Socio-Demographic Factors Associate with Fear of Crime in Bangladesh: A Study in Urban Area

Fear of crime is a concerning issue which led to a whole series of behavioral reactions which negatively affect the quality of life in the society. This study examines the public perceptions of the risks and fear of crime in Bangladesh in relation to urban environment. Drawing upon fear of crime literature and collecting primary data this study will enable researchers to explore the nature of the urban fear of crime in Bangladesh and will find association between socio demographic factor and fear of crime by identifying the possible vulnerable time and place of crime victimization; level of safety at the neighborhood and home and identifying factors affecting victim’s level of fear of crime. The subject of this study was composed of 3957 respondent’s selected from 12th city corporations followed by probability sampling method for collecting information from the general peoples who have victimized and have a fear of crime. The study found that fear of crime is found to be higher with the stranger; people seem to be feared while in the dark time after 6 pm. Many factors affect the fear of crime such as lengthy procedure of criminal justice system, news of crime at their locality, news of crossfire and poor neighborhood physical condition have impact on fear of crime. The perceptions gathered through this study will helps to take important measures and strategies to ensure safe livelihood as well as increase the performance of the law enforcement agencies

The Impact of Human Resource Development (HRD) Practices on Organizational Effectiveness: A Review

This paper attempted to review theoretically the HRD and its matrix and revealed to show the positive relationship between HRD and organizational effectiveness. In HRD shows the different variables (outcomes) such as HRD instruments, HRD processes & climate variables and organizational dimensions. HRD affects the organizational goals which may result from higher productivity, cost reduction, more profits, better image and more satisfied customers and stake holders considered as organization dimensions HRD activities, as such, do not reduce costs, improve quality or quantity, or benefit the enterprise in any way. It is the on-the- job applications of learning that ultimately can reduce costs, improve quality, and so forth. In the organizational context, therefore, HRD means a process which helps employees of an organization to improve their functional capabilities for their present and future roles, to develop their general capabilities, to harness their inner potentialities both for their self and organizational development and, to develop organizational culture to sustain harmonious superior-subordinate relationships, teamwork, motivation, quality and a sense of belongingness. The study also analyses the Kliman Model of HRM to show the path of mechanisms which could lead to competitive advantage. Today’s fast changing environment modern organizations are more careful to sustain in the competitive advantage relating to HRD our study has been developed to help the management students, academicians, and professionals to understand the subject properly and enhance their knowledge about HRD network within the organization for its effectiveness

Tinjauan Tentang Efektivitas Pemidanaan Terhadap Pelaku Tindak Pidana Narkotika di Kabupaten Sidenreng Rappang

v,110 ha

Tinjauan Tentang Efektivitas Pemidanaan Terhadap Pelaku Tindak Pidana Narkotika di Kabupaten Sidenreng Rappang

v,110 ha

Chemical and thermal performance analysis of a solar thermochemical reactor for hydrogen production via two-step WS cycle

Ceria-based H2O/CO2-splitting solar-driven thermochemical cycle produces hydrogen or syngas. Thermal optimization of solar thermochemical reactor (STCR) improves the solar-to-fuel conversion efficiency. This research presents two conceptual designs and thermal modelling of RPC-ceria-based STCR cavities to attain the optimal operating conditions for CeO2 reduction step. Presented hybrid geometries consisting of cylindrical–hemispherical and conical frustum–hemispherical structures. The focal point was positioned at x = 0, -10 mm, and -20 mm from the aperture to examine the flux distribution in both solar reactor configurations. Case-1 with 2 milliradian S.E (slope error) yields a 27% greater solar flux than case-1 with 4 milliradians S.E, despite the 4 milliradian S.E produces an elevated temperature in the reactor cavity. The mean temperature in the reactive porous region was most significant for case-2 (x = -10 mm) with 4 mrad S.E for model-2, reaching 1966 K and 2008 K radially and axially, respectively. In case-2 (x = -10 mm) for 4 mrad S.E, model-1 attained 1720 K. The efficiency analysis shows that the highest conversion efficiency value was obtained to be 7.95% for case-1 with 4 milliradian S.E

In-Depth Analysis of Cement-Based Material Incorporating Metakaolin Using Individual and Ensemble Machine Learning Approaches

In recent decades, a variety of organizational sectors have demanded and researched green structural materials. Concrete is the most extensively used manmade material. Given the adverse environmental effect of cement manufacturing, research has focused on minimizing environmental impact and cement-based product costs. Metakaolin (MK) as an additive or partial cement replacement is a key subject of concrete research. Developing predictive machine learning (ML) models is crucial as environmental challenges rise. Since cement-based materials have few ML approaches, it is important to develop strategies to enhance their mechanical properties. This article analyses ML techniques for forecasting MK concrete compressive strength (fc’). Three different individual and ensemble ML predictive models are presented in detail, namely decision tree (DT), multilayer perceptron neural network (MLPNN), and random forest (RF), along with the most effective factors, allowing for efficient investigation and prediction of the fc’ of MK concrete. The authors used a database of MK concrete mechanical features for model generalization, a key aspect of any prediction or simulation effort. The database includes 551 data points with relevant model parameters for computing MK concrete’s fc’. The database contains cement, metakaolin, coarse and fine aggregate, water, silica fume, superplasticizer, and age, which affect concrete’s fc’ but were seldom considered critical input characteristics in the past. Finally, the performance of the models is assessed to pick and deploy the best predicted model for MK concrete mechanical characteristics. K-fold cross validation was employed to avoid overfitting issues of the models. Additionally, ML approaches were utilized to combine SHapley Additive exPlanations (SHAP) data to better understand the MK mix design non-linear behaviour and how each input parameter’s weighting influences the total contribution. Results depict that DT AdaBoost and modified bagging are the best ML algorithms for predicting MK concrete fc’ with R2 = 0.92. Moreover, according to SHAP analysis, age impacts MK concrete fc’ the most, followed by coarse aggregate and superplasticizer. Silica fume affects MK concrete’s fc’ least. ML algorithms estimate MK concrete’s mechanical characteristics to promote sustainability