Search Results (45)

This comprehensive review critically analyzes the multifaceted role of artificial intelligence (AI) in advancing global sustainability and achieving the Sustainable Development Goals (SDGs). While AI offers powerful solutions for climate action, resource management, and other challenges, its own significant ecological footprint and potential for bias present critical risks that must be proactively managed. This study provides a synthesis of the recent literature (published between 2018 and 2024) to address three primary research questions: (1) What are the main applications of AI for sustainability and their contribution to specific SDGs? (2) What are the primary ecological, socio-economic, and ethical risks of AI adoption? (3) What are the key research gaps and future directions for more sustainable and responsible AI application? A key contribution is a comprehensive, multi-dimensional framework that connects AI applications with an in-depth analysis of their interconnected ecological, algorithmic, and socio-economic risks. This framework, along with a synthesized risk matrix, offers a structured tool for future governance and research, highlighting the need for responsible development to fully leverage AI’s potential for a sustainable future. Full article

This review explores the biofilm architecture and drug resistance of Enterococcus faecalis in clinical and environmental settings. The biofilm in E. faecalis is a heterogeneous, three-dimensional, mushroom-like or multilayered structure, characteristically forming diplococci or short chains interspersed with water channels for nutrient exchange and waste removal. Exopolysaccharides, proteins, lipids, and extracellular DNA create a protective matrix. Persister cells within the biofilm contribute to antibiotic resistance and survival. The heterogeneous architecture of the E. faecalis biofilm contains both dense clusters and loosely packed regions that vary in thickness, ranging from 10 to 100 µm, depending on the environmental conditions. The pathogenicity of the E. faecalis biofilm is mediated through complex interactions between genes and virulence factors such as DNA release, cytolysin, pili, secreted antigen A, and microbial surface components that recognize adhesive matrix molecules, often involving a key protein called enterococcal surface protein (Esp). Clinically, it is implicated in a range of nosocomial infections, including urinary tract infections, endocarditis, and surgical wound infections. The biofilm serves as a nidus for bacterial dissemination and as a reservoir for antimicrobial resistance. The effectiveness of first-line antibiotics (ampicillin, vancomycin, and aminoglycosides) is diminished due to reduced penetration, altered metabolism, increased tolerance, and intrinsic and acquired resistance. Alternative strategies for biofilm disruption, such as combination therapy (ampicillin with aminoglycosides), as well as newer approaches, including antimicrobial peptides, quorum-sensing inhibitors, and biofilm-disrupting agents (DNase or dispersin B), are also being explored to improve treatment outcomes. Environmentally, E. faecalis biofilms contribute to contamination in water systems, food production facilities, and healthcare environments. They persist in harsh conditions, facilitating the spread of multidrug-resistant strains and increasing the risk of transmission to humans and animals. Therefore, understanding the biofilm architecture and drug resistance is essential for developing effective strategies to mitigate their clinical and environmental impact. Full article

Failures in gas path components pose significant risks to aeroengine performance and safety. Traditional fault diagnosis methods often require extensive data and struggle with real-time applications. This study addresses these critical limitations in traditional studies through physics-informed modeling and adaptive estimation. A nonlinear component-level model of the JT9D engine is developed through aero-thermodynamic governing equations, enhanced by a dual-loop iterative cycle combining Newton–Raphson steady-state resolution with integration-based dynamic convergence. An augmented state-space model that linearizes nonlinear dynamic models while incorporating gas path health characteristics as control inputs is novelly proposed, supported by similarity-criterion normalization to mitigate matrix ill-conditioning. A hybrid identification algorithm is proposed, synergizing partial derivative analysis with least squares fitting, which uniquely combines non-iterative perturbation advantages with high-precision least squares. This paper proposes a novel enhanced Kalman filter through integral compensation and three-dimensional interpolation, enabling real-time parameter updates across flight envelopes. The experimental results demonstrate a 0.714–2.953% RMSE in fault diagnosis performance, a 3.619% accuracy enhancement over traditional sliding mode observer algorithms, and 2.11 s reduction in settling time, eliminating noise accumulation. The model maintains dynamic trend consistency and steady-state accuracy with errors of 0.482–0.039%. This work shows marked improvements in temporal resolution, diagnostic accuracy, and flight envelope adaptability compared to conventional approaches. Full article

Aiming at the limitations of existing multisensor fault diagnosis methods for rolling bearings in real industrial scenarios, this paper proposes an innovative intuitionistic fuzzy weighted least squares twin support higher-order tensor machine (IFW-LSTSHTM) model, which realizes a breakthrough in the noise robustness, adaptability to the working conditions, and the class imbalance processing capability. First, the multimodal feature tensor is constructed: the fourier synchro-squeezed transform is used to convert the multisensor time-domain signals into time–frequency images, and then the tensor is reconstructed to retain the three-dimensional structural information of the sensor coupling relationship and time–frequency features. The nonlinear feature mapping strategy combined with Tucker decomposition effectively maintains the high-order correlation of the feature tensor. Second, the adaptive sample-weighting mechanism is developed: an intuitionistic fuzzy membership score assignment scheme with global–local information fusion is proposed. At the global level, the class contribution is assessed based on the relative position of the samples to the classification boundary; at the local level, the topological structural features of the sample distribution are captured by K-nearest neighbor analysis; this mechanism significantly improves the recognition of noisy samples and the handling of class-imbalanced data. Finally, a dual hyperplane classifier is constructed in tensor space: a structural risk regularization term is introduced to enhance the model generalization ability and a dynamic penalty factor is set to set adaptive weights for different categories. A linear equation system solving strategy is adopted: the nonparallel hyperplane optimization is converted into matrix operations to improve the computational efficiency. The extensive experimental results on the two rolling bearing datasets have verified that the proposed method outperforms existing solutions in diagnostic accuracy and stability. Full article

To address the high carbon emissions and resource dependency associated with conventional ordinary Portland cement (OPC) production, this study systematically investigated the preparation processes, hydration mechanisms, and chemical properties of high-belite calcium sulfoaluminate (HBCSA) and calcium sulfoaluminate (CSA) cements based from industrial solid wastes. The results demonstrate that substituting natural raw materials (e.g., limestone and gypsum) with industrial solid wastes—including fly ash, phosphogypsum, steel slag, and red mud—not only reduces raw material costs but also mitigates land occupation and pollution caused by waste accumulation. Under optimized calcination regimes, clinkers containing key mineral phases (C₄A₃S⁻ and C₂S) were successfully synthesized. Hydration products, such as ettringite (AFt), aluminum hydroxide (AH₃), and C-S-H gel, were identified, where AFt crystals form a three-dimensional framework through disordered growth, whereas AH₃ and C-S-H fill the matrix to create a dense interfacial transition zone (ITZ), thereby increasing the mechanical strength. The incorporation of steel slag and granulated blast furnace slag was found to increase the setting time, with low reactivity contributing to reduced strength development in the hardened paste. In contrast, Solid-waste gypsum did not significantly differ from natural gypsum in stabilizing ettringite (AFt). Furthermore, this study clarified key roles of components in HBCSA/CSA systems; Fe₂O₃ serves as a flux but substitutes some Al₂O₃, reducing C₄A₃S⁻ content. CaSO₄ retards hydration while stabilizing strength via sustained AFt formation. CaCO₃ provides nucleation sites and CaO but risks AFt expansion, degrading strength. These insights enable optimized clinker designs balancing reactivity, stability, and strength. Full article

The interactions between dissolved organic matter (DOM) and agrochemicals (e.g., neonicotinoid insecticides, NIs) govern the distribution, migration, and potential environmental risks of agrochemicals. However, the long-term effects of agricultural management on the DOM components and structure, as well as their further influences on the interactions between DOM and agrochemicals, remain unclear. Here, spectroscopic techniques, including Fourier transform infrared spectroscopy, two-dimensional correlation spectroscopy, and three-dimensional excitation–emission matrix fluorescence spectroscopy were employed to delve into the interaction mechanism between the DOM from citrus orchards with distinct cultivation ages (10, 30, and 50 years) and imidacloprid, which is a type of pesticide widely used in agricultural production. The findings revealed that the composition and structure of soil DOM significantly change with increasing cultivation age, characterized by an increase in humic substances and the emergence of new organic components, indicating complex biodegradation and chemical transformation processes of soil organic matter. Imidacloprid primarily interacts with fulvic acid-like fractions of DOM, and its binding affinity decreases with increasing cultivation age. Additionally, the interactions of protein-like fractions with imidacloprid occur after humic-like fractions, suggesting differential binding behaviors among DOM fractions. These results demonstrate that cultivation age significantly influences the composition and structural characteristics of soil DOM in citrus orchards, subsequently affecting its sorption capacity to imidacloprid. This study enhances the understanding of imidacloprid’s environmental behavior and provides theoretical support for the environmental risk management of neonicotinoid pesticides. Full article

Wind turbines are predominantly situated in remote, high-altitude regions, where they face a myriad of harsh environmental conditions. Factors such as high humidity, strong gusts, lightning strikes, and heavy snowfall significantly increase the vulnerability of turbine blades to fatigue damage. This susceptibility poses serious risks to the normal operation and longevity of the turbines, necessitating effective monitoring and maintenance strategies. In response to these challenges, this paper proposes a novel fault detection method specifically designed for analyzing wind turbine blade noise signals. This method integrates the Tyrannosaurus Optimization Algorithm (TROA) with a support vector machine (SVM), aiming to enhance the accuracy and reliability of fault detection. The process begins with the careful preprocessing of raw noise signals collected from wind turbines during actual operational conditions. The method extracts vital features from three key perspectives: the time domain, frequency domain, and cepstral domain. By constructing a comprehensive feature matrix that encapsulates multi-dimensional characteristics, the approach ensures that all relevant information is captured. Rigorous analysis and feature selection are subsequently conducted to eliminate redundant data, thereby focusing on retaining the most significant features for classification. A TROA-SVM classification model is then developed to effectively identify the faults of the turbine blades. The performance of this method is validated through extensive experiments, which indicate that the recognition accuracy rate is 98.7%. This accuracy is higher than that of the traditional methods, such as SVM, K-Nearest Neighbors (KNN), and random forest, demonstrating the proposed method’s superiority and effectiveness. Full article

Predators impact prey populations directly through consumption and indirectly via trait-mediated effects like predator-induced emigration (PIE), where prey alter movement due to predation risk. While PIE can significantly influence prey dynamics, its combined effect with direct predation in fragmented habitats is underexplored. Habitat fragmentation reduces viable habitats and isolates populations, necessitating an understanding of these interactions for conservation. In this paper, we present a reaction–diffusion model to investigate prey persistence under both direct predation and PIE in fragmented landscapes. The model considers prey growing logistically within a bounded habitat patch surrounded by a hostile matrix. Prey move via unbiased random walks internally but exhibit biased movement at habitat boundaries influenced by predation risk. Predators are assumed constant, operating on a different timescale. We examine three predation functional responses—constant yield, Holling Type I, and Holling Type III—and three emigration patterns: density-independent, positive density-dependent, and negative density-dependent emigration. Using the method of sub- and supersolutions, we establish conditions for the existence and multiplicity of positive steady-state solutions. Numerical simulations in one-dimensional habitats further elucidate the structure of these solutions. Our findings demonstrate that the interplay between direct predation and PIE crucially affects prey persistence in fragmented habitats. Depending on the functional response and emigration pattern, PIE can either mitigate or amplify the impact of direct predation. This underscores the importance of incorporating both direct and indirect predation effects in ecological models to better predict species dynamics and inform conservation strategies in fragmented landscapes. Full article

Osteoporosis, a common metabolic bone disorder, leads to increased fracture risk and significant morbidity, particularly in postmenopausal women and the elderly. Traditional treatments often fail to fully restore bone health and may cause side effects, prompting the exploration of regenerative therapies. Adipose-derived stem cells (ADSCs) offer potential for osteoporosis treatment, but their natural inclination toward adipogenic rather than osteogenic differentiation poses a challenge. This study investigates a novel approach combining differentiation inducers (DIs), three-dimensional (3D) hydrogel scaffolds, and photobiomodulation (PBM) to promote osteogenic differentiation of immortalised ADSCs. A dextran-based 3D hydrogel matrix, supplemented with a DI cocktail of dexamethasone, β-glycerophosphate disodium, and ascorbic acid, was used to foster osteogenesis. PBM was applied using near-infrared (825 nm), green (525 nm), and combined wavelengths at fluences of 3 J/cm², 5 J/cm², and 7 J/cm² to enhance osteogenic potential. Flow cytometry identified osteoblast-specific markers, while inverted light microscopy evaluated cellular morphology. Reactive oxygen species assays measured oxidative stress, and quantitative polymerase chain reaction (qPCR) revealed upregulated gene expression linked to osteogenesis. The findings demonstrate that integrating DIs, 3D hydrogels, and PBM effectively drives osteogenic differentiation in immortalised ADSCs. The PBM enhanced osteogenic marker expression, induced morphological changes, and upregulated gene activity, presenting a promising framework for bone regeneration. Future research should assess the stability and functionality of these differentiated cells and explore their applicability in preclinical models of bone injury or degeneration. This integrative approach demonstrated specific efficacy in promoting the osteogenic differentiation of ADSCs, highlighting its potential application in developing targeted treatments for osteoporosis. Full article

Making cities resilient and secure remains a central goal in urban policy strategies, where established methods, technologies, and best experiences are applied or replicated when the knowledge of a threat is already well established. The scientific community and specialized bodies are invited to comprehend and evaluate disastrous events that are still not well explored to broaden the concept of resilient cities. Among these, terrorism in the European-built environment remains an underexplored topic, despite various studies assessing its economic, social, and political dimensions, exploring the radicalist matrix, or examining the post-effects of high-impact disastrous events. Within this framework, this work presents an algorithm for the risk assessment of historic urban open areas (uOAs) in Europe, combining theories of the terrorism phenomenon, the normative experiences, and the phenomenological results of violent acts in uOAs. Specifically, the algorithm is determined by studying physical qualities/properties and elements that usually feature the uOAs, using a limited set of descriptors. The descriptors and their formulation are set starting from their qualification, in compliance with the risk determinant (Hazard, Vulnerability, and Exposure), and discussed starting from participatory methods (Delphi and AHP). The algorithm is finally applied to Italian historic squares, testing the mathematical approach, verifying theories of the phenomenon, and setting up a comprehensive three-dimensional risk matrix for both soft and hard targets. This latest constitutes an operative tool to assess the investigated built environment exposed to terrorist threats aimed at developing more detailed mitigative strategies. Full article

The laboratory is a high-risk place for scientific research and learning, and there are many risk factors and great potential for harm. Hazardous chemicals are important to consider and are the key objects to monitor in a laboratory. In recent years, hazardous chemical fire accidents have occurred in laboratories in various industries, bringing painful lessons and making it urgent to strengthen the safety management of hazardous laboratory chemicals. In this study, a semi-quantitative comprehensive risk assessment model for hazardous chemical laboratory fires was constructed by combining the bowtie model, three-dimensional risk matrix, and analytic network process (ANP). This study applied this method to the management of hazardous chemicals at the TRT Research Institute; evaluated the probability, severity, and preventive components of the corresponding indicators by constructing different index systems; and calculated the evaluation results using the weight of each index. The evaluation results show that the comprehensive likelihood level is 2, the comprehensive severity level is 3, the comprehensive preventive level is 3, and the final calculated comprehensive risk level is tolerable (II). Based on the results of the risk assessment, the corresponding control measures that can reduce the fire risk of hazardous chemicals in the laboratory are proposed according to the actual situation at the TRT Research Institute. Full article

Studying efficient and accurate soil heavy-metal detection technology is of great significance to establishing a modern system for monitoring soil pollution, early warning and risk assessment, which contributes to the continuous improvement of soil quality and the assurance of food safety. Laser-induced breakdown spectroscopy (LIBS) is considered to be an emerging and effective tool for heavy-metal detection, compared with traditional detection technologies. Limited by the soil matrix effect, the LIBS signal of target elements for soil heavy-metal detection is prone to interference, thereby compromising the accuracy of quantitative detection. Thus, a series of signal-enhancement methods are investigated. This study aims to explore the effect of conductive materials of NaCl and graphite on the quantitative detection of lead (Pb) in soil using LIBS, seeking to find a reliable signal-enhancement method of LIBS for the determination of soil heavy-metal elements. The impact of the addition amount of NaCl and graphite on spectral intensity and parameters, including the signal-to-background ratio (SBR), signal-to-noise ratio (SNR), and relative standard deviation (RSD), were investigated, and the mechanism of signal enhancement by NaCl and graphite based on the analysis of the three-dimensional profile data of ablation craters and plasma parameters (plasmatemperature and electron density) were explored. Univariate and multivariate quantitative analysis models including partial least-squares regression (PLSR), least-squares support vector machine (LS-SVM), and extreme learning machine (ELM) were developed for the quantitative detection of Pb in soil with the optimal amount of NaCl and graphite, and the performance of the models was further compared. The PLSR model with the optimal amount of graphite obtained the best prediction performance, with an Rp that reached 0.994. In addition, among the three spectral lines of Pb, the univariate model of Pb I 405.78 nm showed the best prediction performance, with an Rp of 0.984 and the lowest LOD of 26.142 mg/kg. The overall results indicated that the LIBS signal-enhancement method based on conductive materials combined with appropriate chemometric methods could be a potential tool for the accurate quantitative detection of Pb in soil and could provide a reference for environmental monitoring. Full article

Karst terrains can undergo geotechnical issues like subsidence and collapse, occurring both naturally and anthropogenically. The municipality of Sete Lagoas, in the State of Minas Gerais, Brazil, is notable for overexploiting a karst aquifer, resulting in adverse effects such as drying lakes and geotechnical problems. This study aims to assess the progression of geotechnical risk areas in the central urban area from 1940 to 2020 and simulate future scenarios until 2100. To achieve this, historical hydraulic head data, a three-dimensional geological model, and a karst geotechnical risk matrix were used to develop a calibrated FEFLOW numerical model. Results show that before the installation of the first pumping well in 1942, the natural groundwater flow direction was primarily northeast. However, in the 1980s, a cone of depression emerged in the city, creating a zone of influence (ZOI) with a surface area of around 30 km². Between 1940 and 2020, twenty geotechnical collapse events occurred in defined risk zones, often in regions where limestone outcrops or is mantled in association with the ZOI. In future scenarios, if the 2020 total annual groundwater pumping rate (Q = 145,000 m³/d) remains constant until 2100, the geotechnical risk zones will continue expanding laterally. To establish a sustainable risk state, a 40% decrease in the pumping rate (Q = 85,500 m³/d) is necessary. Full article

Osteoporosis represents a widespread and debilitating chronic bone condition that is increasingly prevalent globally. Its hallmark features include reduced bone density and heightened fragility, which significantly elevate the risk of fractures due to the decreased presence of mature osteoblasts. The limitations of current pharmaceutical therapies, often accompanied by severe side effects, have spurred researchers to seek alternative strategies. Adipose-derived stem cells (ADSCs) hold considerable promise for tissue repair, albeit they encounter obstacles such as replicative senescence in laboratory conditions. In comparison, employing ADSCs within three-dimensional (3D) environments provides an innovative solution, replicating the natural extracellular matrix environment while offering a controlled and cost-effective in vitro platform. Moreover, the utilization of photobiomodulation (PBM) has emerged as a method to enhance ADSC differentiation and proliferation potential by instigating cellular stimulation and facilitating beneficial performance modifications. This literature review critically examines the shortcomings of current osteoporosis treatments and investigates the potential synergies between 3D cell culture and PBM in augmenting ADSC differentiation towards osteogenic lineages. The primary objective of this study is to assess the efficacy of combined 3D environments and PBM in enhancing ADSC performance for osteoporosis management. This research is notably distinguished by its thorough scrutiny of the existing literature, synthesis of recent advancements, identification of future research trajectories, and utilization of databases such as PubMed, Scopus, Web of Science, and Google Scholar for this literature review. Furthermore, the exploration of biomechanical and biophysical stimuli holds promise for refining treatment strategies. The future outlook suggests that integrating PBM with ADSCs housed within 3D environments holds considerable potential for advancing bone regeneration efforts. Importantly, this review aspires to catalyse further advancements in combined therapeutic strategies for osteoporosis regeneration. Full article

The conformity assessment of products or a measured value with the given standards is carried out based on the global risk of producers and consumers’ calculations. A product may conform to specifications but be falsely rejected as non-conforming. This is about the producer’s risk. If a product does not meet the requirements but is falsely accepted as conforming, that poses a risk to the consumer. The conventional approach to risk assessment, which yields only a single numerical value for the global risk of producers and consumers, is naturally extended and utilized for assessing risk in measurement models with linear regression. The outcomes of the two-dimensional extension, along a moderate scale, are the parabolas with upwards openings. Risk surfaces were obtained through three-dimensional extension over the area limited by the moderate scale and guard band axes. Four models with different ranges of tolerance intervals were used to test this innovative method of risk assessment in linear regression. The corresponding standard measurement uncertainties were determined by applying a simplified measurement model with the use of comprehensive data on the measurement performance and by determining measurement uncertainty derived from consideration of the functional relationship obtained by linear regression analysis. Models that utilize information from linear regression analysis to determine measurement uncertainty are biased towards risks at the edges of the moderate scale. Testing the model’s performances with metrics related to the confusion matrix, such as the F1 score, further substantiated this assertion. The diagnostic odds ratio has been proven to be extremely effective in identifying the curve along the guard band axis, along which the global risks of producers and consumers are at their lowest. Full article