Effects Of Key Components Research Articles

High-Rate and Durable Zinc-Based Flow Batteries for Large-Scale Energy Storage

Large-scale energy storage is a key enabling technology for the widespread adoption of renewable energy such as wind and solar power. Among various technologies, zinc-based flow batteries stand out as one of the most promising candidates due to the high energy density, low cost, and use of earth-abundant active materials. However, the low operating current density and short cycle life resulting from notorious zinc dendrite formation, large internal resistance, and sluggish reaction kinetics pose a great challenge for their practical applications. Although great efforts have been made to address these issues, previous works mainly focused on one specific aspect, making it challenging to boost the overall performance of zinc-based flow batteries. In this presentation, a systematic study on the effects of key components on the electrochemical performance of zinc-bromine flow batteries will be presented. Our results show that all key components (electrodes, electrolytes, membranes, and flow fields) can exert a significant influence on the electrochemical performance. More impressively, a high areal capacity of >200 mAh/cm2 and an operating current density of as high as 400 mA/cm2 can be achieved by optimizing the design and operating parameters. Underlying mechanisms and perspectives for further improvement will be discussed during the presentation.

An enhanced estimation of distribution algorithm with problem-specific knowledge for distributed no-wait flowshop group scheduling problems

With the trend of economic globalization, distributed manufacturing widely exists in modern manufacturing systems. As an extension of the distributed flowshop scheduling problem, the distributed no-wait flowshop group scheduling problem with sequence-dependent setup times (DNFGSP_SDSTs) is investigated in this article. To address DNFGSP_SDSTs with the criterion of minimizing makespan, this study proposes an enhanced estimation of distribution algorithm·(EEDA) with problem-specific knowledge. First, a mixed integer linear programming(MILP) model of DNFGSP_SDSTs is established. Second, based on the characteristics of DNFGSP_SDSTs, five problem-specific properties about local search operators are derived as prior knowledge to reduce computational cost. Third, two NEH-based two-stage heuristics are presented to construct a high-quality population with diversity. Fourth, a probability model with problem-specific knowledge and a family-based updating mechanism are developed to accumulate valuable pattern information from high-quality solutions, while a sampling strategy is designed to generate new populations with the accumulated information. Fifth, several local search operators are devised to refine the obtained solutions. Furthermore, perturbation and reinitialization methods are developed to avoid premature convergence. Finally, the validity of the MILP model is verified by using the Gurobi solver. The parameters of EEDA are tuned through a design of experiments. The effectiveness of key components in EEDA is confirmed through extensive experiments, and the computational comparisons with the state-of-the-art algorithms indicate the effectiveness of the proposed EEDA for solving DNFGSP_SDSTs.

Employees' in-role and extra-role information security behaviors from the P-E fit perspective

Organizations are increasingly seeking ways to encourage employee in-role and extra-role information security (InfoSec) behaviors for enhancing organizational InfoSec. One important consideration is the InfoSec congruence between the person and the organizational environment. Accordingly, drawing on person–environment (P–E) fit theory, we conducted an empirical investigation that examined (1) the effect of key components in organizational InfoSec management practice on InfoSec value P–E fit, and (2) the role of InfoSec value P–E fit on employees’ InfoSec in-role and extra-role behaviors in the organization. The results suggest that three practices—InfoSec policies; security education, training, and awareness programs; and computer monitoring—cultivate InfoSec value P–E fit, leading to an increase in employees’ InfoSec in-role and extra-role behaviors.

Determinants of Buying Produce on Short-Video Platforms: The Impact of Social Network and Resource Endowment—Evidence from China

In the wake of the COVID-19 pandemic, selling by short video has become a new online selling model that enhances the communication between buyers and sellers. Therefore, it is necessary to identify the key factors influencing consumers’ purchase of agricultural products on short-video platforms. Additionally, it is also important to figure out the influencing mechanism and action path. Specifically, based on the ‘Stimulus-Organism-Response (SOR)’ framework and structural equation model, we delineate and empirically test hypotheses regarding the effects of key components on consumers’ purchase intentions and behaviors. The key components refer to three external stimuli of consumers’ social network, sellers’ resource endowment, and both sides’ infrastructure development levels. Simultaneously, we analyze the mediating role of consumers’ perceived value and perceived risk between external stimuli and consumers’ purchase intentions. This paper argues that short-video merchants improving the influence of their stores and platforms strengthening supervision and management are the keys to ensuring stable growth in consumers’ willingness to purchase agricultural products sold on short videos and promoting the development of the short-video live industry.

Mitigation of Gastric Damage Using Cinnamomum cassia Extract: Network Pharmacological Analysis of Active Compounds and Protection Effects in Rats.

Gastritis is a common disease worldwide that is caused by various causes such as eating habits, smoking, severe stress, and heavy drinking, as well as Helicobacter pylori infections and non-steroidal anti-inflammatory drugs. Cinnamomum cassia is a tropical aromatic evergreen tree commonly used as a natural medicine in Asia and as a functional food ingredient. Studies have reported this species’ anti-obesity, anti-diabetic, and cardiovascular disease suppression effects. We evaluated the potential effects of C. cassia using non-steroidal anti-inflammatory drugs (NSAIDs), ethanol (EtOH), and ethanol/hydrochloric acid (HCl)-induced gastric mucosal injury models. C. cassia extracts reduced the area of gastric mucosa injury caused by indomethacin, NSAID, EtOH, and EtOH/HCl. We also applied a network pharmacology-based approach to identify the active compounds, potential targets, and pharmacological mechanisms of C. cassia against gastritis. Through a network pharmacology analysis, 10 key components were predicted as anti-gastritis effect-related compounds of C. cassia among 51 expected active compounds. The NF-κB signaling pathway, a widely known inflammatory response mechanism, comprised a major signaling pathway within the network pharmacology analysis. These results suggest that the anti-gastritis activities of C. cassia may be induced via the anti-inflammatory effects of key components, which suppress the inflammation-related genes and signaling pathways identified in this study.

Revealing the methanogenic pathways for anaerobic digestion of key components in food waste: Performance, microbial community, and implications

Anaerobic digestion (AD) process is widely considered the most sustainable technology for food waste (FW) disposal due to its advantage of biomethane recovery and beneficial environmental consequences. However, the effects of key components in FW (i.e. starchy food, vegetables, fruits, and meats) on AD process and their methanogenic pathways remain unclear. In this study, the biochemical methane potential (BMP) of cooked rice, cabbage, banana peel, pork and local FW was 288, 283, 254, 630, and 476 NmL CH4/g VSadded, with t80 (time required for 80% methane produced) of 3, 9, 3, 11 and 11 days, respectively. Kinetic analysis suggested diverse hydrolysis rates (0.104–0.679 d-1) and specific methane yields (39–119 NmL CH4/g VSadded/d). The relative abundances of key methanogens in the reactors were diverse, leading to the variation in acetoclastic and hydrogenotrophic methanogenic pathways. This study provides fundamental information for the operation of AD systems with different FW compositions.

Pore-scale modeling of oxygen transport in the catalyst layer of air-breathing cathode in membraneless microfluidic fuel cells

The air-breathing membraneless microfluidic fuel cell is one of the potential micro-fuel cells. Different from conventional membrane-based micro-fuel cells, its cathode catalyst layer directly contacts with flowing aqueous electrolyte. This makes it essential to well understand the oxygen transport, dissolve and reaction in cathode catalyst layer of the microfluidic fuel cell. For the first time, a pore-scale Lattice Boltzmann method model is developed for the cathode catalyst layer with an underneath electrolyte microchannel. The various nanostructured catalyst layers are numerically reconstructed. The effects of key components (ionomer and catalyst mass fractions) and geometric parameters (porosity, isotropy) on oxygen transport and reaction are investigated and discussed. The modeling results suggest that local oxygen concentration and reaction rate are closely correlated to nanostructure. Oxygen transfer can be enhanced by increasing the porosity yet ionomer is found to be extra resistance. The ionomer and catalyst mass fractions have complex effects on oxygen transfer and reaction. Vertical-anisotropic catalyst layers benefit oxygen transfer, thus ordered nanostructure of cathode catalyst layer holds great potentials to enhance performance. The electrolyte flow rate shows the minimum influence on oxygen transfer and reaction. The modeling results can provide insights into multiphysics interaction and characteristics of oxygen transport in air-breathing cathode and also be reference for flow in porous media involving electrochemical reactions.

An empirical study of the impact of consumer emotional engagement and affective commitment in firm-hosted virtual communities

ABSTRACTEmergent perspectives in marketing highlight new opportunities for leveraging social media as a means to build customer–firm relationships through consumer engagement. Drawing from cognitive appraisal theory and aspects of the service dominant logic, this study delineates and empirically tests hypotheses regarding the effects of key components of consumer engagement (cognitive appraisal, affective states, participation) on consumers’ affective commitment, in the context of two service companies where the firms used social media to host virtual communities. The research examines how consumers’ cognitive appraisal of the engagement experience aligned with their online interaction propensity and participation in value-creating activities drive engagement outcomes. The results confirm the need to contextualise, personalise and respond to the consumer’s engagement experience to develop this engagement.

Multiobjective Flexible Job Shop Scheduling Using Memetic Algorithms

In this paper, we propose new memetic algorithms (MAs) for the multiobjective flexible job shop scheduling problem (MO-FJSP) with the objectives to minimize the makespan, total workload, and critical workload. The problem is addressed in a Pareto manner, which aims to search for a set of Pareto optimal solutions. First, by using well-designed chromosome encoding/decoding scheme and genetic operators, the nondominated sorting genetic algorithm II (NSGA-II) is adapted for the MO-FJSP. Then, our MAs are developed by incorporating a novel local search algorithm into the adapted NSGA-II, where some good individuals are chosen from the offspring population for local search using a selection mechanism. Furthermore, in the proposed local search, a hierarchical strategy is adopted to handle the three objectives, which mainly considers the minimization of makespan, while the concern of the other two objectives is reflected in the order of trying all the possible actions that could generate the acceptable neighbor. In the experimental studies, the influence of two alternative acceptance rules on the performance of the proposed MAs is first examined. Afterwards, the effectiveness of key components in our MAs is verified, including genetic search, local search, and the hierarchical strategy in local search. Finally, extensive comparisons are carried out with the state-of-the-art methods specially presented for the MO-FJSP on well-known benchmark instances. The results show that the proposed MAs perform much better than all the other algorithms.

Behavior of Low Frequency Signal Through Stainless Steel Enclosures in Noncontact Safety Switch Application

In this paper, we analyze a noncontact safety system, which consists of a switch and an actuator, designed for ultrahygienic applications using stainless steel enclosures. We explore the effect of different stainless steel thicknesses using different signal frequencies. Analysis of the effect of key components was researched to select suitable electronics to extend the activated switching distance up to 8 mm between switch and actuator. We analyze the transmission and reception of electromagnetic signals for a combination of a specific window frequency, specific material thickness, and content of stainless steel enclosures. We also explore unmet worldwide market demand for noncontact stainless steel frequency-operated switches.