HIFASS-LOPIN-3 Empowerment Programs and the Wellbeing of Orphans and Vulnerable Children in Southern Senatorial District of Cross River State, Nigeria

This study examined the various empowerment programs carried out by Health Initiatives for Safety and Stability in Africa – Local OVC Partner in Nigeria – Region 3 (HIFASS-LOPIN-3) as they affect the wellbeing of orphans and vulnerable children (OVC). Three objectives were outlined to include investigating the relationship between HIFASS-LOPIN-3 educational empowerment program and OVC’s school enrolment; HIFASS-LOPIN-3 provision of health services and OVC’s accessibility to quality healthcare facilities; HIFASS-LOPIN-3 skills acquisition training/financial empowerment program and the income level of OVC/caregivers. An Ex-post facto research design was employed in this study and a sample size of three hundred and eighty (380) respondents was drawn using multi-stage, simple random, stratified and snowball sampling techniques. The reliability test of the instrument was done using the test-retest reliability method. Primary data was obtained using a questionnaire and in-depth interview schedule, while secondary data was sourced from journals, texts, newspapers, internet, unpublished papers. The hypotheses were tested using Pearson’s Product-Moment Correlation Coefficient (r) and Chi-Square statistical techniques, together with frequency/percentage analysis. The study revealed among others that HIFASS-LOPIN-3 educational empowerment has significantly improved OVC’s school enrolment; HIFASS-LOPIN-3 skills acquisition training/financial empowerment program has significantly improved the income level of OVC/caregivers, nevertheless, almost all the OVC who reported having income-generating skills experienced challenges like insufficient start-up capital, insecurity, and crippling economic policies. It was recommended that government should make provision for OVC’s subsidized medical bills and adequate provision for start-up capital/equipment or materials should be made for older OVC/caregivers who receive skills acquisition training.

Effects of intolerance of uncertainty on subjective and psychophysiological measures during fear acquisition and delayed extinction

Individuals who score high in self-reported Intolerance of Uncertainty (IU) tend to find uncertainty unacceptable and aversive. In recent years, research has shed light on the role of IU in modulating subjective (i.e. expectancy ratings) and psychophysiological responses (i.e. skin conductance) across different classical fear conditioning procedures, particularly that of immediate extinction. However, there remain gaps in understanding how IU, in comparison to other negative emotionality traits (STAI-T), impact different types of subjective and psychophysiological measures during different classical fear conditioning procedures. Here, we analyzed IU, STAI-T, subjective (i.e. fear ratings) and psychophysiological (i.e. skin conductance, auditory startle blink) data recorded during fear acquisition training and 24h-delayed extinction training (n = 66). Higher IU, over STAI-T, was: (1) significantly associated with greater fear ratings to the learned fear cue during fear acquisition training, and (2) at trend associated with greater fear ratings to the learned fear versus safe cue during delayed extinction training. Both IU and STAI-T were not related to skin conductance or auditory startle blink during fear acquisition training and delayed extinction training. These results add to and extend our current understanding of the role of IU on subjective and physiological measures during different fear conditioning procedures, particularly that of delayed extinction training. Implications of these findings and future directions are discussed.

Assessment of perceived compliance and barriers to Personal Protective Equipment use among healthcare workers during the COVID-19 pandemic’s second wave surge: “walk to talk” cross-sectional correlational study

Introduction: Due of their near closeness to COVID-19 patients, healthcare workers (HCWs) have a great desire to utilize proper personal protective equipment (PPEs). Aim: Investigating HCWs’ perceptions of PPE compliance and barriers, as well as influencing factors, in order to develop methods to combat the rise in their infection rates. Methodology: During the ‘second wave’ surge, a cross-sectional correlational analysis was conducted over a one-month period. It consists of HCWs from various hospital sectors that admit COVID-19 patients using an online self-administered predesigned tool. Results: Of the 285 recruited participants, 36.1% had previously been diagnosed with COVID-19. Around 71% received training on PPEs use. The perceived compliance was good for (PPEs) usage (mean 2.60 ± 1.10). A significant higher compliance level was correlated with previous diagnosis with COVID-19, working with patients diagnosed with COVID-19, and having a direct contact with a family member older than 45 years old (p<0.01). The main perceived barriers to the use of PPEs were: unavailability of full PPEs (35%), interference with their ability to provide patient care (29%), not enough time to comply with the rigors of PPEs (23.2%), and working in emergency situation (22.5%). With regards to perceived barriers those working with patients diagnosed with COVID-19 and those who reported having a direct contact with a family member older than 45 years old showed significantly higher level of barriers. Conclusion: A series of measures, including prioritization of PPE acquisition, training, and monitoring to guarantee appropriate resources for IPC, are necessary to reduce transmission.

A data multiverse analysis investigating non-model based SCR quantification approaches

Data inevitably need to be processed, typically involving multiple decision nodes with decisions often being equally justifiable. Electrodermal signals are the most common outcome measure in fear conditioning research, but response quantification approaches vary strongly. It remains an open question whether different approaches result in convergent results. Using fear conditioning research as a case example, we identified that baseline-correction (BLC) and trough-to-peak (TTP) quantification are used most frequently in the literature. Furthermore, heterogeneity of specifications in BLC formulas was observed, i.e., within the pre-CS baseline window and the post-CS peak detection or mean detection window. Here we systematically scrutinize the robustness of results when applying different processing methods to one pre-existing dataset (N= 118). The study was pre-registered. We report high agreement between different BLC approaches for US and CS+ trials, but moderate to poor agreement for CS- trials. Furthermore, a specification curve of the main effect of CS discrimination during fear acquisition training from all potential and reasonable combinations of specifications (N=150) and a prototypical TTP approach indicates that resulting effect sizes are largely comparable. Crucially, however, we show that BLC approaches often misclassify the peak SCR - particularly for CS- trials, which leads to a stimulus-specific bias and challenges for post-processing and replicability. Lastly, we investigate how physiologically implausible (negative) skin conductance values in BLC appearing most frequently for CS- (CS- &gt; CS+ &gt; US), correspond to in TTP quantification. We discuss the results in terms of robustness and replicability and provide insights into challenges, opportunities, and implications.

Towards Realizing Intelligent Coordinated Controllers for Multi-USV Systems Using Abstract Training Environments

Unmanned autonomous vehicles for various civilian and military applications have become a particularly interesting research area. Despite their many potential applications, a related technological challenge is realizing realistic coordinated autonomous control and decision making in complex and multi-agent environments. Machine learning approaches have been largely employed in simplified simulations to acquire intelligent control systems in multi-agent settings. However, the complexity of the physical environment, unrealistic assumptions, and lack of abstract physical environments derail the process of transition from simulation to real systems. This work presents a modular framework for automated data acquisition, training, and the evaluation of multiple unmanned surface vehicles controllers that facilitate prior knowledge integration and human-guided learning in a closed-loop. To realize this, we first present a digital maritime environment of multiple unmanned surface vehicles that abstracts the real-world dynamics in our application domain. Then, a behavior-driven artificial immune-inspired fuzzy classifier systems approach that is capable of optimizing agents’ behaviors and action selection in a multi-agent environment is presented. Evaluation scenarios of different combat missions are presented to demonstrate the performance of the system. Simulation results show that the resulting controllers can achieved an average wining rate between 52% and 98% in all test cases, indicating the effectiveness of the proposed approach and its feasibility in realizing adaptive controllers for efficient multiple unmanned systems’ cooperative decision making. We believe that this system can facilitate the simulation, data acquisition, training, and evaluation of practical cooperative unmanned vehicles’ controllers in a closed-loop.

SKILL ACQUISITION AS TOOL FOR SOLVING YOUTH RESTIVENESS AND UNEMPLOYMENT IN NIGERIA: THE ROLE OF NYSC

As of late, the scourge of abducting, cybercrime, terrorism, armed robbery, prostitution, brain drain among others has established an oddity among young people. For this, federal government have acquainted several scheme to assist in checking joblessness among youths. In March 2012, the National Youth Service Corp (NYSC) initiative introduced Skill Acquisition and Entrepreneurship (SAED) Programs into the NYSC orientation course content. The objectives of the scheme incorporate sensitization and mobilization of young graduates for skill acquisition, assistance of preparing and tutoring in business enterprise development. This study look at the usefulness of the NYSC-SAED program in reducing youth restiveness and unemployment among young graduates in Nigeria. 60 Corp members currently serving were interviewed using key informant interview and Focus Group Discussions. Significant theoretical and empirical literature were reviewed. This research was supported on Strain Theory. The study population comprises of 60 Corp members selected arbitrarily from Anambra State. The information gathered were analysed using simple percentages and descriptive statistics. Greater part of the respondents recognized that the NYSC-SAED program has made them to become independent after the service year since they set up their independent company with the little training they got. The study therefore recommend that efforts ought to be geared towards leasing with financial institutions and protection offices to help in giving funds and protection which will help in living condition and improve their businesses. Likewise, SAED handouts ought to be made accessible to all Corp members at the camp at no expense to guarantee full participation by every one of them in the training. Keywords: Skill Acquisition, Training, Unemployment, NYSC-SAED, Mobilization, Entrepreneurship Mentoring.

Classifier-based constraint acquisition

Modeling a combinatorial problem is a hard and error-prone task requiring significant expertise. Constraint acquisition methods attempt to automate this process by learning constraints from examples of solutions and (usually) non-solutions. Active methods query an oracle while passive methods do not. We propose a known but not widely-used application of machine learning to constraint acquisition: training a classifier to discriminate between solutions and non-solutions, then deriving a constraint model from the trained classifier. We discuss a wide range of possible new acquisition methods with useful properties inherited from classifiers. We also show the potential of this approach using a Naive Bayes classifier, obtaining a new passive acquisition algorithm that is considerably faster than existing methods, scalable to large constraint sets, and robust under errors.

The Digital Transformation of the Talent Management Process: A Spanish Business Case

The digital transformation means that companies are redefining the process of talent management. Previous models involved functions, practices and processes that ensured a correct flow of employees towards key positions or a generic talent management view. The digital breakthrough, together with the growing panorama of competition for talent in the market, requires a different focus to enable well-grounded and agile decision-making processes in a sustainable world. The current research considers the functions that applied research has established as the limits of talent management, and that are the key topics in an employee life cycle, namely, talent attraction and acquisition, training, evaluation, and development. In addition, new tools such as employee advocacy and/or brand ambassadors have been added towards to draw conclusions about the future trends of talent management. This article examines the employee life cycle of talent attraction, and acquisition, training, evaluation, and development in the study of the main digital tools utilized in the Spanish market, by both national and multinational corporations. The results indicate that future investments are needed to correlate the digital tools and take advantage of a better employee life cycle management. The main results show a rapid increase in the number and variety of tools used in the talent acquisition process, an expanded use of social networks to enhance the scope of those processes, and conversely, a minor use of digital tools for both talent development and talent retention processes.

The role of intolerance of uncertainty in classical threat conditioning: Recent developments and directions for future research

Intolerance of uncertainty (IU), the tendency to find uncertainty aversive, is an important transdiagnostic dimension in mental health disorders. Over the last decade, there has been a surge of research on the role of IU in classical threat conditioning procedures, which serve as analogues to the development, treatment, and relapse of anxiety, obsessive-compulsive, and trauma- and stressor-related disorders. This review provides an overview of the existing literature on IU in classical threat conditioning procedures. The review integrates findings based on the shared or discrete parameters of uncertainty embedded within classical threat conditioning procedures. Under periods of unexpected uncertainty, where threat and safety contingencies change, high IU, over other self-reported measures of anxiety, is specifically associated with poorer threat extinction learning and retention, as well as overgeneralisation. Under periods of estimation and expected uncertainty, where the parameters of uncertainty are being learned or have been learned, such as threat acquisition training and avoidance learning, the findings are mixed for IU. These findings provide evidence that individual differences in IU play a significant role in maintaining learned fear and anxiety, particularly under volatile environments. Recommendations for future research are outlined, with discussion focusing on how parameters of uncertainty can be better defined to capture how IU is involved in the maintenance of learned fear and anxiety. Such work will be crucial for understanding the role of IU in neurobiological models of uncertainty-based maintenance of fear and anxiety and inform translational work aiming to improve the diagnosis and treatment of relevant psychopathology.