Abstract The dual challenges of waste biomass disposal and escalating contamination by inorganic, organic, and emerging microplastic pollutants demand sustainable, circular remediation strategies. Biochar, a carbon-rich, porous material produced through the thermal decomposition of biomass, has emerged as a multifunctional sorbent capable of adsorbing pollutants, enhancing soil quality, and sequestering carbon. This review critically compares biochars synthesized via conventional pyrolysis (CP) and microwave-assisted pyrolysis (MAP), highlighting their physicochemical differences, sorption mechanisms, and contaminant removal efficiencies. As a key novelty, this review presents the first mechanistically integrated comparison of CP and MAP biochars in relation to their structural characteristics and performance relationships. Factors such as temperature, reactor design, and pyrolysis method significantly influence the properties of biochar, including surface area, porosity, pH, carbon content, and adsorption capacity. Comparative analysis indicates that MAP offers advantages over CP, including faster volumetric heating, higher product yield, enhanced specific surface area, greater selectivity for targeted contaminants, and potentially improved energy efficiency under optimized conditions. The review discusses biochar functionalization, adsorption-reduction methods, and optimization for contaminants such as heavy metals, dyes, medicines, and microplastics. Beyond remediation, biochar is used for soil improvement, composting, catalysis, and electrode materials. Key information gaps persist, including a mechanistic understanding of contaminant interactions, long-term stability, and MAP scalability. Addressing these gaps is critical for optimizing biochar for targeted remediation, incorporating MAP-derived biochar into circular bioeconomy and carbon-neutral methods, and directing the development of next-generation biochars for sustainable pollution management and resource recovery. Graphical Abstract
Poka-Yoke is one of the fundamental Lean tools used to prevent or detect errors. However, the existing theoretical framework and classification models are neither sufficiently systematised nor confirmed by empirical research. This study therefore provides the first comprehensive evaluation of three classification models based on: function (I), principle (II), and device type (III). The first two models are the most commonly used in the relevant literature, while the third was developed by the author and improved through the research conducted for a clearer understanding and more practical application. The models were empirically tested using two criteria: classification accuracy assessment and ease of application assessment. The research included 21 examples of PY solutions from literature and industrial practice, evaluated by 30 experts of various profiles. Statistical analysis of the data confirmed the existence of significant differences between the models. Classification models III was found to be the most accurate and simplest to use, thus confirming its practical value in modern industrial practice. This study contributes to the development of a theoretical and practical framework for the PY method, offering empirically based recommendations for standardisation and wider implementation. These recommendations create the conditions for more effective error management, thereby increasing process reliability and ensuring compliance with Industry 4.0 requirements.
This study investigates how as-built dimensional deviations in a topology-optimized connecting rod manufactured by selective laser melting (SLM) affect structural response. A maraging steel 300 connecting rod was fabricated on a Renishaw 500 S Flex system and digitized using a low-cost structured-light 3D scanner. The scan-derived mesh was aligned to the nominal CAD geometry using principal component analysis initialization and point-to-plane iterative closest point refinement. Full-field deviations were then quantified using signed nearest-neighbor distance and bidirectional Hausdorff distance. The as-built part showed a 3.10
The stochastic delay models provide the realistic way of investigating the infectious diseases. HFMD is a viral disease that affects children predominately, according to WHO and CDC. The main purpose is to study the transmission dynamics of Hand, Foot, and Mouth Disease (HFMD) through stochastic with delay approaches. How is stochastic delay the best strategy for studying the dynamics of disease in a population? For this, we consider the existing deterministic model in literature, with the whole population divided as susceptible S(t), exposed E(t), infected I(t), and recovered humans R(t). After that, we extend the deterministic framework to formulate a stochastic delay model by incorporating stochastic delay differential equations (SDDEs), utilizing transition probabilities and nonparametric perturbation techniques. The analysis rigorously investigates key properties such as positivity, boundedness, disease extinction, and persistence, alongside a thorough examination of the basic reproduction number and its critical role in the disease dynamics. The disease-free equilibrium (DFE) and endemic equilibrium (EE) are two states; local and global stability of second order and sensitivity analysis of parameters are analyzed to verify the model validations. Owing to the complex nonlinear nature of the stochastic delay differential equations in the model, we employed a variety of techniques including the standard Euler–Maruyama method, stochastic Euler, stochastic Runge–Kutta, and a stochastic nonstandard finite difference approach incorporating delays to visualize the results. Ultimately, a comparison of these methods is provided to highlight the superior effectiveness of the nonstandard stochastic methods when delay parameters are involved. This research improves our study of dynamics through stochastic with delay techniques version, which also opens up new directions for dynamical system dynamics and mathematical analysis. The comparison profiles were created for new solutions, and interaction behavior was plotted.
Machine learning (ML) is a rapidly evolving technology with expanding applications across various fields. This paper presents a comprehensive survey of recent ML applications in agriculture for sustainability and efficiency. Existing reviews mainly focus on narrow subdomains or lack a fusion-driven perspectives. This study provides a combined analysis of ML applications in agriculture, structured around five key objectives: (i) Analyzing ML techniques across pre-harvesting, harvesting, and post-harvesting phases. (ii) Demonstrating how ML can be used with agricultural data and data fusion. (iii) Conducting a bibliometric and statistical analysis to reveal research trends and activity. (iv) Investigating real-world case studies of leading artificial intelligence (AI)-driven agricultural companies that use different types of multisensors and multisource data. (v) Compiling publicly available datasets to support ML model training. Going beyond existing previous reviews, this review focuses on how machine learning (ML) techniques, combined with multi-source data fusion (integrating remote sensing, IoT, and climate analytics), enhance precision agriculture by improving predictive accuracy and decision-making. Case studies and statistical insights illustrate the evolving landscape of AI driven smart farming, while future research directions also discusses challenges associated with data fusion for heterogeneous datasets. This review bridges the gap between AI research and agricultural applications, offering a roadmap for researchers, industry professionals, and policymakers to harness information fusion and ML for advancing precision agriculture.
