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Ship maintenance service optimisation is of great significance for improving the competitiveness of shipbuilding enterprises. In this paper, we investigate a ship maintenance service scheduling problem considering the deteriorating maintenance time, distributed maintenance tasks, and limited maintenance teams. The objective is to minimise the service span. First, we construct an initial mixed-integer programming model for the studied problem. Then, through the property analysis of the problem with a single maintenance team, an exact scheduling algorithm is proposed. In addition, the lower bound of the problem with multiple maintenance teams is derived. A scheduled rule is developed to obtain the lower bound for the problem. Based on the property analysis, the original mixed-integer programming model is simplified to an improved mathematical programming model. Since the studied problem is NP-hard, this paper proposes two heuristic algorithms and an integrated metaheuristic algorithm based on the variable neighbourhood search to obtain approximate optimal solutions in a reasonable time. In computational experiments, the two models can solve problems on small scale, while metaheuristics can find approximately optimal solutions in each problem category. Moreover, the computational results validate the performance of the proposed integrated metaheuristic in terms of convergence and stability. [ABSTRACT FROM AUTHOR]

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]

Two-step redox thermochemical cycles, capable of directly converting CO 2 and H 2 O respectively into CO and H 2 , offer a promising synthesis route towards green carbon-neutral fuels. The performance of such two-step cycles depends highly on the thermodynamic properties of splitting materials, particularly the solid-state enthalpy ( Δ h solid) and entropy (Δ s solid) changes during the reduction process. Here, we report an investigation into the roles of the Δ h solid and Δ s solid. We shall show that a high Δ s solid relaxes both reduction temperature and oxygen pressure, but increases oxidant consumption. Conversely, an increase in Δ h solid enhances reduction resistance while promotes oxidation reactions. There are therefore no perfect materials, and a trade-off is needed for an optimal solution. We also defined a thermodynamic region based on Δ h solid and Δ s solid and typical operating conditions, and showed that higher values of both Δ h solid and Δ s solid provided a larger reaction space. While lower Δ h solid and negative Δ s solid may be more suitable for isothermal cycles. Our analyses also suggest future efforts in searching for splitting materials with a high Δ s solid within an appropriate range of Δ h solid (280–460 kJ/mol). [Display omitted] • Trade-off caused by solid state enthalpy and entropy is a key issue. • High solid-state enthalpy and entropy are more suitable for temperature-swing cycle. • A thermodynamic region under typical operating conditions is defined. • Solid-state entropy of different candidate materials is discussed. [ABSTRACT FROM AUTHOR]

There is an ever-increasing number of public users of the internet just to remain learned and updated as well as distribute millions of posts, articles, and videos across platforms such as Twitter, Facebook and YouTube etc. The speedy acceptance of social media has forced to an explosive growth in information-distribution among users, with fake news becoming a fundamental intrusion of our digital day-to-day life. The spread of disinformation is in part associated to social media failing to verify the correctness of a news item. Although it is technically possible, it is challenging for a deep neural network to "attend to" the false alone portions of each news piece because the linguistics of fake news will approximate that of true news. In this paper, a Simple Shallow Perceptron Network (SSPN) has been proposed and will reduce the complications such as the curse of high dimensionality, high training time of Deep Neural Networks, over-fitting and over-thinking problem during network training. The effectiveness of the proposed SSPN model for false news identification using the benchmark LIAR dataset and the more recent FakeNewsNet dataset have also been examined. The proposed model outperformed all other existing models, with an F1-score of 0.73 for the LIAR dataset and 0.90 for the FakeNewsNet dataset. [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 software Defect Prediction (SDP) model uses previously learned data to predict whether a future example (such as a file, class, or module) will be defective or not. Accurate forecast results can help software testers arrange testing resources more efficiently. Nonetheless, there are still difficulties in more exact and reliable defect predictions. This study presents the SDP-CMPOA framework, which stands for Software Defect Prediction Crossover with Brownian Motion-based Pelican Optimization Algorithm. The stages of the approach are pre-processing, feature extraction, feature selection, and detection. The preprocessing stage involves improved data normalization. Higher-order statistical features, enhanced statistical features, and raw features are all taken from the pre-processed data. The primary barrier to precise software defect detection and hence adhering to the ideal feature selection method will be the curse of dimensionality. This research proposes a novel Crossover with the Brownian motion-based Pelican Optimization Algorithm for selecting the best features (CMPOA). Finally, hybrid classifiers like Improved Bidirectional Long Short Term Memory (Bi-LSTM and) Deep Max out models are employed for defect prediction. The classifier analysis of KCI/SDP dataset accuracy of the projected model is 9.17%, 9.6%, 7.20%, 13.3%, and 5.7% better than the other methods such as DBN, SVM, CNN, RF, RNN, and SDP- CMPOA respectively at the 60th learning percentage. The efficiacy of the SDP-CMPOA is examined in comparison to other extant schemes with respect to certain measures. [ABSTRACT FROM AUTHOR]

Global warming's major cause is the emission of greenhouse-effect gases (GHG), especially carbon dioxide (CO 2) whose main source is the combustion of fossil fuels. Fossil fuels serve as the primary energy source in many industries, including shipping, which is the focus of this study. One of the measures proposed to tackle GHG emissions is the development of green shipping corridors - carbon-free shipping routes that require the transition to alternative fuels, which are gaining competitiveness. One of the reasons for that is carbon pricing, which taxes CO 2 emissions. However, the lack of consensus on the most cost-advantageous alternative fuel in the long run results in the delay of the implementation of green shipping corridors. To make it more accessible for stakeholders to conduct an economic analysis of the various options, a framework to determine and minimize the costs of transitioning from fossil fuels to any alternative fuel is proposed, over the period of one voyage, considering the lost opportunity cost, the deployment cost of bunkering vessels at the necessary call ports, the cost of converting the vessel, the car-bon emissions tax cost, and the fuel cost. This will allow stakeholders to choose the most economical alternative fuel, accelerating the development of green shipping corridor initiatives. To validate the effectiveness of the framework, it was applied in a case study involving a shipowner seeking to transition from heavy fuel oil (HFO) to Ammonia, Hydrogen, Liquefied Natural Gas (LNG), or Methanol. This study faced limitations due to the unknown costs of installing bunkering vessels for Ammonia and Hydrogen. However, it evaluates the cost-effectiveness of alternative fuels, providing insights into their short-term economic viability. The results showed that Hydrogen is the most cost-advantageous fuel until a deployment cost per bunkering vessel of 1,990,285$ for a sailing speed of 22 knots and 2,190,171$ for a sailing speed of 18 knots is reached, after which LNG becomes the most economical option regardless of variations in the carbon tax. Moreover, a sensitivity analysis was conducted to determine the effects of variations in parameters, such as carbon tax, fuel prices and vessel conversion costs in the total cost of each fuel option. Results highlighted that even though HFO remains the most economical fuel option, even when considering a high increase in carbon tax, the cost gap between HFO and alternative fuels narrows significantly with the increase in carbon tax. Furthermore, the sailing speed impacts the fuels' competitiveness, as the cost difference between HFO and alternative fuels decreases at higher speeds. • A tool has been developed to guide the transition from fossil fuels, towards green shipping corridors. • An analysis was conducted to determine if increasing carbon taxes would make alternative fuels more financially viable than HFO. • The study identifies hydrogen as cost-advantageous, followed by LNG, influenced by varying carbon taxes. • Results demonstrated that HFO is still the most economical fuel option, even considering a very high carbon tax. • Challenges lie in uncertain bunkering vessel costs, potential profit, and the need for a model extension for fleet use. [ABSTRACT FROM AUTHOR]

Through the use of fuel cells and photovoltaic (PV) solar cells, green energy has become one of the most popular developments in electricity generation in recent years. Accurate modeling of fuel cell and PV cell is critical in the simulation and analysis of energy systems that use both sources. Due to the complexity and nonlinearity of these hybrid systems, the model must be optimized under different operating conditions. In this article, a novel metaheuristic efficient hybrid algorithm is proposed for this purpose. A Hybrid Chaotic Particle Swarm Optimization and Slime Mould Algorithm (HCPSOSMA) has been proposed to estimate the PV/fuel cell system parameters. The hybrid HCPSOSMA algorithm is superior to other algorithms in terms of higher accuracy in searching for optimal solutions and better explorative capability. The problem formulation is based on minimizing the Root Mean Square Error (RMSE) between the calculated values and measured values for both the triple-diode PV model and the proton exchange membrane (PEM) fuel cell. The results show that the hybrid algorithm outperforms other algorithms in determining the best PV/PEMFC parameters that produce accurate V–I and P–I characteristic curves and the RMSE is about 4 × 10–12. Finally, non-parametric tests based on Friedman's ranking, Wilcoxon's rank sum test and Kruskal-Wallis test are implemented to prove the superiority of the proposed hybrid algorithm in the parameter estimation. [ABSTRACT FROM AUTHOR]

This work presents an optimum methodology for producing friction stir spot welding (FSSW) joints using a detachable consumable pin. The study is carried out by welding two AA6061-T6 sheets using a consumable pin of the same material attached to the H13 steel tool. Three methods for attaching the consumable pin to the tool are explored: (1) push-fitting the pin in an indentation on the tool, (2) adhesively bonding the pin to the tool with a cyanoacrylate adhesive, and (3) adhesively bonding the pin to the tool with a thermosetting glue. After welding, the pin gets detached and assimilated into the workpiece. Cyanoacrylate adhesive–bonded consumable pin was the most effective in producing exit-hole–free FSSW joints with good joint strength. The use of lubricants along with adhesive-bonded consumable pins resulted in a reduction of energy requirement by 35 − 42% without compromising joint strength. Optimization was carried out using data from experiments as well as finite element method simulations. For simulating the lap shear test, the Cockcroft-Latham damage model was used. The optimum process parameters for obtaining the maximum joint strength were a rotational speed of 900 revolutions per minute and a plunge rate of 15 mm/min. Based on the study, a strategy for batch production is also proposed for enhancing productivity. [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]

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]

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]

Neem (Azadirachta indica) seed is potential source of oil extraction, which is widely used as an alternative for synthetic chemicals in food preservation, active functional agent in food packaging, medicine, cosmetics and biopesticides. Neem seeds are obtained mainly through manual depulping, which resulted in seed damage, human drudgery, time consuming and yielded poor quality of neem oil. To overcome the limitations of manual process, a study was undertaken to design and develop a Neem fruit depulper with shearing mechanism. The process parameters were optimized by analyzing the effect of independent variables such as: roller speed (450, 550, and 650 rpm), capacity of the depulper (75, 100, 125 and 150 kg/h) and clearance between sieve and cylinder (10, 15, and 20 mm) on depulping efficiency and percentage of broken. The optimized process parameters were found to be roller speed of 550 rpm, capacity 125 kg/h and 15 mm clearance between sieve cylinder and roller. At this optimized condition, the depulping efficiency and broken percentage of the developed neem fruit depulper were recorded as 94.29 (wt.%) and 5.6 (wt.%). The depulped seeds were dried under sun drying and tray drying at the optimized temperature of 60 °C. The sun dried and tray dried neem seeds took 900 and 240 min to reach a final moisture content of 8 (% w.b.) The physicochemical properties (Azadirachtin content, Peroxide value, Acid value, Saponification value, Iodine value, Viscosity, Specific gravity, Density, pH and colour value (L*, a* and b*)) of sun and tray dried samples shows significant difference (p < 0.05) between the treatments. [ABSTRACT FROM AUTHOR]

In airplane structural design, creating a strong yet lightweight structure is crucial. During the preliminary design phase, structural sizing optimization is necessary to achieve an efficient and lightweight structure that meets safety standards. Composite materials offer a high strength-to-weight ratio, enabling a lighter structure without compromising strength. This work focuses on optimizing composite structure wings by considering laminae thicknesses as design variables and minimizing structure weight as the objective, using the gradient-based method. The optimization constraints include the Tsai-Hill criterion, buckling factor, and flutter speed to ensure the structure's safety against static load, buckling phenomena, and flutter phenomena. The optimization results indicate that the buckling factor is the most critical constraint, carrying the highest weight. The weight of the wing structure decreased by 53% and 79% after optimizing with static and flutter constraints, respectively. Despite minimal changes in weight after optimizing with the buckling constraint, the structure now meets the buckling safety criteria and is safe from buckling. [ABSTRACT FROM AUTHOR]

Herein, three g-C3N4(MCN/TCN/UCN), obtained by the direct pyrolysis of melamine/urea/thiourea respectively, were introduced as supports to optimize the NH3-SCR activity of Ce-W-Ti catalyst. Compared to CWT-400-Air, CWT@g-C3N4(2)-300-N2 exhibits lower crystalline anatase TiO2 and larger specific surface area, which improves the dispersion of Ce/W/Ti species on catalysts surface. Furthermore, the introduction of g-C3N4 as supports also contributes to doping C/N elements into Ce-W-Ti catalyst and increases the Ce3+/(Ce3++Ce4+) and Oα/(Oα+Oβ) molar ratios on catalyst surface. These all are advantageous to the NH3-SCR activity. However, UCN shows better promotional effect than MCN and TCN. This might be mainly attributed to the loose and porous stacked layered fold structure of UCN, the larger BET surface area, higher dispersion of Ce/W/Ti species and moderate weak/medium-strong acid sites of CWT@UCN(2)-300-N2. At the same time, the influence of carbon nitride amount, calcination atmosphere and calcination temperature on the NH3-SCR activity of CWT@g-C3N4 catalyst were also investigated. [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]

Electrical parameters are vital for electrical discharge machining (EDM) process performance. The study utilized the combination of Taguchi and grey relational analysis (GRA) to optimize sinking EDM. Optimization of peak current, pulse duration, duty ratio and electrode lifting time affecting material removal rate (MRR), relative electrode wear rate (REWR) and surface roughness (SR) in sinking EDM. Analysis of variance (ANOVA) shows that peak current and pulse duration significantly affect MRR and REWR, respectively. Peak current and pulse duration significantly affect SR. The optimized parameters were subjected to EDM experiments and the results are 8.54% increase in MRR, 11.11% decrease in REWR and 9.42% decrease in SR. It was found that at a peak current of 1A, the discharge pit not only depends on peak current and pulse duration but also on pulse off time. Compared to conventional optimization methods, this study analyzed the microstructure and recast layer of the workpieces. [ABSTRACT FROM AUTHOR]

Plans for expanding protected area systems (prioritizations) need to fulfill conservation objectives. They also need to account for other factors, such as economic feasibility and anthropogenic land‐use requirements. Although prioritizations are often generated with decision support tools, most tools have limitations that hinder their use for decision‐making. We outlined how the prioritizr R package (https://prioritizr.net) can be used for systematic conservation prioritization. This decision support tool provides a flexible interface to build conservation planning problems. It can leverage a variety of commercial (e.g., Gurobi) and open‐source (e.g., CBC and SYMPHONY) exact algorithm solvers to identify optimal solutions in a short period. It is also compatible with a variety of spatially explicit (e.g., ESRI Shapefile, GeoTIFF) and nonspatial tabular (e.g., Microsoft Excel Spreadsheet) data formats. Additionally, it provides functionality for evaluating prioritizations, such as assessing the relative importance of different places selected by a prioritization. To showcase the prioritizr R package, we applied it to a case study based in Washington state (United States) for which we developed a prioritization to improve protected area coverage of native avifauna. We accounted for land acquisition costs, existing protected areas, places that might not be suitable for protected area establishment, and spatial fragmentation. We also conducted a benchmark analysis to examine the performance of different solvers. The prioritization identified 12,400 km2 of priority areas for increasing the percentage of species’ distributions covered by protected areas. Although open source and commercial solvers were able to quickly solve large‐scale conservation planning problems, commercial solvers were required for complex, large‐scale problems.. The prioritizr R package is available on the Comprehensive R Archive Network (CRAN). In addition to reserve selection, it can inform habitat restoration, connectivity enhancement, and ecosystem service provisioning. It has been used in numerous conservation planning exercises to inform best practices and aid real‐world decision‐making. [ABSTRACT FROM AUTHOR]

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. Highlights: 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]

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]

Emotion plays a vital part in the life of humankind. Emotion Recognition (ER) is the necessary task to find one's inner Emotion through speech, text or face. ER process has inevitable places in applications such as education, healthcare, defence, forensics, automation and scientific-based purposes. However, the recognition models face various issues during the process. Due to the large feature sets, the model's computational time and complexity have been increased; it also produced high errors in recognition, which degrades the efficiency of the system. So, a novel Monkey-based V-Net Framework (MbVF) was developed for this research work to classify the Emotion and sentiment patterns using healthcare data. Initially, the healthcare Artificial Intelligence (AI) conversation data from the Coronavirus Tweets NLP-Text Classification dataset was collected and trained for the system. Next, stemming, stop-words removal, tokenization, and noise feature elimination process were performed in the preprocessing step. Then, the Term Frequency based-Inverse Document Frequency (TF-IDF) features were used to choose the best features depending on the fitness features of the proposed MbVF approach. Finally, the ER process was performed to recognize the emotions, such as positive, neutral and negative, using sentiment analysis with the emotion state transition and emotion pattern. The model attained 99.98% accuracy in predicting emotions, and is higher than the other model. [ABSTRACT FROM AUTHOR]

Successful identification of estrum or other stages in a cycling bitch often requires a combination of methods, including assessment of its behavior, exfoliative vaginal cytology, vaginoscopy, and hormonal assays. Vaginoscopy is a handy and inexpensive tool for the assessment of the breeding period. The present study introduces an innovative method for identifying the stages in the estrous cycle of female canines. With a dataset of 210 vaginoscopic images covering four reproductive stages, this approach extracts deep features using the inception v3 and Residual Networks (ResNet) 152 models. Binary gray wolf optimization (BGWO) is applied for feature optimization, and classification is performed with the extreme gradient boosting (XGBoost) algorithm. Both models are compared with the support vector machine (SVM) with the Gaussian and linear kernel, k-nearest neighbor (KNN), and convolutional neural network (CNN), based on performance metrics such as accuracy, specificity, F1 score, sensitivity, precision, matthew correlation coefficient (MCC), and runtime. The outcomes demonstrate the superiority of the deep model of ResNet 152 with XGBoost classifier, achieving an average model accuracy of 90.37%. The method gave a specific accuracy of 90.91%, 96.38%, 88.37%, and 88.24% in predicting the proestrus, estrus, diestrus, and anestrus stages, respectively. When performing deep feature analysis using inception v3 with the same classifiers, the model achieved an accuracy of 89.41%, which is comparable to the results obtained with the ResNet model. The proposed model offers a reliable system for identifying the optimal mating period, providing breeders and veterinarians with an efficient tool to enhance the success of their breeding programs. [ABSTRACT FROM AUTHOR]