Do the engineering education institutions provide soft skills education? Views of South African engineering professionals

Engineering is a profession that utilises knowledge of mathematics and science in order to find solutions to multifaceted problems for the benefit of humans. The advancement of a knowledge-based economy within a globalised world means engineering education institutions are responsible for producing graduates who possess exceptional technical and soft skills. With the universities preferring to provide traditional education and the global corporations looking for versatile graduates, the question arises who is responsible for providing the soft skills education to engineering students of the 21st century. The present study explores qualitative and quantitative data from engineering professionals across South Africa to determine their views on the teaching of soft skills in the engineering curriculum at the engineering education institutions. The data revealed that the engineering professionals are not satisfied with their soft skills training at the educational institutions and would like more focus on such education. The study has implications for the engineering education institutions to balance technical knowledge with soft skills training in their curricula to meet the requirements of the industry. Such education should be incorporated across the board into their professional training.  This will result in empowered graduates who are able to carve their own path to professional succes

The role of working hours, work environment and physical leisure activity on the need for recovery following a day's work among UK white-water raft guides: a within-subjects multilevel approach

Background: White-water raft guides are a growing workforce of the outdoor sector but little is known about how the working environment, workload and physical leisure activity impacts on the need for occupational recovery (the desire to replenish internal resources and recuperate in the time immediately following work) of those working in this physically demanding occupation. Methods: Longitudinal data were collected across an eight month working season at three month intervals. Multilevel analyses tested the within-subject associations between work environment, hours worked and physical leisure activity had on the need for recovery. Results: Working longer across the working season and participating in more physical leisure activity were directly associated with a lower need for occupational recovery. Furthermore, working on natural rivers significantly reduced the need for recovery experienced compared to work on man-made courses. This was regardless of the number of hours of worked in these environments. Discussion: Physical leisure activity may provide a distraction from work, allowing employees to replenish their physical and psychological energy, thus protecting themselves against work-related fatigue. The findings also expand upon the previous literature identifying that working in a natural environment reduces the risk of experiencing work-related fatigue

Influence

Terapi Antiretroviral (ARV) merupakan sesuatu pengobatan pada HIV/AIDS, tetapi ketidak tahuan mereka akan informasi yang berkaitan dengan terapi antiretroviral (ARV) akan menimbulkan ketidak patuhan konsumsi obat (ARV). Penelitian ini betujuan untuk menganalisis Pengaruh Pendidikan dan pekerjaan orang tua terhadap kepatuhan konsusmsi obat ARV pada anak HIV/AIDS. Desain penelitian yang digunakan kuantitatif dengan pendekatan cross sectional. Metode yang digunakan adalah Survei Analitik dengan pengambilan sampel secara Total Sampling. Penelitian ini hampir dilakukan selama 3 bulan dengan mendapatkan data langsung pada orangtua yang memiliki anak HIV/AIDS. Selanjutnya penelitian yang dilakukan, bahwa tingkat pendidikan dan pekerjaan orangtua memberikan pengaruh signifikat terhadap tingkat kepatuhan anak dalam konsumsi ARV. Analisa data yang digunakan adalah uji Paired Sample T-Test. Kesimpulan dari penelitian ini : Ada pengaruh pendidikan terhadap kepatuhan ARV, dan ada pengaruh pekerjaan terhadap kepatuhan ARV. di RSUD Waluyo Djati Kraksaan Kabupaten Probolinggo

Modelling customers credit card behaviour using bidirectional LSTM neural networks

With the rapid growth of consumer credit and the huge amount of financial data developing effective credit scoring models is very crucial. Researchers have developed complex credit scoring models using statistical and artificial intelligence (AI) techniques to help banks and financial institutions to support their financial decisions. Neural networks are considered as a mostly wide used technique in finance and business applications. Thus, the main aim of this paper is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behaviour with respect to two aspects: the probability of single and consecutive missed payments for credit card customers. The proposed model is based on the bidirectional Long-Short Term Memory (LSTM) model to give the probability of a missed payment during the next month for each customer. The model was trained on a real credit card dataset and the customer behavioural scores are analysed using classical measures such as accuracy, Area Under the Curve, Brier score, Kolmogorov–Smirnov test, and H-measure. Calibration analysis of the LSTM model scores showed that they can be considered as probabilities of missed payments. The LSTM model was compared to four traditional machine learning algorithms: support vector machine, random forest, multi-layer perceptron neural network, and logistic regression. Experimental results show that, compared with traditional methods, the consumer credit scoring method based on the LSTM neural network has significantly improved consumer credit scoring

Improving binary classification using filtering based on k-NN proximity graphs

© 2020, The Author(s). One of the ways of increasing recognition ability in classification problem is removing outlier entries as well as redundant and unnecessary features from training set. Filtering and feature selection can have large impact on classifier accuracy and area under the curve (AUC), as noisy data can confuse classifier and lead it to catch wrong patterns in training data. The common approach in data filtering is using proximity graphs. However, the problem of the optimal filtering parameters selection is still insufficiently researched. In this paper filtering procedure based on k-nearest neighbours proximity graph was used. Filtering parameters selection was adopted as the solution of outlier minimization problem: k-NN proximity graph, power of distance and threshold parameters are selected in order to minimize outlier percentage in training data. Then performance of six commonly used classifiers (Logistic Regression, Naïve Bayes, Neural Network, Random Forest, Support Vector Machine and Decision Tree) and one heterogeneous classifiers combiner (DES-LA) are compared with and without filtering. Dynamic ensemble selection (DES) systems work by estimating the level of competence of each classifier from a pool of classifiers. Only the most competent ones are selected to classify a given test sample. This is achieved by defining a criterion to measure the level of competence of base classifiers, such as, its accuracy in local regions of the feature space around the query instance. In our case the combiner is based on the local accuracy of single classifiers and its output is a linear combination of single classifiers ranking. As results of filtering, accuracy of DES-LA combiner shows big increase for low-accuracy datasets. But filtering doesn’t have sufficient impact on DES-LA performance while working with high-accuracy datasets. The results are discussed, and classifiers, which performance was highly affected by pre-processing filtering step, are defined. The main contribution of the paper is introducing modifications to the DES-LA combiner, as well as comparative analysis of filtering impact on the classifiers of various type. Testing the filtering algorithm on real case dataset (Taiwan default credit card dataset) confirmed the efficiency of automatic filtering approach

Returning to work following cancer: a qualitative exploratory study into the experience of returning to work following cancer

The experience of returning to work following cancer is a largely unknown area of cancer research. This preliminary study aimed to explore the factors that influence decisions about return to work eitehr during or after cancer treatment and to identify the important aspects of returning to work. Qalitative data were collected using individual interview (n=19) and two focus groups (n=4, n=6), predominantly with breast cancer survivors. Patterns of returning to work were diverse and a variety of reasons influenced work decisions, including financial concerns and regaining normality. Participants also discussed their ability to work, health professionals' advice, side effects, support and adjustments, and attitudes towards work. Although the majority adapted well, a few encountered difficulties on their return. It is evident that more advice is requried from health professionals about returning to work, along with reasonable support and adjustments from employers to ensure that cancer survivors are able to successfully reintegrate back into the workforce

Tests of self-compacting concrete filled elliptical steel tube columns

YesThis paper presents an experimental study into the axial compressive behaviour of self-compacting concrete filled elliptical steel tube columns. In total, ten specimens, including two empty columns, with various lengths, section sizes and concrete strengths were tested to failure. The experimental results indicated that the failure modes of the self-compacting concrete filled elliptical steel tube columns with large slenderness ratio were dominated by global buckling. Furthermore, the composite columns possessed higher critical axial compressive capacities compared with their hollow section companions due to the composite interaction. However, due to the large slenderness ratio of the test specimens, the change of compressive strength of concrete core did not show significant effect on the critical axial compressive capacity of concrete filled columns although the axial compressive capacity increased with the concrete grade increase. The comparison between the axial compressive load capacities obtained from experimental study and prediction using simple methods provided in Eurocode 4 for concrete-filled steel circular tube columns showed a reasonable agreement. The experimental results, analysis and comparison presented in this paper clearly support the application of self-compacting concrete filled elliptical steel tube columns in construction engineering practice

A deep learning model for behavioural credit scoring in banks

The main aim of this paper is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behaviour concerning three aspects: the probability of single and consecutive missed payments for credit card customers, the purchasing behaviour of customers, and grouping customers based on a mathematical expectation of loss. Two models are developed: the first provides the probability of a missed payment during the next month for each customer, which is described as Missed payment prediction Long Short Term Memory model (MP-LSTM), whilst the second estimates the total monthly amount of purchases, which is defined as Purchase Estimation Prediction Long Short Term Memory model (PE-LSTM). Based on both models, a customer behavioural grouping is provided, which can be helpful for the bank’s decision-making. Both models are trained on real credit card transactional datasets. Customer behavioural scores are analysed using classical performance evaluation measures. Calibration analysis of MP-LSTM scores showed that they could be considered as probabilities of missed payments. Obtained purchase estimations were analysed using mean square error and absolute error. The MP-LSTM model was compared to four traditional well-known machine learning algorithms. Experimental results show that, compared with conventional methods based on feature extraction, the consumer credit scoring method based on the MP-LSTM neural network has significantly improved consumer credit scoring

From Conflict to Assimilation: Strategies of Muslim Immigrants in Papua Special Autonomy Era

This paper aims to explain the forms of Muslim immigrant strategies in Papua in the era of special autonomy. After the implementation of special autonomy in Papua, migrants feel the increasing tension or competition in the economic and political fields. Data obtained through the method of observation, interviews, and literature studies. Observations focused on the economic practices of Muslim migrants in places such as the market in Jayapura, Papua. Interviews were conducted with a number of parties, both Muslim migrants and local Papuans, to obtain information on many things including their response to the presence of Muslim migrants. In addition, data was also obtained through the documentation of literature related to the topic of this paper. The data obtained were then analyzed through the steps of qualitative analysis, namely data reduction, data presentation, drawing conclusions/verification. This paper confirms that Muslim migrants made various efforts to deal with various obstacles in the era of Special Autonomy in Papua in three ways. First, Muslim migrants strengthen the economy, especially the informal sector. Secondly, the political sector is not the main objective of the existence of Muslim migrants. Third, Muslim migrants are not exclusive, especially in establishing communication with indigenous people

Gambaran Nilai Peak Expiratory Flow Rate (Pefr) Pada Pasien Asma Yang Mengikuti Senam Asma Di Pekanbaru

Asthma is a chronic inflammatory associated with airway cause symptoms such as recurrent episodes of wheezing, breathlessness, chest tightness and coughing, especially at night or in the early days and it is reversible with or without treatment. Asthma gymnastic is one of the recommended exercise therapy to help the process of rehabilitation in patients with asthma. To determine the success rate, it is important to evaluate and monitor the effect of asthma gymnastic to lung fuction periodically, with Peak Expiratory Flow Rate (PEFR) examination by using Peak Flow Meter before and after doing gymnastic. This was a cross sectional descriptive study using the total sampling technique to know the overview of PEFR in asthma patients that following asthma gymnastics in pekanbaru. . This research has 32 samples. Result showed that asthma patients who followed gymnastics asma were in the age group 41-50 years old (37,5%), most commonly happened in female (75%), most common had not comorbidities (53,12%), most common patients had a family history of asthma (62,5%), the length of time following asthma gymnastics was the most ≥3 month (59,37%). The degree was the (37,5%). Assessment of asthma control was the most good controlled asthma (46,87%). Assesment of PEFR was the most low obstructive (43,75%)