Effect of Workforce Diversity on Employee Work Performance of Selected Firms in Bangladesh

To analyze the moderating effect of management between workforce diversity and performance of the employees and to determine the relationship between workforce diversity and employee’s performance. Quantitative study was conducted in private sector firms. Sample of 90 employees of different private firms was selected in Dhaka city of Bangladesh. The data was collected through structured questionnaires. Pearson Correlation and Regression was run to analyze the data. Findings show that there is positive and significant relationship between the workforce diversity and the performance of the employees. Sample size was too short it can be replicated in future by large sample in same sector. Relation of management between workforce diversity and employee’s performance can be checked in future. Keywords: Workforce, Diversity, Employee Performance, Bangladesh

Bangladesh baseline surveys on child labour situation in Bidi Industry in Kushtia, Tangail, and Rangpur districts

The International Programme on the Elimination of Child Labour (IPEC) of the International Labour Organization (ILO) has been working in a collaborative effort with the government, workers' and employees' and organizations, and civil society to prevent and eliminate child labour and its worst forms worldwide. As part of its endeavour, Bangladesh has also been striving to address the child labour issue through government, workers' and employers' organizations, NGOs and other international organizations with the launching of an action-oriented US Department of Labor (USDOL) funded Project titled "Preventing and eliminating the worst forms of child labour in selected formal and informal sectors". This research is a part of it

An experimental evaluation of case slicing as a new classification technique

Several classification techniques are designed to discover such classifications when the classifications are unknown. The techniques are tested and evaluated, however, by matching the classifications they recover against expected classifications. Several such techniques may be compared by experimentally evaluating their performance on the same datasets. The goal of this paper is to evaluate the case slicing technique as a new classification technique. The paper achieves this goal in three steps: Firstly, it introduces the case slicing technique as a new approach. Secondly, the paper presents applications of this technique on several datasets. Lastly, it compares the proposed approach with other selected approaches such as the K-Nearest Neighbour (K-NN), Base Learning Algorithm (C4.5) and Naïve Bayes classifier (NB) in solving the classification problems. The results obtained shows that the proposed approach is a promising method in solving decision-making problem

Application of virtual learning environment in the teaching of engineering drawing to enhance students' mental rotation skills

Virtual learning environment seems to transform education process in a more flexible way as compared to other modes of learning and have great potential in ensuring successful learning. This study investigates the effectiveness of virtual learning environment in teaching engineering drawing in order to enhance mental rotation skills. A quasi- experimental design study was used involving engineering students in Universiti Teknologi Malaysia. The intervention group in this study was exposed to virtual learning environment courseware. Students in this study were given a pre-test before the intervention and a post-test prior to the intervention. The result of this research indicates that there are significant improvements in the mental rotation skills of the students who were being exposed to the virtual learning environment. This study also investigates the gender differences in visualization skills. Thus, this study shows that using courseware in the teaching of engineering drawing can act as a catalyst in enhancing productivity and quality of engineering drawing. As a result, students are capable of enhancing their visualization skills which is vital in engineering drawing

An efficient and effective case classification method based on slicing

One of the most important tasks that we have to face in real world applications is the task of classifying particular situations and /or events as belonging to a certain class. In order to solve the classification problem, accurate classifier systems or models must be built. Several computational intelligence methodologies have been applied to construct such a classifier from particular cases or data. This paper introduces a new classification method based on slicing techniques that was proposed for procedural programming languages. The paper also discusses two of common classification algorithms that are used either in data mining or in general AI. The algorithms are: Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5). The paper also studies the comparison between the proposed method and the two selected classification algorithms using several domains

Performance Enhancement of Small-Scale Wind Turbine Featuring Morphing Blades

The demand for renewable energy is driven by the depletion and adverse environmental impacts of fossil fuels. There is a growing global consensus for research and development of renewable energy, including wind. In the current study, National Renewable Energy Laboratory (NREL) Phase VI wind turbine blade is integrated with morphing trailing-edge, installed on the aft-30% blade chord, across outboard 75% blade span. The morphing trailing-edge generates unique topology for each wind speed such that the glide ratio is maximized along the blade span. Three-dimensional transient computational fluid dynamics (CFD) analyses are conducted over low to medium wind speeds to investigate the blade aerodynamics. The analyses exhibit significant increments in the low-speed shaft torque and power of the morphed blades compared to the baseline. The integration of morphing trailing-edge high-lift flow control mechanism on the NREL Phase VI blade enhanced energy harvesting and reduced the wind turbine cut-in wind speed. Comparative investigations are also conducted to assess the improvements in thrust, bending moment, and aerodynamic load distribution, as well as alterations in the pressure, flow field, turbulence, surface flow, and wake. The aeroacoustics directivity of the wind turbines exhibits marginal far-field noise increment in case of morphing trailing-edge integrated blades

Persepsi pekerja terhadap strategi pembangunan kerjaya dalam organisasi: satu kajian di Johor Corporation, Johor Darul Takzim

Pembangunan kerjaya perlu dititikberatkan bagi pihak organisasi kerana ia dapat memberi peluang kepada para pekerja untuk berusaha keras membangunkan kerjaya dan memikirkan tentang kedudukan dan masa depan mereka dalam sesebuah organisasi. Bagi mengkaji pembangunan kerjaya, konsep pembangunan kerjaya amat penting untuk difahami terlebih dahulu. Pembangunan kerjaya merupakan suatu pendekatan formal yang digunakan oleh organisasi untuk membantu individu mendapatkan kemahiran dan pengalaman bagi menjayakan pekerjaan masa kini dan juga pada masa hadapan (Zafir dan Fazilah, 2003). Persepsi sering dikaitkan dengan pandangan seseorang individu. Persepsi setiap orang adalah berbeza mengikut pandangan masing-masing. Persepsi merupakan proses fizikal dan juga kognitif yang kompleks, aktif dan dinamik. Menurut Jaafar (1997), persepsi didefinisikan sebagai proses di mana individu memilih, menyusun dan menerima segala maklumat yang diperolehi melalui deria. Persepsi dapat melahirkan satu gambaran dan kefahaman yang berguna. Memandangkan pembangunan kerjaya dilihat sebagai salah satu aspek pengurusan sumber manusia yang penting, adalah perlu untuk mengkaji persepsi pekerja terhadap aspek strategi pembangunan kerjaya. Ia dapat mengkaji sejauh mana organisasi dan individu pekerja itu memberi perhatian terhadap pembangunan kerjaya pekerja berdasarkan tahap-tahap strategi pembangunan kerjaya yang ingin dikaji

Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference

The warming of the Arctic, also known as Arctic amplification, is led by several atmospheric and oceanic drivers. However, the details of its underlying thermodynamic causes are still unknown. Inferring the causal effects of atmospheric processes on sea ice melt using fixed treatment effect strategies leads to unrealistic counterfactual estimations. Such models are also prone to bias due to time-varying confoundedness. Further, the complex non-linearity in Earth science data makes it infeasible to perform causal inference using existing marginal structural techniques. In order to tackle these challenges, we propose TCINet - time-series causal inference model to infer causation under continuous treatment using recurrent neural networks and a novel probabilistic balancing technique. Through experiments on synthetic and observational data, we show how our research can substantially improve the ability to quantify leading causes of Arctic sea ice melt, further paving paths for causal inference in observational Earth science

Food assimilated by two sympatric populations of the brown planthopper Nilaparvata lugens (Delphacidae) feeding on different host plants contaminates insect DNA detected by RAPD-PCR analysis.

Contamination of insect DNA for RAPD-PCR analysis can be a problem because many primers are non-specific and DNA from parasites or gut contents may be simultaneously extracted along with that of the insect. We measured the quantity of food ingested and assimilated by two sympatric populations of brown planthopper (BPH), Nilaparvata lugens, one from rice and the other from Leersia hexandra (Poaceae), a wetland forage grass, and we also investigated whether host plant DNA contaminates that of herbivore insects in extractions of whole insects. Ingestion and assimilation of food were reduced significantly when individuals derived from one host plant were caged on the other species. The bands, OPA3 (1.25), OPD3 (1.10), OPD3 (0.80), OPD3 (0.60), pUC/M13F (0.35), pUC/M13F (0.20), BOXAIR (0.50), peh#3 (0.50), and peh#3 (0.17) were found in both rice-infesting populations of brown planthopper and its host plant (rice). Similarly, the bands, OPA4 (1.00), OPB10 (0.70), OPD3 (0.90), OPD3 (0.80), OPD3 (0.60), pUC/ M13F (0.35), pUC/M13F (0.20), and BOXAIR (0.50) were found in both Leersia-infesting populations of brown planthopper and the host plant. So, it is clear that the DNA bands amplified in the host plants were also found in the extracts from the insects feeding on them