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This article mainly investigates a proactive replacement policy for a stochastically deteriorating system concurrently subject to two types of shocks. First, the closed-form representation of system reliability function suffering from both a degradation process and environmental shocks is derived based on the degradation-threshold-failure (DTS) modeling framework. An age- and state-dependent competing risks model with mutual dependence between the two failure processes is embedded into system reliability modeling, where two types of shocks are taken into consideration upon arrival of an external shock including a minor one and a major one. Based on which, a bivariate maintenance policy is put forward for the deteriorating system, where the system is proactively replaced before failure at a planned time, or at an appropriate number of minimal repairs, whichever takes place first. The expected long-run cost rate (ELRCR) is formulated, and optimal solutions are evaluated analytically for two special cases. Finally, an illustrative example is redesigned to validate the theoretical results, exploring the significance of two types of shocks and mutual dependence in system reliability modeling, and illustrating the potential applications in maintenance decisions in various manufacturing systems. [ABSTRACT FROM AUTHOR]

The discharge of colored effluent into water bodies is a big concern; hence, the current work was designed to fabricate a superior nanocomposite (NBM) using the Newbouldia laevis husk (NB) and functionalized multiwalled carbon nanotubes (f-MWCNTs) for the adsorption of malachite green oxalate (MGO). Brunauer–Emmett–Teller (BET) surface analysis was used to assess the specific surface area of NB (0.7699 m2 g−1) and NBM (94.006 m2 g−1). Fourier transform infrared spectroscopy (FTIR) was employed to determine the chemical moieties on the surface of the adsorbent. Field emission scanning electron microscopy (FESEM) and thermogravimetric analysis (TGA) were used to analyze the surface morphology and the thermal behavior of the adsorbents. Essential factors of the adsorption process were investigated, and it was revealed that pH 6.0, adsorbent dose of 0.05 g, contact time 80 min, concentration of 100 mg dm−3 and maximum adsorption capacity of 35.78 mg g−1 (NB) and 69.97 mg g−1 (NBM) were the optimal parameters. The NB and NBM adsorption processes followed a pseudo-first-order kinetic model. The exothermic and endothermic adsorptive processes were noticed to be the best descriptions of MGO elimination by NB and NBM, respectively. The uptake of MGO by NB and NBM was best described by models of Freundlich and Langmuir isotherms. Besides, NBM demonstrated uptake efficiency that is >80% after the fourth adsorption/desorption cycle. As a result, NBM has a wide range of possible uses in environmental remediation. The husk of Newbouldia laevis is a frequent waste that must be managed properly. This paper describes the application of Newbouldia laevis husk as a value-added material for the design of a water treatment agent. The use of carbon nanotube in the modification of Newbouldia laevis husk would have a synergistic effect on the overall property of the nanocomposite. Nanocomposite synthesized from multiwalled carbon nanotubes (MWCNTs) and Newbouldia laevis husk were characterized and used for the sequestration of malachite green oxalate from contaminated water. Our primary goal is to optimize the nanocomposite by varying factors of adsorption such as solution pH, equilibrium, kinetic, thermodynamic, and regeneration studies. We believe that this study will contribute to the existing knowledge of Newbouldia laevis husk. Owing to the exceptional potential of the nanocomposite, this adsorbent can be extended to possible field applications. [ABSTRACT FROM AUTHOR]

This study marks the exploration into the production of ectoine, a valuable compound with significant potential as an antioxidant, osmoprotectant, anti-inflammatory agent, and stabilizer of cell membranes, proteins, and DNA integrity. Our focus centred on investigating the presence of ectoine and optimizing its production by the novel ectoine producer bacterial strain, Piscibacillus halophilus. For the optimization of ectoine production the effects of carbon and nitrogen sources, salt, pH, agitation and incubation period were optimized by one-factor-at-a-time. We started with an initial ectoine content of 46.92 mg/L, and through a series of optimization processes, we achieved a remarkable increase, resulting in an ectoine content of 1498.2 mg/L. The bacterial species P. halophilus achieved its highest ectoine production after 48 h of incubation, with conditions set at 10 % (w/v) salinity, pH of 7.50, and an agitation speed of 160 rpm. These precise conditions were found to be the most favourable for maximizing ectoine production by this strain. Besides, we have achieved successful purification of ectoine from the crude extract through a streamlined single-step process. This purification method has delivered an exceptional level of purity, surpassing 99.15 %, and an impressive yield of over 99 %. Importantly, we accomplished this using readily available and cost-effective strong acids (HCl) and strong bases (NaOH) to arrange pH gradients. The use of acid and base in the purification process of ectoine reflects an innovative and sustainable methodology. • Piscibacillus halophilus as an alternative ectoine producer. • Ectoine purification with readily available and cost-effective strong acid (HCl) and strong base (NaOH). • Obtaining ectoine with high purity (99.15 %) and yield (99 %) using an innovative and sustainable methodology. [ABSTRACT FROM AUTHOR]

This work proposes a novel technique to conduct back‐analysis of lateral displacement of deep cement mixing (DCM) columns in deep excavation construction. For the first time, we propose a process to investigate both soil and underground structure end‐to‐end automatically. The novel technique is a complex combination of three crucial factors: (1) a nature‐inspired optimization algorithm (O), (2) a three‐dimensional PLAXIS Geotechnical Engineering software (P) and (3) a modern Python language programming (P), hereinafter called the a nature‐inspired optimization algorithm (O), a three‐dimensional PLAXIS Geotechnical Engineering software (P) and a modern Python language programming (P) (OPP) technique. The novel meta‐heuristic algorithm simulated the co‐evolved partnership behavior of shrimps and goby fishes, termed Shrimp and Goby Association (SGA), which plays an important role in complex analyses. A series of exams to determine the SGA's performance is conducted on 38 benchmark test functions (IEEE Congress on Evolutionary Computation 2017 and 2019) and three real‐world engineering design problems to showcase its applicability. The metaheuristic and PLAXIS 3D analysis work well together, which makes the back‐analysis technique powerfully to determinate stiffness parameters instead of the traditional approaches. Based on the optimized parameters, the lateral deflection of DCM and soil are well predicted for excavation. This study proposes a technique to estimate efficiently the stiffness parameter for very soft soil. As a consequence of the optimization process, an equation to determine the stiffness parameter of DCM columns from laboratory test is also proposed. Based on the obtained results, this research provides a comprehensive methodology for predicting risk, enhancing safety, saving time and money, and effectively designing and constructing underground structures. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive techno-economic analysis of the world's first large-scale commercial Power-to-X (PtX) plant in Kassø, Denmark. This innovative facility combines solar photovoltaic (PV), an electrical grid, green hydrogen production, methanol synthesis, and district heating in an integrated system. We conduct an in-depth analysis to explore optimal operating conditions, the potential for sector coupling within the power and heat sectors, and the economic feasibility of the integrated system. In particular, we study different scenarios considering changes in solar PV power generation, electricity market price, CO 2 price, and methanol price to explore their impacts on the system performance. Our results show that the electrolyzer's operation, a pivotal component of the system, heavily depends on the electricity spot price as well as the methanol price. Based on the green production of methanol, a premium price of 4800 DKK/t (0.64 €/t) is considered in this study (selling price of 8400 DKK/t). The results show that in case of high electricity prices (2022 spot market) or no premium price for methanol, the optimum annual production of methanol decreases by 58% and 67%, respectively. Furthermore, we observe that the variability of solar power output significantly impacts the plant's performance and economic viability, as it supplies about 30% of the plant's power consumption. Although the results are unique to the Kassø plant, the study's techniques and insights can be applied to other comparable systems around the world. The study also highlights how crucial it is to take market, climate, and technical factors into account when developing and running integrated energy systems. • Techno-economic analysis of the world's first large-scale commercial Power-to-X (PtX) plant in Kassø, Denmark. • Insights to optimal operating conditions, sector coupling potential, and economic feasibility of Power-to-Methanol plants. • Analysis of the impact of market dynamics on the operational efficiency and economic viability of the integrated plant. • Study of the role of intermittent solar power and its impact on the performance and economic sustainability of the plant. [ABSTRACT FROM AUTHOR]

Task scheduling is the core research content of cloud computing. It studies how to allocate tasks between computing nodes, so that tasks are evenly allocated or the execution cost of each task is minimized or the overall performance of the system is optimized. Different from the previous task slices that perform independent tasks sequentially in a model with processing time as the goal, we construct a mathematical model with the goal of optimizing response time, in which task slices are executed in parallel. Then, a solution method based on the improved adjustment entropy function is given and a new task scheduling algorithm is designed. Finally, we implement our proposed task scheduling algorithm and compare it with the game theory algorithm and the balanced scheduling algorithm. From the experimental results, by comparing the response time of the task with the size of the control system, the system load and the communication bandwidth, it is found that the task scheduling algorithm we proposed is superior to the game theory algorithm and the balanced scheduling algorithm in response time, and the algorithm we proposed is fair. [ABSTRACT FROM AUTHOR]

Dynamic workloads with varying parameters are delivered to data centers to be scheduled on virtual machines (VMs). These unreliable, inconsistent, shockingly rising workloads with changing resource requisites may cause data center servers to become imbalanced. Consequently, the servers' resource utilization and QoS get degraded which further subsides the energy usage. To meet the requirement for variable on-demand resource provisioning, the current infrastructure must be virtualized to escalate its capability and capacity. Furthermore, because of the existence of competing balancing constraints, load balancing in cloud systems is NP-hard. An efficient load-balancing strategy in coalition with a scheduling technique based on the fusion of the Firefly (FF) and Honeybee algorithm (HA) is implemented to resolve the shortcomings of load balancing. This article proposes an efficient Firefly and Honeybee-based Load Balancing algorithm (FHLBA), to optimize average load, response time, and turnaround time. This methodology not only improvises resource utilization and throughput but also depreciates the makespan, degree of imbalance. It initially executes the FF algorithm to explore potential search space and identify the best feasible mapping of tasks to VMs. Further, the HA technique is applied to eliminate over-usage and under-usage of resources and optimize resource usage as per the workload. The efficacy of the proposed technique is appraised through simulation of the independent and non-preemptive tasks on Cloudsim. A comparison with different mechanisms such as RR, FCFS, SJF, GA, IPSO, IPSO-Firefly, Firefly, OLB, and JAYA is given. The simulation results show the least average load of 0.238 ms, minimum average response time of 13.56 ms, memory usage up to 93%, effective CPU utilization of 98%, a throughput of 73%, and a makespan time of 148 ms, degree of imbalance of 172.01 is achieved with a variable volume of tasks and VMs. [ABSTRACT FROM AUTHOR]

Deep neural networks have shown remarkable performance on a wide range of classification tasks and applications. However, the large model size and the enormous size of the training dataset make the training process slow and often limited by the computing resources. To overcome this limitation, distributed training can be used to accelerate the process by utilizing multiple devices for a single model training. In this work, we evaluate the performance of Microsoft DeepSpeed, a distributed training library, on image classification tasks by comparing the performance of 108 trained neural networks in 27 unique settings. Our experimental results suggest that DeepSpeed may provide limited benefits for simpler learning tasks (e.g. smaller neural network models or simpler datasets). On the other hand, for more complex learning tasks, DeepSpeed can provide up to 8× faster training with possible performance improvement. Our study contributes to a better understanding of the capabilities and limitations of the DeepSpeed library, providing insights into when and where it may be most beneficial to use in image classification settings. [ABSTRACT FROM AUTHOR]

This work presents supervised machine learning techniques for the problem of virtualization design with protection over elastic optical networks (EONs) for predicting the total number of used spectrum slots to support all traffic demands. It considers virtual optical networks (VONs) subject to protection and proposes learning techniques to solve the link capacity problem of EONs with virtualization faster than traditional integer linear programming (ILP) formulations, but keeping the finds near to the optimal ones. The performance of the models were evaluated using statistical metrics, along with the time for training and performing inferences, both using quantitative and qualitative analysis. They showed that the proposed method is effective for predicting the number of required slots (bandwidth) on physical substrate subject to several VONs. [ABSTRACT FROM AUTHOR]

The paper is devoted to a problem of acquiring elastic properties of a composite material from the vibration testing data with a simplified experimental acquisition scheme. The specimen is considered to abide by the linear elasticity laws and subject to viscoelastic damping. The boundary value problem for transverse movement of such a specimen in the frequency domain is formulated and solved with finite-element method. The correction method is suggested for the finite element matrices to account for the mass of the accelerometer. The problem of acquiring the elastic parameters is then formulated as a nonlinear least-square optimization problem. The usage of the automatic differentiation technique for stable and efficient computation of the gradient and hessian allows to use well-studied first and second order local optimization methods. We also explore the possibility of generating initial guesses for local minimization by heuristic global methods. The results of the numerical experiments on simulated data are analyzed in order to provide insights for the following real life experiments. [ABSTRACT FROM AUTHOR]

Asymmetric bioreductions have the potential to synthesize chiral alcohols when catalyzed by biocatalysts. Nevertheless, the (S)-phenyl (pyridin-2-yl)methanol ((S)-2) analgesic synthesis poses significant challenges concerning unsatisfactory substrate amount and production method. Thus, this study proposes an inscribed design-focused multi-response nonlinear optimization model for the asymmetric reduction of the phenyl(pyridin-2-yl)methanone (1) with Leuconostoc pseudomesenteroides N13 biocatalyst. From the novel inscribed design-focused multi-response nonlinear optimization model, optimization conditions of the reaction, such as pH = 6, temperature = 29 °C, incubation time = 53 h, and agitation speed = 153 rpm, were found. Also, the reaction conversion was predicted to be 99%, and the product of the enantiomeric excess (ee) was 98.4% under the obtained optimization conditions. (S)-2 was obtained with 99% ee, 99% conversion, and 98% yield while performing a validation experiment using the determined optimized conditions. In addition, 1 with the amount of 11.9 g was converted entirely to (S)-2 (11.79 g, 98% isolated yield) on a high gram scale. Also, this study is noted as the first example of the gram-scale production of (S)-2 using an optimization strategy and biocatalyst. Further, the applicability of the inscribed design-focused optimization model in biocatalytic reactions has been demonstrated and provides an effective process for the analgesic synthesis of (S)-2, which is a green, cost-effective method of producing chiral aryl heteroaryl methanol. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have been heavily used to minimize the worldwide pollution. Battery storage system is the most important and expensive system in these vehicles. An accurate battery management system (BMS) must be applied to monitor and control the battery states. From these measurements, the residual useful life (RUL) is estimated to avoid any further safety issue which can destroy the battery system or vehicle, or harm the passengers. Many features are required to be measured to estimate the battery states and then estimate the RUL such as battery voltage, current, and temperature according to the time. These measurements are taken using embedded system, and the measured data are stored in data files. Artificial neural networks (ANNs), long short-term memory (LSTM), support vector regressors (SVRs), random forest (RF), and boosting methods have been implemented to estimate the battery RUL. Moreover, many optimization algorithms such as particle swarm optimizer (PSO) and whale optimization algorithm (WOA) are integrated with the extreme learning machine (ELM) to estimate the battery RUL. The root mean square error (RMSE) is considered the main factor that has been used to judge which algorithm is more accurate than the other. It is found that both PSO-ELM and WOA-ELM supersede the machine learning tools, where the RMSE values are 1.46% and 1.51%, respectively. These values are 2.24%, 2.25%, 2.74%, 2.84%, and 3.56% LightGBM, random forest, AdaBoost, XGBoost, and CatBoost algorithms, respectively. [ABSTRACT FROM AUTHOR]

Homogeneous polynomial functions have the potential to provide a general modeling framework for yield surfaces in metal plasticity. They incorporate as particular cases many of the previously proposed yield functions and their fitting capabilities allow for capturing a wide range of yield surface shapes. And yet, there are still two unsolved problems which turn into major obstacles when it comes to actual implementations in both academic and industrial environments: The lack of a general optimization algorithm for the calibration of their parameters and the lack of an efficient computational scheme for their value, gradient and hessian. The difficulty of the first problem is two-fold, necessitating an adequate specification of the experimental input data set and satisfaction of the convexity constraint. The second problem is specific to all high degree polynomials and is comprised of issues such as numerical stability, precision and implementation efficiency. We present practical solutions to both problems: An optimization algorithm that reduces to solving a sequence of quadratic problems and a double Horner evaluation scheme that is optimal (featuring the least number of multiplications). The resulting modeling framework can account for arbitrary input data, experimental or from crystal plasticity predictions. As illustration we show new results regarding the relationship between generalized r-values and the earing profile of deep-drawn cylindrical cups. Practicality is demonstrated by the high level of automation of the entire workflow, from material parameters calibration to finite element simulations, and supporting code (Python scripts and constitutive subroutine) made available at https://github.com/stefanSCS/PolyN. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology for the optimal design of series of sewer pipes including pumping stations. It employs a Shortest Path Algorithm to select the optimal combination of pipe diameters and invert elevations, as well as the optimal pumping features such as the number of pumps, location, and pumping head. The methodology is intended to be applied to the design of any sewer series. Although these are uncommon in real infrastructure, the study allows an analysis of the effect of pipe roughness, inflows, and pipe length on the total cost of the system and pumping features. The methodology was tested in theoretical series of 10 and 20 pipes as well as in a real series that is part of the sewer system of Bogotá, Colombia. The resulting designs suggested that using smooth pipes and reducing the pumping flow rate would decrease the cost of sewer systems in flat terrain. [ABSTRACT FROM AUTHOR]

In this study, we have investigated structural and magnetic properties of the nanoalloys of size 38 atoms of compositions Ni32Co6-nRhn and 55 atoms of compositions Ni42Co13-nRhn. We have performed simulation searches for the lowest energy structures at Gupta level and then performed DFT re-optimization for these structures. The lowest energy structures of Ni32Co6-nRhn and Ni42Co13-nRhn nanoalloys at Gupta level were found to be truncated octahedron and icosahedron structure, respectively. The lowest energy values were found to be at the compositions Ni32Co4Rh2 in Ni32Co6-nRhn and Ni42Co1Rh12 in Ni42Co13-nRhn nanoalloys. Ni atoms full occupy only the surface of the truncated octahedron and icosahedron structure with the exception of bimetallic Ni42Rh13 nanoalloy. Since the energetic stability of the truncated octahedron and icosahedron can be rationalized in terms of local atomic pressure, we have calculated the local atomic pressures of the compositions. We have also investigated the size, geometric structure, and composition effects on magnetic properties as well as stability. [ABSTRACT FROM AUTHOR]

Comet Interceptor (Comet-I) was selected in June 2019 as the first ESA F-Class mission and aims to explore a pristine comet, which will visit the inner Solar System for the first time. Comet-I will hitch a ride to a Sun-Earth L2 quasi-halo orbit, as a co-passenger in ESA's M4 ARIEL's launch. At the time of writing, this is scheduled for 2029. It will then remain there awaiting the right departure conditions to leave definitively and intercept a newly discovered comet. Comet-I will be the first mission to be designed and launched without an already identified target. Given the latter, the Comet-I Target Identification Working Group is tasked with the continuous follow-up of newly discovered comets, to analyse their characteristics as virtual targets for Comet-I. The goal of the paper is to inform the decision-making process that will eventually be necessary to commit Comet-I spacecraft to its final target. Within this context, this paper presents a trajectory optimization process based on Primer Vector theory that considers intercept trajectories with multiple revolutions and multiple deep space manoeuvres (DSM). The process is applied to 21 long period comets, which were discovered since July 1, 2020 and whose characteristics are of interest for the Comet-I mission. In fact, one of the latest of these recently discovered comets, C/2023H2, is the most accessible virtual target discovered at the time of writing, with delta-V intercept costs well within Comet-I capabilities. A complete launch window analysis is thus presented for C/2023H2, which accounts not only for the most optimal delta-V intercept transfer, but also considers all launch options that satisfy all Comet-I's current boundary conditions and flyby constraints. A systematic launch window analysis is made possible by a grid search approach on which departure and fly-by dates are fixed and the remaining design variables, including the times for manoeuvres and their Δ v impulse vector, are optimized via Primer Vector theory. This allows to understand the latest possible departure date, but also the sensitivity to departure and arrival dates of key encounter characteristics such as relative fly-by speed, the solar aspect angle and the Earth distance. • A Primer Vector Theory-based trajectory optimization tool is shown, tailored for ESA's Comet Interceptor (Comet-I). • The method supports trajectories with multiple revolutions around the Sun and multiple impulses. • The method allows rapid grid search over the search space, finding the lowest cost trajectory and other notable solutions. • A novel analysis of the launch window is presented, contributing to the decision-making process of Comet-I. • A multi-manoeuvre strategy benefits Comet-I's trajectory design, lowering the delta-V cost and improving mission flexibility. [ABSTRACT FROM AUTHOR]

Attaining sustainable agriculture requires water consumption management. A water allocation optimization model was developed for the Moghan irrigation network (northwest of Iran) based on the AquaCrop plug-in model. The genetic algorithm was applied to optimize water allocation for five main crops, including wheat, first-cultivation maize, second-cultivation maize, soybeans, and alfalfa. The heuristic economic utility (EU) function was used as the objective function to optimize water allocation. In this function, drained water salinity was applied as a penalty factor to the total benefit, and soil salinity deterioration due to irrigation was also considered as a factor in each crop's benefit. The results showed that the optimal allocated water depth was 17% less than the normal water consumption. Moreover, the application of soil water salinity coefficients did not affect the ratio of EU to EB (economic benefits) for wheat and alfalfa. However, first-cultivation maize, second-cultivation maize, and soybeans cultivation led to a reduction in EU within the study area. A combination of the crops cultivation led to a change in river water quality and an 8.2% reduction in the ratio of EU to EB function. [ABSTRACT FROM AUTHOR]

Autoclave curing is a manufacturing process for high-performance parts based on carbon fiber–reinforced polymers (CFRPs) used for large aircraft parts. Today, this manufacturing process is the reference in terms of quality and, therefore, the manufactured parts' mechanical performance and robustness. However, several parameters can impact the quality of the parts resulting from this process, which requires optimizing key manufacturing parameters. In this study, the effect of autoclave process parameters (i.e., temperature, pressure, and vacuum-pressure) on the glass transition temperature (Tg), laminate compressive modulus (LCM), laminate compressive strength (LCS), and interlaminar shear strength (ILSS) was investigated using three factors, three-level Box–Behnken design (BBD) and response surface methodology (RSM). In addition, the interactions of processing parameters with Tg, LCM, LCS, and ILSS were investigated, making this study an essential investigation for accurately selecting processing parameters. Thus, there is a functionally non-linear relationship between the interaction of the autoclave process parameters. Therefore, these parameters were optimized using RSM with the maximum Tg, LCM, LCS, and ILSS. The optimization and validation of the obtained models were carried out with an average relative error below 3% for all thermomechanical and mechanical properties, indicating that the BBD and optimization were correct. Because of this, the established regression models can accurately predict the Tg, LCM, LCS, and ILSS in autoclaved epoxy/carbon composite laminates. [ABSTRACT FROM AUTHOR]

This study introduces a simulation-based optimization approach that combines a multi-objective genetic algorithm and the finite element method to design sandwich structures to reinforce composite fuselages. The sandwich structure has been parametrized with a set of mixed integer-continuous variables representing the polymer core thickness, the number and weight of laminated fiberglass layers, and size and position of the structure. Automatic scripting builds the geometry of the structure, then mounts it in the fuselage, and finally creates a conformal mesh. During the optimization, each reinforced fuselage is subjected to several load cases specified by the CS-22 EASA design standard, determining the worst load condition. Two objectives were considered: increasing the reinforced fuselage's minimum fiber stress safety factor for improved safety and reducing the number of layers of the sandwich structure to minimize manufacturing costs. Structural constraints were the mass of the sandwich structure and buckling and flexo-torsional deformation of the reinforced fuselage. Results show an efficient material arrangement due to the reduced number of layers in the sandwich structure and a stress safety factor surpassing that set by the standard. Finally, the reinforcement of a glider fuselage to incorporate a retractable electric propulsion system is presented as a real-world industrial application. [ABSTRACT FROM AUTHOR]

This study introduces a new policy for managing parking scarcity called Parking Allocation and Ride-Sharing System (PARS). In PARS, a centralized algorithm allocates parking spacesto drivers who are willing to participate in a coordinated carpool. The algorithm is used to optimize the creation of carpools going to and returning from a particular venue and simultaneously reserve parking for these carpools at the venue. An efficient mixed integer linear programming (MIP) formulation is presented and two heuristics, namely Ride Decomposition (RD) and Quick Converge (QC), are proposed and compared via internally generated experiments. Experimental results show that a commercial solver is able to solve the MIP with thousands of individuals to optimality in minutes. For larger instances, the RD and QC heuristic algorithms can solve the problem, on average, 42.23% and 86.39% faster than the commercial solver and provide solutions that are 3.61% and 3.49% from optimal, respectively. [ABSTRACT FROM AUTHOR]

In this investigation, Ultra-high Hard Armor (UHA) steel plate by Shielded Metal Arc Welding (SMAW) process using the Austenitic Stainless Steel (ASS) electrode. In the first part of this investigation, using the finite element method (FEM), thermal analysis parameters were optimized using bead on plate welds in software code, known as SYSWELD. In the second part, using optimized parameters, a coupled thermo-mechanical analysis on multipass welded fabrication was carried out. Weld thermal cycles were analyzed by simulation and compared with experimentally measured temperature profiles. A double ellipsoidal heat source model was used to simulate the SMAW process. The heating rate and cooling rate were calculated along with residual stresses. This range of heating and cooling rate resulted in untempered martensite in the heat-affected zone (HAZ), confirmed by microstructure analysis and hardness mapping. Good validation was found between the measured and simulated 2D model with a 4–10% variation. This study suggests that numerical simulation is the capable technique in understanding the thermal and mechanical properties that influence the welding of ultra-high hard armor steels joints. [ABSTRACT FROM AUTHOR]

The advancement of transportation machinery has played a crucial role in driving global economic and societal growth. However, this progress has also given rise to challenges, such as the depletion of chemical resources and the escalating impact of climate change. As a result, automobile companies are now prioritizing energy efficiency and transitioning towards eco-friendly vehicles. In response to this demand, various efforts have been made to harvest energy and improve the efficiency of eco-friendly vehicles, with energy-harvesting dampers emerging as a promising solution. This study focuses on the optimization of the shape and design of a rotary power generation system integrated within an energy harvesting system. The primary objective of the rotary power generation system is to convert mechanical motion into electrical energy, thereby enhancing the overall efficiency and sustainability of eco-friendly vehicles. By considering the specific characteristics of SAE 30 W working oil within the damper, the optimal blade shape and generator can be determined to maximize the power generation capabilities of the system. [ABSTRACT FROM AUTHOR]

Growing concerns about seismic events enforced structural engineers and architects to embrace the hazardous effect of ground motion in design. To address this, researchers have developed various base isolation (BI) techniques. This study comprehensively reviews BI system types, techniques, and implementation. Exploring the dynamic response of three-dimensional BI devices and the mutual effects of isolation devices and soil-structure interaction during strong ground motion, the paper covers topics such as seismic isolation of nuclear power plants, cost analysis, and various optimization techniques. Furthermore, the paper investigates the behavior of isolation devices in beyond-design events, including blast and aircraft impact loading. In general, the seismic isolation and control device response is demonstrated through shaking table tests and computational analysis. The study sheds light on the functions of seismic isolation system by comparing them with fixed base structures. Additionally, the paper presents codal recommendations, recent advancements, and current practices, aligning them with historical developments and past reviews of different BI techniques, along with their advantages and disadvantages. In conclusion, the closing remarks emphasize the future research prospects in this field. [ABSTRACT FROM AUTHOR]

This description outlines a Geographic Information System (GIS)-based rainfall-runoff model that simulates the flow of water in a river basin. The model operates on a daily time step and consists of 4 non-linear storage components: Interception, soil moisture, channel, and groundwater. It employs (SCS) Unit Hydrograph model to determine unit hydrograph ordinates. The model replicates the movement and storage of water in various parts of the basin, including vegetation, the soil surface, the soil profile, and groundwater layers. To address uncertainty, a Monte Carlo simulation feature is integrated into the model. Monte Carlo Simulation involves predicting outcomes by generating numerous iterations using estimated ranges of values for variables with inherent uncertainty. This feature generates required number of sample sets with random parameter values. The model is run for all these realizations during a calibration period, and performance metrics like NSE are calculated for each calibration year to assess prediction uncertainty, model parameter weights are computed by normalizing the corresponding likelihood values. These weights sum up to one and represent the probabilistic distribution of predicted variables, illustrating the impact of structural and parameter errors on model predictions. A sensitivity analysis reveals that the Muskingum constants K and X have the greatest influence on model performance, while parameters Фgw, Фsw, Фfc, and Фpc have a minimal effect on the model's performance. The outcomes presented in the findings indicate that the Soil moisture accounting Model successfully forecasted peak discharge by leveraging the existing historical data. The accuracy of both volume and timing in the predictions suggests the model's appropriateness for the examined catchments. [ABSTRACT FROM AUTHOR]

In today's competitive landscape, fulfilling customer expectations and achieving a competitive edge are crucial for business success. These objectives can be attained by effective monitoring in both manufacturing and service sectors to enhance quality, reduce variation, and augment productivity. The control chart, a widely used tool for this purpose, has attracted significant attention from researchers for its ability to detect anomalies and manage out‐of‐control situations. The optimization of control charts, a central focus of this review, not only enhances the detection effectiveness but also maintains the desired false alarm rate, thus ensuring efficient process control without additional cost, complexity, or operational challenges for shop floor personnel. The optimization process involves adjusting charting parameters like the sample size, sampling interval, and control limits within a hypothesis testing framework, thereby achieving optimal system performance. Numerous optimization models have been developed to enhance control chart performance. This paper introduces a classification scheme to analyze and categorize the existing research on control chart optimization. By conducting a thorough review of more than 240 articles, the study pinpoints research gaps and offers valuable insights, thereby advancing the future research in this domain. [ABSTRACT FROM AUTHOR]

Bacteriophages have emerged as effective antimicrobial agents in combating pathogenic bacteria. To successfully apply phages in real‐life scenarios, it is crucial to optimize their large‐scale production, concentration and purification processes. In this study, our objective was to optimize the phage concentration parameters with a high recovery rate within a shorter time and minimum chemical consumption. We isolated and characterized a specific Erwinia amylovora phage. Subsequently, we employed response surface methodology (RSM) combined with a three‐factor central composite design to optimize the phage recovery. The factors considered were polyethylene glycol (PEG) concentration, NaCl concentration and incubation time. The optimized values for PEG, NaCl and incubation time, which resulted in a maximum recovery rate of 85.37%, were determined as 18%, 2.38 M and 0 h, respectively. This finding indicates that the concentration step, which traditionally took up to 18 h with a recovery rate of 65%, can now be accomplished in a significantly shorter time with an improved recovery rate. Our study demonstrates the effectiveness of RSM to identify the optimum conditions for phage precipitation. This approach allows for the rapid and efficient design of precipitation protocols tailored to specific bacteriophages, resulting in shorter processing times and higher recovery rates. [ABSTRACT FROM AUTHOR]

Peptides obtained from hydrolysis have many bioactive properties and have strong biological activities against free radicals and prevent antioxidant processes that cause damage to biological macromolecules and degradation and quality reduction. Due to the large amount of rice production in the world, a large number of rice bran is produced and available. As a suitable and cheap production source, rice bran can be used for the production of plant-derived peptides. Optimizing the conditions of enzymatic hydrolysis of rice by alcalase enzyme was done with the aim of achieving anti-allergenic properties. In order to investigate the antioxidant activities of peptides, iron 3 reduction power tests and DPPH free radical activities and total antioxidant activities were used. In order to optimize the time of the Expert design software and the response surface method with three different changes: this enzyme was applied to the substrate 1-3%, temperature 40-55 degrees Celsius and hydrolysis 30-210 minutes. In the set conditions of the treatment including temperature of 51.5 degrees Celsius, 131.5 minutes and enzyme substrate 3%, it was obtained that DPPH free radical is 37.172%, total antioxidant activity is 1.109% and reducing agent is 2.084%. The obtained results showed that the process of rice bran production by alcalase enzyme leads to the production of peptides with high antianxiety properties and it is noteworthy that they can be used in the production of super-beneficial foods and pharmaceutical industries and also replace them . [ABSTRACT FROM AUTHOR]

This study aimed to optimize the formulation of chitosan -caffeic acid nanogel containing Shirazi thyme (Zataria multiflora) essential oil (ZEO) and nisin. The independent variables (the concentration of chitosan nanogel, Shirazi -thyme, and nisin) were optimized based on the highest zeta potential and encapsulation efficiency, besides the lowest particle size and IC50(DPPH) values. The results of The Box -Behnken experimental design and Stepwise -response surface model showed the optimal nanogel formulation was as follows: chitosan concentration= 0.4 g; ZEO= 157.1 ppm and nisin= 10.1 ppm. The particle size, zeta -potential, antioxidant activity, and encapsulation efficiency of the optimal chitosan -ZEO - nisin nanogel were 411.39±18.11 nm, 32.90±1.10 mV, 0.79±0.06 mg.mL - 1, 71.06 - 82.69% respectively. Moreover, the addition of optimized nanogel to the Iranian white cheese formulation showed that the treated cheese samples with ZEO and nisin (free or encapsulated in chitosan nanogel) improved the microbial quality of chess. The antimicrobial activity of the ZEO and nisin encapsulated in chitosan - caffeic acid nanogel was higher than a free form of ZEO -nisin. The Coliforms population of cheeses treated with sodium nitrate and chitosan nanogel containing ZEO -nisin was acceptable during 60 days of storage. During the storage period, the most changes in the color and texture (hardness) of the cheese samples were related to the control sample, and the least change was obtained for samples treated with sodium -nitrate and chitosan nanogel (P < 0.05). Also, the sensory quality of the sample containing ZEO and nisin was acceptable for the sensory evaluator. The sample containing chitosan nanogel received an acceptable sensory score (>3) during 60 days of storage. In general, the potential of the nanogel in increasing the shelf-life of Iranian white cheese was comparable with sodium nitrate. [ABSTRACT FROM AUTHOR]

Summary: The solution of resource allocation is based on NOMA and OMA structures that are suboptimal to satisfy the demanding QoS and higher data rate requirements compared to EE requirements in 5G cellular networks. In this work, the resource allocation problem in the hybrid MC‐NOMA system is solved. The system achieves the trade‐off between spectral and energy efficiency (EE) with the lowest rate of user requirements. The system's resource allocation, including power allocation, is considered the crucial factor and is identified as a single‐objective problem. The suggested work focuses on user pairing and allocation on paired users following processing. The user grouping is designed to connect near‐ and far‐users with high C3 to increase the fairness of NOMA. As a result, this work considers multiple goals, such as C3 maximization, spectrum efficiency, and energy efficiency. This paper aims to present a novel hybrid optimization model with the combination of coati and bald eagle optimization algorithms, to solve the defined optimization problem. The experimental results show that the suggested system outperforms the conventional algorithms by evaluating different performance measures such as channel correlation, spectrum efficiency, energy efficiency, and power allocation. [ABSTRACT FROM AUTHOR]

This paper combines artificial intelligence deep learning technology to optimize the wine making process of mango wine. Moreover, in view of the shortcomings of traditional electronic nose data processing methods, a deep learning method based on SSAE‐BPNN is proposed for electronic nose data processing. In addition, according to the characteristics of automatic learning features, this paper uses a deep learning method based on SSAE‐BPNN to simplify the process of traditional data processing methods. Finally, this paper constructs an electronic nose system that can be used to identify mango wine making characteristics, and enhances the effect of electronic nose recognition through deep learning. Through the analysis, it can be seen that the mango wine making process optimization method based on artificial intelligence deep learning technology proposed in this paper has a certain effect, and it has optimized the traditional mango wine making process. [ABSTRACT FROM AUTHOR]

Turning biomass waste into briquettes using densification techniques is one of the most promising steps toward mitigating biomass waste pollution and fuel issues in developing countries. Despite the continuous growth of scientific output over the past few decades, only a limited amount of information is available in the literature on biomass briquette optimization and mathematical modeling, as well as the physiochemical characterization of biomass feedstocks and briquette operating variables. In light of this gap in the current literature, this study summarizes the current state of the art and recent advances in biomass-based briquettes generated from agro-residues as an alternative source of clean energy. The primary research method for this study is literature review and conceptual modeling. First, many densification processes, such as piston press, screw press, roller press, hydraulic press, and quality variables such as ash content, calorific value, moisture content, density, compressive strength, shatter index, etc., are thoroughly discussed and compared. Then characteristics of different biomass wastes are studied, together with process parameters, including temperature, type of binder used, particle size, and influence on densification process choice. The current evaluation concentrated on the mathematical modeling and optimization of the briquetting technology and the usefulness of briquettes in applications for heating, cooking, and energy production. Overall, this manuscript will help new researchers understand the basic methodology, classification, limitations, and future perspective of briquetting technology in the production of solid biofuels. [ABSTRACT FROM AUTHOR]

Reasonable workshop layout can reduce the transportation distance, reduce the production cost and improve the production efficiency. Aiming at the problem of unreasonable workshop layout, this paper takes an actual case as the research object and studies its workshop layout. Firstly, the mathematical model with the objectives of minimizing logistics transportation cost and maximizing non-logistics relationship is established. Then, the sparrow search algorithm (SSA) is used to find the optimal layout that satisfies the objective function. Finally, Flexsim software is used to simulate the layout scheme. The results show that compared with the genetic algorithm and the original layout scheme, the layout scheme obtained by the SSA algorithm reduces the total transportation distance by 26 m and 325 m, and improves the non-logistic relationship by 1.6% and 24.84%, respectively. Optimizing the layout using SSA algorithms increases product productivity, which is a good improvement over the original workshop layout. [ABSTRACT FROM AUTHOR]

The area of the brain affected by a brain tumour can be identified using the tumour's shape, size, location, and border. This study seeks to develop a novel system of classification for brain tumours through pre-processing, segmentation, feature extraction, and tumour classification. An improved median filter will be applied to the input image in this initial phase to improve it. In this step, the image is segmented using a U-net model. Then, characteristics based on the Median Binary Pattern (MBP), the loop, the modified Local Gabor Directional Pattern (LGDiP), and the tumour size are retrieved. A hybrid model that fuses DBN and Bi-LSTM is presented to classify cancers. The optimal weights for both classifiers will be tuned during training to improve the classification performance. For this, BMEBEO (Blue Monkey Extended Bald Eagle Optimization) is proposed, which is a hybrid optimization technique. The suggested model obtains the maximum F-measure of 96.16%%. [ABSTRACT FROM AUTHOR]

Machining of titanium matrix composite in unconventional methodology is exceedingly obscure as it possesses advanced strength-to-weight ratio and corrosion resistant. It provides the current state-of-the-art of enrichment of mechanical properties like corrosion, wear, fatigue resistant and biocompatible properties. A novel hybrid desirable multi-objective genetic algorithm (DMOGA) is projected for examination of optimal performance measures of wire-cut electrical discharge machining altering the major input parameters like power (P), peak current (IP) and time-off (Toff). The chief benefit of DMOGA over additional optimization methods lies in its accuracy and robustness. The novelty belongs in its iterative evolution of expansion of effectual grandee set, articulated as population converging to a fitness function. It is outstandingly condensed with disciplined methodology of genetic simulation. Experimental investigation is conceded on material removal rate (MRR), surface roughness (SR), kerf width (KW) and over cut (OC). Optimality set is attained whose combined desirability is 0.72 and improved by DMOGA to 0.75. The final optimal solution is further improved by 4.17% when contrasted with novel DMOGA algorithm to only desirability, when P is 7 W, Toff is 25 µs, IP is 10 A, MRR is 3.68 mm3/min, SR is 1.19 µm, KW is 0.33 mm, OC is 0.08 mm and combined desirability is 0.75. % improvement of MRR is 1.94%, SR is 16.81%, KW is 6.06% and OC is 18.75%. [ABSTRACT FROM AUTHOR]

Numerous studies have been carried out to characterize uncertainties of soil properties, and lots of multivariate databases have been compiled to make the characterization of uncertainties more realistic. However, when it comes to complex situations, that is, for complex critical state constitutive models and soil–structure interface properties, the uncertainty quantification becomes a challenge. This paper aims to quantify the uncertainties of the soil–structure interface properties from laboratory tests. A framework of uncertainty quantification based on a simplified two-dimensional Monte Carlo simulation is first proposed. To validate the performance of the framework, an enhanced hypoplastic interface model considering particle breakage is then proposed and employed in the uncertainty quantification framework. The CMA-ES algorithm is then used to calibrate the uncertainties based on the framework and the hypoplastic soil–structure interface model. The results showed the proposed framework with the enhanced model can capture the uncertainties of the soil–structure interface properties. In the studied experiments, 30 out of 42 experimental curves were found to be well calibrated based on the Kolmogorov–Smirnov normality test. Furthermore, to obtain acceptable results, based on the calibration-validation process, the effect of the selection of experiments on the calibration performance is discussed. Some suggestions on how to choose experiments to calibrate the soil–structure interface properties are summarized, which should be helpful in practice. [ABSTRACT FROM AUTHOR]

Synchronous Reluctance Motors (SynRMs) have become very attractive alternatives to induction motors which have been widely used in industry in recent years. Axial-flux motor (AFM) structures have been studied in the literature as a new field. However, studies on axial-flux synchronous reluctance motors (AF-SynRMs) are very limited. In this study, optimization of an AF-SynRM has been performed to achieve optimal motor performance. In addition, the effects of the permanent magnets (PMs) have been investigated. In this design, PMs have been placed in the barriers using eight different ways, and motor performance has been examined. The placement of magnets on the barriers that are close to the air gap has an increasing effect on the average torque. However, the distorting effect on the torque ripple is seen to be quite high. In particular, the placement of magnet on all barriers improves the machine performance significantly. The negative impact on torque ripple is also low. Therefore, this study aims to provide guidance to researchers working on the design of AF-SynRMs, especially with PMs. [ABSTRACT FROM AUTHOR]

This study proposes the utilization of particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms to optimize the gains in sliding mode control (SMC) for a doubly fed induction generator (DFIG) system. The primary aim is to enhance reference tracking, improve overall system performance, and ensure system stability. The controllers' performance is assessed by evaluating tracking performance and stability through the analysis of noise signals in the stator's active and reactive power controllers. The experiments involve 10 search agents in both PSO and GWO algorithms, with a maximum of 100 iterations. Statistical results reveal that the PSO algorithm demonstrates better convergence stability and yields lower fitness function values, particularly in the case of IAE. Simulations demonstrate that SMC tuning with PSO leads to satisfactory dynamics for active and reactive powers, characterized by fast response and no overshoot. Performance metrics, including IAE and ISE indices, are employed to assess the control strategies, consistently showing that the SMC with PSO controller outperforms the SMC with GWO controller in terms of criteria values. The proposed control strategy utilizing PSO optimization effectively enhances reference tracking and overall performance in DFIG systems. [ABSTRACT FROM AUTHOR]

This study optimized the catalytic performance of Ni/Fibrous Zeolite-Y (Ni/FHY) toward dry reforming of methane (DRM). The optimization study was conducted under three independent variables employing the response surface methodology (RSM), trailed by a regeneration and stability studies. The study focused on the independent parameters of reaction temperature (A) (700 °C–900 °C), gas hourly space velocity (GHSV) (B) (15,000 mL/gcat−1.h−1 – 35,000 mL/gcat−1.h−1), and CH4/CO2 ratio (C) (1.0–3.0). The catalyst exhibited excellent activity (CH4 conversion = 94.5%, CO2 conversion = 88.3%, and H2/CO = 1.05) when all the parameters were under these optimum conditions (A = 833 °C, B = 24,962 mL/gcat−1.h−1, and C = 2.08). The catalytic stability study revealed an excellent performance of Ni/FHY where there was no indication of catalyst deactivation up to 30 h of time-on-stream (TOS). The outcome of the excellent catalytic activity can be explained by Ni-metal inclusion that enabled a substantial increase in the interaction between FHY and Ni, hence, subdued carbon formation. The regeneration study showed a good performance of regeneration owing to the capability of air to regenerate the catalyst completely, thus resulting in an excellent performance with 90 h TOS. [ABSTRACT FROM AUTHOR]

Precise quantification of the JAK2V617F mutation using highly sensitive assays is crucial for diagnosis, treatment process monitoring, and prognostic prediction in myeloproliferative neoplasms' (MPNs) patients. Digital droplet polymerase chain reaction (ddPCR) enables precise quantification of low-level mutations amidst a high percentage of wild type alleles without the need for external calibrators or endogenous controls. The objective of this study was to optimize a ddPCR assay for detecting the JAK2V617F mutation and establish it as a laboratory-developed ddPCR assay in our center. The optimization process involved fine-tuning five key parameters: primer/probe sequences and concentrations, annealing temperature, template amount, and PCR cycles. Our ddPCR assay demonstrated exceptional sensitivity, and the limit of quantification (LoQ) was 0.01% variant allele frequency with a coefficient of variation of approximately 76%. A comparative analysis with quantitative PCR on 39 samples showed excellent consistency (r = 0.988). In summary, through rigorous optimization process and comprehensive analytic performance validation, we have established a highly sensitive and discriminative laboratory-developed ddPCR platform for JAK2V617F detection. This optimized assay holds promise for early detection of minimal residual disease, personalized risk stratification, and potentially more effective treatment strategies in MPN patients and non-MPN populations. [ABSTRACT FROM AUTHOR]

The stationary point of optimization problems can be obtained via conjugate gradient (CG) methods without the second derivative. Many researchers have used this method to solve applications in various fields, such as neural networks and image restoration. In this study, we construct a three-term CG method that fulfills convergence analysis and a descent property. Next, in the second term, we employ a Hestenses-Stiefel CG formula with some restrictions to be positive. The third term includes a negative gradient used as a search direction multiplied by an accelerating expression. We also provide some numerical results collected using a strong Wolfe line search with different sigma values over 166 optimization functions from the CUTEr library. The result shows the proposed approach is far more efficient than alternative prevalent CG methods regarding central processing unit (CPU) time, number of iterations, number of function evaluations, and gradient evaluations. Moreover, we present some applications for the proposed three-term search direction in image restoration, and we compare the results with well-known CG methods with respect to the number of iterations, CPU time, as well as root-mean-square error (RMSE). Finally, we present three applications in regression analysis, image restoration, and electrical engineering. [ABSTRACT FROM AUTHOR]

Despite their widespread applications in sectors such as pulp and paper, textile, food and beverage, pharmaceuticals, and biofuel production, laccases encounter challenges related to their activity and stability under varying reaction conditions. This review accumulates data on the complex interplay between laccase characteristics and reaction conditions for maximizing their efficacy in diverse biotechnological processes. Benefits of organic media such as improved substrate selectivity and reaction control, and their risks such as enzyme denaturation and reduced activity are reported. Additionally, the effect of reaction conditions such as pH and temperature on laccase activity and stability are gathered and reported. Sources like Bacillus pumilus, Alcaligenes faecalis, Bacillus clausii, and Bacillus tequilensis SN4 are producing laccases that are both thermo-active and alkali-active. Additionally, changes induced by the presence of various substances within reaction media such as metals, inhibitors, and organic solvents are also reported. Bacillus pumilus and Bacillus licheniformis LS04 produce the most resistant laccases in this case. Finally, the remarkable laccases have been highlighted and the proper laccase source for each industrial application is suggested. [ABSTRACT FROM AUTHOR]

This article aims to address the issues of high curing temperatures and thermal damage in the production of prefabricated concrete components for high‐speed railways in high‐altitude and high‐latitude cold regions of China. Various steam‐curing processes for concrete are designed to optimize the high‐quality preparation process of steam‐cured concrete prefabricated components in cold environments. With the goal of controlling the residual expansion deformation and considering the overall impact of curing process on the mechanics, durability, and interface transition zone of steam‐cured concrete, the main conclusions obtained in this study are as follows. Within a pre‐curing time of 3–6 h, when the curing temperature is maintained at 45–60°C, the final residual expansion deformation can be controlled below 300 με. The compressive strength, dynamic elastic modulus, peak stress, water absorption and Chloride ion resistance of steam‐cured concrete show the great improvement under above curing processes. Curing at 80°C should be actively avoided, and it is recommended to adopt a 6 h pre‐curing time with a maximum curing temperature of 45°C, especially for cold regions in China. This study can serve as a valuable reference and provide support for the preparation of prefabricated concrete components in Chinese high‐altitude and high‐latitude areas. [ABSTRACT FROM AUTHOR]

Highlights Industrial development has led to an increase in demand for friction materials, resulting in increasingly prominent environmental issues throughout their entire life cycle, and developing green and eco‐friendly brake friction materials has become a focus in the braking field. This paper explores the mechanical and tribological properties of fly ash (industrial waste) and sisal fiber (natural fiber) as eco‐friendly components to enhance brake friction materials. Four samples containing different ratios of fly ash and sisal fiber were prepared and tested with a variable‐speed friction testing machine. The results show that the eco‐friendly alternative combination can effectively improve the friction coefficient, and reduce friction fluctuations and thermal degradation, but the wear rate will also increase accordingly. In addition, the worn morphology reveals the formation of the contact platform and wear mechanism. A hybrid integration of analytic hierarchy process (AHP) and multi‐objective optimization by ratio analysis (MOORA) was used to weigh various evaluation indicators objectively and rank the samples. The sample with 8% fly ash and 6% sisal fiber exhibited the best comprehensive tribological performance. Development of eco‐friendly brake friction composites with natural and waste additives. Investigation of the effect of the natural wastes' ratio on prepared composites' physical, mechanical, and tribological properties. Evaluation of brake friction composites with AHP‐MOORA. Sample S6F8 exhibits the best comprehensive tribological performance. [ABSTRACT FROM AUTHOR]

Sentiment Analysis (SA) is a technique for categorizing texts based on the sentimental polarity of people’s opinions. This paper introduces a sentiment analysis (SA) model with text and emojis. The two preprocessed data’s are data with text and emojis and text without emojis. Feature extraction consists text features and text with emojis features. The text features are features like N-grams, modified Term Frequency-Inverse Document Frequency (TF-IDF), and Bag-of-Words (BoW) features extracted from the text. In classification, CNN (Conventional Neural Network) and MLP (Multi-Layer Perception) use emojis and text-based SA. The CNN weight is optimized by a new Electric fish Customized Shark Smell Optimization (ECSSO) Algorithm. Similarly, the text-based SA is carried out by hybrid Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) classifiers. The bagged data are given as input to the classification process via RNN and LSTM. Here, the weight of LSTM is optimized by the suggested ECSSO algorithm. Then, the mean of LSTM and RNN determines the final output. The specificity of the developed scheme is 29.01%, 42.75%, 23.88%,22.07%, 25.31%, 18.42%, 5.68%, 10.34%, 6.20%, 6.64%, and 6.84% better for 70% than other models. The efficiency of the proposed scheme is computed and evaluated. [ABSTRACT FROM AUTHOR]

Although scientists are generally motivated to adopt sustainable practices in their research, a lack of education is often limiting. Here, I aim to fill this gap by illustrating how to substantially reduce single‐use plastic waste in scientific protocols. This article will outline the translation of the three fundamental principles of reduction, reuse, and miniaturization into experimental practice. The transfection of neurons in sterile conditions will be provided as a concrete example to discuss opportunities for change. Simply by reducing, miniaturizing, and reusing, a decrease in plastic waste of approximately 65% was achieved for exchangeable items. This article demonstrates the feasibility of adopting sustainable practices without compromising workflows or data quality. [ABSTRACT FROM AUTHOR]

Summary: This paper presents Relay node selection scheme and Deep sleep period for power management in Energy Harvesting Wireless Sensor Networks (RD‐EHWSN), a new energy‐saving scheme founded on asynchronous duty cycling. RD‐EHWSN reduces sensor node energy consumption and guarantees equilibrium energy use between sensor nodes in WSN with the energy harvesting capacity by adjusting these sensor nodes duty cycles more drastically and deeply by according to the estimated value of its residual energy on the basis of future‐presented harvested energy, and this is done through the use of a new proposed energy threshold policy. RD‐EHWSN also grips the benefit of transmitter initiated using the low power listening (LPL) technique with short preamble messages and uses a new relay node selection procedure to achieve the load balancing in WSN. We implemented RD‐EHWSN by using OMNeT++/MiXiM. For evaluation, we compared it with PS‐EHWSN, under multiple concurrent multihop traffic flows scenarios and scenarios in which nodes can harvest different energy harvesting rate. In all experiments, RD‐EHWSN significantly outperformed the PS‐EHWSN scheme; the results of simulation demonstrate that our scheme enhances the general yielding of WSN thru lessening the energy consumption and the mean latency, as well as raising the packet delivery ratio and the throughput. Moreover, RD‐EHWSN improves the WSN lifetime and ensures it operates in good condition in the case where the energy harvesting rate is lower by comparing it with the PS‐EHWSN scheme. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed optimization problem of networked Euler–Lagrange (EL) systems with actuator faults and unknown model parameters. In order to solve this issue, some event‐triggered fault‐tolerant (ETFT) optimal coordination algorithms with the mechanism of intermittent communication and intermittent controller update are designed for the first time to compensate for the impact of physical faults on the system, and reduce communication costs and control resource consumption. For practical applications, it is difficult for the system to have a secure environment if there exist malicious attacks blocking network channels among agents. Therefore, the resilient ETFT optimal coordination with intermittent communication subject to asynchronous denial‐of‐service (DoS) attacks is considered. Especially, auxiliary systems with adaptive gains are introduced in the algorithms due to the unavailability of the EL model parameters, which also enable virtual data exchange between agents and protect actual state information. Note that the algorithms are fully distributed as the global topology information is not required. Finally, the feasibility of the proposed algorithms is verified through numerical simulation of examples. [ABSTRACT FROM AUTHOR]

This study evaluated Streptomyces rochei strain NAM-19 solid-state fermentation of agricultural wastes to produce alkaline protease. Alkaline protease production increased with flaxseed, rice bran, and cheese whey fermentation reaching 147 U/mL at 48 h. Statistical optimization of alkaline protease production was performed using the central composite design (CDD). Results of CDD and the optimization plot showed that 4.59 g/L flaxseed, 4.31 g/L rice bran, 4.17 mL cheese whey, and a vegetative inoculum size of 7.0% increased alkaline protease production by 27.2% reaching 186 U/mL. Using the 20–70% ammonium sulfate fractionation method, the optimally produced enzyme was partially purified to fivefold. The partially purified alkaline protease was then covalently immobilized on a biopolymer carrier, glutaraldehyde-polyethylene-imine-κ-carrageenan (GA-PEI-Carr), with 90% immobilization efficiency. Characterizations revealed that immobilization improved thermostability, reusability, optimum temperature, and sensitivity towards metal ions of the free enzyme. The optimal temperature for free and immobilized enzymes was 40 and 50 °C, respectively. Both enzymes had the same optimum pH of 10. Immobilization increased Km from 19.73 to 26.52 mM and Vmax from 56.7 to 62.5 mmol min−1L−1. The immobilized enzyme retained 35% of its initial activity at 70 °C, while the free enzyme retained only 5%. The immobilized enzyme kept 80% of its initial activity at the 20th cycle. After 7 weeks of storage, the free enzyme lost all its initial activity, whereas the immobilized enzyme retained 50%. The free and immobilized enzymes were able to hydrolyze gelatin, and azo-casein demonstrating different relative activity, 85, 80, 90 and 95%, respectively, compared to casein (100%). [ABSTRACT FROM AUTHOR]

Fengycin is an important member of the lipopeptide family with a wide range of applications in the agricultural, food, medical and cosmetic industries. However, its commercial application is severely hindered by low productivity and high cost. Therefore, numerous studies have been devoted to improving the production of fengycin. We summarize these studies in this review with the aim of providing a reference and guidance for future researchers. This review begins with an overview of the synthesis mechanism of fengycin via the non-ribosomal peptide synthetases (NRPS), and then delves into the strategies for improving the fengycin production in recent years. These strategies mainly include fermentation optimization and metabolic engineering, and the metabolic engineering encompasses enhancement of precursor supply, application of regulatory factors, promoter engineering, and application of genome-engineering (genome shuffling and genome-scale metabolic network model). Finally, we conclude this review with a prospect of fengycin production. [ABSTRACT FROM AUTHOR]

Gummy candy is one of the main snacks for children, and conventional samples with high calorie illustrate no nutritional value; therefore, the aim of present research was to develop functional product on priority. Celery (Apium graveolens) puree (25%–50%), boswellia gum (10%–20%), lemon essential oil (0.25%–0.50%), and sugar (10%–20%) in two levels were considered for vegan gummy candy production. Based on central composite design, the 30 types of gummy candies were prepared; afterward, response surface methodology was applied to optimize results determined by texture (hardness, springiness, adhesiveness, gumminess, chewiness, and elasticity characteristics), physicochemical attributes (pH, sugar content, water activity, antioxidant function, and calorie restriction), and also sensory evaluation. In general, elevated concentration of celery puree and boswellia gum‐enhanced hardness, chewiness, and also gumminess for treated products. On the other hand, higher sugar with lemon essential oil improved adhesion, springiness, and elasticity features. More boswellia gum, celery, lemon essential oil, and reduction in sugar elevated water activity and also declined pH for treated samples. The celery puree, boswellia gum, and lemon essential oil significantly enhanced antioxidant function of treated gummy candies. According to attained results, sugar had a remarkable influence on acceptability and in treated samples calorie decreased. Based on all investigated factors, optimal formulation was achieved including 25% celery puree, 20% boswellia gum, 0.450% lemon essential oil, and 13.55% sugar. Regarding the results, obtained gummy candy with high nutritional value and low calorie demonstrated the potential to produce extensively in food sector. [ABSTRACT FROM AUTHOR]