Search Results (18,563)

Modern agriculture must balance productivity with sustainability. In this context, unmanned aerial vehicles (UAVs) offer flexible, cost-effective tools for crop and soil monitoring in precision agriculture. This study aimed to evaluate the potential of UAV-borne RGB imagery, combined with vegetation indices and machine learning, to estimate surface soil properties and crop physiological traits in peanut (Arachis hypogaea) cultivation. A factorial field experiment with four varieties, two planting densities, and two tillage systems was monitored using high-resolution RGB orthomosaics acquired at key phenological stages. From these images, 17 RGB-based indices were computed and related to soil variables and crop traits using Spearman correlation and two regression algorithms: Random Forest (RF) and k-Nearest Neighbors (KNN). RF models outperformed KNN, with the Red Chromatic Coordinate (RCC) index achieving an R² of 0.87 for predicting soil organic matter content. Indices such as visible NDVI and the Green Vegetation Index also provided robust estimates of canopy condition and leaf chlorophyll. Overall, the results demonstrate that UAV RGB imagery, processed through simple vegetation indices and RF models, constitutes an effective, low-cost approach for monitoring key agronomic parameters in peanut farming. Full article

The operation of medical cyclotrons for PET radiopharmaceutical production presents significant radiological and environmental challenges that require systematic risk assessment and evidence-based mitigation strategies. In this study, an integrated framework combining Failure Mode and Effects Analysis (FMEA) with a quantitative Defense Effectiveness Factor (DEF) approach to evaluate and reduce residual risk in a real urban cyclotron facility. High-criticality failure modes (Risk Priority Number $\ge 120$) affecting HVAC systems, stack exhaust, and power supply were identified and validated through a Delphi expert consensus process. These modes were addressed with multi-layered defense-in-depth strategies: redundant systems (occurrence reduction, 60–80% effectiveness), real-time monitoring (detection reduction, 40–50% effectiveness), and design robustness (severity reduction, 70–85% effectiveness). The combined DEF yielded a 96–97% risk reduction. One-way sensitivity analysis confirmed the robustness of these results, with residual annual effective dose to the representative person remaining between 50–88 $\mathsf{\mu }$Sv/year (well below the IAEA 1 mSv/year public dose constraint) even under pessimistic scenarios. Primary exposure pathways were inhalation and cloud gamma from ${}^{18}$F and ${}^{41}$Ar during the early-morning production window, while secondary pathways were negligible due to the short half-lives of the radionuclides. These findings demonstrate that the integration of FMEA with DEF-based defense-in-depth and Gaussian plume modeling provides a transparent, robust, and regulatory-compliant framework for managing radioactive atmospheric emissions in urban medical cyclotron facilities. Full article

Background: Fused deposition modeling (FDM) is one of the most well-known and often published methods for 3D-printed drug delivery systems. In early scientific reports, the active pharmaceutical ingredients were added by soaking, but later, a new milestone was established, after researchers started to manufacture their own filaments by hot-melt extrusion (HME). The number of publications covering this method has multiplied in the last decade, a wide range of natural and synthetic polymers have been tested with versatile active pharmaceutical ingredient components, and various printing parameters and their effects have been investigated. Objectives: In this review, we aim to synthesize how the available quality by design approaches and the scientific results established so far can facilitate the creation of a guideline for appropriate quality production of HME-FDM 3D-printed pharmaceuticals. Methods: Based on PRISMA 2020 guidelines, a systematic search of relevant publications from 2015 to 2025 was carried out using the PubMed database. Twenty-six articles were included, based on number of monitored parameters and methodological description. Reporting of important quality processes and material parameters was assessed. Results: HME, the FDM, and analytical testing experiences were compared and collected into three tables from the selected publications. In two different sections, the pharmacopeial dosage-form tests and the involvement of process analytical technologies (PAT) were also analyzed. We found that reporting of influential parameters is heterogenous, and lack of robust reporting schemes limits the development of QbD approaches. Conclusions: Regarding the data, trends were synthetized, and a guideline was created which is limited by inconsistent parameter reporting. Full article

Cold stress (CS) represents a major environmental factor that adversely affects plant growth, development, and productivity. To cope with low-temperature conditions, plants have evolved sophisticated mechanisms for CS perception and response, mediated through complex cellular signaling networks and physiological processes. Central to these adaptive responses are phytohormones, which function either independently or through synergistic and antagonistic interactions to fine-tune CS tolerance. This review synthesizes current knowledge on the roles of major classical phytohormones and signaling metabolites in regulating CS tolerance in plants. We first outline the molecular mechanisms involved in CS sensing and signal transduction, highlighting the roles of membrane-associated sensors, calcium signaling, and downstream transcriptional networks. Then, we discuss the contributions of key classical phytohormones, including auxin, abscisic acid, ethylene, salicylic acid, cytokinin, jasmonic acid, brassinosteroids, gibberellic acid, strigolactones, and signaling metabolites, including melatonin and gamma-aminobutyric acid, to CS tolerance, highlighting their individual and interacting roles in modulating gene expression regulation, antioxidant defense and physiological adaptations. We also discuss the crosstalk between these hormones, emphasizing the dynamic and often context-dependent nature of their interactions in response to CS. Furthermore, the review highlights recent advances in CRISPR/Cas9-based genome editing strategies targeting phytohormone biosynthesis, signaling, and response pathways to improve CS tolerance in plants. By integrating hormonal signaling, molecular regulation, and modern biotechnological tools, this review provides a comprehensive framework for understanding phytohormone-mediated CS adaptation and offers perspectives for developing climate-resilient crops through genetic and agronomic approaches. Full article

This study presents a wavelet-based framework for mapping inter-well connectivity (IWC) between multiple injectors and producers to support waterflood optimization. The method applies Cross-Wavelet Transform Coherence (CrWTC) with a complex Morlet wavelet to injection and production rate data, enabling the time-localized and frequency-dependent identification of dynamic injector–producer communication. The novelty of this work lies in continuous coherence mapping, the use of the complex Morlet wavelet for improved sensitivity to nonstationary responses, continuous updating as new data become available, and benchmarking on both the Volve and COSTA datasets. Validation using reservoir simulation and field data showed strong qualitative agreement with expected connectivity behavior and demonstrated clearer tracking of connectivity evolution and waterfront movement than the Capacitance Resistance Method (CRM). The proposed approach improves the reliability and interpretability of IWC assessment and offers a practical tool for reservoir surveillance and waterflood management. Full article

Accident reports provide a detailed account of environmental causes, unsafe human behaviors, and subsequent chain reactions. These records serve as essential resources for analyzing accident mechanisms and exploring potential risk patterns within production safety processes. Currently, Graph based Retrieval-Augmented Generation (RAG), which integrates Large Language Models (LLMs) with Knowledge Graphs (KGs), has emerged as a leading approach for complex causal question answering over extensive unstructured accident documentation. However, the application of this technology in the production safety domain still encounters two primary challenges. First, knowledge graph construction using a single granularity fails to capture fine-grained case details and macro-level standard systems. Second, traditional one-step retrieval paradigms lack the capacity to track deep causal chains or interpret the complex logic of multi-factor coupling. To address these limitations, we propose CausalAgent, a hierarchical graph-enhanced multi-agent framework for causal question answering in production safety accident reports. This framework innovatively combines a Hierarchical Causal Graph (HC-Graph) and a Multi-Agent Collaborative Reasoning (MACR) mechanism. Specifically, the HC-Graph employs a two-layer architecture that links a fine-grained instance layer with a national standard causation layer to resolve conflicts in semantic granularity. The MACR mechanism converts complex natural language queries into executable structured queries and logic verification steps through the sequential cooperation of four specialized agents, namely the Graph Parsing Agent, the Problem Analysis Agent, the Query Generation Agent, and the Reasoning Insight Agent. CausalAgent enables in-depth mining of accident causation mechanisms and provides scientific, robust and interpretable intelligent support for data-driven risk assessment and emergency decision-making. Experiments on real-world accident datasets demonstrate that CausalAgent achieves a $100.0\%$ query execution rate and an $87.3\%$ reasoning accuracy, outperforming the SOTA baseline by $45.2\%$ in terms of absolute accuracy. Full article

This study focuses on modelling the spatial dependence of airborne particle contamination using Response Surface Methodology (RSM), with consideration of its implications for technical cleanliness and employee health. The analysis is based on two measurement campaigns conducted in an industrial production hall, where particle concentrations were recorded across multiple size fractions using a TROTEC PC220 device. The results demonstrate that RSM effectively captures nonlinear relationships and spatial gradients, enabling the identification of local extrema and contamination hotspots. Statistical analysis confirmed a significant influence of spatial coordinates on particle concentration across all fractions, with finer particles exhibiting stronger spatial dependence, consistent with aerosol behaviour in indoor environments. Quadratic model terms revealed stable hotspot regions persisting even after corrective measures, indicating persistent contamination sources or structural factors. Residual analysis suggested additional unmodeled local sources or transport mechanisms. Based on the integration of RSM and multi-fraction analysis, a mechanistic contamination model (source–transport–receptor framework with deposition processes) is proposed, linking particle behaviour with surface contamination and potential human exposure. The approach enables data-driven, localised contamination control and supports optimisation of technical cleanliness and occupational health conditions. Full article

SAR interferometry (InSAR) provides a framework for extracting high-resolution topographic information and detecting surface deformation. By analyzing the phase difference between radar acquisitions obtained at different times, one can characterize landscape geometry and surface changes. However, inherent phase noise often compromises the reliability of the resulting interferometric products. Consequently, there is a sustained need for spatial filtering techniques that suppress noise while preserving structural integrity and resolution. This work addresses the challenge of filtering the unwrapped phase, a process traditionally reliant on accurate coherence images to identify reliable pixels. We evaluate three statistically based spatial filters for phase noise reduction. The Enhanced Lee filter, which utilizes spatial adaptation and a physically grounded probability model, serves as the baseline for comparison. We examine the Gierull model, which improves computational efficiency by restricting the parameter space. To further reduce execution time, we propose and evaluate two empirical alternatives: the truncated wrapped normal (TcN) and the truncated wrapped Cauchy (TcC) distributions. Results indicate that these empirical models significantly reduce computational demand without degrading the quality of the filtered phase. We assess performance using a simulated dataset for objective validation alongside InSAR imagery of La Cumbre volcano, Los Alamos, and Robledo volcano. While the proposed models demonstrate significant gains in computational efficiency compared to current methods, we identify numerical integration as a primary bottleneck in the filtering process; this challenge warrants further investigation. Our results indicate that empirical statistical models provide a viable path for accelerated InSAR processing with accuracy equivalent to traditional, computationally intensive approaches. Full article

Background: Post-approval manufacturing changes for biologics require rigorous comparability assessments to ensure uninterrupted quality and clinical performance. CMAB007 (Aomaishu^®), a China-approved (2023) omalizumab biosimilar, underwent process enhancements—including media optimization and anion-exchange chromatography substitution—yielding a 5-fold increase in production without altering the host cell line. Methods: A novel three-arm tiered strategy was adopted to compare post-change CMAB007, pre-change CMAB007, and reference (Xolair^®) products. Critical quality attributes (CQAs) were classified into tiers based on risk impact, with tier-specific acceptance criteria. Comprehensive analytics assessed structure, post-translational modifications, purity/impurities, activity, and Fc-mediated functions. Forced degradation (lyophilized/reconstituted states) and accelerated stability studies were evaluated. Based on the high degree of CMC similarity and to prevent “biological drift”, the pharmacokinetic (PK) and safety comparability of the post-change CMAB007 versus the reference product (Xolair^®) was confirmed in a randomized, double-blind, two-arm study in healthy males (N = 114; single 150 mg subcutaneous administration). The pre-change product was not included in this clinical PK study. Results: Post-change CMAB007 exhibited analytical similarity within tiered acceptance criteria for all CQAs. Stability studies confirmed enhanced robustness under stress conditions. PK equivalence was demonstrated for AUC_0–inf (GMR: 99.82%; 90% CI: 91.46~108.94%), AUC_0–t (99.54%; 91.40~108.41%), and C_max (101.88%; 95.21~109.01%). Immunogenicity (ADA incidence: 10.5% vs. 12.5%, p = 0.742) and safety profiles were comparable. Conclusions: This study pioneers a tiered three-arm comparability strategy for post-approval changes, integrating advanced analytics, risk-based quality assessment, and clinical validation. The approach mitigates “biological drift” risks, ensuring biosimilar quality, efficacy, and safety while enabling sustainable production scalability. Full article

Processing of nickel ores is a key aspect of modern metallurgy due to the growing demand for nickel in stainless steel, battery production, and advanced materials. The depletion of high-grade sulfide ores has shifted attention toward oxidized (lateritic) nickel ores, which are characterized by complex mineralogy and low metal content. This study presents a comparative review of major processing technologies, including pyrometallurgical, hydrometallurgical, and hybrid approaches, with particular emphasis on their applicability to Kazakhstan’s limonitic laterites with high iron and low nickel content. The analysis shows that the most suitable processing routes for such ores include atmospheric acid leaching (AL), high-pressure acid leaching (HPAL), metallothermic reduction, and combined flowsheets integrating thermal and leaching stages. Among these, AL and hybrid approaches are identified as the most promising under resource-constrained conditions. Despite recent technological progress, challenges remain related to energy consumption, economic feasibility, and environmental impact. The study highlights the importance of developing energy-efficient and low-carbon technologies, including hydrogen-based reduction, and provides practical recommendations for selecting and adapting processing methods for Kazakhstan. Full article

The recirculating aquaculture system (RAS) marks a significant shift in global aquaculture, transitioning to controlled, land-based production. This review highlights technological advancements that enable the treatment and reuse of over 90% of water, thereby enhancing water quality and production efficiency. These features position RAS as a cornerstone of sustainable seafood production. This review introduces the RAS Readiness Level (RRL) framework which is a novel, structured approach to assess the commercial maturity of emerging RAS technologies. Applying the RRL to six key technological domains (from digital AI systems to biological PHB recovery) reveals a pervasive pilot-scale purgatory where most innovations stagnate at RRL 4–6. It further addresses advanced processes such as membrane bioreactors, denitrification reactors, and the conversion of waste into valuable products. Furthermore, this review addresses persistent challenges, including high energy demand, economic viability, and the accumulation of pathogens. Finally, it focuses on the emergent integration of the Internet of Things (IoT) and artificial intelligence (AI), which are revolutionizing RAS management through data-driven optimization. By synthesizing current innovations, this review envisions a future of intelligent, closed-loop RAS where advanced IoT- and AI-driven technologies optimize water quality and feeding strategies to minimize ecological impact while enhancing sustainability and productivity. Full article

Diabetic peripheral neuropathy (DPN) is a common and debilitating complication of diabetes mellitus which affects individuals with both type 1 and type 2 diabetes mellitus (T2DM), presenting with sensory loss, pain, and progressive nerve dysfunction. DPN pathogenesis is multifactorial: chronic hyperglycemia activates the polyol, hexosamine, and protein kinase C (PKC) pathways, increases advanced glycation end-products, and drives oxidative stress, mitochondrial dysfunction, inflammation, and impaired neurotrophic signaling. In addition to hyperglycemia-driven mechanisms, dyslipidemia and microvascular insufficiency exacerbate neural ischemia and metabolic stress. Recent mechanistic, animal, and associative human studies further implicate amyloidogenic toxicity, particularly from human islet amyloid polypeptide (hIAPP), as a plausible contributory factor in peripheral nerve degeneration in T2DM, linking protein misfolding and aggregation to axonal damage and demyelination in DPN. Despite increased understanding of these mechanisms, current treatments remain mainly symptomatic. Emerging therapeutic strategies, including antioxidants, anti-inflammatory agents, modulators of mitochondrial function, amyloid oligomer modulators, neurotrophic enhancers, and regenerative approaches such as stem cells and gene-based therapies, offer potential to modify disease progression. The strength of evidence across these methods varies, ranging from mechanistic and animal studies to early human research and, in some cases, randomized clinical trials. Therefore, although several candidates show potential to alter the disease, few have demonstrated consistent benefits on objective measures of nerve structure or function in large clinical trials. This review summarizes the key mechanisms driving DPN in T2DM and highlights promising therapeutic innovations poised for clinical translation. Full article

The cosmetic industry represents a major sector of the global economy and is expected to significantly grow in the coming years. To enhance consumer acceptance and address increasing sustainability concerns, cosmetic companies are actively seeking innovative solutions to mitigate their environmental, economic, and social impacts. In accordance with this, several scientific studies focus on the development, scale-up, and life cycle assessment of sustainable cosmetic products, especially those derived from side streams in accordance with circular economy principles. Various reviews have addressed this topic; however, they typically cover one or two of these dimensions, providing only a partial perspective. In particular, existing studies mainly analyze the types of side streams used and the resulting products, often lacking a comprehensive framework that can effectively support the translation of these approaches into industrial-scale production. The aim of the present review is to address this gap by providing a comprehensive analysis of the maturity level of development, scale-up processes, and life cycle assessment of cosmetic products based on agro-industrial side streams. This analysis is intended to support companies in the transition towards more sustainable practices by reducing carbon footprint and limiting the intensive extraction of virgin raw materials. The different approaches and methodologies proposed for the development and scale-up of sustainable cosmetic products from agro-industrial side streams are also analyzed, considering whether life cycle assessment has been performed. Furthermore, the most suitable business models will be selected as innovative and sustainable value chains capable of generating economic benefits, fostering local development, and enhancing resource efficiency and supply security. Full article

Accurate thermal characterization of building insulation materials is essential for reliable energy performance assessment, regulatory compliance, and the development of high-performance envelopes. On one hand, the growing adoption of innovative insulating products, such as nanoporous materials, aerogel-based composites, bio-based panels, and thin insulating coatings, helps to enhance buildings’ energy efficiency by means of sustainable raw materials. On the other hand, conventional measurement techniques encounter significant challenges, due to their heterogeneity, reduced thickness, and unconventional geometries. In this study, an intra-laboratory comparison of three widely used methods for thermal conductivity determination is presented: the Transient Plane Source (TPS, Hot Disk) method, the Guarded Hot Plate (GHP) method, and the Heat Flow Meter (HFM) method. A total of twelve insulating materials, spanning super-insulating cores, insulating renders, bio-based panels, and nanocomposite coatings, were experimentally characterized under controlled laboratory conditions. A view on the analyzed insulating materials’ cradle-to-grave environmental impact is also given, to enhance the users’ awareness for the highly informed choice. The results highlight systematic differences between transient and steady-state approaches, with TPS measurements generally exhibiting larger deviations for materials characterized by surface roughness, limited thickness, or strong internal heterogeneity. In contrast, GHP and HFM methods show closer agreement when specimen geometry and stabilization requirements are satisfied. The influence of contact resistance, probing depth, specimen preparation, and uncertainty propagation is critically analyzed for each technique. The study provides practical insights into the applicability limits of commonly used thermal characterization methods and emphasizes the importance of selecting measurement techniques in relation to material morphology and testing constraints. These findings support more reliable thermal property assessment of emerging insulation materials and contribute to improved consistency between laboratory measurements and energy performance evaluations for buildings. Full article

Fisheries wastewater contains high levels of suspended solids and organic matter, posing significant environmental risks and necessitating effective and sustainable treatment approaches. This study aims to determine the characteristics of the neem (Azadirachta indica) leaf biocoagulant, assess the interactions among research variables, and optimize its use to reduce total suspended solids (TSS) and chemical oxygen demand (COD) levels in fisheries wastewater. The method used is response surface methodology (RSM), specifically the Box–Behnken Design (BBD), which involves three variables (biocoagulant concentration, fast stirring speed, and sedimentation time) and two responses (TSS and COD removal). Characterization results (Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), and zeta potential) indicated that the biocoagulant contains functional groups such as hydroxyl, carboxyl, and amine, contributing to coagulation–flocculation through adsorption and polymer bridging mechanisms. Statistical analysis confirmed that the developed quadratic models were significant (p-value < 0.05), with high F-values, non-significant lack of fit, and strong coefficients of determination (R² = 0.9111 for TSS and 0.9419 for COD), along with low coefficients of variation (CV < 5%), indicating good model reliability. Although the model generally has a significant effect on the response, the fast stirring speed does not, while the other two factors do. The optimal conditions (based on desirability) were determined to be a biocoagulant concentration of 79.8 mg/L, a fast stirring speed of 100 rpm, and a sedimentation time of 27.5 min. Under these conditions, TSS and COD removals of 88.72% and 79.98%, respectively, were achieved. These findings demonstrate the potential of neem leaf biocoagulant as a sustainable, environmentally friendly alternative to conventional chemical coagulation, supporting cleaner production in aquaculture systems. Full article