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Android malware continues to evolve through obfuscation and polymorphism, posing challenges for both signature-based defenses and machine learning models trained on limited and imbalanced datasets. Synthetic data has been proposed as a remedy for scarcity, yet the role of Large Language Models (LLMs) in generating effective malware data for detection tasks remains underexplored. In this study, we fine-tune GPT-4.1-mini to produce structured records for three malware families: BankBot, Locker/SLocker, and Airpush/StopSMS, using the KronoDroid dataset. After addressing generation inconsistencies with prompt engineering and post-processing, we evaluate multiple classifiers under three settings: training with real data only, real-plus-synthetic data, and synthetic data alone. Results show that real-only training achieves near-perfect detection, while augmentation with synthetic data preserves high performance with only minor degradations. In contrast, synthetic-only training produces mixed outcomes, with effectiveness varying across malware families and fine-tuning strategies. These findings suggest that LLM-generated tabular malware feature records can enhance scarce datasets without compromising detection accuracy, but remain insufficient as a standalone training source. Full article

Flow dynamics in strongly curved channels with submerged vegetation play a crucial role in riverine ecological processes and morphodynamics, yet the combined effects of sharp curvature and rigid submerged vegetation remain inadequately understood. This study presents a comprehensive experimental investigation into the influence of rigid submerged vegetation on the flow characteristics within a 180° strongly curved channel. Laboratory experiments were conducted in a U-shaped flume with varying vegetation configurations (fully vegetated, convex bank only, and concave bank only) and two vegetation heights (5 cm and 10 cm). The density of vegetation ϕ was 2.235%. All experimental configurations exhibited fully turbulent flow conditions (R_e > 60,000) and subcritical flow regimes (F_r < 1), ensuring gravitational dominance and absence of jet flow phenomena. An acoustic Doppler velocimeter (ADV) was employed to capture high-frequency, three-dimensional velocity data across five characteristic cross-sections (0°, 45°, 90°, 135°, 180°). Detailed analyses were performed on the longitudinal and transverse velocity distributions, cross-stream circulation, turbulent kinetic energy (TKE), power spectral density, turbulent bursting, and Reynolds stresses. The results demonstrate that submerged vegetation fundamentally alters the flow structure by increasing flow resistance, modifying the velocity inflection points, and reshaping turbulence characteristics. Vegetation height was found to delay the manifestation of curvature-induced effects, with taller vegetation shifting the maximum longitudinal velocity to the vegetation canopy top further downstream compared to shorter vegetation. The presence and distribution of vegetation significantly impacted secondary flow patterns, altering the direction of cross-stream circulation in fully vegetated regions. TKE peaked near the vegetation canopy, and its vertical distribution was strongly influenced by the bend, causing the maximum TKE to descend to the mid-canopy level. Spectral analysis revealed an altered energy cascade in vegetated regions and interfaces, with a steeper dissipation rate. Turbulent bursting events showed a more balanced contribution among quadrants with higher vegetation density. Furthermore, Reynolds stress analysis highlighted intensified momentum transport at the vegetation–non-vegetation interface, which was further amplified by the channel curvature, particularly when vegetation was located on the concave bank. These findings provide valuable insights into the complex hydrodynamics of vegetated meandering channels, contributing to improved river management, ecological restoration strategies, and predictive modeling. Full article

Autologous fat grafting is the main surgical technique for soft tissue reconstruction. However, its clinical use with more extended volumes is limited by repeated procedures due to the little possibility of banking tissue, donor-site morbidity and unpredictable graft resorption rates. To overcome these problems, adipose tissue engineering has focused on developing injectable scaffolds. Most of them are hydrogels that closely mimic the biological, structural and mechanical characteristics of native adipose tissue. This review provides an overview of current injectable scaffolds designed to restore soft tissue volume defects, emphasizing their translational potential and future directions. Natural injectable scaffolds exhibit excellent biocompatibility but degrade rapidly and lack mechanical strength. Synthetic injectable scaffolds provide tunable elasticity and degradation rates but require biofunctionalization to support cell adhesion and tissue integration. Adipose extracellular matrix-derived injectable scaffolds are fabricated by decellularization of adipose tissue. Accordingly, they combine bio-mimetic structure with intrinsic biological cues that stimulate host-driven adipogenesis and angiogenesis, thus representing a translatable “off-the-shelf” alternative to autologous fat grafting. However, despite this broad spectrum of available injectable scaffolds, the establishment of clinically reliable soft tissue substitutes capable of supporting large-volume and long-lasting soft tissue reconstruction still remains an open challenge. Full article

Periocular recognition is essential when full-face images cannot be used because of occlusion, privacy constraints, or sensor limitations, yet in many deployments, only periocular images are available at run time, while richer evidence, such as archival face photos and textual metadata, exists offline. This mismatch makes it hard to deploy conventional multimodal fusion. This motivates the notion of conditional biometrics, where auxiliary modalities are used only during training to learn stronger periocular representations while keeping deployment strictly periocular-only. In this paper, we propose Multi-Anchor Conditional Periocular Embedding (MACPE), which maps periocular, facial, and textual features into a shared anchor-conditioned space via a learnable anchor bank that preserves periocular micro-textures while aligning higher-level semantics. Training combines identity classification losses on periocular and face branches with a symmetric InfoNCE loss over anchors and a pulling regularizer that jointly aligns periocular, facial, and textual embeddings without collapsing into face-dominated solutions; captions generated by a vision language model provide complementary semantic supervision. At deployment, only the periocular encoder is used. Experiments across five periocular datasets show that MACPE consistently improves Rank-1 identification and reduces EER at a fixed FAR compared with periocular-only baselines and alternative conditioning methods. Ablation studies verify the contributions of anchor-conditioned embeddings, textual supervision, and the proposed loss design. Full article

This paper examines whether information-theoretic complexity measures enhance industry-group return forecasting and portfolio construction within a machine-learning framework. Using daily data for 25 U.S. GICS industry groups spanning more than three decades, we augment gradient-boosted decision tree models with Shannon entropy and fuzzy entropy computed from recent return dynamics. Models are estimated at weekly, monthly, and quarterly horizons using a strictly causal rolling-window design and translated into two economically interpretable allocation rules, a maximum-profit strategy and a minimum-risk strategy. Results show that the top performing strategy, the weekly maximum-profit model augmented with Shannon entropy, achieves an accumulated return exceeding 30,000%, substantially outperforming both the baseline model and the fuzzy-entropy variant. On monthly and quarterly horizons, entropy and fuzzy entropy generate smaller but robust improvements by maintaining lower volatility and better downside protection. Industry allocations display stable and economically interpretable patterns, profit-oriented strategies concentrate primarily in cyclical and growth-sensitive industries such as semiconductors, automobiles, technology hardware, banks, and energy, while minimum-risk strategies consistently favor defensive industries including utilities, food, beverage and tobacco, real estate, and consumer staples. Overall, the results demonstrate that entropy-based complexity measures improve both economic performance and interpretability, yielding industry-rotation strategies that are simultaneously more profitable, more stable, and more transparent. Full article

This study investigates the factors influencing the expansion of organic farming in Europe between 2000 and 2022. Driven by consumer demand and EU support through the Common Agricultural Policy, organic farming has grown significantly. The research uses panel data and linear regression to assess the impact of socio-economic, agronomic, and environmental variables, including GDP, HDI, population density, education, broadband access, pesticide use, and biodiversity indicators. Data sources include FAOSTAT, FiBL, Eurostat, and the World Bank. The analysis also incorporates crop-specific organic farming data and environmental metrics such as ammonia emissions. The results show that expansion is shaped simultaneously by environmental pressures and socio-economic conditions: greater pesticide use, larger land availability, higher human development, and agricultural employment support organic adoption, while intensive livestock-related emissions and indicators of urbanization, such as broadband access, tend to constrain it. Full article

We develop a model of monetary policy committee decision-making, building on the framework of games played through agents (GPTA). Interest groups seek to influence policy by offering action-contingent contracts to committee members. The resulting equilibrium admits a simple characterization and shows how institutional features—such as committee size—shape the extent of external influence. When political pressure pushes for expansive and inflationary policy, larger committees can enhance de facto independence by diluting this influence. We also show that when anti-inflationary pressures dominate, an appropriate choice of committee size can replicate the preference shift towards more conservativeness familiar from delegation frameworks, even when it is not feasible to appoint a conservative central banker in a systematic way. Full article

The debate over CEO compensation persists despite extensive efforts by academics and technocrats to understand its determinants. Most research has focused on how firm-specific characteristics and CEO-specific traits influence CEO compensation. However, the results have been contradictory, indicating that other factors may also play a role. This study examines the impact of macroeconomic factors on the compensation of CEOs. It examines how price variables such as interest rates, inflation, and exchange rates affect the fixed salaries and total compensation of CEOs at six South African banks listed on the Johannesburg Stock Exchange. Conducted over a 15-year period, this quantitative longitudinal study utilized secondary data from annual reports and the IRESS database. Panel data regression analysis was employed to interpret the data. The findings reveal a positive relationship between interest rates and fixed salaries, as well as between exchange rates and fixed salaries. Additionally, interest rates and total compensation are positively related, and exchange rates also have a positive relationship with fixed salaries. Understanding how macroeconomic conditions influence CEO pay helps Compensation Committees contextualize performance. It allows them to differentiate between achievement driven by a CEO’s abilities and that resulting from external factors, ensuring fair compensation and minimizing excessive rewards for “luck”. This knowledge supports the adjustment of incentive plans based on relative performance and economic-adjusted metrics, reducing the cyclical influence of macroeconomic variables on firm performance and, ultimately, CEO compensation. Full article

Financial crises increasingly exhibit complex, interconnected patterns that traditional risk models fail to capture. The 2008 global financial crisis, 2020 pandemic shock, and recent banking sector stress events demonstrate how systemic risks propagate through multiple channels simultaneously—e.g., network contagion, extreme co-movements, and information cascades—creating a multidimensional phenomenon that exceeds the capabilities of conventional actuarial or econometric approaches alone. This paper addresses the fundamental challenge of modeling this multidimensional systemic risk phenomenon by proposing a mathematically formalized three-tier integration framework that achieves 19.2% accuracy improvement over traditional models through the following: (1) dynamic network-copula coupling that captures 35% more tail dependencies than static approaches, (2) semantic-temporal alignment of textual signals with network evolution, and (3) economically optimized threshold calibration reducing false positives by 35% while maintaining 85% crisis detection sensitivity. Empirical validation on historical data (2000–2023) demonstrates significant improvements over traditional models: 19.2% increase in predictive accuracy (R² from 0.68 to 0.87), 2.7 months earlier crisis detection compared to Basel III credit-to-GDP indicators, and 35% reduction in false positive rates while maintaining 85% crisis detection sensitivity. Case studies of the 2008 crisis and 2020 market turbulence illustrate the model’s ability to identify subtle precursor signals through integrated analysis of network structure evolution and semantic changes in regulatory communications. These advances provide financial regulators and institutions with enhanced tools for macroprudential supervision and countercyclical capital buffer calibration, strengthening financial system resilience against multifaceted systemic risks. Full article

Mudstone badlands are critical hotspots of erosion and sediment yield, and their rapid morphological changes serve as an ideal site for studying erosion processes. This study used high-resolution Unmanned Aerial Vehicle (UAV) photogrammetry to monitor erosion patterns on a mudstone badland platform in southwestern Taiwan over a 22-month period. Five UAV surveys conducted between 2017 and 2018 were processed using Structure-from-Motion photogrammetry to generate time-series digital surface models (DSMs). Topographic changes were quantified using DSMs of Difference (DoD). The results reveal intense surface lowering, with a mean erosion depth of 34.2 cm, equivalent to an average erosion rate of 18.7 cm yr⁻¹. Erosion is governed by a synergistic regime in which diffuse rain splash acts as the dominant background process, accounting for approximately 53% of total erosion, while concentrated flow drives localized gully incision. Morphometric analysis shows that erosion depth increases nonlinearly with slope, consistent with threshold hillslope behavior, but exhibits little dependence on the contributing area. Plan and profile curvature further influence the spatial distribution of erosion, with enhanced erosion on both strongly concave and convex surfaces relative to near-linear slopes. The gully network also exhibits rapid channel adjustment, including downstream meander migration and associated lateral bank erosion. These findings highlight the complex interactions among hillslope processes, gully dynamics, and base-level controls that govern badland landscape evolution and have important implications for erosion modeling and watershed management in high-intensity rainfall environments. Full article

The main purpose of this research is to examine the significance of green bonds and green banking loans as financing tools for ecologically sustainable projects in the face of increasing worldwide environmental issues. This research seeks to uncover the determinants of both instruments’ issuance and the obstacles to their acceptance. A thorough systematic literature review will be conducted to assess the efficacy of these tools in improving company financial performance and cost of debt, advancing environmental sustainability, and influencing investor behavior. This methodology guarantees a comprehensive and impartial examination of peer-reviewed publications from reputable sources such as Web of Science and Scopus. Although issues such as greenwashing, market liquidity, and regulatory discrepancies still exist, both tools are growing steadily in the sustainable financing spectrum. The results also suggest that both instruments are influenced by several factors, often overlapping due to their common focus on financing sustainable projects. The credit rating, financial health, and overall environmental performance of the issuing entity significantly influence the attractiveness and pricing of green bonds, as do the market conditions, regulatory frameworks, and certification. The environmental profile and creditworthiness of the borrower are key determinants for green banking loans. The review enhances the current body of knowledge by presenting a theoretical structure for comprehending the dynamics of green debt markets and proposing practical recommendations for policymakers and financial institutions. Furthermore, it emphasizes the deficiencies in existing research, including the need for further longitudinal investigations into green bank loans and a more thorough examination of the notion of ‘greenium’. We searched Web of Science and Scopus up to 26 April 2024. Eligibility criteria included peer-reviewed English-language studies on green bonds or green banking loans. After screening, 128 studies were found to have met the inclusion criteria. Full article

Influenza A viruses exhibit broad host tropism, infecting multiple species including humans, avian species, and swine. Swine influenza virus (SIV), while primarily circulating in porcine populations, demonstrates zoonotic potential with sporadic human infections. In this investigation, we identified two H1N1 subtype swine influenza A virus strains designated A/swine/China/SD6591/2019(H1N1) (abbreviated SD6591) and A/swine/China/SD6592/2019(H1N1) (abbreviated SD6592) in Shandong Province, China. The GenBank accession numbers of the SD6591 viral gene segments are PV464931-PV464938, and the GenBank accession numbers corresponding to each of the eight SD6592 viral gene segments are PV464939-PV464946. Phylogenetic and recombination analyses suggest potential evolutionary differences between the isolates. SD6591 displayed a unique triple-reassortant genotype: comparative nucleotide homology assessments demonstrated that the PB2, PB1, NP, NA, HA, and NEP genes shared the highest similarity with classical swine-origin H1N1 viruses. In contrast, SD6592 maintained genomic conservation with previously characterized H1N1 swine strains, although neither of these two isolates exhibited significant intrasegmental recombination events. Through comprehensive sequence analysis of these H1N1 SIVs, this study provides preliminary insights into their evolutionary history and underscores the persistent risk of cross-species transmission at the human–swine interface. These findings establish an essential foundation for enhancing national SIV surveillance programs and informing evidence-based prevention strategies against emerging influenza threats. Full article

A Security Operation Center (SOC) is a security control center for monitoring, detecting, analyzing, and responding to cybersecurity threats. PT (Perseroan Terbatas) Non-Bank Financial Company (NBFC) has implemented an SOC to secure its information systems, but challenges remain to be solved. These include the absence of impact analysis on financial and regulatory requirements, cost, and effort estimation for recovery; established Key Performance Indicators (KPIs) and Key Risk Indicators (KRIs) for monitoring security controls; and an official program for insider threats. This study evaluates SOC effectiveness at PT NBFC using the ISO 27005:2018 and NIST SP 800-30 frameworks. The research results in a proposed SOC assessment framework, integrating risk assessment, risk treatment, risk acceptance, and monitoring. Additionally, a maturity level assessment was conducted for ISO 27005:2018, NIST SP 800-30, and the proposed framework. The proposed framework achieves good maturity, with two domains meeting the target maturity value and one domain reaching level 4 (Managed and Measurable). By incorporating domains from both ISO 27005:2018 and NIST SP 800-30, the new framework offers a more comprehensive risk management approach, covering strategic, managerial, and technical aspects. Full article

High-arch dams are usually built in high-ground stress distribution areas. The deformation and stability of the abutment slope are directly related to the safety of the construction and operation of these dams. At present, there are few studies on deformation monitoring and analysis of ultra-high-arch dam abutment slopes. In this study, the surface displacement, anchor stress, and anchor cable’s anchoring force of the dam abutment slope of Wudongde Hydropower Station before and after impounding were monitored, and the safety and deformation mechanism of the dam abutment slope were analyzed, focusing on its change amplitude and change trends. Our results indicate that surface displacement and rock mass deformation at the abutment slopes on both banks are minimal, with stability being maintained following excavation and support works and no abnormal deformation occurring during impoundment. Most anchor bolt stresses remained below 50 MPa, with stable readings exceeding 200 MPa at monitored points. The loss rates of the anchor cable’s anchorage force generally fell within ±15%, with variations primarily occurring prior to excavation and support works. Minimal changes were observed before and after impoundment, indicating overall slope stability. The deformation and stress of the dam abutment slope did not exhibit abnormal changes before or after impounding, and the entire slope is in a stable state. These research results provide a reference for the safe operation of Wudongde Hydropower Station. Full article

We examine geographic spillovers in digital credit markets by studying how natural disasters affect borrowing behavior in adjacent, physically undamaged regions. Using granular loan-level data from Indonesia’s largest FinTech lender (2021–2023) and leveraging quasi-random variation in disaster timing and location, we estimate fixed-effects specifications that incorporate spatially lagged disaster exposure (an SLX-type spatial approach) to quantify spillovers. Disasters generate economically significant spillovers in neighboring provinces: a 1% increase in disaster frequency raises local borrowing by 0.036%, approximately 20% of the direct effect. Spillovers vary sharply with geographic connectivity—land-connected provinces experience effects about 6.6 times larger than sea-connected provinces. These results highlight that digital lending platforms can transmit geographically proximate risks beyond directly affected areas through channels that differ from traditional banking networks. The systematic nature of these spillovers suggests that disaster-response strategies may be more effective when they consider adjacent regions. That platform risk management can be strengthened by integrating spatial disaster exposure and connectivity into credit monitoring and decision rules. Full article