Search Results (161)

Volatile organic compounds (VOCs) are highly volatile chemicals in natural and anthropogenic environments, significantly affecting indoor air quality. Major sources of indoor VOCs include emissions from building materials, furnishings, and consumer products. Natural wood products release VOCs, including terpenes and aldehydes, which exert diverse health effects ranging from mild respiratory irritation to severe outcomes, such as formaldehyde-induced carcinogenicity. The temporal dynamics of VOC emissions were investigated, and the toxicological and physiological effects of the VOCs emitted by two types of natural wood, Korean Red Pine (Pinus densiflora) and Japanese Cypress (Chamaecyparis obtusa), were evaluated. Using female C57BL/6 mice as an animal model, the exposure setups included phytoncides, formaldehyde, and intact wood samples over short- and long-term durations. The exposure effects were assessed using oxidative stress markers, antioxidant enzyme activity, hepatic and renal biomarkers, and inflammatory cytokine profiles. Long-term exposure to Korean Red Pine and Japanese Cypress wood VOCs did not induce significant pathological changes. Japanese Cypress exhibited more distinct benefits, including enhanced oxidative stress mitigation, reduced systemic toxicity, and lower pro-inflammatory cytokine levels compared to the negative control group, attributable to its more favorable VOC emission profile. These findings highlight the potential health and environmental benefits of natural wood VOCs and offer valuable insights for optimizing timber use, improving indoor air quality, and informing public health policies. Full article

When emergency incidents, such as bridge damage, abruptly occur on highways and lead to traffic disruptions, the multidimensionality and complexity of driver behaviors present significant challenges to the design of effective emergency response mechanisms. This study introduces a multi-level collaborative emergency mechanism grounded in driver behavior characteristics, aiming to enhance both traffic safety and emergency response efficiency through hierarchical collaboration and dynamic optimization strategies. By capitalizing on human drivers’ perception and decision-making attributes, a driver behavior classification model is developed to quantitatively assess the risk response capabilities of distinct behavioral patterns (conservative, risk-taking, and conformist) under emergency scenarios. A multi-tiered collaborative framework, comprising an early warning layer, a guidance layer, and an interception layer, is devised to implement tailored emergency strategies. Additionally, a rear-end collision risk propagation model is constructed by integrating the risk field model with probabilistic risk assessment, enabling dynamic adjustments to interception range thresholds for precise and real-time emergency management. The efficacy of this mechanism is substantiated through empirical case studies, which underscore its capacity to substantially reduce the occurrence of secondary accidents and furnish scientific evidence and technical underpinnings for emergency management pertaining to highway bridge damage. Full article

Background: Conventional practices for the cultivation of “Cipollotto Nocerino” spring onion are mainly based on growers’ experience, and up to 250 kg/ha for N is commonly furnished among growing cycles. Facing the issue of reduced availability of natural resources for crop production (for example mineral resources), we investigated the optimization of the productivity. Methods: In our research, we tested the use of depletion nutrient supply strategy (CAL-FERT^®) to enhance fertilization in accordance with the principle of sustainable agriculture included in the Farm to Fork strategy. In our study, besides the common initial fertilization, three different strategies for cover fertilizations have been elaborated with the support of CAL-FERT^® software. The treatments were as follows: (i) commercial standard fertilization as control (named CF); (ii) fertilization equivalent to 50% of the N applied in the control (named F-50); (iii) fertilization corresponding to 25% of the N applied in the control (named F-25); and (iv) strongly reduced fertilization compared to the control (named F-0). The parameters investigated included the following: plant height, yield, SPAD index, nitrogen use efficiency, dry matter, soluble solid content, and pyruvate contents in bulbs and leaves. Nitrogen content was also analyzed for both hypogeous and epigeous apparatuses. Results: Among the most interesting vegetative results, plant height and SPAD readings were reduced only by the extreme treatment F-0 compared with the other treatments at 104 days after planting. Regarding qualitative and productive parameters, the treatments F-50 and F-25 showed the highest yield without prejudging Soluble Solid Content and reducing pungency. Conclusion: In nutritional experiments, onion could be considered as a crop model to investigate quality in vegetables due to its consumption as fresh product and for its particular response, in terms of yield and quality, to fertilization. The use of simulation software can support the identification of strategies to reduce the nutrient supply without any detrimental effect on yield and other vegetative and qualitative parameters in onion crops. Full article

Internal leakage within the valve body constitutes a severe potential safety hazard in industrial fluid control systems, attributable to its high concealment and the resultant difficulty in detection via conventional methodologies. Acoustic emission (AE) technology, functioning as an efficient non-destructive testing approach, is capable of capturing the transient stress waves induced by leakage, thereby furnishing an effective means for the real-time monitoring and quantitative assessment of internal leakage within the valve body. This paper conducts a systematic review of the theoretical foundations, signal-processing methodologies, and the latest research advancements related to the technology for detecting internal leakage in the valve body based on acoustic emission. Firstly, grounded in Lechlier’s acoustic analogy theory, the generation mechanism of acoustic emission signals arising from valve body leakage is elucidated. Secondly, a detailed analysis is conducted on diverse signal processing techniques and their corresponding optimization strategies, encompassing parameter analysis, time–frequency analysis, nonlinear dynamics methods, and intelligent algorithms. Moreover, this paper recapitulates the current challenges encountered by this technology and delineates future research orientations, such as the fusion of multi-modal sensors, the deployment of lightweight deep learning models, and integration with the Internet of Things. This study provides a systematic reference for the engineering application and theoretical development of the acoustic emission-based technology for detecting internal leakage in valves. Full article

In the contemporary big data era, data-driven prognostic and health management (PHM) methodologies have emerged as indispensable tools for ensuring the secure and reliable operation of complex equipment systems. Central to these methodologies is the accurate prediction of remaining useful life (RUL), which serves as a pivotal cornerstone for effective maintenance and operational decision-making. While significant advancements in computer hardware and artificial intelligence (AI) algorithms have catalyzed substantial progress in AI-based RUL prediction, extant research frequently exhibits a narrow focus on specific algorithms, neglecting a comprehensive and comparative analysis of AI techniques across diverse equipment types and operational scenarios. This study endeavors to bridge this gap through the following contributions: (1) A rigorous analysis and systematic categorization of application scenarios for equipment RUL prediction, elucidating their distinct characteristics and requirements. (2) A comprehensive summary and comparative evaluation of several AI algorithms deemed suitable for RUL prediction, delineating their respective strengths and limitations. (3) An in-depth comparative analysis of the applicability of AI algorithms across varying application contexts, informed by a nuanced understanding of different application scenarios and AI algorithm research. (4) An insightful discussion on the current challenges confronting AI-based RUL prediction technology, coupled with a forward-looking examination of its future prospects. By furnishing a meticulous and holistic understanding of the traits of various AI algorithms and their contextual applicability, this study aspires to facilitate the attainment of optimal application outcomes in the realm of equipment RUL prediction. Full article

The sintering of coal gangue ceramsites (CGCs) using belt roasting technology involves the recirculation of flue gases and variations in oxygen concentrations. This study investigates the effects of temperature and pyrolysis atmosphere on the pore structure of CGCs at three temperature levels: 600 °C, 950 °C, and 1160 °C. The results revealed that apparent porosity is primarily influenced by O₂-promoted weight loss and the densification process, while closed porosity is affected by pyrolysis reactions and crystal phase transformations. Below 950 °C, enhancing the oxidative atmosphere facilitates the preparation of porous CGCs, whereas above 950 °C, reducing the oxidative atmosphere favors the preparation of high-strength CGCs. These findings provide valuable insights for the industrial production of CGCs, offering a basis for optimizing sintering parameters to achieve the desired material properties. The latest production equipment, furnished with adjustable atmospheres (such as belt sintering roasters), can better regulate the mechanical properties of the products. Full article

The horizontal tube falling film heat exchangers (HTFFHEs), which exhibit remarkable advantages such as high efficiency in heat and mass transfer, low resistance, and a relatively simple structural configuration, have found extensive applications. Complex flow phenomena and the coupled processes of heat and mass transfer take place within it. Given that the heat and mass transfer predominantly occur at the gas-liquid interface, the flow characteristics therein emerge as a significant factor governing the performance of heat and mass transfer. This article elaborates on the progress of experimental and simulation research approaches with respect to flow characteristics. It systematically reviews the influence patterns of various operating parameters, namely parameters of gas, solution and internal medium, as well as structural parameters like tube diameter and tube spacing, on the flow characteristics, such as the flow regime between tubes, liquid film thickness, and wettability. This review serves to furnish theoretical underpinnings for optimizing the heat and mass transfer performance of the horizontal tube falling film heat exchanger. It is further indicated that the multi-dimensional flow characteristics and their quantitative characterizations under the impacts of different airflow features will constitute the focal research directions for horizontal tube falling film heat exchangers in the foreseeable future. Full article

This research examines the effects of vertical jacking construction on the mechanical behavior of shield tunnels. Model tests simulating vertical jacking were performed utilizing a purpose-built apparatus to quantify the reaction forces generated by the diffusion block during the jacking operation. A systematic analysis was conducted on the mechanical responses of shield tunnel lining segments and their interconnecting joints. Utilizing Particle Flow Code (PFC) methodology, a deformation prediction model specifically tailored for vertical jacking conditions was formulated. Correlating simulation results with experimental measurements quantified the sensitivity of tunnel deformation to grouting reinforcement, enabling the identification of an optimal reinforcement zone. Key findings reveal that the jacking reaction force distribution exhibits pronounced nonlinearity: a substantial increase precedes failure, followed by rapid post-failure reduction and eventual stabilization in advanced jacking stages. Tunnel convergence deformation evolves through four distinct phases: significant growth, rapid attenuation, gradual diminution, and final stabilization. The primary zone of influence encompasses the opening ring and its two adjacent rings. Jacking induces longitudinal bending deformation, with maximum joint opening occurring at the opening ring. Abrupt longitudinal load fluctuations cause dislocation between the opening ring and neighboring rings. Internal segment stresses exhibit initial tensile and compressive increases followed by subsequent relaxation. Externally applied grouting reinforcement effectively attenuates jacking-induced tunnel deformation. An optimal reinforcement range was determined at the 60° position relative to the segment springline, substantially lowering resource consumption and construction risks compared to conventional reinforcement strategies. These outcomes furnish theoretical underpinnings and technical benchmarks for optimizing engineering design and facilitating the implementation of vertical jacking technology. Full article

This article investigates a class of interval-valued multiobjective optimization problems (IVMOPs). We define the Hestenes–Stiefel (HS)-type direction for the objective function of IVMOPs and establish that it has a descent property at noncritical points. An Armijo-like line search is employed to determine an appropriate step size. We present an HS-type conjugate direction algorithm for IVMOPs and establish the convergence of the sequence generated by the algorithm. We deduce that the proposed algorithm exhibits a linear order of convergence under appropriate assumptions. Moreover, we investigate the worst-case complexity of the sequence generated by the proposed algorithm. Furthermore, we furnish several numerical examples, including a large-scale IVMOP, to demonstrate the effectiveness of our proposed algorithm and solve them by employing MATLAB. To the best of our knowledge, the HS-type conjugate direction method has not yet been explored for the class of IVMOPs. Full article

This paper discusses the energy-saving control method of a pipeline network system based on reinforcement learning and a genetic algorithm and compares it with traditional control methods such as constant-pressure control and non-frequency conversion control. The purpose is to improve the operational efficiency of an offshore oil and gas field water injection system. This paper simulates and verifies the experimental platform of a water injection system pipe network in offshore oil and gas fields and evaluates the optimization effect of different control strategies under different flow rates. The experimental results reveal that under a varying flow rate, the water injection system harnessing the GA and RL exhibits a remarkable energy-saving advantage over traditional control methods. Specifically, the GA strategy achieves an average energy-saving rate of 22.51%, with a maximum energy-saving rate of 38.14% under low flow rate, while the RL strategy attains an average energy-saving rate of 18.39%. These methodologies not only furnish novel solutions for the real-time optimal scheduling of water injection systems in offshore oil and gas fields but also proffer practical guidance, thereby paving the way for technological advancement and sustainable development in the industry. Full article

The purpose of this study was to evaluate the effects of different nitrogen (N), phosphorus (P), and potassium (K) fertilization ratios on the carbon (C), N, and P contents and their ecological stoichiometric characteristics in the leaf–soil–microbial system of Sapindus saponaria and elucidate their relationship with yield. A “3414” experimental design was employed in a 6-year-old Sapindus saponaria woodland located in Fujian Province of China. Fourteen N–P–K fertilization treatments with three replicates were established. Leaf, soil, and microbial samples were collected and analyzed for C, N, and P contents. Redundancy Analysis (RDA), Partial Least Squares Path Modeling (PLS–PM), and the entropy-weighted technique of ranking preferences by similarity to optimal solutions (TOPSIS) were utilized to assess the relationships among variables and determine optimal fertilization strategies. It was found through research that different fertilization treatment methods have a significant impact on both the soil nutrient content and the C, N, and P contents of soil microorganisms. Compared with the control group, soil organic C, total N, and total P, and microbial C, N, and P contents increased by 14.25% to 52.61%, 3.90% to 39.84%, 9.52% to 150%, 6.65% to 47.45%, 11.84% to 46.50%, and 14.91% to 201.98%, respectively. Results from Redundancy Analysis (RDA) indicated that soil organic C, total N, and total P exerted a significant influence on the leaf nutrients. PLS-PM demonstrated that fertilization indirectly affected leaf nutrient accumulation and yield by altering soil properties, with soil total phosphorus and leaf phosphorus being key determinants of yield. Additionally, soil microbial entropy impacted yield by regulating microbial biomass stoichiometric ratios. The entropy-weighted TOPSIS model identified the N₂P₂K₂ treatment (600 kg/ha N, 500 kg/ha P, and 400 kg/ha K) as the most effective fertilization strategy. Optimizing N–P–K fertilization ratios significantly enhances leaf nutrient content and soil microbial biomass C, N, and P, thereby increasing Sapindus saponaria yield. This research clarifies the underlying mechanisms through which fertilization exerts an impact on the C–N–P stoichiometry within the leaf–soil–microbial system. Moreover, it furnishes a scientific foundation for the optimization of fertilization management strategies in Sapindus saponaria plantations. Full article

Colorectal cancer (CRC) remains a principal contributor to oncological mortality worldwide, with chronic inflammation serving as a fundamental driver of its pathogenesis. Protease-activated receptor-2 (PAR-2), a G-protein-coupled receptor, orchestrates inflammation-driven tumorigenesis by potentiating NF-κB and Wnt/β-catenin signaling, thereby fostering epithelial–mesenchymal transition (EMT), immune evasion, and therapeutic resistance. Despite its pathological significance, targeted modulation of PAR-2 remains an underexplored avenue in CRC therapeutics. Oleocanthal (OC), a phenolic constituent of extra virgin olive oil, is recognized for its potent anti-inflammatory and anti-cancer properties; however, its regulatory influence on PAR-2 signaling in CRC is yet to be elucidated. This study interrogates the impact of OC on PAR-2-mediated inflammatory cascades using HT-29 and Caco-2 CRC cell lines subjected to lipopolysaccharide (LPS)-induced activation of PAR-2. Expression levels of PAR-2 and TNF-α were quantified through Western blotting and RT-PCR, while ELISA assessed TNF-α secretion. Intracellular calcium flux, a pivotal modulator of PAR-2-driven oncogenic inflammation, was evaluated via Fluo-4 calcium assays. LPS markedly elevated PAR-2 expression at both mRNA and protein levels in CRC cells (p < 0.01, one-way ANOVA). OC administration (20–150 μg/mL) elicited a dose-dependent suppression of PAR-2, with maximal inhibition at 100–150 μg/mL (p < 0.001, Tukey’s post hoc test). Concomitant reductions in TNF-α transcription (p < 0.01) and secretion (p < 0.001) were observed, corroborating the anti-inflammatory efficacy of OC. Additionally, OC ameliorated LPS-induced calcium dysregulation, restoring intracellular calcium homeostasis in a concentration-dependent manner (p < 0.01). Crucially, OC exhibited selectivity for PAR-2, leaving PAR-1 expression unaltered (p > 0.05), underscoring its precision as a therapeutic agent. These findings position OC as a selective modulator of PAR-2-driven inflammation in CRC, disrupting the pro-tumorigenic microenvironment through attenuation of TNF-α secretion, calcium dysregulation, and oncogenic signaling pathways. This study furnishes mechanistic insights into OC’s potential as a nutraceutical intervention in inflammation-associated CRC. Given the variability in OC bioavailability and content in commercial olive oil, future investigations should delineate optimal dosing strategies and in vivo efficacy to advance its translational potential in CRC therapy. Full article

Given an untrimmed video and a natural language query, the video temporal grounding task aims to accurately locate the target segment within the video. Functioning as a critical conduit between computer vision and natural language processing, this task holds profound importance in advancing video comprehension. Current research predominantly centers on enhancing the performance of individual models, thereby overlooking the extensive possibilities afforded by multi-model synergy. While knowledge flow methods have been adopted for multi-model and cross-modal collaborative learning, several critical concerns persist, including the unidirectional transfer of knowledge, low-quality pseudo-label generation, and gradient conflicts inherent in cooperative training. To address these issues, this research proposes a Multi-Model Collaborative Learning (MMCL) framework. By incorporating a bidirectional knowledge transfer paradigm, the MMCL framework empowers models to engage in collaborative learning through the interchange of pseudo-labels. Concurrently, the mechanism for generating pseudo-labels is optimized using the CLIP model’s prior knowledge, bolstering both the accuracy and coherence of these labels while efficiently discarding extraneous temporal fragments. The framework also integrates an iterative training algorithm for multi-model collaboration, mitigating gradient conflicts through alternate optimization and achieving a dynamic balance between collaborative and independent learning. Empirical evaluations across multiple benchmark datasets indicate that the MMCL framework markedly elevates the performance of video temporal grounding models, exceeding existing state-of-the-art approaches in terms of mIoU and Rank@1. Concurrently, the framework accommodates both homogeneous and heterogeneous model configurations, demonstrating its broad versatility and adaptability. This investigation furnishes an effective avenue for multi-model collaborative learning in video temporal grounding, bolstering efficient knowledge dissemination and charting novel pathways in the domain of video comprehension. Full article

Self-evaluation empowers students to progress independently and adapt their pace according to their unique circumstances. A critical facet of self-assessment and personalized learning lies in furnishing learners with formative feedback. This feedback, dispensed following their responses to self-assessment questions, constitutes a pivotal component of formative assessment systems. We hypothesize that it is possible to generate explanations that are useful as formative feedback using different techniques depending on the type of self-assessment question under consideration. This study focuses on a subject taught in a computer science program at a Spanish distance learning university. Specifically, it delves into advanced data structures and algorithmic frameworks, which serve as overarching principles for addressing complex problems. The generation of these explanatory resources hinges on the specific nature of the question at hand, whether theoretical, practical, related to computational cost, or focused on selecting optimal algorithmic approaches. Our work encompasses a thorough analysis of each question type, coupled with tailored solutions for each scenario. To automate this process as much as possible, we leverage natural language processing techniques, incorporating advanced methods of semantic similarity. The results of the assessment of the feedback generated for a subset of theoretical questions validate the effectiveness of the proposed methods, allowing us to seamlessly integrate this feedback into the self-assessment system. According to a survey, students found the resulting tool highly useful. Full article

Energy harvesting wireless sensor networks (EH-WSNs) appear as the fundamental backbone of research that attempts to expand the lifespan and efficiency of sensor networks positioned in resource-constrained environments. This review paper provides an in-depth examination of latest developments in this area, highlighting the important components comprising routing protocols, energy management plans, cognitive radio applications, physical layer security (PLS), and EH approaches. Across a well-ordered investigation of these features, this article clarifies the notable developments in technology, highlights recent barriers, and inquires avenues for future revolution. This article starts by furnishing a detailed analysis of different energy harvesting methodologies, incorporating solar, thermal, kinetic, and radio frequency (RF) energy, and their respective efficacy in non-identical operational circumstances. It also inspects state-of-the-art energy management techniques aimed at optimizing energy consumption and storage to guarantee network operability. Moreover, the integration of cognitive radio into EH-WSNs is acutely assessed, highlighting its capacity to improve spectrum efficiency and tackle associated technological problems. The present work investigates ground-breaking methodologies in PLS that uses energy-harvesting measures to improve the data security. In this review article, these techniques are explored with respect to classical encryption and discussed from network security points of view as well.The assessment furthers criticizes traditional routing protocols and their significance in EH-WSNs as well as the balance that has long been sought between energy efficiency and security in this space. This paper closes with the importance of continuous research to tackle existing challenges and to leverage newly available means as highlighted in this document. In order to adequately serve the increasingly changing requirements of EH-WSNs, future research will and should be geared towards incorporating AI techniques with some advanced energy storage solutions. This paper discusses the integration of novel methodologies and interdisciplinary advancements for better performance, security, and sustainability for WSNs. Full article