Renewed talent management: more productive development teams with digitalization supported HR tools

In most companies and organizations, performance is related to talent management and skills to analyze what and why people are working on. However, many companies do fail to implement a long-term strategy for the performance enhancement activities, considering the talents they have recruited. In this article, we propose a tool for HR work, in context of talent management and how to utilize people skills and productivity analytics to improve team performance and related KPIs. A project data-based case study is illustrated, in which a set of devel-oper and content marketers were analyzed as core team members. In practice, the presented framework makes an important contribution to decision-making activities, where people analytics and proper software tools are used to build new novel knowledge into talent pool of the team. With the framework-based analysis, it is possible to analytically compare team members’ performance and enhance the team’s skill and structural development which means that we can employ analytics to find best performers and set their roles for more optimally working teams. Our research supports the concept of using the right framework can make a big positive difference in team analytics.

Numerical simulation of the influence of pores sizes on moisture migration dynamic in masonry wall

The sometimes extreme hydro-climatic stresses that buildings undergo can lead to significant deterioration which can lead to their collapse. The concern to realize durable works and ensuring a comfortable framework for the life of occupants leads to seek effective solutions, as well for the new construction as for the renovation of old construction, answering the sempiternal problem of harmful action of water on buildings materials. This paper proposes a numerical simulation of moisture migration in concrete building walls, the aim being to highlight the influence of pore size on the kinetics of moisture migration, and its gradient in the wall. A mathematical model taking into account the mechanisms of moisture migration due to liquid moisture gradient and by vapor diffusion is proposed; the discrete formulation of the equa-tion by the numerical scheme of Crank Nicolson is then carried out, and results from computer modeling using Matlab software version 7.10.0.499 (R2010a), show that pore size is a key parameter that influences the dynamics of moisture migration in the wall. Indeed, this parameter qualitatively and quantitatively influences the kinetics of moisture migration, as well as it gradient in the concrete wall. It appears a greater migration dynamic when the pores sizes decrease, means a greater kinetics of moisture migration and lower moisture gradient in the walls at the hygrometric equilibrium, for a decreasing pore size.

Why so few EVs are in Kuwait and how to amend it

The automotive industry is at a crossroad. Electric Vehicles (EV) now pose an existential threat to the Internal Combustion Engine (ICE). In some Northern European nations over 50% of new cars sold are EVs, owing in large part to substantial financial incentives to buy and own an EV, such as tax discounts when purchasing an EV, fuel savings, and preferential use of transportation infrastructure. These countries have pledged to cease all imports of non-EVs by 2035. On the other end of the spectrum are Gulf Cooperation Council (GCC) countries, where EVs account for less than 1 percent of vehicles on the road, due in large part to financial and non-financial impediments to buying and owning an EV. In addition, the price per kilometer driven in the GCC is considerably lower with gasoline than with electricity, which contradicts the European experience where cost savings from electricity versus gasoline can be around 8 to 1. Furthermore, as there is an absence of purchase and ownership/utilization taxation of vehicles in the GCC, no tax discount can be levied, in contrast to the EV tax incentives common in Europe. This paper explores which qualities of driving and owning an EV in the GCC are necessary to persuade certain kinds of new automobile consumers to pay a higher purchasing price for owning an EV as opposed to an ICE, in spite of higher costs for electricity compared to gasoline per kilometer driven. This pilot study attempts to provide an insight to new car purchasing behavior among consumers in Kuwait via a qualitative innovative approach known as ‘Q Methodology’. Interestingly, the factors that emerged from the research represent three subjective perspectives of new car purchase in Kuwait which were labeled as Factor 1, ‘Value Seeker’; Factor 2, ‘Safety Seeker’; and Factor 3, ‘Performance Seeker’. The study concludes that given financial constraints, the ‘Value Seeker’ group is not likely to become an early adopter of EVs in the GCC region. Conversely, the ‘Performance Seeker’, which includes mainly younger men who are more likely to view the fast acceleration of EVs as a deciding factor, and the ‘Safety Seekers’, who are mainly younger women who would value the environmental aspects of EVs as well as the quiet driving experience and low maintenance requirements are determining factors for EV adoption in the GCC region in the future.

Diagnosis of Medical Images Using Cloud-Deep Learning System

Diagnosis of brain tumors is one of the most severe medical problems that affect thousands of people each year in the United States. Manual classification of cancerous tumors through examination of MRI images is a difficult task even for trained professionals. It is an error-prone procedure that is dependent on the experience of the radiologist. Brain tumors, in particular, have a high level of complexity. Therefore, computer-aided diagnosis systems designed to assist with this task are of specific interest for physicians. Accurate detection and classification of brain tumors via magnetic resonance imaging (MRI) examination is a famous approach to analyze MRI images. This paper proposes a method to classify different brain tumors using a Convolutional Neural Network (CNN). We explore the performance of several CNN architectures and examine if decreasing the input image resolution affects the model's accuracy. The dataset used to train the model has initially been 3064 MRI scans. We augmented the data set to 8544 MRI scans to balance the available classes of images. The results show that the design of a suitable CNN architecture can significantly better diagnose medical images. The developed model classification performance was up to 97\% accuracy.

Using Haar-like Features and SVM Classifier for Quality Assurance in a Surgical Mask Production Line

With the recent increase for demand of surgical masks, the design and development of mask production lines has become an ever pressing issue. These production lines produce low cost high quantity products. As there are errors during the production, it is important to be able to detect invalid masks to assure that the produced masks are of consistent quality. Manual quality assurance using human operators is an error prone and a costly solution. In this article we describe an image classification method, which is using a low-cost Commercial Camera System and relies on Haar-like features combined with Maximum Relevance, Minimum Redundancy feature selection to detect the invalid masks at the end of the production process. The classification method consists of Preprocessing, Feature Selection and SVM Training. We have tested the method on a database of 150 000 images and it provides a high accuracy method which we use in the Production Line.

Dynamic-IoTrust: A Dynamic Access Control for IoT Based on Smart Contracts

The Internet of things (IoT) is an active, real-world area in need of more investigation. One of the top weaknesses in security challenges that IoTs face, the centralized access control server, which can be a single point of failure. In this paper, Dynamic-IoTrust, a decentralized access control smart contract based aims to overcome distrusted, dynamic, trust and authentication issues for access control in IoT. It also integrates dynamic trust value to evaluate users based on behavior. In particular, the Dynamic-IoTrust contains multiple Main Smart Contract, one Register Contract, and one Judging Contract to achieve efficient distributed access control management. Dynamic-IoTrust provides both static access rights by allowing predefined access control policies and also provides dynamic access rights by checking the trust value and the behavior of the user. The system also provides to detected user misbehavior and make a decision for user trust value and penalty. There are several levels of trusted users to access the IoTs device. Finally, the case study demonstrates the feasibility of the Dynamic-IoTrust model to offer a dynamic decentralized access control system with trust value attribute to evaluate the internal user used IoTs devices.

Assessing readiness for knowledge management implementation in mental hospitals, Indonesia

For Southeast Asia’s largest population, the prevalence of the emotional disorder has increased from 6% in 2013 up to 9.8% in 2018. Aligned with the e-government program, Knowledge Management (KM) offers an easier, faster, and transparent mental health services. However, the implementation relies on various factors. We conducted a workshop at 7 (seven) mental hospitals. The questionnaires were used to identify the factors that consist of awareness & commitment, strategy, culture, structure, people, and information technology (IT). We examine the hypothesis factors of the relationship by employing the statistical analyses of correlation. This study provides at testing the relationship between factors of people, process, and technology, for KM implementation in Indonesian mental hospitals. The results of the study confirm that the relationship between Process to Technology and People has a positive effect on significance. Meanwhile, the adop-tion of the existing technical facilities have not significant support for the needs of the KM. To sum up, the study suggests further improve-ment of leadership and systems in order to serve the best of mental healthcare.

Analysis of traffic density onthree-way junction in Jalan Sukabangun 2 using Vissim simulations model

VISSIM is a Simulations model which means a city traffic simulation model. VISSIM is a simulation software used by professionals to create simulations from dynamic traffic scenarios before making real plans. This research was conducted to determine how traffic performance and traffic performance optimization at the junctions between the existing conditions and the Vissim program caused by congestion. An effective method for overcoming non-jammed junctions can be made using the VISSIM method. This study was conducted at THREE-WAY JUNCTION in Jl. Sukabangun 2 (South) - Jl. R.A Abusamah (West) - Jl. Sukabangun 2 (Utara) - Jl. BeringinSukabangun 2 (East). In this study, three variations were used, namely the traffic light method, the method of forbidden turning right, and the method of dividing the road and turning signs. The results of PTV Vissim simulation showed that the traffic light method has a quite high queue length, namely 79m compared to the existing condition of 63m, for the vehicle delay in this method is 98.954s. On the method of forbidden turning right from the direction of Jl. BeringinSukabangun 2 (East) has a low queue length of 0.287m compared to the existing condition of 63m. The vehicle delay in this method is 13.307s. The method of dividing the road and turning signs, the queue length is quite low at 1.147m compared to the existing condition of 63m. The vehicle delay in this method is 30,169s. The results of the simulation revealed that the most effective method at THREE-WAY JUNCTION in jalanSukabangun 2 is method of forbidden turning right, dividing the roads and turning signs.

Optimal power flow-path determination for voltage control in electricity distribution using the modified dijkstra’s algorithm

This paper examines the electric power distribution network system of the Port Harcourt Electricity Distribution Company (PHEDC); its shortcomings, costs and voltage loss in distribution with a view to finding optimal solution through determination of optimal power flow path. The Modified Dijsktra’s Algorithm was applied to generate optimal flow path model of the distribution network with seven (7) nodes from Afam Thermal Power Station (source) to the Calabar Distribution Centre (destination) via the interconnected substations. The structural design of the PHEDC distribution network and a review of relevant literatures on shortest path problems were adopted. The modified Dijkstra’s algorithm was simulated using JavaScript and is able to run on any web browser (Google Chrome, Mozilla Firefox, etc). It was applied to a practical 330kV network using the relevant data obtained from the company and the result shows the negative effect of distance on voltage quality. It was observed that the Modified Dijkstra’s Algorithm is suitable for determining optimal power flow path with up to 98 percent level of accuracy because of its suitability for determining the shortest route in both transportation and power energy distribution as well as its overall performance with minimal memory space and fast response time.

Data mining based investigation of the impact of imbalanced dataset over fractured zone detection

Several studies have been conducted in recent years to discriminate between fractured (FZs) and non-fractured zones (NFZs) in oil wells. These studies have applied data mining techniques to petrophysical logs (PLs) with generally valuable results; however, identifying fractured and non-fractured zones is difficult because imbalanced data is not treated as balanced data during analysis. We studied the importance of using balanced data to detect fractured zones using PLs. We used Random-Forest and Support Vector Machine classifiers on eight oil wells drilled into a fractured carbonite reservoir to study PLs with imbalanced and balanced datasets, then validated our results with image logs. A significant difference between accuracy and precision indicates imbalanced data with fractured zones categorized as the minor class. The results indicated that the accuracy of imbalanced and balanced datasets is similar, but precision is significantly improved by balancing, regardless of how low or high the calculated indices might be.