Search Results (437)

Emerging digital technologies are transforming how consumers participate in financial markets, yet their benefits depend critically on the speed, reliability, and transparency of the underlying platforms. Online stock trading platforms must maintain high efficiency underload to ensure a good user experience. This paper presents performance analysis under various load conditions based on the containerized stock exchange system. A comprehensive data logging pipeline was implemented, capturing metrics such as API response times, database query times, and resource utilization. We analyze the collected data to identify performance patterns, using both statistical analysis and machine learning techniques. Preliminary analysis reveals correlations between application processing time and database load, as well as the impact of user behavior on system performance. Association rule mining is applied to uncover relationships among performance metrics, and multiple classification algorithms are evaluated for their ability to predict user activity class patterns from system metrics. The insights from this work can guide optimizations in similar distributed web applications to improve scalability and reliability under a heavy load. By framing performance not merely as a technical property but as a determinant of financial decision-making and well-being, the study contributes actionable insights for designers of consumer-facing fintech services seeking to meet sustainable development goals through trustworthy, resilient digital infrastructure. Full article

Mining by-products present both an environmental challenge and a resource opportunity. This review investigates their potential application in road pavement construction, focusing on materials such as fly ash, slag, sulphur, red mud, tailings, and silica fume. Drawing from laboratory and field studies, the review examines their roles across pavement layers—subgrade, base, subbase, asphalt mixtures, and rigid pavements—emphasising mechanical properties, durability, moisture resistance, and ageing performance. When properly processed or stabilised, many of these wastes meet or exceed conventional performance standards, contributing to reduced use of virgin materials and greenhouse gas emissions. However, issues such as variability in composition, leaching risks, and a lack of standardised design protocols remain barriers to adoption. This review aims to consolidate current research, evaluate practical feasibility, and identify directions for future studies that would enable the responsible and effective reuse of mining waste in transportation infrastructure. Full article

Introduction: Huntington’s disease (HD) disrupts cortico-striato-thalamocortical circuits decades before clinical onset. Electroencephalography (EEG) offers millisecond temporal resolution, low cost, and broad accessibility, yet its mechanistic and biomarker potential in HD remains underexplored. We conducted a mechanistic review to synthesize half a century of EEG findings, identify reproducible electrophysiological signatures, and outline translational next steps. Methods: Two independent reviewers searched PubMed, Scopus, Google Scholar, ResearchGate, and the Cochrane Library (January 1970–April 2025) using the terms “EEG” OR “electroencephalography” AND “Huntington’s disease”. Clinical trials published in English that reported raw EEG (not ERP-only) in human HD gene carriers were eligible. Abstract/title screening, full-text appraisal, and cross-reference mining yielded 22 studies (~700 HD recordings, ~600 controls). We extracted sample characteristics, acquisition protocols, spectral/connectivity metrics, and neuroclinical correlations. Results: Across diverse platforms, a consistent spectral trajectory emerged: (i) presymptomatic carriers show a focal 7–9 Hz (low-alpha) power loss that scales with CAG repeat length; (ii) early-manifest patients exhibit widespread alpha attenuation, delta–theta excess, and a flattened anterior-posterior gradient; (iii) advanced disease is characterized by global slow-wave dominance and low-voltage tracings. Source-resolved studies reveal early alpha hypocoherence and progressive delta/high-beta hypersynchrony, microstate shifts (A/B ↑, C/D ↓), and rising omega complexity. These electrophysiological changes correlate with motor burden, cognitive slowing, sleep fragmentation, and neurovascular uncoupling, and achieve 80–90% diagnostic accuracy in shallow machine-learning pipelines. Conclusions: EEG offers a coherent, stage-sensitive window on HD pathophysiology—from early thalamocortical disinhibition to late network fragmentation—and fulfills key biomarker criteria. Translation now depends on large, longitudinal, multi-center cohorts with harmonized high-density protocols, rigorous artifact control, and linkage to clinical milestones. Such infrastructure will enable the qualification of alpha-band restoration, delta-band hypersynchrony, and neurovascular coupling as pharmacodynamic readouts, fostering precision monitoring and network-targeted therapy in Huntington’s disease. Full article

More and more sustainability data are being generated from green buildings and from urban and civil infrastructures. For decades, various systems have been developed, and their data have been collected and stored. More detailed, real-time, and cost-effective data, however, are still in short supply. To address this gap, one of the main objectives of the present study is to propose the GREEN method for opinion analysis to support the development of green infrastructure. Google Search was used to gather substantial amounts of information reflecting the views of both ordinary individuals and professionals regarding the benefits, drawbacks, challenges, and limitations of green infrastructure. Previously, however, such data have not been employed to improve green infrastructure by means of opinion analytics. The GREEN method was developed for the analysis of green infrastructure (GI) and its context, enabling multiple-criteria, neural network, correlation, and regression analyses across micro-, meso-, and macro-environmental scales. A total of 788 global regression (R² = 0.997) and neural network (R² = 0.596) GREEN models were developed and tested. In addition, 34 regression models for 12 (R² = 0.817) and 20 (R² = 0.511) cities were created for the world and separate cities (Munich (R^{2 aver} = 0.801) and London (R^{2 aver} = 0.817)). The GREEN method is a new way to analyze stakeholder opinions on sustainable green infrastructure and its context. With the objective of making green infrastructure more efficient and reducing carbon emissions, the Construction Material Reuse Optimization (SOLUTION) Portal was created as part of this research. The portal generates multiple options and proposes optimal alternatives for reused construction products. The results show that the GREEN method and SOLUTION Portal are reliable tools for evidence-based and rational green infrastructure development. Full article

Sjögren’s Disease (SjD), Rheumatoid Arthritis (RA), and Systemic Lupus Erythematosus (SLE) are autoimmune diseases with overlapping genetic features, yet the etiologies of these diseases are poorly understood. Using these rheumatic diseases as an example of proof of concept, our aim was to develop a tool that simplifies analysis of gene–disease associations applicable to any disease and to perform comparisons. This tool is meant to provide insights into associated gene symbols and gene expression data to identify candidate biomarkers in common among these diseases. The Diseasesv2.0 and GTExv8 databases were utilized for data collection, providing searchable disease names, affiliated gene symbols, confidence scores (ranging from 0 to 5, with 5 being the most confident), and gene expression across the panel of 54 tissue types present in GTExv8. Data infrastructure was established on a Postgres database using Plotlyv5.17.0 and Streamlitv1.27.2 Python packages. The resulting database was used to investigate the genetic associations among SjD, RA, and SLE, including confidence scores from 2.50 to 5.00. STRINGv12 analysis determined significant pathways (FDR < 0.05). Analysis using our tool revealed the following refined gene associations for each disease: SjD based on ‘Sjogren’ search term (n = 12 genes), RA (n = 231 genes), and SLE (n = 137 genes). We found seven genes in common, namely, CD4, CD8A, IL6, IL17A, TNFS13B, TNF, and TRIM21. With the exception of IL17A, these genes were expressed in tissue types known or suggested to be affected by SjD. STRINGv12 determined significant KEGG pathways involving interleukin signaling, cytokine signaling, and the immune system. We developed a tool that simplifies the data mining process, allowing users to search for diseases of interest and view common gene associations and gene expression. Some of the genes identified through our tool may be further explored to better understand SjD pathogenesis and systemic impact. Full article

Geological hazards pose significant threats to ecological stability, human lives, and infrastructure, necessitating precise and robust risk assessment methodologies. This study evaluates geological hazard risks in Dawukou District, Shizuishan City, Ningxia Hui Autonomous Region, using the information value (IV) model. The study systematically identifies susceptibility, hazard, and vulnerability factors influencing geological disaster risks by integrating diverse datasets encompassing geological conditions, meteorological parameters, and anthropogenic activities. The key findings reveal that hilly landforms, slope gradients, and vegetation indices are the dominant contributors to hazard development. Additional factors, including lithology, fault proximity, and precipitation, were also found to play critical roles. The results categorize the district into four risk zones: high-risk areas (1.55% of the total area), moderate-risk areas (10.16%), Low-risk areas (23.32%), and very-low-risk areas (64.97%). These zones exhibit a strong spatial correlation with geomorphic features, tectonic activity, and human engineering interventions, such as mining and infrastructure development. High-risk zones are concentrated near mining regions and fault lines with steep slopes, while low-risk zones are predominantly in flat plains and urban centers. The reliability of the risk assessment was validated through cross-referenced geological hazard occurrence data and Receiver Operating Characteristic (ROC) curve analysis, achieving a high predictive accuracy (AUC = 0.88). The study provides actionable insights for disaster prevention, mitigation strategies, and urban planning, offering a scientific basis for resource allocation and sustainable development. The methodology and findings serve as a replicable framework for geological hazard risk assessments in similar regions facing diverse environmental and anthropogenic challenges. Full article

The transformation of the energy market in Poland over the last 20 years has been a process deeply rooted in European Union policies and initiatives, including emissions trading (EU ETS), climate and energy packages and the European Green Deal. Poland, historically dependent on coal, continues to struggle with systemic problems such as low grid flexibility, ageing infrastructure, high CO₂ emissions and the socio-economic costs of the transition in mining regions. The research methodology is based on analysis of reports, scientific articles, EU documents and statistical data. So far, there is a research gap in the research area, mainly concerning two problems. The first is the lack of a multifaceted, integrated analysis of Poland’s energy transition, taking into account not only technological changes and RES participation, but also systemic problems (infrastructure, policy, social acceptance). The second, in turn, relates to the need to identify the impact of EU regulation as a driving force, not just an obstacle. The objective of the article is to provide a comprehensive analysis of the Polish energy market in the context of the EU over the past 20 years, covering (1) systemic problems of the Polish power sector, (2) the impact of key EU initiatives and regulations, (3) the development of renewable energy sources, (4) the modernisation and digitalisation of the grid, (5) current and future market trends, and (6) the main challenges of the transition. The analysis shows that Poland’s electricity sector is still dominated by coal, but its share is steadily decreasing, from ~85% in 2015 to about 60% in 2023. At the same time, the share of renewable energy sources (mainly wind and photovoltaics) has increased from ~10% to ~27%. Nevertheless, the gap with the EU average remains significant. Full article

Mining activities in Pakistan’s mineral-rich provinces threaten freshwater security through groundwater depletion, contamination, and flood-induced pollution. This study develops an Inclusive Disaster Risk Reduction (IDRR) framework integrating governance, social, environmental, and technical (GSET) dimensions to holistically assess mining-induced water hazards across Balochistan, Khyber Pakhtunkhwa, and Punjab. Using GIS-based spatial risk mapping with multi-layer hydrological modeling, we combine computational analysis and participatory validation to identify vulnerability hotspots and prioritize high-risk mines. Community workshops involving women water collectors, indigenous leaders, and local experts enhanced map accuracy by translating indigenous knowledge into spatially referenced mitigation plans and integrating gender-sensitive metrics to address gendered water access disparities. Key findings reveal severe groundwater depletion, acid mine drainage, and gendered burdens near Saindak and Cherat mines. Multi-sectoral engagements secured corporate commitments for water stewardship and policy advances in inclusive governance. The framework employs four priority-ranked risk categories (Governance-Economic 15%, Social-Community 30%, Environmental 40%, Technical-Geological 15%) derived via local stakeholder collaboration, enabling context-specific interventions. Despite data limitations, the GIS-driven methodology provides a scalable model for regions facing socio-environmental vulnerabilities. The results demonstrate how community participation directly shaped village-level water management alongside GSET analysis to craft equitable risk reduction strategies. Spatially explicit risk maps guided infrastructure upgrades and zoning regulations, advancing SDG 6 and 13 progress in Pakistan. This work underscores the value of inclusive, weighted frameworks for sustainable mining–water nexus management in Pakistan and analogous contexts. Full article

This paper discusses the viability and efficiency of gravity energy storage (GES) systems utilizing abandoned coal mine shafts in Poland as a new frontier of energy management within the broader framework of sustainable energy transition. After a thorough analysis of shaft infrastructure, economic factors, and regulatory environment, the research demonstrates how GES is in line with circular economy and sustainability principles yet there are certain technical and financial limitations—smaller lifting capacities and expensive adjustments, for instance—that are currently stalling its large-scale adoption. The results highlight the importance of harmonizing such repurposing efforts with the available renewable energy infrastructure and call for aggressive policy, technological, and funding efforts to sustain the conceptual promise with actual fulfilment. Full article

In recent years, land subsidence in the Northern Anhui Plain has become increasingly pronounced, posing serious risks to infrastructure and groundwater management. However, quantitative assessments of its driving mechanisms remain limited. This study focuses on Bozhou, a typical resource-based city, and employs 186 Sentinel-1 SAR images and SBAS-based interferometric analysis to retrieve the spatiotemporal evolution of land subsidence from 2022 to 2024. Results show that the peak subsidence rate reaches 102 mm/year and has its major distribution in the central and eastern sectors of Bozhou. Temporally, the subsidence pattern shows an initial intensification followed by gradual stabilization. Furthermore, a GeoDetector-based analysis indicates that excessive groundwater extraction and coal mining are the dominant factors, with significant interactive enhancement effects. These findings provide crucial insights for the prevention and mitigation of land subsidence in resource-based cities. Full article

Landslide prevention is crucial, particularly for protecting roads and infrastructure in rock landslide-prone areas. This global issue has garnered significant attention from researchers worldwide. This study addresses landslide prevention by modeling the factor of safety (FoS) for slope stability through the Geological Strength Index (GSI), limit equilibrium method (LEM), and finite element method (FEM). A GSI analysis was conducted using RocLab software version 1.0, and slope modeling was performed using RocScience SLIDE version 6.0 and RS2 version 11. The results revealed various cohesion and friction angles across six slopes, with Slope 5 exhibiting the highest FoS values (up to 3.27 with the FEM) and Slope 1 exhibiting the lowest (1.59 with the FEM). All slopes, designed with a uniform geometry, remained stable, exhibiting FoS values greater than 1.1. This study further provides an optimal slope design for the open pit in the andesite mining plan at Anugerah Berkah Sejahtera. These findings highlight the important role of accurate modeling in the assessment of slope stability. With a suggested safe slope height of 10 m and an angle of 80° (FoS = 1.62), slope stability analysis based on the factor of safety (FoS) showed that single slopes made of andesite maintain stability at steep angles. Claystone slopes, however, have a maximum slope height of 30 m at 20° (FoS = 1.27) and 27 m at 50° (FoS = 1.34), requiring more conservative geometries to maintain their stability. For an overall slope that comprises both rock types, a height of 30 m with a slope angle of 60° is recommended (FoS = 1.23) to ensure stability. The critical design condition for a claystone slope occurs at a height of 30 m with a slope angle of 50°, yielding a factor of safety (FoS) of 0.92, which indicates instability (FoS < 1.1). Similarly, a 35 m-high slope with a slope angle of 20° produced an FoS of 1.04, and a 35 m-high slope with a slope angle of 50° produced an FoS of 0.89, further confirming instability. For the overall slope configuration, instability occurs at a height of 30 m with a slope angle of 65° that produces an FoS of 1.09. Full article

Urban expansion in coastal areas involves infrastructure development, industrial growth, and mining activities. These coastal environments face various environmental and geological hazards that require geo-engineers to devise solutions. An integrated geophysical approach aims to address such complex challenges as sea level rise, sea water intrusion, shoreline erosion, landslides and previous anthropogenic activity in coastal settings. In this study, the proposed methodology involves the systematic application of geophysical methods (FDEM, 3D GPR, 3D ERT, seismic), starting with a broad-scale survey and then proceeding to a localized exploration, in order to identify lithostratigraphy, bedrock depth, sea water intrusion and detect anthropogenic buried features. The critical aspect is to leverage the unique strengths and limitations of each method within the coastal environment, so as to derive valuable insights for survey design (extension and orientation of measurements) and data interpretation. The coastal zone of Throrikos valley, Attica, Greece, serves as the test site of our geophysical investigation methodology. The planning of the geophysical survey included three phases: The application of frequency-domain electromagnetic (FDEM) and 3D ground penetrating radar (GPR) methods followed by a 3D electrical resistivity tomography (ERT) survey and finally, using the seismic refraction tomography (SRT) and multichannel analysis of surface waves (MASW). The FDEM method confirmed the geomorphological study findings by revealing the paleo-coastline, superficial layers of coarse material deposits and sea water preferential flow due to the presence of anthropogenic buried features. Subsequently, the 3D GPR survey was able to offer greater detail in detecting the remains of an old marble pier inland and top layer relief of coarse material deposits. The 3D ERT measurements, deployed in a U-shaped grid, successfully identified the anthropogenic feature, mapped sea water intrusion, and revealed possible impermeable formation connected to the bedrock. ERT results cannot clearly discriminate between limestone or deposits, as sea water intrusion lowers resistivity values in both formations. Finally, SRT, in combination with MASW, clearly resolves this dilemma identifying the lithostratigraphy and bedrock top relief. The findings provide critical input for engineering decisions related to foundation planning, construction feasibility, and preservation of coastal infrastructure. The methodology supports risk-informed design and sustainable development in areas with both natural and cultural heritage sensitivity. The applied approach aims to provide a complete information package to the modern engineer when faced with specific challenges in coastal settings. Full article

As coal’s share in primary energy consumption wanes, the annual increase in abandoned coal mines presents escalating safety and environmental concerns. This paper delves into cutting-edge models and attributes of integrating pumped storage hydropower systems with subterranean reservoirs and advanced wastewater treatment facilities within these decommissioned mines. By utilizing the expansive underground voids left by coal extraction, this method aims to achieve multifaceted objectives: efficient energy storage and generation, reclamation of mine water, and treatment of urban sewage. This research enhances the development and deployment of pumped storage technology in the context of abandoned mines, demonstrating its potential for fostering sustainable energy solutions and optimizing urban infrastructure. This study not only facilitates the progressive transformation and modernization of energy cities but also provides crucial insights for future advances in ecological mining practices, energy efficiency, emission mitigation, and green development strategies in the mining industry. Full article

Cemented paste backfill (CPB) plays an important role in sustainable mining by providing structural support and reducing surface subsidence. While traditional destructive testing methods such as unconfined compressive strength (UCS) tests offer valuable understanding of material strength, they require a lot of resources, are time-consuming, and environmentally unfriendly. However, non-destructive testing (NDT) techniques such as ultrasonic pulse velocity (UPV), electrical resistivity (ER), and acoustic emission (AE) provide sustainable alternatives by preserving sample integrity, minimizing waste, and enabling real-time monitoring. This study systematically reviews and quantitatively compares the effectiveness of UPV, ER, and AE in predicting the strength of CPB. Meta-analysis of 30 peer-reviewed studies reveals that UPV and AE provide the most consistent and reliable correlations with UCS, with R² values of 0.895 and 0.896, respectively, while ER shows more variability due to its sensitivity to environmental factors. Additionally, a synthetic model combining UPV, AE and ER demonstrates improved accuracy in predicting strength. This hybrid approach enhances predictions of material performance while supporting sustainability in mining and construction. Our research advocates for better testing practices and presents a promising direction for future infrastructure projects, where real-time, non-invasive monitoring can enhance material performance evaluation and optimize resource use. Full article

Accurate extraction of dynamically evolving haul roads in open-pit mines from very-high-resolution (VHR) satellite imagery remains a critical challenge due to domain gaps between urban and mining environments, prohibitive annotation costs, and morphological irregularities. This paper introduces OMRoadNet, an unsupervised domain adaptation (UDA) framework for open-pit mine road extraction, which synergizes self-training, attention-based feature disentanglement, and morphology-aware augmentation to address these challenges. The framework employs a cyclic GAN (generative adversarial network) architecture with bidirectional translation pathways, integrating pseudo-label refinement through confidence thresholds and geometric rules (eight-neighborhood connectivity and adaptive kernel resizing) to resolve domain shifts. A novel exponential moving average unit (EMAU) enhances feature robustness by adaptively weighting historical states, while morphology-aware augmentation simulates variable road widths and spectral noise. Evaluations on cross-domain datasets demonstrate state-of-the-art performance with 92.16% precision, 80.77% F1-score, and 67.75% IoU (intersection over union), outperforming baseline models by 4.3% in precision and reducing annotation dependency by 94.6%. By reducing per-kilometer operational costs by 78% relative to LiDAR (Light Detection and Ranging) alternatives, OMRoadNet establishes a practical solution for intelligent mining infrastructure mapping, bridging the critical gap between structured urban datasets and unstructured mining environments. Full article