Social, Economic and Human Capital: Risk or Protective Factors in Sexual Violence?

In Chile, studies on protective factors and risk factors for sexual violence are limited and very few have incorporated analysis of different types of capital (social, economic, human) as social resources in the protection against sexual violence. The objective of this research is to evaluate to what extent the stock of different capitals act together, as either protective or risk factors in sexual violence in different interpersonal environments. The sample consisted of 1665 women between 15 and 30 years of age (M = 23.47, SD = 4.41). Artificial neural network analysis and social network analysis were used. The nodes representative of human and economic capital have a protective role of low relevance due to their position in the network, while the nodes of social capital acquire a structural relevance due to the central positions of the network. It is concluded that the structural social capital of neighborhood networks constitutes the main protective factor for sexual violence in all areas, and in turn, the structural social capital of networks with non-significant others was the main risk factor in sexual victimization.

Generalized 2HDM with wrong-sign lepton-Yukawa coupling, in light of $$g_{\mu }-2$$ and lepton flavor violation at the future LHC

To explain the observed muon anomaly and simultaneously evade bounds from lepton flavor violation in the same model parameter space is a long-cherished dream. In view of a generalized Two Higgs Doublet Model, with a Yukawa structure as a perturbation of Type-X, we are able to get substantial parameter space satisfying these criteria. In this work, we focus on a region with “wrong-sign” lepton-Yukawa coupling which gives rise to interesting phenomenological consequences. Performing a simple cut-based analysis, we show that at 14 TeV run of the LHC with $$300 \mathrm{{fb}}^{-1}$$ 300 fb - 1 integrated luminosity, part of the model parameter space can be probed with significance "Equation missing" which further improves with Artificial Neural Network analysis.

A Two-Stage SEM—Artificial Neural Network Analysis of the Rewards Effects on Self Perceived Performance in Healthcare

Healthcare managers consider the rewards and performances of employees as central elements of their activities due to the challenges caused by the phenomenon of healthcare employees’ emigrating to higher-income countries, which has reduced patient satisfaction and led to a negative image of hospitals. In this context, this paper analyzes how employee rewards influence the employees’ self-perceived performances in the hospital units of the emergency medical system in Romania. Using structural equation modeling, we analyzed the relationships between the investigated variables, showing that financial motivation and the recognition of employees’ merits are central to employees’ self-perceived performances. Ensuring equity also has a positive impact on how the reward package is established and managed. While financial rewards are the most important incentives to increase efforts to exhibit higher performances, recognition has a long-term motivational effect.

Why have consumers opposed, postponed, and rejected Innovations during a pandemic? A Study of mobile payment Innovations

The highly infectious nature of the COVID-19 virus has made the use of contactless payment methods a health exigency. Yet, consumers are resisting using mobile payments (m-payments) during the pandemic, a confounding behavior that needs to be better understood. The present study explicates this behavior by examining consumer resistance to m-payments during the COVID-19 pandemic. In addition, it provides more granular findings by measuring three levels of resistance/non-adoption, namely, postponement, opposition, and rejection. In this way, the study adds depth to the literature, which has largely examined resistance at an aggregate level to yield generic findings. Toward this end, the study draws upon the Innovation Resistance Theory (IRT) to propose that usage, value, risk, tradition, and image barriers influence the three levels of resistance/non-adoption differently. An artificial neural network analysis (ANN) of the data collected from 406 non-users of m-payments confirmed that the influence of the five barriers varies for the three levels of resistance/non-adoption. The results further suggest that the usage barrier is the most significant contributor to opposition and rejection intentions toward m-payments, whereas the image barrier is the most influential for postponement intentions. This study thus makes a useful contribution to theory and practice.

Paradigm Shifting—The Use of Mobile Communications at Work and the Subsequent Effects

The physical distancing measures generated by the COVID-19 pandemic have forced companies to rethink employment patterns and to pay much more attention to the possibility of carrying out work through telework. The expansion of telework, a phenomenon that manifested itself even before the pandemic’s health crisis, has gained a particular momentum, changing how work is carried out. The main purpose of this paper is to study the main macroeconomic effects of the accelerated expansion of telework on the economic performance and the employment structure by the economic sectors of the workforce. Using artificial neural network analysis and structured equation modeling, the study highlights the significant influences of telework on economic performance and speeding up the transition service-based economy. The share of teleworkers has a significantly positive influence on economic performance. Moreover, the employees’ use of computers, mobile devices, and the internet has a strong mediation effect on the relationship between telework and employment in services. Given these considerations, teleworking is a phenomenon that will become a permanent feature of the future.

Estimation of Flow Discharge Model at Temef Watershed - East Nusa Tenggara Using TRMM Satellite Data

Data collection based on satellite TRMM (Tropical Rainfall Measuring Mission) presents one of the good alternatives in estimating rainfall. TRMM technology can minimize manual rainfall recording errors and improve rainfall accuracy for hydrological analysis. The analysis method used in this research is divided into 3 (three) stages, namely Hydrology analysis, Statistical Analysis and Artificial Neural Network Analysis. From the results of TRMM JAXA analysis in the Temef Watershed Area of East Nusa Tenggara Province obtained TRMM JAXA satellite rainfall relationship to observation data shows rainfall patterns between the two data are interconnected but for cases with very high observation rainfall, TRMM rainfall data tends to be low. From statistical method analysis, the relationship between observation rainfall and TRMM JAXA rainfall obtained results with a "Very Strong" interpretation indicated by the results of 9 years calibration and 1 year validation where the selected equation is a polynomial equation (y=-0,0123x2 + 1,5553x + 20,222). Rain data correction results simulated with Debit data to see the relationship between rain and discharge that occurred, this analysis using Artificial Neural Network with Backpropagation method, the results showed a "Strong" interpretation where statistically the value of Nash-Sutcliffe Efficiency (NSE) 0.920, the coefficient value of correlation of field discharge and TRMM rainfall is 0,877 % and the relative error occurred is 2,62%