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Youth involved with the juvenile justice system have higher rates of unmet social and psychological service needs than the general population. Yet, scant research has addressed the potentially complex needs of youth on probation. This study is thus a first step in improving our ability to promote positive youth development and improved outcomes from youth on probation. It uses administrative data on probation youth from FY2011-2013 in a mid-Atlantic state. We estimated latent class models based on the ordinal scoring (high, moderate, none/low) of the 25 subscales from a standardized assessment administered at intake. In order to test whether groups were distinct, we examined a range of variables, including the official risk classification and recidivism rates measured by officieal rearrests and reconvictions. We report the demographic differences between the estimated groups, as well as adverse childhood experiences, school and employment status, previous legal history, and substance use history. Ultimately, the seven-class model produced four groups that others have noted a relatively low needs (lowest need), relatively high needs (complex needs), substance use service needs, and mental health service needs. The other three groups that emerged include two gender specific groups (one for male and one for female high-need groups) and a group with skills needs that lacks supportive and protective skills. The analyses will facilitate a better appreciation for the service needs of moderate risk youth. Youth on probation are not a uniform population; they reflect tremendous heterogeneity, and probation systems should embrace systemic responsivity to provide appropriate services to improve youth outcomes. Selleckchem SRI-011381 Advancing efforts to provide a broader spectrum of services that address multi-morbid conditions can ensure that youth have opportunities to improve their quality of life during the period of supervision.In this paper, we propose a comprehensive analytics framework that can serve as a decision support tool for HR recruiters in real-world settings in order to improve hiring and placement decisions. The proposed framework follows two main phases a local prediction scheme for recruitments' success at the level of a single job placement, and a mathematical model that provides a global recruitment optimization scheme for the organization, taking into account multilevel considerations. In the first phase, a key property of the proposed prediction approach is the interpretability of the machine learning (ML) model, which in this case is obtained by applying the Variable-Order Bayesian Network (VOBN) model to the recruitment data. Specifically, we used a uniquely large dataset that contains recruitment records of hundreds of thousands of employees over a decade and represents a wide range of heterogeneous populations. Our analysis shows that the VOBN model can provide both high accuracy and interpretability insights to HR professionals. Moreover, we show that using the interpretable VOBN can lead to unexpected and sometimes counter-intuitive insights that might otherwise be overlooked by recruiters who rely on conventional methods. We demonstrate that it is feasible to predict the successful placement of a candidate in a specific position at a pre-hire stage and utilize predictions to devise a global optimization model. Our results show that in comparison to actual recruitment decisions, the devised framework is capable of providing a balanced recruitment plan while improving both diversity and recruitment success rates, despite the inherent trade-off between the two.Respiratory viral infections such as coronavirus (COVID-19) will cause a great mortality, especially in people who underly lung diseases such as chronic obstructive pulmonary and asthma. Very recently, the COVID-19 outbreak has exposed the lack of quick approaches for screening people who may have risen risk of pathogen contact. One proposed non-invasive potential approach to recognize the viral infection is analysis of exhaled gases. It has been indicated that the nitric oxide is one of most important biomarkers which might be emanated by respiratory epithelial cells. Using density functional theory calculations, here, we introduced a novel Au-decorated BN nanotube-based breathalyzer for probable recognition of NO gas released from the respiratory epithelial cells in the presence of interfering CO2 and H2O gases. This breathalyzer benefits from different advantages including high sensitivity (sensing response = 101.5), high selectivity, portability, short recovery time (1.8 μs at 298 K), and low cost.Today, the traditional approach used to conduct phishing attacks through email and spoofed websites has evolved to include social network sites (SNSs). This is because phishers are able to use similar methods to entice social network users to click on malicious links masquerading as fake news, controversial videos and other opportunities thought to be attractive or beneficial to the victim. SNSs are a phisher's "market" as they offer phishers a wide range of targets and take advantage of opportunities that exploit the behavioural vulnerabilities of their users. As such, it is important to further investigate aspects affecting behaviour when users are presented with phishing. Based on the literature studied, this research presents a theoretical model to address phishing susceptibility on SNSs. Using data collected from 215 respondents, the study examined the mediating role that information processing plays with regard to user susceptibility to social network phishing based on their personality traits, thereby identifying user characteristics that may be more susceptible than others to phishing on SNSs. The results from the structural equation modeling (SEM) analysis revealed that conscientious users were found to have a negative influence on heuristic processing, and are thus less susceptible to phishing on SNSs. The study also confirmed that heuristic processing increases susceptibility to phishing, thus supporting prior studies in this area. This research contributes to the information security discipline as it is one of the first to examine the effect of the relationship between the Big Five personality model and the heuristic-systematic model of information processing.With today's world revolving around online interaction, dating applications (apps) are a prime example of how people are able to discover and converse with others that may share similar interests or lifestyles, including during the recent COVID-19 lockdowns. To connect the users, geolocation is often utilized. However, with each new app comes the possibility of criminal exploitation. For example, while apps with geolocation feature are intended for users to provide personal information that drive their search to meet someone, that same information can be used by hackers or forensic analysts to gain access to personal data, albeit for different purposes. This paper examines the Happn dating app (versions 9.6.2, 9.7, and 9.8 for iOS devices, and versions 3.0.22 and 24.18.0 for Android devices), which geographically works differently compared to most notable dating apps by providing users with profiles of other users that might have passed by them or in the general radius of their location. Encompassing both iOS and Android devices along with eight varying user profiles with diverse backgrounds, this study aims to explore the potential for a malicious actor to uncover the personal information of another user by identifying artifacts that may pertain to sensitive user data.This article analyzes ways that logistical services add value to products over and above traditional utility tradeoffs as a result of a confluence of aesthetic, economic, and cultural facets that we identify as enchantment. We delve into how enchantment might be digitally articulated through mediated (as opposed to brick and mortar) consumption, paying attention to the cultural techniques and affordances of the platforms and spaces. Through a series of case studies, we examine ways in which enchantment can occur in digitally mediated shopping environments. Our case studies suggest that enchanted digital logistical channels extend the value-creating functions of traditional logistics by creating consumption circumstances that surprise and intrigue, that suggest perceived scarcity, and that use social media to approach consumers in multiple ways to procure enchantment.With the frequent occurrence of irregular events in recent years, the tourism industry in some areas, such as Hong Kong, has suffered great volatility. To enhance the predictive accuracy of tourism demand forecasting, a decomposition-ensemble approach is developed based on the complete ensemble empirical mode decomposition with adaptive noise, data characteristic analysis, and the Elman's neural network model. Using Hong Kong tourism demand as an empirical case, this study firstly investigates how data characteristic analysis is used in a decomposition-ensemble approach. The empirical results show that the proposed model outperforms other models in both point and interval forecasts for different prediction horizons, indicating the effectiveness of the proposed approach for forecasting tourism demand, especially for time series with complexity.Personal stress is a prevalent problem in a connected world. For salespeople, demands of a connected workplace have largely eliminated boundaries between personal and work life, allowing stress from personal issues to spill over into their work. Thus, problems of health, relationships, and finances are no longer "left at home" for salespeople. Rather, a less central workplace model (e.g., remote workplaces and mobile platforms) and 24/7 work expectations expand the workplace, which comingles personal and work demands. Utilizing a sample of 331 salespeople, we study personal stressors that cross boundaries into the workplace and find that they play a critical role in the formation of burnout across its dimensions, which leads to reduced salesperson performance. Our research contributes to the sales literature by investigating individual personal stressors via Job Demands and Conservation of Resources theories and offers insights for managers of salespeople that face both personal and work stress.The Covid-19 pandemic has disrupted business schools worldwide mainly through the shift to emergency remote teaching. I provide a rudimentary framework to understand this transformation through recognizing the changes in the university, the business world and the student. This disruption is going to lead to a decade-long technology-led remaking of business education. Finally, I share a triphasic model that maps the evolution of online learning in business schools.As a worldwide disaster, the COVID-19 crisis is profoundly affecting the development of the global economy and threatening the survival of firms worldwide. It seems unavoidable that this natural disruption has hit the global economy and produced a huge crisis for firms. This study explores how firms in China are innovating their marketing strategies by critically identifying the typology of firms' marketing innovations using two dimensions, namely, motivation for innovations and the level of collaborative innovations. This research also explores the influence of the external environment, internal advantages (e.g., dynamic capabilities and resource dependence), and characteristics of firms on Chinese firms' choice and implementation of marketing innovation strategies. It provides valuable insights for firms to respond successfully to similar crisis events in the future.
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