Search results for: maximal data sets

Authors: Khuram Nawaz Sadozai, Rizwan Ahmad, Munawar Raza Kazmi, Awais Habib

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Climate change and crop production are intrinsically associated with each other. Therefore, this research study is designed to assess the impact of climate change on pulses production in Southern districts of Khyber Pakhtunkhwa (KP) Province of Pakistan. Two pulses (i.e. chickpea and mung bean) were selected for this research study with respect to climate change. Climatic variables such as temperature, humidity and precipitation along with pulses production and area under cultivation of pulses were encompassed as the major variables of this study. Secondary data of climatic variables and crop variables for the period of thirty four years (1986-2020) were obtained from Pakistan Metrological Department and Agriculture Statistics of KP respectively. Panel data set of chickpea and mung bean crops was estimated separately. The analysis validate that both data sets were a balanced panel data. The Hausman specification test was run separately for both the panel data sets whose findings had suggested the fixed effect model can be deemed as an appropriate model for chickpea panel data, however random effect model was appropriate for estimation of the panel data of mung bean. Major findings confirm that maximum temperature is statistically significant for the chickpea yield. This implies if maximum temperature increases by 1 0C, it can enhance the chickpea yield by 0.0463 units. However, the impact of precipitation was reported insignificant. Furthermore, the humidity was statistically significant and has a positive association with chickpea yield. In case of mung bean the minimum temperature was significantly contributing in the yield of mung bean. This study concludes that temperature and humidity can significantly contribute to enhance the pulses yield. It is recommended that capacity building of pulses growers may be made to adapt the climate change strategies. Moreover, government may ensure the availability of climate change resistant varieties of pulses to encourage the pulses cultivation.

Keywords: climate change, pulses productivity, agriculture, Pakistan

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Authors: E. R. Swart, S. J. Gismondi, N. R. Swart, C. E. Bell

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We present an heuristic algorithm that decides graph non-Hamiltonicity. All graphs are directed, each undirected edge regarded as a pair of counter directed arcs. Each of the n! Hamilton cycles in a complete graph on n+1 vertices is mapped to an n-permutation matrix P where p(u,i)=1 if and only if the ith arc in a cycle enters vertex u, starting and ending at vertex n+1. We first create exclusion set E by noting all arcs (u, v) not in G, sufficient to code precisely all cycles excluded from G i.e. cycles not in G use at least one arc not in G. Members are pairs of components of P, {p(u,i),p(v,i+1)}, i=1, n-1. A doubly stochastic-like relaxed LP formulation of the Hamilton cycle decision problem is constructed. Each {p(u,i),p(v,i+1)} in E is coded as variable q(u,i,v,i+1)=0 i.e. shrinks the feasible region. We then implement the Weak Closure Algorithm (WCA) that tests necessary conditions of a matching, together with Boolean closure to decide 0/1 variable assignments. Each {p(u,i),p(v,j)} not in E is tested for membership in E, and if possible, added to E (q(u,i,v,j)=0) to iteratively maximize |E|. If the WCA constructs E to be maximal, the set of all {p(u,i),p(v,j)}, then G is decided non-Hamiltonian. Only non-Hamiltonian G share this maximal property. Ten non-Hamiltonian graphs (10 through 104 vertices) and 2000 randomized 31 vertex non-Hamiltonian graphs are tested and correctly decided non-Hamiltonian. For Hamiltonian G, the complement of E covers a matching, perhaps useful in searching for cycles. We also present an example where the WCA fails.

Keywords: Hamilton cycle decision problem, computational complexity theory, graph theory, theoretical computer science

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Authors: Hyun-Woo Cho

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The detection and identification of multivariate manufacturing processes are quite important in order to maintain good product quality. Unusual behaviors or events encountered during its operation can have a serious impact on the process and product quality. Thus they should be detected and identified as soon as possible. This paper focused on the efficient representation of process measurement data in detecting and identifying abnormalities. This qualitative method is effective in representing fault patterns of process data. In addition, it is quite sensitive to measurement noise so that reliable outcomes can be obtained. To evaluate its performance a simulation process was utilized, and the effect of adopting linear and nonlinear methods in the detection and identification was tested with different simulation data. It has shown that the use of a nonlinear technique produced more satisfactory and more robust results for the simulation data sets. This monitoring framework can help operating personnel to detect the occurrence of process abnormalities and identify their assignable causes in an on-line or real-time basis.

Keywords: detection, monitoring, identification, measurement data, multivariate techniques

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Authors: Kamel Belammi, Houria Fatrim

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imbalanced data set, a problem often found in real world application, can cause seriously negative effect on classification performance of machine learning algorithms. There have been many attempts at dealing with classification of imbalanced data sets. In medical diagnosis classification, we often face the imbalanced number of data samples between the classes in which there are not enough samples in rare classes. In this paper, we proposed a learning method based on a cost sensitive extension of Least Mean Square (LMS) algorithm that penalizes errors of different samples with different weight and some rules of thumb to determine those weights. After the balancing phase, we applythe different classifiers (support vector machine (SVM), k- nearest neighbor (KNN) and multilayer neuronal networks (MNN)) for balanced data set. We have also compared the obtained results before and after balancing method.

Keywords: multilayer neural networks, k- nearest neighbor, support vector machine, imbalanced medical data, least mean square algorithm, diabetes

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Authors: Hyun-Woo Cho

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Unexpected operational events or abnormalities of industrial processes have a serious impact on the quality of final product of interest. In terms of statistical process control, fault detection and diagnosis of processes is one of the essential tasks needed to run the process safely. In this work, nonlinear representation of process measurement data is presented and evaluated using a simulation process. The effect of using different representation methods on the diagnosis performance is tested in terms of computational efficiency and data handling. The results have shown that the nonlinear representation technique produced more reliable diagnosis results and outperforms linear methods. The use of data filtering step improved computational speed and diagnosis performance for test data sets. The presented scheme is different from existing ones in that it attempts to extract the fault pattern in the reduced space, not in the original process variable space. Thus this scheme helps to reduce the sensitivity of empirical models to noise.

Keywords: fault diagnosis, nonlinear technique, process data, reduced spaces

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Authors: Rishabh Srivastav, Divyam Sharma

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We propose an algorithm to cluster categorical data named as ‘Genetic algorithm initialized rough set based fuzzy K-Modes for categorical data’. We propose an amalgamation of the simple K-modes algorithm, the Rough and Fuzzy set based K-modes and the Genetic Algorithm to form a new algorithm,which we hypothesise, will provide better Centroid Model clustering results, than existing standard algorithms. In the proposed algorithm, the initialization and updation of modes is done by the use of genetic algorithms while the membership values are calculated using the rough set and fuzzy logic.

Keywords: categorical data, fuzzy logic, genetic algorithm, K modes clustering, rough sets

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Authors: Nur Aziza Luxfiati

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Clustering is partitioning data sets into sub-sets or groups in such a way that elements certain properties have shared property settings with a high level of similarity within one group and a low level of similarity between groups. . The K-Means algorithm is one of thealgorithmsclustering as a grouping tool that is most widely used in scientific and industrial applications because the basic idea of the kalgorithm is-means very simple. In this research, applying the technique of clustering using the k-means algorithm as a method of solving the problem of national development imbalances between regions in Central Java Province based on macroeconomic indicators. The data sample used is secondary data obtained from the Central Java Provincial Statistics Agency regarding macroeconomic indicator data which is part of the publication of the 2019 National Socio-Economic Survey (Susenas) data. score and determine the number of clusters (k) using the elbow method. After the clustering process is carried out, the validation is tested using themethodsBetween-Class Variation (BCV) and Within-Class Variation (WCV). The results showed that detection outlier using z-score normalization showed no outliers. In addition, the results of the clustering test obtained a ratio value that was not high, namely 0.011%. There are two district/city clusters in Central Java Province which have economic similarities based on the variables used, namely the first cluster with a high economic level consisting of 13 districts/cities and theclustersecondwith a low economic level consisting of 22 districts/cities. And in the cluster second, namely, between low economies, the authors grouped districts/cities based on similarities to macroeconomic indicators such as 20 districts of Gross Regional Domestic Product, with a Poverty Depth Index of 19 districts, with 5 districts in Human Development, and as many as Open Unemployment Rate. 10 districts.

Keywords: clustering, K-Means algorithm, macroeconomic indicators, inequality, national development

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Authors: Zhen Peng, Baifeng Wu

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Modern computing technology enters the era of parallel computing with the trend of sustainable and scalable parallelism. Single Instruction Multiple Data (SIMD) is an important way to go along with the trend. It is able to gather more and more computing ability by increasing the number of processor cores without the need of modifying the program. Meanwhile, in the field of scientific computing and engineering design, many computation intensive applications are facing the challenge of increasingly large amount of data. Data parallel computing will be an important way to further improve the performance of these applications. In this paper, we take the accurate collision detection in building information modeling as an example. We demonstrate a model for constructing a data parallel algorithm. According to the model, a complex object is decomposed into the sets of simple objects; collision detection among complex objects is converted into those among simple objects. The resulting algorithm is a typical SIMD algorithm, and its advantages in parallelism and scalability is unparalleled in respect to the traditional algorithms.

Keywords: data parallelism, collision detection, single instruction multiple data, building information modeling, continuous scalability

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Authors: Xulu Yao, Moi Hoon Yap, Yanlong Zhang

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As a branch of artificial neural network, deep learning is widely used in the field of image recognition, but the lack of its dataset leads to imperfect model learning. By analysing the data scale requirements of deep learning and aiming at the application in GUI generation, it is found that the collection of GUI dataset is a time-consuming and labor-consuming project, which is difficult to meet the needs of current deep learning network. To solve this problem, this paper proposes a semi-supervised deep learning model that relies on the original small-scale datasets to produce a large number of reliable data sets. By combining the cyclic neural network with the generated countermeasure network, the cyclic neural network can learn the sequence relationship and characteristics of data, make the generated countermeasure network generate reasonable data, and then expand the Rico dataset. Relying on the network structure, the characteristics of collected data can be well analysed, and a large number of reasonable data can be generated according to these characteristics. After data processing, a reliable dataset for model training can be formed, which alleviates the problem of dataset shortage in deep learning.

Keywords: GUI, deep learning, GAN, data augmentation

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Authors: V. Antevska, B. Dejanova, L. Todorovska, J. Pluncevic, E. Sivevska, S. Petrovska, S. Mancevska, I. Karagjozova

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Introduction: The aim of this study was to optimise the laboratory oxidative stress (OS) markers in soccer players. Material and methods: In a number of 37 soccer players (21±3 years old) and 25 control subjects (sedenters), plasma samples were taken for d-ROMs (reactive oxygen metabolites) and NO (nitric oxide) determination. The d-ROMs test was performed by measurement of hydroperoxide levels (Diacron, Italy). For NO determination the method of nitrate enzyme reduction with the Greiss reagent was used (OXIS, USA). The parameters were taken after the training of the soccer players and were compared with the control group. Training was considered as maximal exercise treadmill test. The criteria of maximum loading for each subject was established as >95% maximal heart rate. Results: The level of d-ROMs was found to be increased in the soccer players vs. control group but no significant difference was noticed. After the training d-ROMs in soccer players showed increased value of 299±44 UCarr (p<0.05). NO showed increased level in all soccer players vs. controls but significant difference was found after the training 102±29 μmol (p<0.05). Conclusion: Due to these results we may suggest that the measuring these OS markers in sport medicine may be useful for better estimation and evaluation of the training program. More oxidative stress should be used to clarify optimization of the training intensity program.

Keywords: oxidative stress markers, soccer players, training, sport

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Authors: Marwa Hussein Mohamed, Mohamed Helmy Khafagy, Samah Ahmed Senbel

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Map Reduce is a programming model used to handle and support massive data sets. Rapidly increasing in data size and big data are the most important issue today to make an analysis of this data. map reduce is used to analyze data and get more helpful information by using two simple functions map and reduce it's only written by the programmer, and it includes load balancing , fault tolerance and high scalability. The most important operation in data analysis are join, but map reduce is not directly support join. This paper explains two-way map-reduce join algorithm, semi-join and per split semi-join, and proposes new algorithm hash semi-join that used hash table to increase performance by eliminating unused records as early as possible and apply join using hash table rather than using map function to match join key with other data table in the second phase but using hash tables isn't affecting on memory size because we only save matched records from the second table only. Our experimental result shows that using a hash table with hash semi-join algorithm has higher performance than two other algorithms while increasing the data size from 10 million records to 500 million and running time are increased according to the size of joined records between two tables.

Keywords: map reduce, hadoop, semi join, two way join

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Authors: Rock-Hyun Choi, Won-Seok Kang, Chang-Sik Son

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Physical activity is important for healthy living. Recently wearable devices which motivate physical activity are quickly developing, and become cheaper and more comfortable. In particular, fitness trackers provide a variety of information and need to provide well-analyzed, and user-friendly results. In this study, frequency analysis was performed to classify various data sets of Fitbit into simple activity status. The data from Fitbit cloud server consists of 263 subjects who were healthy factory and office workers in Korea from March 7th to April 30th, 2016. In the results, we found assumptions of activity state classification seem to be sufficient and reasonable.

Keywords: activity status, fitness tracker, heart rate, steps

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Authors: Mohammad Soltani, Sajad Ahmadizad, Fatemeh Hoseinzadeh, Atefe Sarvestan

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The exercise recovery is an important variable in designing resistance exercise training. This study determined the effects of passive and active recoveries on responses of platelet indices and hemodynamic variables to resistance exercise. Twelve healthy subjects (six men and six women, age, 25.4 ±2.5 yrs) performed two types of resistance exercise protocols (six exercises including upper- and lower-body parts) at two separate sessions with one-week intervening. First resistance protocol included three sets of six repetitions at 80% of 1RM with 2 min passive rest between sets and exercises; while, the second protocol included three sets of six repetitions at 60% of 1RM followed by active recovery included six repetitions of the same exercise at 20% of 1RM. The exercise volume was equalized. Three blood samples were taken before exercise, immediately after exercise and after 1-hour recovery, and analyzed for fibrinogen and platelet indices. Blood pressure (BP), heart rate (HR) and rate pressure product (RPP), were measured before, immediately after exercise and every 5 minutes during recovery. Data analyzes showed a significant increase in SBP (systolic blood pressure), HR, rate of pressure product (RPP) and PLT in response to resistance exercise (P<0.05) and that changes for HR and RPP were significantly different between two protocols (P<0.05). Furthermore, MPV and P_LCR did not change in response to resistance exercise, though significant reductions were observed after 1h recovery compared to before and after exercise (P<0.05). No significant changes in fibrinogen and PDW following two types of resistance exercise protocols were observed (P>0.05). On the other hand, no significant differences in platelet indices were found between the two protocols (P>0.05). Resistance exercise induces changes in platelet indices and hemodynamic variables, and that these changes are not related to the type of recovery and returned to normal levels after 1h recovery.

Keywords: hemodynamic variables, platelet indices, resistance exercise, recovery intensity

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Authors: Shaleni Bajpai, V. Niveditha

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A fashion accessory is a product which used to give secondary way to the wearer’s outfit. The term came into use in the 19th century and was specifically chosen to complement the wearer’s look. The aim of project was to introduce the unconventional materials for statement jewellery. The materials used for statement jewellery were waste Cd’s, and scrap fabric. These materials were amalgamated with the traditional raw materials such as beads, sequins, charms and chains to form unique jewellery sets. The sets were divided into two categories based on the type of raw material used i.e. Category 1: Clef-Cd Jewellery, Category 2: Crumb-Fabric Jewellery. Each Jewellery set consisted of a necklace, a pair of earrings, a ring and a bracelet.

Keywords: statement jewellery, unconventional, crumb fabric, Cd’s

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Authors: Zong Yan Cai, Wen-Chyuan Chen, Chih-Min Wu

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The purpose of this study was to investigate the acute effects of local vibration on muscle activation, metabolic and hormone responses. Totally 12 healthy, physically inactive, male adults participated in this study and completed LV exercise session. During LV exercise session, four custom-made vibrations (diameter: 20 mm; thickness: 8 mm; weight: 0.022 g) were locally placed over the belly of the thigh of each subject’s non-dominant leg in supine lying position, and subjects received 10 sets for 1 min at the frequency of 35-40Hz, with 1–2 min of rest between sets. The surface electromyography (EMG) were obtained from the vastus medialis and rectus femoris, and the subjects’ rating of perceived exertion (RPE) and heart rate (HR) were measured. EMG data, RPE values as well as HR were obtained by averaging the results of 10 sets of each exercise session. Blood samples were drawn before exercise, immediately after exercise, and 15min and 30min after exercise in each session for analysis of lactic acid (LA), growth hormone (GH), testosterone (T) and cortisol (C). The results indicated that the HR did not increase after LV (63.18±3.5 to 63.25±2.58 beat/min, p > 0.05). The average RPE values during the LV exposure were at 2.86±0.39. The root mean square % EMG values from the vastus medialis and rectus femoris were 19.02±2.19 and 8.25±2.20 respectively. There were no significant differences after acute LV exercise among LA, GH and T values as compared with baseline values (LA: 0.68±0.11 to 0.7±0.1 mmol/L; GH: 0.06±0.05 to 0.57±0.27 ng/mL; T: 551.33±46.62 to 520.42±43.78 ng/dL, p>0.05). However, the LV treatment caused a significant decrease in C values after exercise (16.56±1.05 to 11.64±1.85 nmol/L, p<0.05). In conclusion, acute LV exercise only slightly increase muscle activation which may not cause effective exercise response. However, acute LV exercise reduces C level, which may reduce the catabolic response. The probable reason might partly due to the vibration rhythmically which massage on muscles.

Keywords: cortisol, growth hormone, lactic acid, testosterone

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Authors: Travis Morris

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The purpose of this study is to measure the intersection of environmental security entities in terrorist propaganda. To the best of author’s knowledge, this is the first study of its kind to examine this intersection within terrorist propaganda. Rosoka, natural language processing software and frame analysis are used to advance our understanding of how environmental frames function as emotive triggers. Violent jihadi demagogues use frames to suggest violent and non-violent solutions to their grievances. Emotive triggers are framed in a way to leverage individual and collective attitudes in psychological warfare. A comparative research design is used because of the differences and similarities that exist between two variants of violent jihadi propaganda that target western audiences. Analysis is based on salience and network text analysis, which generates violent jihadi semantic networks. Findings indicate that environmental frames are used as emotive triggers across both data sets, but also as tactical and information data points. A significant finding is that certain core environmental emotive triggers like “water,” “soil,” and “trees” are significantly salient at the aggregate level across both data sets. All environmental entities can be classified into two categories, symbolic and literal. Importantly, this research illustrates how demagogues use environmental emotive triggers in cyber space from a subcultural perspective to mobilize target audiences to their ideology and praxis. Understanding the anatomy of propaganda construction is necessary in order to generate effective counter narratives in information operations. This research advances an additional method to inform practitioners and policy makers of how environmental security and propaganda intersect.

Keywords: propaganda analysis, emotive triggers environmental security, frames

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Authors: Analen Malnegro, Gina Malacas

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Dominating sets and related topics have been studied extensively in the past few decades. A dominating set of a graph G is a subset D of V such that every vertex not in D is adjacent to at least one member of D. The domination number γ(G) is the number of vertices in a smallest dominating set for G. Some problems involving detection devices can be modeled with graphs. Finding the minimum number of devices needed according to the type of devices and the necessity of locating the object gives rise to locating-dominating sets. A subset S of vertices of a graph G is called locating-dominating set, LD-set for short, if it is a dominating set and if every vertex v not in S is uniquely determined by the set of neighbors of v belonging to S. The location-domination number λ(G) is the minimum cardinality of an LD-set for G. The complement of a graph G is a graph Ḡ on same vertices such that two distinct vertices of Ḡ are adjacent if and only if they are not adjacent in G. An LD-set of a graph G is global if it is an LD-set of both G and its complement Ḡ. The global location-domination number λg(G) is defined as the minimum cardinality of a global LD-set of G. In this paper, global LD-sets on the join of two graphs are characterized. Global location-domination numbers of these graphs are also determined.

Keywords: dominating set, global locating-dominating set, global location-domination number, locating-dominating set, location-domination number

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Authors: Shaharuddin Salleh, Sara Marouf, Hosein Mohammadi

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Directional sensor networks (DSNs) have recently attracted a great deal of attention due to their extensive applications in a wide range of situations. One of the most important problems associated with DSNs is covering a set of targets in a given area and, at the same time, maximizing the network lifetime. This is due to limitation in sensing angle and battery power of the directional sensors. This problem gets more complicated by the possibility that targets may have different coverage requirements. In the present study, this problem is referred to as priority-based target coverage (PTC). As sensors are often densely deployed, organizing the sensors into several cover sets and then activating these cover sets successively is a promising solution to this problem. In this paper, we propose a learning automata-based algorithm to organize the directional sensors into several cover sets in such a way that each cover set could satisfy coverage requirements of all the targets. Several experiments are conducted to evaluate the performance of the proposed algorithm. The results demonstrated that the algorithms were able to contribute to solving the problem.

Keywords: directional sensor networks, target coverage problem, cover set formation, learning automata

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Authors: Yuhanis Yusof, Zuriani Mustaffa, Siti Sakira Kamaruddin

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Data mining is a fundamental technique in identifying patterns from large data sets. The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical. Prior to that, data are consolidated so that the resulting mining process may be more efficient. This study investigates the effect of different data normalization techniques, which are Min-max, Z-score, and decimal scaling, on Swarm-based forecasting models. Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC). Forecasting models are later developed to predict the daily spot price of crude oil and gasoline. Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max. Nevertheless, the GWO is more superior that ABC as its model generates the highest accuracy for both crude oil and gasoline price. Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.

Keywords: artificial bee colony, data normalization, forecasting, Grey Wolf optimizer

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Authors: Usama Massoud, Abeer A. Kenawy, El-Said A. Ragab, Abbas M. Abbas, Heba M. El-Kosery

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Vertical Electrical Sounding (VES) and Transient Electro Magnetic (TEM) survey have been applied for characterizing the groundwater aquifers at El Sadat industrial area. El-Sadat city is one of the most important industrial cities in Egypt. It has been constructed more than three decades ago at about 80 km northwest of Cairo along the Cairo–Alexandria desert road. Groundwater is the main source of water supplies required for domestic, municipal, and industrial activities in this area due to the lack of surface water sources. So, it is important to maintain this vital resource in order to sustain the development plans of this city. In this study, VES and TEM data were identically measured at 24 stations along three profiles trending NE–SW with the elongation of the study area. The measuring points were arranged in a grid like pattern with both inter-station spacing and line–line distance of about 2 km. After performing the necessary processing steps, the VES and TEM data sets were inverted individually to multi-layer models, followed by a joint inversion of both data sets. Joint inversion process has succeeded to overcome the model-equivalence problem encountered in the inversion of individual data set. Then, the joint models were used for the construction of a number of cross sections and contour maps showing the lateral and vertical distribution of the geo-electrical parameters in the subsurface medium. Interpretation of the obtained results and correlation with the available geological and hydrogeological information revealed TWO aquifer systems in the area. The shallow Pleistocene aquifer consists of sand and gravel saturated with fresh water and exhibits large thickness exceeding 200 m. The deep Pliocene aquifer is composed of clay and sand and shows low resistivity values. The water bearing layer of the Pleistocene aquifer and the upper surface of Pliocene aquifer are continuous and no structural features have cut this continuity through the investigated area.

Keywords: El Sadat city, joint inversion, VES, TEM

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Authors: Elham Azimzadeh, Saeedeh Sepehri, Hamidollah Hassanlouei

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Obesity is associated with postural instability, might influence the quality of daily life, and could be considered a potential factor for falling in obese people. The fat body mass especially in the abdominal area may increase body sway. Furthermore, loss of visual feedback may induce a larger postural sway in obese people. Moreover, Muscle fatigue may impair the work capacity of the skeletal muscle and may alter joint proprioception. So, the purpose of this study was to investigate the effect of physical fatigue and visual feedback on body sway and strength of lower extremities in obese people. 12 obese (4 female, 8 male; BMI >30 kg/m2), and 12 normal weight (4 female, 8 male; BMI: 20-25 kg/m2) subjects aged 37- 47 years participated in this study. The postural stability test on the Biodex balance system was used to characterize postural control along the anterior-posterior (AP) and mediolateral (ML) directions in eyes open and eyes closed conditions and maximal voluntary contraction (MVC) of knee extensors and flexors were measured before and after the high-intensity exhausting exercise protocol on the ergometer bike to confirm the presence of fatigue. Results indicated that the obese group demonstrated significantly greater body sway, in all indices (ML, AP, overall) compared with the normal weight group (eyes open). However, when visual feedback was eliminated, fatigue impaired the balance in the overall and AP indicators in both groups; ML sway was higher only in the obese group after exerting the fatigue in the eyes closed condition. Also, maximal voluntary contraction of knee extensors was impaired in the fatigued normal group but, there was no significant impairment in knee flexors MVC in both group. According to the findings, peripheral fatigue was associated with altered postural control in upright standing when eyes were closed, and that mechanoreceptors of the feet may be less able to estimate the position of the body COM over the base of support in the loss of visual feedback. This suggests that the overall capability of the postural control system during upright standing especially in the ML direction could be lower due to fatigue in obese individuals and could be a predictor of future falls.

Keywords: maximal voluntary contraction, obesity, peripheral fatigue, postural control, visual feedback

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Authors: Emmanuel Kamal Aziz Saba, Gihan Abd El-Lateif Younis El-Tantawi, Mohammed Hamdy Zahran, Ibrahim Khalil Ibrahim, Mohammed Abd El-Salam Shehata, Hussein Al-Moghazy Sultan, Medhat Mohamed Anwar

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Examination of the external anal sphincter muscle strength of voluntary contraction is essential in initial assessment and assessment of efficacy of rehabilitation of patients with faecal incontinence (FI) and obstructed defecation (OD). The present study was conducted to evaluate the digital assessment of the external anal sphincter muscle strength of voluntary contraction by using Modified Oxford Scale (MOS) in comparison to anal manometry squeeze pressure. The present cross-sectional study included 65 patients. There were 40 patients (61.5 %) with FI and 25 patients (38.5 %) with OD. All patients were subjected to history taking, clinical examination including assessment of the external anal sphincter muscle strength of voluntary contraction by using MOS and anal manometry (mean squeeze pressure and maximal squeeze pressure). There was a statistically significant positive correlation between MOS and anal manometry squeeze pressures including mean squeeze pressure and maximal squeeze pressure among FI group and OD group. In conclusion, assessment of the external anal sphincter muscle strength of voluntary contraction by using MOS is a valid method and can substitute anal manometry assessment.

Keywords: anal manometry, external anal sphincter muscle, Modified Oxford Scale, muscle strength

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Authors: Mary Joy C. Montenegro, Voltaire M. Mistades

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This action research was implemented to enhance the teaching of linear motion and to improve students' conceptual understanding and engagement using a developed 7R-based module called 'module on vectors and one-dimensional kinematics' (MVOK). MVOK was validated in terms of objectives, contents, format, and language used, presentation, usefulness, and overall presentation. The validation process revealed a value of 4.7 interpreted as 'Very Acceptable' with a substantial agreement (0. 60) from the validators. One intact class of 46 Grade 12 STEM students from one of the public schools in Paranaque City served as the participants of this study. The students were taught using the module during the first semester of the academic year 2019–2020. Employing the mixed-method approach, quantitative data were gathered using pretest/posttest, activity sheets, problem sets, and survey form, while qualitative data were obtained from surveys, interviews, observations, and reflection log. After the implementation, there was a significant difference of 18.4 on students’ conceptual understanding as shown in their pre-test and post-test scores on the 24-item test with a moderate Hake gain equal to 0.45 and an effect size of 0.83. Moreover, the scores on activity and problem sets have a 'very good' to 'excellent' rating, which signifies an increase in the level of students’ conceptual understanding. There also exists a significant difference between the mean scores of students’ engagement overall (t= 4.79, p = 0.000, p < 0.05) and in the dimension of emotion (t = 2.51, p = 0.03) and participation/interaction (t = 5.75, p = 0.001). These findings were supported by gathered qualitative data. Positive views were elicited from the students since it is an accessible tool for learning and has well-detailed explanations and examples. The results of this study may substantiate that using MVOK will lead to better physics content understanding and higher engagement.

Keywords: conceptual understanding, engagement, linear motion, module

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Authors: Harshani Uggallage, Kapila D. Dissanayaka

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A Sulforhodamine-B assay-guided fractionation on Nigella sativa seeds was conducted to determine the presence of cytotoxic compounds against human hepatoma (HepG2) cells. Initially, a freeze-dried sample of Nigella sativa seeds was sequentially extracted into solvents of increasing polarities. Crude extracts from the sequential extraction of Nigella sativa seeds in chloroform and ethyl acetate showed the highest cytotoxicity. The combined mixture of these two extracts was subjected to bioassay guided fractionation using a modified Kupchan method of partitioning, followed by Sephadex® LH-20 chromatography. This chromatographic separation process resulted in a column fraction with a convincing IC50 (half-maximal inhibitory concentration) value of 13.07µg/ml, which is considerable for developing therapeutic drug leads against human hepatoma. Reversed phase High-Performance Liquid Chromatography (HPLC) was finally conducted for the same column fraction, and the result indicates the presence of one or several main cytotoxic compounds against human HepG2 cells.

Keywords: cytotoxic compounds, half-maximal inhibitory concentration, high-performance liquid chromatography, human HepG2 cells, nigella sativa seeds, Sulforhodamine-B assay

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Authors: Taehan Bae

Abstract:

In this paper, a class of length-biased Weibull mixtures is presented to model loss severity data. The proposed model generalizes the Erlang mixtures with the common scale parameter, and it shares many important modelling features, such as flexibility to fit various data distribution shapes and weak-denseness in the class of positive continuous distributions, with the Erlang mixtures. We show that the asymptotic tail estimate of the length-biased Weibull mixture is Weibull-type, which makes the model effective to fit loss severity data with heavy-tailed observations. A method of statistical estimation is discussed with applications on real catastrophic loss data sets.

Keywords: Erlang mixture, length-biased distribution, transformed gamma distribution, asymptotic tail estimate, EM algorithm, expectation-maximization algorithm

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Authors: Sammani Danwawu Abdullahi

Abstract:

Vertex Enumeration Algorithms explore the methods and procedures of generating the vertices of general polyhedra formed by system of equations or inequalities. These problems of enumerating the extreme points (vertices) of general polyhedra are shown to be NP-Hard. This lead to exploring how to count the vertices of general polyhedra without listing them. This is also shown to be #P-Complete. Some fully polynomial randomized approximation schemes (fpras) of counting the vertices of some special classes of polyhedra associated with Down-Sets, Independent Sets, 2-Knapsack problems and 2 x n transportation problems are presented together with some discovered open problems.

Keywords: counting with uncertainties, mathematical programming, optimization, vertex enumeration

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Authors: Xiuqin Ma, Hongwu Qin

Abstract:

A new efficient normal parameter reduction algorithm of soft set in decision making was proposed. However, up to the present, few documents have focused on real-life applications of this algorithm. Accordingly, we apply a New Efficient Normal Parameter Reduction algorithm into real-life datasets of online shopping, such as Blackberry Mobile Phone Dataset. Experimental results show that this algorithm is not only suitable but feasible for dealing with the online shopping.

Keywords: soft sets, parameter reduction, normal parameter reduction, online shopping

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Authors: Miguel Cañon, Darwin Mena, Ivan Cabeza

Abstract:

In this work was compared and evaluated the applicability of statistical methods for the estimation of missing precipitations data in the basin of the river Lenguazaque located in the departments of Cundinamarca and Boyacá, Colombia. The methods used were the method of simple linear regression, distance rate, local averages, mean rates, correlation with nearly stations and multiple regression method. The analysis used to determine the effectiveness of the methods is performed by using three statistical tools, the correlation coefficient (r2), standard error of estimation and the test of agreement of Bland and Altmant. The analysis was performed using real rainfall values removed randomly in each of the seasons and then estimated using the methodologies mentioned to complete the missing data values. So it was determined that the methods with the highest performance and accuracy in the estimation of data according to conditions that were counted are the method of multiple regressions with three nearby stations and a random application scheme supported in the precipitation behavior of related data sets.

Keywords: statistical comparison, precipitation data, river subbasin, Bland and Altmant

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Authors: Yasin Kutuk, Bengi Yanik Ilhan

Abstract:

Previous studies investigate the wage differentiations among regions in Turkey between couples who work in the same industry and those who work in different industries by using the models that is appropriate for cross sectional data. However, since there is no available panel data for this investigation in Turkey, pseudo panels using repeated cross-section data sets of the Household Labor Force Surveys 2004-2014 are employed in order to open a new way to examine wage differentiation patterns. For this purpose, household heads are separated into groups with respect to their household composition. These groups’ membership is assumed to be fixed over time such as age groups, education, gender, and NUTS1 (12 regions) Level. The average behavior of them can be tracked overtime same as in the panel data. Estimates using the pseudo panel data would be consistent with the estimates using genuine panel data on individuals if samples are representative of the population which has fixed composition, characteristics. With controlling the socioeconomic factors, wage differentiation of household income is affected by social, cultural and economic changes after global economic crisis emerged in US. It is also revealed whether wage differentiation is changing among the birth cohorts.

Keywords: wage income, same industry, pseudo panel, panel data econometrics

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Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon

Abstract:

In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.

Keywords: deep learning, indoor quality, metabolism, predictive model

Procedia PDF Downloads 233