Search Results (488)

A hybrid AI and LLM-enabled architecture is presented for real-time decision support in industrial batch processes, where supervision still relies heavily on human operators and ad hoc SCADA logic. Unlike algorithmic contributions proposing novel AI methods, this work addresses the practical integration and deployment challenges arising when applying existing AI techniques to safety-critical industrial environments with legacy PLC/SCADA infrastructure and real-time constraints. The framework combines deterministic rule-based agents, fuzzy and statistical enrichment, and large language models (LLMs) to support monitoring, diagnostic interpretation, preventive maintenance planning, and operator interaction with minimal manual intervention. High-frequency sensor streams are collected into rolling buffers per active process instance; deterministic agents compute enriched variables, discrete supervisory states, and rule-based alarms, while an LLM-driven analytics agent answers free-form operator queries over the same enriched datasets through a conversational interface. The architecture is instantiated and deployed in the Clean-in-Place (CIP) system of an industrial beverage plant and evaluated following a case study design aimed at demonstrating architectural feasibility and diagnostic behavior under realistic operating regimes rather than statistical generalization. Three representative multi-stage CIP executions—purposively selected from 24 runs monitored during a six-month deployment—span nominal baseline, preventive-warning, and diagnostic-alert conditions. The study quantifies stage-specification compliance, state-to-specification consistency, and temporal stability of supervisory states, and performs spot-check audits of numerical consistency between language-based summaries and enriched logs. Results in the evaluated CIP deployment show high time within specification in sanitizing stages (100% compliance across the evaluated runs), coherent and mostly stable supervisory states in variable alkaline conditions (state-specification consistency ${\Gamma }_s\ge 0.98$), and data-grounded conversational diagnostics in real time (median numerical error below 3% in audited samples), without altering the existing CIP control logic. These findings suggest that the architecture can be transferred to other industrial cleaning and batch operations by reconfiguring process-specific rules and ontologies, though empirical validation in other process types remains future work. The contribution lies in demonstrating how to bridge the gap between AI theory and industrial practice through careful system architecture, data transformation pipelines, and integration patterns that enable reliable AI-enhanced decision support in production environments, offering a practical path toward AI-assisted process supervision with explainable conversational interfaces that support preventive maintenance decision-making and equipment health monitoring. Full article

Cleaning historical silk textiles is a particularly sensitive operation that requires precise control to prevent mechanical or chemical damage. In this study, we investigate using flexible PVA–borax-based gels to remove soot from silk, i.e., polyvinyl alcohol–borax (PVA-B) gels and polyvinyl alcohol–borax–agarose double network gels (PVA-B/AG DN) loaded with different cleaning agents—namely, 30% ethanol and 1% Ecosurf EH-6—in addition to plain gels loaded with water. These gel formulations were tested on simplified model systems (SMS) and were applied using two methods: placing and tamping. The cleaning results were compared with a traditional contact-cleaning approach; micro-vacuuming followed by sponging. Visual inspection, 3D opto-digital microscopy, colorimetry, and machine-learning-assisted (ML) soot counting were exploited for the assessment of cleaning efficacy. Rheological characterization provided information about the flexibility and handling properties of the different gel formulations. Among the tested systems, the DN gel containing only water, applied by tamping, was easy to handle and demonstrated the highest soot-removal effectiveness without leaving residues, as confirmed by micro-Fourier Transform Infrared (micro-FTIR) analysis. Scanning electron microscope (SEM) micrographs proved the structural integrity of the treated silk fibers. Overall, this work allows us to conclude that PVA–borax-based gels offer an effective, adaptable, and low-risk cleaning strategy for historical silk fabrics. Full article

Biofouling on ship hulls significantly increases hydrodynamic drag, fuel consumption, and greenhouse gas emissions, while also facilitating the spread of invasive species in regional and global waters, thereby threatening marine biodiversity. To address these environmental and economic issues, we developed an innovative robotic platform for in-water hull cleaning. The platform utilizes a cavitation-based cleaning module that removes biofouling while minimizing hull surface damage and preventing the spread of detached particles into the marine environment. This paper describes the design, operation, and testing of a developed robotic cleaning system prototype. Emphasis is placed on integrating components and sensors for continuous monitoring of key seawater parameters (temperature, salinity, turbidity, dissolved oxygen, chlorophyll-a, etc.) before, during, and after underwater cleaning. Results from real-sea trials show the platform’s effectiveness in removing biofouling and its minimal environmental impact, confirming its potential as a sustainable solution for in-water hull cleaning. Full article

Sustainability in the agri-food sector has become a cornerstone of global efforts to combat climate change, ensure food security through climate-smart agriculture, and strengthen economic resilience. Sustainability reporting within agri-food systems has gained increasing regulatory significance with the introduction of mandatory frameworks such as the Turkish Sustainability Reporting Standards (TSRSs). This article searches for the sustainability reports of agri-business firms listed in BIST in Turkey. Although TSRS reporting is not yet mandatory for the agribusiness sector, this study examines the first TSRS-aligned sustainability reports published by eight agri-food companies, excluding the retail sector. The analysis assesses how effectively these reports address sector-specific environmental and social challenges defined in the GRI 13 Agriculture, Aquaculture and Fishing Sector Standard and their alignment with the United Nations Sustainable Development Goals (SDGs). Using a structured content analysis approach, disclosure patterns were examined at both thematic and company levels. The findings indicate that TSRS-aligned reports place strong emphasis on environmental and climate-related disclosures, particularly emissions, climate adaptation and resilience, water management, and waste. In contrast, agro-ecological and land-based impacts—such as soil health, pesticide use, and ecosystem conversion—are weakly addressed. Economic disclosures are predominantly framed around climate-related financial risks and supply chain traceability, while social reporting focuses mainly on occupational health and safety, employment practices, and food safety, with limited attention to labor and equity issues across the broader value chain. Company-level results reveal marked heterogeneity, with internationally active firms demonstrating deeper alignment with GRI 13 requirements. From an SDG alignment perspective, high levels of coverage are observed across all companies for SDG 13 (Climate Action), SDG 12 (Responsible Consumption and Production), and SDG 6 (Clean Water and Sanitation). By contrast, SDGs critical to agro-ecological integrity and social equity—namely SDG 1 (No Poverty), SDG 2 (Zero Hunger), SDG 10 (Reduced Inequalities), and SDG 15 (Life on Land)—are weakly represented or entirely absent. Overall, the results suggest that while TSRS-aligned reporting enhances transparency in climate-related domains, it achieves only selective alignment with the SDG agenda. This underscores the need for a stronger integration of sector-specific sustainability priorities into mandatory sustainability reporting frameworks. Full article

The global shipping industry is surging ahead, and with it, a quiet revolution is taking place on the water: marine lithium-ion batteries have emerged as a crucial clean energy carrier, powering everything from ferries to container ships. When these vessels dock, they increasingly rely on shore power charging systems to refuel—essentially, plugging in instead of idling on diesel. But predicting how much power they will need is not straightforward. Think about it: different ships, varying battery sizes, mixed charging technologies, and unpredictable port stays all come into play, creating a load profile that is random, uneven, and often concentrated—a real headache for grid planners. So how do you forecast something so inherently variable? This study turned to the Monte Carlo method, a probabilistic technique that thrives on uncertainty. Instead of seeking a single fixed answer, the model embraces randomness, feeding in real-world data on supply modes, vessel types, battery capacity, and operational hours. Through repeated random sampling and load simulation, it builds up a realistic picture of potential charging demand. We ran the numbers for a simulated fleet of 400 vessels, and the results speak for themselves: load factors landed at 0.35 for conventional AC shore power, 0.39 for high-voltage DC, 0.33 for renewable-based systems, 0.64 for smart microgrids, and 0.76 when energy storage joined the mix. Notice how storage and microgrids really smooth things out? What does this mean in practice? Well, it turns out that Monte Carlo is not just academically elegant, it is practically useful. By quantifying uncertainty and delivering load factors within confidence intervals, the method offers port operators something precious: a data-backed foundation for decision-making. Whether it is sizing infrastructure, designing tariff incentives, or weighing the grid impact of different shore power setups, this approach adds clarity. In the bigger picture, that kind of insight matters. As ports worldwide strive to support cleaner shipping and align with climate goals—China’s “dual carbon” ambition being a case in point—achieving a reliable handle on charging demand is not just technical; it is strategic. Here, probabilistic modeling shifts from a simulation exercise to a tangible tool for greener, more resilient port energy management. Full article

In this work we report the results of analysis of the acoustic signal generated by the interaction of a nanosecond laser pulse (30 mJ, 1064 nm) with various residues placed on a silica wafer. The signal was captured by a unidirectional microphone placed 30 mm from the laser-generated plasma. The examined sample classes, other than the clean wafer, included particles from soils and rocks, carbonates, nitro precursors, ash, coal, smeared diesel, and particles of explosives. We tested three types of explosives, namely PETN, RDX, and HMX, having different origins. For the explosives, the acoustic signal showed a faster rise, larger amplitude, different width, and attenuation compared with the other sample classes. By subtracting the acoustic signal from the wafer at the same position, obtained after four cleaning laser pulses, the contribution of echoes was eliminated and true differences between the residue and substrate became evident. Through four different features in the subtracted signal, it was possible to classify explosives without the presence of false positives; the estimated limit of detection was 15 ng, 9.6 ng, and 18 ng for PETN, RDX, and HMX, respectively, where the mass was extrapolated from nano-printed samples and LIBS spectra acquired simultaneously. Furthermore, HMX was distinguished from the other two explosives in 90% of the cases; diesel and coal were also recognized. We also found that explosives deposited through wet transfer behaved as inert substances for the tested masses up to 30 ng. Full article

This study investigates the flow around Kline-Fogleman (KF) airfoils using Particle Image Velocimetry (PIV) in a wind tunnel at Reynolds number Re = 6.8 × 10⁴. Three configurations are tested: a clean NACA 0015 airfoil and two modified versions with a step on either the pressure or suction side. Velocity fields are used to calculate lift via the Kutta-Joukowski theorem. Results show that the KF airfoil with a step on the pressure side achieves a 14.8% higher maximum lift coefficient and delayed stall. In contrast, placing the step on the suction side reduces maximum lift by 4%. The KF airfoil with pressure-side step shows potential for low Reynolds number applications where higher lift and larger stall angles are required. Full article

Fault-tolerant control in safety-critical industrial systems demands adaptive responses to equipment degradation, parameter drift, and sensor failures while maintaining strict operational constraints. Traditional model-based controllers struggle under these conditions, requiring extensive retuning and dense instrumentation. Recent safe multi-agent reinforcement learning (MARL) frameworks with control barrier functions (CBFs) achieve real-time constraint satisfaction in robotics and power systems, yet assume comprehensive state observability—incompatible with sensor-hostile industrial environments where instrumentation degradation and contamination risks dominate design constraints. This work presents a safety-aware multi-agent deep reinforcement learning framework for adaptive fault-tolerant control in sensor-lean industrial environments, achieving formal safety through learned implicit barriers under partial observability. The framework integrates four synergistic mechanisms: (1) multi-layer safety architecture combining constrained action projection, prioritized experience replay, conservative training margins, and curriculum-embedded verification achieving zero constraint violations; (2) multi-agent coordination via decentralized execution with learned complementary policies. Additional components include (3) curriculum-driven sim-to-real transfer through progressive four-stage learning achieving 85–92% performance retention without fine-tuning; (4) offline extended Kalman filter validation enabling 70% instrumentation reduction (91–96% reconstruction accuracy) for regulatory auditing without real-time estimation dependencies. Validated through sustained deployment in commercial beverage manufacturing clean-in-place (CIP) systems—a representative safety-critical testbed with hard flow constraints (≥1.5 L/s), harsh chemical environments, and zero-tolerance contamination requirements—the framework demonstrates superior control precision (coefficient of variation: 2.9–5.3% versus 10% industrial standard) across three hydraulic configurations spanning complexity range 2.1–8.2/10. Comprehensive validation comprising 37+ controlled stress-test campaigns and hundreds of production cycles (accumulated over 6 months) confirms zero safety violations, high reproducibility (CV variation < 0.3% across replicates), predictable complexity–performance scaling ($R^2=0.89$), and zero-retuning cross-topology transferability. The system has operated autonomously in active production for over 6 months, establishing reproducible methodology for safe MARL deployment in partially-observable, sensor-hostile manufacturing environments where analytical CBF approaches are structurally infeasible. Full article

Zeolites and molecular sieves have demonstrated remarkable potential in adsorption, ion exchange, and separation processes since their industrial revolution in the 1950s. Zeolites and molecular sieves are materials of choice in separation applications because of their well-defined microporous architecture, remarkable shape-selectiveness, and tunable characteristics. The adsorption process can be evaluated using an isotherm to determine the feasibility of gas mixture separation for practical applications. We will also discuss the basic structure of zeolites and molecular sieves based on different metals, along with their distinctive properties in detail. The purpose of this review is to contextualize the importance of zeolites and molecular sieves in adsorption and separation applications. The review has been divided into groups based on how zeolites as well as molecular sieves are established in the adsorption and separation processes. The fundamental adsorption characteristics, structures, and various separation methods that make zeolites appealing for different uses are covered. By incorporating knowledge of adsorption mechanisms, isotherms, and material changes, this review discusses the most recent developments. To augment zeolite-based materials for certain pollutant removal applications, it offers a strategic framework for future study. In this review, we will comprehensively discuss a range of separation and adsorption applications, including wastewater purification, CO₂ capture from flue gases, and hydrogen storage. Furthermore, the review will explore emerging prospects of zeolites and molecular sieves in innovative fields such as energy storage, oil refining, and environmental remediation. Emphasis will be placed on understanding how their tunable pore structures, surface chemistry, and metal incorporation can enhance performance and broaden their applicability in sustainable and clean energy systems. Full article

Silver tarnishing in museum environments depends on multiple, interacting factors that are not often studied in situ. With the aim of addressing the problem in real-world scenarios, this study presents a one-year assessment at the National Archaeological Museum of Spain, in Madrid, a museum that houses a significant collection of silver objects. Pure Ag coupons were placed in four display cases—two designs with different airtightness—and in an adjacent gallery. Tarnishing was quantified by colorimetry, gravimetry, and galvanostatic reduction, and analyzed in relation to environmental parameters (T/RH) and gaseous pollutants (H₂S, SO₂, HF, HCl, formic and acetic acids), measured with passive samplers. Coupons showed different degrees of tarnish, with annual corrosion rates ranging from IC1 (very low) to IC2 (low), without a straightforward relation to hydrogen sulfide concentrations. Electrochemical profiles and XPS on representative coupons identified Ag₂S as the dominant product, with AgCl and minor Ag₂SO₄ in the coupons exposed outside the airtight cases, indicating different contributions inside and outside the cases. Findings highlight that sulfide concentration is not the sole driver; case airtightness, internal materials, cleaning products used on adjacent areas, and, possibly, other aspects influence silver tarnishing. Full article

Pane Toscano DOP, a traditional sourdough bread from Italy, has a limited shelf life, typically lasting only a few days. Extending its shelf life without the use of synthetic preservatives is essential to meet the rising demand for clean-label products and to reduce food waste. This study aimed to identify the most effective packaging strategy to extend the shelf life of Pane Toscano DOP. Two packaging systems were evaluated: single-package and bag-in-bag systems. In the single-package setup, bread was packaged in PET/PE under different headspace conditions: ambient air (C1), air + Everfresh^® Spray (EVF, a natural aromatic extract) (C2), CO₂ only (C3), and CO₂ + EVF (C4). In the bag-in-bag system, bread was first placed in a PLA primary package containing air and then enclosed within a PET/PE secondary package filled with either air (T1), air + EVF (T2), CO₂ only (T3), or CO₂ + EVF (T4). Shelf life of bread under different packaging conditions was evaluated based on the appearance of visible mold growth. T4 exhibited the longest shelf life, maintaining acceptable quality for 41 days, followed by T3 with 34 days. Air packaged samples C1 and T1 had the shortest shelf life of only 6 days, while C2, T2, and C3 each maintained quality for 20 days. These findings demonstrate that the use of modified atmosphere packaging, bag-in-bag systems, and aromatic headspace extracts can significantly extend the shelf life of artisanal breads, such as Pane Toscano DOP. This approach offers viable alternatives to synthetic preservatives while maintaining traditional product formulations. Full article

The shift to renewable energy is a key goal for the European Union as it aims for climate neutrality; however, the effectiveness of climate-focused funding instruments varies significantly across member states. This research investigates the influences of mitigation investments, R&D spending, environmental tax revenues, subsidies, GDP growth, and capital formation on renewable energy expansion within the EU-27, placing particular emphasis on the structural differences between Old Member States (OMS) and New Member States (NMS). The study utilizes robust long-run estimation techniques alongside causality analysis over a span of 13 years, from 2010–2023. The findings highlight notable distinctions among the EU-27, OMS, and NMS regions. While the EU-27 and OMS show that funds designated for climate mitigation and R&D are critical drivers of the clean energy transition, in the NMS, environmental taxes, subsidies, innovation, and gross fixed capital formation play vital roles in advancing this transition. Furthermore, economic development shows mixed results in achieving sustainable objectives, underscoring the necessity for climate-oriented funding and initiatives. Therefore, policy measures should focus on mitigation finance and innovation across the EU, while the design of subsidies and environmental tax structures must be tailored to each region to ensure a fair and expedited transition. Full article

This study presents a decision tree model-based approach to classify rural net migration across Colombian departments using sociodemographic and economic variables. In the model formulation, immigration is considered the movement of people to a destination area to settle there, while emigration is the movement of people from that specific area to other places. The target variable was defined as a binary category representing positive (when the immigration is greater than emigration) or negative net migration. Four classification models were trained and evaluated: Decision Tree, Random Forest, AdaBoost, and XGBoost. Data were preprocessed using cleaning techniques, categorical variable encoding, and class balance assessment. Model performance was evaluated using various metrics, including accuracy, precision, sensitivity, $F_1$ score, and the area under the ROC curve. The results show that Random Forest achieves the highest accuracy, precision, sensitivity, and $F_1$ score in the 10-variable and 15-variable settings, while XGBoost is competitive but not dominant. Furthermore, the importance of the model was analyzed to identify key factors influencing migration patterns. This approach allows for a more precise understanding of regional migration dynamics in Colombia and can serve as a basis for designing informed public policies. Full article

Gold mining has played a central role in shaping Peruvian society from pre-Inca civilizations to the present. However, existing literature offers fragmented perspectives, often focusing on isolated themes such as metallurgy, colonial mercury use, or environmental degradation, without integrating these across time and territory. This review addresses that gap by offering a place-based synthesis that combines archaeological, historical, legal, environmental, and comparative insights. Drawing on both Spanish-language sources and international literature, the paper reconstructs Peru’s gold mining trajectory through five historical phases—pre-Inca, Inca, colonial, republican, and contemporary—highlighting continuities and ruptures in governance, labor systems, and environmental impacts. The analysis reveals persistent challenges in Peru’s gold sector, including informality, mercury pollution, and weak institutional capacity. Compared to other mining economies such as Chile, Ghana, and South Africa, Peru exhibits greater fragmentation and limited integration of mining into national development strategies. The review also explores the role of gold in the global energy transition, emphasizing its relevance in clean technologies and green finance, and identifies policy gaps that hinder Peru’s alignment with sustainability goals. By bridging linguistic and disciplinary divides, this synthesis contributes to a more inclusive historiography of extractive industries and underscores the need for interdisciplinary approaches to mining governance. Ultimately, the paper calls for a reimagining of Peru’s gold sector, one that prioritizes environmental justice, social equity, and long-term resilience. Full article

Real-time monitoring is essential for maintaining water quality and optimizing aquaculture productivity. Ion-selective electrodes (ISEs) are widely used to measure key parameters such as pH, nitrate, and dissolved oxygen in aquatic environments. However, these sensors are prone to fouling, the non-specific adsorption of organic, inorganic, and biological matter, which leads to potential drift (e.g., 1–10 mV/h), loss of sensitivity (e.g., ~40% in 20 days), and reduced lifespan (e.g., 3 months), depending on membrane formulation and environmental conditions. This review summarizes current research from mostly the last two decades with around 150 scientific studies on fouling phenomena affecting ISEs, as well as recent advances in fouling detection, cleaning, and antifouling strategies. Detection methods range from electrochemical approaches such as potentiometry and impedance spectroscopy to biochemical, chemical, and spectroscopic techniques. Regeneration and antifouling strategies combine mechanical, chemical, and material-based approaches to mitigate fouling and extend sensor longevity. Special emphasis is placed on environmentally safe antifouling coatings and material innovations applicable to long-term monitoring in aquaculture systems. The combination of complementary antifouling measures is key to achieving accurate, stable, and sustainable ISE performance in complex water matrices. Full article