Unraveling Predatory Pricing:Insights From Case Studies and Game Theory Modeling

This paper develops a game-theory model for predatory pricing via in-depth analyses of three case studies:Brooke Group Ltd.v.Brown&Williamson Tobacco Corp.,Matsushita Electric Industries Co.v.Zenith Radio Corporation,and AKZO Chemie BV v.Commission of the European Communities.This model is based on subsequent action game theory models and rational economics behavior,offering a chronological outline of the“predation”stages.It presents the predator’s decisions,the prey’s potential responses,possible loops,and the two distinctive outcomes.The analysis of the model in context of the three case studies demonstrates its practicality in assessing real-life predatory pricing scenarios and players’strategies.It’s flexibility also allows applications in related fields.Overall,this paper offers a comprehensive framework that bridges the gap between law,economics,and game theory in the study of predatory pricing,informing future research in this area.

Artificial Intelligence Embedded Object-Oriented Methodology For Model Based

The paper presents the coupling of artificial intelligence-AI and Object-oriented methodology applied for the construction of the model-based decision support system MBDSS.The MBDSS is designed for support the strategic decision making lead to the achievemellt of optimal path towardsmarket economy from the central planning situation in China. To meet user's various requirements,a series of innovations in software development have been carried out, such as system formalization with OBFRAMEs in an object-oriented paradigm for problem solving automation and techniques of modules intelligent cooperation, hybrid system of reasoning, connectionist framework utilization,etc. Integration technology has been highly emphasized and discussed in this article and an outlook to future software engineering is given in the conclusion section.

Hero: Automated Detection System for Prescription Stimulant Overdose via AI-Based Emotion Inference, Metabolite Detection, and Biometric Measurement

Over the past year, approximately 10,000 Americans have died by psychostimulant overdose, and over 50% of these deaths were caused by prescription stimulant misuse. A comprehensive approach to detect a drug overdose in the environment where it occurs is imperative to reduce the number of prescription stimulant overdose-related deaths. Teenagers are at the highest risk for prescription stimulant overdose, so this study proposes a multi-factor overdose detection system named Hero which is designed to noninvasively operate within the context of a teen’s life. Hero monitors five factors that indicate stimulant abuse: extreme mood swings, presence of amphetamine metabolite in sweat excreted from the fingertip, heart rate, blood pressure, and respiration rate. An algorithm to detect extreme mood swings in a teen’s outgoing SMS messages was developed by collecting over 3.6 million tweets, creating groups of tweets for euphoria and melancholy using guidelines adapted from DSM-5 criteria, and training six Artificial Intelligence models. These models were used to create a dual-model-based extreme mood swing detection algorithm that was accurate 96% of the time. A biochemical strip, which consisted of a diagnostic measure that changes color when in contact with amphetamine metabolite and a control measure that changes color when the appropriate volume of sweat is excreted, was created. A gold nanoparticle-based diagnostic measure and pH-based control measure were evaluated individually and on the overall strip. The diagnostic measure had an accuracy of 90.62% while the control measure had 84.38% accuracy. Lastly, a vital sign measurement algorithm was built by applying photoplethysmography image processing techniques. A regression model with height, age, and gender features was created to convert heart rate to blood pressure, and the final algorithm had an accuracy of 97.86%. All five of these factors work together to create an accurate and easily integrable system to detect overdoses in real-time and prevent prescription stimulant abuse-related deaths.

Balancing timeliness of reporting with increasing testing probability for epidemic data

Reporting of epidemiological data requires coordinated action by numerous agencies,across a multitude of logistical steps.Using collated and reported information to inform direct interventions can be challenging due to associated delays.Mitigation can,however,occur indirectly through the public generation of concern,which facilitates adherence to protective behaviors.We utilized a coupled-dynamic multiplex network model with a communication-and disease-layer to examine how variation in reporting delay and testing probability are likely to impact adherence to protective behaviors,such as reducing physical contact.Individual concern mediated adherence and was informed by new-or active-case reporting,at the population-or community-level.Individuals received information from the communication layer:direct connections that were sick or adherent to protective behaviors increased their concern,but absence of illness eroded concern.Models revealed that the relative benefit of timely reporting and a high probability of testing was contingent on how much information was already obtained.With low rates of testing,increasing testing probability was of greater mitigating value.With high rates of testing,maximizing timeliness was of greater value.Population-level reporting provided advanced warning of disease risk from nearby communities;but we explore the relative costs and benefits of delays due to scale against the assumption that people may prioritize community-level information.Our findings emphasize the interaction of testing accuracy and reporting timeliness for the indirect mitigation of disease in a complex social system.

Optogenetics in oral and craniofacial research

Optogenetics combines optics and genetic engineering to control specific gene expression and biological functions and has the advantages of precise spatiotemporal control,noninvasiveness,and high efficiency.Genetically modified photosensory sensors are engineered into proteins to modulate conformational changes with light stimulation.Therefore,optogenetic techniques can provide new insights into oral biological processes at different levels,ranging from the subcellular and cellular levels to neural circuits and behavioral models.Here,we introduce the origins of optogenetics and highlight the recent progress of optogenetic approaches in oral and craniofacial research,focusing on the ability to apply optogenetics to the study of basic scientific neural mechanisms and to establish different oral behavioral test models in vivo(orofacial movement,licking,eating,and drinking),such as channelrhodopsin(ChR),archaerhodopsin(Arch),and halorhodopsin from Natronomonas pharaonis(NpHR).We also review the synergic and antagonistic effects of optogenetics in preclinical studies of trigeminal neuralgia and maxillofacial cellulitis.In addition,optogenetic tools have been used to control the neurogenic differentiation of dental pulp stem cells in translational studies.Although the scope of optogenetic tools is increasing,there are limited large animal experiments and clinical studies in dental research.Potential future directions include exploring therapeutic strategies for addressing loss of taste in patients with coronavirus disease 2019(COVID-19),studying oral bacterial biofilms,enhancing craniomaxillofacial and periodontal tissue regeneration,and elucidating the possible pathogenesis of dry sockets,xerostomia,and burning mouth syndrome.