Search Results (64,948)

Lightweight design of the skeletal structure is a critical challenge in the development of Blended-Wing-Body Underwater Gliders (BWBUGs). However, existing studies often rely on empirically derived configurations for parameter optimization, which limits the potential to fully exploit structural performance. To address this issue, this paper proposes a design approach for BWBUG skeletal structures that integrates topology optimization with data-driven optimization, termed the TD-Method. Specifically, the TD-Method first applies topology optimization to identify load transfer paths within the BWBUG structure, thereby generating an initial configuration and a parameterized model for subsequent optimization. On this basis, data-driven optimization is employed to extensively explore the design space, enabling lightweight structural design under specified constraints. Finally, a comparative analysis with existing methods demonstrates that the TD-Method achieves superior skeletal structures with enhanced performance, confirming both the effectiveness and advantages. Full article

Background/Objectives: Antimicrobial resistance and bacterial persistence underscore the need to develop new chemotypes with multifunctional antibacterial mechanisms. This study aimed to design, synthesize, and evaluate curcumin-inspired 3,5-diarylidene-4-piperidones as versatile small molecules exhibiting antibacterial, antibiofilm, anti-efflux, DNA gyrase-inhibitory, and antiproliferative properties. Methods: A targeted series of triazole-conjugated 3,5-diarylidene-4-piperidones was synthesized through copper-catalyzed azide-alkyne cycloaddition click chemistry and subsequently characterized using standard spectroscopic techniques. The compounds were assessed for antibacterial activity against Staphylococcus aureus, Enterococcus faecalis, and Escherichia coli. Selected active compounds underwent further evaluation for DNA gyrase inhibition, antibiofilm activity against multidrug-resistant S. aureus ATCC 33591, ethidium bromide accumulation, and antiproliferative effects on HCT116 and MCF7 cancer cells, with RPE1 cells serving as a control to evaluate cytotoxicity in normal cells. Additionally, computational studies, including QSAR analysis and molecular docking, were conducted to bolster structure–activity relationships and provide mechanistic insights. Results: Several derivatives demonstrated selective antibacterial activity against Gram-positive bacteria, particularly S. aureus, while exhibiting limited or no efficacy against E. coli. Compounds 7n and 7l emerged as the most potent against S. aureus, with minimum inhibitory concentrations (MICs) of 7.8 and 8.2 μM, respectively. Notably, compound 7l inhibited S. aureus DNA gyrase supercoiling, displaying an IC₅₀ of 3.20 μM, comparable to ciprofloxacin. Compound 7e exhibited the strongest antibiofilm activity against multidrug-resistant S. aureus, whereas compound 7a resulted in the highest accumulation of ethidium bromide, indicating robust anti-efflux activity. Antiproliferative assays revealed that select halogenated derivatives were effective against HCT116 and MCF7 cells, while the most promising antibacterial compounds exhibited minimal cytotoxicity toward RPE1 cells. Quantitative structure–activity relationship (QSAR) and docking studies supported the observed structure–activity relationships and suggested potential interactions with the ATPase binding site of DNA gyrase B. Conclusions: Triazole-conjugated 3,5-diarylidene-4-piperidones are promising multifunctional scaffolds with selective anti-S. aureus activity, antibiofilm and anti-efflux properties, and, for compound 7l, potent DNA gyrase inhibition. These findings support further optimization of this chemotype as a platform for developing antibacterial agents with polymechanistic activity. Full article

Driven by the growing demand for functional textiles featuring excellent waterproofness, moisture permeability and mechanical robustness in outdoor sportswear, medical protection and technical apparel, traditional pongee—despite its desirable softness, high wrinkle resistance and good stability as an ideal substrate fabric—is severely restricted in further application by its intrinsically poor hydrostatic pressure resistance in extremely wet environments. Accordingly, we developed a modified waterborne polyurethane (WPU) coating for pongee substrates to fabricate functional textiles that maintain high hydrostatic pressure resistance while possessing good mechanical properties and increased UV absorption. In this study, by using the sol–gel method, an amorphous silicon dioxide (SiO₂) coating layer was constructed on the surface of titanium dioxide (TiO₂) particles, forming silica-modified titania particles (SiO₂/TiO₂). These SiO₂-modified particles were subsequently physically blended with an anionic waterborne polyurethane system that had been previously modified with a polyester-type modifier A to enhance its hydrostatic pressure resistance. The resulting composite coating was designed to combine the high hydrostatic pressure resistance inherited from the modified WPU matrix, the mechanical reinforcement and increased UV absorption contributed by SiO₂/TiO₂, and satisfactory water repellency on fabric substrates. The results indicate that the incorporation of an appropriate amount of modifier A into the prepolymer system significantly enhances hydrostatic pressure resistance while maintaining high elongation at break. At a SiO₂/TiO₂ loading of 0.2 wt%, the composite film exhibits optimal comprehensive performance, characterized by superior mechanical properties, low water absorption, and static water contact angles exceeding 100° for coated fabrics. SiO₂/TiO₂ composite WPU coatings substantially improve hydrostatic pressure resistance across various fabrics, with 380T polyester taffeta demonstrating the best performance. This resistance remains remarkably stable after standard washing, indicating excellent wash fastness and practical applicability. Full article

The housing sector in major cities is facing escalating challenges due to rapid population growth and land scarcity. Consequently, vertical growth has been adopted as a strategic solution to optimize land use while balancing economic, social, and environmental needs. This study examines the phenomenon of vertical growth of the Dammam Metropolitan Area (DMA) in Saudi Arabia, from an urban sustainability perspective, focusing on evaluating the current state of multi-story buildings, their determinants, and their impact on quality of life and infrastructure efficiency. This study utilizes a systematic review methodology and a conceptual approach to develop an integrated framework for sustainable vertical growth. Furthermore, an empirical validation was conducted by projecting this framework onto vertical housing projects in Dammam, focusing on challenges related to design, construction quality, shared service management, and the suitability of apartments for family needs. The results indicate that the shift toward vertical growth achieves land-use efficiency, limits random horizontal expansion, and provides economic opportunities. However, it faces social and cultural constraints, most notably the resistance of some families to changing traditional ownership patterns, limited privacy and green spaces, and challenges in building maintenance and operations. The study highlights the importance of integrating urban planning, governance, architectural design, and infrastructure to ensure the sustainability of vertical growth and provide suitable housing alternatives. The study recommends further field research to assess social acceptance, improve quality-of-life indicators, and develop policies encouraging sustainable vertical expansion in alignment with Saudi Vision 2030 and the 2030 Sustainable Development Goals (SDGs), ensuring cities are more resilient, efficient, sustainable, and liveable. Full article

As sustainable finance continues to advance, an important question is how scientifically designed and well-targeted policies can curb corporate ESG greenwashing and improve the quality of firms’ ESG and sustainability disclosure. From the perspective of fiscal–financial policy coordination, we exploit the green loan interest subsidy policy (GLIS) as a quasi-natural experiment and develop an analytical framework around four policy components: commercial banks’ information screening, local governments’ green screening, the subsidy instrument’s leverage and certification effects, and firms’ internal green governance. Within this framework, we examine whether the GLIS can restrain corporate ESG greenwashing. Using Chinese listed firms from 2009 to 2022 as the sample and identifying the effect through a multi-period difference-in-differences (DID) model, we find that the GLIS significantly curbs corporate ESG greenwashing. In exploring the underlying channels, we find that the GLIS curbs corporate ESG greenwashing by strengthening commercial banks’ information screening, enhancing local governments’ green screening, easing firms’ external financing constraints, and reinforcing firms’ internal green governance. Further analysis indicates that the inhibitory effect of the GLIS on corporate ESG greenwashing is more pronounced among non-state-owned firms, firms in the growth stage, firms in heavily polluting industries, and firms located in regions with weaker resource endowments. In addition, the stronger a firm’s digital technology R&D capability and corporate governance capability, the greater the restraining effect of the GLIS on its ESG greenwashing. By systematically evaluating the governance effect of fiscal–financial policy coordination on corporate ESG greenwashing, our study provides useful insights for governments seeking to improve green finance policies and optimize the coordination of green policy instruments. Full article

Background/Objectives: Cyclotides are plant-derived macrocyclic peptides distinguished by their head-to-tail cyclized backbone and cystine knot motif, which confer remarkable stability against thermal, enzymatic, and chemical degradation. These features, combined with a compact and rigid structure, position cyclotides as promising scaffolds for future antibacterial agents in response to the escalating threat of multidrug-resistant (MDR) pathogens and the stagnation of conventional antibiotic discovery pipelines. This review summarizes the structural features, antibacterial mechanisms, bioengineering strategies, and translational potential of cyclotides against MDR infections. Methods: A narrative review of the literature was conducted using recent original research articles and reviews on cyclotide structure, antibacterial activity, bioengineering, computational modeling, and pharmaceutical applications. Results: Cyclotides exhibit potent antimicrobial activity, primarily through membrane disruption mediated by amphipathic surfaces and affinity for anionic bacterial membranes. Some variants also demonstrate anti-virulence and antibiofilm properties, broadening their therapeutic relevance for difficult-to-treat infections. Bioengineering approaches, including epitope grafting and rational design, have improved selectivity and potency while reducing cytotoxicity. Advances in computational modeling, molecular dynamics, and artificial intelligence have accelerated the prediction and optimization of antimicrobial activity, toxicity, and pharmacokinetic properties. Conclusions: Innovations in synthesis, including recombinant expression and enzymatic ligation, are helping overcome translational barriers related to cost and scalability. Although challenges remain in oral bioavailability and systemic delivery, strategies such as lipidation and scaffold modification support the development of cyclotide-based therapeutics as adaptable platforms for peptide drug discovery. Full article

In order to study the optimal size and sealing performance of the foot-shaped slip ring for reciprocating seal, the loading method of fluid pressure penetration is used to simulate the effect of fluid medium pressure on the seal, and the multi-objective optimization of the geometry of the slip ring is carried out based on optimization software to obtain the best combination of parameters for the foot-shaped slip ring. The effects of slip ring geometry, pre-compression and working pressure on Von Mises stress and contact pressure were investigated using the finite element method. The results show that the optimized geometry of the foot-shaped slip ring can reduce the maximum contact stress on the main sealing surface from 108.5 MPa to 75.22 MPa (a reduction of 30.7%) and decrease the maximum Von Mises stress of the slip ring from 62.84 MPa to 41.57 MPa (a reduction of 33.8%), thereby greatly reducing the wear of the slip ring while ensuring reliable sealing. In the static sealing condition, a smaller pre-compression (1.2–1.3 mm) leads to stress concentration in the O-ring, and the recommended pre-compression range is 1.4–1.6 mm. In the dynamic sealing condition, the effect of pre-compression on the sealing performance is greater than that of reciprocating motion speed on the sealing performance, and the foot-shaped slip ring seal is found to be more suitable for low-speed operation at 0.1–0.2 m/s. The optimized design provides a data-driven methodology for enhancing the reliability and service life of reciprocating seals in high-pressure environments. Full article

The Charpy impact resistance of monolithic high-strength and dissimilar structured steel was studied. A gas metal arc welding process was used to fabricate the structured steel by depositing a layer of austenitic stainless steel, followed by a layer of hardfacing material over the high-strength steel plate. ANSYS LS-DYNA^TM was used to simulate pendulum–striker impacts on steel Charpy samples. A Cowper–Symonds constitutive model was employed to capture the strain rate behavior. The corresponding material constitutive model parameters were obtained from the literature for the monolithic materials; an iterative numerical optimization method was used to couple the parameters of the structured steel simulation and experimental results. Numerical simulation results showed close agreement with experimental ones. Simulation is a valuable tool for explaining the fracture mechanism in the Charpy impact test and can be used to efficiently design parts made of structured steel that will be subjected to impacts or high-speed deformations. Full article

The positioning and clamping process in aircraft assembly exhibits pronounced long-term temporal correlations and intense human–machine interactions. Consequently, assembly quality depends heavily on operator compliance and consistency. Capturing long-term, strongly correlated features in complex industrial environments remains a significant challenge. To overcome this, this study proposes a Long-Term Strongly Associated Action Recognition Network (LTSA-Net) tailored for aircraft assembly positioning and clamping tasks. Based on the C3D backbone, the model first incorporates the SimAM attention mechanism and BN modules to significantly enhance focus on critical spatiotemporal features. To address the challenge of capturing long-term temporal dependencies, LTSFEM is designed to extract global temporal information accurately. Furthermore, to balance structural lightweight design with real-time inference requirements, the CWSTB module is integrated to achieve substantial parameter compression. In addition, a dedicated aircraft assembly positioning and clamping dataset was constructed, and a robust training framework was established using the AdamW optimizer and Mixup data augmentation. Experimental results demonstrate that LTSA-Net achieves a recognition accuracy of 98.82% on the LTSA-Dataset, with a per-frame inference time of 42 ms, successfully meeting the dual requirements of high precision and real-time performance in industrial scenarios, and providing a practical technical solution for intelligent monitoring of aircraft assembly processes. Full article

Although interior-point methods (IPMs) have transformed mathematical programming since 1984, the original projective Karmarkar algorithm is rarely documented step by step on reproducible engineering examples that combine algorithmic transparency with real resource allocation constraints. This article therefore does not propose a new variant of Karmarkar’s algorithm; rather, its scientific contribution is the reproducible MATLAB implementation, canonical-form conversion, and comparative validation of the original projective method against the revised Simplex method and Barnes’ affine scaling variant in two engineering settings. The case studies are (i) the minimum-weight plastic design of a rigid frame with seven candidate plastic hinge locations and six collapse mechanisms and (ii) the optimal allocation of crop patterns in the Caplina Valley (Tacna, Southern Peru), an arid irrigated system with an irrigated command area of 1253 ha, monthly labor availability of 22,239 jornales, and water availability derived from Caplina River discharges at 75% persistence. For Case I, the algorithm reached F = 1.001 in the normalized dual space, which corresponds to F = 4.251 in the original structural objective after applying the scaling factor 17/4; relative to the analytical optimum F* = 4.25, this gives |4.251 − 4.25|/4.25 = 2.4 × 10⁻⁴ after 20 iterations. For Case II, the model yielded the maximum net production value of USD 703,135.92, allocating 948.47 ha among 12 crops while satisfying water, labor, market, and land constraints. The double validation confirms the algorithm’s strictly interior trajectory, polynomial-time rationale, and transparent internal parameters (α = 0.7968, ε = 10⁻⁸), making the implementation a reproducible benchmark for educational use and for future AI–operations research hybrid solvers in regions with limited access to commercial optimization software. Full article

The transition towards sustainable manufacturing necessitates complex optimization that integrates economic goals with environmental factors, such as energy consumption and greenhouse gas emissions. This research addresses the critical challenge of optimizing the Incoming Quality Control (IQC) policy for raw material batches. The primary objective is formulated as a multi-criteria control problem that jointly minimizes the weekly final product cost, carbon footprint, and energy consumption. To handle sequential decision making under uncertainty, we adopt a scalarized reinforcement learning (RL) reward that combines these objectives into a single value function and explores different trade-offs through alternative weight configurations. To effectively handle the uncertainty in incoming quality and the sequential decision making required for dynamic control, the optimization problem is modeled as a Bayesian Adaptive Markov Decision Process (BAMDP). To maintain computational tractability despite the continuous belief space inherent in the BAMDP formulation, we employ a Deep Q-Network (DQN) architecture acting as an approximate dynamic programming solver. The Bayesian framework represents model uncertainty explicitly, updates beliefs as new inspection evidence becomes available, and allows prior domain knowledge on supplier quality to be incorporated into the learning process. The BAMDP formulation is used to learn a set of adaptive inspection policies that adjust the IQC strategy over time to achieve conflicting goals: reducing inspection costs while maintaining standard quality, minimizing energy consumption, and lowering CO₂-equivalent emissions. The goal is to find robust policies that balance these trade-offs under different quality and demand conditions. This methodology aligns with the principles of Industry 5.0 by leveraging advanced artificial intelligence (AI) methods, such as reinforcement learning (RL), coupled with a stochastic simulation of the production system, based on a geometric/physical model of the component’s tolerance chains, to support decision-makers in designing and assessing sustainable IQC strategies. Comparative simulations on the case study, including a benchmark against ISO 2859-1 sampling plans, confirm that this dynamic and risk-aware optimization paradigm can reduce overall cost, energy use, and environmental impact across various quality conditions, while preserving outgoing quality. Full article

Soluble solids content (SSC) is one of the most important indicators of sweetness, ripeness, and market quality in sweet cherries. However, conventional SSC determination is destructive, labor-intensive, and unsuitable for rapid or large-scale quality assessment. Therefore, there is a need for fast, non-destructive, and data-driven sensing approaches that can estimate internal fruit quality without damaging the sample. This study aimed to develop a non-destructive approach for SSC prediction in sweet cherries by combining open-ended coaxial probe dielectric spectroscopy with deep learning models. An open-ended coaxial probe measurement system was designed and developed to determine the dielectric properties of sweet cherries and was coupled with an Agilent E4991A impedance analyzer operating over a frequency range of 5–3005 MHz. A total of 10,080 dielectric measurements and 2100 reference SSC measurements were collected over 26 experimental days. The dielectric constant (ε′), loss factor (ε″), and loss tangent (tan δ) were extracted and used to construct separate ε′, ε″, tan δ, and integrated combined datasets. Six deep learning architectures, namely convolutional neural network (CNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU), CNN-LSTM, and convolutional long short-term memory (ConvLSTM), were trained and optimized using Bayesian optimization and early stopping. CNN achieved the best performance on the tan δ dataset (test R² = 0.9099, RMSE = 0.8354 °Brix, MAE = 0.6599 °Brix), whereas GRU yielded the highest accuracy on the integrated combined dataset (test R² = 0.8622, RMSE = 1.0331 °Brix, MAE = 0.7958 °Brix). ConvLSTM provided the most consistent performance across all four datasets (test R² = 0.8081–0.8651), demonstrating strong predictive capability and practical computational efficiency. These findings confirm the potential of reduced-range dielectric spectroscopy combined with deep learning for rapid, non-destructive SSC assessment in sweet cherries. Full article

The Annual Delivery Program (ADP) for Liquefied Natural Gas (LNG) represents a complex maritime inventory-routing problem that requires the precise synchronization of production and distribution. This study introduces a novel Mixed Integer Linear Programming (MILP) model designed to optimize vessel routing and scheduling over a one-year horizon under a direct-shipment assumption. The model minimizes total logistics costs, encompassing both fixed annual fleet costs and daily operating costs. The novelty of the model can be summarized in two aspects. First, it simultaneously optimizes several decisions: the assignment of frequency of deliveries to customers, the assignment of vessels to customers, cargo load sizes, and vessel routing and scheduling. The key distinction is that, unlike existing formulations that take the frequency of deliveries to customers as a fixed parameter, this frequency is itself a decision variable selected from a customer-specific discrete set; the selected frequency partitions the planning horizon into uniform windows and sets each delivery’s cargo load size to the exact demand accumulated over its window from daily demand data. Second, it incorporates several relaxations of selected variables and valid inequalities that enable us to solve the complex model for moderate size problems within a reasonable computational time using the exact optimization approach. Using this novel model, we carried out extensive numerical analysis based on cost and operational parameter scenarios and developed important insights for the characteristics of a solution to the problem. Full article

High sound pressure level (SPL) acoustic sensing requires miniaturized microphones that can operate under large acoustic loading while maintaining mechanical linearity, sufficient sensing response, and broadband audio frequency behavior. This work targets high-SPL operation and numerically investigates a graphene piezoresistive MEMS microphone based on a membrane-supported beam–island diaphragm. The proposed structure retains a continuous membrane for acoustic load bearing, while the upper beam–island topology redirects deformation-induced strain toward beam root regions where graphene piezoresistors are placed. This design is intended to increase the local strain available for piezoresistive readout without simply relying on larger global diaphragm deflection. Finite-element analysis was used to optimize the diaphragm geometry and evaluate strain enhancement, pressure response linearity, modal behavior, and harmonic response. Under the 170 dB SPL reference condition, the optimized structure increases the peak structural strain from 47.83 με in a thickness-equivalent solid diaphragm to 562.53 με, achieving an approximately 11.8-fold enhancement in local sensing strain while maintaining a highly linear pressure response (R2 > 0.9999). Additionally, the results also show that the sensor exhibits a high first natural frequency of 64.07 kHz and a small response variation of approximately 0.94 dB within the 0–20 kHz target frequency range, indicating excellent dynamic stability and high-fidelity signal transduction characteristics. To connect the structural response with piezoresistive readout, first-order electromechanical output estimation was further performed using representative graphene gauge factors, quarter-bridge readout assumptions, contact resistance correction, and Johnson-noise-limited signal-to-noise ratio estimation. A ±5% geometric tolerance check further indicates that the membrane side length is the most fabrication-sensitive parameter, while the selected design remains generally robust except for reduced linearity margin under positive membrane side-length deviation. These results demonstrate the potential of the proposed graphene-based MEMS microphone for high-SPL broadband acoustic sensing applications in harsh and high-intensity acoustic environments. Full article

Flooding has become increasingly severe due to rapid urbanization and changing hydrological conditions, necessitating effective and sustainable mitigation strategies. This study investigates the hydraulic performance of a hybrid flood defense system comprising a dike, a moat, and vegetation under varying surface roughness conditions. The results demonstrate that increasing roughness significantly enhances flood mitigation performance by improving energy dissipation and delaying the propagation of floodwater. A maximum energy reduction of approximately 75.56% and a delay in floodwater arrival of up to 65% were observed under higher roughness conditions. In contrast, increasing flow intensity reduced system efficiency, highlighting the importance of optimizing roughness under varying hydraulic conditions. The findings reveal that surface roughness is the dominant factor controlling flow resistance, turbulence generation, and hydraulic jump formation within the system. The novelty of this study lies in systematically quantifying the combined effect of roughness across structural and vegetative components within a hybrid defense framework. These results provide a practical basis for the design and optimization of eco-engineered flood defense systems, offering a cost-effective approach for reducing flood risk in riverine environments. Full article