A novel siphon-based divide-and-conquer(SbDaC)policy is presented in this paper for the synthesis of Petri net(PN)based liveness-enforcing supervisors(LES)for flexible manufacturing systems(FMS)prone to deadlocks or l...A novel siphon-based divide-and-conquer(SbDaC)policy is presented in this paper for the synthesis of Petri net(PN)based liveness-enforcing supervisors(LES)for flexible manufacturing systems(FMS)prone to deadlocks or livelocks.The proposed method takes an uncontrolled and bounded PN model(UPNM)of the FMS.Firstly,the reduced PNM(RPNM)is obtained from the UPNM by using PN reduction rules to reduce the computation burden.Then,the set of strict minimal siphons(SMSs)of the RPNM is computed.Next,the complementary set of SMSs is computed from the set of SMSs.By the union of these two sets,the superset of SMSs is computed.Finally,the set of subnets of the RPNM is obtained by applying the PN reduction rules to the superset of SMSs.All these subnets suffer from deadlocks.These subnets are then ordered from the smallest one to the largest one based on a criterion.To enforce liveness on these subnets,a set of control places(CPs)is computed starting from the smallest subnet to the largest one.Once all subnets are live,this process provides the LES,consisting of a set of CPs to be used for the UPNM.The live controlled PN model(CPNM)is constructed by merging the LES with the UPNM.The SbDaC policy is applicable to all classes of PNs related to FMS prone to deadlocks or livelocks.Several FMS examples are considered from the literature to highlight the applicability of the SbDaC policy.In particular,three examples are utilized to emphasize the importance,applicability and effectiveness of the SbDaC policy to realistic FMS with very large state spaces.展开更多
Several rheocasting processes are developed or applied worldwide in the metal forming industry.One of the new rheocasting processes is the gas induced semi-solid(GISS) process.The GISS process utilizes the principle o...Several rheocasting processes are developed or applied worldwide in the metal forming industry.One of the new rheocasting processes is the gas induced semi-solid(GISS) process.The GISS process utilizes the principle of rapid heat extraction and vigorous local extraction using the injection of fine gas bubbles through a graphite diffuser.Several forming processes such as die casting,squeeze casting,gravity casting,and rheo-extrusion of the semi-solid slurries prepared by the GISS process have also been conducted.The GISS process is capable of processing various alloys including cast aluminum alloys,die casting aluminum alloys,wrought aluminum alloys,and zinc alloys.The GISS process is currently developed to be used commercially in the industry with the focus on forming semi-solid slurries containing low fractions solid(< 0.25) into parts.The research and development activities of the GISS process were discussed and the status of the industrial developments of this process was reported.展开更多
The gas induced semi-solid(GISS) is a rheocasting process that produces semi-solid slurry by applying fine gas bubble injection through a graphite diffuser.The process is developed to be used in the die casting indust...The gas induced semi-solid(GISS) is a rheocasting process that produces semi-solid slurry by applying fine gas bubble injection through a graphite diffuser.The process is developed to be used in the die casting industry.To apply the GISS process with a die casting process,a GISS maker unit is designed and attached to a conventional die casting machine with little modifications.The commercial parts are developed and produced by the GISS die casting process.The GISS die casting shows the feasibility to produce industrial parts with aluminum 7075 and A356 with lower porosity than liquid die casting.展开更多
Noise pollution is one of the most significant harmful physical factors in the industrial and occupational environments.Due to the high costs of exposure to excessive noise;continuous sound evaluation,propose and impl...Noise pollution is one of the most significant harmful physical factors in the industrial and occupational environments.Due to the high costs of exposure to excessive noise;continuous sound evaluation,propose and implement noise control plans in occupational environments is necessary.Thus,the present study aimed to review environmental sound measurements,drawing of noise maps,and prioritizing the engineering noise control methods using the Analytic Hierarchy Process(AHP).This study was a descriptive-analytical study that aimed to assess occupational noises and present a control plan in the City Gas Stations(CGSs)of Kerman,Iran in 2021.The present study was done in two phases.In the first phase,six CGSs were investigated to measure and evaluate the noise.In addition,the noise map of a CGS was drawn using the Surfer software.Finally,the AHP was used in the second phase of the research to prioritize the control measures.In this phase,four criteria and ten alternatives were identified.According to first phase results,the sound pressure level(SPL)of the stations varied from 76 to 98 dBA.Besides,the majority of the studied stations had a sound level higher than 85 dBA(danger zone).The second phase of the study showed that out of the four evaluated criteria,the executability criterion had the highest impact and the cost criterion had the lowest impact on the selection of control measures with a weight of 0.587 and 0.052,respectively.Based on the results of prioritization of the alternatives,using a silenced regulator(weight of 0.223)and increasing the thickness of the tube(weight of 0.023)had the highest and lowest priorities among the alternatives,respectively.The use of engineering noise control methods such as using silenced regulators was the best way to control the noises of CGSs.Additionally;it is noteworthy that AHP is a practical method for prioritizing alternatives to achieve the most accurate decision-making.The results of AHP can be of great help to health and safety experts and managers in choosing the sound engineering control measures more precisely.展开更多
The design of the geometric shape of a helicopter fuselage poses a serious challenge for designers. The most important parameter in determining the shape of the helicopter fuselage is its aerodynamic coefficients. The...The design of the geometric shape of a helicopter fuselage poses a serious challenge for designers. The most important parameter in determining the shape of the helicopter fuselage is its aerodynamic coefficients. These coefficients are determined using two methods： wind tunnel test and computational fluid dynamics（CFD） simulation. The first method is expensive, time-consuming and limited. In addition, estimates in regions away from data can be poor. The second method,due to the limitations of numerical solution, the number of nodes and the used solution, is often inaccurate. In this paper, with the aim of accelerating the design process and achieving results with reasonable engineering accuracy, an engineering-statistical model which is useful for estimating the aerodynamic coefficients was developed, which mitigated the drawbacks of these two methods.First, by combining CFD simulation and regression techniques, an engineering model was presented for the estimation of aerodynamic coefficients. Then, by using the data from a wind tunnel test and implementation of statistical adjustment, the engineering model was modified and an engineering-statistical model was obtained. By spending less time and cost, the final model provided the aerodynamic coefficients of a helicopter fuselage at the desired angles of attack with reasonable accuracy. Finally, three numerical examples were provided to illustrate the application of the proposed model. Comparative results demonstrate the effectiveness of the engineering-statistical model in estimating the aerodynamic coefficients of a helicopter fuselage.展开更多
With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial transformation.The Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized ...With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial transformation.The Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether.In industry 4.0,powerful IntrusionDetection Systems(IDS)play a significant role in ensuring network security.Though various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of networks.This is because conventional Machine Learning(ML)approaches are inadequate and insufficient to address the demands of dynamic IIoT networks.Further,the existing Deep Learning(DL)can be employed to identify anonymous intrusions.Therefore,the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection(HGSODLID)model for the IIoT environment.The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format.The HGSO algorithm is employed for Feature Selection(HGSO-FS)to reduce the curse of dimensionality.Moreover,Sparrow Search Optimization(SSO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the network.Finally,the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model.The proposed HGSODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.展开更多
The present study focuses on the flow of a yield-stress(Bingham)nanofluid,consisting of suspended Fe3O4 nanoparticles,subjected to a magnetic field in a backward-facing step duct(BFS)configuration.The duct is equipped...The present study focuses on the flow of a yield-stress(Bingham)nanofluid,consisting of suspended Fe3O4 nanoparticles,subjected to a magnetic field in a backward-facing step duct(BFS)configuration.The duct is equipped with a cylindrical obstacle,where the lower wall is kept at a constant temperature.The yield-stress nanofluid enters this duct at a cold temperature with fully developed velocity.The aim of the present investigation is to explore the influence of flow velocity(Re=10 to 200),nanoparticle concentration(ϕ=0 to 0.1),magnetic field intensity(Ha=0 to 100),and its inclination angle(γ=0 to 90)and nanofluid yield stress(Bn=0 to 20)on the thermal and hydrodynamic efficiency inside the backward-facing step.The numerical results have been obtained by resolving the momentum and energy balance equations using the Galerkin finite element method.The obtained results have indicated that an increase in Reynolds number and nanoparticle volume fraction enhances heat transfer.In contrast,a significant reduction is observed with an increase in Hartmann and Bingham numbers,resulting in quasi-immobilization of the fluid under the magnetic influence and radical solidification of this type of fluid,accompanied by the suppression of the vortex zone downstream of the cylindrical obstacle.This study sheds light on the complexity of this magnetically influenced fluid,with potential implications in various engineering and materials science fields.展开更多
To satisfy the instancy requirements of resources integration inside and outside the enterprises for resolving the uniformity of information resources for steel enterprises in competing environment, a control model of...To satisfy the instancy requirements of resources integration inside and outside the enterprises for resolving the uniformity of information resources for steel enterprises in competing environment, a control model of ISPS-ERP system integration is proposed. The schemes based on regeneration of operation flow are designed here. The well effects are achieved during the implementing course in XG enterprise.展开更多
Transportation systems are rapidly transforming in response to urbanization,sustainability challenges,and advances in digital technologies.This review synthesizes the intersection of artificial intelligence(AI),fuzzy ...Transportation systems are rapidly transforming in response to urbanization,sustainability challenges,and advances in digital technologies.This review synthesizes the intersection of artificial intelligence(AI),fuzzy logic,and multi-criteria decision-making(MCDM)in transportation research.A comprehensive literature search was conducted in the Scopus database,utilizing carefully selected AI,fuzzy,and MCDM keywords.Studies were rigorously screened according to explicit inclusion and exclusion criteria,resulting in 73 eligible publications spanning 2006-2025.The review protocol included transparent data extraction on methodological approaches,application domains,and geographic distribution.Key findings highlight the prevalence of hybrid fuzzyAHPand TOPSIS methods,the widespread integration of machine learning for prediction and optimization,and a predominant focus on logistics and infrastructure planning within the transportation sector.Geographic analysis underscores a marked concentration of research activity in Asia,while other regions remain underrepresented,signaling the need for broader international collaboration.The review also addresses persistent challenges such asmethodological complexity,data limitations,and model interpretability.Future research directions are proposed,including the integration of reinforcement learning,real-time analytics,and big data-driven adaptive solutions.This study offers a comprehensive synthesis and critical perspective,serving as a valuable reference for researchers,practitioners,and policymakers seeking to enhance the efficiency,resilience,and sustainability of transportation systems through intelligent decision-making frameworks.展开更多
Ductile iron represents an optimal solution for saving material and costs in producing large heavy-section castings in the energy sector.It aimed to investigate the influence of very long solidification time(3,10 and ...Ductile iron represents an optimal solution for saving material and costs in producing large heavy-section castings in the energy sector.It aimed to investigate the influence of very long solidification time(3,10 and 20 h)in different casting zones(casting center and transition zone)on the microstructure and mechanical properties of non-standard heavy-section ferritic ductile iron(EN-GJS-400-15)castings.The different solidification conditions significantly influenced the microstructure(graphite and ferrous matrix).The extent of phenomena such as degenerate graphite,solidification defects,hard carbides,and intergranular pearlitic areas and the microstructural coarsening were proportional to the solidification time and attributable to the combined effect of limited undercooling,solid solution diffusion mechanisms,and segregation phenomena.For comparable solidification time,the transition zone was characterized by larger nodules,comparable nodularity,and lower nodule count than the casting center due to more effective diffusion phenomena during cooling.Moreover,the lower segregation phenomena in the transition zone reduced the amount of pearlite and carbides in the intercellular zones.Hardness was only slightly influenced by the different solidification conditions and did not represent a reliable indicator of the microstructural inhomogeneities.These results are essential to refine casting simulations for producing large ferritic ductile iron castings,considering the wide microstructural variability within non-standard heavy-section castings caused by significantly different solidification conditions.展开更多
Rail profile optimization is a critical strategy for mitigating wear and extending service life.However,damage at the wheel-rail contact surface goes beyond simple rail wear,as it also involves fatigue phenomena.Focus...Rail profile optimization is a critical strategy for mitigating wear and extending service life.However,damage at the wheel-rail contact surface goes beyond simple rail wear,as it also involves fatigue phenomena.Focusing solely on wear and not addressing fatigue in profile optimization can lead to the propagation of rail cracks,the peeling of material off the rail,and even rail fractures.Therefore,we propose an optimization approach that balances rail wear and fatigue for heavy-haul railway rails to mitigate rail fatigue damage.Initially,we performed a field investigation to acquire essential data and understand the characteristics of track damage.Based on theory and measured data,a simulation model for wear and fatigue was then established.Subsequently,the control points of the rail profile according to cubic non-uniform rational B-spline(NURBS)theory were set as the research variables.The rail’s wear rate and fatigue crack propagation rate were adopted as the objective functions.A multi-objective,multi-variable,and multi-constraint nonlinear optimization model was then constructed,specifically using a Levenberg Marquardt-back propagation neural network as optimized by the particle swarm optimization algorithm(PSO-LM-BP neural network).Ultimately,optimal solutions from the model were identified using a chaos microvariation adaptive genetic algorithm,and the effectiveness of the optimization was validated using a dynamics model and a rail damage model.展开更多
The flexible job shop scheduling problem(FJSP)is commonly encountered in practical manufacturing environments.A product is typically built by assembling multiple jobs during actual manufacturing.AGVs are normally used...The flexible job shop scheduling problem(FJSP)is commonly encountered in practical manufacturing environments.A product is typically built by assembling multiple jobs during actual manufacturing.AGVs are normally used to transport the jobs from the processing shop to the assembly shop,where they are assembled.Therefore,studying the integrated scheduling problem with its processing,transportation,and assembly stages is extremely beneficial and significant.This research studies the three-stage flexible job shop scheduling problem with assembly and AGV transportation(FJSP-T-A),which includes processing jobs,transporting them via AGVs,and assembling them.A mixed integer linear programming(MILP)model is established to obtain optimal solutions.As the MILP model is challenging for solving large-scale problems,a novel co-evolutionary algorithm(NCEA)with two different decoding methods is proposed.In NCEA,a restart operation is developed to improve the diversity of the population,and a multiple crossover strategy is designed to improve the quality of individuals.The validity of the MILP model is proven by analyzing its complexity.The effectiveness of the restart operator,multiple crossovers,and the proposed algorithm is demonstrated by calculating and analyzing the RPI values of each algorithm's results within the time limit and performing a paired t-test on the average values of each algorithm at the 95%confidence level.This paper studies FJSP-T-A by minimizing the makespan for the first time,and presents a MILP model and an NCEA with two different decoding methods.展开更多
Background This study aimed to explore the influence of prior gaming experience on the intensity and onset of cybersickness symptoms by comparing the effects of virtual reality(VR)immersion in gamers and nongamers.Met...Background This study aimed to explore the influence of prior gaming experience on the intensity and onset of cybersickness symptoms by comparing the effects of virtual reality(VR)immersion in gamers and nongamers.Methods This study involved 50 male participants,with equal numbers of gamers and nongamers,who were subject to a VR environment using head-mounted displays in a sitting position for a 15-minute session.The intensity of cybersickness symptoms,such as nausea,oculomotor disturbances,and disorientation,was measured using a simulator sickness questionnaire,and the onset of cybersickness was measured using the fast motion sickness scale.Physiological indices were measured based on heart rate variability(HRV)parameters.Results This study found that prior gaming experience significantly affected symptoms of cybersickness during VR immersion.Nongamers experienced more severe symptoms,including higher levels of nausea,disorientation,and oculomotor disturbances,with symptoms appearing earlier than those in gamers.These differences were linked to increased fluctuations in HRV and reduced parasympathetic activity in nongamers,indicating higher autonomic nervous system strain.By contrast,gamers showed more stable HRV responses,suggesting better physiological adaptability to VR environments.Conclusion These findings indicate that familiarity of gamers with dynamic visual and sensory inputs may help them manage VR-induced sensory conflicts more effectively.展开更多
Robust numerical tools are essential for enabling the use of hybrid rocket engines(HREs)in future space applications.In this context,Computational Fluid Dynamics(CFD)transient simulations can be employed to analyse an...Robust numerical tools are essential for enabling the use of hybrid rocket engines(HREs)in future space applications.In this context,Computational Fluid Dynamics(CFD)transient simulations can be employed to analyse and predict relevant fluid dynamics phenomena within the thrust chamber of small-scale HREs.This work applies such techniques to investigate two unexpected behaviours observed in a 10 N-class hydrogen peroxide-based hybrid thruster:an uneven regression rate during High-Density Polyethylene(HDPE)and Acrylonitrile Butadiene Styrene(ABS)fuel tests,and non-negligible axial consumption in the ABS test case.The present study seeks to identify their fluid-dynamic origins by analysing key aspects of the thruster’s internal ballistics.The impact of recirculation zones and mixing on regression rates is quantified,as is the effect of grain heating on performance.Although already known in the present scientific literature,these phenomena prove to become particularly relevant for small-scale engines.Furthermore,the study demonstrates how appropriate numerical tools can replicate experimental findings,helping to foresee and mitigate undesirable behaviours in the design phases of future HRE propulsion systems.CFD results match the final HDPE grain geometry,reproducing the uneven port diameters with a maximum error below 9%.For ABS,axial regression is accurately captured,confirming the model’s reliability.Furthermore,average regression rates differ by only 1.60%and 1.20%for HDPE and ABS,respectively,while mass consumption is reproduced within 1.70%for HDPE and 3.01%for ABS.Overall,the results of the work demonstrate the reliability of the numerical approach adopted.This enriches the analysis capabilities devoted to 10 N-class engines,provides an additional tool for simulating the internal ballistics of small-scale hybrid thrusters,and integrates the existing literature with new insights into their fluid dynamics.展开更多
The valuation of financial derivatives often assumes risk neutrality with respect to the risk-neutral martingale measure,which prevents arbitrage opportunities.However,casual traders may still incur substantial losses...The valuation of financial derivatives often assumes risk neutrality with respect to the risk-neutral martingale measure,which prevents arbitrage opportunities.However,casual traders may still incur substantial losses when trading at this risk-neutral price,especially when the price has to be paid now and the payoff is only realized in the future.This study proposes a new valuation framework that provides risksensitive investors with an additional safeguard.The proposed framework embraces a worst-case perspective while exploiting the underlier’s stochastic process,representing a combination of robust optimization and stochastic programming.Notably,it aims to mitigate losses in the likelier scenarios of the underlying asset’s prices.When the underlier’s returns are independent and lognormally but not necessarily identically distributed,our approach for pricing variance and volatility swaps could be greatly simplified,benefit from parallel computing,and be solved by a two-dimensional grid search.We further derive a closed-form solution in some special stationary cases and provide experimental results to highlight the effect of risk aversion on fending off sizable trading losses.展开更多
Ceramic materials with intricate structures can be efficiently fabricated using stereolithography(SLA)based 3D printing technology,offering advantages over traditional methods.Sintering temperature has primary effect ...Ceramic materials with intricate structures can be efficiently fabricated using stereolithography(SLA)based 3D printing technology,offering advantages over traditional methods.Sintering temperature has primary effect on properties of ceramics.This study investigated the crucial sintering temperature for 3D printed ceramics to ensure the desired properties.The results indicate that all samples exhibit a consistent layered structure across the experimental sintering temperatures.When the sintering temperature is increased from 1,250℃ to 1,350℃,the grain's morphology changes from spherical to plate-like.Surface morphology analysis reveals a decrease in surface roughness at sintering temperatures above 1,350℃.Mechanical tests show improved flexural strength and stiffness as the sintering temperature rises.Friction and wear experiments demonstrate that as the sintering temperature increases from 1,450℃ to 1,550℃,the wear pattern on ceramic surfaces transitions from deep pits to shallow grooves.The increase in sintering temperature effectively enhances the wear resistance of 3D printed alumina ceramics.This improvement plays a significant role in expanding the application field of these ceramics,prolonging the lifespan of parts,reducing production costs,enhancing performance,and promoting environmental protection.In this study,ceramics achieve the highest strength and best wear resistance when sintered at 1,600℃,resulting in the best overall performance.展开更多
The total replacement of old fossil fuels poses obstacles,making the production of efficient biogasoline vital.Despite its potential as an environmentally friendly fossil fuel substitute,the life cycle assessment(LCA)...The total replacement of old fossil fuels poses obstacles,making the production of efficient biogasoline vital.Despite its potential as an environmentally friendly fossil fuel substitute,the life cycle assessment(LCA)of palm oil-derived biogasoline remains underexplored.This study investigated the production of biogasoline fromcrude palm oil(CPO)based biorefinery using catalytic cracking over mesoporousγ-Al_(2)O_(3) catalyst and LCA analysis.High selectivity of converting CPO into biogasoline was achieved by optimizing catalytic cracking parameters,including catalyst dose,temperature,and contact time.γ-Al_(2)O_(3) and CPO were characterized by several methods to study the physical and chemical properties.The physical properties of biogasoline,such as density,calorific value,viscosity,and flash point,were investigated.An overall yield of 60.11%was achieved after catalytic cracking produced several C5-C11 short-chain hydrocarbons.Additionally,this research proposes innovative emission reduction strategies,including waste-to-biogasoline conversion and the use of biodegradable feedstocks that enhance the sustainability of biogasoline production.LCA ofγ-Al_(2)O_(3)’s energy and environmental implications reveals minor effects on global warming(0.0068%)and freshwater ecotoxicity(0.187%).LCAs show a 0.085%impact in the energy sector.This focus on both ecological impacts and practical mitigation strategies deepens the understanding of biogasoline production.展开更多
Effective thermal management is paramount for successfully deploying lithium-ion batteries in residential settings as storage systems for the exploitation of renewable sources.Uncontrolled temperature increases within...Effective thermal management is paramount for successfully deploying lithium-ion batteries in residential settings as storage systems for the exploitation of renewable sources.Uncontrolled temperature increases within battery packs can lead to critical issues such as cell overheating,potentially culminating in thermal runaway events and,in extreme cases,leading to fire or explosions.Thiswork presents a comprehensive numerical thermalmodel of a battery pack made of prototype pouch cells based on lithium ferrophosphate(LFP)chemistry.The multi-physical model is specifically developed to investigate real-world operating scenarios and to assess safety considerations.The considered energy storage system is a battery designed for residential applications,in its integration with a photovoltaic(PV)installation.The actual electrochemical behavior of the prototype cell during the charging and discharging processes is modeled and validated on the ground of experimental data.The essential steps leading to the numerical schematization of the battery pack are then presented to apply themodel to two different use scenarios,differing for the user loads.The first scenario corresponds to a typical residential load,with standby lights being active during the night,solar generation with its peak at noon,and appliance use shifting in the afternoon and the evening.In the second scenario,a double demand for energy is present thatmakes the battery never reach 100%of the State of Charge(SoC)and dischargemore rapidly with respect to what occurs under the first scenario.Comparing the simulated temperature with the assumed C-rate,namely the charge or discharge current divided by the battery nominal capacity,it is found that peaks coincide with the charging phase;subsequently,the current tends to a zero value,and consequently,the temperature suddenly reaches the value of the environment.Finally,the model is also utilized to simulate a condition of thermal runaway by introducing critical conditions within a specific pouch cell.In this simulation,the thermal exchange between the cell in thermal runaway and the rest of the system remains within acceptable limits.This occurs due to the short duration of the process and to the module casing coated with an insulating material.The work provides an essential foundation for conducting numerical simulations of battery packs operating also at higher power levels.展开更多
This study examines the impact of futures trading on market efficiency and price discovery in the U.S.real estate investment trusts(REITs)market.First,we present inconclusive evidence regarding efficiency improvement ...This study examines the impact of futures trading on market efficiency and price discovery in the U.S.real estate investment trusts(REITs)market.First,we present inconclusive evidence regarding efficiency improvement in the U.S.REIT spot market following the introduction of futures trading.To investigate the interplay between spot and futures markets,we analyze their respective roles in price discovery and find that,unlike in stock and bond markets,the spot market predominantly exhibits price leadership in the U.S.REITs market,despite the growing market size of futures.We find evidence that the limited role of futures in price discovery is associated with an increase in speculative demand,which outweighs hedging pressure.These findings suggest that policymakers should carefully monitor investor trading motives in the U.S.REITs market and consider revising market regulations to enhance liquidity,ensuring that increased liquidity does not primarily result from heightened speculative demand.展开更多
For any country,the availability of electricity is crucial to the development of the national economy and society.As a result,decision-makers and policy-makers can improve the sustainability and security of the energy...For any country,the availability of electricity is crucial to the development of the national economy and society.As a result,decision-makers and policy-makers can improve the sustainability and security of the energy supply by implementing a variety of actions by using the evaluation of these factors as an early warning system.This research aims to provide a multi-criterion decision-making(MCDM)method for assessing the sustainability and security of the electrical supply.The weights of criteria,which indicate their relative relevance in the assessment of the sustainability and security of the energy supply,the MCDM method allow users to express their opinions.To overcome the impact of uncertainty and vagueness of expert opinion,we explore the notion of picture fuzzy theory,which is a more efficient and dominant mathematical model.Recently,the theory of Aczel-Alsina operations has attained a lot of attraction and has an extensive capability to acquire smooth approximated results during the aggregation process.However,Choquet integral operators are more flexible and are used to express correlation among different attributes.This article diagnoses an innovative theory of picture fuzzy set to derive robust mathematical methodologies of picture fuzzy Choquet Integral Aczel-Alsina aggregation operators.To prove the intensity and validity of invented approaches,some dominant properties and special cases are also discussed.An intelligent decision algorithm for the MCDM problem is designed to resolve complicated real-life applications under multiple conflicting criteria.Additionally,we discussed a numerical example to investigate a suitable electric transformer under consideration of different beneficial key criteria.A comparative study is established to capture the superiority and effectiveness of pioneered mathematical approaches with existing methodologies.展开更多
基金The authors extend their appreciation to King Saud University,Saudi Arabia for funding this work through the Ongoing Research Funding Program(ORF-2025-704),King Saud University,Riyadh,Saudi Arabia.
文摘A novel siphon-based divide-and-conquer(SbDaC)policy is presented in this paper for the synthesis of Petri net(PN)based liveness-enforcing supervisors(LES)for flexible manufacturing systems(FMS)prone to deadlocks or livelocks.The proposed method takes an uncontrolled and bounded PN model(UPNM)of the FMS.Firstly,the reduced PNM(RPNM)is obtained from the UPNM by using PN reduction rules to reduce the computation burden.Then,the set of strict minimal siphons(SMSs)of the RPNM is computed.Next,the complementary set of SMSs is computed from the set of SMSs.By the union of these two sets,the superset of SMSs is computed.Finally,the set of subnets of the RPNM is obtained by applying the PN reduction rules to the superset of SMSs.All these subnets suffer from deadlocks.These subnets are then ordered from the smallest one to the largest one based on a criterion.To enforce liveness on these subnets,a set of control places(CPs)is computed starting from the smallest subnet to the largest one.Once all subnets are live,this process provides the LES,consisting of a set of CPs to be used for the UPNM.The live controlled PN model(CPNM)is constructed by merging the LES with the UPNM.The SbDaC policy is applicable to all classes of PNs related to FMS prone to deadlocks or livelocks.Several FMS examples are considered from the literature to highlight the applicability of the SbDaC policy.In particular,three examples are utilized to emphasize the importance,applicability and effectiveness of the SbDaC policy to realistic FMS with very large state spaces.
基金supports from several sources including the Thai Research Fund (No. MRG5280215)Prince of Songkla University (No. AGR530031M)the Royal Golden Jubilee Ph.D. Program (No. PHD/0134/2551 and PHD/0173/2550)
文摘Several rheocasting processes are developed or applied worldwide in the metal forming industry.One of the new rheocasting processes is the gas induced semi-solid(GISS) process.The GISS process utilizes the principle of rapid heat extraction and vigorous local extraction using the injection of fine gas bubbles through a graphite diffuser.Several forming processes such as die casting,squeeze casting,gravity casting,and rheo-extrusion of the semi-solid slurries prepared by the GISS process have also been conducted.The GISS process is capable of processing various alloys including cast aluminum alloys,die casting aluminum alloys,wrought aluminum alloys,and zinc alloys.The GISS process is currently developed to be used commercially in the industry with the focus on forming semi-solid slurries containing low fractions solid(< 0.25) into parts.The research and development activities of the GISS process were discussed and the status of the industrial developments of this process was reported.
基金supports from Prince of Songkla University (No.AGR530031M)the Royal Golden Jubilee Ph.D program (No.PHD/0173/2550)
文摘The gas induced semi-solid(GISS) is a rheocasting process that produces semi-solid slurry by applying fine gas bubble injection through a graphite diffuser.The process is developed to be used in the die casting industry.To apply the GISS process with a die casting process,a GISS maker unit is designed and attached to a conventional die casting machine with little modifications.The commercial parts are developed and produced by the GISS die casting process.The GISS die casting shows the feasibility to produce industrial parts with aluminum 7075 and A356 with lower porosity than liquid die casting.
文摘Noise pollution is one of the most significant harmful physical factors in the industrial and occupational environments.Due to the high costs of exposure to excessive noise;continuous sound evaluation,propose and implement noise control plans in occupational environments is necessary.Thus,the present study aimed to review environmental sound measurements,drawing of noise maps,and prioritizing the engineering noise control methods using the Analytic Hierarchy Process(AHP).This study was a descriptive-analytical study that aimed to assess occupational noises and present a control plan in the City Gas Stations(CGSs)of Kerman,Iran in 2021.The present study was done in two phases.In the first phase,six CGSs were investigated to measure and evaluate the noise.In addition,the noise map of a CGS was drawn using the Surfer software.Finally,the AHP was used in the second phase of the research to prioritize the control measures.In this phase,four criteria and ten alternatives were identified.According to first phase results,the sound pressure level(SPL)of the stations varied from 76 to 98 dBA.Besides,the majority of the studied stations had a sound level higher than 85 dBA(danger zone).The second phase of the study showed that out of the four evaluated criteria,the executability criterion had the highest impact and the cost criterion had the lowest impact on the selection of control measures with a weight of 0.587 and 0.052,respectively.Based on the results of prioritization of the alternatives,using a silenced regulator(weight of 0.223)and increasing the thickness of the tube(weight of 0.023)had the highest and lowest priorities among the alternatives,respectively.The use of engineering noise control methods such as using silenced regulators was the best way to control the noises of CGSs.Additionally;it is noteworthy that AHP is a practical method for prioritizing alternatives to achieve the most accurate decision-making.The results of AHP can be of great help to health and safety experts and managers in choosing the sound engineering control measures more precisely.
文摘The design of the geometric shape of a helicopter fuselage poses a serious challenge for designers. The most important parameter in determining the shape of the helicopter fuselage is its aerodynamic coefficients. These coefficients are determined using two methods： wind tunnel test and computational fluid dynamics（CFD） simulation. The first method is expensive, time-consuming and limited. In addition, estimates in regions away from data can be poor. The second method,due to the limitations of numerical solution, the number of nodes and the used solution, is often inaccurate. In this paper, with the aim of accelerating the design process and achieving results with reasonable engineering accuracy, an engineering-statistical model which is useful for estimating the aerodynamic coefficients was developed, which mitigated the drawbacks of these two methods.First, by combining CFD simulation and regression techniques, an engineering model was presented for the estimation of aerodynamic coefficients. Then, by using the data from a wind tunnel test and implementation of statistical adjustment, the engineering model was modified and an engineering-statistical model was obtained. By spending less time and cost, the final model provided the aerodynamic coefficients of a helicopter fuselage at the desired angles of attack with reasonable accuracy. Finally, three numerical examples were provided to illustrate the application of the proposed model. Comparative results demonstrate the effectiveness of the engineering-statistical model in estimating the aerodynamic coefficients of a helicopter fuselage.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R319)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR44The authors are thankful to the Deanship of Scientific Research at Najran University for funding thiswork under theResearch Groups Funding program Grant Code(NU/RG/SERC/11/4).
文摘With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial transformation.The Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether.In industry 4.0,powerful IntrusionDetection Systems(IDS)play a significant role in ensuring network security.Though various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of networks.This is because conventional Machine Learning(ML)approaches are inadequate and insufficient to address the demands of dynamic IIoT networks.Further,the existing Deep Learning(DL)can be employed to identify anonymous intrusions.Therefore,the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection(HGSODLID)model for the IIoT environment.The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format.The HGSO algorithm is employed for Feature Selection(HGSO-FS)to reduce the curse of dimensionality.Moreover,Sparrow Search Optimization(SSO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the network.Finally,the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model.The proposed HGSODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.
文摘The present study focuses on the flow of a yield-stress(Bingham)nanofluid,consisting of suspended Fe3O4 nanoparticles,subjected to a magnetic field in a backward-facing step duct(BFS)configuration.The duct is equipped with a cylindrical obstacle,where the lower wall is kept at a constant temperature.The yield-stress nanofluid enters this duct at a cold temperature with fully developed velocity.The aim of the present investigation is to explore the influence of flow velocity(Re=10 to 200),nanoparticle concentration(ϕ=0 to 0.1),magnetic field intensity(Ha=0 to 100),and its inclination angle(γ=0 to 90)and nanofluid yield stress(Bn=0 to 20)on the thermal and hydrodynamic efficiency inside the backward-facing step.The numerical results have been obtained by resolving the momentum and energy balance equations using the Galerkin finite element method.The obtained results have indicated that an increase in Reynolds number and nanoparticle volume fraction enhances heat transfer.In contrast,a significant reduction is observed with an increase in Hartmann and Bingham numbers,resulting in quasi-immobilization of the fluid under the magnetic influence and radical solidification of this type of fluid,accompanied by the suppression of the vortex zone downstream of the cylindrical obstacle.This study sheds light on the complexity of this magnetically influenced fluid,with potential implications in various engineering and materials science fields.
文摘To satisfy the instancy requirements of resources integration inside and outside the enterprises for resolving the uniformity of information resources for steel enterprises in competing environment, a control model of ISPS-ERP system integration is proposed. The schemes based on regeneration of operation flow are designed here. The well effects are achieved during the implementing course in XG enterprise.
文摘Transportation systems are rapidly transforming in response to urbanization,sustainability challenges,and advances in digital technologies.This review synthesizes the intersection of artificial intelligence(AI),fuzzy logic,and multi-criteria decision-making(MCDM)in transportation research.A comprehensive literature search was conducted in the Scopus database,utilizing carefully selected AI,fuzzy,and MCDM keywords.Studies were rigorously screened according to explicit inclusion and exclusion criteria,resulting in 73 eligible publications spanning 2006-2025.The review protocol included transparent data extraction on methodological approaches,application domains,and geographic distribution.Key findings highlight the prevalence of hybrid fuzzyAHPand TOPSIS methods,the widespread integration of machine learning for prediction and optimization,and a predominant focus on logistics and infrastructure planning within the transportation sector.Geographic analysis underscores a marked concentration of research activity in Asia,while other regions remain underrepresented,signaling the need for broader international collaboration.The review also addresses persistent challenges such asmethodological complexity,data limitations,and model interpretability.Future research directions are proposed,including the integration of reinforcement learning,real-time analytics,and big data-driven adaptive solutions.This study offers a comprehensive synthesis and critical perspective,serving as a valuable reference for researchers,practitioners,and policymakers seeking to enhance the efficiency,resilience,and sustainability of transportation systems through intelligent decision-making frameworks.
文摘Ductile iron represents an optimal solution for saving material and costs in producing large heavy-section castings in the energy sector.It aimed to investigate the influence of very long solidification time(3,10 and 20 h)in different casting zones(casting center and transition zone)on the microstructure and mechanical properties of non-standard heavy-section ferritic ductile iron(EN-GJS-400-15)castings.The different solidification conditions significantly influenced the microstructure(graphite and ferrous matrix).The extent of phenomena such as degenerate graphite,solidification defects,hard carbides,and intergranular pearlitic areas and the microstructural coarsening were proportional to the solidification time and attributable to the combined effect of limited undercooling,solid solution diffusion mechanisms,and segregation phenomena.For comparable solidification time,the transition zone was characterized by larger nodules,comparable nodularity,and lower nodule count than the casting center due to more effective diffusion phenomena during cooling.Moreover,the lower segregation phenomena in the transition zone reduced the amount of pearlite and carbides in the intercellular zones.Hardness was only slightly influenced by the different solidification conditions and did not represent a reliable indicator of the microstructural inhomogeneities.These results are essential to refine casting simulations for producing large ferritic ductile iron castings,considering the wide microstructural variability within non-standard heavy-section castings caused by significantly different solidification conditions.
基金supported by the National Natural Science Foundation of China(No.52388102)the Sichuan Science and Technology Program(No.2023ZDZX0008)China.The authors would like to thank the Guoneng Shuo-Huang Railway Development Company,China for providing vehicle parameters and line data for this project.The authors would also like to acknowledge the Xplorer Prize for sponsoring the project.
文摘Rail profile optimization is a critical strategy for mitigating wear and extending service life.However,damage at the wheel-rail contact surface goes beyond simple rail wear,as it also involves fatigue phenomena.Focusing solely on wear and not addressing fatigue in profile optimization can lead to the propagation of rail cracks,the peeling of material off the rail,and even rail fractures.Therefore,we propose an optimization approach that balances rail wear and fatigue for heavy-haul railway rails to mitigate rail fatigue damage.Initially,we performed a field investigation to acquire essential data and understand the characteristics of track damage.Based on theory and measured data,a simulation model for wear and fatigue was then established.Subsequently,the control points of the rail profile according to cubic non-uniform rational B-spline(NURBS)theory were set as the research variables.The rail’s wear rate and fatigue crack propagation rate were adopted as the objective functions.A multi-objective,multi-variable,and multi-constraint nonlinear optimization model was then constructed,specifically using a Levenberg Marquardt-back propagation neural network as optimized by the particle swarm optimization algorithm(PSO-LM-BP neural network).Ultimately,optimal solutions from the model were identified using a chaos microvariation adaptive genetic algorithm,and the effectiveness of the optimization was validated using a dynamics model and a rail damage model.
基金Supported by National Natural Science Foundation of China(Grant Nos.52205529 and 62303204)the Youth Innovation Team Program of Shandong Higher Education Institution(Grant No.2023KJ206)the Guangyue Youth Scholar Innovation Talent Program support received from Liaocheng University(Grant No.LCUGYTD2022-03)。
文摘The flexible job shop scheduling problem(FJSP)is commonly encountered in practical manufacturing environments.A product is typically built by assembling multiple jobs during actual manufacturing.AGVs are normally used to transport the jobs from the processing shop to the assembly shop,where they are assembled.Therefore,studying the integrated scheduling problem with its processing,transportation,and assembly stages is extremely beneficial and significant.This research studies the three-stage flexible job shop scheduling problem with assembly and AGV transportation(FJSP-T-A),which includes processing jobs,transporting them via AGVs,and assembling them.A mixed integer linear programming(MILP)model is established to obtain optimal solutions.As the MILP model is challenging for solving large-scale problems,a novel co-evolutionary algorithm(NCEA)with two different decoding methods is proposed.In NCEA,a restart operation is developed to improve the diversity of the population,and a multiple crossover strategy is designed to improve the quality of individuals.The validity of the MILP model is proven by analyzing its complexity.The effectiveness of the restart operator,multiple crossovers,and the proposed algorithm is demonstrated by calculating and analyzing the RPI values of each algorithm's results within the time limit and performing a paired t-test on the average values of each algorithm at the 95%confidence level.This paper studies FJSP-T-A by minimizing the makespan for the first time,and presents a MILP model and an NCEA with two different decoding methods.
基金Supported by Rekognisi Tugas Akhir(RTA)Grant(No.5286/UN1.P1/PT.01.03/2024)from Universitas Gadjah Mada,Indonesia.
文摘Background This study aimed to explore the influence of prior gaming experience on the intensity and onset of cybersickness symptoms by comparing the effects of virtual reality(VR)immersion in gamers and nongamers.Methods This study involved 50 male participants,with equal numbers of gamers and nongamers,who were subject to a VR environment using head-mounted displays in a sitting position for a 15-minute session.The intensity of cybersickness symptoms,such as nausea,oculomotor disturbances,and disorientation,was measured using a simulator sickness questionnaire,and the onset of cybersickness was measured using the fast motion sickness scale.Physiological indices were measured based on heart rate variability(HRV)parameters.Results This study found that prior gaming experience significantly affected symptoms of cybersickness during VR immersion.Nongamers experienced more severe symptoms,including higher levels of nausea,disorientation,and oculomotor disturbances,with symptoms appearing earlier than those in gamers.These differences were linked to increased fluctuations in HRV and reduced parasympathetic activity in nongamers,indicating higher autonomic nervous system strain.By contrast,gamers showed more stable HRV responses,suggesting better physiological adaptability to VR environments.Conclusion These findings indicate that familiarity of gamers with dynamic visual and sensory inputs may help them manage VR-induced sensory conflicts more effectively.
文摘Robust numerical tools are essential for enabling the use of hybrid rocket engines(HREs)in future space applications.In this context,Computational Fluid Dynamics(CFD)transient simulations can be employed to analyse and predict relevant fluid dynamics phenomena within the thrust chamber of small-scale HREs.This work applies such techniques to investigate two unexpected behaviours observed in a 10 N-class hydrogen peroxide-based hybrid thruster:an uneven regression rate during High-Density Polyethylene(HDPE)and Acrylonitrile Butadiene Styrene(ABS)fuel tests,and non-negligible axial consumption in the ABS test case.The present study seeks to identify their fluid-dynamic origins by analysing key aspects of the thruster’s internal ballistics.The impact of recirculation zones and mixing on regression rates is quantified,as is the effect of grain heating on performance.Although already known in the present scientific literature,these phenomena prove to become particularly relevant for small-scale engines.Furthermore,the study demonstrates how appropriate numerical tools can replicate experimental findings,helping to foresee and mitigate undesirable behaviours in the design phases of future HRE propulsion systems.CFD results match the final HDPE grain geometry,reproducing the uneven port diameters with a maximum error below 9%.For ABS,axial regression is accurately captured,confirming the model’s reliability.Furthermore,average regression rates differ by only 1.60%and 1.20%for HDPE and ABS,respectively,while mass consumption is reproduced within 1.70%for HDPE and 3.01%for ABS.Overall,the results of the work demonstrate the reliability of the numerical approach adopted.This enriches the analysis capabilities devoted to 10 N-class engines,provides an additional tool for simulating the internal ballistics of small-scale hybrid thrusters,and integrates the existing literature with new insights into their fluid dynamics.
基金supported by the Ministry of Education,Singapore,under its Academic Research Fund Tier 2 Grant MOE-T2EP20222-0003.
文摘The valuation of financial derivatives often assumes risk neutrality with respect to the risk-neutral martingale measure,which prevents arbitrage opportunities.However,casual traders may still incur substantial losses when trading at this risk-neutral price,especially when the price has to be paid now and the payoff is only realized in the future.This study proposes a new valuation framework that provides risksensitive investors with an additional safeguard.The proposed framework embraces a worst-case perspective while exploiting the underlier’s stochastic process,representing a combination of robust optimization and stochastic programming.Notably,it aims to mitigate losses in the likelier scenarios of the underlying asset’s prices.When the underlier’s returns are independent and lognormally but not necessarily identically distributed,our approach for pricing variance and volatility swaps could be greatly simplified,benefit from parallel computing,and be solved by a two-dimensional grid search.We further derive a closed-form solution in some special stationary cases and provide experimental results to highlight the effect of risk aversion on fending off sizable trading losses.
基金supported by the Xinjiang Tianchi Talent Introduction Plan (51052300585)the Fundamental Research Funds for Autonomous Region Universities (XJEDU2022P002)+1 种基金the Natural science foundation project of Xinjiang Uygur Autonomous Region (2023D01C192,2023D01C33)the Tianshan Innovation Team Program of Xinjiang Uygur Autonomous Region (2023D14001)。
文摘Ceramic materials with intricate structures can be efficiently fabricated using stereolithography(SLA)based 3D printing technology,offering advantages over traditional methods.Sintering temperature has primary effect on properties of ceramics.This study investigated the crucial sintering temperature for 3D printed ceramics to ensure the desired properties.The results indicate that all samples exhibit a consistent layered structure across the experimental sintering temperatures.When the sintering temperature is increased from 1,250℃ to 1,350℃,the grain's morphology changes from spherical to plate-like.Surface morphology analysis reveals a decrease in surface roughness at sintering temperatures above 1,350℃.Mechanical tests show improved flexural strength and stiffness as the sintering temperature rises.Friction and wear experiments demonstrate that as the sintering temperature increases from 1,450℃ to 1,550℃,the wear pattern on ceramic surfaces transitions from deep pits to shallow grooves.The increase in sintering temperature effectively enhances the wear resistance of 3D printed alumina ceramics.This improvement plays a significant role in expanding the application field of these ceramics,prolonging the lifespan of parts,reducing production costs,enhancing performance,and promoting environmental protection.In this study,ceramics achieve the highest strength and best wear resistance when sintered at 1,600℃,resulting in the best overall performance.
基金The contract No.PRJ-395/DPKS/2022 or 2383/PKS/ITS/2022 on 14 November 2022.
文摘The total replacement of old fossil fuels poses obstacles,making the production of efficient biogasoline vital.Despite its potential as an environmentally friendly fossil fuel substitute,the life cycle assessment(LCA)of palm oil-derived biogasoline remains underexplored.This study investigated the production of biogasoline fromcrude palm oil(CPO)based biorefinery using catalytic cracking over mesoporousγ-Al_(2)O_(3) catalyst and LCA analysis.High selectivity of converting CPO into biogasoline was achieved by optimizing catalytic cracking parameters,including catalyst dose,temperature,and contact time.γ-Al_(2)O_(3) and CPO were characterized by several methods to study the physical and chemical properties.The physical properties of biogasoline,such as density,calorific value,viscosity,and flash point,were investigated.An overall yield of 60.11%was achieved after catalytic cracking produced several C5-C11 short-chain hydrocarbons.Additionally,this research proposes innovative emission reduction strategies,including waste-to-biogasoline conversion and the use of biodegradable feedstocks that enhance the sustainability of biogasoline production.LCA ofγ-Al_(2)O_(3)’s energy and environmental implications reveals minor effects on global warming(0.0068%)and freshwater ecotoxicity(0.187%).LCAs show a 0.085%impact in the energy sector.This focus on both ecological impacts and practical mitigation strategies deepens the understanding of biogasoline production.
文摘Effective thermal management is paramount for successfully deploying lithium-ion batteries in residential settings as storage systems for the exploitation of renewable sources.Uncontrolled temperature increases within battery packs can lead to critical issues such as cell overheating,potentially culminating in thermal runaway events and,in extreme cases,leading to fire or explosions.Thiswork presents a comprehensive numerical thermalmodel of a battery pack made of prototype pouch cells based on lithium ferrophosphate(LFP)chemistry.The multi-physical model is specifically developed to investigate real-world operating scenarios and to assess safety considerations.The considered energy storage system is a battery designed for residential applications,in its integration with a photovoltaic(PV)installation.The actual electrochemical behavior of the prototype cell during the charging and discharging processes is modeled and validated on the ground of experimental data.The essential steps leading to the numerical schematization of the battery pack are then presented to apply themodel to two different use scenarios,differing for the user loads.The first scenario corresponds to a typical residential load,with standby lights being active during the night,solar generation with its peak at noon,and appliance use shifting in the afternoon and the evening.In the second scenario,a double demand for energy is present thatmakes the battery never reach 100%of the State of Charge(SoC)and dischargemore rapidly with respect to what occurs under the first scenario.Comparing the simulated temperature with the assumed C-rate,namely the charge or discharge current divided by the battery nominal capacity,it is found that peaks coincide with the charging phase;subsequently,the current tends to a zero value,and consequently,the temperature suddenly reaches the value of the environment.Finally,the model is also utilized to simulate a condition of thermal runaway by introducing critical conditions within a specific pouch cell.In this simulation,the thermal exchange between the cell in thermal runaway and the rest of the system remains within acceptable limits.This occurs due to the short duration of the process and to the module casing coated with an insulating material.The work provides an essential foundation for conducting numerical simulations of battery packs operating also at higher power levels.
基金supported by the National Research Foundation of Korea funded by the Ministry of Science and ICT(2022R1A2C1004258:Kwangwon Ahn)the Sogang University Research Grant of 2023(No.202310016.01:Sungbin Sohn).
文摘This study examines the impact of futures trading on market efficiency and price discovery in the U.S.real estate investment trusts(REITs)market.First,we present inconclusive evidence regarding efficiency improvement in the U.S.REIT spot market following the introduction of futures trading.To investigate the interplay between spot and futures markets,we analyze their respective roles in price discovery and find that,unlike in stock and bond markets,the spot market predominantly exhibits price leadership in the U.S.REITs market,despite the growing market size of futures.We find evidence that the limited role of futures in price discovery is associated with an increase in speculative demand,which outweighs hedging pressure.These findings suggest that policymakers should carefully monitor investor trading motives in the U.S.REITs market and consider revising market regulations to enhance liquidity,ensuring that increased liquidity does not primarily result from heightened speculative demand.
文摘For any country,the availability of electricity is crucial to the development of the national economy and society.As a result,decision-makers and policy-makers can improve the sustainability and security of the energy supply by implementing a variety of actions by using the evaluation of these factors as an early warning system.This research aims to provide a multi-criterion decision-making(MCDM)method for assessing the sustainability and security of the electrical supply.The weights of criteria,which indicate their relative relevance in the assessment of the sustainability and security of the energy supply,the MCDM method allow users to express their opinions.To overcome the impact of uncertainty and vagueness of expert opinion,we explore the notion of picture fuzzy theory,which is a more efficient and dominant mathematical model.Recently,the theory of Aczel-Alsina operations has attained a lot of attraction and has an extensive capability to acquire smooth approximated results during the aggregation process.However,Choquet integral operators are more flexible and are used to express correlation among different attributes.This article diagnoses an innovative theory of picture fuzzy set to derive robust mathematical methodologies of picture fuzzy Choquet Integral Aczel-Alsina aggregation operators.To prove the intensity and validity of invented approaches,some dominant properties and special cases are also discussed.An intelligent decision algorithm for the MCDM problem is designed to resolve complicated real-life applications under multiple conflicting criteria.Additionally,we discussed a numerical example to investigate a suitable electric transformer under consideration of different beneficial key criteria.A comparative study is established to capture the superiority and effectiveness of pioneered mathematical approaches with existing methodologies.
