Proposed New Framework Scheme for Path Loss in Wireless Body Area Network

Recent technical developments in wi-fi networking, microelectronic integration and programming, sensors and the Internet have enabled us to create and enforce a range of new framework schemes to fulfil the necessities of healthcare-related wireless body area network (WBAN). WBAN sensors continually screen and measure patients’ indispensable signs and symptoms, and relay them to scientific monitoring for diagnosis. WBAN has a range of applications, the most necessary of which is to help patients suffering diseases to stay alive. The quality instance is the coronary heart implant sensor, whose video display unit monitors coronary heart sign and continuously transmits it. This setup eliminates the need for patients to visit the medical doctor frequently. Instead, they can take a seat at home and acquire an analysis and prescription for the disease. Today, a sizable effort is being made to increase low-power sensors and gadgets for utility in WBAN. A new framework scheme that addresses route loss in WBAN and discusses its penalties in depth is endorsed in this paper. The new framework scheme is applied to three case scenarios to obtain parameters by measuring vital information about the human body. On-body and intrabody conversation simulations are conducted. On-body conversation findings show that the route loss between transmitter and receiver rises with growing distance and frequency

Measuring maladaptive avoidance: from animal models to clinical anxiety

Avoiding stimuli that predict danger is required for survival. However, avoidance can become maladaptive in individuals who overestimate threat and thus avoid safe situations as well as dangerous ones. Excessive avoidance is a core feature of anxiety disorders, post-traumatic stress disorder (PTSD), and obsessive-compulsive disorder (OCD). This avoidance prevents patients from confronting maladaptive threat beliefs, thereby maintaining disordered anxiety. Avoidance is associated with high levels of psychosocial impairment yet is poorly understood at a mechanistic level. Many objective laboratory assessments of avoidance measure adaptive avoidance, in which an individual learns to successfully avoid a truly noxious stimulus. However, anxiety disorders are characterized by maladaptive avoidance, for which there are fewer objective laboratory measures. We posit that maladaptive avoidance behavior depends on a combination of three altered neurobehavioral processes: (1) threat appraisal, (2) habitual avoidance, and (3) trait avoidance tendency. This heterogeneity in underlying processes presents challenges to the objective measurement of maladaptive avoidance behavior. Here we first review existing paradigms for measuring avoidance behavior and its underlying neural mechanisms in both human and animal models, and identify how existing paradigms relate to these neurobehavioral processes. We then propose a new framework to improve the translational understanding of maladaptive avoidance behavior by adapting paradigms to better differentiate underlying processes and mechanisms and applying these paradigms in clinical populations across diagnoses with the goal of developing novel interventions to engage specific identified neurobehavioral targets.

A Survey of Opponent Modeling in Adversarial Domains

Opponent modeling is the ability to use prior knowledge and observations in order to predict the behavior of an opponent. This survey presents a comprehensive overview of existing opponent modeling techniques for adversarial domains, many of which must address stochastic, continuous, or concurrent actions, and sparse, partially observable payoff structures. We discuss all the components of opponent modeling systems, including feature extraction, learning algorithms, and strategy abstractions. These discussions lead us to propose a new form of analysis for describing and predicting the evolution of game states over time. We then introduce a new framework that facilitates method comparison, analyze a representative selection of techniques using the proposed framework, and highlight common trends among recently proposed methods. Finally, we list several open problems and discuss future research directions inspired by AI research on opponent modeling and related research in other disciplines.

The Chinese Latin Alphabet: A Revolutionary Script in the Global Information Age

This article rethinks the history of Chinese script reforms and proposes a new genealogy for the Chinese Latin Alphabet (CLA), invented in 1931 by Chinese and Russian revolutionaries in the Soviet Union. Situating script reforms within a global information age that emerged out of the nineteenth-century communications revolution, the article historicizes the CLA within a technologically and ideologically contrived Sino-Soviet space. In particular, it shows the intimate links between the CLA and the Unified New Turkic Alphabet (UNTA), which grew out of a latinization movement based in Baku, Azerbaijan. The primary purpose of the UNTA was to latinize the Arabic script of the Turkic people living in Soviet Central Asia, but it was immediately exported to the non-Turkic world as well in an effort to latinize languages across Eurasia and ignite revolutionary internationalism. This article investigates the forgotten figures involved in carrying the Latin alphabet from Baku to Shanghai and offers a new framework to scrutinize the history of language, scripts, and knowledge production across Eurasia.

Synthetic Biology Approaches to Hydrocarbon Biosensors: A Review

Monooxygenases are a class of enzymes that facilitate the bacterial degradation of alkanes and alkenes. The regulatory components associated with monooxygenases are nature’s own hydrocarbon sensors, and once functionally characterised, these components can be used to create rapid, inexpensive and sensitive biosensors for use in applications such as bioremediation and metabolic engineering. Many bacterial monooxygenases have been identified, yet the regulation of only a few of these have been investigated in detail. A wealth of genetic and functional diversity of regulatory enzymes and promoter elements still remains unexplored and unexploited, both in published genome sequences and in yet-to-be-cultured bacteria. In this review we examine in detail the current state of research on monooxygenase gene regulation, and on the development of transcription-factor-based microbial biosensors for detection of alkanes and alkenes. A new framework for the systematic characterisation of the underlying genetic components and for further development of biosensors is presented, and we identify focus areas that should be targeted to enable progression of more biosensor candidates to commercialisation and deployment in industry and in the environment.

Mental Health in 3D

There is a wealth of research on the depiction and impact of mental health representations in traditional media; however, less is known about video games. As the dominant form of media in the 21st century, video games uniquely portray mental illness in traditional ways as well as in ways unique to video games, such as in-game mechanics (e.g., sanity meters) and player-driven decision making. This paper outlines the importance of cultural messages relating to mental illness as conveyed through video games in terms of content and influence and presents a multi-dimensional model of analysis for the representation of mental illness in digital games. The aim of this paper is to provide a foundation for understanding how mental illness is represented in digital games, provide a new perspective for thinking critically about representation of mental illness in games, and overview a new framework for assessing video game content in this area.

Haplotype-based inference of the distribution of fitness effects

Recent genome sequencing studies with large sample sizes in humans have discovered a vast quantity of low-frequency variants, providing an important source of information to analyze how selection is acting on human genetic variation. In order to estimate the strength of natural selection acting on low-frequency variants, we have developed a likelihood-based method that uses the lengths of pairwise identity-by-state between haplotypes carrying low-frequency variants. We show that in some non-equilibrium populations (such as those that have had recent population expansions) it is possible to distinguish between positive or negative selection acting on a set of variants. With our new framework, one can infer a fixed selection intensity acting on a set of variants at a particular frequency, or a distribution of selection coefficients for standing variants and new mutations. We show an application of our method to the UK10K phased haplotype dataset of individuals.

At the Margins of the Global Market

Contemporary scholars debate the factors driving despotic labour conditions across the world economy. Some emphasize the dominance of global market imperatives and others highlight the market's reliance upon extra-economic coercion and state violence. At the Margins of the Global Market engages in this debate through a comparative and world-historical analysis of the labour regimes of three global commodity-producing subregions of rural Colombia: the coffee region of Viejo Caldas, the banana region of Urabá, and the coca/cocaine region of the Caguán. By drawing upon insights from labour regimes, global commodity chains, and world historical sociology, this book offers a novel understanding of the broad range of factors - local, national, global, and interregional - that shape labour conditions on the ground in Colombia. In doing so, it offers a critical new framework for analysing labour and development dynamics that exist at the margins of the global market.