Search Results (601)

Androgenetic alopecia (AGA) involves dihydrotestosterone-driven follicular miniaturization compounded by oxidative stress and perifollicular inflammation, while current pharmacotherapies remain limited by adverse effects. Neem (Azadirachta indica A. Juss., Meliaceae) leaves, a phenolic- and limonoid-rich plant widely used in Southeast Asian traditional medicine, were fermented for seven days with a tri-culture consortium of Saccharomyces cerevisiae, Lactobacillus plantarum, and Aspergillus niger (TRI-NE) to evaluate whether expanded microbial diversity enhances bioactivity relative to the unfermented extract (UN-NE). TRI-NE significantly increased total phenolic content, rising from 315.30 to 564.70 mg GAE/g by day 7, alongside an overall enhancement of antioxidant capacity across all assays. Moreover, untargeted metabolomics revealed compositional remodeling, with 67.47% of significantly altered metabolite features upregulated after fermentation, including selective enrichment of quercetin despite reductions in other polyphenols. In human hair follicle dermal papilla cells (HFDPCs) and complementary models, TRI-NE consistently outperformed UN-NE, enhancing paracrine-mediated fibroblast proliferation, preserving cell viability under potassium-channel blockade, suppressing lipopolysaccharide-induced inflammatory nitric oxide production, and attenuating oxidative membrane damage. At the transcriptional level, TRI-NE downregulated androgen metabolism (SRD5A1 and SRD5A2) and pro-regression genes (TGFB1) while upregulating Wnt/β-catenin (CTNNB1), Sonic Hedgehog (SHH, SMO, and GLI1), and angiogenic (VEGF) pathway genes, with effects matching or exceeding standard hair-loss therapeutics. These findings indicate that TRI-NE confers superior bioactivity over the unfermented extract, supporting its potential as a multi-target cosmeceutical candidate for AGA. Full article

Financial crises are often expected to trigger industry renewal through processes of creative destruction; however, this outcome is not automatic. This study examines why the mechanisms associated with creative destruction failed to emerge in Lebanon’s banking sector during the crisis. Using a qualitative longitudinal case study and process-tracing approach, the analysis draws on regulatory documents, financial data, institutional reports, and secondary sources covering the period from 2014 to 2024. The findings show that successive regulatory interventions contained short-term pressures while preventing the restructuring mechanisms associated with industry renewal from emerging. Instead, these responses preserved a weakened banking structure and reinforced three interconnected mechanisms: strategic inertia, capability erosion, and the failure of the restructuring mechanisms associated with creative destruction. Over time, banks lost much of their capacity to perform core financial functions, public trust declined, and households and firms increasingly relied on cash and informal financial channels. The consequences extended beyond the banking sector, weakening financial intermediation, investment, monetary stability, and broader economic recovery. The study contributes to strategic management theory by showing that creative destruction is not an automatic response to crisis but an institutionally mediated process. The Lebanese case demonstrates how weak governance, the absence of credible restructuring, and limited mechanisms for institutional exit and resource reallocation can prevent industry renewal from emerging. It further shows how prolonged reliance on short-term crisis management can erode organizational capabilities and preserve dysfunctional institutions rather than promote adaptation and renewal. Full article

Aqueous zinc-ion batteries are promising candidates for large-scale energy storage as high-safety, low-cost, and abundant zinc resources. Compared with conventional zinc foil, zinc powder (Zn-P) anodes display controllable capacity by regulating zinc powder loading, electrode thickness and pore structure, providing opportunities to improve zinc utilization, reduce costs and enhance manufacturing compatibility. However, Zn-P anodes are dynamic particle-composite electrodes in which reaction interfaces, pore channels, particle contacts, and electronic networks continuously evolve during cycling and storage. Their electrochemical behavior therefore reflects coupled effects of Zn²⁺ plating/stripping, hydrogen evolution, corrosion, by-product-induced pore blockage, stress evolution, contact loss, and calendar aging. This review focuses on the structural characteristics, failure mechanisms, interfacial reaction regulation, electrode engineering, and practical evaluation of zinc powder anodes. It subsequently analyzes the application boundaries of zincophilic metals, carbon materials, MXene, Meta–Organic Frameworks/Covalent Organic Frameworks (MOFs/COFs), artificial interphases, electrolyte additives, slurry engineering, current collectors, and manufacturing strategies. Then, it points out their mechanisms for regulating reaction selectivity, ion/electron transport, and structural stability. Further, the practical evaluation criteria are given, including Zn loading, the negative/positive capacity ratio (N/P ratio), Zn utilization, electrolyte dosage, calendar life and pouches/large-scale cells under limited zinc conditions. Overall, Zn-P anode development should focus on interfacial reaction selectivity, powder structure, electrode manufacturing and limited-zinc evaluation. Full article

Ecologically fragile and economically underdeveloped mountainous regions face the dual challenge of pursuing catch-up development while maintaining ecological security. Although scientific and technological innovation is widely viewed as a key driver of green transition, county-level evidence remains limited on whether, how, and under what conditions innovation improves green development efficiency (GDE) in such constrained settings. Using a balanced panel of 62 mountainous counties in Hebei Province, China, from 2007 to 2022, this study measures GDE with a super-efficiency SBM model, constructs a composite index of technological innovation with the entropy weight method, and estimates dynamic panel models to examine the direct effect, mediating pathway, and heterogeneity of technological innovation. The results are as follows: (1) Technological innovation significantly promotes GDE, but the effect is mainly released through a lagged and cumulative process rather than fully in the contemporaneous period. This finding suggests that innovation dividends in mountainous counties depend on local technology absorption, organizational adjustment, and complementary infrastructure. (2) Mechanism identification confirms that frontier advancement capability, captured by the Technical Change (TC) index derived from the Malmquist–Luenberger framework, serves as a crucial endogenous mediation channel. Innovation systematically lifts the green efficiency ceiling by accelerating the outward shift in the production possibility frontier toward pollution and carbon reduction, yielding a highly significant indirect effect. (3) The innovation–GDE relationship is conditioned by institutional settings and local absorptive capacity. The effect is strongest in counties included in the 2010 cohort of National Key Ecological Function Zones, whereas in low-efficiency counties the innovation effect is positive but weaker and less persistent, so short-term gains do not automatically accumulate into long-term efficiency advantages. These findings provide county-level empirical evidence for designing time-phased and institutionally differentiated green innovation policies in ecologically constrained, underdeveloped regions. Full article

Common prosperity places the distributional consequences of digitalization at the center of China’s current development agenda. This study develops a two-level analytical framework linking macro-level structures to household income positions. The empirical analysis combines a prefecture-level panel for 2009–2022 with the China Family Panel Studies to examine how city-level digital development relates to the urban–rural income gap. City-level estimates indicate that more advanced digital economies are associated with a wider urban–rural income gap during the sample period. The mechanism estimates are consistent with possible channels involving skill-biased technological progress and unequal digital access and absorptive capacity. Moderation tests indicate that interaction between an “enabling government” and an “efficient market” can mitigate this widening trend. Evidence from the Broadband China pilot further suggests that infrastructure expansion without complementary institutions may intensify polarization, highlighting the limits of policy intervention. At the micro level, digital participation exhibits an inclusive yet asymmetric pattern. Internet use is associated with lower income-based relative deprivation in both groups, with a slightly larger estimated reduction among urban households. Taken together, the evidence points to a distributional tension between digital expansion and equity and supports policies that pair connectivity with stronger capabilities and inclusive institutions. Full article

Small objects in remote sensing images often exhibit blurred edges and dense distributions. This makes it difficult to precisely localize object regions. These challenges are especially pronounced on devices with limited computational capacity, where accuracy and efficiency are both critical. To address these challenges, we propose MFRA-YOLOv11, an enhanced YOLOv11s-based network for remote sensing small object detection under the horizontal bounding box paradigm, which integrates multiscale feature extraction and object region awareness to improve detection accuracy. First, in the backbone, we introduce the CSP bottleneck with triple attention aggregation module to emphasize object regions. This module combines channel, coordinate, and kernel attention to aggregate features, enhancing the localization and representation of small objects. Second, a multiscale feature extraction module is integrated into the neck to enhance feature representation across different scales capturing multiscale features along horizontal and vertical directions under varied receptive fields, further boosting small object detection. Finally, we incorporate an adaptive multi-receptive field module into the detection head, which adaptively selects appropriate receptive fields for feature maps of varying granularity, aiding the head in accurate object localization. We validated the accuracy of MFRA-YOLOv11 on the NWPU VHR-10, VEDAI, and DOTA datasets. Compared to YOLOv11, our model achieves 3.0%, 2.7%, and 3.6% improvements in mAP₅₀ on these three datasets, respectively, and 2.4%, 3.5%, and 4.0% improvements in mAP_50–95, with only a slight increase in computational cost (15.9% in parameters and 10.2% in GFLOPs). Full article

Solid-state sodium metal batteries are promising for large-scale energy storage owing to their high safety, abundant sodium resources, and low material cost. However, NASICON-type solid-state electrolytes, such as Na₃Zr₂Si₂PO₁₂, still suffer from limited room-temperature ionic conductivity and poor ceramic densification. Herein, a multivalent co-doping strategy using Zn²⁺, Sc³⁺, Hf⁴⁺, and Nb⁵⁺ was employed to regulate the crystal structure, sintering behavior, and Na⁺ transport properties of NASICON electrolytes. The effects of isovalent and aliovalent dopants were systematically investigated by XRD, Rietveld refinement, SEM-EDS, bond-valence-site calculations, and electrochemical impedance spectroscopy. The optimized monoclinic Na_3.167Zn_0.167Hf_0.167Zr_1.5Nb_0.167Si₂PO₁₂ electrolyte delivered a room-temperature total ionic conductivity of 1.16 mS cm⁻¹ and an activation energy of 0.35 eV, mainly due to optimized Na⁺ migration-channel geometry and enhanced ceramic densification. Na||Na symmetric cells exhibited stable cycling for 500 h with a maximum polarization voltage of 20 mV. Furthermore, solid-state Na||Na₃V₂(PO₄)₃ cells retained 95% capacity after 624 cycles at 1 C. This work provides a feasible doping strategy for advanced NASICON electrolytes and solid-state sodium metal batteries. Full article

As health services shift to digital delivery, older adults face practical online risks, including operational errors, difficulty interpreting clinical information, unclear privacy consent procedures, and telecommunications fraud. Existing empirical work mostly adopts questionnaire and interview designs to document older adults’ real-world digital usage, yet few studies systematically analyze policy documents as an external intervention system and assess how they address four core usage barriers: limited digital proficiency, constrained service access, weak sustained participation, and individualized online safety threats. This paper analyzes 1008 policy records extracted from the Peking University Legal Database covering 2007–2025, and develops a four-dimensional analytical framework split into B1 (Digital Capacity and Age-Friendly Support), B2 (Digital Health Management Support), B3 (Home- and Community-Based Care Support), and B4 (Institutional Care and Risk Governance). The analytical toolkit combines a Capability–Opportunity–Motivation–Behavior (COM-B) diagnostic model, an M1–M4 intervention-mechanism coding protocol, Latent Dirichlet Allocation (LDA) topic modeling, and two indices: the Digital Adaptation Policy Orientation Index (DAPOI) and the Behavioral Intervention Diversity Index (BIDI). Coding outputs reveal a pronounced structural imbalance in policy allocation for older adults’ digital health in China: community and institutional governance clauses (B3, B4) dominate the corpus, while person-centred support including one-on-one operational coaching, clinical data interpretation guidance and offline manual backup channels accounts for a marginal share of all coded entries (B1, B2). Age-friendly digital policies issued circa 2020 serve primarily short-term emergency adjustments instead of permanent institutional arrangements. Built on full-text policy coding outputs, this work defines policy-supported digital resilience as a holistic protective system formed by coordinated institutional clauses. Every result reported is sourced exclusively from supply-side policy records, which provides policy-level evidence for subsequent behavioral research on older adults’ digital risk management and digital welfare construction. Full article

A self-driven terahertz metamaterial liquid biosensor composed of channel-structured metamaterials and a quartz microcavity is proposed for glioblastoma sample detection. The device transports liquid samples via capillary force. The permittivity (ε_eff) of liquid suspensions is comprehensively analyzed to provide theoretical support for the detection. For suspensions with the same contents, ε_eff decreases with increasing concentration, while under the same concentration condition, ε_eff decreases as particle size grows. An electric dipole resonance is excited at ~1.54 THz with theoretical sensitivity ≥ 242 GHz/RIU (where RIU denotes refractive index unit). For the same type of cell discrimination, the frequencies of the biosensor’s feature peaks shift from ~1.14, ~1.18, and ~1.20 THz with the rise in cell concentration of glioblastoma stem cell (GSC) suspension from 4 × 10⁵, 6 × 10⁵ to 8 × 10⁵ cells/mL, respectively. In addition, the GSC, U87 and U251—whose average diameters increase in that order—tested at the same concentration of 4 × 10⁵ cells/mL lead to feature peak shifts from ~1.14, ~1.17, and ~1.20 THz. Clear THz differences exist between healthy and patient serum, with peaks at 1.18 and 1.19 THz. The sensor effectively distinguishes cell suspensions but has limited serum discrimination capacity. The sensor retains cell morphology, requires little pretreatment, and is low-cost and fast for rapid glioblastoma clinical screening. Full article

Bluetooth Low Energy (BLE) resolvable private addresses (RPAs) reduce passive tracking, but RPA-resolving creates receiver-side work: a scanner whose bonded identity set exceeds controller resolving-list capacity which can be forced into repeated host-side Identity Resolving Key (IRK) searches by floods of syntactically valid unknown private-address candidates. We formulate this as capacity-constrained resolving scheduling and propose P3, our persistence-aware admission-control scheduler for BLE RPA resolution, evaluated in its main form P3-Persist. After cache and resolving-list fast paths miss and the unresolved-work budget is exhausted, P3 grants a small reserve only to over-the-air RPA values that reappear. This uses visible repetition rather than an identity-correlated pre-resolution label. In paired simulations, identical visible traces produced identical initial pre-resolution decisions, but later modeled delay/defer features were distinguishable in some tested conditions; accordingly, no end-to-end side-channel-free claim is made. In the evaluated 20-seed, 1800 s simulation matrix (N = 512, RL = 8), P3-Persist keeps modeled unique-flood work in the same bounded approximately 45k AES-equivalent/min regime as budget-only limiting while increasing modeled legitimate resolution to 0.9569 under medium flood and 0.9557 under heavy flood, compared to 0.57–0.62 for budget-only baselines. A 20-seed epoch–loss–density experiment shows that service recovery is conditional rather than universal, and a denser adaptive-address search identifies an overlapping near-cap region around m = 320 and r = 1, with a highest confirmed mean of 146,196.29 AES-equivalent/min (96.69% of the modeled cap). These results establish bounded-work and conditional service-recovery behavior within the simulator; they do not establish production-firmware effectiveness, end-to-end side-channel freedom, on-device timing, energy, current, latency, or queueing behavior. Full article

Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is essential for operational safety and predictive maintenance. However, accurate RUL estimation from incomplete degradation trajectories remains challenging because of limited observable history, nonlinear capacity degradation, local fluctuations, and error accumulation during long-term forecasting. This study proposes GLF-Former, a Transformer-based framework with a global–local dual-branch architecture for battery capacity and RUL prediction. The global branch represents long-term degradation trends, whereas the local branch focuses on short-term capacity variations. A structure-aware attention (SAA) module and an aligned channel-fusion (ACF) module are further introduced to enhance global–local feature interaction and adaptively recalibrate the fused degradation representations. The proposed model was evaluated on representative cells from the CALCE, NASA, and TJU public datasets under multiple prediction starting points. Across the 12 evaluated cell starting point settings, GLF-Former achieved capacity-prediction MAE values of 0.0019–0.0068 Ah and RMSE values of 0.0026–0.0152 Ah, with R² values ranging from 0.9644 to 0.9997. The corresponding mean AE values ranged from 0.4 to 1.1 cycles across the evaluated settings. The results demonstrate that GLF-Former achieves competitive capacity-prediction and RUL-estimation performance under the evaluated settings, particularly when the available degradation history is limited. Full article

IEEE 802.11ax adopts Orthogonal Frequency Division Multiple Access (OFDMA), which divides a wireless channel into multiple Resource Units (RUs) with different sizes and transmission rates. Most existing studies on uplink random access assume that all stations contend for RUs of the same size and mainly improve channel access by adjusting contention parameters, such as the OFDMA backoff mechanism. As a result, the transmission requirements of stations are not sufficiently considered, leading to inefficient RU utilization. Stations with heavy traffic may not obtain adequate transmission resources, whereas stations with light traffic may occupy unnecessarily large RUs. To address this limitation, this paper proposes a Traffic-Aware Distributed Selection (TADS) scheme for IEEE 802.11ax uplink random access with different RU sizes. Each station estimates its traffic demand based on the mean data rate, delay bound, and buffer status. It also estimates the expected service capacity of each RU size using the transmission capacity and collision probability. The station selects the smallest RU size whose expected service capacity satisfies its traffic demand. If no RU size can satisfy the demand, the RU size with the highest ratio of expected service capacity to traffic demand is selected. When multiple RUs of the selected size are available, the station randomly selects one of them. In TADS, each station determines its RU independently without additional scheduling by the access point. Simulation results show that TADS achieves higher network throughput than conventional random RU selection under data, real-time, and mixed traffic scenarios. The results also show that the performance gain depends on the available RU configuration, with greater benefits when different RU sizes are available. Full article

Using data from Chinese listed non-financial firms from 2015 to 2023, this study examines how environmental, social, and governance (ESG) rating divergence affects banks’ loan limit decisions and the underlying decision-making mechanisms within the sustainable finance system. Drawing on cognitive psychology, we conceptualize bank lending as an organizational decision-making process in which conflicting ESG signals create information ambiguity and influence banks’ risk assessments. Greater ESG rating divergence is associated with significantly lower total annual bank loan limits. Mechanism analyses provide evidence consistent with heightened bank concerns about firm default risk, whereas we find no supporting evidence for the information-quality channel captured by discretionary accruals. Further analysis reveals an asymmetric response to conflicting ESG information: banks appear to place greater weight on relatively unfavorable ESG ratings, while favorable ratings do not exert a comparable moderating effect. The negative association between ESG rating divergence and bank loan limits is more evident among non-state-owned enterprises, firms with weaker repayment capacity, firms with lower financial information disclosure quality, and firms without third-party ESG assurance. These findings extend the literature on ESG rating divergence and sustainable finance by showing how conflicting ESG information is associated with contractual credit allocation through banks’ risk assessment and asymmetric information-processing behavior. They also highlight the importance of improving ESG information governance and banks’ capacity to evaluate inconsistent sustainability signals. Full article

Compact sub-6 GHz and wireless local area network (WLAN) multiple-input multiple-output (MIMO) antennas require broad impedance coverage and low inter-port coupling within limited footprints. This work presents a two-port Koch-fractal U-slot antenna with a defected ground structure (DGS) on RT/duroid 5880. The design evolves from a rectangular monopole through Koch-edge shaping, U-slot loading, and ground-plane defects. The fabricated two-port prototype exhibits a measured −10 dB impedance bandwidth of 3.07–6.02 GHz, covering n78, n79, and WLAN, while the measured inter-port isolation exceeds 18.13 dB. The fabricated single-element prototype provides measured realized gains of 1.92, 2.34, and 2.05 dBi at 3.5, 4.7, and 5.5 GHz, respectively. Measurement-derived MIMO metrics yield an envelope correlation coefficient not exceeding 0.002, diversity gain close to 10 dB, channel capacity loss of 0.07–0.10 bits/s/Hz, mean effective gain near −3.1 dB with zero port imbalance, and acceptable in-phase total active reflection coefficient behavior. WLAN-band quadrature phase-shift keying tests at 5.18, 5.50, and 5.825 GHz produce error vector magnitude values of 5.4–9.1%, with derived bit error rate estimates below 10⁻⁶ under an additive white Gaussian noise assumption. The design provides wide measured bandwidth, good isolation, low correlation, and WLAN-band signal-domain validation in a simple printed structure. Full article

Green finance has emerged as a critical mechanism for aligning capital markets with climate and sustainability objectives, particularly as economies face mounting pressure to transition away from carbon-intensive growth models. In hydrocarbon-dependent regions such as the Gulf Cooperation Council (GCC), this transition poses distinctive challenges that require integrated institutional, policy, and financial frameworks. The global transition toward sustainable finance has gathered significant momentum, with green capital markets emerging as a central mechanism for channeling investment toward climate and development objectives. Hydrocarbon-dependent economies face a distinctive challenge in this transition, as they must reconcile resource-based growth models with rising pressures for environmental accountability and low-carbon diversification. This study develops an integrated theoretical framework to examine how Gulf Cooperation Council (GCC) financial systems are transitioning toward green capital markets, drawing on institutional theory, environmental policy pathway analysis, and climate-finance alignment models. Using descriptive statistics from regional stock exchanges covering 2015–2024, the study maps key trends in sustainable asset growth, institutional investor preferences, and regulatory evolution across the GCC. Findings indicate progressive alignment with global ESG norms; sustainable asset valuations grew at 23.5% CAGR (UAE) and 18.7% CAGR (Saudi Arabia). A fixed-effects panel regression with panel-corrected standard errors is estimated across all six GCC economies; regulatory framework maturity emerges as the strongest predictor of green bond issuance (β = 0.47, p < 0.01). Cumulative green bond issuances reached USD 52.6 billion (2015–2024), with renewable energy accounting for 58.1% of the sectoral allocation and green transportation recording a 55.9% CAGR (2020–2024). Policy recommendations focus on GCC-wide harmonization of mandatory ESG disclosure, adoption of a unified green bond taxonomy, and expansion of concessional green financing mechanisms. Substantial cross-country heterogeneity is documented, driven by differences in energy policy commitment, financial market maturity, and institutional capacity. The proposed framework offers specific policy guidance to accelerate green financial integration in the GCC, emphasizing regulatory harmonization, institutional capacity-building, and alignment with SDG targets 7 and 13. The study contributes to the limited evidence base on green finance in hydrocarbon-dependent economies and provides a foundation for future empirical research. Given the small panel dimensions (N = 6 cross-sectional units; T = 10 years), this study is positioned as exploratory rather than confirmatory: the panel-regression estimates and the hypothesized institutional-to-policy-to-finance sequence are interpreted as associational patterns consistent with the proposed framework rather than as definitive causal tests, and the reported coefficients are offered as indicative magnitudes to be re-examined as longer GCC green-finance time series become available. Full article