Unlocking the Network Traffic Password of the Sudden Popularity of Oriental Selection:Consumer Behavior Decision-Making Mechanism Based on Mental Accounting Theory

This paper explores the decision-making mechanism of the consuming behavior hidden behind the sudden popularity of the Oriental Selection Company in terms of the mental accounting theory.Firstly,according to the“Non-alternative”characteristics of mental accounting,this paper expounds how the strategy of the bilingual live-streaming of Oriental Selection promoters stimulates consumers’desire to buy the advertised products and services whilst using the utility theory of mental accounting to analyze how Oriental Selection promoters improve consumers’acquisition utility and total utility.Secondly,we sum up the successful experiences of Oriental Selection:The live-streaming industry should apply the theory of mental accounting in effectively overcoming the shortcomings of the live-streamed marketing by stimulating consumers’desire and influencing their decision-making behavior through the streaming of content that triggers them to make purchases.This is achievable by abandoning the traditional ways of loudly urging consumers to buy goods.Finally,this paper puts forward some suggestions on how to use the mental accounting theory in promoting sustainable consumption and points out the prospects for Oriental Selection.

The modulation recognition based on decision-making mechanism and neural network integrated classifier

A neural network integrated classifier(NNIC) designed with a new modulation recognition algorithm based on the decision-making tree is proposed in this paper.Firstly,instantaneous parameters are extracted in the time domain by the coordinated rotation digital computer(CORDIC) algorithm based on the extended convergence domain and feature parameters of frequency spectrum and power spectrum are extracted by the time-frequency analysis method.All pattern identification parameters are calculated under the I/Q orthogonal two-channel structure,and constructed into the feature vector set.Next,the classifier is designed according to the modulation pattern and recognition performance of the feature parameter set,the optimum threshold is selected for each feature parameter based on the decision-making mechanism in a single classifier,multi-source information fusion and modulation recognition are realized based on feature parameter judge process in the NNIC.Simulation results show NNIC is competent for all modulation recognitions,8 kinds of digital modulated signals are effectively identified,which shows the recognition rate and anti-interference capability at low SNR are improved greatly,the overall recognition rate can reach 100%when SNR is12dB.

The underlying mechanism of variety–water–nitrogen–stubble damage interactions on yield formation in ratoon rice with low stubble height under mechanized harvesting

Agronomic measures are the key to promote the sustainable development of ratoon rice by reducing the damage from mechanical crushing to the residual stubble of the main crop, thereby mitigating the impact on axillary bud sprouting and yield formation in ratoon rice. This study used widely recommended conventional rice Jiafuzhan and hybrid rice Yongyou 2640 as the test materials to conduct a four-factor block design field experiment in a greenhouse of the experimental farm of Fujian Agricultural and Forestry University, China from 2018 to 2019.The treatments included fertilization and no fertilization, alternate wetting and drying irrigation and continuous water flooding irrigation, and plots with and without artificial crushing damage on the rice stubble. At the same time, a 13C stable isotope in-situ detection technology was used to fertilize the pot experiment. The results showed significant interactions among varieties, water management, nitrogen application and stubble status.Relative to the long-term water flooding treatment, the treatment with sequential application of nitrogen fertilizer coupled with moderate field drought for root-vigor and tiller promotion before and after harvesting of the main crop, significantly improved the effective tillers from low position nodes. This in turn increased the effective panicles per plant and grains per panicle by reducing the influence of artificial crushing damage on rice stubble and achieving a high yield of the regenerated rice. Furthermore, the partitioning of 13C assimilates to the residual stubble and its axillary buds were significantly improved at the mature stage of the main crop, while the translocation rate to roots and rhizosphere soil was reduced at the later growth stage of ratooning season rice. This was triggered by the metabolism of hormones and polyamines at the stem base regulated by the interaction of water and fertilizer at this time. We therefore suggest that to achieve a high yield of ratoon rice with low stubble height under mechanized harvesting, the timely application of nitrogen fertilizer is fundamental,coupled with moderate field drying for root-vigor preservation and tiller promotion before and after the mechanical harvesting of the main crop.

Preliminary discussion on the ignition mechanism of exploding foil initiators igniting boron potassium nitrate

Exploding foil initiator(EFI)is a kind of advanced device for initiating explosives,but its function is unstable when it comes to directly igniting pyrotechnics.To solve the problem,this research aims to reveal the ignition mechanism of EFIs directly igniting pyrotechnics.An oscilloscope,a photon Doppler velocimetry,and a plasma spectrum measurement system were employed to obtain information of electric characteristics,impact pressure,and plasma temperature.The results of the electric characteristics and the impact pressure were inconsistent with ignition results.The only thing that the ignition success tests had in common was that their plasma all had a relatively long period of high-temperature duration(HTD).It eventually concludes that the ignition mechanism in this research is the microconvection heat transfer rather than the shock initiation,which differs from that of exploding foil initiators detonating explosives.Furthermore,the methods for evaluating the ignition success of semiconductor bridge initiators are not entirely applicable to the tests mentioned in this paper.The HTD is the critical parameter for judging the ignition success,and it is influenced by two factors:the late time discharge and the energy of the electric explosion.The longer time of the late time discharge and the more energy of the electric explosion,the easier it is to expand the HTD,which improves the probability of the ignition success.

Antibacterial mechanism of kojic acid and tea polyphenols against Escherichia coli O157:H7 through transcriptomic analysis

Escherichia coli O157:H7 is one of the major foodborne pathogenic bacterial that cause infectious diseases in humans.The previous found that a combination of kojic acid and tea polyphenols exhibited better activity against E.coli O157:H7 than using either alone.This study aimed to explore responses underlying the antibacterial mechanisms of kojic acid and tea polyphenols from the gene level.The functional enrichment analysis by comparing kojic acid and tea polyphenols individually or synergistically against E.coli O157:H7 found that acid resistance systems in kojic acid were activated,and the cell membrane and genomic DNA were destructed in the cells,resulting in“oxygen starvation”.The oxidative stress response triggered by tea polyphenols inhibited both sulfur uptake and the synthesis of ATP,which affected the bacteria's life metabolic process.Interestingly,we found that kojic acid combined with tea polyphenols hindered the uptake of iron that played an essential role in the synthesis of DNA,respiration,tricarboxylic acid cycle.The results suggested that the iron uptake pathways may represent a novel approach for kojic acid and tea polyphenols synergistically against E.coli O157:H7 and provided a theoretical basis for bacterial pathogen control in the food industry.

Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees

While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review

Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems.

Microdynamic mechanical properties and fracture evolution mechanism of monzogabbro with a true triaxial multilevel disturbance method

The far-field microdynamic disturbance caused by the excavation of deep mineral resources and underground engineering can induce surrounding rock damage in high-stress conditions and even lead to disasters.However,the mechanical properties and damage/fracture evolution mechanisms of deep rock induced by microdynamic disturbance under three-dimensional stress states are unclear.Therefore,a true triaxial multilevel disturbance test method is proposed,which can completely simulate natural geostress,excavation stress redistribution(such as stress unloading,concentration and rotation),and subsequently the microdynamic disturbance triggering damaged rock failure.Based on a dynamic true triaxial test platform,true triaxial microdynamic disturbance tests under different frequency and amplitudes were carried out on monzogabbro.The results show that increasing amplitude or decreasing frequency diminishes the failure strength of monzogabbro.Deformation modulus gradually decreases during disturbance failure.As frequency and amplitude increase,the degradation rate of deformation modulus decreases slightly,disturbance dissipated energy increases significantly,and disturbance deformation anisotropy strengthens obviously.A damage model has been proposed to quantitatively characterize the disturbance-induced damage evolution at different frequency and amplitude under true triaxial stress.Before disturbance failure,the micro-tensile crack mechanism is dominant,and the micro-shear crack mechanism increases significantly at failure.With the increase of amplitude and frequency,the micro-shear crack mechanism increases.When approaching disturbance failure,the acoustic emission fractal dimension changes from a stable value to local large oscillation,and finally increases sharply to a high value at failure.Finally,the disturbance-induced failure mechanism of surrounding rock in deep engineering is clearly elucidated.

Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

Failure mechanism and infrared radiation characteristic of hard siltstone induced by stratification effect

The deformation in sedimentary rock induced by train loads has potential threat to the safe operation of tunnels. This study investigated the influence of stratification structure on the infrared radiation and temporal damage mechanism of hard siltstone. The uniaxial compression tests, coupled with acoustic emission(AE) and infrared radiation temperature(IRT) were conducted on siltstones with different stratification effects. The results revealed that the stratigraphic structure significantly affects the stress-strain response and strength degradation characteristics. The mechanical parameters exhibit anisotropy characteristics, and the stratification effect exhibits a negative correlation with the cracking stress and peak stress. The failure modes caused by the stratification effect show remarkable anisotropic features, including splitting failure(Ⅰ: 0°-22.50°, Ⅱ: 90°), composite failure(45°), and shearing failure(67.50°). The AE temporal sequences demonstrate a stepwise response characteristic to the loading stress level. The AE intensity indicates that the stress sensitivity of shearing failure and composite failure is generally greater than that of splitting failure. The IRT field has spatiotemporal migration and progressive dissimilation with stress loading and its dissimilation degree increases under higher stress levels. The stronger the stratification effect, the greater the dissimilation degree of the IRT field. The abnormal characteristic points of average infrared radiation temperature(AIRT) variance at local stress drop and peak stress can be used as early and late precursors to identify fracture instability. Theoretical analysis shows that the competitive relationship between compaction strengthening and fracturing damage intensifies the dissimilation of the infrared thermal field for an increasing stress level. The present study provides a theoretical reference for disaster warnings in hard sedimentary rock mass.

Macro-micro behaviors and failure mechanism of frozen weakly cemented mudstone

Understanding the mechanical properties and multiscale failure mechanism of frozen soft rock is an important prerequisite for the construction safety of tunnels,artificially frozen ground and other infrastructure in cold regions.In this study,the triaxial compression test are performed on mudstone in the weakly cemented soft rock strata in the mining area of western China,and the mechanical characteristics and failure mechanism of weakly cemented mudstone are systematically investigated under the combined action of freezing and loading.Furthermore,the quantitative relationship between the microstructural parameters and the macroscopic strength and deformation parameters is established based on fractal theory.Thus,the failure mechanism of frozen weakly cemented mudstone is revealed on both micro- and macro-scales.The results show that temperature and confining pressure significantly affects the elastic modulus and peak strength of weakly cemented mudstone.With decreasing temperature,the compressive strength increases,while the corresponding peak strain decreases gradually.On the deformation curve,the plastic deformation stage is shortened,and the brittle fracture feature at the post-peak stage is more prominent,and the elastic modulus correspondingly increases with decreasing temperature.Under low-temperature conditions,most of the weakly cemented mudstone undergoes microscopic shear failure along the main fracture surface.The micro-fracture morphology characteristics of weakly cemented mudstone under different temperatures are quantified via the fractal dimension,and an approximately exponential relationship can be obtained among the fractal dimension and the temperature,compressive strength and elastic modulus.

Activity-dependent mechanisms of neuroprotection:promising avenues against dementia

The study of the brain and its complex functions is highly fascinating and,at the same time,extremely important.Indeed,furthering our understanding of the biology of neurons and synapses is a prerequisite to uncover the mechanisms involved in memory formation and the coordination of movement as well as their alterations occurring in several neurological disorders.

Antagonism effect of residual S triggers the dual-path mechanism for water oxidation

Transition metal chalcogenides(TMCs)are recognized as pre-catalysts,and their(oxy)hydroxides derived from electrochemical reconstruction are the active species in the water oxidation.However,understanding the role of the residual chalcogen in the reconstructed layer is lacking in detail,and the corresponding catalytic mechanism remains controversial.Here,taking Cu_(1-x)Co_(x)S as a platform,we explore the regulating effect and existence form of the residual S doped into the reconstructive layer for oxygen evolution reaction(OER),where a dual-path OER mechanism is proposed.First-principles calculations and operando~(18)O isotopic labeling experiments jointly reveal that the residual S in the reconstructive layer of Cu_(1-x)Co_(x)S can wisely balance the adsorbate evolution mechanism(AEM)and lattice oxygen oxidation mechanism(LOM)by activating lattice oxygen and optimizing the adsorption/desorption behaviors at metal active sites,rather than change the reaction mechanism from AEM to LOM.Following such a dual-path OER mechanism,Cu_(0.4)Co_(0.6)S-derived Cu_(0.4)Co_(0.6)OSH not only overcomes the restriction of linear scaling relationship in AEM,but also avoids the structural collapse caused by lattice oxygen migration in LOM,so as to greatly reduce the OER potential and improved stability.

The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

Spherical q-linearDiophantine fuzzy sets(Sq-LDFSs)provedmore effective for handling uncertainty and vagueness in multi-criteria decision-making(MADM).It does not only cover the data in two variable parameters but is also beneficial for three parametric data.By Pythagorean fuzzy sets,the difference is calculated only between two parameters(membership and non-membership).According to human thoughts,fuzzy data can be found in three parameters(membership uncertainty,and non-membership).So,to make a compromise decision,comparing Sq-LDFSs is essential.Existing measures of different fuzzy sets do,however,can have several flaws that can lead to counterintuitive results.For instance,they treat any increase or decrease in the membership degree as the same as the non-membership degree because the uncertainty does not change,even though each parameter has a different implication.In the Sq-LDFSs comparison,this research develops the differentialmeasure(DFM).Themain goal of the DFM is to cover the unfair arguments that come from treating different types of FSs opposing criteria equally.Due to their relative positions in the attribute space and the similarity of their membership and non-membership degrees,two Sq-LDFSs formthis preference connectionwhen the uncertainty remains same in both sets.According to the degree of superiority or inferiority,two Sq-LDFSs are shown as identical,equivalent,superior,or inferior over one another.The suggested DFM’s fundamental characteristics are provided.Based on the newly developed DFM,a unique approach tomultiple criterion group decision-making is offered.Our suggestedmethod verifies the novel way of calculating the expert weights for Sq-LDFSS as in PFSs.Our proposed technique in three parameters is applied to evaluate solid-state drives and choose the optimum photovoltaic cell in two applications by taking uncertainty parameter zero.The method’s applicability and validity shown by the findings are contrasted with those obtained using various other existing approaches.To assess its stability and usefulness,a sensitivity analysis is done.

Obituary:Prof.Yun Zhang(1963-2023)-A scientist focused on toxins and their underlying mechanisms to decipher human diseases

Prof.Yun Zhang was born on 9 July 1963 in Kunming,Yunnan,China,during a tumultuous period which he often referenced.Throughout his life,he harbored a steadfast belief in using knowledge to unravel the mysteries of human diseases.His educational journey was marked by frequent changes in schools due to his parents’occupational relocations.However,despite these challenges,he consistently displayed diligence and was admitted to the East China University of Science and Technology,Shanghai,after completing high school in 1980.He remained an active and loyal member of the School of Biotechnology at the university.

A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying capacity.This approach can capture the imprecision and ambiguity often present in human perception.With the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making algorithms.By implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

Chlorfenapyr poisoning:mechanisms,clinical presentations,and treatment strategies

BACKGROUND:Chlorfenapyr is used to kill insects that are resistant to organophosphorus insecticides.Chlorfenapyr poisoning has a high mortality rate and is difficult to treat.This article aims to review the mechanisms,clinical presentations,and treatment strategies for chlorfenapyr poisoning.DATA RESOURCES:We conducted a review of the literature using PubMed,Web of Science,and SpringerLink from their beginnings to the end of October 2023.The inclusion criteria were systematic reviews,clinical guidelines,retrospective studies,and case reports on chlorfenapyr poisoning that focused on its mechanisms,clinical presentations,and treatment strategies.The references in the included studies were also examined to identify additional sources.RESULTS:We included 57 studies in this review.Chlorfenapyr can be degraded into tralopyril,which is more toxic and reduces energy production by inhibiting the conversion of adenosine diphosphate to adenosine triphosphate.High fever and altered mental status are characteristic clinical presentations of chlorfenapyr poisoning.Once it occurs,respiratory failure occurs immediately,ultimately leading to cardiac arrest and death.Chlorfenapyr poisoning is diflcult to treat,and there is no specific antidote.CONCLUSION:Chlorfenapyr is a new pyrrole pesticide.Although it has been identified as a moderately toxic pesticide by the World Health Organization(WHO),the mortality rate of poisoned patients is extremely high.There is no specific antidote for chlorfenapyr poisoning.Therefore,based on the literature review,future efforts to explore rapid and effective detoxification methods,reconstitute intracellular oxidative phosphorylation couplings,identify early biomarkers of chlorfenapyr poisoning,and block the conversion of chlorfenapyr to tralopyril may be helpful for emergency physicians in the diagnosis and treatment of this disease.

A reduced combustion mechanism of ammonia/diesel optimized with multi-objective genetic algorithm

For the deep understanding on combustion of ammonia/diesel,this study develops a reduced mechanism of ammonia/diesel with 227 species and 937 reactions.The sub-mechanism on ammonia/interactions of N-based and C-based species(N—C)/NOx is optimized using the Non-dominated Sorting Genetic Algorithm II(NSGA-II)with 200 generations.The optimized mechanism(named as 937b)is validated against combustion characteristics of ammonia/methane(which is used to examine the accuracy of N—C interactions)and ammonia/diesel blends.The ignition delay times(IDTs),the laminar flame speeds and most of key intermediate species during the combustion of ammonia/methane blends can be accurately simulated by 937b under a wide range of conditions.As for ammonia/diesel blends with various diesel energy fractions,reasonable predictions on the IDTs under pressures from 1.0 MPa to5.0 MPa as well as the laminar flame speeds are also achieved by 937b.In particular,with regard to the IDT simulations of ammonia/diesel blends,937b makes progress in both aspects of overall accuracy and computational efficiency,compared to a detailed ammonia/diesel mechanism.Further kinetic analysis reveals that the reaction pathway of ammonia during the combustion of ammonia/diesel blend mainly differs in the tendencies of oxygen additions to NH_2 and NH with different equivalence ratios.