Search Results (49,314)

Recent discoveries of fluorite–barite deposits in the Donghai–Linshu area in northern Jiangsu Province, China, underscore the region’s mineral potential, yet detailed geological investigations remain limited. In this study, we examined monzonite and quartz monzonite from drill cores in the Jiashan mining area using petrography, U–Pb zircon dating, zircon trace element geochemistry, whole-rock geochemistry, and zircon Lu–Hf isotopes. Laser ablation inductively coupled plasma mass spectrometry (LA–ICP–MS) zircon U–Pb analyses were conducted to constrain the crystallization ages of the monzonite (127.06 ± 0.54 Ma and 126.83 ± 0.75 Ma) and quartz monzonite (127.2 ± 0.5 Ma and 128.59 ± 0.62 Ma) to the Early Cretaceous, marking a significant magmatic event. Many of the zircons contain inherited Neoproterozoic cores (718–760 Ma and 800–860 Ma), indicating the assimilation of deep crustal materials of this age. The monzonite is metaluminous, with moderate SiO₂ (61.61–62.41 wt.%), high alkalis (Na₂O + K₂O = 7.48–7.92 wt.%), and A/CNK = 0.72–0.91. The quartz monzonite has higher SiO₂ (66.26–68.18 wt.%) and alkalis (8.32–9.33 wt.%). Both rock types exhibit similar trace and rare earth element patterns: enrichment in large-ion lithophile and light rare earth elements, depletions in Nb, Ta, and Ti, no significant Zr-Hf depletion, and weak negative Eu anomalies (δEu ≈ 0.84–1.00). Their low Zr + Nb + Ce + Y contents, Ga/Al ratios, and TFeO/MgO ratios indicate that they have an I-type granite affinity. The Early Cretaceous zircons have highly negative ε_Hf(t) values (−33.7 to −23.5) and ancient two-stage model ages (2622–3247 Ma), which are consistent with derivation from Archean crust. The inherited Neoproterozoic zircons have younger Paleo–Mesoproterozoic T_DM2 ages. The evidence suggests that both intrusions were mainly generated by partial melting of ancient Archean basement, with minor mantle input. The magma generation was likely triggered by crustal anatexis induced by the underplating of mantle-derived magmas in an extensional tectonic regime, coeval with Early Cretaceous magmatism in the Sulu orogen. Full article

Unmanned Aerial Systems (UASs) are playing an increasingly critical role in both civilian and defense applications. However, their heavy reliance on unencrypted Global Navigation Satellite System (GNSS) signals, particularly GPS, makes them highly susceptible to signal spoofing attacks, posing severe operational and safety threats. This paper introduces a comprehensive, AI-driven multi-layer sensor framework that simultaneously enables real-time spoofing detection and secure command-and-control (C2) communication in lightweight UAS platforms. The proposed system enhances telemetry reliability through a refined preprocessing pipeline that includes a novel GPS Drift Index (GDI), robust statistical normalization, cluster-constrained oversampling, Kalman-based noise reduction, and quaternion filtering. These sensing layers improve anomaly separability under adversarial signal manipulation. On this enhanced feature space, a differentiable architecture search (DARTS) approach dynamically generates lightweight neural network architectures optimized for fast, onboard spoofing detection. For secure command and control, the framework integrates a low-latency cryptographic layer utilizing PRESENT-128 encryption and CMAC authentication, achieving confidentiality and integrity with only 1.79 ms latency and a 0.51 mJ energy cost. Extensive experimental evaluations demonstrate the framework’s outstanding detection accuracy (99.99%), near-perfect F1-score (0.999), and AUC (0.9999), validating its suitability for deployment in real-world, resource-constrained UAS environments. This research advances the field of AI-enabled sensor systems by offering a robust, scalable, and secure navigation framework for countering GPS spoofing in autonomous aerial vehicles. Full article

Nature-based Solutions (NbS) such as rolled cover crops are increasingly adopted in rainfed vineyards to reduce soil degradation and drought risk, but their effectiveness depends on local soil physical conditions. We compared spontaneous inter-row vegetation managed by mowing (Control) with a cereal-based rolled cover crop (C-R) in two vineyards of the Oltrepò Pavese (Northern Italy) with contrasting texture, structure, and slope: Canevino (CNV) and Santa Maria della Versa (SMV). From 2021 to 2025, continuous soil moisture monitoring was combined with field measurements of saturated hydraulic conductivity (K_s) and bulk density, interpreted using temporal indicators (MRD, ITS) and a drought index (SWDI) calibrated to field moisture thresholds. During wet phases, average saturation at 50 cm was consistently higher at SMV (about 78 to 84 percent) than at CNV (about 68 to 75 percent). Under water-limited conditions, management contrasts were most evident at SMV: at 50 cm during the post-termination dry phase, saturation remained around 70 percent under C-R versus about 64 percent under the Control, and K_s was higher under C-R (8.32 × 10⁻⁶ m/s in topsoil) than under the Control (7.39 × 10⁻⁶ m/s). At CNV, SWDI at 50 cm indicated a moderate improvement in one agronomic year (median −1.2 under C-R versus −5.3 under the Control in 2021 to 2022), while a full tillage operation in 2024 defined a disturbed phase that was interpreted separately. SWDI occasionally suggested severe drought levels not fully matching field evidence, highlighting the need for site-calibrated reference thresholds in structured fine-textured soils. Overall, soil physical properties set the hydrological envelope, while rolled cover management can enhance buffering and preserve conductive pathways during dry phases; therefore, NbS performance should be evaluated with site-adapted monitoring and cautious inference from temporally autocorrelated time series. Full article

This study examines the determinants of inflation volatility in Tunisia, focusing on central bank independence (CBI), economic transparency, and macroeconomic fundamentals. Although CBI is widely regarded as essential for monetary credibility, its effectiveness depends on its institutional framework. Our contribution is twofold. First, we develop a theoretical framework based on game theory to illustrate how the effectiveness of economic transparency and CBI shapes the welfare of both the central bank and the private sector in the presence (or not) of fiscal policy. Second, we use a binary threshold nonlinear autoregressive distributed lag (NARDL) model to capture long-run relationships and a Markov-switching GARCH (MS-GARCH) framework to model volatility dynamics. As a continuous measure, CBI has no significant impact on volatility. Paradoxically, high de jure independence in a binary regime is associated with a slight increase in inflation fluctuations. This indicates that legal independence alone is insufficient without fiscal discipline or effective coordination between the monetary and fiscal authorities. Notably, under fiscal pressure, greater CBI substantially reduces inflation volatility, highlighting the need for a coherent macroeconomic framework. Economic transparency generally increases short-term volatility but stabilizes inflation when supported by credible fiscal signals. Among the macroeconomic fundamentals, volatility in broad money is strongly destabilizing, whereas fluctuations in industrial production and the real exchange rate are largely insignificant. Government spending and exposure to external shocks, including import prices and geopolitical risks, further amplify this volatility. The observed negative trend over time reflects gradual improvements owing to policy reforms. Policy recommendations emphasize the establishment of genuinely independent and credible monetary institutions, enhancing coordination with fiscal policy, improving communication strategies, and strengthening risk management. Full article

To improve the dynamic response and steady-state frequency quality of a wind–storage coordinated system during primary frequency regulation, and to address the secondary frequency dip caused by rotor kinetic energy recovery when a doubly fed induction generator (DFIG)-based wind turbine (DFIG-WT) participates in frequency support, this paper proposes a coordinated wind–storage primary frequency regulation strategy. This strategy synergistically controls the wind turbine’s rotor kinetic energy recovery and exploits the advantages of hybrid energy storage system (HESS). During the DFIG-WT control stage, an adaptive weighted model is developed for the inertial and droop power contributions of the DFIG-WT based on the available rotor kinetic energy, enabling a rational distribution of primary frequency regulation power. In the control segment of HESS, an adaptive complementary filtering frequency division strategy is proposed. This approach integrates an adaptive adjustment method based on state of charge (SOC) to control both the battery energy storage system (BESS) and supercapacitor (SC). Additionally, the BESS assists in completing the rotor kinetic energy recovery process. Through simulation experiments, the results demonstrate that under operating conditions of 9 m/s wind speed and a 30 MW step disturbance, the proposed adaptive weight integrated inertia control elevates the frequency nadir to 49.84 Hz and reduces the secondary frequency dip to 0.0035 Hz. Under the control strategy where wind and storage coordinated participate in frequency regulation and BESS assist in rotor kinetic energy recovery, secondary frequency dips were eliminated, with steady-state frequency rising to 49.941 Hz. The applicability of this strategy was further validated under higher wind speeds and larger disturbance conditions. Full article

Traffic systems are quintessential complex systems, characterized by nonlinear interactions, multiscale dynamics, and emergent spatiotemporal patterns over complex networks. These properties make traffic prediction highly challenging, as it requires jointly modeling stable global topology and time-varying local dependencies. Existing graph neural networks often rely on predefined or static learnable graphs, overlooking hidden dynamic structures, while most RNN- or CNN-based approaches struggle with long-range temporal dependencies. This paper proposes a Spatiotemporal Causal–Trend Network (SCTN) tailored to complex transportation networks. First, we introduce a dual-path adaptive graph learning scheme: a static graph that captures global, topology-aligned dependencies of the complex network, and a dynamic graph that adapts to localized, time-varying interactions. Second, we design a Gated Temporal Attention Module (GTAM) with a causal–trend attention mechanism that integrates 1D and causal convolutions to reinforce temporal causality and local trend awareness while maintaining long-range attention. Extensive experiments on two real-world PeMS traffic flow datasets demonstrate that SCTN consistently achieves superior accuracy compared to strong baselines, reducing by 3.5–4.5% over the best-performing existing methods, highlighting its effectiveness for modeling the intrinsic complexity of urban traffic systems. Full article

In this study, an electrochemical valorization strategy on liquid byproducts from hazelnut shell gasification was developed to couple waste remediation with energy-efficient hydrogen production. The aqueous phase, rich in organic compounds, is processed in an anion exchange membrane (AEM) cell, where pure hydrogen evolved at the cathode while organic pollutants are oxidized at the anode. First, the feedstock is thoroughly characterized using gas chromatography–mass spectrometry (GC-MS), identifying a complex matrix of water-soluble aromatic compounds such as phenols, catechols, and other aromatics compounds, with concentrations reaching up to 2.9 g/kg for catechols. Then, the electro-reforming process is optimized using Nickel oxide–hydroxide (Ni(O)OH) electrodes with a loading of 0.75 mg/cm². This methodology relies on the favorable thermodynamics of organic oxidation, which requires a lower onset potential (0.4 V) compared to the oxygen evolution reaction (OER) observed in the alkaline control (0.52 V), and the low overpotential of the Nickel oxide–hydroxide electrode towards the oxidized species. Consequently, the organic load undergoes progressive oxidation into hydrophilic and less bioaccumulating species and carbon dioxide, allowing for the simultaneous generation of pure hydrogen at the cathode at a reduced cell voltage. Elevated stability was observed, with a substantial abatement—78% of the initial organic load—of organic compounds achieved over 80 h at a fixed cell voltage of 0.5 V, and a specific energy consumption for hydrogen production of $38.5 \mathrm{M}\mathrm{J}\cdot {\mathrm{k}\mathrm{g}}_{{\mathrm{H}}_2}^{-1}$. This represents a step forward in the development of technologies that reduce the energy intensity of hydrogen generation while valorizing biomass gasification residues. Full article

The Social Internet of Things (SIoT) enables collaborative service provisioning among interconnected devices by leveraging socially inspired trust relationships. This paper proposes a socially driven SIoT protocol for trust-aware service selection, enabling dynamic friendship formation and ranking among distributed service-providing devices based on observed execution behavior. The protocol integrates detection accuracy, round-trip time (RTT), processing time, and device characteristics within a graph-based friendship model and employs PageRank-based scoring to guide service selection. Industrial computer vision workloads are used as a representative testbed to evaluate the proposed SIoT trust-evaluation framework under realistic execution and network constraints. In homogeneous environments with comparable service-provider capabilities, friendship scores consistently favor higher-accuracy detection pipelines, with F1-scores in the range of approximately 0.25–0.28, while latency and processing-time variations remain limited. In heterogeneous environments comprising resource-diverse devices, trust differentiation reflects the combined influence of algorithm accuracy and execution feasibility, resulting in clear service-provider ranking under high-resolution and high-frame-rate workloads. Experimental results further show that reducing available network bandwidth from 100 Mbps to 10 Mbps increases round-trip communication latency by approximately one order of magnitude, while detection accuracy remains largely invariant. The evaluation is conducted on a physical SIoT testbed with three interconnected devices, forming an 11-node, 22-edge logical trust graph, and on synthetic trust graphs with up to 50 service-providing nodes. Across all settings, service-selection decisions remain stable, and PageRank-based friendship scoring is completed in approximately 20 ms, incurring negligible overhead relative to inference and communication latency. Full article

Background and Objectives: Sternocleidomastoid (SCM) dysfunction is commonly implicated in several musculoskeletal conditions. Accordingly, shear-wave elastography has been used to characterize SCM stiffness in asymptomatic and clinical cohorts. However, the only reproducibility study available reported limited reliability, so clinical interpretations should be made with caution. Therefore, this study revisits key methodological aspects of that protocol to assess intra-examiner reliability and includes two examiners with different levels of expertise to evaluate inter-examiner reliability. Materials and Methods: A longitudinal observational study was conducted, recruiting twenty-five asymptomatic participants. Two examiners with different experience levels participated in this study after following structured training. For each side, images were obtained in immediate succession in the sequence experienced–novice–experienced–novice (with side order randomized), using an ROI spanning full muscle thickness, stabilizing approximately 10 s before freezing to record Young’s modulus and shear-wave speed. Results: Inter-examiner agreement was good–excellent: single-measurement ICCs were 0.77–0.86, improving to 0.79–0.87 when averaging two trials, which also reduced the standard error of measurement (SEM) and minimal detectable changes (MDCs). Between-examiner mean differences were small and nonsignificant (p ≥ 0.068). Intra-examiner reliability was excellent (ICC ≈ 0.93–0.94) with small absolute errors. Precision was high (SEM ~5–6 kPa; 0.22 m/s), and MDCs were ~15–16 kPa and ~0.60 m/s, with no trial-to-trial bias (all p ≥ 0.311). Conclusions: The revised protocol showed excellent intra-examiner repeatability and good–excellent inter-examiner reliability with minimal bias. Averaging two acquisitions improved precision, while a single operator optimized longitudinal stability. Full article

In this study, the biocatalytic activity of four steroid-transforming strains isolated from the African frog Xenopus laevis and identified as Streptomyces rochei towards pregnane steroids has been investigated. All the isolated strains facilitated the reduction of the C20-carbonyl group and the structures of the metabolites were confirmed by mass spectrometric (MS) and ¹H NMR spectroscopic analyses. Hydrocortisone and progesterone were poorly transformed by the streptomycete strains, whereas cortexolone (Reichstein’s substance S) was effectively biotransformed, yielding more than 90% of 17α,20β,21α-trihydroxy-4-pregnen-3-one (20β-S). Primarily, 20α-reduction was detected when the microbial isolates were incubated with 17α-hydroxyprogesterone with the yield of 17α,20α-dihydroxy-4-pregnen-3-one (17,20α-P) reaching 70%. The biological activity of 20β-S was evaluated in Danio rerio. The results demonstrated that 20β-S modulated stress- and anxiety-related behavioral responses and activated Pgr-dependent transcriptional pathways in the brain and ovarian tissues. These observations support the potential relevance of the synthesized progestin as a functional regulator in teleost physiology. The findings enhance our understanding of the biodiversity of steroid-transforming actinomycetes inhabiting amphibians and can be successfully employed for the effective microbiological synthesis of biologically active 20-hydroxylated progestins that serve as bioregulators in teleosts. Full article

Falls are one of the most common and devastating effects of neurological diseases, especially in patients with stroke outcomes, Parkinson’s Disease (PD), and Multiple Sclerosis (MS). To prevent negative outcomes and guide tailored rehabilitation, it is necessary to identify risk factors early. The current study aims to assess whether and how the risk of falling is related to spatiotemporal and kinematic parameters in stroke, PD, and MS. It also seeks to determine how these factors can help manage patients and identify more personalized and appropriate rehabilitation treatments. Ninety patients with neurological disorders (stroke, PD, and MS) underwent eight weeks of home-based rehabilitation using the ARC Intellicare device or following a paper-based protocol. At baseline (T0) and at the end of the protocol (T2), they were assessed using the Silver Index of the hunova^® robotic platform to evaluate fall risk, and instrumental gait analysis to record spatiotemporal and kinematic parameters of walking. Statistical analysis showed moderate and significant correlations between the Silver Index and gait spatiotemporal parameters such as stance and swing phase, both in affected (T0, p = 0.007; T2, p = 0.017) and unaffected side (T0, p = 0.022; T2, p = 0.008), double support in affected side (T0, p = 0.002; T2, p = 0.005), cycle length in affected (T0, p = 0.007; T2, p = 0.003) and unaffected side (T0, p = 0.008; T2, p = 0.003), and cadence (T0, p = 0.025; T2, p = 0.003) in stroke patients. No significant results emerged in the PD and MS. No population showed significant correlations between the Silver Index and gait kinematic parameters. The Silver Index may reflect distinct patterns of instability in post-stroke gait, but in PD and MS, multiple factors influence the risk of falling that instrumental gait analysis cannot fully capture, requiring a more extensive and multidimensional approach that includes cognitive aspects. Full article

Autonomous driving perception systems are vulnerable to physical adversarial attacks. Existing defenses largely adopt loosely coupled architectures where visual and kinematic cues are processed in isolation, thus failing to exploit physical spatiotemporal consistency as a structural prior and often struggling to balance adversarial robustness, transferability, accuracy, and efficiency under realistic attacks. We propose a physics-aware trajectory–appearance consistency defense that detects and corrects spatiotemporal inconsistencies by tightly coupling visual semantics with physical dynamics. The module combines a dual-stream spatiotemporal encoder with endogenous feature orchestration and a frequency-domain kinematic embedding, turning tracking artifacts that are usually discarded as noise into discriminative cues. These inconsistencies are quantified by a Trajectory–Appearance Mutual Exclusion (TAME) energy, which supports a physics-aware switching rule to override flawed visual predictions. Operating on detector backbone features, outputs, and tracking states, the defense can be attached as a plug-in module behind diverse object detectors. Experiments on nuScenes, KITTI, and BDD100K show that the proposed defense substantially improves robustness against diverse categories of attacks: on nuScenes, it improves Correction Accuracy (CA) from 86.5% to 92.1% while reducing the computational overhead from 42 ms to 19 ms. Furthermore, the proposed defense maintains over 71.0% CA when transferred to unseen detectors and sustaining 72.4% CA under adaptive attackers. Full article

We investigate whether sophisticated deep learning architectures justify their computational cost for short-term cryptocurrency price forecasting. Our study evaluates a 2.1M-parameter (M represents millions (e.g., 2.1M = 2,100,000 parameters), with all RMSE values reported in USD) wavelet-enhanced transformer that decomposes the Fear and Greed Index (FGI) into multiple timescales before integrating these signals with technical indicators. Using Diebold–Mariano tests with HAC-corrected variance, we find that all models—including our wavelet–transformer, ARIMA, XGBoost, LSTM, and vanilla Transformer—fail to significantly outperform the O(1) naive persistence baseline at the 1-day horizon (DM statistic = +19.13, $p<0.001$, naive preferred). Our model achieves an RMSE of USD 2005 versus USD 1986 for naive (ratio 1.010), requiring 3909× more inference time (2.43 ms vs. 0.0006 ms) for a statistically worse performance. These results provide strong empirical support for the Efficient Market Hypothesis in cryptocurrency markets: even sophisticated multi-scale architectures combining wavelet decomposition, cross-attention, and auxiliary technical indicators cannot extract profitable short-term signals. Through systematic ablation, we identify positional encoding as the only critical architectural component—its removal causes 30% RMSE degradation. Our findings carry important implications, as follows: (1) short-term crypto forecasting faces fundamental predictability limits, (2) architectural complexity provides negative ROI in efficient markets, and (3) rigorous statistical validation reveals that apparent improvements often represent noise rather than signal. Full article

Polyphenols are a structurally diverse group of plant secondary metabolites widely recognized for their antioxidant, anti-inflammatory, antimicrobial, and chemoprotective properties, which have stimulated their extensive use in food, pharmaceutical, nutraceutical, and cosmetic products. However, their chemical heterogeneity, wide polarity range, and strong interactions with plant matrices pose major challenges for efficient extraction, separation, and reliable analytical characterization. This review provides a critical overview of contemporary strategies for the extraction, separation, and identification of polyphenols from plant-derived matrices. Conventional extraction methods, including maceration, Soxhlet extraction, and percolation, are discussed alongside modern green technologies such as ultrasound-assisted extraction, microwave-assisted extraction, pressurized liquid extraction, and supercritical fluid extraction. Particular emphasis is placed on environmentally friendly solvents, including ethanol, natural deep eutectic solvents, and ionic liquids, as sustainable alternatives that improve extraction efficiency while reducing environmental impact. The review further highlights chromatographic separation approaches—partition, adsorption, ion-exchange, size-exclusion, and affinity chromatography—and underlines the importance of hyphenated analytical platforms (LC–MS, LC–MS/MS, and LC–NMR) for comprehensive polyphenol profiling. Key analytical challenges, including matrix effects, compound instability, and limited availability of reference standards, are addressed, together with perspectives on industrial implementation, quality control, and standardization. Full article

Alternaria alternata is a common postharvest mold affecting tomato products, including cherry tomatoes, and causing their contamination with mycotoxins. When consumers encounter moldy fruits, some may remove the visibly contaminated part and consume the rest, to reduce waste. However, the extent to which A. alternata toxins migrate beyond visible fungal growth remains unclear, potentially posing health risks. This study investigated (i) the within-fruit migration of A. alternata in cherry tomatoes together with the associated mycotoxin production, and (ii) the diffusion of purified Alternaria toxins in tomatoes in the absence of any fungal activity. Toxins were quantified using LC-MS/MS, while fungal colonization was assessed through visual inspection and DNA quantification across fruit sections. In the absence of fungal growth, toxins remained largely confined to the spiking site and were degraded over time. In contrast, in inoculated samples, Alternaria DNA was detected at notable levels even in sections lacking visible fungal growth, while Alternaria toxins were found both in these regions and in lower fruit sections where fungal DNA was below the qPCR detection limit. These findings highlight the limitations of relying solely on visual inspection to assess food safety. A consumer recommendation is proposed to help minimize health risks while reducing food waste. Full article