Search Results (29,716)

Exchange rate volatility has intensified in recent decades, yet its systematic implications for banking-sector stability remain contested. This study investigates whether exchange rate volatility constitutes a meaningful source of financial fragility using a global panel of 103 countries over the period 2000–2021. Financial stability is proxied by the banking-sector Z-score, while exchange rate volatility is estimated using a EGARCH-based framework to capture time-varying uncertainty. To address cross-sectional dependence, heterogeneity, and endogeneity, the analysis employs Driscoll–Kraay fixed effects, two-step system GMM, and quantile regressions. The results reveal that exchange rate volatility exerts a statistically and economically significant negative effect on banking stability, reducing Z-scores across countries and income groups. The findings remain robust across alternative specifications and estimators. Bank-level fundamentals—capitalisation, liquidity, and credit—enhance stability, whereas higher non-performing loans and risk exposure amplify fragility. Macroeconomic conditions also matter, with stronger growth, institutional quality and external balances supporting resilience, while inflation, economic policy uncertainty and expansionary government spending weaken stability. By integrating time-varying volatility modelling with dynamic panel techniques in a large cross-country setting, this study provides new global evidence that exchange rate volatility is not merely a macroeconomic fluctuation but a structural source of banking-sector risk. The findings carry important implications for macroprudential policy, foreign-exchange management, and coordinated monetary–fiscal responses aimed at safeguarding financial stability in open economies. Full article

Microaeration has been applied to enhance anaerobic digestion (AD), although the underlying mechanisms remain unclear. This work proposes that improving methanogenic activity can be achieved by alleviating micronutrient deficiencies and enhancing digestibility. The microaeration technique was employed to enhance the methanogenic activity of cassava wastewater (CW) both with and without added cassava residue (CR) and to improve CR digestibility in a continuous stirred tank reactor (CSTR) at 37 °C. The sole CW had the optimal COD loading rate of 1.71 kg/m³d. The addition of CR at 1000 mg/L to the CW resulted in the greatest methanogenic improvement of 88% compared with the sole CW and provided the greatest digestibility of CR. Under the optimal specific O₂ dosage rate (3 mL/LRd), the improvements in CH₄ yields were 251% and 140% in comparison to those of the sole CW and the CW with added CR, respectively. Additionally, it achieved substantial improvements in digestibility for the cellulose (59%), hemicellulose (61%), and remaining starch (67%) fractions of added CR. However, lignin degradation remained unaffected, a potential area for future optimization. This work opens new avenues for enhancing biogas production from wastewater by adding agricultural residue in conjunction with microaeration. Full article

Artificial intelligence and machine learning techniques can be used for performing magnetic testing on spacecraft that has historically been difficult and risky to perform. Some of the difficulty arises from the need to take these measurements from within the turbulent near-field area of the spacecraft. Some methods of testing require the spacecraft to be hoisted in the air and swung while the measurements are being taken so that any magnetic signatures in the test area can be removed. These new artificial intelligence and machine learning techniques can be used to determine the magnetic torque of complex magnetic systems. Here we will describe a test method that collects such data and poses much less risk to the spacecraft. We will also show some combinations of hyper-parameters that can be used to increase the speed and accuracy of the models. Some models were able to achieve over 96.6% accuracy of multipole determination on simulated data and over a 99.99% accuracy of dipole moment determination on simulated data. Applications include attitude control systems (ACS), science instrument locations, and data analysis. Full article

This study concurrently addressed the separation method for carbon dots derived from Glycyrrhizae Radix et Rhizoma Praeparata cum Melle (GRRPM) and the in vitro evaluation of their anti-allergic biological activity. Glycyrrhizae Radix et Rhizoma Praeparata cum Melle Carbon Dots (GRRPM-CDs) were prepared via decoction followed by dialysis, and their properties were characterized using High-Performance Liquid Chromatography (HPLC) and nanomaterial techniques. Anti-allergic activity was evaluated using a C48/80-induced RBL-2H3 mast cell degranulation model. Safety and efficacy were assessed using the CCK-8 assay, direct intervention, and drug-containing serum methods. The release of β-hexosaminidase (β-hex), histamine (HIS), interleukin-4 (IL-4), and tumor necrosis factor-α (TNF-α) was measured by ELISA, and key proteins in the MAPK signaling pathway were analyzed by Western blot. GRRPM-CDs inhibited mast cell degranulation and the release of allergic and inflammatory mediators in a dose-dependent manner. They also significantly downregulated the phosphorylation levels of the JNK, ERK, and p38 proteins in the MAPK signaling pathway. GRRPM-CDs exhibit significant anti-allergic activity, likely via suppression of the MAPK pathway. These findings provide new insights into the bioactive components of processed Glycyrrhiza and suggest potential avenues for developing novel therapies for allergic diseases. Full article

In this paper, an efficient and accurate framework for nonlinear spacetime fractional diffusion equations is proposed. The methods are based on the spectral deferred correction technique, which employs a compact difference scheme as the preconditioner via the Picard integral collocation formulation. The nonlinear term is incorporated into the preconditioner in a way similar to linear systems without using Newtonian methods. The preconditioner is proven to be a stable operator, and the resulting spectral deferred correction method maintains an arbitrary order of accuracy and excellent stability. Due to the dense property of the central finite difference approximation of the fractional Laplacian ${(-\Delta )}^s$, a dual accelerated algorithm for the exact computation of the matrix–vector product is presented by introducing the discrete sine transform. The numerical results demonstrate that the proposed new methods are highly efficient and precise. Full article

Arrhythmia is a common and potentially life-threatening cardiovascular condition. Photoplethysmography (PPG) has emerged as a noninvasive alternative to electrocardiography for cardiac rhythm monitoring, yet most PPG-based methods remain limited to binary classification. In this study, a new deep learning approach is suggested for categorizing six arrhythmia types from PPG data: sinus rhythm (SR), premature ventricular contraction (PVC), premature atrial contraction (PAC), ventricular tachycardia (VT), supraventricular tachycardia (SVT), and atrial fibrillation (AF). The raw PPG signal is enhanced by extracting its first and second derivatives to capture morphological features not readily apparent in the original signal. A hybrid architecture, VGG-BiLSTM, is utilized, merging VGG convolutional layers for spatial features extraction with bidirectional long short-term memory layers for modeling temporal dependencies. A stratified data splitting strategy is further adopted to address class imbalance across arrhythmia types. A publicly available dataset containing 46,827 PPG segments from 91 individuals was employed to assess the effectiveness of the suggested technique. The method yielded an overall accuracy, sensitivity, specificity and F1 score of 88.7%, 78.5%, 97.6% and 80.5% correspondingly. Full article

Genetically modified (GM) lactic acid bacteria (LAB) are gaining attention as tools for innovation in the food sector, health applications, and industrial processes. LAB have long been used safely due to their GRAS/QPS status, making them suitable for improving fermentation and synthesizing specific and beneficial metabolites. Advances in genomics and gene editing have significantly expanded the available tools, ranging from classical mutagenesis to site-specific recombination, homologous recombination in non-coding regions, CRISPR-based systems, and food-grade chromosomal integration. These approaches enable the insertion of desired genes and the development of engineered strains with tailored functionalities. GM-LAB are also being studied as live delivery systems for therapeutic molecules, including cytokines, hormones, antimicrobial peptides, and vaccine antigens. Engineered strains of Lactococcus lactis and Lactobacillus spp. have yielded promising outcomes in applications such as mucosal immunization, modulation of inflammatory and metabolic responses, and inhibition of pathogenic microorganisms, including multidrug-resistant bacteria. From an industrial perspective, several studies highlight their potential for cost-effective recombinant protein production and the synthesis of high-value metabolites through fermentation. However, within the European Union, their use is subject to stringent regulatory oversight, requiring comprehensive molecular and environmental risk assessments, careful evaluation of horizontal gene transfer, and a preference for markerless chromosomal integrations. Despite these constraints, GM-LAB offer significant potential to improve food quality, sustainability, and human health. Full article

Cobalt–copper bimetallic oxides (CoCuO_x) show great potential for constructing high-performance micro-supercapacitors (MSCs) for micro-electronic applications. However, their poor conductivity and complex preparation procedures significantly hinder their broad applications. To address these challenges, oxygen-vacancy-modified CoCuO_x-based binder-free electrodes were fabricated using a one-step computer numerical control wire electrical discharge machining (CNCWEDM) strategy. This approach enabled the fabrication of CoCuO_x-based maze-like MSCs (CoCuMMSCs) with designable electrochemical performance, which could be simply controlled by their geometric shape and machining voltage. Subsequently, theoretical simulations were conducted for studying the effect of MSCs geometric shape on their capacitive behavior. Remarkably, the CoCuMMSCs fabricated by a machining voltage of 100 V achieved the maximum capacitance of 32.8 mF cm⁻² at 0.15 mA cm⁻². Furthermore, the CoCuMMSCs demonstrated outstanding performance at ultrahigh scan rates of up to 50,000 mV s⁻¹, exceeding by more than two orders of magnitude the values previously reported in the literature. The obtained results proved that the development of the CNCWEDM technique facilitated manufacturing CoCuMMSCs devices with excellent performance by the comprehensive utilization of oxygen-vacancy incorporation, synergistic effect of cobalt and copper oxides, binder-free electrode design, proper device construction and controllable machining voltage. The advanced CNCWEDM strategy creates a new pathway for the high-efficiency fabrication of high-performance bimetallic-oxide-based micro-electronic devices, such as MSCs, intelligent micro-sensors and micro-batteries. Full article

The growth of Internet of Things (IoT) applications is driving demand for Low-Power Wide-Area Networks (LPWANs) to support higher data rates with the same energy efficiency. While Long Range (LoRa) provides excellent noise immunity and receiver sensitivity, its data rate might be insufficient for some applications, including those real-time applications in which LoRa is required to have infrequent transmissions to maintain low power consumption. In this paper, a novel modulation is introduced to address these limitations by utilizing narrowband chirp to represent a data symbol with chirp slopes, called a multi-slope chirp signal. At the receiver, a new blind non-coherent detection technique is also presented to recover the proposed signal. The simulation results confirm that the proposed scheme can successfully transmit information at 2 to 4 bits per symbol, and when compared to LoRa SF 6, it reduces the Time-on-Air (ToA) by half and also achieves an improvement in spectral efficiency in the frequency domain. Full article

Background: The estimation of the postmortem interval (PMI) remains a complex challenge in forensic medicine. While macroscopic, biochemical, and molecular methods are well-documented, postmortem functional approaches at the organ level are largely underexplored. This pilot study investigated the feasibility of utilizing an isolated ex vivo kidney perfusion model to assess residual postmortem renal function—specifically glomerular filtration and tubular solute handling—as a potential chronological marker for PMI. Methods: Sixteen adult New Zealand rabbits were euthanized and randomly assigned to four postmortem interval groups (1, 5, 10, and 15 h). An unoxygenated, room-temperature crystalloid perfusion system was established to mimic natural postmortem decay. Initially, 32 kidneys were perfused; two were excluded due to anuria, resulting in 30 successfully analyzed kidneys. To strictly eliminate pseudoreplication bias, bilateral functional data were mathematically aggregated at the subject level, establishing the individual rabbit (n = 16) as the statistical unit. Results: Following statistical adjustment at the subject level, none of the measured functional parameters exhibited statistically significant chronological variation across the postmortem intervals (all p > 0.05; statistical significance defined as p < 0.05). Glomerular filtration was profoundly depressed across all groups, with adjusted inulin clearance ranging between 0.0031 and 0.0086 mL/min/g (peaking nonsignificantly at 10 h). Furthermore, active tubular reabsorption was virtually nonexistent; calculated reabsorbed loads for evaluated solutes, particularly potassium and sodium, yielded predominantly negative values. This phenomenon indicates a complete absence of physiological active reabsorption, reflecting instead a massive passive leakage of intracellular electrolytes into the tubular fluid due to cellular autolysis. Conclusions: Within this specific experimental setup, the isolated kidney perfusion model failed to demonstrate reproducible, time-dependent renal function useful for PMI estimation. These findings indirectly suggest that, unlike the prolonged supravital physiological resilience observed in skeletal muscle, highly metabolically active renal tissue rapidly loses its complex functional capacity following somatic death. Future studies exploring supravital renal function should consider targeting the immediate early postmortem period (0–1 h) or integrating advanced organ preservation techniques to unmask residual cellular capabilities. Full article

This study investigates the impact of revenue diversification on the performance and stability of listed Tunisian banks over the period 2008–2023, with the objective of assessing whether diversification strategies enhance bank performance and promote financial stability in an emerging-market context. The analysis relies on a panel dataset of Tunisian listed banks and employs a two-stage least squares (2SLS) estimation approach to address potential endogeneity issues, using ownership structure as an instrumental variable. Bank performance is measured by Return on Assets (ROA) and Net Interest Margin (NIM), while financial stability is captured by the Z-score. The empirical results show that revenue diversification has a positive and significant effect on bank performance, as measured by ROA, and on financial stability. However, it exerts a negative and significant impact on NIM, indicating that although diversification improves overall performance and strengthens stability, it may weaken traditional intermediation income. This study contributes to the limited literature on banking in emerging markets by jointly examining performance and stability effects while addressing endogeneity concerns through robust econometric techniques, and by providing new evidence from the Tunisian banking sector, which has experienced significant political and economic disruptions during the study period. Full article

Heart disease remains a leading cause of mortality worldwide, and timely and accurate diagnosis is crucial for improving patient outcomes. Medical imaging plays a pivotal role in this process, yet traditional diagnostic methods often suffer from limitations, including dependency on manual interpretation, susceptibility to observer variability, and inefficiency in handling large-scale data. Deep learning has emerged as an innovative technology in medical imaging, providing unparalleled advancements in feature extraction, segmentation, classification, and prediction tasks. Despite its proven potential, comprehensive reviews of deep learning methods specifically targeted at cardiac imaging remain scarce. This review paper seeks to bridge this gap by analyzing the state-of-the-art deep learning applications for heart disease diagnosis, covering the period from 2015 to 2025. Employing a well-structured methodology, this review categorizes and examines studies based on imaging modalities: Ultrasound (US), Magnetic Resonance Imaging (MRI), X-ray, Computed Tomography (CT), and Electrocardiography (ECG). For each modality, the analysis focuses on utilized datasets, processing techniques (e.g., extraction, segmentation and classification), and paradigms (e.g., transfer learning, federated learning, explainability, interpretability, and uncertainty quantification). Additionally, the types of heart disease addressed and prediction accuracy metrics are also scrutinized. These findings point toward future opportunities, including the study of data quality, optimization, transfer learning, uncertainty quantification and model explainability or interpretability. Furthermore, exploring advanced techniques such as recurrent expansion, transformers, and other architectures may unlock new pathways in cardiac imaging research. This review is a critical synthesis offering a roadmap for researchers and practitioners to advance the application of deep learning in heart disease diagnosis. Full article

The hydrogen fuel cell electric vehicles (FCEVs) are becoming a worldwide recognized eco-friendly choice which produces no tailpipe emissions while providing better energy efficiency than traditional internal combustion engine vehicles. The review delivers an in-depth evaluation of FCEVs through their assessment which focuses on their transportation and power generation functions. The research investigates hydrogen production methods together with storage and distribution systems and vehicle integration practices and performance enhancement techniques. The paper highlights major technical challenges such as high production costs, limited refueling infrastructure, storage inefficiencies, and fuel cell durability. The research uses battery electric and hybrid vehicle comparisons to assess FCEV market competitiveness. The life-cycle environmental impact assessment proves that using clean hydrogen sources and sustainable end-of-life strategies is essential for achieving FCEV operational capabilities. The review examines new electrochemistry materials science and hybridization solutions which have become essential methods for creating better efficiency and durability while decreasing costs. The study shows how policy regulations and collaborative programs fast-track hydrogen adoption through their impact on future hydrogen grid integration and renewable hydrogen production and circular economy methods. The review shows how experts from different fields reached their achievements while still facing challenges to improve FCEVs as fundamental components of environmentally friendly transportation systems and clean energy networks. Full article

In this study, chicken bone biochar (CBC) was prepared from waste chicken bones via oxygen-limited pyrolysis. A magnetic component (Fe₃O₄) was introduced, and the composite was embedded in a sodium alginate (SA) gel network, successfully constructing magnetic chicken bone biochar/sodium alginate composite gel beads (M-CBC/SA). The experimental results showed that under the conditions of pH = 4.5, 25 °C, and an adsorbent dosage of 0.5 g/L, the removal efficiency of M-CBC/SA toward 50 mg/L U(VI) reached 91.67%, corresponding to an adsorption capacity of 91.67 mg/g. The adsorption process followed the pseudo-second-order kinetic model and the Langmuir isotherm model, with a theoretical maximum adsorption capacity of 322.58 mg/g, indicating that the adsorption was dominated by monolayer chemisorption. The material exhibited excellent magnetic separability and good anti-interference ability against coexisting ions such as K⁺, Na⁺, Cl⁻, and SO₄²⁻, and its adsorption behavior was only weakly affected by ionic strength. Characterization by XRD, FTIR, XPS, SEM-EDS and other techniques revealed that the immobilization mechanism of U(VI) involved the synergistic effects of dissolution–precipitation (the formation of a new autunite phase), surface complexation (involving hydroxyl and phosphate groups), ion exchange (exchange with Ca²⁺), and electrostatic attraction. Using waste chicken bones as the raw material, this composite achieves both efficient uranium immobilization and convenient magnetic separation, fully embodying the environmental concept of “treating waste with waste”, and shows promising application prospects in the treatment of uranium-containing wastewater. Full article

We introduce and study a new subclass of Janowski-type harmonic close-to-convex functions in the open unit disk defined via the Jackson q-derivative operator. The structure of the operator naturally reflects certain symmetric properties in the analytic representation of the considered harmonic mappings. By applying subordination techniques, we establish sufficient conditions for sense-preserving close-to-convexity and distortion estimates. The extreme points of the class are determined, and its topological properties are examined, showing that the class is convex and compact. We further obtain the radius of starlikeness and prove that the class is closed under convolution. Moreover, as $q\to 1^{-}$, the operator reduces to the classical derivative, and our results recover several known results in the existing literature, demonstrating that the present work extends and generalizes earlier findings. Full article