Search results for: develeopmental features

Authors: Puran Chandra Joshi

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The Himalaya is the newest mountain system on this earth. This highest as well as fragile mountain system is still rising up. The tectonic activities have been experienced by this entire area, so the geomorphology of the region is affected by it. As we know, geomorphology is the study of landforms and their processes on the earth surface. These landforms are very important for human beings and other creatures on this planet. Present paper traces out the geomorphological features and their significance along Dhauli Ganga river valley in the Himalaya. Study area falls in higher Himalaya, which has experienced glacial and fluvial processes. Dhauli Ganga river is a considerable tributary of river kali, which is the part of huge Gangetic system. Dhauli originates in the form of two tributaries from valley glaciers of the southern slopes of Kumaun-Tibbet water divide. The upper catchment of this river has been carved by the glacial activity. The area of investigation is a remote regionin, Kumaun Himalaya. The native people do seasonal migration due to harsh winters. In summers, they return back with their cattle. In this season, they also grow potatoes and pulses, especiallybeanson river terraces. This study is important for making policies in the entire area. Area has witnessed big landslide in the recent past. So, the present study becomes more important.

Keywords: himalaya, geomorphology, glacial, tectonics

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Authors: Xunan Huang, Caicai Zhang

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This paper investigated the code-mixing features in Mandarin-English bilingual children in Singapore. First, it examined whether the code-mixing rate was different in Mandarin Chinese and English contexts. Second, it explored the syntactic categories of code-mixing in Singapore bilingual children. Moreover, this study investigated whether morphological information was preserved when inserting syntactic components into the matrix language. Data are derived from the Singapore Bilingual Corpus, in which the recordings and transcriptions of sixty English-Mandarin 5-to-6-year-old children were preserved for analysis. Results indicated that the rate of code-mixing was asymmetrical in the two language contexts, with the rate being significantly higher in the Mandarin context than that in the English context. The asymmetry is related to language dominance in that children are more likely to code-mix when using their nondominant language. Concerning the syntactic categories of code-mixing words in the Singaporean bilingual children, we found that noun-mixing, verb-mixing, and adjective-mixing are the three most frequently used categories in code-mixing in the Mandarin context. This pattern mirrors the syntactic categories of code-mixing in the Cantonese context in Cantonese-English bilingual children, and the general trend observed in lexical borrowing. Third, our results also indicated that English vocabularies that carry morphological information are embedded in bare forms in the Mandarin context. These findings shed light upon how bilingual children take advantage of the two languages in mixed utterances in a bilingual environment.

Keywords: bilingual children, code-mixing, English, Mandarin Chinese

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Authors: Kent Salomonsson, Xuefang Zhao, Sara Kallin

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Human soft tissue is loaded and deformed by any activity, an effect known as a stress-strain relationship, and is often described by a load and tissue elongation curve. Several advances have been made in the fields of biology and mechanics of soft human tissue. However, there is limited information available on in vivo tissue mechanical characteristics and behavior. Confident mechanical properties of human soft tissue cannot be extrapolated from e.g. animal testing. Thus, there is need for non invasive methods to analyze mechanical characteristics of soft human tissue. In the present study, the internal mechanical conditions of the lower limb, which is subject to an external load, is studied by use of the finite element method. A detailed finite element model of the lower limb is made possible by use of MRI scans. Skin, fat, bones, fascia and muscles are represented separately and the material properties for them are obtained from literature. Previous studies have been shown to address macroscopic deformation features, e.g. indentation depth, to a large extent. However, the detail in which the internal anatomical features have been modeled does not reveal the critical internal strains that may induce hypoxia and/or eventual tissue damage. The results of the present study reveals that lumped material models, i.e. averaging of the material properties for the different constituents, does not capture regions of critical strains in contrast to more detailed models.

Keywords: FEM, tissue, indentation, properties

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Authors: Meysam Saidi

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While the specific externalities and required regulatory measures in relation to Islamic banking are fairly uncertain, the business is growing across the world. Unofficial data indicate that the Islamic Finance market is growing with annual rate of 15% and it has reached 1.3 $ trillion size. This trend is associated with inherent systematic connection of Islamic financial institutions to other entities and different sectors of economies. Islamic banking has been subject of market development policies in major economies, most notably the UK. This trend highlights the need for identification of distinct risk features of Islamic banking and crafting customized regulatory measures. So far there has not been a significant systemic crisis in this market which can be attributed to its distinct nature. However, the significant growth and spread of its products worldwide necessitate an in depth study of its nature for customized congruent regulatory measures. In the post financial crisis era some market analysis and reports suggested that the Islamic banks fairly weathered the crisis. As far as heavily blamed conventional financial products such as subprime mortgage backed securities and speculative credit default swaps were concerned the immunity claim can be considered true, as Islamic financial institutions were not directly exposed to such products. Nevertheless, similar to the experience of the conventional banking industry, it can be only a matter of time for Islamic banks to face failures that can be specific to the nature of their business. Using the experience of conventional banking regulations and identifying those peculiarities of Islamic banking that need customized regulatory approach can aid to prevent major failures. Frank Knight has stated that “We perceive the world before we react to it, and we react not to what we perceive, but always to what we infer”. The debate over congruent Islamic banking regulations might not be an exception to Frank Knight’s statement but I will try to base my discussion on concrete evidences. This paper first analyzes both theoretical and actual features of Islamic banking in order to ascertain to its peculiarities in terms of market stability and other externalities. Next, the paper discusses distinct features of Islamic financial transactions and banking which might require customized regulatory measures. Finally, the paper explores how a more transparent path for the Islamic banking regulations can be drawn.

Keywords: Islamic banking, regulation, risks, capital requirements, customer protection, financial stability

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Authors: Diana Ruth Caga-Anan

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Classroom management is a critical skill but the styles are constantly evolving. It is constantly under pressure particularly in the college education level due to diversity in student profiles, modes of delivery, and marketization of higher education. This study sought to analyze the extent of implementation of classroom management practices (CMPs) of the college instructors of the Hotel, Restaurant, and Institution Management of a premier university in the Philippines. It was also determined if their length of teaching affects their classroom management style. A questionnaire with sixteen 'evidenced-based' CMPs grouped into five critical features of classroom management, adopted from the literature search of Simonsen et al. (2008), was administered to 4 instructor-respondents and to their 88 students. Weighted mean scores of each of the CMPs revealed that there were differences between the instructors’ self-scores and their students’ ratings on their implementation of CMPs. The critical feature of classroom management 'actively engage students in observable ways' got the highest mean score, corresponding to 'always' from the instructors’ self-rating and 'frequently' from their students’ ratings. However, 'use a continuum of strategies to respond to inappropriate behaviors' got the lowest scores from both the instructors and their students corresponding only to 'occasionally'. Analysis of variance showed that the only CMP affected by the length of teaching is the practice of 'prompting students to respond'. Based on the findings, some recommendations for the instructors to improve on the critical feature where they scored low are discussed and suggestions are included for future research.

Keywords: classroom management, CMPs, critical features, evidence-based classroom management practices

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Authors: Walid Moudani

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The healthcare environment is generally perceived as being information rich yet knowledge poor. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. In fact, valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, a proficient methodology for the extraction of significant patterns from the coronary heart disease warehouses for heart attack prediction, which unfortunately continues to be a leading cause of mortality in the whole world, has been presented. For this purpose, we propose to enumerate dynamically the optimal subsets of the reduced features of high interest by using rough sets technique associated to dynamic programming. Therefore, we propose to validate the classification using Random Forest (RF) decision tree to identify the risky heart disease cases. This work is based on a large amount of data collected from several clinical institutions based on the medical profile of patient. Moreover, the experts’ knowledge in this field has been taken into consideration in order to define the disease, its risk factors, and to establish significant knowledge relationships among the medical factors. A computer-aided system is developed for this purpose based on a population of 525 adults. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.

Keywords: multi-classifier decisions tree, features reduction, dynamic programming, rough sets

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Authors: Philipp Sommer, Amgad Agoub

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The construction sector significantly impacts global CO₂ emissions, particularly through the energy usage of residential buildings. To address this, various governments, including Germany's, are focusing on reducing emissions via sustainable refurbishment initiatives. This study examines the application of machine learning (ML) to estimate energy demands dynamically in residential buildings and enhance the potential for large-scale sustainable refurbishment. A major challenge in Germany is the lack of extensive publicly labeled datasets for energy performance, as energy performance certificates, which provide critical data on building-specific energy requirements and consumption, are not available for all buildings or require on-site inspections. Conversely, England and other countries in the European Union (EU) have rich public datasets, providing a viable alternative for analysis. This research adapts insights from these English datasets to the German context by developing a comprehensive data schema and calibration dataset capable of predicting building energy demand effectively. The study proposes a minimal feature set, determined through feature importance analysis, to optimize the ML model. Findings indicate that ML significantly improves the scalability and accuracy of energy demand forecasts, supporting more effective emissions reduction strategies in the construction industry. Integrating energy performance certificates into municipal heat planning in Germany highlights the transformative impact of data-driven approaches on environmental sustainability. The goal is to identify and utilize key features from open data sources that significantly influence energy demand, creating an efficient forecasting model. Using Extreme Gradient Boosting (XGB) and data from energy performance certificates, effective features such as building type, year of construction, living space, insulation level, and building materials were incorporated. These were supplemented by data derived from descriptions of roofs, walls, windows, and floors, integrated into three datasets. The emphasis was on features accessible via remote sensing, which, along with other correlated characteristics, greatly improved the model's accuracy. The model was further validated using SHapley Additive exPlanations (SHAP) values and aggregated feature importance, which quantified the effects of individual features on the predictions. The refined model using remote sensing data showed a coefficient of determination (R²) of 0.64 and a mean absolute error (MAE) of 4.12, indicating predictions based on efficiency class 1-100 (G-A) may deviate by 4.12 points. This R² increased to 0.84 with the inclusion of more samples, with wall type emerging as the most predictive feature. After optimizing and incorporating related features like estimated primary energy consumption, the R² score for the training and test set reached 0.94, demonstrating good generalization. The study concludes that ML models significantly improve prediction accuracy over traditional methods, illustrating the potential of ML in enhancing energy efficiency analysis and planning. This supports better decision-making for energy optimization and highlights the benefits of developing and refining data schemas using open data to bolster sustainability in the building sector. The study underscores the importance of supporting open data initiatives to collect similar features and support the creation of comparable models in Germany, enhancing the outlook for environmental sustainability.

Keywords: machine learning, remote sensing, residential building, energy performance certificates, data-driven, heat planning

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Authors: Omar A. Abuobidalla, Mingyuan Chen, Satyaveer S. Chauhan

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Railroad transportation of hazardous materials freights is important to the North America economics that supports the national’s supply chain. This paper introduces various extensions of the dynamic hazardous materials trip plan problems. The problem captures most of the operational features of a real-world railroad transportations systems that dynamically initiates a set of blocks and assigns each shipment to a single block path or multiple block paths. The dynamic hazardous materials trip plan policies have distinguishing features that are integrating the blocking plan, and the block activation decisions. We also present a non-linear mixed integer programming formulation for each variant and present managerial insights based on a hypothetical railroad network. The computation results reveal that the dynamic car scheduling policies are not only able to take advantage of the capacity of the network but also capable of diminishing the population, and environment risks by rerouting the active blocks along the least risky train services without sacrificing the cost advantage of the railroad. The empirical results of this research illustrate that the issue of integrating the blocking plan, and the train makeup of the hazardous materials freights must receive closer attentions.

Keywords: dynamic car scheduling, planning and scheduling hazardous materials freights, airborne hazardous materials, gaussian plume model, integrated blocking and routing plans, box model

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Authors: Sultan Mahmud, Qamar Zaman, Travis Esau, Young Chang

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Strawberry powdery mildew (PM) is a serious disease that has a significant impact on strawberry production. Field scouting is still a major way to find PM disease, which is not only labor intensive but also almost impossible to monitor disease severity. To reduce the loss caused by PM disease and achieve faster automatic detection of the disease, this paper proposes an approach for detection of the disease, based on image texture and classified with support vector machines (SVMs) and k-nearest neighbors (kNNs). The methodology of the proposed study is based on image processing which is composed of five main steps including image acquisition, pre-processing, segmentation, features extraction and classification. Two strawberry fields were used in this study. Images of healthy leaves and leaves infected with PM (Sphaerotheca macularis) disease under artificial cloud lighting condition. Colour thresholding was utilized to segment all images before textural analysis. Colour co-occurrence matrix (CCM) was introduced for extraction of textural features. Forty textural features, related to a physiological parameter of leaves were extracted from CCM of National television system committee (NTSC) luminance, hue, saturation and intensity (HSI) images. The normalized feature data were utilized for training and validation, respectively, using developed classifiers. The classifiers have experimented with internal, external and cross-validations. The best classifier was selected based on their performance and accuracy. Experimental results suggested that SVMs classifier showed 98.33%, 85.33%, 87.33%, 93.33% and 95.0% of accuracy on internal, external-I, external-II, 4-fold cross and 5-fold cross-validation, respectively. Whereas, kNNs results represented 90.0%, 72.00%, 74.66%, 89.33% and 90.3% of classification accuracy, respectively. The outcome of this study demonstrated that SVMs classified PM disease with a highest overall accuracy of 91.86% and 1.1211 seconds of processing time. Therefore, overall results concluded that the proposed study can significantly support an accurate and automatic identification and recognition of strawberry PM disease with SVMs classifier.

Keywords: powdery mildew, image processing, textural analysis, color co-occurrence matrix, support vector machines, k-nearest neighbors

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Authors: A. Hameed, N. Upreti, P. Srivastava

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Main objective the present study is to investigate microscopic, sub-microscopic, clay mineralogical and geochemical characteristics of three calcic soil profiles from the western Thar Desert for the last 30 ka paleoclimatic information. Thin-sections of the soils show weakly to moderately developed pedofeatures dominated by powdery to well-indurated pedogenic calcium carbonate. Sub-microscopy of the representative calcretes show extensive growth of fibrous palygorskite in pore spaces of micritic and sparitic nodules. XRD of the total clay ( < 2 µm) and fine clay ( < 0.2 µm) fractions of the soils show dominance of smectite, palygorskite, chlorite, mica, kaolinite and small amounts of quartz and feldspar. Formation of the palygorskite is attributed to pedogenic processes associated with Bw, Bss and Bwk horizons during drier conditions over the last 30 ka. Formation of palygorskite was mainly favoured by strongly evaporating percolating water and precipitation of secondary calcite, high pH (9-10), high Mg, Si and low Al activities during pedogenesis. Age estimate and distribution of calcretes, palygorskite, and illuvial features indicate fluctuating monsoonal strength during MIS3-MIS1 stages. The pedogenic features in calcic soils of western Thar suggest relatively arid conditions during MIS3-MIS2 transition and LGM time that changed to relatively wetter conditions during post LGM time and again returned to dry conditions at ~4 ka in MIS1.

Keywords: palygorskite, clay minerals, Thar, aridisol, late quaternary

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Authors: Kambiz Teimour Najad

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The basic methodology of GPR involves the use of a transmitting antenna to send electromagnetic waves into the subsurface, which then bounce back to the surface and are detected by a receiving antenna. The transmitter and receiver antennas are typically placed on the ground surface and moved across the area of interest to create a profile of the subsurface. The GPR system consists of a control unit that powers the antennas and records the data, as well as a display unit that shows the results of the survey. The control unit sends a pulse of electromagnetic energy into the ground, which propagates through the soil or rock until it encounters a change in material or structure. When the electromagnetic wave encounters a buried object or structure, some of the energy is reflected back to the surface and detected by the receiving antenna. The GPR data is then processed using specialized software that analyzes the amplitude and travel time of the reflected waves. By interpreting the data, GPR can provide information on the depth, location, and nature of subsurface features and structures. GPR has several advantages over other geophysical survey methods, including its ability to provide high-resolution images of the subsurface and its non-invasive nature, which minimizes disruption to the site. However, the effectiveness of GPR depends on several factors, including the type of soil or rock, the depth of the features being investigated, and the frequency of the electromagnetic waves used. In environmental hazard assessments, GPR can be used to detect buried structures, such as underground storage tanks, pipelines, or utilities, which may pose a risk of contamination to the surrounding soil or groundwater. GPR can also be used to assess soil stability by identifying areas of subsurface voids or sinkholes, which can lead to the collapse of the surface. Additionally, GPR can be used to map the extent and movement of groundwater contamination, which is critical in designing effective remediation strategies. the methodology of GPR in environmental hazard assessments involves the use of electromagnetic waves to create high of the subsurface, which are then analyzed to provide information on the depth, location, and nature of subsurface features and structures. This information is critical in identifying and mitigating environmental hazards, and the non-invasive nature of GPR makes it a valuable tool in this field.

Keywords: GPR, hazard, landslide, rock fall, contamination

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Authors: Selda Uzal Seyfi

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Goat milk is a quite important in human nutrition. In order to meet the demand to the goat and sheep milk occurring in the recent years, an increase is seen in the demand to housing projects, which will enable animals to be sheltered in the suitable environments. This study was carried out in between 2012 and 2013, in order to identify the existing cases of sheep and goat housings in the province Konya and their possibilities to be developed. In the study, in the province Konya, 25 pieces of sheep and goat farms and 46 pieces of sheep and goat housings (14 sheep housings, 3 goat housings, and 29 housings, in which both sheep and goat are bred ) that are present in the farm were investigated as material. In the study, examining the general features of the farms that are present in the region and structural features of housings that are present in the farms, it is studied whether or not they are suitable for animal breeding. As a result of the study, the barns were evaluated as insufficient in terms of barn design, although 48% of they were built after 2000. In 63% of housings examined, stocking density of resting area was below the value of 1 m2/animal and in 59% of the housings, stocking density of courtyard area was below the 2 m2/animal. Feeding length, in 57% of housings has a value of 0.30 m and below. In the region, it will be possible to obtain the desired productivity level by building new barn designs, developed in accordance with the animal behaviors and welfare. Carrying out the necessary works is an important issue in terms of country and regional economy.

Keywords: barn design, goat housing, sheep housing, structural analysis

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Authors: C. Manjula, Lilly Florence

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Software technology is developing rapidly which leads to the growth of various industries. Now-a-days, software-based applications have been adopted widely for business purposes. For any software industry, development of reliable software is becoming a challenging task because a faulty software module may be harmful for the growth of industry and business. Hence there is a need to develop techniques which can be used for early prediction of software defects. Due to complexities in manual prediction, automated software defect prediction techniques have been introduced. These techniques are based on the pattern learning from the previous software versions and finding the defects in the current version. These techniques have attracted researchers due to their significant impact on industrial growth by identifying the bugs in software. Based on this, several researches have been carried out but achieving desirable defect prediction performance is still a challenging task. To address this issue, here we present a machine learning based hybrid technique for software defect prediction. First of all, Genetic Algorithm (GA) is presented where an improved fitness function is used for better optimization of features in data sets. Later, these features are processed through Decision Tree (DT) classification model. Finally, an experimental study is presented where results from the proposed GA-DT based hybrid approach is compared with those from the DT classification technique. The results show that the proposed hybrid approach achieves better classification accuracy.

Keywords: decision tree, genetic algorithm, machine learning, software defect prediction

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Authors: Rongbo Shen, Jianhua Yao, Kezhou Yan, Kuan Tian, Cheng Jiang, Ke Zhou

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This study proposes a generative adversarial networks (GAN) framework to perform synthetic sampling in feature space, i.e., feature augmentation, to address the class imbalance problem in medical image analysis. A feature extraction network is first trained to convert images into feature space. Then the GAN framework incorporates adversarial learning to train a feature generator for the minority class through playing a minimax game with a discriminator. The feature generator then generates features for minority class from arbitrary latent distributions to balance the data between the majority class and the minority class. Additionally, a data cleaning technique, i.e., Tomek link, is employed to clean up undesirable conflicting features introduced from the feature augmentation and thus establish well-defined class clusters for the training. The experiment section evaluates the proposed method on two medical image analysis tasks, i.e., mass classification on mammogram and cancer metastasis classification on histopathological images. Experimental results suggest that the proposed method obtains superior or comparable performance over the state-of-the-art counterparts. Compared to all counterparts, our proposed method improves more than 1.5 percentage of accuracy.

Keywords: class imbalance, synthetic sampling, feature augmentation, generative adversarial networks, data cleaning

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Authors: Reshaa Alruwaili

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The primary aim of this study was to examine the hypothesis presented by Self Determination theory which suggests that autonomy impacts positively the expression of love. Other hypotheses were also examined which suggest that other variables explain the expression of love, including: dyadic adjustment (dyadic consensus, dyadic satisfaction and dyadic cohesion), couple satisfaction, age, gender, the length of marriage, number of children and attachment styles. The participants were Saudi couples, which provided the opportunity to consider the influence of Saudi culture on the expression of love. A questionnaire was employed to obtain measures of all the relevant variables, including a measure of expression of love that was built from 27 items, constituting verbal, physical and caring features, and a measure of autonomy based on three features: authorship, interest-taking and susceptibility. Data were collected from both members of 34 Saudi couples. Descriptive analysis of both expression of love and autonomy was conducted. Correlation and regression were used to assess the relationships between expression of love and autonomy and other variables. Results indicated that Saudi couples who most often express their love tend to be more than somewhat autonomous. Not much difference was found between husbands and wives in expressing love, although wives were slightly more autonomous than husbands. Expression of love was enhanced by the autonomy of the participants to a greater extent when dyadic satisfaction was controlled, since the latter was negatively correlated with autonomy and had no effect on the expression of love. Basic psychological needs, dyadic consensus and dismissive-avoidant attachment improve the expression of love, while it is decreased by the number of children.

Keywords: autonomy, determination theory, expression of love, dyadic adjustment

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Authors: L. Bendifallah, S. Doumandji, K. Louadi, S. Iserbyt, F. Acheuk

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Due to their ecological key position and diversity, plant-bee relationships constitute excellent models to understand the processes of food specialisation. The purpose of this study is to define and identify the most important species of bees foraging broadbean flowers, we estimated morphological, phonological and behavioural features. We discuss the results by considering the food specialisation level of the visitor. In the studied populations (Algiers, Algeria), visiting bees belong to four different genus: Apis, Andrena, Eucera and Xylocopa. Eucera is foraging broad beans flowers during months of April, May. The genus Andrena and Xylocopa were found on weeds after the flowering period of beans. The two species have not a preferred type of vegetation compared to Eucera. The main pollinators were generalist bees such as Apis mellifera L. and Xylocopa pubescens Spinola (Apidae), and specialist bees such Eucera numida Lep. (Apidae). The results show that no one of the studied species, neither the specialist, nor the generalist ones, share adaptative morphological or behavioural features that may improve foraging on Vicia faba. However, there is a narrow synchronisation between the daily and yearly phenologies of Eucera numida and those of V. faba. This could be an adaptation of the specialist bee to its host plant. Thus, the food specialisation of Eucera numida, as for most specialist bees, would be more linked to its adapted phenology than to an adapted morphology.

Keywords: Vicia faba, bees, pollinators, Algeria

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Authors: Aleksandr S. Ermolenko

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Allometry is an important factor of morphological integration, contributing to the organization of the phenotype and its variability. The allometric change in the shape of the hand is particularly important in primate evolution, as the hand has important taxonomic features. Some of these features are known to parts with the shape, especially the ratio of the lengths of the index and ring fingers (2d: 4d ratio). The hand is a fairly well-studied system in the context of the evolutionary development of complex morphological structures since it consists of various departments (basipodium, metapodium, acropodium) that form a single structure –autopodium. In the present study, we examined the allometric variability of acropodium. We tested the null hypothesis that there would be no difference in allometric variation between the two components. Geometric morphometry based on a procrustation of 16 two-dimensional (2D) landmarks was analyzed using multivariate shape-by-size regressions in samples from 100 people (50 men and 50 women). The results obtained show that men have significantly greater allometric variability for the ring finger (variability in the transverse axis prevails), while women have significantly greater allometric variability for the index finger (variability in the longitudinal axis prevails). The influence of the middle finger on the shape of the hand is typical for both men and women. The influence of the little finger on the shape of the hand, regardless of gender, was not revealed. The results of this study support the hypothesis that allometry contributes to the organization of variation in the human hand.

Keywords: human hand, size and shape, 2d:4d ratio, geometric morphometry

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Authors: Zdzisław Kobos, Robert Jędrys, Zbigniew Wochyński

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Motor activity and good health are sine qua non determinants of a proper practice of the profession, especially aviation. Therefore, candidates to the aviation are selected according their psychomotor ability by both specialist medical commissions. Moreover, they must past an examination of the physical fitness. During the studies in the air force academy, eye-hand coordination is formed in two stages. The future aircraft pilots besides all-purpose physical education must practice specialist training on SAGI. Training includes: looping, aerowheel, and gyroscope. Aim of the training on the above listed apparatuses is to form eye-hand coordination during the tasks in the air. Such coordination is necessary to perform various figures in the real flight. Therefore, during the education of the future pilots, determinants of the effective ways of this important parameter of the human body functioning are sought for. Several studies of the sport psychology indicate an important role of the temperament as a factor determining human behavior during the task performance and acquiring operating skills> Polish psychologist Jan Strelau refers to the basic, relatively constant personality features which manifest themselves in the formal characteristics of the human behavior. Temperament, being initially determined by the inborn physiological mechanisms, changes in the course of maturation and some environmental factors and concentrates on the energetic level and reaction characteristics in time. Objectives. This study aimed at seeking a relationship between temperamental features and eye-hand coordination formation during training on SAGI. Material and Methods: Group of 30 students of pilotage was examined in two situations. The first assessment of the eye-hand coordination level was carried out before the beginning of a 30-hour training on SAGI. The second assessment was carried out after training completion. Training lasted for 2 hours once a week. Temperament was evaluated with The Formal Characteristics of Behavior − Temperament Inventory (FCB-TI) developed by Bogdan Zawadzki and Jan Strelau. Eye-hand coordination was assessed with a computer version of the Warsaw System of Psychological Tests. Results: It was found that the training on SAGI increased the level of eye-hand coordination in the examined students. Conclusions: Higher level of the eye-hand coordination was obtained after completion of the training. Moreover, a relationship between eye-hand coordination level and selected temperamental features was statistically significant.

Keywords: temperament, eye-hand coordination, pilot, SAGI

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Authors: Yahya Toyin Muritala, Nonglaksana Kama, Ahmad Yani

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The aim of the paper is to investigate various linguistic techniques in enhancing and facilitating the acquisition of the practical knowledge of Arabic grammatical structuring among non-Arab learners of the standard classical Arabic language in non-Arabic speaking academic settings in the course of the current growth of the internationalism and cultural integration in some higher institutions. As the nature of the project requires standard investigations into the unique principal features of Arabic structurings and implications, the findings of the research work suggest some principles to follow in solving the problems faced by learners while acquiring grammatical aspects of Arabic language. The work also concentrates on the the structural features of the language in terms of inflection/parsing, structural arrangement order, functional particles, morphological formation and conformity etc. Therefore, grammatical aspect of Arabic which has gone through major stages in its early evolution of the classical stages up to the era of stagnation, development and modern stage of revitalization is a main subject matter of the paper as it is globally connected with communication and religion of Islam practiced by millions of Arabs and non-Arabs nowadays. The conclusion of the work shows new findings, through the descriptive and analytical methods, in terms of teaching language for the purpose of effective global communication with focus on methods of second language acquisitions by application.

Keywords: language structure, Arabic grammar, classical Arabic, intercultural communication, non-Arabic speaking environment and prospects

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Authors: Ashfaq Khan, Aftab Khan, Mushtaq Khan, Sarem Sattar, Mohammad A Sheikh, Lin Li

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Chip removal processes are one of key processes of the manufacturing industry where chip removal is conducted by tool inserts of exceptionally hard materials. Tungsten carbide has been extensively used as tool insert for machining processes involving chip removal processes. These hard materials are generally fabricated by single step sintering process as further modification after fabrication in these materials cannot be done easily. Advances in tool surface modification have revealed that advantages such as improved tribological properties and extended tool life can be harnessed from the same tool by texturing the tool rake surface. Moreover, it has been observed that the shape and location of the texture also influences the behavior. Although texturing offers plentiful advantages the challenge lies in the generation of textures on the tool surface. Extremely hard material such as diamond is required to process tungsten carbide. Laser is unique processing tool that does not have a physical contact with the material and thus does not wear. In this research the potential of utilizing laser for texturing of tungsten carbide to develop custom features would be studied. A parametric study of texturing of Tungsten Carbide with a femtosecond laser would be conducted to investigate the process parameters and establish the feasible processing window. The effect of fluence, scan speed and number of repetition would be viewed in detail. Moreover, the mechanism for the generation of features would also be reviewed.

Keywords: laser, texturing, femtosecond, tungsten carbide

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Authors: Justyna Skrzyńska, Zdzisław Kobos, Zbigniew Wochyński

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The assessment of characterologic type is an essential element which decides about the proper task performance in the Air Forces. The aim of the research was to specify the percentage distribution of characterologic features by cadets studying particular courses in Polish Air Force Academy with the use of questionnaire. 34 first-year cadets chosen by lot and disunited into aircrafts pilots (N-10), helicopter pilots (N-13) and navigators(N-11) participated in the research. All of the questioned have had their psychomotor education examined in Military Aviation Medicine Institute in Warsaw, Poland. Moreover all of them are characterised by very good fitness. In the research, an anonymous poll(based on Myers-Briggs Type Indicator) appraising cadets’ characterologic type has been used. Cadets were provided with the same accommodation and nutrition. The findings have shown that percentage distribution was diversified, however it could be distinctly observed that most of future helicopter pilots (69%) are introverts whereas the majority of aircrafts pilots (70%) and navigators (100%) are extraverts. Moreover, it was also observed that 70% of cadets studying aircrafts pilotage run regular lifestyle and have judging skill according to Myers-Briggs Type Indicator. In future navigators group, 73% of students do not have this characteristic. The research has shown that cadets studying pilotage are more likely to demonstrate the characteristics which are essential for a performance of the important tasks in pilots environment than the cadets studying navigation.

Keywords: pilot, Myers-Briggs Type indicator, questionnaire research, cadets, psychomotor education

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Authors: Isaac Owusu Asante, Yushi Jiang, Hailin Tao

Abstract:

Live streaming marketing, the new electronic commerce element, became an optional marketing channel following the COVID-19 pandemic. Many sellers have leveraged the features presented by live streaming to increase sales. Studies on live streaming have focused on gaming and consumers’ loyalty to brands through live streaming, using interview questionnaires. This study, however, was conducted to measure real-time observable interactions between consumers and sellers. Based on the affordance theory, this study conceptualized constructs representing the interactive features and examined how they drive consumers’ purchase willingness during live streaming sessions using 1238 datasets from Amazon Live, following the manual observation of transaction records. Using structural equation modeling, the ordinary least square regression suggests that live viewers, new followers, live chats, and likes positively affect purchase willingness. The Sobel and Monte Carlo tests show that new followers, live chats, and likes significantly mediate the relationship between live viewers and purchase willingness. The study introduces a new way of measuring interactions in live streaming commerce and proposes a way to manually gather data on consumer behaviors in live streaming platforms when the application programming interface (API) of such platforms does not support data mining algorithms.

Keywords: livestreaming marketing, live chats, live viewers, likes, new followers, purchase willingness

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Authors: Shreeyam, Ranjan Kumar Sah, Shivangi

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Recognizing and solving handwritten mathematical expressions can be a challenging task, particularly when certain characters are segmented and classified. This project proposes a solution that uses Convolutional Neural Network (CNN) and image processing techniques to accurately solve various types of equations, including arithmetic, quadratic, and trigonometric equations, as well as logical operations like logical AND, OR, NOT, NAND, XOR, and NOR. The proposed solution also provides a graphical solution, allowing users to visualize equations and their solutions. In addition to equation solving, the platform, called CNNCalc, offers a comprehensive learning experience for students. It provides educational content, a quiz platform, and a coding platform for practicing programming skills in different languages like C, Python, and Java. This all-in-one solution makes the learning process engaging and enjoyable for students. The proposed methodology includes horizontal compact projection analysis and survey for segmentation and binarization, as well as connected component analysis and integrated connected component analysis for character classification. The compact projection algorithm compresses the horizontal projections to remove noise and obtain a clearer image, contributing to the accuracy of character segmentation. Experimental results demonstrate the effectiveness of the proposed solution in solving a wide range of mathematical equations. CNNCalc provides a powerful and user-friendly platform for solving equations, learning, and practicing programming skills. With its comprehensive features and accurate results, CNNCalc is poised to revolutionize the way students learn and solve mathematical equations. The platform utilizes a custom-designed Convolutional Neural Network (CNN) with image processing techniques to accurately recognize and classify symbols within handwritten equations. The compact projection algorithm effectively removes noise from horizontal projections, leading to clearer images and improved character segmentation. Experimental results demonstrate the accuracy and effectiveness of the proposed solution in solving a wide range of equations, including arithmetic, quadratic, trigonometric, and logical operations. CNNCalc features a user-friendly interface with a graphical representation of equations being solved, making it an interactive and engaging learning experience for users. The platform also includes tutorials, testing capabilities, and programming features in languages such as C, Python, and Java. Users can track their progress and work towards improving their skills. CNNCalc is poised to revolutionize the way students learn and solve mathematical equations with its comprehensive features and accurate results.

Keywords: AL, ML, hand written equation solver, maths, computer, CNNCalc, convolutional neural networks

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Authors: Taerim Kim, Shin Ahn, Chang Hwan Sohn, Dong Woo Seo, Won Young Kim

Abstract:

Object: Reversible cerebral vasospasm syndrome (RCVS) remains an underrated cause of thunderclap headache which shares similar history of the ‘worst-ever’ headache with subarachnoid hemorrhage (SAH) to the emergency physicians. This study evaluated the clinical manifestations, radiological features, and outcomes of patients with RCVS so that the physicians could raise the high index of suspicion to detect RCVS in more patients with thunderclap headache before having life-threatening complications. Methods: The electric medical records of 18 patients with diagnostic criteria of RCVS at the emergency department (ED) between January 2013 and December 2014 were retrospective reviewed. Results: The mean age was 50.7 years, and 80% were women. Patients with RCVS visit an average of 4.7 physicians before receiving an accurate diagnosis and mean duration of symptom until diagnosis is 9.3 days. All patients except one experienced severe headache, from 8 to 10 pain intensity on a numerical rating scale (NRS). 44% of patients had nausea as an associated symptom, 66% of patients experienced worsening of headache while gagging, leaning forward, defecating, urinating or having sex. The most frequently affected vessels are middle cerebral arteries demonstrating the characteristic diffuse “string of beads” appearance. Four patients had SAH as a complication. Conclusion: Patients with RCVS have a unique set of clinical and imaging features. Emergency physicians should raise the high index of suspicion to detect RCVS in more patients with thunderclap headache before life-threatening complications.

Keywords: headache, thunderclap, subarachnoid haemorrhage, stroke

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Authors: Stelios A. Mitilineos, Nicolas-Alexander Tatlas, Georgia Korompili, Lampros Kokkalas, Stelios M. Potirakis

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Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) is a widespread chronic disease that mostly remains undetected, mainly due to the fact that it is diagnosed via polysomnography which is a time and resource-intensive procedure. Screening the disease’s symptoms at home could be used as an alternative approach in order to alert individuals that potentially suffer from OSAHS without compromising their everyday routine. Since snoring is usually linked to OSAHS, developing a snore detector is appealing as an enabling technology for screening OSAHS at home using ubiquitous equipment like commodity microphones (included in, e.g., smartphones). In this context, this study developed a snore detection tool and herein present the approach and selection of specific sound features that discriminate snoring vs. environmental sounds, as well as the performance of the proposed tool. Furthermore, a Real-Time Snore Detector (RTSD) is built upon the snore detection tool and employed in whole-night sleep sound recordings resulting to a large dataset of snoring sound excerpts that are made freely available to the public. The RTSD may be used either as a stand-alone tool that offers insight to an individual’s sleep quality or as an independent component of OSAHS screening applications in future developments.

Keywords: obstructive sleep apnea hypopnea syndrome, apnea screening, snoring detection, machine learning, neural networks

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Authors: Sarah Hamdan Al-Jahdali, Khaled Alsaad, Abdullah Al-Sayyari

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Objectives: To study the prevalence, clinicopathological features, risk factors and outcome of biopsy proven polyoma (BK) virus infection among Saudi kidney transplant recipients and compare them to negative BK virus group. Methods: We retrospectively reviewed the charts of all the patients with biopsy-proven polyoma (BK) virus infection in King Abdulaziz Medical City in Riyadh between 2005 and 2011. The details of clinical presentation, the indication for kidney biopsy, the laboratory findings at presentation, the natural history of the disease, thepathological findings, the prognosis as well as the response to therapy were all recorded. Results: Kidney biopsy was performed in 37 cases of unexplained graft dysfunction. BK virus was found in 10 (27%). Out of those 10, 3 (30%) ended with graft failure. BK virus occurred in all patients who received ATG induction therapy 100% versus 59.3% in the non BK virus patients (p=0.06). Furthermore, the risk of BK virus was much less in those who received acyclovir as an anti-viral prophylaxis as compared to those who did not receive it (p=0.01). Also, patients with BK virus weighed much less (mean 46.7±20.6 Kgs) than those without BK virus at time of transplantation (mean 64.3±12.1). Graft survival was better among deceased donor kidneys compared to living ones (P=0.016) and with older age (P=0.005). Conclusion: Our findings suggest the involvement of ATG induction therapy, the lack of antiviral prophylaxis therapy and lower weight at transplant as significant risk factors for the development of BK virus infection.

Keywords: BKVAN, BKV, kidney transpant, Saudi Arabia

Procedia PDF Downloads 276

Authors: U. Borasi, Amit Kr. Jain, Rakesh, Piyush Jain

Abstract:

Data collection, data preparation, model training, model evaluation, and deployment are all processes in a typical machine learning workflow. Training data needs to be gathered and organised. This often entails collecting a sizable dataset and cleaning it to remove or correct any inaccurate or missing information. Preparing the data for use in the machine learning model requires pre-processing it after it has been acquired. This often entails actions like scaling or normalising the data, handling outliers, selecting appropriate features, reducing dimensionality, etc. This pre-processed data is then used to train a model on some machine learning algorithm. After the model has been trained, it needs to be assessed by determining metrics like accuracy, precision, and recall, utilising a test dataset. Every time a new model is built, both data pre-processing and model training—two crucial processes in the Machine learning (ML) workflow—must be carried out. Thus, there are various Machine Learning algorithms that can be employed for every single approach to data pre-processing, generating a large set of combinations to choose from. Example: for every method to handle missing values (dropping records, replacing with mean, etc.), for every scaling technique, and for every combination of features selected, a different algorithm can be used. As a result, in order to get the optimum outcomes, these tasks are frequently repeated in different combinations. This paper suggests a simple architecture for organizing this largely produced “combination set of pre-processing steps and algorithms” into an automated workflow which simplifies the task of carrying out all possibilities.

Keywords: machine learning, automation, AUTOML, architecture, operator pool, configuration, scheduler

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Authors: Yen-Fen Tseng

Abstract:

Studying migration regimes enables one to identify clusters of countries with policy features in common. A few researchers have pointed out the origin of hardship experienced by foreign workers in Taiwan, Japan, and South Korea, stems from their exclusionary migration regime. This paper aims to understand the causes and consequences of the East Asia migration regime, exploring the common exclusionary policies features of Taiwan, Japan, and South Korea, focusing on the foreign labor policy. It will then present explanations as to factors shaping migration regime; the perspective of factors within political system is adopted, as opposed to political economy and pluralist society approach. In the minds of political elites across East Asia, there exists a powerful belief in mono-ethnicity, namely, the benefits of mono-ethnicity and the social ill of “minority problems”. Guest workers policies of various alterations become the compromise between the want for foreign labor and the desire to maintain mono-ethnicity. The paper discusses the absence of immigrant settlement and formation of ethnic communities as a result of the reluctant hosts. Migrant workers in these societies commonly suffer from irregular working conditions as well as unprotected rights out of their denied legality. The case of Taiwan will be presented with greater details, drawing on data from both first-hand and secondary sources.

Keywords: migration regime, guest worker policies, East Asia, society

Procedia PDF Downloads 377

Authors: Sadia Idrees, Gianpaolo Vignali, Simeon Gill

Abstract:

The study aims to establish the virtual size and fit technology features to enhance fashion online retailing platforms, utilising digital human measurements to provide customised style and function to consumers. A few firms in the UK have launched advanced interactive fashion shopping domains for personalised shopping globally, aided by the latest internet technology. Virtual size and fit interfaces have a great potential to provide a personalised better-fitted garment to promote mass customisation globally. Made-to-measure clothing, consuming unstitched fabric is a common practice offered by fashion brands in Pakistan. This product is regarded as economical and sustainable to be utilised by consumers in Pakistan. Although the manual sizing system is practiced to sell garments online, virtual size and fit visualisation and recommendation technologies are uncommon in Pakistani fashion interfaces. A comparative assessment of Pakistani fashion brand websites and UK technology-driven fashion interfaces was conducted to highlight the vast potential of the virtual size and fit technology. The results indicated that web 2.0 technology adopted by Pakistani apparel brands has limited features, whereas companies practicing web 3.0 technology provide interactive online real-store shopping experience leading to enhanced customer satisfaction and globalisation of brands.

Keywords: e-commerce, mass customization, virtual size and fit, web 3.0 technology

Procedia PDF Downloads 134

Authors: Abubakar Ibrahim, Muhammad Garba, Adelusi Oluwaseyi Abiodun

Abstract:

Website development and administration has already become a very critical issue in many organisations due to the fact that most of the organisations have embraced the use of the internet to deliver their services and products seamlessly but even with huge advantages of being present on the internet, and website are very difficult and expensive to develop and maintain. In recent years, a number of open-source web Contents Management System (CMS) have been developed to allow organisations to internally develop and maintain their websites without the need to hire professional web developers to provide such services for them. This study aimed at performing a comparative analysis of the two most widely used open source CMS Joomla and wordpress, based on the following criteria: intuitiveness, responsiveness richness in features, meeting expectation, fill secured, ease of navigation, structure, and performance. Two identical applications were developed using the said CMS. In this study, a purposive sampling technique was adopted to administer the questionnaires, and a total of 50 respondents were selected to surf sites and fill out a questionnaire based on their experience on the two sites. Gt-matrix was used to carry out further analysis of the applications. The result shows that Joomla is the best for developing an e-commerce site due to the fact that it is best in terms of performance, better structure, meeting user expectations, rich features, and functionality. Even though Wordpress is intuitive and easy for navigation. One can still argue that Joomla is superior.

Keywords: open source, content management system, Joomla, WordPress

Procedia PDF Downloads 51