Investigating The Relationship Between Adverse Events And Infrastructure Development In An Active War Theater Using Soft Computing Techniques

The military recently recognized the importance of taking sociocultural factors into consideration. Therefore, Human Social Culture Behavior (HSCB) modeling has been getting much attention in current and future operational requirements to successfully understand the effects of social and cultural factors on human behavior. There are different kinds of modeling approaches to the data that are being used in this field and so far none of them has been widely accepted. HSCB modeling needs the capability to represent complex, ill-defined, and imprecise concepts, and soft computing modeling can deal with these concepts. There is currently no study on the use of any computational methodology for representing the relationship between adverse events and infrastructure development investments in an active war theater. This study investigates the relationship between adverse events and infrastructure development projects in an active war theater using soft computing techniques including fuzzy inference systems (FIS), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFIS) that directly benefits from their accuracy in prediction applications. Fourteen developmental and economic improvement project types were selected based on allocated budget values and a number of projects at different time periods, urban and rural population density, and total adverse event numbers at previous month selected as independent variables. A total of four outputs reflecting the adverse events in terms of the number of people killed, wounded, hijacked, and total number of adverse events has been estimated. For each model, the data was grouped for training and testing as follows: years between 2004 and 2009 (for training purpose) and year 2010 (for testing). Ninety-six different models were developed and investigated for Afghanistan iv and the country was divided into seven regions for analysis purposes. Performance of each model was investigated and compared to all other models with the calculated mean absolute error (MAE) values and the prediction accuracy within ±1 error range (difference between actual and predicted value). Furthermore, sensitivity analysis was performed to determine the effects of input values on dependent variables and to rank the top ten input parameters in order of importance. According to the the results obtained, it was concluded that the ANNs, FIS, and ANFIS are useful modeling techniques for predicting the number of adverse events based on historical development or economic projects’ data. When the model accuracy was calculated based on the MAE for each of the models, the ANN had better predictive accuracy than FIS and ANFIS models in general as demonstrated by experimental results. The percentages of prediction accuracy with values found within ±1 error range around 90%. The sensitivity analysis results show that the importance of economic development projects varies based on the regions, population density, and occurrence of adverse events in Afghanistan. For the purpose of allocating resources and development of regions, the results can be summarized by examining the relationship between adverse events and infrastructure development in an active war theater; emphasis was on predicting the occurrence of events and assessing the potential impact of regional infrastructure development efforts on reducing number of such events

A cross-sectional study of the relationships between work-related affective feelings expressed by workeres in Turkey

Understanding employees' feelings at work plays a significant role in developing practical and effective organizational and human resource management policies and practices. Furthermore, work-related emotions may have a considerable effect on workers’ health and wellbeing and affect work effectiveness and work performance. The objectives of the current study were to investigate the relationships among four work-related (WOR) affective feelings (WORAF) and to validate the WORAF questionnaire in a Turkish sample. A survey was performed including four constructs: (1) WOR feelings of happiness, (2) WOR feelings of anxiety, (3) WOR feelings of anger, and (4) WOR feelings of dejection. A total of 322 workers from various companies in Turkey completed a paper-based survey. A research model was developed, and its main components were estimated with partial least squares structural equation modeling (PLS-SEM). The results revealed that dejection and anger at work play a critical role in experienced anxiety in occupational settings. Similarly, dejection, anger, and anxiety at work play a crucial role in perceived happiness at work

The relationships between the use of smart mobile technology, safety knowledge and propensity to follow safe practices at work

The main objective of this study was to investigate the relationships between the use of smart technology (mobile phones) and the implicit (tacit) and explicit safety knowledge of employees and their propensity to follow safe practices at work. A survey was performed with seven constructs: (a) use of mobile technology; (b) tacit safety knowledge; (c) explicit safety knowledge of unsafe behaviors; (d) attitudes toward safety: emotional aspects; (e) safety culture: behavioral and psychological aspects of work; (f) safety culture: aspects of work; (g) safety culture: regulations at work. Workers from three manufacturing companies located in southeastern Poland completed a paper-based survey. The results revealed that using mobile technology positively influenced the explicit safety knowledge of employees, as well as their assessed safety culture, in terms of behavioral aspects and their attitudes toward safety expressed through the psychological aspects of safety culture

A fuzzy overlay model for mapping adverse event risk in an active war theatre

WOS: 000443895900008This study discusses a series of fuzzy overlay analysis performed within a Geographic Information System (GIS) on recent adverse events throughout the war in Afghanistan. Three types of input variables are considered in terms of number of people killed, wounded and hijacked over the period 2004-2010 in order to identify the risk level in Afghanistan using fuzzy GIS approach. To conclude, most risky areas are accumulated in the eastern region of the country and major population centres. The proposed approach could enable military decision-makers to obtain a better understanding of the socio-spatial dynamic of incidents in Middle East.Office of Naval Research [10523339]This work was supported by the Office of Naval Research [grant number 10523339]