Search results for: human machine interface

Authors: H.-H. Doh, J.-M. Yu, Y.-J. Kwon, J.-H. Shin, H.-W. Kim, S.-H. Nam, D.-H. Lee

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This paper suggests a decision tree based approach for flexible job shop scheduling with multiple process plans, i. e. each job can be processed through alternative operations, each of which can be processed on alternative machines. The main decision variables are: (a) selecting operation/machine pair; and (b) sequencing the jobs assigned to each machine. As an extension of the priority scheduling approach that selects the best priority rule combination after many simulation runs, this study suggests a decision tree based approach in which a decision tree is used to select a priority rule combination adequate for a specific system state and hence the burdens required for developing simulation models and carrying out simulation runs can be eliminated. The decision tree based scheduling approach consists of construction and scheduling modules. In the construction module, a decision tree is constructed using a four-stage algorithm, and in the scheduling module, a priority rule combination is selected using the decision tree. To show the performance of the decision tree based approach suggested in this study, a case study was done on a flexible job shop with reconfigurable manufacturing cells and a conventional job shop, and the results are reported by comparing it with individual priority rule combinations for the objectives of minimizing total flow time and total tardiness.

Keywords: flexible job shop scheduling, decision tree, priority rules, case study

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Authors: G. Tamilpavai, C. Vishnuppriya

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Bioinformatics is an active research area which combines biological matter as well as computer science research. The longest common subsequence (LCSS) is one of the major challenges in various bioinformatics applications. The computation of the LCSS plays a vital role in biomedicine and also it is an essential task in DNA sequence analysis in genetics. It includes wide range of disease diagnosing steps. The objective of this proposed system is to find the longest common subsequence which presents in a normal and various disease affected human DNA sequence using Self Organizing Map (SOM) and LCSS. The human DNA sequence is collected from National Center for Biotechnology Information (NCBI) database. Initially, the human DNA sequence is separated as k-mer using k-mer separation rule. Mean and median values are calculated from each separated k-mer. These calculated values are fed as input to the Self Organizing Map for the purpose of clustering. Then obtained clusters are given to the Longest Common Sub Sequence (LCSS) algorithm for finding common subsequence which presents in every clusters. It returns nx(n-1)/2 subsequence for each cluster where n is number of k-mer in a specific cluster. Experimental outcomes of this proposed system produce the possible number of longest common subsequence of normal and disease affected DNA data. Thus the proposed system will be a good initiative aid for finding disease causing sequence. Finally, performance analysis is carried out for different DNA sequences. The obtained values show that the retrieval of LCSS is done in a shorter time than the existing system.

Keywords: clustering, k-mers, longest common subsequence, SOM

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Authors: A. Degale Desta, Cheng Jian

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Deep learning application in computer vision is rapidly advancing, giving it the ability to monitor the public and quickly identify potentially anomalous behaviour from crowd scenes. Therefore, the purpose of the current work is to improve the performance of safety of people in crowd events from panic behaviour through introducing the innovative idea of Aggregation of Ensembles (AOE), which makes use of the pre-trained ConvNets and a pool of classifiers to find anomalies in video data with packed scenes. According to the theory of algorithms that applied K-means, KNN, CNN, SVD, and Faster-CNN, YOLOv5 architectures learn different levels of semantic representation from crowd videos; the proposed approach leverages an ensemble of various fine-tuned convolutional neural networks (CNN), allowing for the extraction of enriched feature sets. In addition to the above algorithms, a long short-term memory neural network to forecast future feature values and a handmade feature that takes into consideration the peculiarities of the crowd to understand human behavior. On well-known datasets of panic situations, experiments are run to assess the effectiveness and precision of the suggested method. Results reveal that, compared to state-of-the-art methodologies, the system produces better and more promising results in terms of accuracy and processing speed.

Keywords: action recognition, computer vision, crowd detecting and tracking, deep learning

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Authors: Rahul Paul, Kedar Nath Das

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The field of machine learning (ML) is experiencing rapid development, resulting in a multitude of theoretical advancements and extensive practical implementations across various disciplines. The objective of ML is to facilitate the ability of machines to perform cognitive tasks by leveraging knowledge gained from prior experiences and effectively addressing complex problems, even in situations that deviate from previously encountered instances. Reinforcement Learning (RL) has emerged as a prominent subfield within ML and has gained considerable attention in recent times from researchers. This surge in interest can be attributed to the practical applications of RL, the increasing availability of data, and the rapid advancements in computing power. At the same time, optimization algorithms play a pivotal role in the field of ML and have attracted considerable interest from researchers. A multitude of proposals have been put forth to address optimization problems or improve optimization techniques within the domain of ML. The necessity of a thorough examination and implementation of optimization algorithms within the context of ML is of utmost importance in order to provide guidance for the advancement of research in both optimization and ML. This article provides a comprehensive overview of the application of metaheuristic evolutionary optimization algorithms in conjunction with RL to address a diverse range of scientific challenges. Furthermore, this article delves into the various challenges and unresolved issues pertaining to the optimization of RL models.

Keywords: machine learning, reinforcement learning, loss function, evolutionary optimization techniques

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Authors: Anukriti Verma, Bhawna Rathi, Shivani Sharda

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Reactive Arthritis (ReA) is a disorder that causes inflammation in joints due to certain infections at distant sites in the body. ReA begins with stiffness, pain, and inflammation in these areas especially the ankles, knees, and hips. It gradually causes several complications such as conjunctivitis in the eyes, skin lesions in hand, feet and nails and ulcers in the mouth. Nowadays the diagnosis of ReA is based upon a differential diagnosis pattern. The parameters for differentiating ReA from other similar disorders include physical examination, history of the patient and a high index of suspicion. There are no standard lab tests or markers available for ReA hence the early diagnosis of ReA becomes difficult and the chronicity of disease increases with time. It is reported that enteric disorders such as Inflammatory Bowel Disease (IBD) that is inflammation in gastrointestinal tract namely Crohn’s Disease (CD) and Ulcerative Colitis (UC) are reported to be linked with ReA. Several microorganisms are found such as Campylobacter, Salmonella, Shigella and Yersinia causing IBD leading to ReA. The aim of our study was to perform the in-silico analysis in order to find interactions between microorganisms and human host causing IBD leading to ReA. A systems biology approach for metabolic network reconstruction and simulation was used to find the essential genes of the reported microorganisms. Interactomics study was used to find the interactions between the pathogen genes and human host. Genes such as nhaA (pathogen), dpyD (human), nagK (human) and kynU (human) were obtained that were analysed further using the functional, pathway and network analysis. These genes can be used as putative drug targets and biomarkers in future for early diagnosis, prevention, and treatment of IBD leading to ReA.

Keywords: drug targets, inflammatory bowel disease, reactive arthritis, systems biology

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Authors: Amina Ben Abla, Sylvie Changotade, Geraldine Rohman, Guilhem Boeuf, Cyrine Dridi, Ahmed Elmarjou, Florence Dufour, Didier Lutomski, Abdellatif Elm’semi

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Understanding cell adhesion and interaction with the extracellular matrix is essential for biomedical and biotechnological applications, including the development of biomaterials. In recent years, numerous biomaterials have emerged and were used in the field of tissue engineering. Nevertheless, the lack of interaction of biomaterials with cells still limits their bio-integration. Thus, the design of bioactive biomaterials to improve cell attachment and proliferation is of growing interest. In this study, bio-functionalized material was developed combining a synthetic polymer scaffold surface with selected domains of type III human fibronectin (FNIII-DOM) to promote cell adhesion and proliferation. Bioadhesive ligand includes cell-binding domains of human fibronectin, a major ECM protein that interacts with a variety of integrins cell-surface receptors, and ECM proteins through specific binding domains were engineered. FNIII-DOM was produced in bacterial system E. coli in 5L fermentor with a high yield level reaching 20mg/L. Bioactivity of the produced fragment was validated by studying cellular adhesion of human cells. The adsorption and immobilization of FNIII-DOM onto the polymer scaffold were evaluated in order to develop an innovative biomaterial.

Keywords: biomaterials, cellular adhesion, fibronectin, tissue engineering

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Authors: Azizah Hashim, Rohana Ngah

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Transformation of Economic Development is aimed to transform Malaysia to become a high-income developed nation with a knowledge-based economy by 2020. To achieve this national agenda, the country needs to further strengthen its economic development, growth and well-being. Therefore, this study aspires to examine the relationship between market orientation, human resource management and adoption of information communication technology and SMEs performance and cash management as a mediator. This study will employ quantitative approaches. Questionnaires will be distributed to managers and owners in service sectors. The data collected will be analyzed using SPSS and Structural Equation Modelling. Resource Based Theory (RBT) adopts as an integral part of management literature that explains the performance of organizations through building resources and implement of their strategies.

Keywords: small medium enterprises (SMEs), market orientation, human resource management, adoption of information communication technology

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Authors: Angela Maria Fasnacht

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Detection of anomalies due to contaminants’ presence, also known as early detection systems in water treatment plants, has become a critical point that deserves an in-depth study for their improvement and adaptation to current requirements. The design of these systems requires a detailed analysis and processing of the data in real-time, so it is necessary to apply various statistical methods appropriate to the data generated, such as Spearman’s Correlation, Factor Analysis, Cross-Correlation, and k-fold Cross-validation. Statistical analysis and methods allow the evaluation of large data sets to model the behavior of variables; in this sense, statistical treatment or analysis could be considered a vital step to be able to develop advanced models focused on machine learning that allows optimized data management in real-time, applied to early detection systems in water treatment processes. These techniques facilitate the development of new technologies used in advanced sensors. In this work, these methods were applied to identify the possible correlations between the measured parameters and the presence of the glyphosate contaminant in the single-pass system. The interaction between the initial concentration of glyphosate and the location of the sensors on the reading of the reported parameters was studied.

Keywords: glyphosate, emergent contaminants, machine learning, probes, sensors, predictive

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Authors: Jacqueline Miller, Dongyuan Y. Wang, F. Dan Richard

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Before trying to make an ethical machine that holds a higher ethical standard than humans, a better understanding of human moral standards that could be used as a guide is crucial. How humans make decisions in dangerous driving situations like moral dilemmas can contribute to developing acceptable ethical principles for autonomous vehicles (AVs). This study uses a driving simulator to investigate whether drivers make utilitarian choices (choices that maximize lives saved and minimize harm) in unavoidable automobile accidents (moral dilemmas) with time pressure manipulated. This study also investigates how impulsiveness influences drivers’ behavior in moral dilemmas. Manipulating time pressure results in collisions that occur at varying time intervals (4 s, 5 s, 7s). Manipulating time pressure helps investigate how time pressure may influence drivers’ response behavior. Thirty-one undergraduates participated in this study using a STISM driving simulator to respond to driving moral dilemmas. The results indicated that the percentage of utilitarian choices generally increased when given more time to respond (from 4 s to 7 s). Additionally, participants in vehicle scenarios preferred responding right over responding left. Impulsiveness did not influence utilitarian choices. However, as time pressure decreased, response time increased. Findings have potential implications and applications on the regulation of driver assistance technologies and AVs.

Keywords: time pressure, automobile moral dilemmas, impulsiveness, reaction time

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Authors: Bruno Henrique Rocha Fernandes, Amir Rezaee, Jucelia Appio

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The relation between Human Resource Management (HRM) practices and organizational performance remains the subject of substantial literature. Though many studies demonstrated positive relationship, still major influencing variables are not yet clear. This study considers the Person-Environment Fit (PE Fit) and its components, Person-Supervisor (PS), Person-Group (PG), Person-Organization (PO) and Person-Job (PJ) Fit, as possible explanatory variables. We analyzed PE Fit as a moderator between HRM practices and financial performance in the “best companies to work” in Brazil. Data from HRM practices were classified through the High Performance Working Systems (HPWS) construct and data on PE-Fit were obtained through surveys among employees. Financial data, consisting of return on invested capital (ROIC) and price earnings ratio (PER) were collected for publicly traded best companies to work. Findings show that PO Fit and PJ Fit play a significant moderator role for PER but not for ROIC.

Keywords: financial performance, human resource management, high performance working systems, person-environment fit

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Authors: Damião Sousa, Hasan Turkez, Ozlem Tozlu, Tamires Lima

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Glioblastoma (GBM) is an aggressive cancer from the brain and with high prevalence and significant morbimortality. Therefore, it is necessary to investigate new therapeutic options against this pathology. Thus, the purpose of this study was to evaluate the antitumor activity from Mentha crispa essential oil (MCEO), its major constituent rotundifolone (ROT) and a series of six analogues on human U87MG glioblastoma cell line. The antitumor effects of the compounds on human U87MG-GBM cell line were assessed using in vitro cell viability assays. In addition, biosafety tests were performed on cultured human blood cells. The data show that MCEO, 1,2-perillaldehyde epoxide (EPER1) and perillaldehyde (PALD) were the most cytotoxic compounds against the U87MG cells, with IC50 values of 16.263, 15.087 and 14.888 μg/mL, respectively. The treatment with MCEO, EPER1 and PALD did not lead to damage in blood cells. These chemical analogues may be useful as prototypes for development of novel antitumor drugs due to their promising activities and toxicological safety.

Keywords: antitumor activity, cancer, natural products, terpenes

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Authors: Mohd Fitri Mansor, Noor Hidayah Abu, Hussen Nasir

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Human capital is one of valuable assets to the organization in order to sustain organization performance and to achieve both employees and employer objectives. The aim of the study is to examine the powerful of both Human Resource practices (i.e. Training & Development and Compensation & Rewards) towards sustaining SME’s performance. The objectives of the current study are to examine the relationship between training and development as well as compensation and rewards in sustaining Malaysian SME’s performance. Finally, is to identify the strongest variable contribute to the sustainability of SMEs performance. The result from 80 Malaysian SME’s owners found that both variables training & development and compensation & rewards significantly contributes to the sustainability of SME,s performance. Meanwhile, the strongest variable contributes to the sustainability of SMEs performance was training and development. The study contributes to the knowledge and awareness to the SME’s owners an important or the powerful of human resource practices in sustaining their organization performance.

Keywords: training and development, compensation and rewards, sustainability, SME’s performance

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Authors: Sawarni Hasibuan, Rudi Effendi Listyanto

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The industrial sector is the largest consumer of energy consumption in Indonesia. The ceramics industry includes one of seven industries categorized as an energy-intensive industry. Energy costs on the ceramic floor production process reached 40 percent of the total production cost. The kiln is one of the machines in the ceramic industry that consumes the most gas energy reach 51 percent of gas consumption in ceramic production. The purpose of this research is to make improvement of energy consumption in kiln machine part with the innovation of burner tube to support the sustainability of Indonesian ceramics industry. The tube burner is technically designed to be able to raise the temperature and stabilize the air pressure in the burner so as to facilitate the combustion process in the kiln machine which implies the efficiency of gas consumption required. The innovation of the burner tube also has an impact on the decrease of the combustion chamber pressure in the kiln and managed to keep the pressure of the combustion chamber according to the operational standard of the kiln; consequently, the smoke fan motor power can be lowered and the kiln electric energy consumption is also more efficient. The innovation of burner tube succeeded in saving consume of gas and electricity respectively by 0.0654 GJ and 1,693 x 10-3 GJ for every ton of ceramics produced. Improvement of this energy consumption not only implies the cost savings of production but also supports the sustainability of the Indonesian ceramics industry.

Keywords: sustainable ceramic industry, burner tube, kiln, energy efficiency

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Authors: Yan Cheng, Yijun Shao, James Rudolph, Charlene R. Weir, Beth Sahlmann, Qing Zeng-Treitler

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Supervised predictive models require labeled data for training purposes. Complete and accurate labeled data, i.e., a ‘gold standard’, is not always available, and imperfectly labeled data may need to serve as an alternative. An important question is if the accuracy of the labeled data creates a performance ceiling for the trained model. In this study, we trained several models to recognize the presence of delirium in clinical documents using data with annotations that are not completely accurate (i.e., weakly-supervised learning). In the external evaluation, the support vector machine model with a linear kernel performed best, achieving an area under the curve of 89.3% and accuracy of 88%, surpassing the 80% accuracy of the training sample. We then generated a set of simulated data and carried out a series of experiments which demonstrated that models trained on imperfect data can (but do not always) outperform the accuracy of the training data, e.g., the area under the curve for some models is higher than 80% when trained on the data with an error rate of 40%. Our experiments also showed that the error resistance of linear modeling is associated with larger sample size, error type, and linearity of the data (all p-values < 0.001). In conclusion, this study sheds light on the usefulness of imperfect data in clinical research via weakly-supervised learning.

Keywords: weakly-supervised learning, support vector machine, prediction, delirium, simulation

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Authors: R. Kanthavel, R. Yogesh Kumar, T. Narendrakumar, B. Santhosh, S. Surya Prakash

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This paper primarily focuses on developing an affordable personal assistant and the implementation of it in the field of Artificial Intelligence (AI) to create a virtual assistant/friend. The problem in existing home automation techniques is that it requires the usage of exact command words present in the database to execute the corresponding task. Our proposed work is ATOM a.k.a ‘Artificial intelligence Talking Omnipresent Machine’. Our inspiration came from an unlikely source- the movie ‘Iron Man’ in which a character called J.A.R.V.I.S has omnipresence, and device controlling capability. This device can control household devices in real time and send the live information to the user. This device does not require the user to utter the exact commands specified in the database as it can capture the keywords from the uttered commands, correlates the obtained keywords and perform the specified task. This ability to compare and correlate the keywords gives the user the liberty to give commands which are not necessarily the exact words provided in the database. The proposed work has a higher flexibility (due to its keyword extracting ability from the user input) comparing to the existing work Intelligent Home automation System (IHAS), is more accurate, and is much more affordable as it makes use of WI-FI module and raspberry pi 2 instead of ZigBee and a computer respectively.

Keywords: home automation, speech recognition, voice control, personal assistant, artificial intelligence

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Authors: Anna Maria Re

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Taking advantage of the ecolinguistic theoretical and practical analysis, the work intends the prophetic, punctual, and at times disturbing language used by David Quammen in Spillover, questioning it from an ecological perspective and contributing to the search for new stories. In the famous volume, the author illustrates a literary history of the great epidemics and pandemics, demonstrating that viruses are nature's inevitable response to man's assault on ecosystems. In doing so, he introduces new words, which have tamed our anxieties in recent years since writing as a human artistic expression can mirror the human conscience. Writing in the Anthropocene, coining a new reference lexicon with respect to what is happening, means offering a form to the idea of survival of the planet, imagining the human being grappling with an environment whose conformation he himself has helped to change with a language that is no longer effective in describing the world as we have known it and that quickly needs a radical overhaul. Following the methodology proposed in Ecolinguistics: language, ecology and the stories we live by, the analysis in the paper will enhance the language that encodes new stories based on: ideologies, framings, metaphors, evaluations, identities, convictions, and salience.

Keywords: Anthropocene, pandemic, spillover, virus, zoonosis

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Authors: Diane H. Dotson, Shari L. Rodriguez

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Habitat for predators is on the decline worldwide, which often brings humans and predators into conflict over remaining shared space and common resources. While the direct impacts of human predator conflict on humans (i.e., attacks on livestock or humans resulting in injury or death) are well documented, the indirect impacts of conflict on humans (i.e., downstream effects such as fear, stress, opportunity costs, PTSD) have not been addressed. We interviewed 437 people living in 54 villages on the periphery of Kanha National Park, India, to assess the amount and severity of direct and indirect impacts of predator conflict. ​While 58% of livestock owners believed that predator attacks on livestock guards occurred frequently and 62% of those who collect forest products believed that predator attacks on those collecting occurred frequently, less than 20% of all participants knew of someone who had experienced an attack. Data related to indirect impacts suggest that such impacts are common; 76% of participants indicated they were afraid a predator will physically injure them. Livestock owners reported that livestock guarding took time away from their primary job (61%) and getting enough sleep (73%), and believed that it increased their vulnerability to illnesses (80%). These results suggest that the perceptions of risk of predator attack are likely inflated, yet the costs of human predator impacts may be substantially higher than previously estimated, particularly related to human well-being, making the implementation of appropriate and effective conservation and conflict mitigation strategies and policies increasingly urgent.

Keywords: direct impacts, indirect impacts, human-predator conflict, India

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Authors: Kalaashini Ramachandran, Maznah Dahlui, Nik Daliana Nik Farid

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WHO recommends all babies to be exclusively breastfed and donor milk is the next best alternative in the absence of mother’s own milk. The establishment of a human milk bank (HMB) is still being debated due to religious concerns in Malaysia leading to informal milk sharing practices, but little is known on the knowledge, attitude and perception of women towards HMB and its benefits. This study hypothesizes that there is no association between knowledge and attitude and the acceptance towards the establishment of human milk bank among Malaysian women and healthcare providers. The aim of this study is to determine the drivers and barriers among Malaysian towards the acceptance of an HMB. A cross-sectional study with 367 participants was enrolled within a period of 3 months to answer an online self-administered questionnaire. Data on sociodemographic, knowledge on breastfeeding benefits, knowledge and attitude on HMB and its specific issues were analyzed in terms of frequency and then proceed to multiple logistic regression. Majority of the respondents are of Islamis religion (73.3%), have succeesfully completed their tertiary education (82.8%), and are employed (70.8%). Only 55.9% of respondents have heard of an HMB stating internet as their main source of information but a higher prevalence is agreeable to the establishment of a human milk bank (67.8%). Most respondents have a good score on knowledge of breastfeeding benefits and on HMB specific issues (70% and 54.2% respectively) while 63.8% of them have a positive attitude towards HMB. In the multivariate analysis, mothers with a good score on general knowledge of breastfeeding (AOR: 1.715) were more likely to accept the establishment of an HMB while Islamic religion was negatively associated with its establishment (AOR:0.113). This study has found a high prevalence rate of mothers who are willing to accept the establishment of an HMB. This action can be potentially shaped by educating mothers on the benefits of breastfeeding as well as addressing their religious concerns so the establishment of a religiously abiding HMB in Malaysia may be accepted without compromising their belief or the health benefit of donor milk.

Keywords: acceptability, attitude, human milk bank, knowledge

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Authors: C. Brierley, H. El-Farahaty, A. Farhan

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The Leeds Parallel Corpus of Arabic-English Constitutions is a parallel corpus for the Arabic legal domain. Analysis of legal language via Corpus Linguistics techniques is an important development. In legal proceedings, a corpus-based approach to disambiguating meaning is set to replace the dictionary as an interpretative tool, and legal scholarship in the States is now attuned to the potential for Text Analytics over vast quantities of text-based legal material, following the business and medical industries. This trend is reflected in Europe: the interdisciplinary research group in Computer Assisted Legal Linguistics mines big data collections of legal and non-legal texts to analyse: legal interpretations; legal discourse; the comprehensibility of legal texts; conflict resolution; and linguistic human rights. This paper focuses on ‘dignity’ as an important aspect of the overarching concept of human rights in current constitutions across the Arab world. We have compiled a parallel, Arabic-English raw text corpus (169,861 Arabic words and 205,893 English words) from reputable websites such as the World Intellectual Property Organisation and CONSTITUTE, and uploaded and queried our corpus in Sketch Engine. Our most challenging task was sentence-level alignment of Arabic-English data. This entailed manual intervention to ensure correspondence on a one-to-many basis since Arabic sentences differ from English in length and punctuation. We have searched for morphological variants of ‘dignity’ (رامة ك, karāma) in the Arabic data and inspected their English translation equivalents. The term occurs most frequently in the Sudanese constitution (10 instances), and not at all in the constitution of Palestine. Its most frequent collocate, determined via the logDice statistic in Sketch Engine, is ‘human’ as in ‘human dignity’.

Keywords: Arabic constitution, corpus-based legal linguistics, human rights, parallel Arabic-English legal corpora

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Authors: Shamim Akhtar

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The purpose of the present research was to analyse the performance level of district administrators and school heads teachers at secondary school level. The sample of the study was head teachers and teachers of secondary schools. In survey three scales were used, two scales were for the head teachers, one five point scale was for analysing the working efficiency of educational administrators and other seven points scale was for head teachers for analysing their own performance and one another seven point rating scale similar to head teacher was for the teachers for analysing the working performance of their head teachers. The results of the head teachers’ responses revealed that the performance of their District Educational Administrators was average and for the performance efficiency of the head teachers, researcher constructed the rating scales on seven parameters of management likely academic management, personnel management, financial management, infra-structure management, linkage and interface, student’s services, and managerial excellence. Results of percentages, means, and graphical presentation on different parameters of management showed that there was an obvious difference in head teachers and teachers’ responses and head teachers probably were overestimating their efficiency; but teachers evaluated that they were performing averagely on majority statements. Results of t-test showed that there was no significance difference in the responses of rural and urban teachers but significant difference in male and female teachers’ responses showed that female head teachers were performing their responsibilities better than male head teachers in public sector schools. When efficiency of the head teachers on different parameters of management were analysed it was concluded that their efficiency on academic and personnel management was average and on financial management and on managerial excellence was highly above of average level but on others parameters like infra-structure management, linkage and interface and on students services was above of average level on most statements but highly above of average on some statements. Hence there is need to improve the working efficiency in academic management and personnel management.

Keywords: educational administration, educational management, parameters of management, education

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Authors: Shan Zhang, Peter Suechting

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Predicting trade patterns is critical to decision-making in public and private domains, especially in the current context of trade disputes among major economies. In the past, U.S. recycling has relied heavily on strong demand for recyclable materials overseas. However, starting in 2017, a series of new recycling policies (bans and higher inspection standards) was enacted by multiple countries that were the primary importers of recyclables from the U.S. prior to that point. As the global trade flow of recycling shifts, some new importers, mostly developing countries in South and Southeast Asia, have been overwhelmed by the sheer quantities of scrap materials they have received. As the leading exporter of recyclable materials, the U.S. now has a pressing need to build its recycling industry domestically. With respect to the global trade in scrap materials used for recycling, the interest in this paper is (1) predicting how the export of recyclable materials from the U.S. might vary over time, and (2) predicting how international trade flows for recyclables might change in the future. Focusing on three major recyclable materials with a history of trade, this study uses data-driven and machine learning (ML) algorithms---supervised (shrinkage and tree methods) and unsupervised (neural network method)---to decipher the international trade pattern of recycling. Forecasting the potential trade values of recyclables in the future could help importing countries, to which those materials will shift next, to prepare related trade policies. Such policies can assist policymakers in minimizing negative environmental externalities and in finding the optimal amount of recyclables needed by each country. Such forecasts can also help exporting countries, like the U.S understand the importance of healthy domestic recycling industry. The preliminary result suggests that gravity models---in addition to particular selection macroeconomic predictor variables--are appropriate predictors of the total export value of recyclables. With the inclusion of variables measuring aspects of the political conditions (trade tariffs and bans), predictions show that recyclable materials are shifting from more policy-restricted countries to less policy-restricted countries in international recycling trade. Those countries also tend to have high manufacturing activities as a percentage of their GDP.

Keywords: environmental economics, machine learning, recycling, international trade

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Authors: Md. Khayrul Bashar, Md. Saiful Islam, Kimiko Yamashita, Yano Midori

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Despite having some progress in human authentication, conventional biometrics (e.g., facial features, fingerprints, retinal scans, gait, voice patterns) are not robust against falsification because they are neither confidential nor secret to an individual. As a non-invasive tool, electrocardiogram (ECG) has recently shown a great potential in human recognition due to its unique rhythms characterizing the variability of human heart structures (chest geometry, sizes, and positions). Moreover, ECG has a real-time vitality characteristic that signifies the live signs, which ensure legitimate individual to be identified. However, the detection accuracy of the current ECG-based methods is not sufficient due to a high variability of the individual’s heartbeats at a different instance of time. These variations may occur due to muscle flexure, the change of mental or emotional states, and the change of sensor positions or long-term baseline shift during the recording of ECG signal. In this study, a new method is proposed for human identification, which is based on the extraction of the local roughness of ECG heartbeat signals. First ECG signal is preprocessed using a second order band-pass Butterworth filter having cut-off frequencies of 0.00025 and 0.04. A number of local binary patterns are then extracted by applying a moving neighborhood window along the ECG signal. At each instant of the ECG signal, the pattern is formed by comparing the ECG intensities at neighboring time points with the central intensity in the moving window. Then, binary weights are multiplied with the pattern to come up with the local roughness description of the signal. Finally, histograms are constructed that describe the heartbeat signals of individual subjects in the database. One advantage of the proposed feature is that it does not depend on the accuracy of detecting QRS complex, unlike the conventional methods. Supervised recognition methods are then designed using minimum distance to mean and Bayesian classifiers to identify authentic human subjects. An experiment with sixty (60) ECG signals from sixty adult subjects from National Metrology Institute of Germany (NMIG) - PTB database, showed that the proposed new method is promising compared to a conventional interval and amplitude feature-based method.

Keywords: human identification, ECG biometrics, local roughness patterns, supervised classification

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Authors: Elham Rajabian

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The ascent of cybersecurity incidents is a rising threat to most organisations in general, while the impact of the incidents is unique to each of the organizations. It is a need for behavioural sciences to concentrate on employees’ behaviour in order to prepare key security mitigation opinions versus cybersecurity incidents. There are noticeable differences among users of a computer system in terms of complying with security behaviours. We can discuss the people's differences under several subjects such as delaying tactics on something that must be done, the tendency to act without thinking, future thinking about unexpected implications of present-day issues, and risk-taking behaviours in security policies compliance. In this article, we introduce high-profile cyber-attacks and their impacts on weakening cyber resiliency in organizations. We also give attention to human errors that influence network security. Human errors are discussed as a part of psychological matters to enhance compliance with the security policies. The organizational challenges are studied in order to shape a sustainable cyber risks management approach in the related work section. Insiders’ behaviours are viewed as a cyber security gap to draw proper cyber resiliency in section 3. We carry out the best cybersecurity practices by discussing four CIS challenges in section 4. In this regard, we provide a guideline and metrics to measure cyber resilience in organizations in section 5. In the end, we give some recommendations in order to build a cybersecurity culture based on individual behaviours.

Keywords: cyber resilience, human factors, cybersecurity behavior, attitude, usability, security culture

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Authors: Ilya B. Tsyrlov, Dmitry Y. Oshchepkov

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A computational search for dioxin response elements (DREs) in genes of proteins comprising the Ah receptor (AhR) cytosolic core complex was performed by highly efficient tool SITECON. Eventually, the following number of new DREs in 5’flanking region was detected by SITECON: one in AHR gene, five in XAP2, eight in HSP90AA1, and three in HSP90AB1 genes. Numerous DREs found in genes of AhR and AhR cytosolic complex members would shed a light on potential mechanisms of expression, the stoichiometry of unliganded AhR core complex, and its degradation vs biosynthesis dynamics resulted from treatment of target cells with the AhR most potent ligand, 2,3,7,8-TCDD. With human viruses, reduced susceptibility to TCDD of geneencoding HIV-1 P247 was justified by the only potential DRE determined in gag gene encoding HIV-1 P24 protein, whereas the regulatory region of CMV genes encoding IE gp/UL37 has five potent DRE, 1.65 kb/UL36 – six DRE, pp65 and pp71 – each has seven DRE, and pp150 – ten DRE. Also, from six to eight DRE were determined with SITECON in the regulatory region of HSV-1 IE genes encoding tegument proteins, UL36 and UL37, and of UL19 gene encoding bindingglycoprotein C (gC). So, TCDD in the low picomolar range may activate in human cells AhR: Arnt transcription pathway that triggers CMV and HSV-1 reactivation by binding to numerous promoter DRE within immediate-early (IE) genes UL37 and UL36, thus committing virus to the lytic cycle.

Keywords: dioxin response elements, Ah receptor, AhR: Arnt transcription pathway, human and viral genes

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Authors: Prakash Singh

Abstract:

Far-reaching organizational and structural changes are required to address the severe imbalances in the provision and strong bureaucratic controls in many organizations at all levels. Transformational collegial leadership (TCL) is a collective action generated by transforming leadership which empowers all individuals who participate in this process, whether it is in business or education, or in any other organization. Recent research is strongly supportive of collegiality as a key factor in transforming traditional bureaucratic management practices (TBMPs) in organizations. The primary objective of this study was therefore to gauge the extent to which organizations have affected a shift from TBMPs towards TCL and how this can affect the development of human resources. This qualitative study investigated the impact of TCL on developing human resources in two organizations which were purposively selected to participate in this study. The findings of this study affirm that human resource development and TCL embrace the values of collaboration, confrontation, authenticity, trust, support and openness. In order to overcome TBMPs, a clear sense of direction must be communicated by collegial leaders throughout their organizations so that lower-level employees can initiate actions without the same degree of vulnerability. The TCL approach must respond to needs amongst followers and must look for motives, extrinsic and intrinsic, to satisfy those needs by enhancing opportunities, empowering people and giving more freedom, performance evaluation and the full support of the leader. Evidently, the collegial model emphasizes the valuing of individuals where there is a breadth of leadership and an absence of hierarchy.

Keywords: bureaucratic management model, human resource development, transformational collegial leadership model, transforming leader

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Authors: Mahnaz Hosseinzadeh, Shaghayegh Rezaee Amiri

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This paper presents a methodology for serial production planning problem in wire and cable manufacturing process that addresses the problem of input-output imbalance in different consecutive stations, hoping to minimize the halt of machines in each stage. To this end, a linear Goal Programming (GP) model is developed, in which four main categories of constraints as per the number of runs per machine, machines’ sequences, acceptable inventories of machines at the end of each period, and the necessity of fulfillment of the customers’ orders are considered. The model is formulated based upon on the real data obtained from IKO TAK Company, an important supplier of wire and cable for oil and gas and automotive industries in Iran. By solving the model in GAMS software the optimal number of runs, end-of-period inventories, and the possible minimum idle time for each machine are calculated. The application of the numerical results in the target company has shown the efficiency of the proposed model and the solution in decreasing the lead time of the end product delivery to the customers by 20%. Accordingly, the developed model could be easily applied in wire and cable companies for the aim of optimal production planning to reduce the halt of machines in manufacturing stages.

Keywords: goal programming approach, GP, production planning, serial manufacturing process, wire and cable industry

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Authors: Makda Yeshitela Kifele

Abstract:

Hair treatment prevents the hair from loss of volume, changing colour, and damaging its properties of the hair. Hair is the beauty of human beings that makes people beautiful and takes the other hearts to see them and to give them an appreciation for their effort to treat their hair and save it from damage. There are different methods to protect human hair from loss and damage that influence human psychology better than the problems. Chemicals products are available in the world that keeps safely the hair and provide beauty for the hair. But chemical products have side effects and are not cost-effective. Even some of the chemicals are allergic for users and left some changes in the hair. Indigenous hair treatment is an effective method that reduces the bad effects and the problems of the chemical that are lefts in human being’slife. Indigenous hair treatment can treat the hair safely and effectively that does not have much effect or spots in the human hair the users rather, it improves some attributes of the hair such that shine, quality, quantity improvements, length, and flexibility can be modified by these indigenous treatments. Rate is the local plant that plays a significant role in hair treatment. Rate is the local plant that can be available everywhere in the country, and anybody can be used for hair treatments. For this research, 50 women are identified as sample populations with different hair characteristics. The treatments were collected from the fields and squeezed into the pots to be prepared as specimens. The squeezed plants were deposited in the refrigerator for three days with some amounts of salts to prevent some bacteria. Chemical analysis has been done to sort out some detrimental substances. So the result showed that there are no detrimental substances that affect the hair properties and the health of the users. The sample population used the oil for one month without any other oily cosmetics that disturbs the treatment. The output is very effective and brings shining the hair, preventing greying of the hair, showing fast-growing, increasing the volume of the hair, and becoming flexible and curly, straight hair, thicker, and with no allergic effects.

Keywords: indigenous, chemicals, curly, treatment

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Authors: Christopher Thornton, Niina Kolehmainen, Kianoush Nazarpour

Abstract:

Background: Accelerometers are widely used to measure physical activity behavior, including in children. The traditional method for processing acceleration data uses cut points, relying on calibration studies that relate the quantity of acceleration to energy expenditure. As these relationships do not generalise across diverse populations, they must be parametrised for each subpopulation, including different age groups, which is costly and makes studies across diverse populations difficult. A data-driven approach that allows physical activity intensity states to emerge from the data under study without relying on parameters derived from external populations offers a new perspective on this problem and potentially improved results. We evaluated the data-driven approach in a diverse population with a range of rapidly evolving physical and mental capabilities, namely very young children (9-38 months old), where this new approach may be particularly appropriate. Methods: We applied an unsupervised machine learning approach (a hidden semi-Markov model - HSMM) to segment and cluster the accelerometer data recorded from 275 children with a diverse range of physical and cognitive abilities. The HSMM was configured to identify a maximum of six physical activity intensity states and the output of the model was the time spent by each child in each of the states. For comparison, we also processed the accelerometer data using published cut points with available thresholds for the population. This provided us with time estimates for each child’s sedentary (SED), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA). Data on the children’s physical and cognitive abilities were collected using the Paediatric Evaluation of Disability Inventory (PEDI-CAT). Results: The HSMM identified two inactive states (INS, comparable to SED), two lightly active long duration states (LAS, comparable to LPA), and two short-duration high-intensity states (HIS, comparable to MVPA). Overall, the children spent on average 237/392 minutes per day in INS/SED, 211/129 minutes per day in LAS/LPA, and 178/168 minutes in HIS/MVPA. We found that INS overlapped with 53% of SED, LAS overlapped with 37% of LPA and HIS overlapped with 60% of MVPA. We also looked at the correlation between the time spent by a child in either HIS or MVPA and their physical and cognitive abilities. We found that HIS was more strongly correlated with physical mobility (R²HIS =0.5, R²MVPA= 0.28), cognitive ability (R²HIS =0.31, R²MVPA= 0.15), and age (R²HIS =0.15, R²MVPA= 0.09), indicating increased sensitivity to key attributes associated with a child’s mobility. Conclusion: An unsupervised machine learning technique can segment and cluster accelerometer data according to the intensity of movement at a given time. It provides a potentially more sensitive, appropriate, and cost-effective approach to analysing physical activity behavior in diverse populations, compared to the current cut points approach. This, in turn, supports research that is more inclusive across diverse populations.

Keywords: physical activity, machine learning, under 5s, disability, accelerometer

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Authors: Ruchi Sharma, Rajasekhar Balasubramanian

Abstract:

Motor vehicles emit a number of air pollutants, among which fine particulate matter (PM₂.₅) is of major concern in cities with high population density due to its negative impacts on air quality and human health. Typically, people spend more than 80% of their time indoors. Consequently, human exposure to traffic-related PM₂.₅ in indoor environments has received considerable attention. Most of the public residential buildings in tropical countries are designed for natural ventilation where indoor air quality tends to be strongly affected by the migration of air pollutants of outdoor origin. However, most of the previously reported traffic-related PM₂.₅ exposure assessment studies relied on ambient PM₂.₅ concentrations and thus, the health impact of traffic-related PM₂.₅ on occupants in naturally ventilated buildings remains largely unknown. Therefore, a systematic field study was conducted to assess indoor human exposure to traffic-related PM₂.₅ with and without mitigation measures in a typical naturally ventilated residential apartment situated near a road carrying a large volume of traffic. Three PM₂.₅ exposure scenarios were simulated in this study, i.e., Case 1: keeping all windows open with a ceiling fan on as per the usual practice, Case 2: keeping all windows fully closed as a mitigation measure, and Case 3: keeping all windows fully closed with the operation of a portable indoor air cleaner as an additional mitigation measure. The indoor to outdoor (I/O) ratios for PM₂.₅ mass concentrations were assessed and the effectiveness of using the indoor air cleaner was quantified. Additionally, potential human health risk based on the bioavailable fraction of toxic trace elements was also estimated for the three cases in order to identify a suitable mitigation measure for reducing PM₂.₅ exposure indoors. Traffic-related PM₂.₅ levels indoors exceeded the air quality guidelines (12 µg/m³) in Case 1, i.e., under natural ventilation conditions due to advective flow of outdoor air into the indoor environment. However, while using the indoor air cleaner, a significant reduction (p < 0.05) in the PM₂.₅ exposure levels was noticed indoors. Specifically, the effectiveness of the air cleaner in terms of reducing indoor PM₂.₅ exposure was estimated to be about 74%. Moreover, potential human health risk assessment also indicated a substantial reduction in potential health risk while using the air cleaner. This is the first study of its kind that evaluated the indoor human exposure to traffic-related PM₂.₅ and identified a suitable exposure mitigation measure that can be implemented in densely populated cities to realize health benefits.

Keywords: fine particulate matter, indoor air cleaner, potential human health risk, vehicular emissions

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Authors: Asma Moomal, Zukarnain Zakaria

Abstract:

In this uncertain, highly competitive and hasty moving era, most of the organizations are surviving under the pressure of complex dynamics, fierce competition and many challenges in terms of global talent management within the global market. One key result of these challenges is that the organizations have to be organized and good at handling human capital if they want to gain sustainable and steady success in near future. By keeping in mind the importance of global competition, many human resource (HR) professionals are diagnosing the complexities in managing talent of human capital at global level, especially those of multinational enterprises (MNEs). As, there has been little research in the country regarding identification of the GTM in MNEs, this paper reviewed the relevant literature in order to examine the role of GTM strategies in enhancing the organizational development in the MNEs of Malaysia. The data collection technique used in this study was done through the secondary data resources (i.e. the existing literature analysis). This study contributes to extend our understanding of the impact of GTM on organizational development of MNEs within the country.

Keywords: Global Talent Management (GTM), multinational enterprises (MNEs), organizational development, talent

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