Search Results (27,469)

Efficient co-utilization of mixed sugars from lignocellulosic hydrolysates is often hindered by carbon catabolite repression and pretreatment-derived inhibitors. In this study, a co-fermentation strategy using Saccharomyces cerevisiae (S. cerevisiae) and Enterococcus mundtii (E. mundtii) was developed to simultaneously produce ethanol and lactic acid from non-detoxified corncob hydrolysate. Co-fermentation performed at 39 °C significantly improved substrate utilization compared with monoculture systems, achieving pentose and total sugar utilization percentages of 67.1% and 83.7%, respectively. S. cerevisiae preferentially consumed glucose and effectively detoxified furfural and 5-hydroxymethylfurfural (5-HMF), thereby alleviating inhibitory stress and carbon catabolite repression on E. mundtii. By optimizing the inoculation sequence, a 3 h delayed inoculation of E. mundtii significantly enhanced pentose utilization from 68.6% to 80.2% and increased total sugar utilization to 90.4%. This synergistic co-fermentation strategy provides an effective approach for improving mixed-sugar utilization and multi-product bioconversion efficiency in lignocellulosic biorefineries. Full article

Objective: To evaluate parental attitudes towards vaccination in children with inflammatory bowel disease (IBD), assess the level of adherence to immunization schedules, and identify key barriers hindering vaccination. Materials and Methods: A comparative survey was conducted involving 215 respondents, divided into an IBD group (109 parents of children with IBD) and a control group (106 parents of healthy children). The majority of respondents were mothers (96%) with higher education (81% and 79%, respectively) residing in a major metropolitan area. We assessed demographic data, vaccination history of both children and parents, sources of medical information, and reasons for vaccine refusal. Results: Routine vaccination coverage in children under 6 years of age was high and comparable in both groups (>93%). The majority of parents in the IBD group (n = 68; 62%) expressed a positive attitude towards vaccination. However, following the onset of IBD, only 24 (22%) continued vaccination, while 85 (78%) reported a categorical refusal to continue immunization. It was found that parents tend to misinterpret normal post-vaccination reactions as vaccine complications. A significant factor contributing to refusal is the lack of information from attending physicians and reliance on the Internet as a primary information source. Additionally, low rates of adult revaccination were noted, with over 30% of parents in both groups not being vaccinated in adulthood. Conclusions: The low vaccination rate in children with IBD after disease onset is driven not by initial anti-vaccination sentiment, but by acquired fears and a lack of professional communication from primary care providers and specialists. Improving immunization coverage requires the active implementation of educational programs for parents regarding vaccine safety during immunosuppressive therapy, as well as the development of specific guidelines for attending physicians. Full article

With the rise of smart cities, technology has enabled more efficient urban management. A key part of this is the Internet of Vehicles (IoVs), which connects vehicles to smart city systems to improve transportation safety and efficiency. This integrated system enables wireless connection between vehicles, allowing for the sharing of essential traffic information. However, with all this connectivity, there are growing concerns about IoV security and privacy. This paper presents a new privacy-preserving authentication scheme for Autonomous Vehicles (AVs) in the IoV field using physical unclonable functions (PUFs). This scheme employs a bilinear pairing-based encryption technique that supports search over encrypted data. The primary aim of this scheme is to authenticate AVs inside the IoV architecture. A novel PUF design generates random keys for our authentication technique, hence boosting security. This dual-layer security strategy safeguards against a range of cyber threats, including identity fraud, man-in-the-middle attacks, and unauthorized access to personal user data. The PUF design will guarantee the true randomness of the AVs’ users’ secret keys. To handle the large amount of data involved, we use hardware acceleration with different Field-Programmable Gate Arrays (FPGAs). Our examination of privacy and security demonstrates the achievement of the defined design goals. The proposed authentication framework was fully implemented and validated on FPGA platforms to demonstrate its hardware feasibility and efficiency. The integrated heterogeneous PUF achieves an average reliability exceeding $98.5\%$ across a wide temperature range, while maintaining near-ideal randomness with an average Hamming weight of $49.7\%$ over multiple challenge sets. Furthermore, the uniqueness metric approaches $49.9\%$, confirming strong inter-device distinguishability among different PUF instances. The complete authentication architecture was synthesized on Nexys-100T, Zynq-104, and Kintex-116 devices, where the design utilizes less than $80\%$ of slice Look-Up Tables (LUTs), under $27\%$ of on-chip memory resources, and below $16\%$ of DSP blocks, demonstrating low hardware overhead. Full article

The rapid growth of the Internet of Things (IoT) has exposed billions of interconnected, heterogeneous, and resource-constrained devices to increasingly sophisticated threats. To evaluate the readiness of current intrusion detection systems (IDSs), this study reviews 32 recent IoT-IDS proposals spanning conventional, machine-learning, deep-learning, and hybrid approaches. Each system is assessed against 10 criteria that reflect practical IoT requirements, including real-time performance, latency, lightweight design, detection accuracy, mitigation capabilities, integrated detection-and-mitigation workflows, adaptability, resilience to advanced attacks, validation in realistic environments, and scalability. The results indicate that although many approaches achieve high detection accuracy, most do not meet real-time and lightweight thresholds commonly cited in IoT deployment literature. Mitigation features are often absent, adaptability is rarely implemented, and 29 out of 32 studies rely solely on offline datasets, thereby limiting confidence in their robustness to deployment. Scalability remains the most significant limitation, as none of the reviewed IDSs have tested their performance under realistic multi-node or high-traffic conditions, even though scalability is critical for large IoT ecosystems. Overall, the review suggests that future IoT IDS research should move beyond accuracy-focused models and toward lightweight, adaptive, and autonomous solutions that incorporate mitigation, support real-time inference, and undergo standardized evaluations under real-world operating conditions. Full article

With the rapid development of autonomous driving, path tracking has emerged as a pivotal research direction. Model predictive control (MPC) has become one of the most prevailing approaches for path tracking, owing to its superior capacity in dealing with multi-constrained control problems and compatibility with the symmetry of vehicle dynamic systems. Nevertheless, conventional MPC suffers from performance degradation in path tracking when vehicle localization drift occurs, referring to the noticeable deviation between sensor-measured position and actual physical position over time, which is mainly induced by sensor noise and outliers. To overcome these limitations and enhance the accuracy and stability of path tracking, this paper presents a location-adaptive model predictive control framework. Specifically, a supervisor is designed to detect localization drift, and a Runge–Kutta-based location estimator is activated to predict the current vehicle state once drift is identified. Furthermore, a linear time-varying MPC is utilized to compute the desired control input for real-time multi-objective optimization. A set of co-simulations based on Simulink and CarSim are conducted to validate the effectiveness of the proposed strategy. Numerical results demonstrate that the presented method outperforms traditional MPC in terms of tracking accuracy and stability under localization drift conditions. Full article

Underwater vehicles performing sampling tasks often encounter significant buoyancy variations due to payload adjustments and environmental changes, which severely challenge the stability and accuracy of controllers. To address this issue, this paper proposes a hybrid control framework that integrates Proximal Policy Optimization (PPO) with adaptive PID tuning. The framework employs PPO to dynamically adjust PID parameters online while incorporating output saturation, stepwise quantization, and dead zone filtering to ensure control safety and actuator longevity. A dual-error state representation—combining instantaneous error and its derivative—along with actuator command buffering is introduced to compensate for system lag and inertia. Comparative simulations and experimental tests demonstrate that the proposed method achieves faster convergence, lower steady-state error, and smoother control signals compared to both conventional PID and pure PPO-based control. The framework is validated through pool tests and field trials, confirming its robustness under realistic hydrodynamic disturbances. This work provides a practical and safe solution for adaptive depth control of sampling-capable AUVs operating in dynamic underwater environments. Full article

Objectives: Unexplained syncope (US) persists despite extensive diagnostic evaluations, with autonomic dysfunction as a central mechanism. Sleep apnea (SA) may contribute through intermittent hypoxemia and autonomic imbalance. We evaluated the impact of SA treatment on syncope recurrence, nocturnal heart rate variability (HRV), and quality of life in patients with US. Methods: We conducted a prospective multicentre study in three hospitals in Galicia (Spain), including adults with US who underwent home respiratory polygraphy. SA was diagnosed according to guideline criteria, and treatment was prescribed when indicated (positive airway pressure therapy, positional therapy, and/or weight management). Symptoms, syncope burden, nocturnal heart rate variability derived from the ECG signal, and quality of life (SF-36 and a 0–100 visual analogue scale) were assessed at baseline and after 12 months. Results: Of 141 patients, 99 met treatment criteria, and 67 completed the 12-month follow-up. Mean age was 64.5 years; 59.6% were men; mean AHI was 25.9/h. After therapy, daytime sleepiness (Epworth score decreased from 8 to 5; p = 0.001), fatigue, nocturnal awakenings, and syncopal episodes decreased from 62.6% to 16.2%, 56.6% to 16.2%, and 3 to 0, respectively (all p < 0.001). HRV showed increased RR interval (p < 0.001) and RMSSD (p = 0.04). Quality of life improved in vitality (SF-36 vitality domain increased from 44 to 50; p = 0.02) and on the visual analogue scale (0–100: 50 to 70; p = 0.002). Conclusions: In this prospective cohort of patients with US and SA, therapy for SA was associated with fewer syncope recurrences, improvements in nocturnal respiratory indices, and selected heart rate variability measures, and better self-reported fatigue and vitality. Given the single-arm design and potential adherence and selection biases, these findings should be interpreted with caution and warrant confirmation in controlled studies. Full article

Soil salinity is a major constraint on global agricultural productivity. This study evaluated the efficacy of a cell-free extract from Trichoderma hamatum (designated BEYF) in enhancing salt stress tolerance in lettuce (Lactuca sativa). Lettuce plants under normal and salt-stressed conditions exposed to 200 mM NaCl were treated with either water or YF (the working solution of BEYF) at concentrations of 0.05, 0.10, and 0.25 mg/L. Compared to the control, YF application significantly improved plant growth under salt stress, as indicated by increased plant height, biomass, leaf area, and other agronomic traits. Physiologically, YF mitigated oxidative membrane damage, as indicated by reduced electrolyte leakage and malondialdehyde (MDA) content, while promoting the accumulation of the osmoprotectant proline. Histochemical staining further confirmed that YF effectively suppressed hydrogen peroxide (H₂O₂) accumulation and preserved cell viability under salt stress. At the molecular level, YF significantly up-regulated the expression of key stress-responsive genes, including those involved in abscisic acid biosynthesis (NCED1, NCED2), signaling (WRKY58), and proline synthesis (P5CSs). Collectively, our findings demonstrate that BEYF enhances lettuce salt tolerance through integrated physiological, cellular, and transcriptional adaptations, supporting its potential as a sustainable biostimulant for improving crop cultivation in saline soils. Full article

Cancer-associated fibroblasts (CAFs), the major constituent of the tumor microenvironment, are considered the most active cells and key contributors to tumor resistance, recurrence, and metastasis. Therefore, we have investigated here the potential normalization of the active features of breast CAFs with decorin (DCN), a small leucine-rich proteoglycan that acts as an oncogene suppressor. We have first shown that rhDCN modulates the expression of a plethora of proteins involved in different signaling pathways, including STAT3/NF-κB and ERK. Consequently, rhDCN repressed the important active CAF biomarkers α-SMA, IL-6, and SDF-1 through inhibition of the STAT3/AUF-1 pathway, in cells grown as 2D and 3D cultures. Furthermore, rhDCN had a strong downregulation effect on FAP-α, a key biomarker of active CAFs, and suppressed their proliferative and invasive capacities through upregulation of p16 and p21, and downregulation of MMP-2 and MMP-9. Furthermore, rhDCN suppressed the paracrine effects of active CAFs in promoting epithelial-to-mesenchymal transition (EMT) and cancer stem cells in breast cancer cells, both in vitro and in orthotopic tumor xenografts. Importantly, rhDCN-related normalization of active CAFs features was persistent through cellular passaging, and was not accompanied by cytotoxicity. Together, these findings have revealed rhDCN as a promising anti-breast cancer therapeutic cytokine through suppression of the non-cell-autonomous cancer-promoting effects of active CAFs. Full article

Maritime autonomous systems require robust predictive capabilities to anticipate vessel motion and environmental dynamics. While transformer architectures have revolutionized AIS-based trajectory prediction and demonstrated feasibility for sonar frame forecasting, their application to maritime radar frame prediction remains unexplored, creating a critical gap given radar’s all-weather reliability for navigation. This survey reviews predictive modeling approaches relevant to maritime radar, with emphasis on transformer architectures for spatiotemporal sequence forecasting, where existing representative methods are analyzed according to data type, architecture, and prediction horizon. Our review shows that, while the literature has demonstrated transformer-based frame prediction for sonar sensing, no prior work addresses transformer-based maritime radar frame prediction, thereby defining a clear research gap and motivating concrete research directions for future work in this area. Full article

For battery-powered Smart Road components deployed in locations without access to the electrical grid, limited energy availability represents a major challenge to long-term autonomous operation. While photovoltaic panels are the most commonly used energy-harvesting solution, their effectiveness depends strongly on environmental and climatic conditions and may be insufficient in shaded areas or in highly dynamic road environments. Road infrastructure, however, inherently provides additional and largely underutilized energy sources, among which thermoelectric energy generated by temperature gradients within the road structure is particularly promising. This review addresses the problem of identifying viable alternatives or complements to photovoltaic energy harvesting by focusing on thermoelectric transducers as a potential power source for Smart Road applications. The objective of the article is to provide a comprehensive overview of the physical principles underlying thermoelectric transducers, the different architectures of thermoelectric modules, and their practical applicability in road transportation systems. Particular attention is devoted to implementation approaches that do not interfere with traffic flow or compromise road safety, as well as to existing applications of thermoelectric energy harvesting in transportation infrastructure. In addition, the review discusses the potential and limitations of concentrated thermoelectric transducers for increasing power density. By synthesizing current research results, this work evaluates the feasibility, advantages, and challenges of thermoelectric energy harvesting to extend the operational lifetime of autonomous Smart Road components and identifies directions for future research. Full article

Radar image extrapolation serves as a fundamental methodology in meteorological forecasting, facilitating precise short-term weather prediction through spatiotemporal sequence analysis. Conventional approaches remain constrained by progressive image degradation and artifacts, substantially limiting operational forecasting reliability. This research introduces E-HEOA—an enhanced deep learning architecture with integrated hyperparameter optimization. Our framework incorporates two fundamental innovations: (a) a hybrid metaheuristic optimizer merging a Gaussian-mutated ESOA and Cauchy-mutated HEOA for autonomous learning rate and dropout optimization and (b) embedded ConvLSTM2D modules for enhanced spatiotemporal feature preservation. Experimental validation on 170,000 radar echo samples demonstrates superior performance, demonstrating considerable enhancement in almost all aspects relative to the baseline model while establishing new state-of-the-art benchmarks in prediction fidelity, convergence efficiency, and structural similarity metrics. Full article

Building resilient cities that can survive, adapt, and thrive amid climate and ecological challenges has become a global priority, yet achieving this goal requires adequate financial support. This study investigates the impact of green finance on urban ecological resilience (UER) by exploiting the establishment of China’s Green Finance Reform and Innovation Pilot Zones (GFPZs) as a policy shock. Using a DPSIR-based (driving force–pressure–state–impact–response) evaluation framework and a staggered difference-in-differences approach with panel data from 277 cities (2011–2022), the empirical results show that (1) the GFPZ policy significantly enhances UER; (2) green finance improves UER through three transmission channels—government environmental governance, green technological innovation, and public environmental participation; (3) the policy effects display clear spatial and structural heterogeneity, with stronger impacts in southern, less-developed, and non-traditional industrial cities, as well as positive local effects, negative spatial spillovers, and significant synergies with national big data pilot zones. This study clarifies how financial instruments contribute to building resilient cities and offers insights for embedding green finance into urban ecological strategies. Full article

Generative Artificial Intelligence (Gen AI) offers significant potential to support requirements engineering (RE) education; however, its integration poses challenges regarding accuracy and student engagement. While Gen AI cannot independently specify requirements without hallucinating or overstepping scope, it can serve as a powerful partner in a hybrid intelligence workflow. In this paper, we address the challenge of translating high-level motivational models into detailed user stories, a process that is traditionally labour-intensive for novices. We introduce a structured, human-in-the-loop workflow that uses Gen AI to refine and polish user stories while strictly preserving student agency. By grounding the output from Gen AI in a validated motivational model, the workflow minimises the risk of metacognitive offloading, requiring students to actively critique and validate the initially generated requirements. Our analysis of instructional artefacts demonstrates that Gen AI helps in three ways: suggesting structural improvements, offering alternative professional phrasing, and enhancing readability. However, we also identify risks of intent drift and scope expansion, reinforcing the need for rigorous human oversight. The findings advocate for a pedagogical approach where the Gen AI system acts as a reflective assistant rather than an autonomous generator. Full article

Objectives: To evaluate regional anatomical differences in brain volume, surface area, and cortical thickness between children with breath-holding spells (BHSs) and a control group using morphometric MRI analyses. Methods: Three-dimensional T1-weighted cranial MRI data from 48 children with BHSs and 50 control children were retrospectively analyzed, yielding volumetric, surface area, and cortical thickness measures for 135 brain regions. All measurements were assessed relative to total intracranial volume (ICV). Group comparisons were performed using analysis of covariance with age, sex, and ICV as covariates, followed by Benjamini–Hochberg false discovery rate correction (q < 0.05). Results: The BHS group exhibited reduced bilateral amygdala volumes (left: q = 0.042; right: q = 0.038). Both cortical thickness and volume were reduced in the right anterior insula (thickness: q = 0.046; volume: q = 0.049). In addition, cortical thickness was reduced in the bilateral anterior cingulate cortices (left: p = 0.019, q = 0.045; right: p = 0.017, q = 0.043) as well as in the right medial frontal cortex (p = 0.009, q = 0.036). Subregional cerebellar analysis demonstrated volume reductions in the right lobule VI (q = 0.031), left lobule VIIA (Crus I) (q = 0.043), and vermis IX–X (q = 0.039). Conclusions: Detecting measurable morphometric changes in brain regions involved in autonomic and emotional regulation in children with BHSs will contribute to understanding the neurobiological characteristics associated with BHSs. Full article