Search Results (6,965)

Precise forecasting of the initial productivity rates of infill wells is essential for the effective exploitation of offshore reservoirs characterized by high water-cut. However, conventional reservoir simulation and basic machine learning models often suffer from high computational complexity and low interpretability. This research introduces a hybrid data-driven framework that combines ensemble feature engineering with a random forest model optimized through the crayfish optimization algorithm. The primary controlling factors were identified through a majority voting mechanism involving five feature selection algorithms. Subsequently, the COA was utilized to optimize the parameters of the random forest algorithm to improve its predictive robustness. The proposed EFE-COA-RF model achieves a testing MAE of 6.831 and an R² of 0.954, outperforming standard machine learning models and other optimization-based variants. The complete training process requires approximately 10.8 min, whereas the prediction time for the testing set is approximately 0.03 s. These results demonstrate that the proposed framework provides an accurate, interpretable, and efficient tool for rapid productivity evaluation in mature offshore oilfields. Full article

Effective project selection is a critical determinant of success for Design for Six Sigma (DFSS), particularly in automotive environments defined by high technical complexity and constrained resources. Because these selection tasks involve competing priorities, they are fundamentally multi-criteria decision-making (MCDA) problems that directly impact a company’s economic performance. This paper proposes a hybrid decision-support framework that integrates the Analytic Hierarchy Process (AHP) with a normalized scoring model. In this approach, classical AHP pairwise comparisons are used to derive consistent criteria weights, while project alternatives are evaluated on a 1–10 normalized scale to ensure the model remains scalable and practical for an industrial setting. The framework was empirically validated through a case study in an automotive company evaluating twelve DFSS project concepts. The results reveal that experts prioritize Product Quality (33%) and Cost/Functionality (33%) above all other factors, with these two criteria accounting for 66% of the total decision weight. Furthermore, the study established classification rules where projects scoring above 7.2 showed high implementation potential, while those below 5.2 were frequently discontinued. This structured approach enables a transparent and justifiable prioritization process that supports economic and operational sustainability by significantly reducing wasted engineering hours and prototype costs. Full article

The design of active pneumatic upper limb exoskeletons is complicated by the challenge of reliably determining a kinematically safe workspace. Existing analytical kinematic methods are not sufficient to predict geometric collisions between elements of closed kinematic chains, which poses risks of mechanical damage and threats to user safety during exoskeleton operation. This paper proposes a hybrid algorithm for verifying the workspace of a pneumatic exoskeleton, combining analytical modelling in MATLAB R2020b based on the Product of Exponentials (PoE) method with high-performance static simulation in the Unity environment. At the initial stage, a discrete set comprising 758 million positions of the upper exoskeleton manipulator was generated. Subsequently, a multithreaded two-stage filtering process was implemented: analytical verification of rod stroke limits and angular constraints, followed by the detection of physical intersections of solid-state meshes using the PhysX engine. The results indicate that while the analytical model filters out 99.6% of invalid configurations. Yet, among the remaining positions—formally correct from a mathematical standpoint—up to 50% lead to critical geometric collisions or breaks in the kinematic chain. The computational efficiency of the proposed architecture enabled full static workspace verification in under 20 min. A reachable zone topology was established, revealing pronounced asymmetry and the presence of a “manoeuvrability core” in the user’s anterior hemisphere. The developed algorithm generates a verified set of kinematically safe exoskeleton states, providing a foundation for the kinematic safety layer of a hierarchical control system. These findings demonstrate the necessity of complementing analytical kinematics with physical collision detection when designing hybrid kinematic mechanisms, and the approach can be applied to verify collision-free movement trajectories in various robotic systems. The approach can be applied to verify collision-free movement trajectories in simulation, with physical validation deferred to future work. Full article

Microwave irradiation, being an electromagnetic wave, facilitates volumetric heating through various dielectric heating modes such as dipolar polarization and ionic conduction. In this review, an attempt has been made to critically discuss various principles associated with microwave–material interactions. The review has given particular importance to recent developments in microwave-assisted material synthesis and processing of various materials such as metals, ceramics, polymers, nanoparticles, and food materials. The microwave method has various advantages over conventional heating methods in terms of reaction kinetics, product uniformity, and energy efficiency. The review also critically discusses some of the challenges faced by microwave–material interactions and how they can be addressed by adopting new strategies, such as hybrid heating and reactor innovations. In addition, future research directions have also been outlined to take microwave technologies to new heights in material processing. Full article

Prompt injection, ranked first in the OWASP Top 10 for Large Language Model (LLM) applications, enables adversaries to override system instructions and exfiltrate sensitive information by crafting inputs that blur the boundary between data and control. While application-layer defenses such as PromptShield and Prompt-G have advanced, they operate in isolation from enterprise Security Operations Center (SOC) infrastructure and lack the session-level visibility required to detect multi-turn fragmented campaigns. This paper presents a hybrid detection framework that instruments a Phi-3 Mini Instruct gateway to emit structured telemetry, correlates events in Elastic SIEM using four expert-authored detection rules, and augments rule coverage with a One-Class Support Vector Machine (OCSVM) trained exclusively on 1200 benign interactions. Evaluated against 1100 prompts (900 malicious from CySecBench, 200 benign from Stanford Alpaca), the framework achieves a precision of 0.971, a recall of 0.810, and an F1-score of 0.883, and it reduces the Attack Success Rate (ASR) to 19.0% with a Mean Time to Detection (MTTD) of 2.3 s under the evaluated Phi-3 Mini configuration. The OCSVM layer accounts for 162 of 243 incremental true positives over the baseline, identifying attacks whose behavioral feature vectors deviate from the benign manifold. The framework is architected around OpenAI-compatible gateway telemetry and is therefore designed for vendor-neutral integration; however, broader validation across model families, prompt templates, and application domains is required before making general claims about cross-model performance or production-scale effectiveness. Full article

Neurodegenerative disorders, including Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS), arise from highly interconnected molecular and cellular abnormalities that progressively lead to neuronal dysfunction, synaptic failure, and cell death. This review provides a unified framework to understand the interrelated molecular mechanisms driving these diseases, with a focus on identifying key disease-specific intervention nodes. Core contributors include oxidative stress, mitochondrial dysfunction, protein aggregation, neuroinflammation, and emerging roles of peroxisomal dysfunction in redox imbalance, lipid dysregulation, and inflammatory amplification. Single-target therapies often show limited efficacy due to the complex, interconnected nature of these pathways. In contrast, polypharmacology, which targets multiple disease-relevant mechanisms simultaneously, offers a more promising therapeutic strategy. This review critically examines how pathway crosstalk drives neurodegenerative progression, with particular emphasis on mitochondrial–ROS–inflammatory signaling, aggregation–proteostasis failure, synaptic–neuroimmune dysfunction, and gut–brain communication. It evaluates various multi-node intervention strategies, including multi-target-directed ligands (MTDLs), molecular hybrids, natural products, drug repurposing, and nanocarrier-based delivery systems. Advances in network pharmacology, artificial intelligence (AI), bioinformatics, and multi-omics have enhanced the identification of actionable therapeutic nodes, candidate compounds, and brain-targeted delivery platforms. Notably, the NOD-like receptor pyrin domain-containing protein 3 (NLRP3) inflammasome and cyclic GMP–AMP synthase (cGAS)—stimulator of interferon genes (STING) pathways—play distinct roles in neuroinflammation, amplifying neuronal damage by releasing inflammatory cytokines and inducing mitochondrial dysfunction. However, successful translation into clinical practice remains constrained by challenges such as blood–brain barrier penetration, patient heterogeneity, and biomarker limitations. The review advocates for a shift towards mechanism-informed, patient-stratified polypharmacological strategies to better address the network pathology of neurodegeneration, despite significant translational hurdles. Full article

Sex control technology has become a key technique in aquatic animal breeding, as many aquatic species exhibit distinct sexual dimorphism in growth, reproduction, immunity, and other economically important traits. Therefore, methods such as regulating sex ratios and establishing unisexual populations can significantly enhance aquaculture productivity and breeding efficiency. Recent years have seen a rapid advancement in the field of research on the mechanisms of sex determination and differentiation in aquatic animals, as well as sex control technologies. This review summarizes the latest advances in research on the mechanisms of sex formation in aquatic animals, including genetic sex determination, environmental sex determination, and genotype-environment interactions. Furthermore, this review outlines the major sex-linked genes and molecular markers used for genetic sex identification, introduces key male and female regulatory factors involved in gonadal differentiation, and explores the application of major sex control methods in aquaculture breeding, including techniques such as interspecific hybridization, environmental regulation, hormone induction, parthenogenesis, and gene editing. Full article

Maize productivity in Southeastern Europe is increasingly affected by climatic variability, necessitating agronomic strategies to maintain yield under irrigated conditions. This study evaluated the effects of conventional tillage, minimum tillage, and no-tillage on maize yield, yield components, and weed dynamics, and analyzed the interaction between tillage intensity and hybrid performance under irrigated cambic chernozem conditions in Southeastern Romania. A three-year field experiment (2023–2025) was conducted as a randomized complete block design with three replications using three maize hybrids (P0900, P0937, and P1441) under sprinkler irrigation. Grain yield, kernel weight per ear, kernel number per ear, thousand-kernel weight, plant density, and weed density were analyzed using ANOVA, linear mixed models, and regression analysis. Grain yield ranged from 10.66 to 11.46 t ha⁻¹ across years, with the hybrid exerting the strongest effect on all productivity parameters. P0900 recorded the highest yield (12.43 t ha⁻¹) and the lowest associated weed density. Weed density increased from 207.44 plants m⁻² under conventional tillage to 266.11 plants m⁻² under no-tillage and was negatively associated with yield components and grain yield. Significant tillage × weed-density interactions indicated steeper productivity declines in reduced-tillage systems, particularly no-tillage. The results suggest that the agronomic performance of conservation-oriented tillage systems under irrigation depends strongly on hybrid adaptability and effective weed-management strategies. Full article

Autonomous mobility is reshaping how travel time is perceived, experienced, and monetised. Most existing studies have examined the value of time (VOT), willingness to pay (WTP), comfort and safety perception, digital services, and user perception as isolated phenomena, with limited efforts to integrate these dimensions into unified analytical frameworks. This study aims to address the fragmented nature of existing research by developing an integrated understanding of user behaviour in autonomous mobility, linking VOT, WTP, psychological constructs, and service-related factors within a unified analytical perspective. A systematic review methodology following PRISMA 2020 guidelines was applied. A total of 81 peer-reviewed studies published between 2015 and 2026 were included and analysed, focusing on Private Autonomous Vehicles (PAVs) and Shared Autonomous Vehicles (SAVs). The results reveal three main trends. First, autonomous travel introduces greater flexibility in time use and enables productive or leisure activities during travel. Second, behavioural aspects of VOT and WTP are strongly influenced by psychological constructs such as trust, safety, and risk perception. Third, notable differences emerge between PAV and SAV contexts, particularly in terms of comfort, control, and safety perception. The literature predominantly employs stated preference surveys, discrete choice models, and hybrid models incorporating psychological factors. However, fragmentation persists in modelling behavioural aspects of time perception and shared mobility services. This study provides a structured synthesis of existing evidence and highlights key research gaps by integrating economic, psychological, and service-related dimensions. The findings emphasise the importance of context-specific and psychologically informed modelling approaches to better understand user acceptance and behavioural adaptation in autonomous mobility systems. Full article

Peanut (Arachis hypogaea L.) production faces persistent threats from various infectious diseases. Planting healthy varieties with robust botanical defense networks is critical for minimizing future costs. Non-expressor of pathogenesis-related (NPR) regulators are involved in immune activation and act as key targets for deeper stress adaptation, and are thus promising targets for genetic enhancement. In this study, we characterized the peanut NPR4B protein and demonstrated its local subcellular binding to the nucleus. Ectopic overexpression of AhNPR4B in Arabidopsis thaliana significantly enhanced resistance to the necrotrophic pathogen Botrytis cinerea and enhanced cold tolerance, as supported by quantitative and statistical analyses (p < 0.05). As regards underlying molecular events, Y2H (Yeast 2-Hybrid) analysis revealed a binding in vitro physical relation of AhPR2-like to AhNPR4B. This binding was demonstrated in vivo through BiFC (Bimolecular Fluorescence Complementation). These results suggest that the AhNPR4B-AhPR2-like complex may act as a key regulatory module associated with biotic and abiotic stress signaling, potentially contributing to broad-spectrum stress resistance. These findings provide foundational insights into the functional roles of AhNPR4B and its interaction with AhPR2-like in regulating stress resistance and support its potential as a candidate target for future genetic improvements to enhance stress resilience in peanuts. Full article

This study addresses the critical challenge of sediment-driven degradation in aging hydropower infrastructure by implementing a novel Digital Operational Technology modernization framework at the AES Chivor Hydropower Plant in Colombia. While conventional sediment monitoring relies on sporadic manual campaigns, this research introduces a continuous, real-time sensing architecture that integrates hybrid acoustic–optical sensors, covering a range of 10 to 6000 mg/L, directly into the plant’s SCADA (Supervisory Control and Data Acquisition) system. The novelty of this approach lies in the seamless coupling of high-frequency physical data (15 min sampling) with an Operational Decision Support Module, enabling adaptive turbine management. Statistical validation against laboratory gravimetric standards yielded a robust correlation of 0.93, confirming the system’s precision in quantifying suspended sediment concentrations. By identifying critical fine particle fractions in real time, the proposed model enables a precision-based maintenance strategy that significantly reduces unscheduled production downtime and mitigates accelerated wear in Pelton turbines. These findings provide a scalable benchmark for extending the operational life of large-scale hydropower facilities facing advanced sedimentation risks through digital transformation. Full article

Background: The litchi fruit borer, Conopomorpha sinensis (Lepidoptera: Gracillariidae), is a devastating pest affecting litchi and longan production across Asia. Although a reference mitochondrial genome (mitogenome) has been published, its utility is limited by the lack of precise geographical data and raw sequencing data. Methods: In this study, we sequenced and characterized the complete mitogenome of C. sinensis collected from Taiwan using a hybrid assembly of Illumina and Oxford Nanopore technologies. Results: The assembled mitogenome is 17,301 bp in length with a mean sequencing depth of 19,155-fold, comprising 13 protein-coding genes (PCGs), 22 transfer RNA genes, two ribosomal RNA genes, and an AT-rich control region. Notably, we identified a rare tRNA gene rearrangement (trnR-trnA-trnN-trnS1-trnE-trnF) that deviates from the ancestral lepidopteran ditrysian pattern. Comparative analysis revealed a 94.65% overall sequence identity with the reference mitogenome, though the PCGs remained highly conserved at 99.35%. Variant analysis demonstrated that this divergence is predominantly driven by structural variations (228 indels) rather than nucleotide substitutions (2 SNPs) across the entire mitogenome; furthermore, 94.7% of the indels were identified in the control region and intergenic spacers. Subtle differences in codon usage were also observed in the ND6 start codon (ATT vs. ATA) and COX1 stop codon (TAA vs. T). Phylogenetic and molecular clock analyses robustly clustered the Taiwan specimen within the C. sinensis clade. Molecular dating estimates that the Conopomorpha lineage originated during the Late Cretaceous (~77.23 Ma). Notably, the divergence between the Taiwan specimen and the reference lineage was estimated to be negligible (<0.01 Ma) within the protein-coding regions, demonstrating a high degree of purifying selection that maintains coding-sequence stability across geographically distinct specimens, even as substantial variation accumulates in non-coding genomic regions. Conclusions: These findings provide high-resolution genomic resources and a temporal framework for the evolutionary study of Gracillariidae, offering foundational tools for targeted pest management. Full article

Soil salinization is a major abiotic stress limiting maize (Zea mays L.) growth and productivity worldwide. Recently, many genes involved in salt stress have been identified. However, the molecular mechanisms underlying salt tolerance in maize remain largely elusive. In this study, we identified a member of the ZmPIRIN family genes, ZmPRN1, acting as a negative regulator in response to salt stress. The expression levels of ZmPRN1 were down-regulated under salt and H₂O₂ treatment. Subcellular localization analysis showed that ZmPRN1 is localized to the chloroplast. Under salt stress, the Zmprn1-Mu mutant exhibited higher survival rates and lower reactive oxygen species (ROS) accumulation compared to wild-type plants. Whereas, ZmPRN1 overexpression lines were more sensitive to salt stress, and had higher ROS levels and lower chlorophyll content than wild-type plants. Transcriptome analysis showed that the differentially expressed genes (DEGs) were mainly involved in the oxidation-reduction process. Furthermore, yeast-two hybrid and split-luciferase complementation assays revealed that ZmPRN1 can interact with the chloroplast NDH complex subunit NDF4, the RING-type E3 ubiquitin ligase RING371, and the auxin-responsive protein IAA27. Collectively, our findings demonstrated that ZmPRN1 negatively regulates salt tolerance in maize by modulating ROS homeostasis, providing a valuable genetic resource for breeding salt-tolerant maize varieties. Full article

Tobacco use remains a major public health challenge in Jordan, where cigarette smoking and waterpipe use are both common and dual use is increasingly prevalent. Community pharmacies are highly accessible healthcare settings, yet structured smoking-cessation services remain underutilized. This study evaluated the clinical effectiveness and implementation of Dual-Quit Digital, a pharmacist-delivered cessation counseling program tailored to the type of tobacco used, paired with a 6-month automated WhatsApp^® (Menlo Park, CA, USA) follow-up system. We conducted a pragmatic, two-arm, parallel-group, Hybrid Type 2 cluster randomized controlled trial in 16 community pharmacies in Jordan, randomized 1:1 to intervention or usual care. A total of 320 adult tobacco users were enrolled (160 per arm). The intervention combined a structured in-pharmacy pharmacist consultation, tailored behavioral support, phenotype-stratified pharmacotherapy support, and 6 months of semi-automated WhatsApp^® follow-up with telepharmacy escalation for predefined red-flag responses. The control arm received usual care, consisting of opportunistic brief advice and standard over-the-counter sales without proactive follow-up. The primary outcome was biochemically verified continuous abstinence at 6 months, defined as exhaled carbon monoxide (CO) < 10 ppm and analyzed using intention-to-treat principles. Secondary outcomes included 7-day point prevalence abstinence (PPA) at 3 and 6 months, 30-day PPA at 6 months, both-product abstinence among baseline dual users, pharmacotherapy uptake and adherence, and implementation-relevant outcomes, including service reach, feasibility of recruitment, and digital engagement metrics. All 16 pharmacies were retained, and all 320 randomized participants were included in the intention-to-treat analysis. At 6 months, CO-verified continuous abstinence was achieved by 26.3% of participants in the intervention arm compared with 11.3% in the control arm (adjusted odds ratio [aOR] 2.84, 95% CI 1.55–5.18; p < 0.001). The intervention also improved 7-day PPA at 3 months (33.1% vs. 15.6%; aOR 2.68, 95% CI 1.56–4.60; p < 0.001), 7-day PPA at 6 months (30.6% vs. 14.4%; aOR 2.62, 95% CI 1.48–4.62; p = 0.001), and 30-day PPA at 6 months (28.1% vs. 11.9%; aOR 2.89, 95% CI 1.59–5.24; p < 0.001). Among baseline dual users, both-product abstinence was higher in the intervention arm (21.9% vs. 7.8%; aOR 3.30, 95% CI 1.12–9.75; p = 0.026). Pharmacotherapy initiation was more frequent in the intervention arm (72.5% vs. 28.1%; p < 0.001), as was self-reported adherence for at least 8 weeks among initiators (56.0% vs. 26.7%; p = 0.002). In the intervention arm, active patient response rates to scheduled WhatsApp^® messages remained substantial, with 88.1% responding at Week 1, 73.8% at Week 4, 67.5% at Month 3, and 61.3% at Month 6; 145 red-flag triggers were captured from 62 participants, and 84.1% of escalations resulted in successful pharmacist follow-up within 48 h. The Dual-Quit Digital model significantly improved smoking-cessation outcomes compared with usual care and proved operationally feasible. These findings support integrating phenotype-stratified pharmacist counselling, pharmacotherapy support, and low-burden digital follow-up as a pragmatic cessation model for Jordan and similar settings. Full article

Reliable detection of hydrogen leakage is essential for the safe operation of hydrogen-related facilities. In this work, we propose a compact fiber Bragg grating (FBG) hydrogen sensor that exhibits high sensitivity. The sensor is based on an FBG encapsulated in a capillary, deposited with a hybrid coating of Pt/WO₃ nanosheets and Nafion, which can effectively prevent the detachment of sensitive materials and facilitate mass production. The optimized sensor exhibits a wavelength shift of 1383 pm and a response time of 16 s towards 1% H₂ in air at room temperature, outperforming other FBG hydrogen sensors. In addition, the sensor displays nearly linear response and good repeatability during the hydrogen exposure process. Furthermore, the response of the sensor to hydrogen is much higher than that of other reducing gases. Nevertheless, more than 80% of the sensitivity of this sensor can be maintained even in 85% humidity atmosphere. This work presents an effective strategy to improve the performance of FBG hydrogen sensors, which can promote their potential application for hydrogen detection. Full article