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A Flow Structure Interaction Method for Towed Cable System
Full text linkAbstract: The ocean towed cable system is a classic example of fluid-structure interaction (FSI). This interaction can exhibit stability or oscillation between a highly deformable moving cable and the surrounding turbulent flow. However, in dynamic simulations of towed cable systems, a constant drag coefficient for an infinite circular cylinder is often used based on experimental data. An innovative fluid-structure interaction method is introduced to obtain accurate drag distribution along cable to couple with towed system dynamics. A modified nodal position finite element method (NPFEM) coupled with Reynolds-averaged Navier-Stokes (RANS) approach has been utilized to predict hydrodynamic forces along the cable. A data exchange algorithm has been developed specifically for fluid-structure interaction within the towed cable system where the cable profile is transferred to construct the flow domain while hydrodynamics is interpolated for NPFEM analysis. A topology partition around cable is applied. A multiblock grid is generated around cable. The simulation results of the fluid-structure interaction of the towing system are verified. This FSI scheme reveals how strongly hydrodynamics determine cable dynamics and induce vortex structure vibrations around a towed cable system. Parametrically controlled structured grid generation and their applicability for complex flow fields have also been discussed. Detailed descriptions of boundary layer separation evolution around spatially distributed cable are provided. This FSI scheme reveals a real strongly hydrodynamic determined cable dynamics and vortex structure induced vibrations around a towed cable system. The proposed method enhances predictive accuracy of the towed system dynamics response
Recent Initiatives on Fossil Fuel Transition towards Renewable Energy for Combating Climate Change and a Net-Zero Energy Future
Full text linkThis study presents the recent trends in the transition from fossil fuels towards renewable energy for combating climate change and achieving a net-zero energy target by 2030 as per United Nations Sustainable Development Goal-7 (Energy for All). However, the Net Zero target is difficult to achieve unless effective energy conservation and energy efficiency policies, regulations, and financial investment, are not initiated along with the major energy transition to renewable energy. Therefore, the study\u27s objective is to present the current status of initiatives by different countries including India to address this problem as per the recommendations of various Conference of Parties including COP-29. The case study of India shows that enhanced energy efficiency, energy conservation, effective solar energy policies, and regulations for high energy-consuming sectors like industry, agriculture, buildings, domestic and awareness among society are important for achieving realistic targets. The Chhattisgarh State study identifies the high energy-consuming sectors, leading to a 2.7 million kWh reduction in energy consumption in the past two decades through various initiatives. These measures are leading India towards an efficient Net-Zero energy transition in a realistic way. The study results are of importance for follow-up action in developing and least-developed countries worldwide
Reflections on the Potential of Applying Artificial Intelligence (AI) and Machine Learning (ML) for Screening Topical Hemostatic Agents Based on Inorganic Solids for Hemorrhage Control
Full text linkDespite significant advances in medical interventions, fatal traumatic hemorrhage remains a leading cause of death worldwide. This persistent challenge has driven extensive research and development efforts aimed at creating more effective hemostatic agents to control bleeding. While most existing hemostatic agents are organic in nature, recent studies have highlighted the promising potential of mineral and synthetic inorganic materials for hemorrhage control. These materials demonstrate remarkable properties, such as rapid water adsorption from blood via their porous structures, which leads to the local concentration of proteins and cellular elements crucial for clot formation. Additionally, their negatively charged surfaces create a favorable environment for the activation of the intrinsic coagulation cascade. Although a variety of minerals and synthetic inorganic materials are currently employed as topical hemostatic agents, a vast array of emerging classes of inorganic materials remains underexplored. Many of these materials possess untapped hemostatic potential, but their properties and mechanisms for controlling bleeding are poorly understood. Moreover, synthesizing these materials with the precise characteristics required for effective hemostasis presents significant challenges. Recent advances in artificial intelligence (AI) offer a promising avenue to address these hurdles. By leveraging the growing availability of large datasets and sophisticated algorithms, AI can identify complex relationships within multidimensional systems, such as the synthesis of advanced inorganic materials. This capability is particularly critical for materials lacking well-characterized mechanisms or those with implications for hemostasis disorders, such as severe bleeding or thrombosis. AI-driven approaches could enable the design of innovative topical hemostatic agents capable of rapidly diagnosing and efficiently intervening in life-threatening situations, revolutionizing the field of hemorrhage control
Consumer Feedback Sentiment Classification Improved Via Modified Metaheuristic Optimization Natural Language Processing
Full text linkThis study investigates the synergy between the virtual and real-world economies through e-commerce, where seller reputation is critical in guiding consumer decisions. As traditional businesses shift towards online retail, user reviews become essential, offering feedback to both sellers and potential buyers. Sentiment analysis through machine learning (ML) techniques presents significant advantages for consumers and retailers alike. This research proposes a novel approach combining bidirectional encoder representations from transformers (BERT) embeddings with an optimized XGBoost classification model to enhance sentiment analysis performance. A modified metaheuristic algorithm, derived from the firefly algorithm (FA), is introduced to optimize the model. Testing on publicly available datasets demonstrates that models optimized by this algorithm achieved a peak accuracy of .881336. Further statistical analyses substantiate these improvements, and SHAP interpretation on the best-performing model identifies key features impacting model predictions, shedding light on factors driving customer sentiment insights
Research on Few-Shot Defect Detection Algorithm Based on Federated Learning
Full text linkThe algorithm based on deep learning has been widely used in defect detection in all walks of life, but the performance of the deep learning model depends mainly on rich annotation data. However, in the actual scene, obtaining large-scale, high-quality data to ensure users\u27 privacy and safety is challenging, which limits its further promotion in specific application fields. To solve this problem, we propose a federated few-shot defect detection framework, which uses the privacy protection of the federated framework to jointly train independent few-shot tasks distributed on different clients to obtain a few-shot model that can quickly adapt to new tasks with limited data. We have done many experiments to evaluate our framework\u27s effectiveness, and the results show that our framework is superior to the baseline and achieves the same performance as the model trained with a lot of data
Modeling and Control Experiments of a Fishtail-Like Pneumatic Soft Actuator
Full text linkAs the exploration of deep-sea resources continues, underwater actuators with conventional motors as the main building blocks can no longer meet the increasingly demanding needs. Inspired by bionics, researchers have started to work on underwater actuators with bionic structures. In this study, we designed and implemented a novel Fishtail-like Pneumatic Soft Actuator (FPSA). This innovative actuator configuration is inspired by the tail structure of Body and/or Caudal Fin (BCF) mode fish. The actuator\u27s motion is achieved by controlling the expansion and contraction of the pneumatic soft muscles on both sides. And by constructing an experimental platform, we conducted an in-depth performance characterization, revealing the existence of a frequency-dependent nonlinear hysteresis characteristic of the FPSA. In order to accurately characterize this property, we built a dynamic model of the FPSA and successfully identified the uncertain parameters in the model by applying the nonlinear least squares method. The validation results show that the constructed model can accurately describe the nonlinear hysteresis characteristics of the FPSA. Finally, we successfully realized the high-precision trajectory tracking control of the endpoint of the FPSA using a PID controller. This result provides relevant ideas for the research of novel underwater bionic actuators
Fungal Foams from Teak Leaves: Effect of Cold Shock and Species Variation on Growth and Mechanical Strength
Full text linkFungal foams represent sustainable alternatives to synthetic materials, yet optimization of growth and mechanical properties remains challenging. This study evaluated the effects of cold shock treatments at 0 °C for different interval days (3, 5, 7 days) on Pleurotus species including P. djamor, P. florida, and P. sajor-caju cultivated on teak leaves. Mycelial growth rates, mechanical properties (hardness, springiness, resilience), and scanning electron microscopy (SEM) were systematically evaluated. Results demonstrated that 3-day cold shock treatment consistently maximized growth, with P. florida (W3) achieving 1.41 cm/day. Mechanical testing revealed superior performance in 3-day treated samples: P. florida (W3) recorded peak hardness at 7904 g.sec, while springiness and resilience value reached 0.681 and 0.399 respectively for P. sajor-caju (G3) surpassing controls samples. SEM confirmed denser, thicker and intertwined hyphal networks in cold-treated samples, correlating with mechanical properties. These findings establish 3-day cold shock as an effective, non-chemical strategy to enhance fungal foam quality from agricultural residues.
A Systematic Literature Review of the Pecking Order Theory on Financing Decisions
Full text linkThis study is a Systematic Literature Review (SLR) that aims to evaluate the application of Pecking Order Theory (POT) in corporate financing decisions published between 2020 and 2025. The article selection process was conducted using the PRISMA 2020 protocol, with relevant articles selected through a search in the Scopus database, based on inclusion criteria that included: time range (2020–2025), field of study (Business Management and Accounting), document type (article), keywords (Pecking Order Theory), language (English), and open access status. Of the total 1027 articles found, 31 articles met the inclusion criteria and were used as research objects. The results showed that POT theory remains relevant in explaining corporate financing behavior, especially in developing countries, although some findings indicate a combination with other theories, such as Trade-Off Theory (TOT). Empirical findings reveal that factors such as profitability, liquidity, company growth, and tangible assets have a significant influence on the order of corporate funding preferences, which begins with the utilization of internal funds, followed by debt, and finally the issuance of new shares. This study also reveals that although POT is widely applicable, its application shows variability influenced by factors such as economic context, regulations, and company characteristics
Artificial Intelligence and FinTech Applications for Reducing Payment Disputes in the Construction Industry: A Review of Secondary Evidence
Full text linkPayment delays and disputes are persistent challenges in the construction industry, leading to financial strain, project interruptions, and weakened stakeholder relationships. This paper reviews secondary evidence published between 2021 and 2025 to examine how financial technologies (FinTech) and artificial intelligence (AI) can reduce payment-related disputes. The review synthesizes findings on blockchain-based smart contracts, BIM-integrated payment systems, digital payment platforms, and supply-chain financing tools, highlighting their potential to enhance transparency, automate workflows, and accelerate payment cycles. AI applications, including invoice analysis, claim verification, contract compliance monitoring, and risk prediction, further strengthen the accuracy and efficiency of financial processes. Despite these benefits, adoption remains limited due to technical complexity, dependence on high-quality data, implementation costs, and stakeholder scepticism. The study identifies strategies to address these barriers and highlights the complementary role of AI and FinTech in transforming construction payment management. The findings offer practical insights for researchers, practitioners, and policymakers seeking to leverage digital and AI-enabled solutions to reduce disputes, improve project outcomes, and promote a more transparent and accountable construction industry
Enhancing IRS Localization via Deep Learning-Based AOA and Distance Estimation
Full text linkIn Intelligent Reflecting Surface (IRS)-assisted communication systems, accurate user localization, particularly Angle-Of-Arrival (AoA) and range estimation are challenging due to the computational complexity and limited resolution of traditional Multiple Signal Classification (MUSIC) algorithms. This paper introduces a hybrid IRS framework that combines machine learning with a modified MUSIC algorithm to achieve high-precision localization and enhanced security. The system integrates two Convolutional Neural Networks (CNNs): RefineNet, which refines AoA and range estimates from MUSIC pseudo-spectra, and ElementNet, which optimizes the number and placement of active IRS elements to balance accuracy with resource efficiency. Notably, ElementNet shows that only eight active elements are sufficient to obtain 90% of the best achievable localization accuracy, highlighting the efficiency of the proposed design. Validation on the DeepMIMO dataset demonstrates superior range accuracy and AoA precision. This work sheds light on the secure and high-precision localization for diverse wireless applications.