Search Results (4,041)

To control the quality of mineral-medicinal waters and ensure their therapeutic benefits, spas often rely on periodic discrete sampling to analyze the physico-chemical properties of their pools. The AQUAPRED project aims to digitize this process by deploying IoT systems within the spa facilities, enabling real-time data acquisition via calibrated multi-parameter probes. Using data collected by these pilot systems, we develop and validate a predictive machine learning model capable of forecasting the short-term evolution of the thermal water properties. Historical data from each facility allow the model to learn the specifics dynamics of each spa. As a practical application, we propose an anomaly detection module based on residual analysis from predicted and observed values. Significant discrepancies signal events of interest and emergent trends, such as anomalous readings, contamination or sensor drift. The methodology is evaluated using real data from six spas associated with the AQUAPRED project. The results demonstrate the model’s effectiveness and support its feasibility for deployment in other thermal establishments. Full article

This paper reported the grey water footprint (GWF) mitigation resulting from a brewery industry wastewater treatment using malt dust-derived biochar. The GWF was assessed based on chemical oxygen demand (COD) and total suspended solids (TSS) removal. A new data-driven parametric index (GWFI_BP) was reported that uses the GWF tool. A data-driven model was designed in order to define the impact of the dual advantages of biochar application relative to the Conventional Activated Sludge (CAS) process. A GWF reduction of approximately 21.59% was found for the biochar application. Full article

The present study investigates the presence, sources, and impacts of microplastics (MPs) in different soil types, including agricultural, urban, and forest areas, through a synthesis of results of published scientific papers. MPs originate from a variety of human activities, such as the widespread use of plastic mulch in agriculture and the application of organic fertilizers and treated sewage sludge, as well as from vehicle tire wear, industrial processes, and the gradual degradation of plastic products in the environment. In urban soils, the main sources of MPs are related to road traffic, industrial activity, and landfills, while in forest soils, concentrations are generally lower. However, MPs in forest areas are thought to be carried there by the air, by runoff, or from nearby areas with human activity. Available data show that larger MP particles tend to remain in the surface layers of the soil, while smaller particles can penetrate deeper soil layers, increasing their bioavailability and the likelihood of interaction with microorganisms and plant root systems. In terms of their chemical composition, polyethylene (PE) and polypropylene (PP) polymers dominate in agricultural soils, which is directly linked to agricultural practices, while polystyrene (PS) and polyvinyl chloride (PVC) are more frequently detected in urban soils. The morphological types of MPs include fragments, fibers, and films, while their color characteristics provide clues to possible sources of origin, such as plastic ground covers, tire wear, and packaging materials. Overall, the study’s results underscore the growing environmental significance of MP soil pollution and highlight the need for more effective management and recycling of plastic materials, as well as for further interdisciplinary research aimed at understanding the mechanisms of transport, accumulation, and long-term ecological effects of microplastics in terrestrial ecosystems. Full article

Aerobic ceramic membrane bioreactors (AeCeMBR) have shown great potential in treating wastewater (WW) from the textile industry; however, their operation faces challenges such as process variability, membrane contamination, and the need for accurate prediction of treated water quality under varying conditions. In this study, chemical oxygen demand (COD) and turbidity were selected as key indicators, as they directly reflect organic load removal and solids separation efficiency in MBR systems. The effect of four operational parameters: hydraulic retention time (HRT), organic loading rate (OLR), mixed liquor suspended solids (MLSS), and transmembrane pressure (TMP), was investigated using a response surface methodology (RSM) based on a Box–Behnken design. A random forest (RF) model coupled with Monte Carlo simulation (MC) was also developed using 174 experimental data points to enhance predictive power and quantify uncertainty. The RSM model showed strong agreement with experimental results (coefficient of determination (R²) > 0.95), achieving approximately 96% removal for both COD and turbidity, with validation errors of less than 2%. MC simulation (10,000 iterations) was applied to assess the effect of ±10% variance under operating conditions, providing a probabilistic view of system performance. The RF-MC framework demonstrated high predictive accuracy, with strong correlations between predicted and observed values (R² = 0.92 for COD and 0.97 for turbidity) and low uncertainty. Overall, this study proposes an integrated RSM, RF–MC approach for AeCeMBR systems, providing a robust and uncertainty-aware framework for process optimization and performance prediction under changing operating conditions. Full article

Chemical analysts and archeologists are increasingly interested in organic remains that penetrate the porous structures of ceramic vessels. Fatty acids and archaeological biomarkers are chemical compounds that are particularly important for determining the contents of ceramic vessels. This study involved gas chromatography coupled with mass spectrometry (GC–MS) analysis of organic residues extracted from 56 Neolithic pottery samples found in 18 different settlements in Poland. Fatty acid ratios, including the newly proposed C15:0/C17:0 ratio (pentadecanoic acid/heptadecanoic acid) for the identification of dairy products and archaeological biomarker analysis, were used to determine the possible origin of these residues. The data obtained from the gas chromatography studies were statistically analyzed using principal component analysis (PCA), k-means clustering, and PERMANOVA to determine differences in the diet of the people inhabiting individual settlements. The obtained results allowed us to determine that the Neolithic diet was probably similar in different regions of Poland and throughout different periods of the Neolithic era. However, because of the large difference in variance between the different sample groups, we believe that research should continue and that a larger number of samples per settlement or historical period should be examined. We can conclude that all samples contained residues of mixed animal and plant origin, and the food stored in these vessels was likely subjected to thermal processing. Full article

Per- and polyfluoroalkyl substances (PFAS) are ultra-persistent contaminants characterized by exceptional chemical stability, high mobility, and widespread environmental occurrence, posing significant challenges for remediation. Phytotechnology has emerged as a promising nature-based approach, yet its effectiveness is strongly governed by PFAS physicochemical properties and plant–soil interactions. This review provides a mechanistic synthesis linking PFAS environmental behavior with phytotechnology performance by examining PFAS sources, transport pathways, and structure-dependent properties that control persistence, partitioning, and mobility, with an emphasis on differences between short- and long-chain compounds. These characteristics determine bioavailability and influence treatment outcomes. Plant uptake mechanisms, including root absorption, xylem translocation, and tissue accumulation, are discussed alongside rhizosphere processes such as sorption, microbial interactions, and hydrological dynamics that regulate PFAS retention and redistribution. Current evidence indicates that phytotechnology functions primarily as a form of phytomanagement rather than a destructive solution, as mineralization is limited and field-scale treatment remains low. Instead, plant–soil–microbe systems reduce PFAS mobility and exposure through stabilization and sequestration. Future research should prioritize strategies for short-chain PFAS, integration with sorptive amendments, and data-driven approaches to optimize phytomanagement performance. Full article

An aeolian event and a fluvial episode affected Gale crater, Mars, prior to 3.6 billion years ago. Both were short-lived and catastrophic. The same two events also modified the Southern Highlands of the red planet during the same time interval. We show that events in Gale crater were a part of those that modified vast areas of the southern hemisphere of Mars. As such, the patterns documented in Gale crater are consistent with reshaping of large portions of Mars by short-lived catastrophic events by wind and water, although data from other regions are needed to establish this on a planetary scale. The study is based on data collected by the Curiosity rover during the past 14 years. The aeolian event excavated Gale crater and formed two distinct morphological provinces with two contrasting rock types. One was the cone-shaped ancestral Aeolis Mons, informally known as Mt. Sharp, that consists of sandstone, siltstone, and mudstone. The other was the nearly flat hollowed margin, the ancestral crater floor, that was initially covered by loose pebbles, cobbles, and boulders which were reworked and lithified to a conglomeratic rock unit later. Commonly reported Martian aeolian erosion rates cannot account for the abrasion and transport of 39,000 km³ of sediments out of Gale crater. This conclusion is supported by little modification of Gale crater during the past 3.6 billion years by ordinary winds. Our evaluation indicates that the excavation of Gale crater took place by a powerful aeolian perturbation that resembled a sand-blasting operation. It was short-lived, had extremely high erosion rates, and occurred during a cold and dry climate. The fluvial episode followed the aeolian event. The study of its sedimentary record indicates that it began with intense precipitation-driven great floods that eroded the ancestral Mt. Sharp, carved large canyons on its slope, and reworked gravels of the ancestral crater floor into giant bedforms. Flood waters also formed a deep lake that experienced one rise and one fall of lake-level and had a dynamic storm-driven sedimentation. The fluvial episode was also short-lived and indicates catastrophic actions of water during a warm and wet climate. As such, this study suggests that the extensive reshaping of the red planet during the Late Noachian period, including formation of valley networks, occurrence of hundreds of crater lakes, and excavation of numerous craters, were also due to short-lived, intense, climate-related perturbations by powerful wind and water rather than by ordinary, slow rate, long-duration processes. Another implication of the study is for the mineralogical evolution of Martian sedimentary rocks. It indicates that the Late Noachian period may have been mostly cold and dry, similar to the modern Mars. Its low water/rock ratio and cold temperatures halted chemical weathering that resulted in preservation of highly unstable minerals such as olivine and pyroxene. The fluvial perturbation with its high water/rock ratio was not long and/or warm enough to alter or significantly affect the mineralogy by weathering at the source region, or during the transport, or at the depositional site. Full article

Mulberry species (Morus spp.) provide phytochemically distinct plant materials in which leaves are typically characterized by high levels of iminosugars (notably 1-deoxynojirimycin), flavonols/flavones, and polysaccharides, whereas fruits—especially Morus nigra—contain substantial amounts of anthocyanins alongside other phenolic compounds and polysaccharides. Importantly, the composition and biological properties of mulberry-derived products depend not only on species and plant part (leaf vs. fruit), but also on preparation and processing variables, including drying, maceration, fermentation, and extraction, or fractionation strategy (e.g., aqueous vs. hydroalcoholic extracts or enriched fractions). Such technological factors may substantially influence the chemical composition, bioavailability, and functionality of mulberry-derived preparations and thereby modify their interactions with gut microbiota and host metabolic processes. Available preclinical studies indicate that mulberry leaf- and fruit-derived preparations can affect gut microbial composition or activity in experimental models of metabolic dysfunction. Reported findings frequently include enrichment of microbial taxa commonly regarded as beneficial, such as Bifidobacterium, Lactobacillus, and Akkermansia, normalization of dysbiosis-associated microbial patterns, and increased production of short-chain fatty acids, particularly acetate, propionate, and butyrate. These microbial changes are sometimes observed alongside improvements in metabolic parameters such as glucose regulation, lipid profile, adiposity, or inflammatory markers. However, reported responses differ across plant parts, species, and preparation approaches, indicating that phytochemical composition and processing strategy are likely to influence biological outcomes. Interpretation of the current evidence is limited by the predominance of non-human studies and by incomplete or inconsistent reporting of extract composition, processing conditions, and standardization procedures. These factors reduce comparability between studies and complicate mechanistic interpretation of microbiome-related effects. Overall, existing preclinical data support the possibility that mulberry-derived preparations may influence metabolic health through microbiota-associated pathways shaped by both botanical origin and preparative technology. Well-designed human intervention studies using chemically characterized and standardized preparations, together with comprehensive gut microbiome analyses, are needed to determine the translational relevance of these observations and to identify which mulberry-derived preparations offer the greatest potential for supporting gut and metabolic health. Full article

The removal of recalcitrant industrial dyes from wastewater has emerged as a critical environmental challenge, particularly in the context of the accelerating decline of global freshwater reserves. Given that these contaminants originate predominantly from the effluents of textile, chemical, and related manufacturing sectors, the deployment of advanced treatment technologies prior to discharge is imperative to mitigate their ecological impact. This study investigates the photocatalytic degradation of indigo carmine using a synthesized MgAl–LDH material. LDH is shown to act as an active photocatalytic component rather than a support, with its remarkably simple synthesis offering a practical alternative to the complex catalysts dominating the current literature. The catalyst’s structural, morphological, and surface characteristics were comprehensively validated through XRD, SEM, EDX, and BET analyses. The catalyst was evaluated under varying hydrogen peroxide doses and across an initial dye concentration range of 5 × 10⁻⁵ to 5 × 10⁻⁴ M. Increasing the H₂O₂ volume (3.5–20 mL, corresponding to H₂O₂ excess ratios of 17.5–100) significantly enhanced the oxidation rate, whereas higher dye concentrations reduced efficiency due to photon competition and partial saturation of catalytic sites. These experiments provided the basis for extracting kinetic parameters and assessing the mechanistic pathways governing the photocatalytic process. The kinetic behavior of indigo carmine degradation was evaluated by fitting the experimental data to zero-order, first-order, and second-order empirical models to identify the rate law that best describes the reaction. Reusability tests showed that MgAl–LDH maintains high activity over multiple cycles, with only a moderate decline, demonstrating its stability and suitability for practical wastewater treatment applications. Full article

Tritium breeding blanket is a key component of future fusion power plants, and its performance depends on the selection, fabrication, and qualification of lithium-based ceramic material. Among the proposed lithium ceramics materials, the main candidates for ceramic breeders are lithium orthosilicate (Li₄SiO₄) and lithium metatitanate (Li₂TiO₃). These advanced ceramics and their biphasic composites are the leading candidates due to their high lithium density, favorable tritium breeding ratio (TBR ≈ 1.15–1.25 with Be₁₂Ti multiplier and 90% ⁶Li enrichment), and robust thermo-mechanical behavior within the 200–900 °C operational window of helium-cooled pebble bed (HCPB) blankets. This review provides an engineering-oriented assessment covering fabrication routes (solid-state, hydrothermal, melt-based, drip casting, powder injection molding, microwave sintering, and digital light processing additive manufacturing); microstructure–property relationships and performance under neutron irradiation; and tritium generation, retention, and release as functions of chemical composition, defect structure, and operating temperature. Induced radioactivity of Li-based ceramics and key impurity elements is quantified using activation formalisms applied to WWR-K reactor conditions, providing guidance for raw-material selection and waste-management assessment. Authors’ original contributions include (i) an empirical model of pebble crush load vs. biphasic composition (R² > 0.99); (ii) two universal semi-empirical kinetic models (exponential growth and non-linear strength degradation, R² = 0.97–0.99) for nine structural and mechanical parameters of Li₂TiO₃ under He²⁺ and H⁺ irradiation; (iii) a consolidated table of Arrhenius tritium diffusion parameters from reactor experiments and DFT; and (iv) an induced radioactivity calculation for the biphasic system with two-exponential post-irradiation decay analysis. The review identifies biphasic Li₄SiO₄–Li₂TiO₃ composites with ~30 ± 5 mol.% Li₂TiO₃ as particularly promising and formulates specific data gaps and modeling needs for the reliable deployment of ceramic breeder pebbles in helium-cooled fusion blanket systems. It should be specifically noted that Li₄SiO₄ pebbles fabricated via the melt method, as an example, typically exhibit exceptionally high densities, generally exceeding 90% of the theoretical density (TD). Building on the calculation of induced radioactivity, it is crucial to consider the microstructural distribution of highly radioactive nuclides (e.g., Co, Mn) within the ceramic matrix. If these impurities segregate at grain boundaries rather than being homogeneously distributed, there is a potential pathway to develop targeted wet-chemical methods, such as selective acid leaching, to remove these impurities post-irradiation, thereby lowering the waste disposal classification. Full article

This study examines the influence of the chemical nature and molecular structure of external electron-donor compounds on the slurry synthesis of isotactic polypropylene in a hydrocarbon diluent using a titanium–magnesium Ziegler–Natta catalyst. Propylene polymerization was carried out at constant temperature, pressure, hydrogen concentration, and fixed molar ratios of the catalyst system components. Alkoxysilanes with different structures were used as external donors: dicyclopentyldimethoxysilane, cyclohexylmethyldimethoxysilane, diisobutyldimethoxysilane, and diethylaminotriethoxysilane; di-n-butyl phthalate was used as a reference compound. It was shown that external donors decrease catalytic activity relative to the donor-free system but increase stereospecificity, as indicated by a lower xylene-soluble fraction and a higher isotactic index of polypropylene. Dicyclopentyldimethoxysilane demonstrated the strongest stereoregulating effect, providing the lowest content of xylene-soluble polymer. The donor structure significantly affected the molecular weight, rheological, and thermal characteristics of polypropylene, including melt flow rate, viscosity-average molecular weight, melting temperature, melting enthalpy, and crystallinity. Comparison with literature data for catalyst systems differing in internal and external donors, as well as in synthesis conditions, showed that catalyst activity and the stereospecificity of the catalyst system may be determined not only by process parameters but also by the electron-donor environment of the active sites. The results support established concepts of external-donor action in Ziegler–Natta catalysis and provide a comparative assessment of alkoxysilane donors and di-n-butyl phthalate under slurry polymerization conditions. Full article

The Beijing–Tianjin–Hebei (BTH) region faces complex air pollution driven by alternating particulate matter (PM) and ozone (O₃) dominance, regional transport, topography, and meteorology. This study develops a hybrid framework integrating air quality index (AQI) records, pollutants, meteorological variables, and MEIC emissions from the BTH region (2018–2025) to capture spatiotemporal evolution and short-term predictability. Results show a seasonal AQI cycle (winter/spring highs, summer/autumn lows) with a summer PM–O₃ seesaw. Spatially, three zones were identified: the northern and coastal ecological barrier zone, the central compound-pollution plain zone, and the southern heavy-industrial zone. Random Forest identifies PM as the dominant AQI compositional contributor, with visibility, dew point, humidity, and MEIC emissions (particulates, NH₃, organics) as key correlates. Forecast evaluation reveals progressive improvement: ARMA captures linear baselines (R² = 0.318, MAPE = 33.26%), XGBoost improves statistical prediction by incorporating nonlinear feature interactions and lagged meteorology (R² = 0.567, MAPE = 24.81%), and LSTM shows the strongest statistical predictive performance (R² = 0.613, MAPE = 22.32%). The improvement of LSTM over XGBoost is incremental and reflects enhanced data-driven representation of short-term AQI–meteorology temporal dependence, rather than identification of physical pollution mechanisms. Regional disparities persist, with higher predictability in the southern heavy-industrial zone and lower accuracy in the northern and coastal ecological barrier zone affected by intermittent dust intrusions and frontal passages. Overall, the results suggest that LSTM may support data-driven short-term AQI warning, but source-oriented mitigation still requires process-based tools, such as chemical-transport or source-apportionment models. Full article

Proactive accident prediction is a fundamental prerequisite for the environmental and social sustainability of high-risk sectors. Accident prediction research has expanded rapidly across transportation, construction, fire safety, chemical/process industries, and mining, yet many models that perform well in offline benchmarks fail in field deployment because algorithm capability, data regime, and operational constraints are misaligned. This review synthesizes cross-industry evidence on how accident prediction is practiced under distinct data conditions, including spatiotemporal, multimodal, and data-scarce settings, and compares mainstream methods from statistical baselines to machine learning and deep learning in terms of deployability rather than accuracy alone. Building on this synthesis, we propose the Model–Data–Scenario Adaptation (MDSA) framework, a systems-level protocol that operationalizes deployment-aware model selection through a multi-dimensional scoring rubric and an iterative validation loop. MDSA balances predictive performance with interpretability, robustness, data dependency, and implementation cost. A chemical industry case study demonstrates how accuracy-centric selection can fail operationally and how MDSA yields a more viable choice under real constraints. The framework ultimately facilitates long-term sustainable risk governance by balancing predictive performance with operational constraints, thereby contributing to the United Nations Sustainable Development Goals (SDGs 3, 8, 9, and 11). Full article

Fermented foods are chemically complex systems in which substrate composition, microbial community dynamics, and physicochemical conditions interact to generate thousands of metabolites across diverse chemical classes. Conventional targeted analytical approaches quantify predefined compounds with high precision but operate within a restricted chemical space, systematically excluding emergent features central to product identity, safety, and sensory character. Untargeted metabolomics addresses this limitation by capturing global chemical fingerprints of fermented matrices, enabling discovery-driven investigation across a broad fraction of the metabolome. This review examines the application of untargeted metabolomics across key research areas in fermented food science, including fermentation monitoring, microbial interactions, flavour development, process optimisation, post-fermentation stability, and safety assessment. Across these domains, untargeted approaches reveal system-level metabolic relationships beyond the reach of targeted analyses, while also presenting interpretive challenges. A central limitation is the annotation bottleneck: despite high feature detection rates, only a small fraction of signals are structurally identified, constraining mechanistic interpretation and cross-study comparability. Additional challenges in data processing, statistical validation, and interlaboratory reproducibility further limit data interpretation. Addressing these constraints through improved spectral libraries, standardised workflows, and integration with complementary omics is essential for advancing untargeted metabolomics towards robust knowledge generation in fermented food systems. Full article

This work aims to study and improve the long-term stability of conductimetric biosensors for urea detection in clinical and environmental samples, which are fundamentally limited by complex thermal and temporal drifts due to temperature-sensitive enzyme kinetics, variations in ionic mobility, and the progressive degradation of the sensing layer. The biosensor targets the urea concentration range 0.01–30 mM, validated against experimental data and covering the clinically relevant range for blood urea detection (2.5–7.5 mM), urine (20–40 mM), and environmental monitoring applications. Conventional calibration techniques, such as the conventional calibration method (based on reference measurements), and purely deterministic correction methods, such as deterministic methods (based on known fixed equations), often prove insufficient because they struggle to capture the non-stationary and inherently stochastic nature of these drifts. In this work, we propose an original hybrid probabilistic framework that synergistically combines machine learning and Bayesian inference for robust adaptive drift compensation. A Random Forest model is first implemented to model the deterministic nonlinear relationships between environmental parameters (temperature, pH, CO₂ concentration) and the sensor response. The residual temporal drift is then explicitly modeled as a non-stationary latent stochastic process using Bayesian inference based on a Gaussian process. This approach allows continuous online model updating, real-time uncertainty quantification, and automatic detection of anomalies. The models were trained and validated on a large dataset obtained from multiphysics simulations carried out in COMSOL Multiphysics 5.6. These simulations incorporated enzymatic reactions, thermal effects, and chemical dynamics taking place inside the sensor. Experimental results show that the hybrid approach substantially enhances sensor performance, lowering the root mean square error (RMSE) to below 0.8 μS/cm (corresponding to less than 0.5% of the full-scale response) over a wide temperature range (15–45 °C) and across extended operating periods. This represents a clear improvement over conventional compensation method. By merging the predictive power of ensemble learning with a probabilistic Bayesian model of dynamic drift, this study introduces a fresh perspective on the design of intelligent, self-adaptive, and drift-resistant conductimetric biosensors. The proposed framework holds strong potential for reliable, long-term autonomous operation in urea reliable, long-term autonomous operation in urea monitoring across biomedical diagnostics (kidney/liver function assessment) and environmental surveillance (water eutrophication prevention). Full article