Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Lean Six Sigma in Industry: A Sugar Company Case Study

 Abstract — The integration between lean manufacturing and six sigma methodology – Lean Six Sigma (LSS) – is often used in many industries, e.g., to overcome nonvalue adding practices and wastes. Many Researchers applied LSS in industries like oilfield, food, ores, foundry clusters, etc. Also, it is used in Services, e.g., to improve quality of performance and increase market share. These services are, e.g., hospitals, educational institutes, healthcare. LSS tools used in both application types may be similar, but the results gained differ quantitively and qualitatively. This paper shows different types of applications used LSS and the tools used, whether in industries or services, and the results reached in each due to the usage of various tools. A case study was held in one of the sugar companies in Egypt, showing the impact of this method and its tools to improve such an industry. The five stages were applied with the suitable tools reaching promising results, as will be
The Performance Study on Combined Cooling Heating and Power System Based on Micro Turbine

Firstly,combined cooling heating and power(CCHP)systems are introduced.The mathematical model of main components is built in order to study the micro CCHP system in Shanghai Company.Based on the mode,the operating performances of the system under different ambient temperature are simulated.The operating performance of this system throughout the year is further simulated based on the previous conclusion.The results show that the environmental effects on the CCHP system performance.When the system does not run during the transitional seasons,the average overall energy efficiency of a year is more than 70%.
Exotic fish and decreasing habitats vis-à-vis conservation of freshwater fish biodiversity of Bangladesh

Multiple stressors including biological invasion have long been recognised in conservation of freshwater fish biodiversity. Works in this line have, however, been still scanty in Asia and South America – the continents known for rich biodiversity. In this study, the first of its type in Bangladesh, impacts of exotic fish and declining habitats on native fishes have been investigated. Regression and correlation analyses of 16 years production data of ponds, practically the only habitat where exotic fish are cultured commonly with indigenous ones, reveals that a maximum of 25% of the abundance decrease of native fish could be due to the effects of five commonly cultured exotic fish. Rest of the decline might largely be due to shrinkage, degradation, and destruction of habitats. Inland habitats other than ponds shrink, commonly, by about 80 % during the dry season, and most rivers are heavily polluted except for the brief mid rainy season. Decline in native fish populations may result in reduced genetic diversity. Another potential danger is the probable genetic introgression from the less fit hatchery-originated fish with the wild ones. The study concludes that the native freshwater fishes are endangered by declining habitats, exotic fishes, genetic consequences of hatchery supplementation
The moderating effect of audit committee chairman’s multiple directorships on the relationship between audit committee characteristics and firm performance

The aim of this study was to investigate the impact of corporate governance characteristics, particularly the characteristics of audit committees (ACs), on the performance of finance companies. Specifically, it sought to determine if the multiple directorships of the audit committee chairperson (ACC) moderate the relationship between the AC characteristics (i.e., independence, size, meeting frequency, and expertise) and the performance of finance companies. Multiple directorships have become an increasing issue in a number of countries around the world (Saleh, Shurafa, Shukeri, Nour, & Maigosh, 2020). In Malaysia, based on the mandatory listing requirements for the Bursa Malaysia, for example, directors can have up to 25 directorships, 15 of which can be in non-public corporations and the remaining 10 in public organizations. The study was conducted on 140 observations obtained from 28 financial companies listed on Bursa Malaysia over the period 2015–2019. The Pearson correlation coefficient and multiple linear regressions were employed. The study findings show that the corporate governance characteristics, specifically the AC characteristics (i.e., independence, size, meeting frequency, and expertise), have a substantial influence on finance company performance but the fact that the ACC has multiple directorships has no significant moderating impact on corporate performance’s associations with AC expertise and independence. On the other hand, the study results show that the fact that the ACC has multiple directorships has a strong moderating impact on the corporate performance’s associations with AC meeting frequency and size.
Neighborhood Information-Based Method for Multivariate Association Mining

Most current data is multivariable, exploring and identifying valuable information in these datasets has far-reaching impacts. In particular, discovering meaningful hidden association patterns in multivariate plays an important role. Plenty of measures for multivariate association have been proposed, yet it is still an open research challenge for effectively capturing association patterns among three or more variables, especially the scenario without any prior knowledge about those relationships. To do so, we desire a distribution-free, association type-independent and non-parametrical measure. For practical applications, such a measure should comparable, interpretable, scalable, intuitive, reliability, and robust. However, no exiting measures fulfill all of these desiderata. In this paper, taking advantage of the neighborhood information of a sample, we propose MNA, a maximal neighborhood multivariate association measure that satisfies all the above criteria. Extensive experiments on synthetic and real data show it outperforms state-of-the-art multivariate association measures.
Physical Fitness and Upper Limb Asymmetry in Young Padel Players: Differences between Genders and Categories

This study aimed to assess the physical fitness and upper body asymmetries of young padel players aged between 13 and 16 years and to determine the possible differences between genders and categories. A total of 60 padel players were divided into four groups: under-14 male (n = 15; age: 13.75 ± 0.45 years; height: 1.64 ± 0.07 m; weight: 54.7 ± 8.3 kg), under-14 female (n = 15; age: 13.75 ± 0.44 years; height: 1.60 ± 0.05 m; weight: 51.5 ± 6.0 kg), under-16 male (n = 15; age: 15.44 ± 0.51 years; height:1.71 ± 0.04 m; weight: 63.88 ± 6.2 kg) and under-16 female (n = 15; age:15.46 ± 0.52 years; height:1.63 ± 0.05 m; weight: 55.08 ± 3.6 kg). Handgrip strength, ischiosural flexibility, gestural speed of the dominant arm, vertical jump, cardiorespiratory capacity, lateral movement, lateral acceleration and reaction time were measured. Male players showed better results in manual grip strength, vertical jump power, cardiorespiratory capacity and lateral movement (p < 0.05). Moreover, males presented a higher percentage of asymmetry in upper limb strength. Female players showed better reaction time and greater flexibility (p < 0.05). Regarding the differences between categories, the under-16 players showed greater flexibility, gestural speed, vertical jump power, cardiorespiratory capacity and lateral movement compared to the under-14 players. These results can be used as reference values for coaches/physical trainers of younger categories to improve health control and physical performance planning.
Challenges in watch-centric cross-device applications

People are increasingly interacting with an ecology of devices that include smartwatches, smartphones, tablets and other traditional desktop-based devices. As a small wearable device, the smartwatch has the ability to become a central mediating device that instruments the human hand into a reconfigurable tool that provides other devices with personal access to users personal information, thus, mediating interaction between other devices. In this paper, we discuss a number of interaction techniques and applications that demonstrate how a smartwatch can allow for interaction within an ecology of devices. We summarize open challenges and describe directions for future work.
Reconfigurable wearable to monitor physiological variables and movement

This article presents a preliminary prototype of a wearable instrument for oxygen saturation and ECG monitoring. The proposed measuring system is based on the light reflection variability of a LED emission on the subject temple. Besides, the system has the capacity to incorporate electrodes to obtain ECG measurements. All measurements are stored and transmitted to a mobile device (tablet or smartphone) through a Bluetooth link.
Assessing Sumatran Peat Vulnerability to Fire under Various Condition of ENSO Phases Using Machine Learning Approaches

In recent decades, catastrophic wildfire episodes within the Sumatran peatland have contributed to a large amount of greenhouse gas emissions. The El-Nino Southern Oscillation (ENSO) modulates the occurrence of fires in Indonesia through prolonged hydrological drought. Thus, assessing peatland vulnerability to fires and understanding the underlying drivers are essential to developing adaptation and mitigation strategies for peatland. Here, we quantify the vulnerability of Sumatran peat to fires under various ENSO conditions (i.e., El-Nino, La-Nina, and Normal phases) using correlative modelling approaches. This study used climatic (i.e., annual precipitation, SPI, and KBDI), biophysical (i.e., below-ground biomass, elevation, slope, and NBR), and proxies to anthropogenic disturbance variables (i.e., access to road, access to forests, access to cities, human modification, and human population) to assess fire vulnerability within Sumatran peatlands. We created an ensemble model based on various machine learning approaches (i.e., random forest, support vector machine, maximum entropy, and boosted regression tree). We found that the ensemble model performed better compared to a single algorithm for depicting fire vulnerability within Sumatran peatlands. The NBR highly contributed to the vulnerability of peatland to fire in Sumatra in all ENSO phases, followed by the anthropogenic variables. We found that the high to very-high peat vulnerability to fire increases during El-Nino conditions with variations in its spatial patterns occurring under different ENSO phases. This study provides spatially explicit information to support the management of peat fires, which will be particularly useful for identifying peatland restoration priorities based on peatland vulnerability to fire maps. Our findings highlight Riau’s peatland as being the area most prone to fires area on Sumatra Island. Therefore, the groundwater level within this area should be intensively monitored to prevent peatland fires. In addition, conserving intact forests within peatland through the moratorium strategy and restoring the degraded peatland ecosystem through canal blocking is also crucial to coping with global climate change.
Novel Compensation Parameter Design Methodology and Maximum Efficiency Tracking Control Strategy for Inductive Power Transfer System

This paper presents a novel compensation parameter design methodology and maximum efficiency tracking (MET) control strategy for inductive power transfer (IPT) system with an inductance and double capacitance-series (LCC-S) compensation topology. The compensation parameters of a conventional IPT converter are designed for the required output voltage by considering the fluctuation ranges of the battery voltage and coupling coefficient, but without considering the efficiency characteristics of the IPT converter. Additionally, the IPT system efficiency fluctuates with the load, because the battery equivalent resistance continuously changes according to the state-of-charge. To address these problems, this paper presents a compensation parameter design methodology based on a loss analysis of the IPT converter. The losses of the transmitter and receiver in the IPT converter with the optimal compensation parameters are compared and analyzed according to the load. Based on these analyses, a control strategy is proposed for tracking the maximum efficiency according to the load without real-time communication. To validate the proposed compensation parameter design methodology and maximum-efficiency tracking control strategy, a 1.85 kW experimental prototype is configured.
IRW (Isoleucine–Arginine–Tryptophan) Improves Glucose Tolerance in High Fat Diet Fed C57BL/6 Mice via Activation of Insulin Signaling and AMPK Pathways in Skeletal Muscle

IRW (Isoleucine–Arginine–Tryptophan), has antihypertensive and anti-inflammatory properties in cells and animal models and prevents angiotensin-II- and tumor necrosis factor (TNF)-α-induced insulin resistance (IR) in vitro. We investigated the effects of IRW on body composition, glucose homeostasis and insulin sensitivity in a high-fat diet (HFD) induced insulin resistant (IR) model. C57BL/6 mice were fed HFD for 6 weeks, after which IRW was incorporated into the diet (45 or 15 mg/kg body weight (BW)) until week 14. IRW45 (at a dose of 45 mg/kg BW) reduced BW (p = 0.0327), fat mass gain (p = 0.0085), and preserved lean mass of HFD mice (p = 0.0065), concomitant with enhanced glucose tolerance and reduced fasting glucose (p < 0.001). In skeletal muscle, IRW45 increased insulin-stimulated protein kinase B (AKT) phosphorylation (p = 0.0132) and glucose transporter 4 (GLUT4) translocation (p < 0.001). Angiotensin 2 receptor (AT2R) (p = 0.0024), phosphorylated 5′-AMP-activated protein kinase (AMPKα) (p < 0.0124) and peroxisome proliferator-activated receptor gamma (PPARγ) (p < 0.001) were enhanced in skeletal muscle of IRW45-treated mice, as was the expression of genes involved in myogenesis. Plasma angiotensin converting enzyme-2 (ACE2) activity was increased (p = 0.0016). Uncoupling protein-1 in white adipose tissue (WAT) was partially restored after IRW supplementation. IRW improves glucose tolerance and body composition in HFD-fed mice and promotes glucose uptake in skeletal muscle via multiple signaling pathways, independent of angiotensin converting enzyme (ACE) inhibition.
The Management of Female Genital Schistosomiasis in Tanzania: Challenges and Call for ‎Action ‎‎

Female Genital Schistosomiasis (FGS) is among the neglected gynecological manifestation of urogenital schistosomiasis in Tanzania. Little is known about the challenges in the diagnosis, treatment, and prevention of FGS in Tanzania. The challenges could potentially threaten the successful control of the FGS. Therefore, the letter explains the challenges in the FGS management and recommends the appropriate measures that could be taken to initiate the fight against the FGS in Tanzania.
Cavity‐backed self‐phased circularly polarized antenna based on stepped‐width dipole with wide axial ratio beamwidth

A cavity‐backed self‐phased circularly polarized (CP) antenna based on stepped‐width dipoles with wide axial ratio beamwidth is proposed for global positioning system (GPS) applications. The radiator of the antenna consists of two pairs of stepped‐width dipoles, contributing to the CP radiation. The working principle of CP radiation is elaborated by using two simplified models. Then, a feeding network consisting of a Y‐shaped structure and two pairs of identical coplanar striplines (CPSs) is utilized to feed the radiator. Moreover, a cavity‐backed reflector is employed in this antenna to achieve a unidirectional radiation with wide axial ratio (AR) beamwidth. Measured results show that the proposed antenna achieves a wide impedance bandwidth of 43% from 1.37 to 2.12 GHz with Return Loss > 15 dB, and a 3‐dB axial ratio (AR) bandwidth of 6.98% from 1.52 to 1.63 GHz. Particularly, the antenna shows a right‐hand circular polarization (RHCP) radiation with wide 3‐dB AR beamwidth of 205° and 215° in the XOZ and YOZ plane at the center frequency of 1.575 GHz, respectively.
A numerical–experimental approach to assess emission performance of new generation engines during the cold transient

Modern vehicles equipped with a spark ignition engine come with fuel injection and electronic mixture control, in combination with a three-way catalyst: as a result, carbon monoxide and unburned hydrocarbon cold emissions represent a significant share of total emissions if compared with those given off in hot conditions, so implying direct consequences on the air quality of metropolitan areas. The purpose of this research is to investigate the engine efficiency and emissive performance of last generation engines during the cold-start phase. For this aim, an experimental–analytical procedure was optimised and applied to study the cold emission behaviour of two motorcycles, characterised by similar technical specifications; the exhaust emissions of these motorcycles were measured on a chassis dynamometer in the laboratories of the National Research Council (CNR-Italy). By using this calculation procedure and the emission results measured during the experimental tests, the duration of the cold phase and the total emissions released during the cold-start transient were evaluated for carbon monoxide and unburned hydrocarbons. The average values of cold emission factors and warm-up transient durations obtained for unburned hydrocarbons were 1.05 g/km and 162 s, respectively. Regarding carbon monoxide, the average values of cold emission factors and warm-up transient durations were 14.9 g/km and 152 s, respectively. The results of this study are very useful for better characterising the emission levels of the last generation motorcycles under real urban conditions.
Method of Fault Diagnosis for Power Transformer with Multi-parameters

According to the shortcomings of existing transformer fault comprehensive diagnosis methods,combining the data of dissolved gas in oil and the off-line electrical test data,one kind of transformer fault synthetic diagnosis model based on wavelet neural network(WNN) optimized by adaptive genetic algorithm(AGA) and D-S evidence theory fusion technique is proposed in this paper for completely synthetically diagnosing the transformer fault.According to the divisions of fault characteristic parameters,multi-neural networks are constructed to reflect the transformer faults from different aspects.The basic probability assignment(BPA) of evidence body is constructed combining the evidence importance and the outputs of neutral network in order to sufficiently display the believable value between the evidence bodies and the single fault model recognition.The diagnostic results indicate that the fault synthesis diagnostic method based on information fusion and multi-parameters has better diagnostic performance than method based on single-parameter,and a new idea is provided to diagnose other high reliability devices.
Challenges of STEM Vocations in Secondary Education

Contribution: There is a significant decline in technological vocations despite a huge increase in labor demand for professionals in these sectors. This technology-related problem has become a major concern for governments, organizations, and education systems. Students’ motivation toward Science, Technology, Engineering, and Mathematics (STEM) may be due to the influence of their immediate environment, known role models and the involvement of families and teachers, as well as the search for more positive and inclusive perceptions of science and technology in our lives. The present study shows the perception and motivation of secondary students and teachers toward STEM and its importance in society. Background: The low presence of students in STEM could be explained by the perception and knowledge of students and teachers toward technology. Studies show that students largely lose interest in STEM during secondary school education, and this leads to a decline in technological vocations that carries over to university and the labor market. Due to the economic and social impact of this loss, institutions are launching initiatives to support education and the development of digital competences, with special emphasis on the awakening technological vocations. Research Questions: What is the motivation and perspective of students for STEM studies? Are teachers aware of students’ perceptions? What are the actions taken by teachers to encourage secondary students toward STEM? Methodology: This study follows a descriptive correlational design based on surveys (1562 students and 430 teachers) regarding issues that influence the perceptions of students and teachers toward STEM. Findings: This study demonstrates a divergence between teachers’ and students’ perceptions of STEM-related skills and abilities. Indeed, teachers and parents/guardians need to encourage more young people to engage in STEM activities because of the great influence they have on their decisions. The search for new STEM referents that young people can identify with is another important finding. Furthermore, this study also reveals that technology should be linked to our daily lives and emphasize its role as a social good for society. Finally, integrating STEM subjects into the curriculum and encouraging technological vocations at all stages of education are of utmost importance.
Development of Fully Convolutional Neural Networks Based on Discretization in Time Series Classification

Time Series Classification (TSC) is a crucial area in machine learning. Although applications of Deep Neural Networks (DNNs) in this area have led to relatively good results, classifying this kind of data is a major challenge. This issue is due to the nature of time-series as it involves high data volume, unfavorable elements such as noise, inconsistency, and missing values. Shallow approaches solve these challenges using temporal discretization. Despite offering significant advantages, deep learning (DL) models do not apply temporal discretization in an end-to-end manner. This paper develops three end-to-end DL models, namely FCN-DISC, LSTM-FCN-DISC, and ALSTM-FCN-DISC, to integrate the benefits of temporal discretization and deep network architecture. The proposed models attempt to select the values of the input time-series that play a more effective role in model training by embedding temporal discretization in deep network architecture. These models use two loss functions to construct a discretized time series and optimize network weights. Thus, a new loss function for discretization was also introduced in addition to the cross-entropy. Experiments on univariate TSC datasets demonstrate that the proposed models, in most cases, outperform the state-of-the-art methods.
Effects of thermal loading on the deployment of four-bar linkages for retractable roof structures

The thermal stress due to temperature rise may increase the element strain and internal forces, which may lead to the failure of the deployment of deployable structure. However, few works are carried out to study the dynamic performance of the flexible multibody under different temperatures, especially for retractable roof structures. Based on this idea and formulation of finite element methods, the dynamic equations of the system considering the temperature effect are derived. Then two examples, a parallel four-bar linkage and a non-parallel four-bar linkage, are presented in this paper to study the influence of thermal loading on the structural behavior during the motion. The results show that the effect of temperature changes is slight for the parallel four-bar linkage, but the influence is significant for the non parallel four-bar linkage. It can be concluded that the former case may be more suitable for the deployable structures. DOI: http://dx.doi. org/10.5755/j01.mech. 23.2. 12542