost, three-dimensional, eco-friendly sensor. The sensor is characterised by its simplicity, speed, precision, instantaneous measurement capability, and user-friendliness. The MLP and LVQ algorithms took as input the resistance values measured in two different oil types (diesel, bench oil) after 5–30 h of use. Depending on their degradation levels, they classified the oils as ‘diesel’ or ‘bench oil’ with 99.77% and 100% accuracy. This study encompasses a sensing system with a sensitivity of 50 µS/cm, demonstrating the proposed methodologies’ efficacy. A next-generation decision support system that will perform oil life determination in real time and with excellent efficiency has been introduced into the literature. The components of the sensor structure under scrutiny in this study are conducive to the creation of zero waste, in addition to being environmentally friendly and biocompatible. The developed three-dimensional green sensor simultaneously detects physical (resistance change) and chemical (oxidation-induced polar group formation) degradation by measuring oil conductivity and resistance changes. Measurements were conducted on simulated contaminated samples in a laboratory environment and on real diesel, gasoline, and industrial oil samples. Thanks to its simplicity, rapid applicability, and low cost, the proposed method enables real-time data collection and decision-making in industrial maintenance processes, contributing to the development of predictive maintenance strategies. It also supports environmental sustainability by preventing unnecessary oil changes and reducing waste. Full article

Biosorbents are materials of biological origin (microbial, biomass-derived waste, or industrial by-products) used to adsorb or absorb pollutants. They have been used to remove various contaminants, including heavy metals, dyes, and pharmaceuticals. Their effectiveness is due to the different functional groups that interact with pollutants, including hydroxyl, amino, carboxyl, and phosphate groups. This review explores the various kinds of biosorbents (classification), mechanisms, and factors influencing biosorption, such as biomass content, time, temperature, pH, and concentration of pollutants, synthesis methods of biosorbents, and the current state of research on biosorbents. The review highlights the advantages of biosorbents, along with the challenges encountered, such as difficulty in regeneration and variability in performance. Finally, the review identifies research gaps and future directions, including exploration of modified/synthetic biosorbents for the removal of multi-component pollutants. Full article

This study investigates contaminants in metalworking fluids (MWFs) from an industrial band saw, focusing on microparticle classification and microbial quantification linked to fluid degradation. Most particles were under 50 µm, primarily aluminum and iron oxides from tool wear; oxygen- and sulfur-containing particles suggested corrosion. Microbiological analysis showed high contamination, with culturable microorganisms exceeding 1000 CFU/mL. A pathogenic strain associated with biodeterioration was identified, underscoring the need for microbial control. Filtration and ozonation have been used as decontamination methods to improve the purity and biological stability of the process fluid. Filtration enabled selective removal of metallic microparticles. Among six nanofiber filters, the Berry filter achieved the highest efficiency (70.8%) for particles ≥ 7.3 µm, while other filters were faster but less efficient. Ozonation proved highly effective for microbiological decontamination, reducing viable microorganisms by over 95%, improving visual clarity, and lowering pH from 9 to 8 while remaining within operational limits. Unlike filtration, ozonation significantly reduced microbial load. The combination of both methods is proposed as a sustainable strategy for maintaining process fluid quality under industrial conditions. These findings support integrated decontamination approaches to extend fluid life, reduce fresh MWF consumption and waste, and enhance workplace hygiene and safety in machining operations. Full article

The significant expansion of the construction sector and corresponding depletion of natural sand resources have intensified the search for sustainable alternatives, with waste foundry sand (WFS) emerging as a promising candidate. This systematic review evaluates the environmental performance and engineering feasibility of using WFS as a substitute for natural sand in construction. A PRISMA-guided search identified 152 peer-reviewed studies published between 2001 and 2024, which were categorized into four thematic areas: material characterization, construction applications, environmental impacts, and regulatory frameworks. The findings indicate that substituting 10–30% of natural sand with WFS in concrete and asphalt can deliver compressive strength within ±5% of control mixes and reduce water absorption by 5–15% at optimal replacement levels. However, contamination risks remain a concern, as chromium and copper concentrations in raw WFS have been reported at up to 931 mg/kg and 3318 mg/kg, respectively. To address these risks and ensure responsible reuse, a six-stage framework is proposed in this study, comprising end-of-waste classification, contaminant assessment, material preprocessing, certification, and regulatory monitoring. A comprehensive decision tree is also presented to guide the feasibility assessment of WFS reuse based on contaminant levels and material performance. Full article

This study addresses the use of porcelain polishing waste as a pyro-expansive agent in clay-based formulations for the production of lightweight aggregates, aiming to reduce the consumption of natural resources and mitigate environmental impacts. In line with circular economy principles and sustainable construction goals, this study investigates the potential use of porcelain polishing waste as a pyro-expansive agent in clay-based formulations for producing sustainable lightweight aggregates. Using the Taguchi method and ANOVA, the effects of key processing parameters were evaluated. The results demonstrated a broad range of volumetric changes, from shrinkage of 40.84% to expansion of 91.69%, depending on the formulation and processing conditions. The aggregates exhibited specific mass values ranging from 0.99 g/cm³ to 2.36 g/cm³, water absorption up to 3.29%, and mechanical strength from 4.57 MPa to 39.87 MPa. Notably, nine of the sixteen experimental conditions met the technical standards for classification as LWA, indicating suitability for applications in high-strength, structural, and non-structural lightweight concretes, as well as lightweight mortars. The performance of these materials was directly linked to the chemical and mineralogical characteristics of the precursors and the proportion of pyro-expansive waste used. Overall, the findings suggest that 50% of the produced aggregates are viable for high-performance concrete applications, offering an environmentally responsible alternative to virgin raw materials and contributing to sustainable waste valorization in the ceramic and construction industries. Full article

We present a comprehensive study of LED-based optical sensing systems for the classification of waste materials, analyzing recent developments in the field. Accurate identification of materials such as plastics, glass, aluminum, and paper is a crucial yet challenging task in waste management for recycling. The first approach uses short-wave infrared reflectance spectroscopy with commercial Germanium photodetectors and selected LEDs to keep data complexity and cost at a minimum while achieving classification accuracies up to 98% with machine learning algorithms. The second system employes a voltage-tunable Germanium-on-Silicon photodetector that operates across a broader spectral range (400–1600 nm), in combination with three LEDs in both the visible and short-wave infrared bands. This configuration enables an adaptive spectral response and simplifies the optical setup, supporting energy-efficient and scalable integration. Accuracies up to 99% were obtained with the aid of machine learning algorithms. Across all systems, the strategic use of low-cost LEDs as light sources and compact optical sensors demonstrates the potential of light-emitting devices in the implementation of compact, intelligent, and sustainable solutions for real-time material recognition. This article explores the design, characterization, and performance of such systems, providing insights into the way light-emitting and optoelectronic components can be leveraged for advanced sensing in waste classification applications. Full article

The colorimetric food freshness indicator (CFFI) is a promising technology in intelligent food packaging, offering the capability for real-time monitoring of food freshness through colorimetric changes. This technology holds significant promise in mitigating food waste and enhancing transparency across the supply chain. This paper provides a comprehensive review of the classification system for the CFFI, encompassing colorimetric films and sensor arrays. It explores their applications across key perishable food categories, including meats, seafoods, fruits, and vegetables. Furthermore, this paper offers an in-depth analysis of three critical challenges currently hindering technological advancement: safety concerns, stability issues, and limitations in sensitivity and selectivity. In addressing these challenges, this paper proposes forward-looking solutions and outlines potential research directions aimed at overcoming these bottlenecks, thereby fostering substantial progress in the development of this field. Full article

Human activities and river course change have a complex reciprocal interaction. The river channel is altered by human activity, and these alterations have an impact on the activities and settlements along the riverbank. Understanding the relationship between urbanization and changes in river morphology is crucial for effective river management, safeguarding the urban environment, and mitigating flood hazards. In this context, this study has been conducted to investigate the interrelationship between morphological dynamics, built-up growth, and urban flood risk along the Manohara River in Kathmandu Valley, Nepal. The Sinuosity Index was used to analyze variation in river courses and instability from 1996 to 2023. Built-up change analysis is carried out using supervised maximum likelihood classification method and rate of change is calculated for built-up area growth (2003–2023) and building construction between 2003 and 2021. Flood hazard risk manning was carried out using flood frequency estimation method integrating HEC-GeoRAS modeling. Linear regression and spatial overlay analysis was carried out to examine the interrelationship between river morphology, urban growth, and fold hazed risk. In recent years (2016–2023), the Manohara River has straightened, particularly after 2011. Before 2011, it had significant meandering with pronounced curves and bends, indicating a mature river system. However, the SI value of 1.45 in 2023 and 1.80 in 2003 indicates a significant straightening of high meandering over 20 years. A flood hazard modeling carried out within the active floodplain of the Manohara River shows that 26.4% of the area is under high flood risk and 21% is under moderate risk. Similarly, over 10 years from 2006 to 2016, the rate of built-up change was found to be 9.11, while it was 7.9 between 2011 and 2021. The calculated R² value of 0.7918 at a significance level of 0.05 (with a p value of 0.0175, and a standard error value of 0.07877) indicates a strong positive relationship between decreasing sinuosity and increasing built-up, which demonstrates the effect of built-up expansion on river morphology, particularly the anthropogenic activities of encroachment and haphazard constructions, mining, dumping wastes, and squatter settlements along the active floodplain, causing instability on the river course and hence, lateral shift. The riverbank and active floodplain are not defined scientifically, which leads to the invasion of the river area. These activities, together with land use alteration in the floodplain, show an increased risk of flood hazards and other natural calamities. Therefore, sustainable protection measures must be prioritized in the active floodplain and flood risk areas, taking into account upstream–downstream linkages and chain effects caused by interaction between natural and adverse anthropogenic activities. Full article

Influence-based data selection methods, such as TracIn, aim to estimate the impact of individual training samples on model predictions and are increasingly used for dataset curation and reduction. This study investigates whether selecting the most positively influential training examples can be used to create compressed yet effective training datasets for transfer learning in plastic waste classification. Using a ResNet-18 model trained on a custom dataset of plastic waste images, TracIn was applied to compute influence scores across multiple training checkpoints. The top 50 influential samples per class were extracted and used to train a new model. Contrary to expectations, models trained on these highly influential subsets significantly underperformed compared to models trained on either the full dataset or an equally sized random sample. Further analysis revealed that many top-ranked influential images originated from different classes, indicating model biases and potential label confusion. These findings highlight the limitations of using influence scores for dataset compression. However, TracIn proved valuable for identifying problematic or ambiguous samples, class imbalance issues, and issues with fuzzy class boundaries. Based on the results, the utilized TracIn approach is recommended as a diagnostic instrument rather than for dataset curation. Full article

In this study, we adopt a geometallurgical analytical approach common in mineral processing in the characterization of samples of shredded waste printed circuit board (PCB) E-waste, originating from Europe. Conventionally, bulk chemical analysis provides a value for E-waste; however, chemical analysis alone does not provide information on the textural variability, phase complexity, grain size, particle morphology, phase liberation and associations. To address this, we have integrated analysis using binocular microscopy, manual scanning electron microscopy, phase, textural and compositional analyses by automated (SEM-EDS), phase analysis based on (Automated Material Identification and Classification System (AMICS) software, and elemental analysis using micro-XRF. All methods used have strengths and limitations, but an integration of these analytical tools allows the detailed characterization of the texture and composition of the E-waste feeds, ahead of waste reprocessing. These data can then be used to aid the design of optimized processing circuits for the recovery of the key payable components, and assist in the commercial trading of e-scrap. Full article

The transition to efficient production requires innovative water control techniques to maximize irrigation efficiency and minimize waste. Analyzing and optimizing irrigation practices is essential to improve water use and reduce environmental impact. The aim of the research was to identify a discrimination method to analyze the different hydration levels in baby-leaf products. The species being researched was spinach, harvested at the baby leaf stage. Utilizing a large dataset of 261 wavelengths from the hyperspectral imaging system, the feature selection minimum redundancy maximum relevance (FS-MRMR) algorithm was applied, leading to the development of a neural network-based prediction model. Finally, a mathematical classification model K-NN (k-nearest neighbors type) was developed in order to identify a transfer function capable of discriminating the hyperspectral data based on a threshold value of absolute leaf humidity. Five significant wavelengths were identified for estimating the moisture content of baby leaves. The resulting model demonstrated a high generalization capability and excellent correlation between predicted and measured data, further confirmed by the successful training, validation, and testing of a K-NN-based statistical classifier. The construction phase of the statistical classifier involved the use of the experimental dataset and the critical humidity threshold value of 0.83 (83% of leaf humidity) was considered, below which the baby-leaf crop requires the irrigation intervention. High percentages of correct classification were achieved for data within two humidity classes. Specifically, the statistical classifier demonstrated excellent performance, with 81.3% correct classification for samples below the threshold and 99.4% for those above it. The application of advanced spectral analysis and artificial intelligence methods has led to significant progress in leaf moisture analysis and prediction, yielding substantial implications for both agriculture and biological research. Full article

Background/Objectives: Malnutrition is a common problem in children with cerebral palsy (CP). The aim of this study was to investigate the suitability and diagnostic performance of mid-upper arm circumference (MUAC) z-score in diagnosing wasting in children with CP, and its impact on diagnostic accuracy when evaluated concomitantly with additional clinical factors (birth weight, history of phototherapy). Methods: This single-center, cross-sectional study included 83 children with CP, aged 6 months–17 years, followed-up in our clinic. Anthropometric measurements (MUAC, Body Mass Index (BMI)) and clinical data (birth weight, history of phototherapy, Gross Motor Function Classification System (GMFCS)) were prospectively collected. Wasting was defined according to the BMI z-score ≤ −2 criteria. The diagnostic performance of MUAC z-score was evaluated by Receiver Operating Characteristic (ROC) analysis. The contribution of additional covariates was examined using logistic regression analysis and the backward elimination method. Results: MUAC z-score alone demonstrated good discrimination in diagnosing wasting with an Area Under the Curve (AUC) value between 0.805 and 0.821, but its sensitivity was limited (67.0%). No statistically significant difference was found in diagnostic performance between MUAC measurements of the right arm, left arm, and the unaffected arm (p > 0.050). In logistic regression analysis, MUAC z-score (p = 0.001), birth weight (p = 0.014), and a history of phototherapy (p = 0.046) were found to be significantly associated with wasting malnutrition. The simplified model including these variables yielded an AUC value of 0.876. Conclusions: MUAC z-score is a usable tool for wasting malnutrition screening in children with CP. Although its sensitivity is limited when used alone, its diagnostic accuracy increases when evaluated concomitantly with additional clinical factors such as birth weight and a history of phototherapy. This combined approach may offer clinicians a more robust tool for the early diagnosis and management of wasting malnutrition in children with CP. Full article

With increasing urban economic development, some industrial parks and residential areas are being situated adjacent to each other, creating a potential risk of soil and groundwater contamination from the wastewater and solid waste produced by enterprises. This contamination poses a threat to the health of nearby residents. Currently, groundwater pollution prevention and control zoning in China primarily targets groundwater environmental pollution risks and does not consider the health risks associated with groundwater exposure in industry–city integration areas. Therefore, a scientific assessment of environmental risks in industry–city integration areas is essential for effectively managing groundwater pollution. This study focuses on the high frequency and rapid pace of human activities in industry–city integration areas. It combines health risk assessment and groundwater pollution simulation results with traditional groundwater pollution control classification outcomes to develop a groundwater pollution risk zoning framework specifically suited to these integrated areas. Using this framework, we systematically assessed groundwater pollution risks in a representative industry–city integration area in the middle reaches of the Yangtze River in China and delineated groundwater pollution risk zones to provide a scientific basis for local groundwater environmental management. The assessment results indicate that the total area of groundwater pollution risk control zones is 30.37 km², accounting for 19.06% of the total study area. The first-level control zone covers 5.38 km² (3.38% of the total area), while the secondary control zone spans 24.99 km² (15.68% of the total area). The first-level control zone is concentrated within industrial clusters, whereas the secondary control zone is widely distributed throughout the region. In comparison to traditional assessment methods, the zoning results derived from this study are more suitable for industry–city integration areas. This study also provides groundwater management recommendations for such areas, offering valuable insights for groundwater control in integrated industrial–residential zones. Full article