Abstract Spinodal topologies (STs), inspired by material phase transition phenomena, exhibit complex geometries generated by Gaussian random fields (GRFs). These dual-phase structures feature smooth transitions and low-curvature designs, offering enhanced mechanical response compared to traditional surface- or strut-based metamaterials. Their flexible design spaces allow both periodic and non-periodic topologies, making them promising candidates for multiphysics applications. This work presents a computationally efficient data-driven framework for optimizing the mechanical and thermal properties of STs. A convolutional neural network (CNN) is trained on homogenized elastic and thermal properties obtained from finite element analyses. Next, the CNN model is coupled with an optimization solver to design dual-phase STs to improve multiphysics objectives. The inherent variations of STs introduced by the GRF formulation limit the ability of gradient-based optimizers to efficiently search for the design space. To overcome this challenge, a gradient-free global optimization algorithm is integrated into the surrogate model to find optimum designs with superior mechanical and thermal performance.

The majority of the research articles on optimum experimental designs for generalized linear models focus on Poisson regression models with log-link function. In the generalized linear model (GLM) configuration, the information matrix depends on the unknown parameters of the model. In such a case, an experimenter must take the strategy of identifying local optimum designs i.e. first guessing the best value for the parameters and then calculating the optimal designs. In this article, we examine locally D- and A-optimal designs for a Poisson regression model using square root link function. The Equivalence theorem validates the necessary and sufficient conditions of this optimality criterion.

Metaphysical Optimality and Intelligent Design

Anthropogenic pollution of ecosystems, rapid climate change, and biodiversity losses have become an urgent issue in the world and need innovative and scaled-based solutions. The developments in artificial intelligence (AI), bio-engineering and environmental sensing technologies are currently providing unparalleled possibilities in rescuing ecological equilibrium in or affected health and natural ecosystems. This paper offers a novel and unified system that integrates AI-based pollution control, predictive water-quality, and bio-engineered remediation measures to aid the restoration of sustainable ecosystems. The research solution is based on deep-learning models, such as convolutional neural networks, long short-term memory networks, and transformers based on architectures, to be efficient in concluding the presence, classification, and forecasting of pollutant dynamics in terrestrial and aquatic ecosystems. Simultaneously, bio-engineering technologies like engineered microbial communities, hyperaccumulator plants, and optimum bioreactor designs have been used to hasten the degradation, absorption and fixation of contaminants. Moreover, AI-based models of biodiversity sustainability can be applied to measure the changes in distribution of species, habitat suitability and ecosystem resilience when subjected to environmental stressors of various levels. Based on experimental assessments and case studies, it is shown that AI combined with bio-engineered remediation improves the accuracy of identifying the source of the pollution by more than 30 times, it can be found to be more effective in the removal of contaminants, up to 38 times, and that it can maintain beneficial effects on biodiversity in the long-term, which can be achieved by optimised restoration strategies. The conclusions support the radical opportunities of AI-enhanced bio-engineering solutions to the restoration of fast, resilient, and scalable ecosystems. In addition, the research paper points at the major challenges such as the lack of data, ecological complexity, and ethical considerations and explains future research directions to serve the intelligent restoration ecology.

Flexible sensors play a crucial role in various emerging high-tech fields including electronic technology, information industry, and bioengineering. However, achieving excellent comprehensive performances through optimum structural designs remains a great challenge. Here, a facile technique is proposed to prepare high-performance flexible strain sensors based on fern-leaf-inspired hierarchical structures including primary, secondary, tertiary veins, and corrugated surfaces. The primary vein with a deep W-shaped groove plays a critical role in optimizing sensing performances by the formation of staggered cracks and establishment of serpentine conductive paths. This unique sensing mechanism endows the sensor with excellent comprehensive performances including high sensitivity (up to 20940), low detection limit (0.06%), wide detection range (40%), fast response/recovery speed (83/92ms), good cyclic stability (20000 cycles), and outstanding bidirectional bending capacity. As a demonstration of applications, the fern-leaf-inspired sensor has been used for human motion monitoring, Morse code expressing, information encryption, machine learning-assisted bending recognition, and health alert. This innovative design not only shows the application prospect of biomimetic sensors in the fields of health management and information communication, but also provides a new idea for future intelligent interactive systems.

ABSTRACT Plastics converters are increasingly considering high‐performance screws in single‐screw extrusion to meet demanding production requirements. In this context, reliable prediction models for melt conveying support the search for optimum designs without exhaustive experimental trials. Although full‐scale fluid dynamics simulations would closely represent the process, their high computational effort is rarely affordable in an industrial setting. Conversely, fast‐computing extruder calculations using surrogate models fail to capture the influence of channel curvature in multi‐flighted screw segments, thus becoming inaccurate for high‐performance screws. To overcome these limitations, we developed generic dimensionless analytical equations for the local pumping capability, viscous dissipation, and average shear rate in metering channels. First, a comprehensive simulation database for the flow of power‐law fluids through confined curved channel segments was generated that covers a vast range of designs and process configurations in single‐screw extrusion. Subsequently, this database was approximated by continuous functions using knowledge‐guided symbolic regression. The resulting characteristic equations excellently forecast the simulations throughout the entire design space with less than 3.5% average deviation, significantly outperforming existing approximations. When implemented in segmented extrusion calculations, these surrogate models enable quick and reliable statements on melt conveying in both conventional and high‐performance extruders for more efficient screw design and troubleshooting.

The Laboratory experiments were conducted using a second order rotatable design in four dimensions constructed using balanced incomplete block designs. Obtained data was applied in developing a semi-empirical model based on a second degree polynomial for predicting bioethanol yield. The model testing using ANOVA in R resulted in a correlation coefficient of 0.95 and an adjusted R-squared value of 0.911 for the E-optimal design, which indicates a good model fit. The model was used to generate contour plots and response surface for bioethanol yield. A maximum yield of 12.35 g/L of ethanol was realized at factor settings of 54.35 h, 4.96 level of pH, 34.67 0C temperature and 28.03 g/L of substrate concentration using the E-optimal design which was found to be the most efficient design relative to the general design which had an optimal yield of 12.39 g/L at factor settings of 56.45 h, 4.95 pH level, 34.59 0C level of temperature and 28.30 g/l of substrate concentrations. A yield of 12.35 g/L of ethanol for a substrate concentration of 28.03 g/L. translates to 0.441 g of ethanol per gram of substrate comparing well with many other findings in literature from similar studies which is roughly 86% of the theoretical yield (0.511 g/g of substrate). A second order rotatable design in four dimensions constructed using balanced incomplete block designs when the number of replications (r) are less than three the number of times (\(\lambda\)) pairs of treatments occur together ( r<\(\lambda\)) in the design was applied and found reliable in modeling, optimizing and studying the effects of the four factors and their interaction to the processes of fermentation of pineapples peels as substrate for ethanol production using Saccharomyces cerevisiae.

Despite a vast amount of available research on structural optimization, numerical applications addressing real-world structures are very scarce in the literature. Not only does the actual design process in practice have increased complexity and versatility, but also the structures must usually be designed under a high number of load combinations, resulting in prolonged computation times. In this study, the real-world steel structures built in various regions of Türkiye have been optimally redesigned for the minimum weight, and the optimum designs of these structures are compared with their original design-office solutions. A recently developed design-driven optimization technique, called the capacity controlled search algorithm, is employed to deliver the cost-efficient designs of these structures in a timely manner. The present study helps fill the gap in the literature regarding the implementation of optimization techniques in industrial applications while investigating how much cost-savings can actually be achieved through structural optimization compared to the original design-office solutions. The results indicate that about 3%–40% cost reduction is possible through the replacement of conventional trial-and-error based design methodology by an optimum design approach.

Optimised hammer drilling bit design using artificial neural networks trained by FDEM-generated data

In the context of Syria, a nation grappling with protracted conflict, the imperative for reha-bilitation efforts is paramount, particularly in the context of the extensive damage to nu-merous public buildings. Conventional rehabilitation methodologies frequently depend on labour-intensive manual techniques, which are further complicated by the loss of data per-taining to damaged structures. The present document offers a technological and technical framework for the rehabilitation of public buildings that have been damaged by armed con-flict. The framework utilizes Building Information Modeling (BIM), Artificial Intelligence (AI), Virtual Reality (VR), and Augmented Reality (AR) technologies. The framework under consideration is comprised of two phases. The initial phase employs statistical methodolo-gies to assess damaged components and propose technological solutions. The subsequent phase utilizes AI-based generative design techniques to formulate novel post-restoration visions. The verification of these designs is then facilitated through immersive simulations that uti-lize virtual reality (VR) and augmented reality (AR), thereby enabling the selection of opti-mal design alternatives. The proposed framework was applied to a case study of a damaged building in Hama, Syria. This application demonstrated the framework's effectiveness in improving the rehabilitation process and decision-making. The results of the study indicate the viability of incorporating advanced technologies into restoration projects with the ob-jective of enhancing efficiency, accuracy, and stakeholder communication. Furthermore, the integration of such technologies can facilitate training for engineers and students enrolled in engineering colleges. In accordance with the principles of transparency and reproduci-bility, the source code and ancillary materials are disseminated via GitHub[1] . This dissemi-nation encompasses evaluation results and step-by-step documentation, thereby ensuring the highest standards of transparency and reproducibility. [1] https://github.com/baToul214batou/An-Integrated-Framework-for-the-Rehabilitation-of-War-Damaged-Public-Buildings-.git

High-efficiency design solutions for cogeneration steam power plants are studied for different steam consumer requirements (steam pressures between 3.6 and 40 bar and heat flow rates between 10 and 40% of the fuel heat flow rate into the steam generators). Using a genetic algorithm, optimum designs for schemes with extraction-condensing steam turbines, reheat, and supercritical parameters were found considering four objective functions (high global efficiency, low specific investment in equipment, high exergetic efficiency, and high power-to-heat ratio in full cogeneration mode). A second Pareto front was computed from the prior solutions, considering the first two objective functions, resulting in the high-efficiency cogeneration schemes with a primary energy savings (PES) ratio higher than 10%. The results showed that the PES ratio depends strongly on the steam consumer requirements, rising from values under 10% for low heat flow rates and few preheaters to over 25% for a higher number of preheaters, high heat flow rates, and low steam pressures to the consumer. At the same heat flow rate to the consumer, the power-to-heat ratio in full cogeneration mode increases with the decrease in the required steam pressure to the consumer.

Structural properties of materials are important during flexible pavement design since they represent the capacity to withstand traffic loads. It is essential to regularly update the structural inputs for design to achieve optimum pavement designs. Alabama Department of Transportation (ALDOT) updated the structural inputs for hot-mix-asphalt in 2009, but not yet those for base and subgrade materials. Therefore, the goal of this study was to evaluate and update the materials’ structural inputs used by ALDOT for: 1) aggregate base (limestone and sandstone), 2) stabilized base (full-depth reclamation with cement [FDR-C]) and soil-cement [S-C]), and 3) subgrade. For that purpose, numerous laboratory and field-testing results for those materials were used to obtain resilient modulus (M R ) and structural coefficients, which were compared with ALDOT default values using statistical analysis methods. For aggregate base, the M R of limestone was statistically greater while the M R of sandstone was statistically lower than the default design values. For stabilized aggregate base, the M R of FDR-C and S-C were statistically lower than the default values when using 7-day unconfined compressive strength (UCS) and statistically greater when using 28-day UCS. The use of 28-day UCS in existing correlation equations to obtain structural properties of FDR-C and S-C was shown to provide more realistic values and, therefore, is recommended for design purposes. The practical difference between the new base materials properties and the default design values was also evaluated through a case study. For subgrade, new recommended M R values were provided for all soil types.

This study aims to develop an automated screw briquetting system that will be applicable as a small business venture in provincial areas of the Philippines. The existing manual briquette machine was integrated with automation equipment and software to facilitate continuous briquette production. The automated machine uses an induction motor fitted with a Variable Frequency Drive to control the motor’s rotational speed. A Programmable Logic Controller was used to facilitate the overall control of the briquetting system through the synchronization of the sensors and drivers. In addition, the machine features a band heater with a temperature controller and a control panel for safety and ease of operation. A simulation was performed to determine the optimal design configuration of the screw geometry applicable for automation. Experiments were performed by varying the temperature and screw speed to achieve optimal briquette quality. The automation results showed that briquettes of high quality can be produced at a rate of 10kg/hr. An economic feasibility study was done to show the machine’s viability in a provincial small business context.

Abstract In this paper, we investigate the performance optimization of a novel perovskite solar cell (PSC) structure comprising lead-free methylammonium tin iodide (MASnI₃) active layer along with a unique charge transport layers (CTLs) material combination such as NiOx as the hole transport layer (HTL) and ZnO as the electron transport layer (ETL) at room temperature. Unlike traditional toxic lead-based perovskites, our proposed eco-friendly MASnI3-based PSC structure leverages inorganic CTLs to improve the structural stability and charge transport efficiency significantly. This design addresses key limitations such as recombination losses, thermal instability, and toxicity. The use of wider band gap CTLs ensures high optical transparency, and the favourable energy level alignments with the active layer contribute to the enhanced device overall performance and long-term cost-effectiveness. The simulated results are verified with experimental data taken from literature. The effects of several performance-defining structural parameters, such as the thickness of different layers, doping density, and defect density, are considered in the SCAPS-1D-based simulation. The results indicate the possibilities of some optimum designs for the best target performance. The design with optimized structural parameters of the perovskite absorber layer (PAL), HTL, and ETL yields a high-power conversion efficiency (PCE) of 33.46%. The optimized structure also achieves more than 90% quantum efficiency (QE) over a wide visible spectrum coverage (300-870 nm). Comparative analysis with recently published MASnI3-based PSCs having other inorganic CTLs combinations reveals the superiority of our proposed NiOx and ZnO-based alternative PSC architecture. Variations of fill factor (FF), open-circuit voltage (VOC), short-circuit current density (JSC), and quantum efficiency (QE) are also investigated.

Abstract This study aims to find the optimum design of reinforcing layers placed around the hole in a composite laminated plate to maximize its first-ply and ultimate failure strengths with minimum use of reinforcing material. The ultimate failure load is determined using a progressive damage model incorporating the Puck failure criterion and the material property degradation method. A finite element model is developed to simulate the structural response under loading. The model’s accuracy is confirmed by comparing its predictions with existing experimental data. The optimum local reinforcement designs are identified using a modified simulated annealing algorithm, which is made more reliable and easier to apply with the improvements introduced in this study. The optimization variables are chosen as the fiber orientation angles and the dimensions of the reinforcing layers. Optimizations are performed for different configurations of local reinforcements to determine the most effective way of placing the reinforcing layers. Significant improvements in the strength are obtained with minimal added mass through optimization.

This research investigates artificial intelligence techniques in the form of parametric modeling and machine learning to render buildings energy independent and earthquake resilient. The aim is to create a human-oriented, adaptable system for sustainable design in earthquake-prone regions. A mixed-methods design was used collaboratively. Designers, architects, and energy specialists collaboratively created initial design sketches, which were then converted to virtual models via BIM software such as Grasshopper for Rhino and Dynamo for Revit. Site-specific factors—geometry, materials, and energy loads- were coupled with seismic data (2000–2023) and NZEB performance curves (2015–2023). Subsequently, data was utilized to train supervised machine learning models (Random Forest, SVM) to forecast structural and energy results with 89% and 85% accuracy, respectively. Their results were embedded into parametric design cycles, modeling more than 27,000 design variants. Each of these designs was evaluated for international code compliance (Eurocode 8, ASHRAE 90.1, LEED v4) and optimized using a genetic algorithm to provide optimal safety and energy performance. The optimum designs reduced energy consumption by 30% annually and provided 28.6% greater seismic safety. These findings were verified by statistical testing (ANOVA, regression) and pilot field testing, ascertaining predictive efficacy and practical use. This study illustrates how the integration of AI at the initial stages of architectural design enables the development of buildings that not only remain structurally sound and energy-efficient but also are resilient, ecological, and futureresilient. The method encourages collaboration, enhances data-driven imagination, and provides access to resilient, climate-concordant infrastructure.

This research investigates artificial intelligence techniques in the form of parametric modeling and machine learning to render buildings energy independent and earthquake resilient. The aim is to create a human-oriented, adaptable system for sustainable design in earthquake-prone regions. A mixed-methods design was used collaboratively. Designers, architects, and energy specialists collaboratively created initial design sketches, which were then converted to virtual models via BIM software such as Grasshopper for Rhino and Dynamo for Revit. Site-specific factors—geometry, materials, and energy loads- were coupled with seismic data (2000–2023) and NZEB performance curves (2015–2023). Subsequently, data was utilized to train supervised machine learning models (Random Forest, SVM) to forecast structural and energy results with 89% and 85% accuracy, respectively. Their results were embedded into parametric design cycles, modeling more than 27,000 design variants. Each of these designs was evaluated for international code compliance (Eurocode 8, ASHRAE 90.1, LEED v4) and optimized using a genetic algorithm to provide optimal safety and energy performance. The optimum designs reduced energy consumption by 30% annually and provided 28.6% greater seismic safety. These findings were verified by statistical testing (ANOVA, regression) and pilot field testing, ascertaining predictive efficacy and practical use. This study illustrates how the integration of AI at the initial stages of architectural design enables the development of buildings that not only remain structurally sound and energy-efficient but also are resilient, ecological, and futureresilient. The method encourages collaboration, enhances data-driven imagination, and provides access to resilient, climate-concordant infrastructure.

Behavioral research and experimental evidence support the notion that human operators behave differently than machines. The literature indicates that when humans are likely to cause idleness in their immediate environment, they tend to increase their work pace. We analytically investigate the effect of social human work behaviour for designing serial asynchronous production lines. Specifically, we study the optimal allocation of the total workload and buffer space among workstations by modelling the processing times of human operators using a continuous-time Markov chain to optimise expected output or output variability. Our findings suggest that the speed-up pressure and fatigue of human work behaviour when distributing system resources have significant impact and should not be ignored. Furthermore, to minimise deviations from expected performance in a system with high processing time variability, the right distribution of buffer spaces should be prioritised over the allocation of total workload. Finally, our results show that in a system with human operators: (1) the workload allocation that maximises throughput violates all three properties of the classical bowl-phenomenon: reversibility, symmetrical allocation, and monotonicity, (2) optimum designs for output variability differs from the traditional guidelines, (3) pure buffer allocation can be more effective than pure workload allocation if output variability is minimised.

Concrete-Filled Double-Skin Steel Tube (CFDST) columns are a cutting-edge structural option that blends the compressive qualities of concrete with the strength and flexibility of steel. This review study offers a thorough analysis of the body of knowledge on CFDST columns, concentrating on their applications, design factors, and structural behavior. A critical examination is conducted on significant elements, such as dynamic performance, fire resistance, local and global buckling, steel-concrete bonding, and load-bearing capacity. The report also highlights construction-related challenges, such as complex bonding and manufacturing processes—inefficiencies, while exploring alternatives, like implementing shear connections, advanced concrete materials, and optimum designs. According to the assessment, CFDST columns have several important advantages that make them appropriate for high-rise structures, bridges, and infrastructure in challenging conditions. These advantages include their high strength-to-weight ratio, enhanced seismic performance, and sustainability benefits. The knowledge gaps and areas that need more investigation, such as long-term durability and creative design approaches, are also discussed. To ensure that CFDST columns are widely used in contemporary construction, this study intends to direct future research, improve design procedures, and aid in creating engineering standards. The following points may improve the conclusion parameters commonly used in CFDST column studies: Column length-to-outer diameter ratio (L/Do), typically between 3 and 5, and spacing of shear connectors of approximately 50 mm to 200 mm.

Abstract This paper presents the design of optimum wideband compensator filters to improve the passband of comb-based filter classes composed of the cascade of different lengths of comb filters. More specifically, we consider three classes of comb-based functions modified from the literature proposed to increase the aliasing attenuation of comb filters. However, those functions exhibit a high passband droop, which should be compensated to avoid the deformation of a decimated signal. The magnitude characteristic of the filter is in sinusoidal form. The design parameters are amplitudes of the sinusoidal functions obtained using MATLAB Particle Swarm Optimization (PSO). As a result, a minimum of the maximum passband deviation is achieved. The examples of the optimum compensator designs for all three comb-based filter classes are presented. As a result, the proposed decimation filter is less complex compared with the classical Remez-designed decimation filter.