Search Results (9,614)

This study explores the application of generative artificial intelligence (AI) in financial risk forecasting, aiming to assess its potential in enhancing both the accuracy and interpretability of predictive models. Traditional methods often struggle with the complexity and nonlinearity of financial data, whereas generative AI—such as large language models and generative adversarial networks (GANs)—offers novel solutions to these challenges. The study begins with a comprehensive review of current research on generative AI in financial risk prediction, with a focus on its roles in data augmentation and feature extraction. It then investigates techniques such as Generative Adversarial Explanation (GAX) to evaluate their effectiveness in improving model interpretability. Case studies demonstrate the practical value of generative AI in real-world financial forecasting and quantify its contribution to predictive accuracy. Furthermore, the study identifies key challenges—including data quality, model training costs, and regulatory compliance—and proposes corresponding mitigation strategies. The findings suggest that generative AI can significantly improve the accuracy and interpretability of financial risk models, though its adoption must be carefully managed to address associated risks. This study offers insights and guidance for future research in applying generative AI to financial risk forecasting. Full article

Alopecia areata (AA) is a chronic autoimmune disorder characterized by non-scarring hair loss, with subtypes ranging from patchy alopecia (AAP) to alopecia totalis and universalis (AT/AU). The aim of this research is to investigate molecular features across AA severity by performing an integrated analysis of scalp transcriptomic datasets (GSE148346, GSE68801, GSE45512, GSE111061) and matched serum proteomic data from GSE148346. Differential expression analysis indicated that, relative to normal scalp, non-lesional AA tissue shows early immune activation—including Type 1 (C-X-C motif chemokine ligand 9 (CXCL9), CXCL10, CD8a molecule (CD8A), C-C motif chemokine ligand 5 (CCL5)) and Type 2 (CCL13, CCL18) signatures—together with reduced expression of hair-follicle structural genes (keratin 32(KRT32)–35, homeobox C13 (HOXC13)) (FDR < 0.05, |fold change| > 1.5). Lesional AAP and AT/AU scalp showed stronger pro-inflammatory upregulation and greater loss of keratins and keratin-associated proteins (KRT81, KRT83, desmoglein 4 (DSG4), KRTAP12/15) compared with non-lesional scalp (FDR < 0.05, |fold change| > 1.5). Ferroptosis-associated genes (cAMP responsive element binding protein 5 (CREB5), solute carrier family 40 member 1 (SLC40A1), (lipocalin 2) LCN2, SLC7A11) and IRS (inner root sheath) differentiation genes (KRT25, KRT27, KRT28, KRT71–KRT75, KRT81, KRT83, KRT85–86, trichohyalin (TCHH)) were consistently repressed across subtypes, with the strongest reductions in AT/AU lesions versus AAP lesions, suggesting that oxidative-stress pathways and follicular structural integrity may contribute to subtype-specific pathology. Pathway analysis of lesional versus non-lesional scalp highlighted enrichment of IFN-α/γ, cytotoxic, and IL-15 signaling. Serum proteomic profiling, contrasting AA vs. healthy controls, corroborated scalp findings, revealing parallel alterations in immune-related proteins (CXCL9–CXCL10, CD163, interleukin-16 (IL16)) and structural markers (angiopoietin 1 (ANGPT1), decorin (DCN), chitinase-3-like protein 1 (CHI3L1)) across AA subtypes. Together, these data offer an integrated view of immune, oxidative, and structural changes in AA and found ferroptosis-related and IRS genes, along with immune signatures, as potential molecular indicators to support future studies on disease subtypes and therapeutic strategies. Full article

Photocatalytic technologies based on silicon (Si-based) nanostructures offer a promising solution for water purification, hydrogen generation, and the conversion of CO₂ into useful chemical compounds. This review systematizes the diversity of modern approaches to the synthesis and modification of Si-based photocatalysts, including chemical deposition, metal-associated etching, hydrothermal methods, and atomic layer deposition. Heterostructures, plasmonic effects, and co-catalysts that enhance photocatalytic activity are considered. Particular attention is drawn to the silicon doping of semiconductors, such as TiO₂ and ZnO, to enhance their optical and electronic properties. The formation of heterostructures and the evaluation of their efficiency were discussed. Despite the high biocompatibility and availability of silicon, its photocorrosion and limited stability require the development of protective coatings and morphology optimization. The application of machine learning for predicting redox potentials and optimizing photocatalyst synthesis could offer new opportunities for increasing their efficiency. The review highlights the potential of Si-based materials for sustainable technologies and provides a roadmap for further research. Full article

Copper is one of the most used metals today due to its wide range of applications. Traditionally, this metal has been primarily extracted through pyrometallurgical methods, which presents several environmental and energy-related drawbacks. An alternative is hydrometallurgy, which has achieved acceptable copper extraction rates. However, this process has not found widespread industrial application due to operational challenges and the complexity associated with the selective recovery of copper ions from the Pregnant Leach Solution (PLS), especially due to the coexistence of copper and iron ions, complicating the efficient separation of both metals. In this work, the use of aluminum shavings as a cementation agent is proposed, analyzing variables such as the initial shaving concentration (2.5, 5, 10, 15, and 20 g/L), the agitation speed (0, 200, and 400 rpm), and a temperature of 20, 30, and 40 °C. The results demonstrated selective copper cementation, achieving a 100% recovery in 30 min under stirring conditions of 400 rpm. The analysis performed using X-ray Diffraction (XRD) and Scanning Electron Microscopy (SEM) revealed the formation of solid phases such as metallic copper (Cu), aluminum hydroxide [Al(OH)₃], and elemental sulfur (S). Additionally, it was observed that the iron ion concentration remained constant throughout the experiment, indicating a high selectivity in the process. The kinetic analysis revealed that the reaction follows a first-order model without stirring. An activation energy of 62.6 kJ/mol was determined within the experimental temperature range of 20–40 °C, confirming that the process fits the chemical reaction model. These findings provide a deeper understanding of the system’s behavior, highlighting its feasibility and potential for industrial-scale applications. Full article

Using an environmentally friendly approach, we successfully synthesized an asymmetric monomethine cyanine dye, 7-chloro-1-ethyl-4-((3-ethylbenzo[d]thiazol-2(3H)-ylidene)methyl) quinolin-1-ium iodide, named CHLoris (CHL), via a modified Knoevenagel-type condensation. The reaction was carried out mechanochemically in an ethanol–water medium using 1-ethyl-2-methylbenzothiazolium iodide and 4,7-dichloro-1-ethylquinolin-1-ium iodide in the presence of sodium carbonate as a base and catalytic amounts of Hünig’s base. The UV/VIS absorption spectra of CHL in both the buffer solution and ethanol revealed the formation of aggregates in aqueous media. Density Functional Theory (DFT) and Time-Dependent DFT (TDDFT) calculations were employed to support the experimental findings further and provide insights into the self-association behavior of CHL in an aqueous solution. The photophysical properties of the dye were examined in the presence of DNA and RNA, and its performance was compared to that of the commercial dye Thiazole Orange (TO) under identical conditions. The results show that CHL is more sensitive towards RNA. Full article

Background: Demoralization, anxiety, irritability, and depression are common among hospital patients and are associated with poorer outcomes and greater healthcare burden. Early identification is essential, but simultaneous screening across multiple domains is often impractical with questionnaires. Computerized Adaptive Testing (CAT) offers a solution by tailoring item administration, reducing test length while preserving measurement precision. The aim of this study was to develop and validate FRIDA (Four-factor Rapid Interactive Diagnostic Assessment), a freely accessible, web-based CAT for rapid multidimensional screening of psychopathology in hospital patients. Methods: We analysed data from 472 medically ill in-patients at a University Hospital. Item calibration was performed using a four-factor graded response model (demoralization, anxiety, irritability, depression) in the mirt package. CAT simulations were run with 1000 virtual respondents to optimize item selection, exposure control, and stopping rules. The best configuration was applied to the real dataset. Criterion validity for demoralization was evaluated against the Diagnostic Criteria for Psychosomatic Research (DCPR). Results: The four-factor model showed good fit (CFI = 0.947, RMSEA = 0.080). Factor correlations were moderate to high, with the strongest overlap between demoralization and depression (r = 0.93). In simulations, the CAT required, on average, 7.8 items and recovered trait estimates with high accuracy (r = 0.94–0.97). In real patients, mean test length was 11 items, with accuracy of r = 0.95 across domains. FRIDA demonstrated good criterion validity for demoralization (AUC = 0.816; sensitivity 80%, specificity 67.5%). Conclusions: FRIDA is the first freely available, multidimensional CAT for rapid screening of psychopathology in hospital patients. It offers a scalable, efficient, and precise tool for integrating mental health assessment into routine hospital care. Full article

The sudden increase in global energy demand has prompted the integration of Artificial Intelligence and the Internet of Things into the utility grid. The synergy of Artificial Intelligence and the Internet of Things in renewable energy sources has emerged as a promising solution for the development of smart grids and a transformative catalyst that restructures centralized power systems into resilient and sustainable systems. The state-of-the-art of the Internet of Things and Artificial Intelligence is presented in this paper to support the design, planning, operation, management and optimization of renewable energy-based power systems. This paper outlines the benefits of smart and resilient energy systems and the contributions of the Internet of Things across several applications, devices and networks. Artificial Intelligence can be utilized for predictive maintenance, demand-side management, fault detection, forecasting and scheduling. This paper highlights crucial future research directions aimed at overcoming the challenges that are associated with the adoption of emerging technologies in the power system by focusing on market policy and regulation and the human-centric and ethical aspects of Artificial Intelligence and the Internet of Things. The outcomes of this study can be used by policymakers, researchers and development agencies to improve global access to electricity and accelerate the development of sustainable energy systems. Full article

Existing building management systems face critical limitations in real-time data integration, primarily relying on static models that lack dynamic updates from IoT sensors. To address this gap, this study proposes a novel system integrating MQTT over WebSocket with Three.js visualization, enabling real-time sensor-data binding to Building Information Models (BIM). The architecture leverages MQTT’s lightweight publish-subscribe protocol for efficient communication and employs a TCP-based retransmission mechanism to ensure 99.5% data reliability in unstable networks. A dynamic topic-matching algorithm is introduced to automate sensor-BIM associations, reducing manual configuration time by 60%. The system’s frontend, powered by Three.js, achieves browser-based 3D visualization with sub-second updates (280–550 ms latency), while the backend utilizes SpringBoot for scalable service orchestration. Experimental evaluations across diverse environments—including high-rise offices, industrial plants, and residential complexes—demonstrate the system’s robustness: Real-time monitoring: Fire alarms triggered within 2.1 s (22% faster than legacy systems). Network resilience: 98.2% availability under 30% packet loss. User efficiency: 4.6/5 satisfaction score from facility managers. This work advances intelligent building management by bridging IoT data with interactive 3D models, offering a scalable solution for emergency response, energy optimization, and predictive maintenance in smart cities. Full article

CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)-Cas (CRISPR-associated) systems, originally identified as prokaryotic adaptive immune mechanisms, have rapidly evolved into powerful tools for molecular diagnostics. Leveraging their precise nucleic acid targeting capabilities, CRISPR diagnostics offer rapid, sensitive, and specific detection solutions for a wide array of targets. This review delves into the fundamental principles of various Cas proteins (e.g., Cas9, Cas12a, Cas13a) and their distinct mechanisms of action (cis- and trans-cleavage). It highlights the diverse applications spanning infectious disease surveillance, cancer biomarker detection, and genetic disorder screening, emphasizing key advantages such as speed, high sensitivity, specificity, portability, and cost-effectiveness, particularly for point-of-care (POC) testing in resource-limited settings. The report also addresses current challenges, including sensitivity limitations without pre-amplification, specificity issues, and complex sample preparation, while exploring promising future trajectories like the integration of artificial intelligence (AI) and the development of universal diagnostic platforms to enhance clinical translation. Full article

The rapid development of nanotechnology has significantly transformed the design and performance of glucose biosensors, leading to enhanced sensitivity, selectivity, and real-time monitoring capabilities. This review highlights recent advances in glucose-sensing platforms facilitated by nanomaterials, including metal and metal oxide nanoparticles, carbon-based nanostructures, two-dimensional materials, and metal–organic frameworks (MOFs). The integration of these nanoscale materials into electrochemical, optical, and wearable biosensors has addressed longstanding challenges associated with enzyme stability, detection limits, and invasiveness. Special emphasis is placed on non-enzymatic glucose sensors, flexible and wearable devices, and hybrid nanocomposite systems. The multifunctional properties of nanomaterials, such as large surface area, excellent conductivity, and biocompatibility, have enabled the development of next-generation sensors for clinical, point-of-care, and personal healthcare applications. The review also discusses emerging trends such as biodegradable nanosensors, AI-integrated platforms, and smart textiles, which are poised to drive the future of glucose monitoring toward more sustainable and personalized healthcare solutions. Full article

This study employed molecular dynamics simulations to investigate the impact of N-linked glycosylation (GlcNAc₂) at the K20N position on the structural dynamics and stability of the bacterial immunity protein Im7. Simulations were conducted in both aqueous and 2 M urea denaturing environments. The simulation results in aqueous solution indicate that glycosylation has only a minor effect on the protein, consistent with expectations. In contrast, simulations in urea reveal that K20N glycosylation significantly destabilizes Im7. Analyses of RMSD, native contacts, SASA, RMSF, correlation matrix, PCA, helical content and hydrophobic centroid distance consistently demonstrate that K20N glycosylation increases the flexibility of Helix I and Helix II and weakens the concerted motion among helical domains (particularly between Helix I and Helix II/IV). The destabilizing effect of K20N glycosylation on Im7 originates in Helix I (where glycan attaches) and propagates to Helix II and the loop region connecting Helix I and Helix II. The instability of Helix II is closely associated with the disruption of hydrophobic interactions within the hydrophobic core. These findings provide new insights into the relationship between site-specific glycosylation and protein stability. Full article

This paper provides the modeling, implementation, and simulation of fractional-order processes associated with the production of the enriched ¹³C isotope due to chemical exchange processes between carbamate and CO₂. To demonstrate and simulate the process most effectively, an execution of a new approximating solution of fractional-order systems is required, which has become possible due to the utilization of advanced AI methods. As the separation process exhibits extremely strong nonlinearity and fractional-order-based performance, it was similarly necessary to utilize the fractional-order system theory to mathematically model the operation, which consists of the comparison of its output with an integrator function. The learning of the dynamic structure’s parameters of the derived fractional-order model is performed by neural networks, which are AI-based domain solutions. Thanks to the approximations executed, the concentration dynamics of the enriched ¹³C isotope can be simulated and predicted with a high level of precision. The solutions’ effectiveness is corroborated by the model’s response comparison with the reaction of the actual process. The current implementation uses neural networks trained specifically for this purpose. Furthermore, since the isotopic separation processes are long-settling-time processes, this paper proposes some control strategies that are developed for the ¹³C isotopic separation process, in order to improve the system performances and to avoid the loss of enriched product. The adaptive controllers were tuned by imposing them to follow the output of a first-order-type transfer function, using a PI or a PID controller. Finally, the paper confirms that AI solutions can successfully support the system throughout a range of responses, which paves the way for an efficient design of the automatic control for the ¹³C isotope concentration. Such systems can similarly be implemented in other industrial processes. Full article

The increasing adoption of electric delivery fleets introduces significant challenges related to uneven energy utilization and suboptimal scheduling efficiency. Vehicle-to-Vehicle (V2V) energy sharing presents a promising solution, but its effectiveness critically depends on precise matching and co-optimization within dynamic urban traffic environments. This paper proposes a hierarchical optimization framework to minimize total fleet operational costs, incorporating a comprehensive analysis that includes battery degradation. The core innovation of the framework lies in coupling high-level path planning with low-level real-time speed control. First, a high-fidelity energy consumption surrogate model is constructed through model predictive control simulations, incorporating vehicle dynamics and signal phase and timing information. Second, the spatiotemporal longest common subsequence algorithm is employed to match the spatio-temporal trajectories of energy-provider and energy-consumer vehicles. A battery aging model is integrated to quantify the long-term costs associated with different operational strategies. Finally, a multi-objective particle swarm optimization algorithm, integrated with MPC, co-optimizes the rendezvous paths and speed profiles. In a case study based on a logistics network, simulation results demonstrate that, compared to the conventional station-based charging mode, the proposed V2V framework reduces total fleet operational costs by a net 12.5% and total energy consumption by 17.4% while increasing the energy utilization efficiency of EV-Ps by 21.4%. This net saving is achieved even though the V2V strategy incurs a marginal increase in battery aging costs, which is overwhelmingly offset by substantial savings in logistical efficiency. This study provides an efficient and economical solution for the dynamic energy management of electric fleets under realistic traffic conditions, contributing to a more sustainable and resilient urban logistics ecosystem. Full article

Tidal inlets regulate the exchange of water and sediment between the open sea and adjacent basins. In many locations, engineering interventions combined with coastal protections and polders have intensified erosion and scouring. This study reports on a three-year monitoring program following the implementation of a Nature-based Solution (NbS) at a previous engineering tidal inlet in the Zwin, located along the Belgian–Dutch coast. In 2019, large-scale modifications to the intertidal zone and the opening of a dyke doubled the surface area of the tidal inlet and its associated tidal marsh. Results revealed rapid and substantial morphological adjustments: the main channel deepened, widened, and migrated eastward. Sediment balance analyses showed stability at the inlet entrance but material loss further inland. Tidal prism and cross-sectional measurements indicated a fourfold increase in tidal prism immediately after NbS implementation, triggering strong channel responses. Within a year, the channel cross-sectional area reached a new equilibrium, which remained stable in the following years. These patterns highlight active sediment transport driven by coupled hydrodynamic and morphodynamic processes. Using an extensive data set, a conceptual model is presented to illustrate how the NbS influenced tidal inlet dynamics through the interaction of flow and sedimentation processes. Full article

Although heart transplantation remains the gold standard in the treatment of advanced heart failure, the limited availability of donor organs and the growing number of patients requiring long-term care have necessitated wider implementation of mechanical circulatory support (MCS). Ventricular assist devices (VADs) substantially improve survival and quality of life, yet their clinical use is still constrained by serious complications, most notably local infections at percutaneous exit sites. This challenge persists across all device generations, from extracorporeal pulsatile pumps to contemporary continuous-flow systems. While fourth-generation concepts based on transcutaneous energy transfer are under development, unresolved issues such as thermal tissue injury continue to impede their adoption. This review critically examines current evidence on local infections, with particular emphasis on the role of biomaterials in bacterial colonization. The clinical burden and microbial etiology, dominated by Staphylococcus aureus and Staphylococcus epidermidis, are outlined, together with the limitations of existing material solutions, which lack durable antimicrobial activity. These infections frequently result in tissue necrosis, sepsis, rehospitalization, and elevated treatment costs, and their management is further complicated by the global rise in antimicrobial resistance. By synthesizing available data and identifying key shortcomings of current materials, this review underscores the urgent need for next-generation biomaterials with enhanced biocompatibility, resistance to microbial adhesion, and intrinsic or functionalized antimicrobial activity. Such advances are essential to improve the long-term safety and clinical outcomes of MCS therapy. Full article