Polymer-modified bentonite (PMB) is much more effective at containing chemically aggressive liquids than conventional bentonite. The PMB manufacturing process typically utilizes natural, high-quality sodium bentonite (NaB) owing to its excellent hydrophilicity and swelling capacity. However, calcium bentonite (CaB), which is much more abundant worldwide, is rarely used for containment applications owing to its poor hydrophilicity. This study proposed a polymerization method that transforms sodium-activated calcium bentonite (NCB) into PMB to achieve low hydraulic conductivity (k) to aggressive liquids. The mechanism for its low k was revealed through characterization techniques and analyses (e.g. X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and Brunauer-Emmett-Teller (BET)). The results showed that the PMB had a small amount of polymer elution (indicating better interface stability) and thus exhibited excellent barrier properties under chemically aggressive conditions, with the k of <10−11 m/s for 0.6 mol/L NaCl solution, which is four orders of magnitude lower than that of the NCB (k = 3 × 10−7 m/s). Various microscopic analyses indicated that the selected monomers were successfully polymerized, and intercalated into and grafted onto the montmorillonite layers of bentonite. The formed polymer network increased the swelling capability of PMB granules, decreased the pore size, and created narrow and tortuous flow pathways leading to a very low k to aggressive liquids.
Engineering geology. Rock mechanics. Soil mechanics. Underground construction
Parisa Kanani, Mohammad Javad Omidi, Mahmoud Modarres-Hashemi
et al.
This paper introduces a novel high altitude platform station (HAPS)-based integrated sensing and communication (ISAC) system, referred to as HAPS-ISAC, designed to enhance the capabilities of future 6G networks by simultaneously optimizing communication and sensing functions. HAPS operates as a super-macro base station in the stratosphere, utilizing advanced beamforming techniques within a multiple-input multiple-output (MIMO) architecture, supplemented by multiple-input single-output (MISO) configurations, effectively enabling the system to serve ground communication users (CUs) while conducting high-resolution sensing of potential targets. A Rician channel model is employed to capture both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. The performance of the system is optimized through a non-convex optimization problem that maximizes the minimum beampattern gain towards desired sensing angles while ensuring that the signal-to-interference-plus-noise ratio (SINR) requirements for CUs are satisfied, all under the power constraints of the HAPS. Compared to the traditional terrestrial and UAV-based ISAC systems, HAPS-ISAC delivers sustained and reliable service over extensive areas, leading to significantly improved overall performance. Simulation results show that HAPS-ISAC significantly improves SINR, resource allocation, sensing accuracy, and fairness, outperforming existing technologies. This establishes HAPS-ISAC as a key enabler for 6G networks and advances intelligent infrastructures like IoT and smart cities.
Telecommunication, Transportation and communications
Abstract The cross-grooved domed rupture disc (CDR) is widely used due to its rapid response and the absence of fragment spattering. The bursting performance of the CDR is primarily affected by the operating conditions including the applied pressure, clamp fillet radius, and pressure rise rate. In this study, a systematic investigation was conducted to examine the dynamic bursting pressure (DBP) of the CDR under varying operating conditions. A numerical model of the CDR was developed and validated against the dynamic bursting test results. The applied pressure conditions, including the operating pressure and back pressure, were thoroughly analyzed. The results indicate that an increase in applied pressure leads to a decrease in DBP. In certain cases, the presence of back pressure can cause premature failure. This behavior is attributed to the combined effects of residual stress and plastic deformation. Furthermore, due to shear stress at the boundary, the DBP of the CDR decreases as the clamp fillet radius increases. Finally, as the pressure rise rate increases, the DBP initially rises and then declines, which can be attributed to the strain rate effect of the material.
Abstract China’s rapid urbanization has intensified energy consumption and carbon emissions, underscoring the urgency of low-carbon urban renewal strategies. The impacts of urban renewal on the community deserve to be evaluated to inform the mode and direction of implementation. However, the knowledge base lacks a comprehensive framework for evaluating the public sense of gain (SOG). This study aims to develop and measure indicators of public SOG in low-carbon urban renewal. A questionnaire survey was conducted among residents from different occupations, and an empirical study was carried out in Fuzhou. Both traditional fuzzy and cloud model-based fuzzy comprehensive evaluation methods were applied. The results showed that the economic dimension exerted the strongest influence, with an expected value of 4.04 in the cloud model. In addition, structural equation modeling (SEM) was used to perform a first-order confirmatory factor analysis (CFA), which validated the internal structure of the SOG evaluation system. All standardized loadings exceeded 0.6, and composite reliability (CR) values exceeded 0.8, verifying the model’s good fit. The proposed evaluation framework helps fill a methodological gap and provides a rational tool to measure public SOG in low-carbon urban renewal. The findings could support more inclusive and sustainable urban redevelopment practices and offer practical guidance for policy and implementation.
History of scholarship and learning. The humanities, Social Sciences
The behavior of the Rucklidge-type dynamical system was investigated, providing some semi-analytical solutions, in this paper. This system was analytically investigated by means of the Optimal Auxiliary Functions Method (OAFM) for two cases. An exact parametric solution was obtained. The effect of the physical parameters was investigated on the asymptotic behaviors and damped oscillations of the solutions. Damped oscillations are essential for analyzing and designing various mechanical, biological, and electrical systems. Many of the applications involving these systems represent the main reason of this work. A comparison between the obtained results via the OAFM, the analytical solution obtained with the iterative method, and the corresponding numerical solution was performed. The accuracy of the analytical and corresponding numerical results is illustrated by graphical and tabular representations.
Por meio de uma revisão de literatura exploratória, este artigo discute a necessidade de promover hábitos sustentáveis de transporte nas escolas, dada a crescente degradação ambiental e as altas emissões de carbono pelo setor de transportes. Embora existam várias estratégias para promover o transporte sustentável, a eficácia dessas intervenções depende de mudanças comportamentais. As discussões são pautadas nas buscas realizadas, em uma análise bibliométrica e outra de narrativas. As buscas inserem no âmbito brasileiro os conceitos de “educação sobre transporte sustentável” e “educação e desenvolvimento sustentável - EDS”. Com relação à temática, a análise bibliométrica destaca: periódicos adequados para a publicação de estudos futuros; o caráter multidisciplinar; e a escassez de estudos. A análise de narrativas sugere três hipóteses para futuros estudos: a relação entre a escassez de estudos e a menor necessidade de conscientização infantil em países desenvolvidos; o impacto positivo de uma base sólida de conscientização ambiental no currículo escolar; e a insuficiência das práticas educacionais atuais nas escolas brasileiras. Contudo, o artigo oferece insights para que práticas educacionais promovam hábitos de transporte sustentáveis desde a infância.
Monitoring of cracks and crack growth rates is a crucial aspect of structural health monitoring for concrete infrastructure, and multiple manual and automatic monitoring techniques have emerged over the years. This study focuses on an in-depth review of concrete crack sensing using distributed fiber optic sensing (DFOS) technology. DFOS provides the option to sample distributed data points through dedicated optical fibers or cables, thereby effectively addressing the spatial limitations associated with conventional discrete point sensors such as foil strain gauges and transducers. The main findings include that (1) smart concrete crack sensing generally involves three objectives: detecting crack initiation, identifying the crack location and determining the crack width and its evolution; (2) for DFOS used for crack sensing, the three main sensing principles are to measure localized strain spikes in optical fibers or cables that span across cracks, to detect signal intensity losses caused by micro-bending of optical fibers in proximity to cracks and to measure precise local temperature variations within the crack areas; (3) strain-based crack sensing has become the predominant method due to its superior sensing performance and application versatility. This dominance is supported by extensive experimental demonstrations and successful implementations in field monitoring practices; (4) the sensitivity of optical fibers or cables to concrete cracks depends on the installation method, while quantitative crack width measurements require the precise determination of crack locations followed by a subsequent integration or exponential fitting of strain along the length at fiber-concrete interface. This study helps to advance the application of the smart DFOS for structural health monitoring and maintenance of concrete infrastructures.
Materials of engineering and construction. Mechanics of materials
The research involved replacing fine particles with solid waste rubber powder to prepare the toughened cement stabilized macadam/TCSM. Based on vertical full-scale model tests of frost heave and thaw settlement, vertical stress transmission tests between layers, and inter-layer interface adhesion effect evaluation tests, the freeze-thaw deformation and inter-layer interface contact characteristics of TCSM were discussed. The results indicated: 1) Under test conditions where cement and rubber powder were added simultaneously, the TCSM could effectively eliminate thaw settlement deformation, with frost heave deformation reduced from 2.53 mm to 0.26 mm, a decrease of 90.4 %. The hydration and adhesion of cement, combined with the elastic filling of rubber powder, played a positive role in resisting volume deformation caused by ice-water phase transitions. 2) The contact area between the TCSM and the surface of the sub-grade fill material was more extensive than that of cement stabilized macadam under the same conditions, exhibited better stress diffusion behaviour. Under low stress conditions, stress diffusion effects were pronounced, while under high stress conditions, stress concentration continued to increase. 3) Under identical micro-deformation conditions, the fixed interface bending load-bearing capacity was 4 times that of a free interface for the composite inter-layer structure, and a calculation method for the interface adhesion contact coefficient/IBC was provided. The TCSM shown the better inter-layer adhesion effect when combined with SBS/rubber powder blended modified asphalt mixture, with an index value of 3.9 under small deformation conditions of 0.2 mm, approaching the ideal fixed state. The research could understand the freeze-thaw deformation characteristics and its contact state with the upper and lower layers for TCSM, which was crucial for enhancing the toughness of the subgrade surface layer.
Materials of engineering and construction. Mechanics of materials
Tat-Dat Bui, Hien Minh Ha, Thi Phuong Thuy Tran
et al.
This study is to build a causality model to implement energy security strategies (ESSs) in approaching a world-regions comparison. This study contributes to ESSs by indicating a set of valid attributes and those attributes are interrelationships in nature. There is major global interest in ESSs due to the pressure to ensure sustainable energy supply sources. An adequate energy source is decisive for ensuring stable economic growth, enhancing social development, and protecting the environment. Nonetheless, in reviewing the energy literature, generating strategic attributes is still lacking, which leads to difficulties for policymakers in building, executing, and assessing energy policies. This study utilizes a hybrid method: text mining, cluster analysis, fuzzy Delphi method, fuzzy decision-making trial and evaluation laboratory, and entropy weight method. As a result, five aspects and 22 criteria from the data pool are validated. The causal model shows that the energy control system, strategic collaboration and technological capability are the priority. In practice, the effect aspects are waste-to-energy and energy resilience. Although the research trends on ESSs in different regions are quite similar, each continent still has unique concerns such as European countries with distributed energy resources, Asia and Oceania with decarbonization, African countries with new technologies, and Americas with energy planning.
Abstract With the aim to enhance prediction accuracy for nonlinear time series, this paper put forward an improved deep Echo State Network based on reservoir states reconstruction driven by a Self-Normalizing Activation (SNA) function as the replacement for the traditional Hyperbolic tangent activation function to reduce the model’s sensitivity to hyper-parameters. The Strategy was implemented in a two-state reconstruction process by first inputting the time series data to the model separately. Once, the time data passes through the reservoirs and is activated by the SNA activation function, the new state for the reservoirs is created. The state is input to the next layer, and the concatenate states module saves. Pairs of states are selected from the activated multi-layer reservoirs and input into the state reconstruction module. Multiple input states are transformed through the state reconstruction module and finally saved to the concatenate state module. Two evaluation metrics were used to benchmark against three other ESNs with SNA activation functions to achieve better prediction accuracy.
Abolfazl Eslami, Masoud Nobahar, Mohammad Esmailzade
Field tests are the most suitable method to determine geotechnical parameters. Owing to some restrictions in field tests, physical modeling has been widely accepted as a proper method to define mathematical correlations among geotechnical parameters. This study investigates correlations between parameters derived from cone penetrometer tests. The tests were performed in a cylindrical chamber with a height and diameter of 1000 mm to minimize the boundary effect. Coastal poorly graded sand sampled from the Babolsar region, adjacent to the Caspian Sea, was used. Some correlations among geotechnical parameters, including cone resistance, dynamic cone resistance, dynamic penetration index, modulus of elasticity, internal friction angle, and relative density, are presented. All correlations were categorized into three main categories: soil stiffness, penetration strength, and geotechnical parameters. The results had reasonable accuracy and precision. The average R<sup>2</sup> value of the obtained results was approximately 94. The investigations into the inherent CPT also indicated that the strength parameter had more accuracy than stiffness and other sand parameters. Specifically, the R<sup>2</sup> value for the correlation between the results of various penetration tests, considered strength parameters, averaged 97. In contrast, the R<sup>2</sup> value for the correlation between the elasticity modulus and cone penetration test results was 86.
This research investigated the effects of acetone and metal nano magnesium oxides (MNMgOs) on combustion and emissions in diesel engines. Tests were conducted at various load conditions at a speed of 1500 rpm. The results showed that the use of a low percentage (2 %) of acetone led to increases in instantaneous energy changes, cylinder pressures (CP), heat release rates (HRR), mean gas temperatures (MGT), and pressure rise rates (PRR). However, an increase in acetone concentration and the use of Acetone + MNMgOs resulted in a decrease in combustion parameters. Concentrations of 2 %, 5 %, and 10 % acetone reduced carbon monoxide (CO) emissions by 7.85 %, 11.35 %, and 2.23 % respectively, hydrocarbons (HC) by 10.84 %, 15.86 %, and 3.02 % respectively, and nitrogen oxides (NOx) emissions by 3.79 %, 2.02 %, and 10.25 % respectively. The combined use of acetone and MNMgOs resulted in a reduction of 1.10 % in CO emissions, 1.48 % in HC emissions, and 10.62 % in NOx emissions.
Dense matching plays an important role in 3D modeling from satellite images. Its purpose is to establish pixel-by-pixel correspondences between two stereo images. The most well-known algorithm is the semi-global matching (SGM), which can generate high-quality 3D models with high computational efficiency. Due to the complex coverage and imaging condition, SGM cannot cope with these situation well. In recent years, deep learning-based stereo matching has attracted wide attention and shown overwhelming benefits over traditional algorithms in terms of precision and completeness. However, existing models are usually evaluated by using close-ranging datasets. Thus, this study investigates the recent deep learning models and evaluate their performance on both close-ranging and satellite image datasets. The results demonstrate that deep learning network can better adapt to the satellite dataset than the typical SGM. Meanwhile, the generalization ability of deep learning-based models is still low for the real application at recent time.
R. Augustine, Mert Gezek, Nazli Seray Bostanci
et al.
The lack of oxygen supply in engineered constructs has been an ongoing challenge for tissue engineering and regenerative medicine. Upon implantation of an engineered tissue, spontaneous blood vessel formation does not happen rapidly, therefore, there is typically a limited availability of oxygen in engineered biomaterials. Providing oxygen in large tissue-engineered constructs is a major challenge that hinders the development of clinically relevant engineered tissues. Similarly, maintaining adequate oxygen levels in cell-laden tissue engineered products during transportation and storage is another hurdle. There is an unmet demand for functional scaffolds that could actively produce and deliver oxygen, attainable by incorporating oxygen-generating materials. Recent approaches include encapsulation of oxygen-generating agents such as solid peroxides, liquid peroxides, and fluorinated substances in the scaffolds. Recent approaches to mitigate the adverse effects, as well as achieving a sustained and controlled release of oxygen, are discussed. Importance of oxygen-generating materials in various tissue engineering approaches such as ex vivo tissue engineering, in situ tissue engineering, and bioprinting are highlighted in detail. In addition, the existing challenges, possible solutions, and future strategies that aim to design clinically relevant multifunctional oxygen-generating biomaterials are provided in this review paper.
The increased automation and connectivity of vehicles and road infrastructure can make future transportation systems more efficient and smarter and enable new transportation business models. Connected and automated vehicles (CAVs) may form a group of autonomous fleets to transform today’s shared mobility services and also play the role of mobile sensors, which share real-time traffic and road information for transportation management. However, these technological advancements also lead to new cyber and physical threats that cause safety hazards or other undesired consequences. Although there have been a large number of papers about identifying and mitigating each type of threat, the lack of design support still challenges security engineering for developing CAVs. This limits the engineering capabilities of original equipment manufacturers to prioritize among multiple system properties, including safety, security, and privacy, and dealing with ever-changing attack surfaces and the power of attackers. This paper surveys security vulnerabilities and defense mechanisms for CAVs from an engineering design perspective. We illustrate how to identify and mitigate physical threats that compromise the safety of individual vehicles and cyber threats that disrupt newly CAV-enabled transportation services in a systematic way. An integrated security engineering process and a multi-layer design framework are presented for providing traceability and guidance in threat identification and mitigation.
Abstract In this paper, we propose a new multi-objective optimization approach for the pharmaceutical supply chain network (PSCN) design problem to minimize the total cost and the delivery time of pharmaceutical products to the hospital and pharmacy, while maximizing the reliability of the transportation system. A new mixed-integer non-linear programming model was developed for the production-allocation-distribution-inventory-ordering-routing problem. Three new heuristics (H-1), (H-2), and (H-3) have been proposed and to validate the model, two new meta-heuristic algorithms, namely, an Improved Social Engineering Optimization (ISEO) and Hybrid Firefly and Simulated Annealing Algorithm (HFFA-SA) have been developed. The proposed mathematical model has been evaluated through extensive simulation experiments by analyzing different criteria. The results show that the proposed model along with the solution method provides a reliable and powerful instrument to solve the PSCN design problem.
Machine Learning (ML) is increasingly applied for the control of safety-critical Cyber-Physical Systems (CPS) in application areas that cannot easily be mastered with traditional control approaches, such as autonomous driving. As a consequence, the safety of machine learning became a focus area for research in recent years. Despite very considerable advances in selected areas related to machine learning safety, shortcomings were identified on holistic approaches that take an end-to-end view on the risks associated to the engineering of ML-based control systems and their certification. Applying a classic technique of safety engineering, our paper provides a comprehensive and methodological analysis of the safety hazards that could be introduced along the ML lifecycle, and could compromise the safe operation of ML-based CPS. Identified hazards are illustrated and explained using a real-world application scenario—an autonomous shop-floor transportation vehicle. The comprehensive analysis presented in this paper is intended as a basis for future holistic approaches for safety engineering of ML-based CPS in safety-critical applications, and aims to support the focus on research onto safety hazards that are not yet adequately addressed.
Rocha Segundo Iran, Landi Jr Salmon, Afonso Cátia
et al.
The functionalization of asphalt mixtures is carried out in order to provide new capabilities to the road pavements, with major social, environmental and financial benefits. Optical characterization techniques as well as optical processes like photocatalysis play a major role in the development of new asphalt mixtures with smart functions. These advanced capabilities which are being developed in asphalt mixtures are: photocatalytic, superhydrophobic, self-cleaning, de-icing/anti-ice, self-healing, thermochromic, and latent heat thermal energy storage. The main objective of this research work is to stress the importance of optics and photonics technologies giving an overview of advanced functionalized smart asphalt mixtures.