Search Results (4,988)

While higher education institutions strive for academic excellence, they also bear the responsibility of caring for and ensuring the sustainable well-being of their students. After the COVID-19 pandemic, these institutions have transitioned to hybrid and digital education models and have begun to experience the opportunities and threats of digital learning ecosystems. With the introduction of AI technology, this transformation has taken on a new dimension: while students benefit from the flexibility, instant feedback, and personalized learning offered by AI tools, they have also begun to experience new challenges, including cognitive overload, digital fatigue, and social isolation. In this context, the aim of this research is to assess students’ overall psychological well-being and to provide a support system that promotes sustainable well-being by anticipating potential psychological strain and recommending necessary precautions. Accordingly, the purpose of this study, drawing on Self-Determination Theory and Conservation of Resources Theory, is to examine the direct effects of an AI-enabled student support system on sustainable well-being and academic engagement, as well as its indirect effects through self-efficacy and academic resilience. Data will be collected from undergraduate students from a public university in Istanbul. Data will be analyzed in the R statistical environment. We expect that academic resilience, and self-efficacy will mediate the relationship between an AI-enabled student support system and sustainable well-being. At the end of the study, we propose a conceptual model that can be tested empirically by further research. Managerial and further research directions, as well as limitations, are also discussed. Full article

To address the inherent limitations of qualitative hygiene inspections, this study establishes a structured MCDM framework to evaluate kitchen hygiene management in Taiwan’s catering industry by integrating the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The model integrates expert-weighted criteria to facilitate a structured risk-oriented assessment and support sustainable hygiene management through prioritized resource allocation and more systematic hygiene management. The AHP results determined hygiene behavior, cooking and processing, and storage operation management as the most influential criteria, underscoring the critical role of direct food handling practices. The framework was empirically applied to five large-scale catering enterprises and international tourist hotels with multinational operational backgrounds. TOPSIS analysis revealed significant performance variability, with establishment D achieving the highest relative closeness coefficient (0.6125) and establishment E the lowest (0.2358). These findings indicate that operational control measures play a more critical role in food safety and sustainable hygiene governance than supporting infrastructure alone. The proposed model serves as a quantitative decision-support tool for both industry self-assessment and regulatory inspections, facilitating prioritized resource allocation, continuous hygiene improvement, improved food safety governance, and more consistent long-term hygiene management practices. Sensitivity analysis further demonstrated that the overall comparative ranking structure remained generally consistent under alternative normalization conditions, although minor variation was observed between the two highest-performing alternatives. Full article

Against the backdrop of the automotive industry’s transition to low-carbon operations, assessing the sustainability of pure electric vehicle products remains crucial. Existing multi-criteria evaluation methods often follow a compensatory logic, allowing high carbon emissions to be offset by other advantages. This contradicts the core principle that sustainability must be non-negotiable. To address this issue, we propose a two-stage non-compensatory evaluation framework. First, we apply a carbon footprint threshold based on life cycle assessment: any candidate vehicle exceeding this threshold is eliminated. Second, the remaining models are evaluated across ten indicators (economic, social, and technical), and a comprehensive ranking is generated using entropy weighting, fuzzy analytic hierarchy process (FAHP), and the TOPSIS method. This framework has been validated on seven mainstream BEV midsize sedans. The results show that the non-compensatory screening mechanism eliminated two high-carbon-emission models, confirming that environmental criteria must be considered independently. The top-ranked model was not the one with the lowest carbon emissions but rather the one demonstrating balanced performance, indicating that environmental performance and overall competitiveness can be enhanced synergistically. The ranking results remained relatively robust even under a combination of objective and subjective weightings. This study provides a more logically consistent tool for evaluating pure electric vehicles at the product level. Full article

Background/Objectives: Psoriasis is a chronic immune-mediated inflammatory disease increasingly recognized as a systemic disorder associated with significant metabolic and cardiovascular comorbidities. Among these, obesity (defined as BMI > 30 kg/m²) plays a pivotal role, acting both as a risk factor for psoriasis development and as a modifier of disease severity, clinical phenotype, and therapeutic response. The relationship between psoriasis and obesity is bidirectional and sustained by shared inflammatory and metabolic pathways. This review aims to provide a comprehensive and updated synthesis of the epidemiological association between psoriasis and obesity, to elucidate the underlying pathophysiological mechanisms, and to discuss the clinical and therapeutic implications of excess body weight in psoriasis management. Methods: A narrative review of the literature was conducted, including epidemiological studies, mechanistic research, clinical trials, and real-world evidence addressing the interplay between psoriasis and obesity. Relevant data were identified from peer-reviewed publications focusing on inflammatory pathways, metabolic dysfunction, cardiovascular risk, and treatment outcomes in obese patients with psoriasis. The graphical figures included in this manuscript were created with the assistance of a large language model–based image-generation tool, ChatGPT-5 by OpenAI, using author-defined prompts. The prompts requested schematic medical illustrations summarizing the pathophysiological links between obesity and psoriasis, including adipose tissue dysfunction, adipokine imbalance, systemic inflammation, and activation of the IL-23/Th17 axis. For the therapeutic algorithm, the prompt requested a stepwise clinical flowchart for obese patients with psoriasis, including BMI assessment, comorbidity screening, universal weight-management measures, psoriasis severity stratification, obesity-adapted biologic selection, and management of suboptimal response. The generated images were subsequently reviewed, edited, and approved by the authors to ensure scientific accuracy, clarity, and consistency with the manuscript content. Results: Epidemiological evidence consistently demonstrates a higher prevalence of obesity among patients with psoriasis, with obesity independently associated with increased disease severity. Shared mechanisms include adipose tissue–driven cytokine production, dysregulated adipokine secretion, insulin resistance, endothelial dysfunction, and activation of the IL-23/Th17 axis, collectively contributing to systemic inflammation and accelerated atherogenesis. Obesity negatively impacts the efficacy, pharmacokinetics, and long-term drug survival of conventional systemic agents and biologic therapies, leading to suboptimal clinical outcomes. Conclusions: Obesity is a key determinant of psoriasis burden, influencing disease expression, comorbidities, and therapeutic response. Integrating weight reduction strategies into personalized psoriasis management may improve both dermatological outcomes and overall cardiometabolic health, supporting a holistic approach to patient care. Full article

Land-use and land cover (LULC) change is a key driver of environmental dynamics in semi-arid Mediterranean watersheds, strongly influencing hydrological processes, soil degradation, and ecosystem stability. In this context, understanding and predicting spatial–temporal land transformations is essential for sustainable watershed management. This study proposes a nonlinear spatial–temporal modeling framework integrating a hybrid Artificial Neural Network (ANN), Cellular Automata (CA), and Markov chain approach to simulate LULC dynamics in the Sebdou watershed, northwestern Algeria. Multi-temporal Landsat imagery (1985, 2005, and 2025), combined with topographic, socio-economic, and accessibility variables (slope, population density, distance to roads, and hydrographic network), was used to reconstruct historical land-use patterns and identify key driving forces of change. A supervised Maximum Likelihood classification achieved high accuracies, with overall accuracy ranging from 92.87% to 96.26% and Kappa coefficients between 0.85 and 0.91. The ANN model was trained to estimate nonlinear transition potentials, while the CA component incorporated spatial neighborhood effects to simulate land allocation processes. Markov chain analysis provided temporal transition probabilities, enabling the construction of a coupled ANN–CA–Markov framework for scenario-based prediction. Model validation against observed 2025 LULC maps indicated strong agreement in quantity distribution (Kappa histogram = 0.767), while spatial agreement (Kappa = 0.3566) reflected inherent spatial displacement typical of CA-based stochastic allocation. Simulation results for 2045 indicate continued urban expansion along major transport corridors, progressive decline of dense forest cover, and increasing bare soil areas, while agricultural land remains dominant but increasingly fragmented. These trends highlight the growing influence of anthropogenic pressure and accessibility factors on landscape restructuring in semi-arid environments. The proposed hybrid framework provides a robust decision-support tool for anticipating land-use dynamics and assessing future environmental pressures in Mediterranean drylands. Its integration with hydrological and erosion models can further support sustainable watershed planning under combined socio-economic and climatic changes. Full article

The development of agricultural tourism (agritourism) has gained increasing importance as a tool for improving rural economies and promoting sustainable natural resource management. This study proposes a scenario-based framework for agritourism development assessment using hierarchical principal component analysis in Iran. National spatial and statistical datasets, geographic information, topographic maps, and climatic data were used. Sub-criteria related to five main criteria including accessibility, risk and safety, environmental conditions, tourism attractions, and tourism facilities were prepared and normalized. First-level principal component analysis was independently applied to each criterion to reduce data dimensionality and generate weighted potential maps. Then, second-level principal component analysis was used to produce the final agritourism development potential map. To evaluate model robustness, a weight-based sensitivity analysis was performed. In addition, risk-oriented, development-oriented, and environment-oriented management scenarios were defined to examine the effects of alternative priorities on agritourism potential patterns. Results showed that the first principal component explained approximately 44% of the variance in accessibility, 48% in environmental conditions, 69% in tourism attractions, 90% in tourism facilities, and 38% in risk and safety, while the first three components explained more than 80% of variability for most criteria. At the second level, the first component explained about 64% of the total variance, indicating its dominant role in shaping the spatial pattern of agritourism potential. The proportion of the very high potential class in accessibility, risk and safety, tourism attractions, tourism facilities, and environmental conditions was 25%, 19.7%, 15.8%, 6.6%, and 1.8%, respectively, whereas only about 10% of the country fell into this class in the final map. Sensitivity analysis revealed that accessibility and environmental conditions had the greatest influence on model stability, whereas tourism attractions showed the most stable behavior. Scenario analysis indicated that the very high potential class increased to 13.9% under the development-oriented scenario, while it decreased to 10% and 9.1% under the environment- and risk-oriented scenarios, respectively. Full article

The paper presents an innovative approach to producing structural connectors for bamboo constructions using recycled plastic. This solution enhances the sustainability of bamboo structures while simultaneously promoting the valorization of plastics waste. The aim is to conduct a preliminary investigation in the possible use of high-density polyethylene (HDPE) as a structural material. Two connectors’ geometries have been developed, both specifically devised for bamboo truss systems: a paddle-shaped design and an oval-shaped design. In both designs, a series of circularly arranged holes enables flexible orientation of the connected elements. The connectors are fabricated melting layers of rough-milled HDPE, sourced from waste materials, which are cast in a mold incorporating an agave braid as a reinforcement. The manufacturing process is intentionally low-tech and accessible, relying only on basic tools and equipment for milling, melting, and casting. This approach makes the proposed connectors particularly suitable for adoption in developing countries. To assess their performance, physical and mechanical tests were conducted on the base material, evaluating density, void content, and tensile strength. The tensile strength of the finished connectors results in an average value of 12.73 MPa, with a standard deviation of 2.34 MPa and a coefficient of variation CV of 18.4%, consistent with the results of tests reported in the literature. Although the sample size is limited, the obtained data are sufficient to assess the feasibility of the proposed solution, demonstrating a reasonable reliability of both the molding process and the mechanical performance of the connector. Full article

Background: Osteoporosis/osteoporotic fractures are identified in both hypercalcemic (HC-HPT) and normocalcemic variant (NC-HPT) of primary hyperparathyroidism (HPT) at various rates. Objective: Noting the need of modern society to easily assess the osteoporotic fracture risk amid the diagnosis of HPT, we aimed to address this gap by analyzing the 10-year fracture risk assessment based on traditional FRAX (Fracture Risk Assessment Tool) model in comparison to the novel algorithm (FRAXplus), according to the adjustment for the presence of HPT, as well as for the use of lumbar bone mineral density (BMD) in menopausal women with HPT versus controls (non-HPT), respectively, between HC-HPT versus NC-HPT. Methods: For each patient, the latest algorithms of FRAX and FRAXplus provided the 10-year fracture risk for major osteoporotic fractures (MOF) and for hip fracture (HF) amid a single-center, retrospective, real-life study. Results: In total, 131 subjects were included: 51.15% had HPT (64.18% of them had HC-HPT) versus age-, menopause duration-, and body mass index-matched (HPT-free) controls. As a result, 10-year fracture risk for MOF and HF was statistically significantly higher in HPT versus controls only for the calculation with femoral neck BMD. FRAXplus showed that for both estimations (MOF and HF) with introduction of lumbar BMD remained higher than controls (4.55% vs. 3.7%, p = 0.004, respectively, 1.05% vs. 0.5%, p = 0.002). In HPT group, 10-year fracture risk for MOF and HF were higher if adjustment for HPT was applied. The highest 10-year fracture risk for MOF was obtained for HPT adjustment with femoral neck BMD (5.9%) versus the estimation without using femoral neck BMD (5.25%, p = 0.001), respectively, versus the probability with adjustment for lumbar BMD (4.55%, p < 0.001). The same observation was for HF: 1.4% versus 1.2% (p = 0.028), respectively, versus 1.05% (p < 0.001). In HPT group, parathormone level positively correlated with 10-year hip fracture risk with HPT adjustment, without femoral neck BMD (r = 0.257, p = 0.049). Bone formation marker P1NP negatively correlated with 10-year fracture risk for MOF without femoral neck BMD (r = −0.416, p = 0.043), respectively, with the estimation including HPT adjustment without femoral neck BMD (r = −0.404, p = 0.05), and with the 10-year HF risk calculated without femoral neck BMD (r = −0.407, p = 0.049). Conclusions: To our best knowledge, this is the first study to address the use of FRAXplus in HPT. The similar values between FRAX-based probabilities without the use of femoral neck BMD in HPT versus non-HPT controls suggested that this traditional estimation might not be so useful in HPT population, thus the need for novel models (HPT adjustment). HPT adjustment (FRAXplus) provided a higher MOF/HF risk versus non-adjustment (FRAX). All 10-year probabilities based on FRAX and FRAXplus models showed similar values in HC-HPT versus NC-HPT, which implies that current algorithms might not make a clear distinction between HPT subtypes, yet the statistically significant results within each of these subgroups sustain the FRAXplus application regardless of the variant. Full article

Background: In the era of digital transformation, public health systems increasingly rely on digital technologies to improve accessibility, efficiency, and patient outcomes. However, policymakers face significant challenges in allocating limited resources across competing digital health investments characterized by uncertainty and dynamic impacts. Methods: This study introduces the Adaptive Impact–Cost Optimization Theory (AICOT), a hybrid framework integrating fuzzy logic and genetic algorithms to optimize digital health investment portfolios. The model defines the Investment Priority Score (IPS) as a function of cost, expected impact, and implementation feasibility, enabling structured evaluation under uncertainty. A fuzzy inference system with centroid-based defuzzification is used to convert qualitative assessments into quantitative scores, while optimization techniques identify optimal portfolios across different fiscal scenarios. The empirical analysis covers 15 OECD countries (2018–2024) using publicly available datasets. Sensitivity analyses assess robustness under inflation, cost shocks, and changing system priorities. Results: The findings show that blended investment strategies combining routine digital health tools with pandemic-oriented infrastructures yield the highest resilience-adjusted efficiency. Results remain stable across sensitivity scenarios, with pandemic surveillance consistently ranking as a top priority even under increased cost conditions. The model effectively captures cross-country heterogeneity, demonstrating adaptability to different levels of digital maturity. Conclusions: AICOT provides a transparent and policy-relevant decision-support framework that improves resource allocation efficiency and reduces unnecessary expenditures. These contributions support long-term financial sustainability and align with global health objectives, including Universal Health Coverage and Sustainable Development Goal 3 (Good Health and Well-being). Full article

This study presents a bibliometric and systematic review of 480 articles meeting the following inclusion criteria: English-language articles, reviews, or proceeding papers focusing on building topics with full text available, retrieved from the Web of Science Core Collection on 9 Jannary 2026 to map the intellectual landscape of smart-sustainable building (SSB) research. Employing the PRISMA framework combined with scientometric mapping (VOSviewer), thematic classification, and qualitative synthesis (no risk of bias assessment was performed as this was a bibliometric review), the analysis reveals exponential publication growth since 2022, identifying three dominant thematic clusters: digital enabling technologies (41.0%), energy systems (30.8%), and advanced building envelopes and materials (28.3%). Keyword analysis identifies “smart buildings,” “green buildings,” and “energy efficiency” as central conceptual anchors, while temporal trends indicate increasing attention to artificial intelligence, digital twins, and blockchain. Notably, 51.4% of articles address two or more themes simultaneously, confirming the field’s interdisciplinary character. Critical analysis reveals persistent fragmentation: sustainable building rating tools (e.g., BREEAM, LEED) and smart building evaluation methods (e.g., Smart Readiness Indicator). Seven challenges, including assessment fragmentation, high costs, and cybersecurity vulnerabilities, are identified as barriers to SSB adoption. Limitations include reliance on a single database (Web of Science) and subjective thematic classification. This review provides a roadmap for future research emphasizing integrated assessment frameworks and interdisciplinary collaboration. Registration: Not pre-registered. Funding: National Key R&D Program of China (2025YFF0521003). Full article

Conserved land represents an important mechanism for protecting ecologically sensitive lands while maintaining working landscapes. Despite their significance, nationwide tools for continuous hydrological monitoring of conservation easement lands remain limited. This study conceptualizes surface-water inundation as an indicator of hydrologic connectivity and ecosystem function, reflecting how water dynamics influence the resilience and ecological performance of conservation easement landscapes. We present a scalable framework to assess inundation dynamics across more than 340,000 conservation sites between 2018 and 2024 by integrating Sentinel-2 satellite imagery, Dynamic World land-cover data, and machine-learning classifiers (Support Vector Machine, Random Forest, and CART) within the Google Earth Engine platform. Spectral water indices (NDWI, MNDWI, and NDMI) were combined with Dynamic World classifications to generate quarterly inundation maps at 10 m spatial resolution, enabling consistent detection of surface-water presence across space and time. Among the evaluated classifiers, the Support Vector Machine (SVM) model achieved the highest performance in surface-water detection. Results reveal strong regional and seasonal variability in inundation patterns across conservation land. Higher inundation frequencies were observed in the Midwest, Gulf Coast, and Pacific Northwest, where wetland-associated easements showed persistent inundation (>50%) during spring and early summer, highlighting their role in supporting biodiversity, groundwater recharge, and flood mitigation. Overlay analysis with the National Wetlands Inventory (NWI) and SSURGO hydric soils confirmed a strong spatial correspondence between inundation occurrence and wetland-prone landscapes, extending the same Sentinel-2 and machine-learning framework to conservation land and enabling the first systematic cross-program comparison of hydrological dynamics across two major U.S. conservation mechanisms. This work highlights the critical role of conservation lands including Conservation Reserve Program (CRP) areas and conservation easements in supporting inundation dynamics and hydrological connectivity. These functions are essential for sustaining wetland habitats, maintaining water quality, and enhancing flood mitigation at the national scale. Full article

Rural low-altitude tourism serves as an important carrier for the deep integration of general aviation technology and agricultural culture and tourism, driven by the comprehensive promotion of the rural revitalization strategy and the accelerated rise of the low-altitude economy. However, systematic sustainability assessment tools suitable for complex rural scenes remain lacking. This study aimed to fill this gap and constructed a multi-dimensional evaluation framework. The framework included five main dimensions: the integration of low-altitude general technology and digital infrastructure, the digital protection and activation of rural cultural heritage, the economic and social benefits of agricultural culture and tourism integration, ecological coordination and community inclusiveness, and airspace governance and policy support. Twenty-one secondary indicators supplemented these dimensions. The triangular fuzzy number-TOPSIS group decision method determined the indicator weights and reduced subjective uncertainty in expert evaluation. The TOPSIS method quantitatively evaluated and ranked five typical villages: Anji in Zhejiang, Yangshuo in Guangxi, Yuanjiajie in Hunan, Nantai in Gansu, and Lingshui in Hainan. The results show that Zhejiang Anji leads in comprehensive sustainability, followed by Hunan Yuanjiajie and Guangxi Yangshuo. Sensitivity analysis confirms the robustness of the ranking results. The innovation of this research lies in the integration of frontier elements such as airspace synergy efficiency into the evaluation framework. The application of triangular fuzzy number TOPSIS enhances the methodological rigor and robustness of the evaluation. This study provides practical insights for optimizing rural low-altitude tourism resource allocation, strengthening cultural heritage transmission, and promoting green transformation. Full article

Universities compete for funding, and their positions depend on the results of national assessments and rankings, which are expensive to produce and based on difficult-to-predict expert opinions. Assessment results have a significant impact on a university’s reputation, funding levels, attractiveness to faculty and staff, and success in recruiting top-tier students. Expert assessments and forecasts are widely used, but additional support from trusted AI tools is desirable. Several attempts have been made to use various machine learning methods, but confidence in such solutions is limited due to perceived difficulties in clearly and reliably justifying the resulting predictions. This research aims to present a proposal for using neural network models, accompanied by explanations of their predictions, to support trustworthy and sustainable assessment of university competitiveness. This methodological contribution enhances the transparency and interpretability of the assessment process and is further supported by empirical studies based on data from selected universities. A Fully Connected Neural Network (FCNN) is used for the calculations, and the local interpretable model-agnostic explanations (LIME) method is applied to explain the prediction results. The results confirm the usefulness of the proposed model and provide a solid foundation for improving evaluation systems and building trust in AI applications for assessing universities’ competitive position and the benefits of scientific research for society. Full article

Tofu wastewater (TWW), characterized as a high-strength organic effluent with elevated chemical oxygen demand (COD) and low pH, presents significant environmental challenges, including eutrophication, soil degradation, and greenhouse gas emissions. Conventional disposal methods have proven inadequate in mitigating these risks; however, thermophilic anaerobic digestion (TAD) has emerged as a viable technology for bioenergy recovery. Nonetheless, TAD is impeded by rapid acidification, ammonia and hydrogen sulfide inhibition, and the accumulation of volatile fatty acids (VFAs). This review introduces nano-calcium-peroxide-modified biochar (nano-CaO₂/BC) as a multifunctional additive designed to establish an integrated framework for intervention, risk mitigation, and resource recovery. The proposed amendment synergistically combines the adsorptive and microbial-supportive properties of biochar with the controlled oxidative and alkaline characteristics of nano-CaO₂. Under thermophilic conditions, the slow hydrolysis of nano-CaO₂ generates transient microaerobic zones that enhance polymer hydrolysis, suppress ammonia (NH₃) and hydrogen sulfide (H₂S) formation, and facilitate the oxidation of inhibitory VFAs, concurrently releasing calcium hydroxide (Ca(OH)₂) for sustained pH buffering. Utilizing failure mode and effects analysis (FMEA) as a semi-quantitative assessment tool, the results indicate that the composite significantly reduces risk priority numbers associated with acidification, ammonia toxicity, and sulfide inhibition when compared with conventional TAD methods. The resultant digestates, which are enriched in nutrients and recalcitrant carbon, possess the potential to serve as valuable soil amendments, thereby contributing to a circular bioeconomy. A techno-economic assessment grounded in unit cost analysis suggests that positive net benefits may be realized through enhanced biogas recovery and the mitigation of environmental penalties. However, empirical validation at the pilot scale is essential to substantiate the projected performance. This review underscores critical knowledge gaps and proposes a systematic experimental framework aimed at translating the conceptual risk mitigation strategy into practical applications. Full article

Deep phosphate mining operations face complex, dynamic working conditions characterized by the superimposed disturbances of high temperature, high stress, and high strain. The occurrence of rockburst disasters demonstrates a clear pattern of dynamic evolution. Traditional rockburst risk assessment methods mostly adopt a static analysis approach, making it difficult to accurately grasp the dynamic characteristics of the entire process of a rockburst from inception and development to occurrence, and also making it hard to meet the practical work requirements of deep phosphate mining safety management. To address this engineering problem, this study constructs a superimposed analysis model for the risk of underground rockburst accidents in deep phosphate drilling based on a dynamic fault tree, and strives to tackle the complex dynamic issues in rockburst risk analysis and prediction. This model retains the technical advantages of traditional fault tree logical reasoning, integrates the time-series analysis function of dynamic fault trees, and organizes and describes various risk factors of deep phosphate rockbursts, as well as the concurrent, selective, and time-overlapping correlations among each factor. Finally, by introducing dynamic logic gates such as priority gates and standby gates, combined with the quantitative representation of rockburst risk stacking effects, it achieves dynamic risk assessment and accurate prediction of rockburst disasters. The model construction strictly follows the core processes of top event definition, hierarchical decomposition of risk factors, and dynamic logic structure construction, and organically integrates risk stacking theory with the dynamic fault tree method, forming an emergency rockburst risk prediction system that can provide technical support for reducing the probability of deep phosphate rockburst accidents. The rock fracture risk superposition model developed in this study aims to provide a tool for risk identification and spatial superposition analysis in deep phosphate mining, minimizing disturbances to the mine’s ecological environment, and offering theoretical support and technical methods for safe and green mining, sustainable development, and high-quality exploitation of deep phosphate resources. Full article