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Building energy simulations often rely on abstract assumptions when it comes to natural ventilation, such as ‘windows always open [or closed]’ or ‘windows open when outdoor temperature is below a certain threshold.’ However, simulations based on these assumptions fail to fully exploit the cooling potential of natural ventilation, as its effectiveness can be enhanced or diminished by various factors, including the presence of thermal mass. This issue also extends to smart home controls, where determining the window schedule becomes challenging without information about the building's response to outdoor conditions. To address these issues, this study has developed an analytical model for window operation schedules that leverages the passive cooling from natural ventilation. The analytical model was validated against a Modelica simulation. A case study utilizing the BESTEST model of ANSI/ASHRAE Standard 140 underwent validation with EnergyPlus simulations, showing strong concordance. The algorithm provides window schedule recommendations adapted to various airflow rates, thermal masses, and climate variations. The case study demonstrated that proper window scheduling could reduce indoor temperature by up to 8 °C under the given simulation settings, thereby improving resilience and indicating potential energy savings. Furthermore, the paper explores the potential opportunities and challenges this approach presents, especially for building simulation and smart home applications.

Connected and Autonomous Vehicles (CAV) require positioning that is consistently reliable and accurate. This is achieved through the choice of sensors and the real-time selection of high-quality measurements. Global Navigation Satellite Systems (GNSS) are the foundation to achieve accurate absolute positioning. GNSS Common-mode Errors (CME)mitigation can be realized with Differential GNSS (DGNSS) approach and Precise Point Positioning (PPP) techniques. With the evolution of the International GNSS Service (IGS) Multi-GNSS Experiment (MGEX), Real-time PPP (RT-PPP) corrections for multi-GNSS have only recently become accessible.GNSS measurements are prone to outliers. This results in an inherent performance versus risk trade-off in CAV state estimation applications. Recently proposed Risk-Averse Performance Specified (RAPS) methods address this trade-off by optimally selecting a subset of measurements to minimize risk while achieving a target performance. The existing RAPS literature presents cases where the performance specification is stated for the full information matrix. However, those methods are not computationally efficient as required for real-time and do not address situations where that specification is infeasible.This dissertation focuses on the Diagonal Performance-Specified RAPS (DiagRAPS) formulation. This dissertation begins with a review of GNSS measurement models and real-time CME mitigation techniques, such as DGNSS, PPP, and Virtual Network DGNSS (VN-DGNSS). It then develops the theory of DiagRAPS for both binary and non-binary measurement selection variables. Algorithms suitable for real-time applications are proposed within Linear Programming (LP) and Mixed-Integer Linear Programming (ILP) optimization frameworks, achieving polynomial time complexity. The convergence and computation costs of these algorithms are discussed. For binary DiagRAPS, a novel convex reformulation is derived, leading to a globally optimal solution that can be solved using existing tools. Additionally, a soft constraint optimization approach is proposed for situations when the specified performance is unfeasible. Finally, this dissertation evaluates DiagRAPS state estimation approaches using real-world multi-GNSS data from challenging environments for both DGNSS and RT-PPP applications. The results reveal that the locally optimal approach achieves state estimation performance comparable to the global solution. Both binary and non-binary DiagRAPS outperform traditional methods. Notably, the non-binary approach yielded the lowest computation cost and the best overall performance.

Purpose: To determine the minimum number of events (training or matches) for producing valid acceleration–speed (AS) profiles from global navigation satellite system (GNSS) data. Methods: Nine elite female soccer players participated in a 4-week training camp consisting of 19 events. AS profile metrics calculated from different combinations of athlete events were compared to force–velocity (FV) profile metrics from 2 × 40-m stand-alone sprint effort trials, using the same GNSS 10-Hz technology. Force–velocity profiles were calculated, from which AS profiles were obtained. AS profiles from training and matches were generated by plotting acceleration and speed points and performing a regression through the maximal points to obtain the AS metrics (theoretical maximal speed, x-intercept [in meters per second], theoretical maximal acceleration, y-intercept [in meters per second squared], and the slope per second). A linear mixed model was performed with the AS metrics as the outcome variables, the number of events as a fixed effect, and the participant identifier as a mixed effect. Dunnett post hoc multiple comparisons were used to compare the means of each number of event grouping (1–19 events) to those estimated from the dedicated sprint test. Results: Theoretical maximal speed and theoretical maximal acceleration means were no longer significantly different from the isolated sprint reference with 9 to 19 (small to trivial differences = −0.31 to −0.04 m·s−1, P =.12–.99) and 6 to 19 (small differences = −0.4 to −0.28 m·s−2, P =.06–.79) events, and the slopes were no longer different with 1 to 19 events (trivial differences = 0.06–0.03 s−1, P =.35–.99). Conclusions: AS profiles can be estimated from a minimum of 9 days of tracking data. Future research should investigate methodology resulting in AS profiles estimated from fewer events. [ABSTRACT FROM AUTHOR]

Sustainability outcomes are influenced by the laws and configurations of natural and engineered systems as well as activities in socio-economic systems. An important subset of human activity is the creation and implementation of institutions, formal and informal rules shaping a wide range of human behavior. Understanding these rules and codifying them in computational models can provide important missing insights into why systems function the way they do (static) as well as the pace and structure of transitions required to improve sustainability (dynamic). Here, we conduct a comparative synthesis of three modeling approaches- integrated assessment modeling, engineering-economic optimization, and agent-based modeling-with underexplored potential to represent institutions. We first perform modeling experiments on climate mitigation systems that represent specific aspects of heterogeneous institutions, including formal policies and institutional coordination, and informal attitudes and norms. We find measurable but uneven aggregate impacts, while more politically meaningful distributional impacts are large across various actors. Our results show that omitting institutions can influence the costs of climate mitigation and miss opportunities to leverage institutional forces to speed up emissions reduction. These experiments allow us to explore the capacity of each modeling approach to represent insitutions and to lay out a vision for the next frontier of endogenizing institutional change in sustainability science models. To bridge the gap between modeling, theories, and empirical evidence on social institutions, this research agenda calls for joint efforts between sustainability modelers who wish to explore and incorporate institutional detail, and social scientists studying the socio-political and economic foundations for sustainability transitions.

This work uses supervised learning to optimize the package design with validated experimental results for piezoelectric micromachined ultrasonic transducers (PMUTs) to increase and alter the sound pressure level (SPL). Advancements as compared to the state-of-art include: (1) a neural network model to achieve a mean squared error of less than 0.65 dB2 post 100 epochs; (2) increased vibration amplitude by 17.9 dBV at the first-mode resonance frequency of 33.5 kHz; and (3) SPL enhancements below the 20 kHz frequency range such as the magnitude increases of more than 60 dBV at 5 kHz. As such, the package design shifts the emitting acoustic energy from the ultrasound to audio range in favor of various applications, including audio speakers.

BACKGROUND: Given sleeps crucial role in health and cognition, numerous sleep-based brain interventions are being developed, aiming to enhance cognitive function, particularly memory consolidation, by improving sleep. Research has shown that Transcranial Alternating Current Stimulation (tACS) during sleep can enhance memory performance, especially when used in a closed-loop (cl-tACS) mode that coordinates with sleep slow oscillations (SOs, 0.5-1.5Hz). However, sleep tACS research is characterized by mixed results across individuals, which are often attributed to individual variability. OBJECTIVE/HYPOTHESIS: This study targets a specific type of SOs, widespread on the electrode manifold in a short delay (global SOs), due to their close relationship with long-term memory consolidation. We propose a model-based approach to optimize cl-tACS paradigms, targeting global SOs not only by considering their temporal properties but also their spatial profile. METHODS: We introduce selective targeting of global SOs using a classification-based approach. We first estimate the current elicited by various stimulation paradigms, and optimize parameters to match currents found in natural sleep during a global SO. Then, we employ an ensemble classifier trained on sleep data to identify effective paradigms. Finally, the best stimulation protocol is determined based on classification performance. RESULTS: Our study introduces a model-driven cl-tACS approach that specifically targets global SOs, with the potential to extend to other brain dynamics. This method establishes a connection between brain dynamics and stimulation optimization. CONCLUSION: Our research presents a novel approach to optimize cl-tACS during sleep, with a focus on targeting global SOs. This approach holds promise for improving cl-tACS not only for global SOs but also for other physiological events, benefiting both research and clinical applications in sleep and cognition.

The upstart is commonly used on bars in artistic gymnastics following a release and regrasp skill, where the gymnast will perform a flighted element before catching the bar. The variability of the flighted element leads to varying initial conditions prior to the upstart. The aim of the study was to understand how technique can be manipulated in order to ensure success at the task despite this variability. More specifically, the study aimed to quantify the ranges of initial angular velocity a gymnast could cope with in an upstart using (a) a fixed timing technique, (b) with one additional parameter to modify timings as a function of initial angular velocity, and (c) a further additional parameter to extend the range. Relationships were established, using computer simulation modeling, between the movement pattern parameters, which defined the technique, and the initial angular velocity of the upstart. A two-parameter relationship outperformed both the one-parameter relationship and the fixed timing solution in terms of the range of initial angular velocities the model could cope with. One of the two parameters governed the time by which the initiation of the shoulder extension should be reduced as a function of increased initial angular velocity, and the other parameter performed the same function for the remaining timing parameters at the hip and shoulder. The present study suggests that gymnasts, and, therefore, humans, may be able to modify movement patterns to cope with uncertain initial conditions using a relatively small number of parameters. [ABSTRACT FROM AUTHOR]

Summary: The wireless sensor network (WSN) has been assimilated into modern society and is utilized in many crucial application domains, including animal monitoring, border surveillance, asset monitoring, and so forth. These technologies aid in protecting the place of the event's occurrence from the adversary. Maintaining privacy concerning the source location is challenging due to the sensor nodes' limitations and efficient routing strategies. Hence, this research introduces a novel source location privacy preservation using the awareness‐based Gannet with random‐Dijkstra's algorithm (AGO‐RD). The network is initialized by splitting the hotspot and non‐hotspot region optimally using the proposed awareness‐based Gannet (AGO) algorithm. Here, the multi‐objective fitness function is utilized to initialize the network based on factors like throughput, energy consumption, latency, and entropy. Then, the information is forwarded to the phantom node in the non‐hotspot region to preserve the source location's privacy, which is far from the sink node. The proposed random‐Dijkstra algorithm is utilized to route the information from the phantom node to the sink with more security. Analysis of the proposed AGO‐RD‐based source location privacy preservation technique in terms of delay, throughput, network lifetime, and energy consumption accomplished the values of 6.52 ms, 95.68%, 7109.9 rounds, and 0.000125 μJ. [ABSTRACT FROM AUTHOR]

The use of lignocellulosic biomass to create natural flavor has drawn attention from researchers. A key flavoring ingredient that is frequently utilized in the food industry is vanillin. In this present study, Pediococcus acidilactici PA VIT effectively involved in the production of bio-vanillin by using Ferulic acid as an intermediate with a yield of 11.43 µg/mL. The bio-vanillin produced by Pediococcus acidilactici PA VIT was examined using FTIR, XRD, HPLC, and SEM techniques. These characterizations exhibited a unique fingerprinting signature like that of standard vanillin. Additionally, the one variable at a time method, placket Burmann method, and response surface approach, were employed to optimize bio-vanillin. Based on the central composite rotary design, the most important process factors were determined such as agitation speed, substrate concentration, and inoculum size. After optimization, bio-vanillin was found to have tenfold increase, with a maximum yield of 376.4 µg/mL obtained using the response surface approach. The kinetic study was performed to analyze rate of reaction and effect of metal ions in the production of bio-vanillin showing Km of 10.25, and Vmax of 1250 were required for the reaction. The metal ions that enhance the yield of bio-vanillin are Ca2+, k+, and Mg2+ and the metal ions that affects the yield of bio-vanillin are Pb+ and Cr+ were identified from the effect of metal ions in the bio-vanillin production. • Identification and characterization of Pediococcus Acidilactici capable of catalyzing the production of bio vanillin. • Optimization of fermentation conditions to enhance the biotransformation efficiency and yield of vanillin. • Evaluating bio-vanillin produced through biotransformation, demonstrating its suitability for various applications. • A detailed kinetic study, shedding light on the reaction rates involved in the process of bio vanillin production. [ABSTRACT FROM AUTHOR]

Automated Storage and Retrieval System (AS/RS) is driven by multiple motors for loading and unloading the items (z-axis) onto the fork or stacker, then moving the items horizontally (x-axis) and vertically (y-axis) at a time. Thus, it is practical to determine the speed configuration for each movement. To be responsive, it is reasonable to set the speed as fast as possible. However, high speed leads to high energy consumption which is undesirable in the context of green warehousing. Given that the speed changes dynamically, it is important to have an advanced optimization model for balancing both objectives and providing accurate estimation. This study proposed metamodel-based simulation–optimization (MSO) allowing to jointly optimize four speed-related variables namely horizontal speed (x), vertical speed (y), fork or depth speed (z), and acceleration/deceleration under the dynamicity of AS/RS. A case study was given in a warehouse comprising five cranes and ten racks. Using Desirability Function Analysis, the optimal speed configuration is obtained efficiently for minimizing travel time and energy consumption of AS/RS. The result also shows that row-based storage provides better responsiveness and energy efficiency than random-based storage. Further, rack design also indicates a significant impact on the AS/RS speed configuration. [ABSTRACT FROM AUTHOR]

The stability of the injection molding process is critical to efficient production. However, environment and production disturbances pose challenges to maintaining a stable molding process. A two-stage dynamic adjustment strategy is proposed in this paper to increase weight consistency, considering fluctuation of density and fluidity based on products' types and structure. This study investigates the factors influencing the weight stability of injection molding products during the injection and holding stages. Experiments show that the injection stage is affected by density fluctuations, while melt fluidity fluctuations influence the holding stage. Simulation experiments prove the efficacy of dynamic adjustment strategies for injection-sensitive products (ISP) and holding-sensitive products (HSP). Thinner products as ISPs benefit from adjustments of the injection stage parameters, while thicker products as HSPs require optimization of holding stage parameters. To prove its validity, this strategy was tested on two molds and improved weight stability by 60.0% and 34.8%, respectively. [ABSTRACT FROM AUTHOR]

This research studied the effect of rice husk ash (RHA) during the turning of AlSi10Mg aluminum alloy. Stir casting was the chosen fabrication technique for this investigation. The combined effects of the following turning process parameters including cutting speed (s), feed (f), depth of cut (d), and weight fraction of reinforcement ( w t ) on the responses; that is the tool wear and surface roughness were studied. Taguchi L 16 orthogonal array was adopted to determine the experimental runs. The analysis of variance (ANOVA) for the tool wear and the surface roughness showed that the feed and the cutting speed were the most significant factors influencing the tool wear and surface roughness, respectively. Using single objective optimization also affirmed the feed and cutting speed as the most influencing parameters for the tool wear and surface roughness, respectively. The predicted model for tool wear and surface roughness obtained from the regression equation shows a good correlation with the experimental results. A correlation of 95% and 94% of tool wear and surface roughness was determined between the predicted and corresponding experimental results. [ABSTRACT FROM AUTHOR]

The sixth generation (6G) networks demand high security, low latency, and highly dependable standards and capacity. One of the essential components of 6G networks is flexible wireless network slicing. In this paper, we propose a hybrid model that combines a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM). The hybrid model is applied to the Unicauca IP Flow Version2 dataset. The CNN handles the automated feature section, while the BiLSTM is utilized for categorizing the suitable network slices. This hybrid model is capable of offering a reliable and effective network slice to the end user. The proposed hybrid model has an overall recognition rate of 97.21%, which reflects the applicability of the proposed approach. A stratified 10-fold cross-validation is used to assess the applicability of the proposed model. The main challenge for network service providers is to assign slices correctly. A clever method is needed to make a standard for accurately assigning network slices to an unidentified device when it asks for them. For each incoming request for new traffic, the proposed model forecasts the suitable network slice [ABSTRACT FROM AUTHOR]

The main goal of this paper is to develop a new class of sixth- and fourth-order implicit Runge–Kutta methods to treat linear and nonlinear initial value ordinary differential equations (ODEs) in applied science and engineering. In this regard, a completely unconventional approach is taken to derive the new formulations. They are derived through a computational approach without involving in the rigorous theory of order conditions. The new formulation has two distinctive parts, namely integration and interpolation. In the first part, we introduce a form of multi-stage single-step numerical integrator with unknown weights. These weights are determined by four simple interconnectivity rules that relate them to minimize the error of the integrator. Some of these rules are based on our expertise gathered from the available prominent quadratures. However, the most important one, the so-called fundamental weighting rule (FWR), is obtained from evolutionary and statistical computations. The second part of this research focuses on developing efficient tools for evaluating the internal stages of the integrator. Accordingly, a series of strong Hermite interpolators has been extended to approximate the solution values at the internal stages. They effectively collaborate with the integrator in several numerical algorithms, the so-called α I I - (q + r) P algorithms, to solve applied ODEs. Development of the integrator, its weighting rules, and the interpolators constitutes the central contribution of this research. Numerical studies demonstrate that the presented algorithms exhibit a high level of accuracy when approximating high-frequency problems as well as long-term solutions of linear and nonlinear ODEs. [ABSTRACT FROM AUTHOR]

Fatigue in asphalt pavements is a major deterioration caused by repeated traffic loading. The objectives of this research were to investigate the fatigue performance of graphene oxide (GO) modified asphalt mixtures and evaluate the influence of temperature and stress level on the fatigue life of GO asphalt mixtures using response surface methodology (RSM). For this purpose, an indirect tensile fatigue test was conducted. The results showed that the application of GO increased the fatigue life of the asphalt mixture. The RSM analysis proposed a quadratic model with a high determination coefficient (R2 = 0.9977) to fit the experimental data. The ANOVA test showed that temperature, stress level, and GO percentage had a pronounced effect on the fatigue performance of the mixture. Furthermore, multi-objective optimization was used to achieve an optimized mixture proportion and verify the model's reliability by conducting validation experiments. The predicted and actual results were found to be in excellent agreement, with a variation of only 2.12%, suggesting that the model can accurately predict the fatigue life of the GO-modified asphalt mixture. [ABSTRACT FROM AUTHOR]

The occurrence of natural disasters, accelerated by climate change, has become a continuous menace to the environment and consequently impacts the socioeconomic well-being of people. Flood events are natural disasters resulting from excessive rainfall duration, intensity, and snow melt. Flood disaster management systems that are machine learning-based have been increasingly suggested and applied to forestall the impacts of floods on the environment in terms of monitoring and warning. This study aims to critically review various studies conducted on flood management systems to identify applicable data sources and machine learning models. The study applied Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to source data from an academic database using some selected keywords, which were identified for the review process after filtering a total number of forty-two pertinent research papers was used. The review identified different combinations of flood data, flood management techniques, flood models, application of machine learning in flood predictions, optimization techniques, data processing techniques, and evaluation techniques. The study concluded that a standard approach should be applied in building robust and efficient flood disaster management systems. Lastly, informed future research directions on using machine learning for flood prediction and susceptibility mapping are provided. [ABSTRACT FROM AUTHOR]

Damage to the environment or the ecosystem because of natural lightning or human-induced circumstances can lead to losses that reverberate across decades. Harnessing satellite images can be an imperative change that can help avoid relentless and escalating fires. Continuous and comprehensive coverage by remote sensing enables the detection of small plumes of fire or smoke that can emerge as wildfire if not detected early. Mainly, research has been focused on only limited predefined classes. To overcome this limitation the dataset provided by the Moderate Resolution Imaging Spectroradiometer (MODIS), referred as USTC_SmokeRS is utilized in this study. This dataset includes smoke and situations that resemble smoke, such as clouds, dust, and haze. USTC- SmokeRS dataset helps in developing a system that can adapt across diverse geographical regions. This article employs three different pre-trained models: MobilenetV2, InceptionV3, and DenseNet201, which are optimized and are used for the smoke classification task. Along with optimization of the models, an ensemble algorithm is also proposed to combine the capabilities of the three individual models. The proposed ensemble model achieved the highest accuracy of 95.73% on this dataset. Further, the Grad-CAM technique is adapted for localization task. [ABSTRACT FROM AUTHOR]

The edible plant oils production is associated with the release of different types of by-products. The latter represent cheap and available substrates to produce valuable compounds, such as flavours and fragrances, biologically active compounds and bio-based polymers. Elizabethkingia meningoseptica Oleate hydratases (Em_OhyA) can selectively catalyze the conversion of unsaturated fatty acids, specifically oleic acid, into hydroxy fatty acids, which find different industrial applications. In this study, Design-of-experiment (DoE) strategy was used to screen and identify conditions for reaching high yields in the reaction carried out by Escherichia coli whole-cell carrying the recombinant enzyme Em_OhyA using Waste Cooking Oils (WCO)-derived free fatty acids (FFA) as substrate. The identified reaction conditions for high oleic acid conversion were also tested on untreated triglycerides-containing substrates, such as pomace oil, sunflower oil, olive oil and oil mill wastewater (OMW), combining the triglyceride hydrolysis by the lipase from Candida rugosa and the E. coli whole-cell containing Em_OhyA for the production of hydroxy fatty acids. When WCO, sunflower oil and OMW were used as substrate, the one-pot bioconversion led to an increase of oleic acid conversion compared to the standard reaction. This work highlights the efficiency of the DoE approach to screen and identify conditions for an enzymatic reaction for the production of industrially-relevant products. • Enzymatic production of hydroxy fatty acids for different application, such as flavors. • Screening of biocatalytic hydration of crude free fatty acids from waste cooking oils. • Design-of-experiment used to identify interactions between parameters. • Production of enzymatic cascade reactions. • Reproducibility of results on triglyceride-containing industrial feedstocks. [ABSTRACT FROM AUTHOR]

The fog computing environment expands the capabilities of cloud computing by moving computing, storage, and networking services closer to IoT devices. These resource‐constrained IoT devices often face challenges like high task failure rates and extended execution latency due to data traffic congestion. Distributing IoT services through task offloading across different layers of computing paradigms enhances QoS (Quality of Service) parameters. This endeavor aims to allocate custom workflow‐based real‐time tasks or jobs for processing across various cloud/fog/edge layers, optimizing QoS factors like makespan, energy consumption, and cost. In the fog computing environment, challenges arise due to uncertainties related to job execution locations and the ability to predict future user requirements. Fuzzy logic offers low‐complexity solutions for handling unpredictable and rapidly changing conditions. This paper proposes a hybrid fog‐cloud‐based computing architecture and an intelligent fuzzy logic‐based computation offloading approach. This approach effectively allocates workloads among edge, fog, and cloud layers, resulting in improvements in makespan time (7.51%), energy consumption (4.63%), and cost (13.60%). The proposed method selects suitable processing units or compute nodes for job execution, utilizing heterogeneous resources. Simulation results demonstrate that the proposed methodology outperforms current state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

Summary: In the ever‐evolving realm of cloud computing, the challenge of intrusion detection has grown increasingly intricate and vital. With the proliferation of cyber‐attacks and the widespread use of virtualized environments, there is a pressing need for network security solutions that are not only innovative and robust but also easily comprehensible. These solutions must possess the ability to effectively detect malicious activities, provide visibility into network operations, adapt to changing requirements, and promptly alert stakeholders to any suspicious behavior. In this study, we introduce a groundbreaking approach known as fuzzy logic‐driven optimized long short‐term memory (LSTM)—FLOLSTM, specifically designed for hypervisor‐based environments. By integrating fuzzy logic with an optimized LSTM neural network, FLOLSTM aims to significantly enhance the detection of attack traffic within hypervisor networks by combining the interpretability and uncertainty management capabilities of fuzzy logic with the temporal pattern recognition prowess of LSTM. The research methodology involves meticulous data collection from hypervisor monitors, followed by rigorous cleaning and preprocessing to enhance data reliability. Subsequently, the preprocessed data is input into the FLOLSTM classifier to identify malicious activities. Furthermore, the performance of the LSTM is fine‐tuned using the waterwheel plant optimization (WPO) algorithm. Experimental evaluations compare the efficiency of the proposed FLOLSTM with existing techniques across various metrics including accuracy, recall, precision, F‐measure, specificity, false‐positive rate (FPR), and false‐negative rate (FNR). Overall, the FLOLSTM model represents a significant advancement in intrusion detection for cloud environments, offering a potent blend of interpretability, accuracy, and efficiency. Its superior performance underscores its capacity to enhance network security and effectively mitigate cyber threats in dynamic and virtualized settings, thereby making a substantial impact on the field of cyber security. [ABSTRACT FROM AUTHOR]

ABSTRACT Model‐free iterative learning control (ILC) can lead to high performance by attenuating repeating disturbances completely, using dedicated experiments on the real system to replace the traditional model. The aim of this paper is to develop a fast data‐driven method for MIMO ILC that uses random learning in the form of efficient unbiased gradient estimates. This is achieved by developing a stochastic conjugate gradient algorithm, in which the search direction and optimal step size are generated using dedicated experiments. The approach is applied to MIMO automated feedforward tuning. Simulation and experimental results show that the method is superior to earlier stochastic and deterministic methods. [ABSTRACT FROM AUTHOR]

Highlights In a time when global initiatives to lower CO2 emissions are accelerating the shift towards bio‐based products, various efforts are being made to develop wood plastic composites (WPCs) and similar bio‐composites using a variety of raw materials. This paper first showcases benchmark material properties serving as standards and models the composition, for polypropylene‐based WPCs integrating wood flour and maleic anhydride‐modified polypropylene. Subsequently, by applying statistical modeling techniques, we demonstrate an adaptive experimental design that efficiently and rapidly adjusts formulations. Beginning with an empirical dataset (24 samples), we established high‐performing models that articulate the individual contributions of the constituents on ten vital properties of WPCs. Our adaptive experimental design, leveraging nonlinear partial least squares regression and the Bayesian optimization, successfully refined formulations to enhance bending and impact strengths. Notably, we proposed optimal conditions starting from just eight samples with minimal iterations. This study shows that statistical techniques can quickly optimize WPC formulations, making the overall development process faster. By addressing the multiple conflicting properties, these techniques can greatly reduce the time and effort needed for development. Applied several regression methods to analyze ten properties of PP‐based WPCs. Showcased distinct impacts of the formulations of WF and MAPP on WPCs. Implemented the adaptive experimental design for formulation optimization. Enhanced bending and impact strengths in the formulations of WPCs. Advanced sustainable material development with data‐driven approach. [ABSTRACT FROM AUTHOR]

This article addresses the challenges of load curve variability in integrated energy systems (IES) and emphasizes carbon neutralization in response to global environmental concerns. It introduces a game-theoretic approach to understand the collaborative dynamics within an integrated energy-hydrogen system (HGESS). The study develops a collaborative game model for the IES-HGESS partnership, focusing on carbon neutralization considerations. Using game theory, it investigates the determinants shaping this partnership and extends to create a hybrid model for day-ahead and real-time markets. To foster enduring cooperation, the study introduces a cooperation profit coefficient and devises an income distribution strategy for HGESS-IES interactions. A load management program is incorporated to reduce costs and enhance profitability. Addressing complexity and constraints, the article introduces an advanced multi-objective particle swarm optimization method. Tested on a sample system under various conditions, the results demonstrate that the alliance between IES and HGESS can significantly enhance net profits, achieving mutual benefit and a win-win outcome while advancing sustainability in energy management. • "IES load curve challenges and carbon neutralization". • "Game theory in IES-HGESS dynamics and cooperative models". • "Cooperation profit coefficient for enduring IES-HGESS collaboration". • "Load management program to reduce costs and enhance profitability". • "Multi-objective particle swarm optimization for IES-HGESS alliance profitability". [ABSTRACT FROM AUTHOR]

A sandwich plate with a corrugation and auxetic honeycomb hybrid core is constructed, and its sound insulation and optimization are investigated. First, the motion governing equation of the sandwich plate is established by the third-order shear deformation theory (TSDT), and then combined with the fluid-structure coupling conditions, and the sound insulation is solved. The theoretical results are validated by COMSOL simulation results, and the effects of the structural parameter on the sound insulation are analyzed. Finally, the standard genetic algorithm is adopted to optimize the sound insulation of the sandwich plate. [ABSTRACT FROM AUTHOR]

Availability contracting is a prominent procurement strategy among many defense and other capital equipment suppliers and their customers. Under availability contracting, the revenue generated for a service provider is a function of equipment/system availability over the predetermined contract period. A major challenge in implementing availability contracting is predicting the availability of equipment, defining the target availability, and determining the best contract duration based on the target availability. In this paper, we extend the classical operational availability model for estimating equipment availability under hierarchical maintenance, which is frequently used for complex equipment such as aircraft. Under an availability contract, bounds have been proposed on system-level operational availability. The current study also develops optimization models for finding the optimal availability contract duration under different scenarios: stochastic and non-stochastic contract durations; contract written at parts level, system level; minimizing total maintenance costs with minimum level of target operational availability as a constraint etc. The study also shows that the optimal contract duration is a non-decreasing function of spares on hand, and the inherent availability of system part. The results from the study have been validated using numerical study with both simulated and real data. [ABSTRACT FROM AUTHOR]

Summary: Photovoltaic (PV) systems benefit from the cascaded H‐bridge multilevel inverter (CHMLI) topology due to its high flexibility and efficiency. However, PV mismatches in three‐phase grid‐connected systems result in an unbalanced power supply, which leads to an unbalanced grid current. Proportional integral derivative (PID) controllers can be used to control H‐bridge inverters because of their simplicity of tuning as well as their basic structure. However, due to the nonlinearity and high sensitivity of the PID controller, its performance declines as a result of high overshoot, high settling time, and high rise time during significant external disturbances. Moreover, the switching angle of the inverter must be adjusted to provide the required fundamental voltage while reducing harmonic content. In order to address this problem, a control technique with modulation compensation is proposed. The present work offers a two‐degree‐of‐freedom fractional‐order PID controller with an integrated filter (2DOF–FOPID–IF). Moreover, the optimum switching angles are determined for the CHMLI by employing an improved sparrow optimization (ISO) algorithm. To prove the practicality of the suggested technique, simulation, and experimental data are compared with the existing techniques. The proposed technique shows better performance in terms of settling time, overshoot, rise time, and low harmonics. [ABSTRACT FROM AUTHOR]

An internal thermally coupled air separation column (ITCASC) process is an effective energy‐saving technology in the air separation process. However, a large economic investment is a crucial factor for the widespread use of this technology in practical applications. In this article, an alternative configuration design, namely, top‐integrated (T‐ITCASC), bottom‐integrated (B‐ITCASC), and top‐bottom‐integrated ITCASC (T‐B‐ITCASC) with a focus on energy savings and economic feasibility are studied. A rigorous optimization based on a nonlinear interior‐point algorithm was developed by integrating the dynamic model into the optimization formulation. In the context of ITCASC process design and optimization, numerical simulations demonstrated that T‐ITCASC, B‐ITCASC, and T‐B‐ITCASC configurations improved energy‐saving potential and reduced capital investment, compared to the F‐ITCASC and conventional air separation column (CASC) configurations. Among these optimized configurations, the T‐B‐ITCASC configuration is preferred. [ABSTRACT FROM AUTHOR]

In this paper, the seeded batch cooling crystallization of sodium phosphite (SP) is simulated and optimized through a coupled method of the genetic algorithm and nonlinear programming. At first, the modeling and simulation test methods of the crystallization process are applied for the crystallization of SP, which expands the relevant study of SP from the experiment to the simulation. A comprehensive model is established in MATLAB/Simulink, and based on this model, the results of the common cooling strategy (linear cooling) on the process are investigated. Meanwhile, the process sensitivity to the change of seeding conditions is analyzed. Then, the coupled optimization method based on the genetic algorithm and nonlinear programming is applied to optimize the crystallization process for the first time, and the obtained optimized cooling strategy is compared to the result of the traditional nonlinear programming method (NLPM). The traditional NLPM has more significant effects on large seeding mass and small mean size, while the coupled method has better adaptability. When the coefficient of variation is almost fixed, the cooling strategy obtained by the coupled method could produce more crystals with large mean size. In addition, the end of the process can be reached earlier. The results show that the coupled method is more suitable for the optimization of the batch cooling crystallization of SP. [ABSTRACT FROM AUTHOR]

This work reports the degradation of cellulosic material, i.e., paper, under different environmental conditions (soil and hydrolytic). Models were developed using central composite design (CCD) within the framework of response surface methodology (RSM) and artificial neural network (ANN) techniques. It is done as part of a solid waste management system to eliminate the waste produced by excessive paper usage and obtain optimized degradation conditions of paper. In view of the real environments where paper-based solid wastes are usually exposed, soil degradation and hydrolytic degradation of paper were investigated. The factors pH (4–10), compost ratio (2–5), CaCl2 (5–15 ppm), and time (7–20 days) were independent factors that varied for the study, and degradation conversion was measured as the dependent variable for soil degradation of paper. The factors pH (4–10), CaCl2 (2–5 ppm), tripotassium phosphate (TPP, 2–5 ppm), and time (6–16 days) were independent variables that varied for the study, and degradation conversion was the dependent variable for hydrolytic degradation of paper. The optimal conditions for soil degradation were determined to be a pH of 4, a compost ratio of 5, a salt (CaCl2) addition of 5 ppm, and a duration of 20 days, resulting in the highest observed conversion rate. The coefficient of regression (R2) for CCD is 87.24%, whereas it is 94.82% for ANN. The optimal conditions for achieving maximum conversion in hydrolytic degradation include a pH of 10, a CaCl2 salt concentration of 5 ppm, a TPP salt concentration of 2 ppm, and a duration of 16 days. The coefficient of regression for CCD is 75.15%, while the coefficient of regression for ANN is 93.91%. In both deterioration scenarios, the data exhibited a higher degree of fit when modeled using ANN compared to CCD (RSM). [ABSTRACT FROM AUTHOR]

In this study, a machine learning-based hybrid optimization method is developed to optimize tuned mass damper considering soil-structure interaction. A total of 200,000 time history analyses of the tuned mass damper-structure-soil system are conducted and the results are used to construct the database of six machine learning models. Three meta-heuristic algorithms are used for optimization. A weighted optimization algorithm is developed based on the three individual optimization algorithms. Compared with three individual optimization algorithms, the weighted optimization algorithm can achieve better optimization results. The robustness of the single ground motion-based optimization applied to other ground motions is also investigated. [ABSTRACT FROM AUTHOR]

This paper introduces a new learning-based approach for approximately solving the Kidney-Exchange Problem (KEP), an NP-hard problem on graphs. The KEP consists of, given a pool of kidney donors and patients waiting for kidney donations, optimally selecting a set of donations to optimize the quantity and quality of transplants performed while respecting a set of constraints about the arrangement of these donations. The proposed technique consists of two major steps: the first is a Graph Neural Network (GNN) trained without supervision; the second is a deterministic non-learned search heuristic that uses the output of the GNN to find a valid solution. To allow for comparisons, we also implemented and tested an exact solution method using integer programming, two greedy search heuristics without the machine learning module, and the GNN alone without a heuristic. We analyze and compare the methods and conclude that the learning-based two-stage approach is the best solution quality, outputting approximate solutions on average 1.1 times more valuable than the ones from the deterministic heuristic alone. [ABSTRACT FROM AUTHOR]

This paper proposes a metaheuristic algorithm, called advanced differential evolution (ADE), by improving the DE algorithm. The ADE algorithm was developed with the goal of creating an optimization framework that addresses the challenges of exploration and exploitation balance, avoiding local minima, utilizing chaos theory for diverse initialization, and improving solution quality and convergence speed. By incorporating these features, ADE aims to enhance the effectiveness of optimization processes. The proposed algorithm utilizes chaos theory to generate the initial population, which is subsequently divided into two sub-populations with adaptive sizes. The size of each sub-population is determined using a formula based on the number of iterations during the algorithm's execution. The first sub-population has a larger size in the beginning and the second one has a smaller size, but the total size of these two populations is always constant. The main contribution of this paper is the proposal of two novel improved differential evolution algorithms, namely MDE1 and MDE2, which are utilized for exploration within these sub-populations. The proposed ADE is tested on 29 well-known benchmarks and six engineering problems, and the results are compared with seven other algorithms. Various statistical experiments are carried out showing that the proposed algorithm provides significant superiority over other well-known algorithms. [ABSTRACT FROM AUTHOR]

Summary: A robust method for analysing acrylamide in plantain and cassava snacks was developed using SPME and GC‐FID. Optimised via Box–Benkhen design, the method met European standards with a calibration curve R2 > 99%, detection limit 63 μg kg−1, and quantification limit 212 μg kg−1. Precision was <17.76% as per Horwitz equation. Acrylamide levels in fried ripe plantain were 894.40 ± 56.35 μg kg−1 and in cassava snacks 59.42 ± 2.46 μg kg−1. This validated method offers an efficient means for acrylamide control in thermally processed foods. [ABSTRACT FROM AUTHOR]

In this research, the optimization of the geometric parameters of a 3-layer composite plate with an elliptical cutout in the center was investigated utilizing an artificial neural network and genetic algorithm for the first time to obtain the minimum ratio of linear buckling load to weight and the ratio of the diameters of the ellipse. After completing 294 simulations in Abaqus finite element software and forming an artificial neural network with two hidden layers, the function obtained in the artificial neural network was used as the objective function in the genetic algorithm, and the optimal values for the angle of the ellipse with the horizon axis, the diameter ratio of ellipses as well as the fiber angle of the middle layer of the composite were obtained. using artificial neural networks, three algorithms, Levenberg-Marquardt, Bayesian regularization; and scaled conjugate gradient backpropagation, were compared and the number of neurons in each algorithm was compared. The results showed that Levenberg-Marquardt's algorithm is more accurate compared to other algorithms. In the end, the optimal values for the angle of the ellipse with the horizontal axis; as the fiber angle of the middle layer of the composite and, the ratio of the oval diameters as α = 12.3001 degree , θ = 5.9481 degree and D = 1.5690. [ABSTRACT FROM AUTHOR]

Outstanding progress in manufacturing technology of fiber-reinforced composite materials, allows designers to go beyond classical design rules. Composites with variable stiffness are an innovative division of composite materials that provide more efficient designs for engineers due to their diverse design parameters. In this study, a straightforward methodology is presented for the design of sandwich panels with corrugated variable stiffness core. Based on the homogenization method, a unit-cell of the sandwich panel is used as a representative volume element for the sandwich panel with corrugated core. Due to multiple design variables, conventional numerical and analytical methods cannot be applied to variable stiffness composites, thus PYTHON/ABAQUS framework is developed to determine the extensional and bending behavior of unit-cells. Details of this framework including PYTHON scripting and ABAQUS finite element solver are given along with their corresponding functions. Furthermore, the elitist genetic algorithm is incorporated into a framework for the optimum design of variable stiffness cores. Two optimization problems, namely composites with variable thickness and variable angle are considered for unit-cells, and associated constraints, boundary conditions, and fitness functions are discussed in detail. In addition to the benefits of the proposed framework, the obtained results reveal notable enhancement in the extensional and bending behavior of unit-cell. Results reveal that corresponding displacements are significantly improved (up to 80%) when using the optimum variable thickness case. For variable angle cases, the improvement of displacements is up to about 28%. [ABSTRACT FROM AUTHOR]