Machine Learning for Circular Economy: Predicting Product Lifecycle and Optimizing Waste Management

The transformation from a linear to a circular economy model is a political priority of the European Union to develop sustainable and more efficient raw materials and waste management. The wastewater sector is an important element in the circular economy with a great potential for resource recovery such as phosphorus recovery from waste streams. With this in view, the present study’s aim was to analyse data on municipal wastewater treatment plants (>2,000 p.e.) in Latvia and the present situation with newly developed circularity indicators on waste reduction, pollutants removal, resource recovery and water reuse; thus, enabling evaluation of the level of transformation towards circularity in Latvia’s wastewater treatment plants and their future perspectives. The results showed that the wastewater sector in the country complies with legal obligations, but on the other hand, only a few solutions are practised in compliance with the principles of circular economy, i.e., reduction of wastewater discharge and increasing sewage sludge utilization as fertilizer in agriculture.

Background/Objectives: Acute respiratory distress syndrome (ARDS) represents a critical complication in polytrauma patients, characterized by diffuse lung inflammation and bilateral pulmonary infiltrates with mortality rates reaching 45% in intensive care units (ICU). The heterogeneous nature of ARDS and complex clinical presentation in severely injured patients poses substantial diagnostic challenges, necessitating early prediction tools to guide timely interventions. Machine learning (ML) algorithms have emerged as promising approaches for clinical decision support, demonstrating superior performance compared to traditional scoring systems in capturing complex patterns within high-dimensional medical data. Based on the identified research gaps in early ARDS prediction for polytrauma populations, our study aimed to: (i) develop a balanced random forest (BRF) ML model for early ARDS prediction in critically ill polytrauma patients, (ii) identify the most predictive clinical features using ANOVA-based feature selection, and (iii) evaluate model performance using comprehensive metrics addressing class imbalance challenges. Methods: This retrospective cohort study analyzed 407 polytrauma patients admitted to the ICU of the Center of Traumatology and Major Burns of Ben Arous, Tunisia, between 2017 and 2021. We implemented a comprehensive ML pipeline that incorporates Tomek Links undersampling, ANOVA F-test feature selection for the top 10 predictive variables, and SMOTE oversampling with a conservative sampling rate of 0.3. The BRF classifier was trained with class weighting and evaluated using stratified 5-fold cross-validation. Performance metrics included AUROC, PR-AUC, sensitivity, specificity, F1-score, and Matthews correlation coefficient. Results: Among 407 patients, 43 developed ARDS according to the Berlin definition, representing a 10.57% incidence. The BRF model demonstrated exceptional predictive performance with an AUROC of 0.98, a sensitivity of 0.91, a specificity of 0.80, an F1-score of 0.84, and an MCC of 0.70. Precision-recall AUC reached 0.86, demonstrating robust performance despite class imbalance. During stratified cross-validation, AUROC values ranged from 0.93 to 0.99 across folds, indicating consistent model stability. The top 10 selected features included procalcitonin, PaO2 at ICU admission, 24-h pH, massive transfusion, total fluid resuscitation, presence of pneumothorax, alveolar hemorrhage, pulmonary contusion, hemothorax, and flail chest injury. Conclusions: Our BRF model provides a robust, clinically applicable tool for early prediction of ARDS in polytrauma patients using readily available clinical parameters. The comprehensive two-step resampling approach, combined with ANOVA-based feature selection, successfully addressed class imbalance while maintaining high predictive accuracy. These findings support integrating ML approaches into critical care decision-making to improve patient outcomes and resource allocation. External validation in diverse populations remains essential for confirming generalizability and clinical implementation.

Abstract: This review examines how lean manufacturing principles can be effectively integrated with circular economy (CE) practices to develop sustainable supply chains and promote green infrastructure. Emphasis is placed on waste reduction, recycling, and resource recovery to shift from linear to regenerative systems. Key strategies in the construction sector—such as prefabrication, eco-design, and adaptive resource use—are discussed alongside policy interventions like green procurement and zero-waste initiatives. By bridging lean and circular frameworks through supportive policies, the paper proposes an interdisciplinary approach for enhancing environmental performance. The study concludes with future research directions and identifies challenges and opportunities for practical implementation. Keywords: Lean Manufacturing, Circular Economy, Sustainable Supply Chain, Green Building Practices, Waste Reduction, Policy Incentives, Recycling Strategies

A machine learning approach for hierarchical classification of software requirements

Severe hypoglycemia (SH) is a major complication in adults with type 1 diabetes mellitus (T1DM). The multifactorial etiology of T1DM highlights the need for predictive tools that integrate clinical, behavioral, and technological factors. This study aimed to develop and evaluate machine learning (ML) models for predicting SH by incorporating hypoglycemia problem-solving ability, diabetes technology, and continuous glucose monitoring (CGM) indices. We analyzed data from 247 adults with T1DM (mean age 50.4 ± 13.7 years; 38.1% male; glycosylated hemoglobin 7.7 ± 0.9%) from the FGM-Japan study. A total of 22,517 feature-model combinations were evaluated across 11 ML algorithms, including logistic regression, L1-regularized regression, random forest, LightGBM, XGBoost, SVM, Naïve Bayes, SGD, neural networks, and k-nearest neighbors. Eleven candidate predictors included impaired awareness of hypoglycemia (IAH), diabetic peripheral neuropathy (DPN), CSII, rtCGM, and seven domains of hypoglycemia problem-solving ability. The model performance was assessed with fivefold cross-validation using the receiver operating characteristic-area under the curve (ROC-AUC), accuracy, precision, recall, and F1 score. Class imbalance was addressed using SMOTE. The mean ROC-AUC across models was 0.64 (range: 0.151-0.916). The average accuracy was 0.90, but the precision and recall were consistently low, with a mean recall of 0.08. The high-performing models (ROC-AUC > 0.90) were primarily Random Forest and LightGBM, which frequently incorporated domains such as problem perception, identifying problem attributes, seeking preventive strategies, evaluating strategies, and immediate management. factors. Tree-based models significantly outperformed logistic regression, Naïve Bayes, SVM, and SGD (adjusted p < 0.001), whereas the differences among the tree-based algorithms were not clinically meaningful. Tree-based ML models demonstrated superior discriminative ability for predicting SH in patients with T1DM. Hypoglycemia problem-solving ability was the strongest predictor, underscoring the importance of integrating behavioral self-management skills with clinical and technological factors. University hospital Medical Information Network (UMIN) Center: UMIN000039475), Approval date 13 February 2020.

Machine learning techniques have long been the foundations of speech processing. Bayesian classiflcation, decision trees, unsupervised clustering, the EM algorithm, maximum entropy, etc. are all part of existing speech recognition systems. The success of statistical speech recognition has led to the rise of statistical and empirical methods in natural language processing. Indeed, many of the machine learning techniques used in language processing, from statistical part-of-speech tagging to the noisy channel model for machine translation have roots in work conducted in the speech fleld. However, advances in learning theory and algorithmic machine learning approaches in recent years have led to signiflcant changes in the direction and emphasis of the statistical and learning centered research in natural language processing and made a mark on natural language and speech processing. Approaches such as memory based learning, a range of linear classiflers such as Boosting, SVMs and SNoW and others have been successfully applied to a broad range of natural language problems, and these now inspire new research in speech retrieval and recognition. We have seen an increasingly close collaboration between voice and language processing researchers in some of the shared tasks such as spontaneous speech recognition and understanding, voice data information extraction, and machine translation. The purpose of this special issue was to invite speech and language researchers to communicate with each other, and with the machine learning community on the latest machine learning advances in their work. The call for papers was met with great enthusiasm from the speech and natural language community. Thirty six submissions were received; each paper was reviewed by at least three reviewers. Only ten papers were selected re∞ecting not only some of the best work on machine learning in the areas of natural language and spoken language processing but also what we view as a collection of papers that represent current trends in these areas of research both from the perspective of

The concept of the circular economy (CE) is gaining prominence as a sustainable alternative to the traditional linear economy. Bio-based innovations, which harness biological resources for production, offer promising solutions to resource depletion and environmental degradation. The increasing global emphasis on sustainable development has catalyzed the shift from traditional linear economic models towards a more sustainable and efficient circular economy. Within this transformation, bio-based innovations play a critical role, particularly in reducing environmental impact, enhancing resource efficiency, and stimulating economic growth. This research examines the biochemical processes underlying bio-based innovations and their potential economic benefits within a circular economy framework. It explores the significance of bio-based innovations from both biochemical and economic perspectives, examining their role in fostering a circular economy. The integration of bio-based materials and processes, which utilize renewable biological resources, not only reduces dependency on fossil fuels but also promotes the regeneration of biological systems. Economically, the bio-based sector contributes to job creation, GDP growth, and overall economic resilience. This study also analyzes how bio-based innovations can be harnessed for waste valorization, resource recovery, and environmental sustainability, culminating in a balanced approach to circular economy principles. Using case studies and statistical analysis, the paper presents a comprehensive view of the biochemical mechanisms and economic implications of bio-based innovations, with recommendations for policy and business strategies.

Material Criticality and Circular Economy: Necessity of Manufacturing Oriented Strategies

The concept of circular economy has gained significant traction in recent years as a holistic approach to sustainable development and resource management. This review explores the principles of circular economy and their integration into global supply chain strategies. The transition from a linear 'take-make-dispose' model to a circular model focused on resource recovery, reuse, and regeneration is driving a paradigm shift in how businesses operate and manage their supply chains. Key principles of circular economy, including designing out waste and pollution, keeping products and materials in use, and regenerating natural systems, are examined in the context of supply chain management. Strategies for implementing circular economy principles across various stages of the supply chain, from product design and manufacturing to distribution, consumption, and end-of-life management, are discussed. The review highlights the benefits of adopting circular economy principles in supply chain strategies, including reduced resource consumption, waste reduction, cost savings, and enhanced resilience to external shocks. Case studies and examples from diverse industries illustrate successful implementation of circular economy principles in supply chain management, demonstrating tangible environmental, social, and economic benefits. Challenges and barriers to the adoption of circular economy principles in global supply chains are also addressed, including regulatory hurdles, technological limitations, and cultural barriers. Strategies for overcoming these challenges and promoting a circular mindset among stakeholders are explored, emphasizing the importance of collaboration, innovation, and cross-sector partnerships. The integration of circular economy principles into global supply chain strategies offers significant opportunities for sustainable growth, resilience, and value creation. By embracing the principles of circularity, businesses can unlock new sources of competitive advantage while contributing to the transition towards a more sustainable and regenerative economy. Keywords: Circular Economy, Supply Chain, Sustainability, Integration, Challenges, Opportunities.

The printing industry plays a vital role in communication, education, and the creative economy, yet it is also associated with considerable environmental impacts due to high energy consumption, reliance on petroleum-based raw materials, intensive water use, and significant waste generation. In response to increasing societal pressure, regulatory demands, and sustainability-oriented market expectations, Green Manufacturing has emerged as a critical paradigm to reduce the ecological footprint of the printing sector while maintaining its economic and social functions. This study aims to systematically review the existing body of knowledge on Green Manufacturing in the printing industry by identifying dominant research trends, examining key implementation challenges, and proposing a future-oriented research and policy roadmap. A Systematic Literature Review (SLR) was conducted following PRISMA guidelines, synthesizing 35 peer-reviewed international journal articles published between 2014 and 2024 and indexed in major scientific databases. The findings reveal three dominant research streams: (1) innovation in sustainable raw materials, including plant-based inks and recycled or bio-based substrates; (2) process and energy efficiency improvements through digital printing technologies and waterless printing systems; and (3) the adoption of circular economy principles for waste reduction and resource recovery. Despite technological progress, implementation remains constrained by high initial investment costs, technical performance gaps of green materials, fragmented sustainability standards, and limited awareness among consumers and small and medium-sized enterprises (SMEs). Based on identified gaps, this paper proposes a future roadmap emphasizing locally sourced eco-materials, integration of AI and IoT for process optimization, development of globally harmonized sustainability standards, and circular economy–driven business models. By positioning Green Manufacturing as a socio-technical transformation rather than a purely technological shift, this study contributes strategic insights for researchers, industry practitioners, and policymakers seeking to accelerate a sustainable transition in the printing industry.

Management of phosphate mine waste rock piles (PWRPs) is a critical challenge in the mining industry, particularly in regions like Morocco, which holds the world&amp;#8217;s largest phosphate reserves. To this end, there is a need for an approach that focuses on real-time monitoring of waste rock heterogeneity, enabling more efficient resource recovery and environmental management. This study proposes a novel, multi-scale approach that integrates hyperspectral imaging, field spectroscopy, and explainable machine learning (XML) to characterize and map the mineralogical diversity of PWRPs at the Benguerir mine. &amp;#160;A total of 103 samples were collected from waste rock piles across an area of approximately 60 km&amp;#178;, representing the full spectrum of mineralogical variability. Handheld X-ray fluorescence (XRF) analysis was conducted on the all the samples and revealed the dominance of SiO&amp;#8322; (29.51 wt% &amp;#177; 12.42), CaO (30.16 wt% &amp;#177; 10.17), and P&amp;#8322;O&amp;#8325; (7.23 wt% &amp;#177; 4.21). These XRF analyses indicated the presence of silicate, carbonate, and phosphate-bearing materials. These findings were complemented by both PRISMA hyperspectral imaging, which captured spectral data across the visible to shortwave infrared (VSWIR) range. precise calibration and validation of the remote sensing outputs were conducted using field spectroscopy using the ASD FieldSpec 4 spectroradiometer.To address the complexity of the spectral data, we developed an explainable machine learning framework based on SHapley Additive exPlanations (SHAP) and Convolutional Neural Networks (CNN). This framework not only improved classification accuracy (achieving 0.92 overall accuracy) but also provided interpretable insights into the spectral features driving mineral identification. Our results showed that the used model successfully differentiated four main waste rock categories: carbonate-rich, phosphate-rich, clay-dominated, and siliceous materials. The resulting maps offer a practical tool for real-time waste management and resource recovery. For instance, carbonate-rich materials, characterized by high CaO content, can be identified or used for construction applications, while phosphate-rich zones, with elevated P&amp;#8322;O&amp;#8325; levels, can be flagged for potential recovery and further processing. This targeted approach ensures that waste materials are repurposed efficiently, aligning with circular economy principles. The study highlights the potential for automated, spectroscopy-based monitoring systems to support sustainable mining practices. Overall, this study demonstrates the power of combining cutting-edge remote sensing technologies with explainable machine learning to address the challenges of phosphate waste rock characterization. The methodology provides a scalable, cost-effective solution for mining operations worldwide, with significant implications for environmental sustainability, resource efficiency, and circular economy initiatives.Keywords:&amp;#160;Phosphate mine waste, Hyperspectral imaging, Field spectroscopy, Explainable machine learning (XML), Sustainable mining.

Regression analysis is one of the most widely utilized methods because of its adaptability and simplicity. Recently, the machine learning (ML) approach, which is one aspect of regression methods, has been gaining attention from researchers, including social science, but there are only a few studies that compared the traditional approaches with the ML approach. This study was conducted to explore the usefulness of the ML approach by comparing the ordinary least square estimate (OLS), the stochastic gradient descent algorithm (SGD), and the support vector regression (SVR) with a model predicting and explaining the tuberculosis screening intention. The optimized models were evaluated by four aspects: computational speed, effect and importance of individual predictor, and model performance. The result demonstrated that each model yielded a similar direction of effect and importance in each predictor, and the SVR with the radial kernel had the finest model performance compared to its computational speed. Finally, this study discussed the usefulness and attentive points of the ML approach when a researcher utilizes it in the field of communication.

Machine learning (ML) methods have shown great potential in predicting colorectal cancer (CRC) survival. However, the ML models introduced thus far have mainly focused on binary outcomes and have not considered the time-to-event nature of this type of modeling. This study aims to evaluate the performance of ML approaches for modeling time-to-event survival data and develop transparent models for predicting CRC-specific survival. The data set used in this retrospective cohort study contains information on patients who were newly diagnosed with CRC between December 28, 2012, and December 27, 2019, at West China Hospital, Sichuan University. We assessed the performance of 6 representative ML models, including random survival forest (RSF), gradient boosting machine (GBM), DeepSurv, DeepHit, neural net-extended time-dependent Cox (or Cox-Time), and neural multitask logistic regression (N-MTLR) in predicting CRC-specific survival. Multiple imputation by chained equations method was applied to handle missing values in variables. Multivariable analysis and clinical experience were used to select significant features associated with CRC survival. Model performance was evaluated in stratified 5-fold cross-validation repeated 5 times by using the time-dependent concordance index, integrated Brier score, calibration curves, and decision curves. The SHapley Additive exPlanations method was applied to calculate feature importance. A total of 2157 patients with CRC were included in this study. Among the 6 time-to-event ML models, the DeepHit model exhibited the best discriminative ability (time-dependent concordance index 0.789, 95% CI 0.779-0.799) and the RSF model produced better-calibrated survival estimates (integrated Brier score 0.096, 95% CI 0.094-0.099), but these are not statistically significant. Additionally, the RSF, GBM, DeepSurv, Cox-Time, and N-MTLR models have comparable predictive accuracy to the Cox Proportional Hazards model in terms of discrimination and calibration. The calibration curves showed that all the ML models exhibited good 5-year survival calibration. The decision curves for CRC-specific survival at 5 years showed that all the ML models, especially RSF, had higher net benefits than default strategies of treating all or no patients at a range of clinically reasonable risk thresholds. The SHapley Additive exPlanations method revealed that R0 resection, tumor-node-metastasis staging, and the number of positive lymph nodes were important factors for 5-year CRC-specific survival. This study showed the potential of applying time-to-event ML predictive algorithms to help predict CRC-specific survival. The RSF, GBM, Cox-Time, and N-MTLR algorithms could provide nonparametric alternatives to the Cox Proportional Hazards model in estimating the survival probability of patients with CRC. The transparent time-to-event ML models help clinicians to more accurately predict the survival rate for these patients and improve patient outcomes by enabling personalized treatment plans that are informed by explainable ML models.

The article examines the development of the circular economy in Ukraine, as well as the prospects of implementation of the experience of leading EU countries in the field of circular economy in Ukraine. The relevance of the study is that for now in Ukraine very few steps have been taken towards a circular economy, although everyone understands its importance. First of all, the meaning of the circular economy is revealed and the main advantages of its implementation in Ukraine are determined. The advantages of the transition to a circular economy are as follows: significant material savings, sustainable use of resources, stimulating innovations, the ability to meet the needs of the ever-growing population of the Earth, economic growth, and income. The circular or closed-loop economy involves the recovery, reuse, and rational use of resources and, through services and smart solutions, leads to added value. The state of resource management in Ukraine and the EU, in particular waste management, is analyzed. It is established that in the field of waste management Ukraine lags far behind developed European countries. Today, huge amounts of waste have accumulated in Ukraine, and currently, there is no developed infrastructure to decide this problem, and landfills themselves have become sources of great environmental danger. The new EU policy and the policy of Ukraine in the field of circular economy are analyzed. Taking into account the best European practices, recommendations are given to Ukraine on the transition from a traditional, linear economy to a circular economy. The main ones are as follows: it is necessary to change the structure of public procurement and focus on environmentally friendly types of production; it is necessary to accelerate the transition of Ukraine from the traditional model of public procurement to the concept of “green” procurement; it is necessary to develop an effective legislative and regulatory framework for the functioning of the circular economy; it is necessary to move to more efficient business models (such as ecodesign, repair, reuse, recovery and exchange of products and maximum prevention of waste generation) and stimulate the development of environmentally friendly technologies; it is expedient to develop programs of financing of projects in the field of circular economy; it is necessary to promote better awareness of consumers about the concept of “circular” economy, environmental “footprint” of goods and services, as well as sustainable consumption; it is necessary to change the method of production and consumption of goods from short-term to long-term, as well as to encourage the economy, businesses and consumers to maximize waste recycling. It is also established that in general, Ukraine’s policy in the field of circular economy should be formed and implemented at all three levels: national, regional, and global. However, each of us can begin to change in the direction of a circular economy (for example, through conscious consumption and resource conservation). It is also important to realize the importance of sorting garbage because its removal to landfills and finding new ones is not the best solution. Garbage needs to be recycled, and this can only be done if it is sorted.

ABSTRACTWater and wastewater sector actively participates in circular economy (CE) transition; however, there is no official CE monitoring framework to measure the level of progress toward the CE model in this sector. The paper presents a set of environmental indicators that could support water and wastewater treatment companies in the assessment of CE implementation. CE indicators were grouped into six areas of CE implementation in this sector: reduction—prevention of wastewater generation; reclamation (removal)—contamination removal from water and wastewater; reuse—water reuse for nonpotable usage; recycling—water reuse for potable usage; recovery—recovery of raw materials and energy from water‐based waste; and rethink—rethinking use of raw materials. The following indicators were proposed in specific CE areas: reduction—tap water consumption reduction, wastewater generation reduction, waste reduction, sewage sludge generation reduction, and water footprint; reclamation—pollutants removal from water and pollutants removal from wastewater; reuse—water reuse from wastewater for nonpotable usage and recycling of technical water for nonpotable usage; recycling—water reuse from wastewater for potable usage; recovery—raw materials recovery, bio‐based fertilizers, energy recovery, processing of sewage sludge, composting of sewage sludge, biogas production, and processing of ashes; and rethink—CE technologies, life cycle assessment reports, CE products, and CE business models, industrial symbiosis, and carbon footprint. The proposed CE indicators can be used not only to assess the level of transformation toward CE but also to support further policies on strategic CE initiatives in organizations operating in the water and wastewater sector. Moreover, they could support the strategic planning of further CE initiatives in organizations. The proposed list of CE indicators is not closed because in the coming years, more and more CE solutions are expected to be developed and implemented in water and wastewater treatment plant companies, involving also economic and social aspects, in order to support the sustainable development.