Search results for: maximal data sets

Authors: Sheldon Liu, Gordon Wang, Lei Liu, Xuefeng Liu

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Anomaly detection, also referred to as one-class classification, plays a crucial role in identifying product images that deviate from the expected distribution. This study introduces Data-centric Anomaly Detection with Diffusion Models (DCADDM), presenting a systematic strategy for data collection and further diversifying the data with image generation via diffusion models. The algorithm addresses data collection challenges in real-world scenarios and points toward data augmentation with the integration of generative AI capabilities. The paper explores the generation of normal images using diffusion models. The experiments demonstrate that with 30% of the original normal image size, modeling in an unsupervised setting with state-of-the-art approaches can achieve equivalent performances. With the addition of generated images via diffusion models (10% equivalence of the original dataset size), the proposed algorithm achieves better or equivalent anomaly localization performance.

Keywords: diffusion models, anomaly detection, data-centric, generative AI

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Authors: Gayatri Ghosh

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In this work, a flavour theory of a neutrino mass model based on A_4 symmetry is considered to explain the phenomenology of neutrino mixing. The spontaneous symmetry breaking of A_4 symmetry in this model leads to tribimaximal mixing in the neutrino sector at a leading order. We consider the effect of Z_2 × Z_2 invariant perturbations in neutrino sector and find the allowed region of correction terms in the perturbation matrix that is consistent with 3σ ranges of the experimental values of the mixing angles. We study the entanglement of this formalism on the other phenomenological observables, such as δ_CP phase, the neutrino oscillation probability P(νµ → νe), the effective Majorana mass |mee| and |meff νe |. A Z_2 × Z_2 invariant perturbations in this model is introduced in the neutrino sector which leads to testable predictions of θ_13 and CP violation. By changing the magnitudes of perturbations in neutrino sector, one can generate viable values of δ_CP and neutrino oscillation parameters. Next we investigate the feasibility of charged lepton flavour violation in type-I seesaw models with leptonic flavour symmetries at high energy that leads to tribimaximal neutrino mixing. We consider an effective theory with an A_4 × Z_2 × Z_2 symmetry, which after spontaneous symmetry breaking at high scale which is much higher than the electroweak scale leads to charged lepton flavour violation processes once the heavy Majorana neutrino mass degeneracy is lifted either by renormalization group effects or by a soft breaking of the A_4 symmetry. In this context the implications for charged lepton flavour violation processes like µ → eγ, τ → eγ, τ → µγ are discussed.

Keywords: Z2 × Z2 invariant perturbations, CLFV, delta CP phase, tribimaximal neutrino mixing

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Authors: Elizabeta Krstić Vukelja

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Digitization of personal data is a consequence of the development of information and communication technologies that create a new work environment with many advantages and challenges, but also potential threats to privacy and personal data protection. Regulation (EU) 2016/679 of the European Parliament and of the Council is becoming a law and obligation that should address the issues of personal data protection and information security. The existence of the Regulation leads to the conclusion that national legislation in the field of virtual environment, protection of the rights of EU citizens and processing of their personal data is insufficiently effective. In the health system, special emphasis is placed on the processing of special categories of personal data, such as health data. The healthcare industry is recognized as a particularly sensitive area in which a large amount of medical data is processed, the digitization of which enables quick access and quick identification of the health insured. The protection of the individual requires quality IT solutions that guarantee the technical protection of personal categories. However, the real problems are the technical and human nature and the spatial limitations of the application of the Regulation. Some conclusions will be drawn by analyzing the implementation of the basic principles of the Regulation on the example of the Croatian health care system and comparing it with similar activities in other EU member states.

Keywords: regulation, healthcare system, personal dana protection, quality data assurance

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Authors: Nagi Mekhiel

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Many applications use vector operations by applying single instruction to multiple data that map to different locations in conventional memory. Transferring data from memory is limited by access latency and bandwidth affecting the performance gain of vector processing. We present a memory system that makes all of its content available to processors in time so that processors need not to access the memory, we force each location to be available to all processors at a specific time. The data move in different orbits to become available to other processors in higher orbits at different time. We use this memory to apply parallel vector operations to data streams at first orbit level. Data processed in the first level move to upper orbit one data element at a time, allowing a processor in that orbit to apply another vector operation to deal with serial code limitations inherited in all parallel applications and interleaved it with lower level vector operations.

Keywords: Memory Organization, Parallel Processors, Serial Code, Vector Processing

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Authors: David Percy

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Landslides are a geologic phenomenon that affects a large number of inhabited places and are constantly being monitored and studied for the prediction of future occurrences. Reconstructability analysis (RA) is a methodology for extracting informative models from large volumes of data that work exclusively with discrete data. While RA has been used in medical applications and social science extensively, we are introducing it to the spatial sciences through applications like landslide prediction. Since RA works exclusively with discrete data, such as soil classification or bedrock type, working with continuous data, such as porosity, requires that these data are binned for inclusion in the model. RA constructs models of the data which pick out the most informative elements, independent variables (IVs), from each layer that predict the dependent variable (DV), landslide occurrence. Each layer included in the model retains its classification data as a primary encoding of the data. Unlike other machine learning algorithms that force the data into one-hot encoding type of schemes, RA works directly with the data as it is encoded, with the exception of continuous data, which must be binned. The usual physical and derived layers are included in the model, and testing our results against other published methodologies, such as neural networks, yields accuracy that is similar but with the advantage of a completely transparent model. The results of an RA session with a data set are a report on every combination of variables and their probability of landslide events occurring. In this way, every combination of informative state combinations can be examined.

Keywords: reconstructability analysis, machine learning, landslides, raster analysis

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Authors: Angela McCaskill

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Accounting graduates in most United States universities are not being adequately taught International Financial Reporting Standards (IFRS). As such they are not prepared with the knowledge and skills necessary to remain competitive in international businesses. One of the reasons behind the ill preparation is the lack of specific international accounting instruction available in the U.S. This paper explores the importance of IFRS education through the lenses of graduate accounting majors. The paper specifically explores graduate accounting major’s preparedness in IFRS based on their recent completion of a Master in Accountancy degree where IFRS had been integrated. The data for the study was collected via face-to face and telephone/Skype interviews and questionnaires. After the interview the participants also agreed to answer two supplementary questions. The participants were to determine the amounts that should be reported on the balance sheet under (1) IFRS and (2) U.S. GAAP. These questions intended to test their knowledge of both sets of standards. The sample consisted of on-line and brick and mortar university students enrolled in their graduate program during the period from spring semester 2016 to summer semester 2016. This study shows that a separate course should be devoted to teaching IFRS and convergence related issues. There is a direct correlation between the knowledge level of those students taking an IFRS course and the successful completion of the supplementary questions compared to those who only had IFRS instruction mixed into their U.S. GAAP based instruction. Students who took an international accounting course were better prepared for the IFRS conversion than those who did not have a separate course. Academically, universities need to take a deeper look into the needs of their students and do better at incorporating international standards in their curriculum.

Keywords: accounting education, global accounting standards, international accounting, IFRS and U.S. GAAP convergence, IFRS, U.S. GAAP

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Authors: Tammy Wee, Detlev Remy, Arif Perdana

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In the recent years, data analytics has become the buzzword in the hospitality industry. The hospitality industry is another example of a data-rich industry that has yet fully benefited from the insights of data analytics. Effective use of data analytics can change how hotels operate, market and position themselves competitively in the hospitality industry. However, at the moment, the data obtained by individual hotels remain under-utilized. This research is a preliminary research on data analytics in the hospitality industry, using an in-depth face-to-face interview on one hotel as a start to a multi-level research. The main case study of this research, hotel A, is a chain brand of international hotel that has been systematically gathering and collecting data on its own customer for the past five years. The data collection points begin from the moment a guest book a room until the guest leave the hotel premises, which includes room reservation, spa booking, and catering. Although hotel A has been gathering data intelligence on its customer for some time, they have yet utilized the data to its fullest potential, and they are aware of their limitation as well as the potential of data analytics. Currently, the utilization of data analytics in hotel A is limited in the area of customer service improvement, namely to enhance the personalization of service for each individual customer. Hotel A is able to utilize the data to improve and enhance their service which in turn, encourage repeated customers. According to hotel A, 50% of their guests returned to their hotel, and 70% extended nights because of the personalized service. Apart from using the data analytics for enhancing customer service, hotel A also uses the data in marketing. Hotel A uses the data analytics to predict or forecast the change in consumer behavior and demand, by tracking their guest’s booking preference, payment preference and demand shift between properties. However, hotel A admitted that the data they have been collecting was not fully utilized due to two challenges. The first challenge of using data analytics in hotel A is the data is not clean. At the moment, the data collection of one guest profile is meaningful only for one department in the hotel but meaningless for another department. Cleaning up the data and getting standards correctly for usage by different departments are some of the main concerns of hotel A. The second challenge of using data analytics in hotel A is the non-integral internal system. At the moment, the internal system used by hotel A do not integrate with each other well, limiting the ability to collect data systematically. Hotel A is considering another system to replace the current one for more comprehensive data collection. Hotel proprietors recognized the potential of data analytics as reported in this research, however, the current challenges of implementing a system to collect data come with a cost. This research has identified the current utilization of data analytics and the challenges faced when it comes to implementing data analytics.

Keywords: data analytics, hospitality industry, customer relationship management, hotel marketing

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Authors: Fadzilah Abdul Rahman

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Emotional development is developed between the parents and their child. Behavioural development is also developed between the parents and their child. Social Development is how parents can help their special needs child to adapt to society and to face challenges. In promoting a lifelong learning mindset, enhancing skill sets and readiness to face challenges, parents would be able to counter balance these challenges during their care giving process and better manage their expectations through understanding the hierarchy of needs modality towards a positive attitude, and in turn, improve their quality of life and participation in society. This paper aims to demonstrate how the hierarchy of needs can be applied in various situations of caregiving for parents with a special needs child.

Keywords: hierarchy of needs, parents, special needs, care-giving

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Authors: Djelloul Benatiallah, Ali Benatiallah, Abdelkader Harouz

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Geographic Information Systems (GIS) include various methods and computer techniques to model, capture digitally, store, manage, view and analyze. Geographic information systems have the characteristic to appeal to many scientific and technical field, and many methods. In this article we will present a complete and operational geographic information system, following the theoretical principles of data management and adapting to spatial data, especially data concerning the monitoring of drinking water supply wells (DWS) Adrar region. The expected results of this system are firstly an offer consulting standard features, updating and editing beneficiaries and geographical data, on the other hand, provides specific functionality contractors entered data, calculations parameterized and statistics.

Keywords: GIS, DWS, drilling, Adrar

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Authors: Bergun Meric Bingul, Yezdan Cinel, Murat Son, Cigdem Bulgan, Mensure Aydin

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The aim of this study was to determination of the effects of knee and hip isokinetic performance during the training with the special designed roller-shoes. 30 soccer players participated as subjects and these subjects were divided into 3 groups randomly. Training groups were; with the dynamic training shoes group, without the dynamic training shoes group and control group. Subjects were trained speed strength trainings during 8 weeks (3 days a week and 1 hour a day). 6 exercises were focused on the knee flexors and extensors, also hip adductor and abductor muscles were chosen and performed in 3x30secs at each sets. Control group was not paticipated to the training program. Before and after the training programs knee flexor and extensor muscles and hip abductor and adductor muscles’ peak torques were measured by Biodex III isokinetic dynamometer. Isokinetic strength data were analyzed by using SPSS program. A repeated measures analysis of variance (ANOVA) was used to determine differences among the peak torque values for three groups. The results indicated that soccer players’ peak torque values that the group of using the dynamic training shoes, were found higher. Also, hip adductor and abductor peak torques that the group of using the dynamic training shoes, were obtained better than the other groups. In conclusion, the ground friction forces are an important role of increasing strength. With these shoes, using rollers, soccer players were able to move easily because of the friction forces were reduced and created more range of motion. So, exercises were performed faster than before and strength movements in all angles, it ensured that the active state. This was resulted in a better use of force.

Keywords: isokinetic, soccer, dynamic training shoes, training

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Authors: S. Habte, K. Ouazzane, P. Patel, S. Patel

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The dynamic and highly competitive nature of the consumer electronics retail industry means that businesses in this industry are experiencing different decision making challenges in relation to pricing, inventory control, consumer satisfaction and product offerings. To overcome the challenges facing retailers and create opportunities, we propose a generic data warehousing solution which can be applied to a wide range of consumer electronics retailers with a minimum configuration. The solution includes a dimensional data model, a template SQL script, a high level architectural descriptions, ETL tool developed using C#, a set of APIs, and data access tools. It has been successfully applied by ASK Outlets Ltd UK resulting in improved productivity and enhanced sales growth.

Keywords: consumer electronics, data warehousing, dimensional data model, generic, retail industry

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Authors: Lei Ren, Michael Hartnett, Stephen Nash

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The abundant measured surface current from HF radar system in coastal area is assimilated into model to improve the modeling forecasting ability. A simple sequential data assimilation scheme, Direct Insertion (DI), is applied to update model forecast states. The influence of Direct Insertion data assimilation over time is analyzed at one reference point. Vector maps of surface current from models are compared with HF radar measurements. Root-Mean-Squared-Error (RMSE) between modeling results and HF radar measurements is calculated during the last four days with no data assimilation.

Keywords: data assimilation, CODAR, HF radar, surface current, direct insertion

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Authors: M. A. Hasan, M. R. Islam, R. A. Tarefder

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This study investigates how the site specific traffic data differs from the Mechanistic Empirical Pavement Design Software default values. Two Weigh-in-Motion (WIM) stations were installed in Interstate-40 (I-40) and Interstate-25 (I-25) to developed site specific data. A computer program named WIM Data Analysis Software (WIMDAS) was developed using Microsoft C-Sharp (.Net) for quality checking and processing of raw WIM data. A complete year data from November 2013 to October 2014 was analyzed using the developed WIM Data Analysis Program. After that, the vehicle class distribution, directional distribution, lane distribution, monthly adjustment factor, hourly distribution, axle load spectra, average number of axle per vehicle, axle spacing, lateral wander distribution, and wheelbase distribution were calculated. Then a comparative study was done between measured data and AASHTOWare default values. It was found that the measured general traffic inputs for I-40 and I-25 significantly differ from the default values.

Keywords: AASHTOWare, traffic, weigh-in-motion, axle load distribution

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Authors: Mansoureh Naderipour, Susan Bastani, Mohammad Fazel Zarandi

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Predicting links that may occur in the future and missing links in social networks is an attractive problem in social network analysis. Granular computing can help us to model the relationships between human-based system and social sciences in this field. In this paper, we present a model based on granular computing approach and Type-2 fuzzy logic to predict links regarding nodes’ activity and the relationship between two nodes. Our model is tested on collaboration networks. It is found that the accuracy of prediction is significantly higher than the Type-1 fuzzy and crisp approach.

Keywords: social network, link prediction, granular computing, type-2 fuzzy sets

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Authors: Angga Hidayah Ramadhan, Luciana Andrawina, M. Azani Hasibuan

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Data will be as a core of the decision if it has a good treatment or process, which is process that data into information, and information into knowledge to make a wisdom or decision. Today, many companies have not realize it include XYZ University Admission Directorate as executor of National Admission called Seleksi Masuk Bersama (SMB) that during the time, the workers only uses their feeling to make a decision. Whereas if it done, then that company can analyze the data to make a right decision to get a pin sales from student candidate or registrant that follow SMB as many as possible. Therefore, needs Knowledge Management System (KMS) with Geographic Information System (GIS) use 5C4C that can process that company data becomes more useful and can help make decisions. This information system can process data into information based on the pin sold data with 5C (Contextualized, Categorize, Calculation, Correction, Condensed) and convert information into knowledge with 4C (Comparing, Consequence, Connection, Conversation) that has been several steps until these data can be useful to make easier to take a decision or wisdom, resolve problems, communicate, and quicker to learn to the employees have not experience and also for ease of viewing/visualization based on spatial data that equipped with GIS functionality that can be used to indicate events in each province with indicator that facilitate in this system. The system also have a function to save the tacit on the system then to be proceed into explicit in expert system based on the problems that will be found from the consequences of information. With the system each team can make a decision with same ways, structured, and the important is based on the actual event/data.

Keywords: 5C4C, data, information, knowledge

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Authors: Aidin Foroutan, David S. Wishart, Leluo L. Guan, Carolyn Fitzsimmons

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Maximizing efficiency and growth potential of beef cattle requires not only genetic selection (i.e. residual feed intake (RFI)) but also adequate nutrition throughout all stages of growth and development. Nutrient restriction during gestation has been shown to negatively affect post-natal growth and development as well as fertility of the offspring. This, when combined with RFI may affect progeny traits. This study aims to investigate the impact of selection for divergent genetic potential for RFI and maternal nutrition during early- to mid-gestation, on bull calf traits such as fertility and muscle development using multiple ‘omics’ approaches. Comparisons were made between High-diet vs. Low-diet and between High-RFI vs. Low-RFI animals. An epigenetics experiment on semen samples identified 891 biomarkers associated with growth and development. A gene expression study on Longissimus thoracis muscle, semimembranosus muscle, liver, and testis identified 4 genes associated with muscle development and immunity of which Myocyte enhancer factor 2A [MEF2A; induces myogenesis and control muscle differentiation] was the only differentially expressed gene identified in all four tissues. An initial metabolomics experiment on serum samples using nuclear magnetic resonance (NMR) identified 4 metabolite biomarkers related to energy and protein metabolism. Once all the biomarkers are identified, bioinformatics approaches will be used to create a database covering all the ‘omics’ data collected from this project. This database will be broadened by adding other information obtained from relevant literature reviews. Association analyses with these data sets will be performed to reveal key biological pathways affected by RFI and maternal nutrition. Through these association studies between the genome and metabolome, it is expected that candidate biomarker genes and metabolites for feed efficiency, fertility, and/or muscle development are identified. If these gene/metabolite biomarkers are validated in a larger animal population, they could potentially be used in breeding programs to select superior animals. It is also expected that this work will lead to the development of an online tool that could be used to predict future traits of interest in an animal given its measurable ‘omics’ traits.

Keywords: biomarker, maternal nutrition, omics, residual feed intake

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Authors: Dipo Dunsin, Mohamed C. Ghanem, Karim Ouazzane

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Digital investigators often have a hard time spotting evidence in digital information. It has become hard to determine which source of proof relates to a specific investigation. A growing concern is that the various processes, technology, and specific procedures used in the digital investigation are not keeping up with criminal developments. Therefore, criminals are taking advantage of these weaknesses to commit further crimes. In digital forensics investigations, artificial intelligence is invaluable in identifying crime. It has been observed that an algorithm based on artificial intelligence (AI) is highly effective in detecting risks, preventing criminal activity, and forecasting illegal activity. Providing objective data and conducting an assessment is the goal of digital forensics and digital investigation, which will assist in developing a plausible theory that can be presented as evidence in court. Researchers and other authorities have used the available data as evidence in court to convict a person. This research paper aims at developing a multiagent framework for digital investigations using specific intelligent software agents (ISA). The agents communicate to address particular tasks jointly and keep the same objectives in mind during each task. The rules and knowledge contained within each agent are dependent on the investigation type. A criminal investigation is classified quickly and efficiently using the case-based reasoning (CBR) technique. The MADIK is implemented using the Java Agent Development Framework and implemented using Eclipse, Postgres repository, and a rule engine for agent reasoning. The proposed framework was tested using the Lone Wolf image files and datasets. Experiments were conducted using various sets of ISA and VMs. There was a significant reduction in the time taken for the Hash Set Agent to execute. As a result of loading the agents, 5 percent of the time was lost, as the File Path Agent prescribed deleting 1,510, while the Timeline Agent found multiple executable files. In comparison, the integrity check carried out on the Lone Wolf image file using a digital forensic tool kit took approximately 48 minutes (2,880 ms), whereas the MADIK framework accomplished this in 16 minutes (960 ms). The framework is integrated with Python, allowing for further integration of other digital forensic tools, such as AccessData Forensic Toolkit (FTK), Wireshark, Volatility, and Scapy.

Keywords: artificial intelligence, computer science, criminal investigation, digital forensics

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Authors: Sıdıka Kaya, Gülay Sain Güven, Seda Karsavuran, Onur Toka

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This study is part of an ongoing research project supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Project Number 114K404, and participation to this conference was supported by Hacettepe University Scientific Research Coordination Unit under Project Number 10243. Evaluation of hospital readmissions is gaining importance in terms of quality and cost, and is becoming the target of national policies. In Turkey, the topic of hospital readmission is relatively new on agenda and very few studies have been conducted on this topic. The aim of this study was to determine 30-day readmission rates and risk factors for readmission. Whether readmission was planned, related to the prior admission and avoidable or not was also assessed. The study was designed as a ‘prospective cohort study.’ 472 patients hospitalized in internal medicine departments of a university hospital in Turkey between February 1, 2015 and April 30, 2015 were followed up. Analyses were conducted using IBM SPSS Statistics version 22.0 and SPSS Modeler 16.0. Average age of the patients was 56 and 56% of the patients were female. Among these patients 95 were readmitted. Overall readmission rate was calculated as 20% (95/472). However, only 31 readmissions were unplanned. Unplanned readmission rate was 6.5% (31/472). Out of 31 unplanned readmission, 24 was related to the prior admission. Only 6 related readmission was avoidable. To determine risk factors for readmission we constructed Chi-square automatic interaction detector (CHAID) decision tree algorithm. CHAID decision trees are nonparametric procedures that make no assumptions of the underlying data. This algorithm determines how independent variables best combine to predict a binary outcome based on ‘if-then’ logic by portioning each independent variable into mutually exclusive subsets based on homogeneity of the data. Independent variables we included in the analysis were: clinic of the department, occupied beds/total number of beds in the clinic at the time of discharge, age, gender, marital status, educational level, distance to residence (km), number of people living with the patient, any person to help his/her care at home after discharge (yes/no), regular source (physician) of care (yes/no), day of discharge, length of stay, ICU utilization (yes/no), total comorbidity score, means for each 3 dimensions of Readiness for Hospital Discharge Scale (patient’s personal status, patient’s knowledge, and patient’s coping ability) and number of daycare admissions within 30 days of discharge. In the analysis, we included all 95 readmitted patients (46.12%), but only 111 (53.88%) non-readmitted patients, although we had 377 non-readmitted patients, to balance data. The risk factors for readmission were found as total comorbidity score, gender, patient’s coping ability, and patient’s knowledge. The strongest identifying factor for readmission was comorbidity score. If patients’ comorbidity score was higher than 1, the risk for readmission increased. The results of this study needs to be validated by other data–sets with more patients. However, we believe that this study will guide further studies of readmission and CHAID is a useful tool for identifying risk factors for readmission.

Keywords: decision tree, hospital, internal medicine, readmission

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Authors: Zhiwei Wang

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Much emphasis has been placed on the political dimension of digitised Chinese national(ist) discourses and their embodied national identities, which neglects other important dimensions constitutive of their discursive nature. A further investigation into how Chinese national(ist) discourses are daily (re)shaped online by diverse socio-political actors (especially ordinary users) is crucial, which can contribute to not only deeper understandings of Chinese national sentiments on China’s Internet beyond the excessive focus on their passionate, political-charged facet but also richer insights into the socio-technical ecology of the contemporary Chinese digital (and physical) world. This research adopts an ethnographic methodology, by which ‘fieldsites’ are Sina Weibo and bilibili. The primary data collection method is virtual ethnographic observation on everyday national(ist) discussions on both platforms. If data obtained via observations do not suffice to answer research questions, in-depth online qualitative interviews with ‘key actors’ identified from those observations in discursively (re)producing Chinese national identity on each ‘fieldsite’ will be conducted, to complement data gathered through the first method. Critical discourse analysis is employed to analyse data. During the process of data coding, NVivo is utilised. From November 2021 to December 2022, 35 weeks’ digital ethnographic observations have been conducted, with 35 sets of fieldnotes obtained. The strategy adopted for the initial stage of observations was keyword searching, which means typing into the search box on Sina Weibo and bilibili any keywords related to China as a nation and then observing the search results. Throughout 35 weeks’ online ethnographic observations, six keywords have been employed on Sina Weibo and two keywords on bilibili. For 35 weeks’ observations, textual content created by ordinary users have been concentrated much upon. Based on the fieldnotes of the first week’s observations, multifarious national(ist) discourses on Sina Weibo and bilibili have been found, targeted both at national ‘Others’ and ‘Us’, both on the historical and real-world dimension, both aligning with and differing from or even conflicting with official discourses, both direct national(ist) expressions and articulations of sentiments in the name of presentation of national(ist) attachments but for other purposes. Second, Sina Weibo and bilibili users have agency in interpreting and deploying concrete national(ist) discourses despite the leading role played by the government and the two platforms in deciding on the basic framework of national expressions. Besides, there are also disputes and even quarrels between users in terms of explanations for concrete components of ‘nation-ness’ and (in)direct dissent to officially defined ‘mainstream’ discourses to some extent, though often expressed much more mundanely, discursively and playfully. Third, the (re)production process of national(ist) discourses on Sina Weibo and bilibili depends upon not only technical affordances and limitations of the two sites but also, to a larger degree, some established socio-political mechanisms and conventions in the offline China, e.g., the authorities’ acquiescence of citizens’ freedom in understanding and explaining concrete elements of national discourses while setting the basic framework of national narratives to the extent that citizens’ own national(ist) expressions do not reach political bottom lines and develop into mobilising power to shake social stability.

Keywords: national identity, national(ist) discourse(s), everyday nationhood/nationalism, Chinese nationalism, digital nationalism

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Authors: Shravani Itkelwar, Deepak Tewari, Bhaskar Natarajan

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The energy consumption data plays a vital role in energy efficiency policy design, implementation, and impact assessment. Any demand-side energy management intervention's success relies on the availability of accurate, comprehensive, granular, and up-to-date data on energy consumption. The Building sector, including residential and commercial, is one of the largest consumers of energy in India after the Industrial sector. With economic growth and increasing urbanization, the building sector is projected to grow at an unprecedented rate, resulting in a 5.6 times escalation in energy consumption till 2047 compared to 2017. Therefore, energy efficiency interventions will play a vital role in decoupling the floor area growth and associated energy demand, thereby increasing the need for robust data. In India, multiple institutions are involved in the collection and dissemination of data. This paper focuses on energy consumption data management in the building sector in India for both residential and commercial segments. It evaluates the robustness of data available through administrative and survey routes to estimate the key performance indicators and identify critical data gaps for making informed decisions. The paper explores several issues in the data, such as lack of comprehensiveness, non-availability of disaggregated data, the discrepancy in different data sources, inconsistent building categorization, and others. The identified data gaps are justified with appropriate examples. Moreover, the paper prioritizes required data in order of relevance to policymaking and groups it into "available," "easy to get," and "hard to get" categories. The paper concludes with recommendations to address the data gaps by leveraging digital initiatives, strengthening institutional capacity, institutionalizing exclusive building energy surveys, and standardization of building categorization, among others, to strengthen the management of building sector energy consumption data.

Keywords: energy data, energy policy, energy efficiency, buildings

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Authors: Ombir Singh

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In spite of the religious and cultural pre-dominance of the river Ganga in the Indian ethos, fragmentation and degradation of the river continued down the ages. Recognizing the national concern on environmental degradation of the river and its basin, Ministry of Water Resources, River Development & Ganga Rejuvenation (MoWR,RD&GR), Government of India has initiated a number of pilot schemes for the rejuvenation of river Ganga under the ‘Namami Gange’ Programme. Considering the diversity, complexity, and intricacies of forest ecosystems and pivotal multiple functions performed by them and their inter-connectedness with highly dynamic river ecosystems, forestry interventions all along the river Ganga from its origin at Gaumukh, Uttarakhand to its mouth at Ganga Sagar, West Bengal has been planned by the ministry. For that Forest Research Institute (FRI) in collaboration with National Mission for Clean Ganga (NMCG) has prepared a Detailed Project Report (DPR) on Forestry Interventions for Ganga. The Institute has adopted an extensive consultative process at the national and state levels involving various stakeholders relevant in the context of river Ganga and employed a science-based methodology including use of remote sensing and GIS technologies for geo-spatial analysis, modeling and prioritization of sites for proposed forestation and conservation interventions. Four sets of field data formats were designed to obtain the field based information for forestry interventions, mainly plantations and conservation measures along the river course. In response, five stakeholder State Forest Departments had submitted more than 8,000 data sheets to the Institute. In order to analyze a voluminous field data received from five participating states, the Institute also developed a software to collate, analyze and generation of reports on proposed sites in Ganga basin. FRI has developed potential plantation and treatment models for the proposed forestry and other conservation measures in major three types of landscape components visualized in the Ganga riverscape. These are: (i) Natural, (ii) Agriculture, and (iii) Urban Landscapes. Suggested plantation models broadly varied for the Uttarakhand Himalayas and the Ganga Plains in five participating states. Besides extensive plantations in three type of landscapes within the riverscape, various conservation measures such as soil and water conservation, riparian wildlife management, wetland management, bioremediation and bio-filtration and supporting activities such as policy and law intervention, concurrent research, monitoring and evaluation, and mass awareness campaigns have been envisioned in the DPR. The DPR also incorporates the details of the implementation mechanism, budget provisioned for different components of the project besides allocation of budget state-wise to five implementing agencies, national partner organizations and the Nodal Ministry.

Keywords: conservation, Ganga, river, water, forestry interventions

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Authors: Silvina Caíno-Lores, Jesús Carretero

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Large scale computing infrastructures have been widely developed with the core objective of providing a suitable platform for high-performance and high-throughput computing. These systems are designed to support resource-intensive and complex applications, which can be found in many scientific and industrial areas. Currently, large scale data-intensive applications are hindered by the high latencies that result from the access to vastly distributed data. Recent works have suggested that improving data locality is key to move towards exascale infrastructures efficiently, as solutions to this problem aim to reduce the bandwidth consumed in data transfers, and the overheads that arise from them. There are several techniques that attempt to move computations closer to the data. In this survey we analyse the different mechanisms that have been proposed to provide data locality for large scale high-performance and high-throughput systems. This survey intends to assist scientific computing community in understanding the various technical aspects and strategies that have been reported in recent literature regarding data locality. As a result, we present an overview of locality-oriented techniques, which are grouped in four main categories: application development, task scheduling, in-memory computing and storage platforms. Finally, the authors include a discussion on future research lines and synergies among the former techniques.

Keywords: data locality, data-centric computing, large scale infrastructures, cloud computing

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Authors: Mohammad Soltani, Ayoub Saeidi, Nikoo Khosravi, Hanieh Nohbaradar, Seyedeh Parya Barzanjeh, Hassane Zouhal

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Exercise training and herbs supplement represent have role in the treatment for patients with type 2 diabetes (T2D). However, it is unclear combined effects of exercise training and herbs supplements on diabetic risk markers. This study aimed to determine the effect of 12 weeks of combined exercise and broccoli supplementation on decitin-1 and insulin resistance in men with type 2 diabetes. Forty-four type 2 diabetes men (age, 48.52 ± 4.36) were randomly allocated to training -supplement (TS, n = 11), training- placebo (TP, n = 11), supplement (S, n = 11) and control- placebo (CP, n = 11) groups. The combined exercise program included 12 weeks, three sessions per week, that each session contained 45 minutes of resistance training with intensity 60-70% of one maximal repetition and 30 minutes aerobic training (running) with intensity 60-70% of maximum heart rate. In addition supplement groups consumed 10 grams of Broccoli per day for 12 weeks. Plasma Decitin-1, HOMA-IR, Insulin, glucose and body composition were assessed before and after training. Plasma Dectin-1, HOMA-IR, glucose and BMI significantly decreased in TS, TP and S groups compared with CP group (P < .05). In addition Insulin and skeletal muscles mass showed significant increase in TS and TP groups compared with S and CP groups (P < .05). It is concluded that both combined exercise training (aerobic-resistance) or broccoli supplement can improve plasma Decitin-1 and insulin resistance in two diabetic patients however combine of exercise training and broccoli supplement have more effective on these markers.

Keywords: broccoli supplements, combined training, decitin-1, insulin resistance, type 2 diabetes

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Authors: Harold P. Villota, Alejandro Osorio B.

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The energy meteorology is an area for study energy complementarity and the use of renewable sources in interconnected systems. Due to diversify the energy matrix in Colombia with wind sources, is necessary to know the data bases about this one. However, the time series given by 260 automatic weather stations have empty, and no apply data, so the purpose is to fill the time series selecting two years to characterize, impute and use like base to complete the data between 2005 and 2020.

Keywords: complementarity, wind speed, renewable, colombia, characteri, characterization, imputation

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Authors: Noxolo Mafu

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This study presents an overview of strategic planning in South African higher education institutions by tracing its trends and mystique in order to identify its impact. Over the democratic decades, strategic planning has become integral to institutional survival. It has been used as a potent tool by several institutions to catch up and surpass counterparts. While planning has always been part of higher education, strategic planning should be considered different. Strategic planning is primarily about development and maintenance of a strategic fitting between an institution and its dynamic opportunities. This presupposes existence of sets of stages that institutions pursue of which, can be regarded for assessment of the impact of strategic planning in an institution. The network theory serves guides the study in demystifying apparent organisational networks in strategic planning processes.

Keywords: network theory, strategy, planning, strategic planning, assessment, impact

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Authors: Ibrahim Musa, Mohammed Abdul-Aziz Mabrouk, Yusuf Tanko

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Introduction: Long term physical training affect the performance of athletes especially the females. Soccer which is a team sport, played in an outdoor field, require adequate oxygen transport system for the maximal aerobic power during exercise in order to complete 90 minutes of competitive play. Suboptimal haematological status has often been recorded in athletes with intensive physical activity. It may be due to the iron depletion caused by hemolysis or haemodilution results from plasma volume expansion. There is lack of data regarding the dynamics of red blood cell variables, in male football players. We hypothesized that, a long competitive season involving frequent matches and intense training could influence red blood cell variables, as a consequence of applying repeated physical loads when compared with sedentary. Methods: This cross sectional study was carried on 40 adult males (20 athletes and 20 non athletes) between 18-25 years of age. The 20 apparently healthy male non athletes were taken as sedentary and 20 male footballers comprise the study group. The university institutional review board (ABUTH/HREC/TRG/36) gave approval for all procedures in accordance with the Declaration of Helsinki. Red blood cell (RBC) concentration, packed cell volume (PCV), and plasma volume were measured in fasting state and immediately after exercise. Statistical analysis was done by using SPSS/ win.20.0 for comparison within and between the groups, using student’s paired and unpaired “t” test respectively. Results: The finding from our study shows that, immediately after termination of exercise, the mean RBC counts and PCV significantly (p<0.005) decreased with significant increased (p<0.005) in plasma volume when compared with pre-exercised values in both group. In addition the post exercise RBC was significantly higher in untrained (261.10±8.5) when compared with trained (255.20±4.5). However, there was no significant differences in the post exercise hematocrit and plasma volume parameters between the sedentary and the footballers. Moreover, beside changes in pre-exercise values among the sedentary and the football players, the resting red blood cell counts and Plasma volume (PV %) was significantly (p < 0.05) higher in the sedentary group (306.30±10.05 x 104 /mm3; 58.40±0.54%) when compared with football players (293.70±4.65 x 104 /mm3; 55.60±1.18%). On the other hand, the sedentary group exhibited significant (p < 0.05) decrease in PCV (41.60±0.54%) when compared with the football players (44.40±1.18%). Conclusions: It is therefore proposed that the acute football exercise induced reduction in RBC and PCV is entirely due to plasma volume expansion, and not of red blood cell hemolysis. In addition, the training status also influenced haematological indices of male football players differently from the sedentary at rest due to adaptive response. This is novel.

Keywords: Haematological Indices, Performance Status, Sedentary, Male Football Players

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Authors: Hyun-Woo Cho

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Unexpected events may occur with serious impacts on industrial process. This work utilizes a data representation technique to model and to analyze process data pattern for the purpose of diagnosis. In this work, the use of triangular representation of process data is evaluated using simulation process. Furthermore, the effect of using different pre-treatment techniques based on such as linear or nonlinear reduced spaces was compared. This work extracted the fault pattern in the reduced space, not in the original data space. The results have shown that the non-linear technique based diagnosis method produced more reliable results and outperforms linear method.

Keywords: process monitoring, data analysis, pattern modeling, fault, nonlinear techniques

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Authors: Sorayya Kazemi, Hamed Kharrati, Mehdi Abedinpour Fallah

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This paper presents the design and implementation of a novel electric pressure-sensitive toothbrush, capable of measuring the forces applied to the head of the brush. The developed device is used for gum recession measurement. In particular, the percentage of gum recession is measured by a Programmable Automation controller (PAC). Moreover, the brushing forces are measured by a Force Sensing Resistor (FSR) sensor. These forces are analog inputs of PAC. According to the applied forces during patient’s brushing and the patient’s percentage of gum recession, dentist sets the standard force range. The instrument alarms when the patient applies a force over the set range.

Keywords: gum recession, force sensing resistor, controller, toothbrush

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Authors: Lu Liao

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This paper studies the impact of private labels on the advertising incentives of national brands. The worldwide expansion of private labels over the past two decades not only transformed the choice sets of consumers but also forced manufacturers of national brands to design new marketing strategies to maintain their market positions. This paper first develops a consumer demand model that incorporates spillover effects of advertising for antacids, including private labels and finds positive spillovers of national brands’ advertising on demand for private label antacids. With the demand estimates, it provides a simulation for the equilibrium prices and advertising levels for leading national brands in a counterfactual where private labels are eliminated to quantify national brands’ advertising incentives as a response to the rise of private labels.

Keywords: advertising, private label, marketing, demand

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Authors: Mohamed Sameer Mughal, Andrew D. Ross, Damian J. Fearon

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Background: The fast-paced changing landscape of UK Higher Education Institution (HEIs) is poised by changes and challenges affecting Middle Managers (MM) in their job roles. MM contribute to the success of HEIs by balancing the equilibrium and pass organization strategies from senior staff towards operationalization directives to junior staff. However, this study showcased from the data analyzed during the semi structured interviews; MM job role is becoming more complex due to changes and challenges creating colossal pressures and workloads in day-to-day working. Current development programmes provisions by Human Resources (HR) departments in such HEIs are not feasible, applicable, and matching the true essence and requirements of MM who suggest that programmes offered by HR are too generic to suit their precise needs and require tailor made espousal to work effectively in their pertinent job roles. Methodologies: This study aims to capture demands of MM Development Needs (DN) by means of a conceptual model as conclusive part of the research that is divided into 2 phases. Phase 1 initiated by carrying out 2 pilot interviews with a retired Emeritus status professor and HR programmes development coordinator. Key themes from the pilot and literature review subsidized into formulation of 22 set of questions (Kvale and Brinkmann) in form of interviewing questionnaire during qualitative data collection. Data strategy and collection consisted of purposeful sampling of 12 semi structured interviews (n=12) lasting approximately an hour for all participants. The MM interviewed were at faculty and departmental levels which included; deans (n=2), head of departments (n=4), subject leaders (n=2), and lastly programme leaders (n=4). Participants recruitment was carried out via emails and snowballing technique. The interviews data was transcribed (verbatim) and managed using Computer Assisted Qualitative Data Analysis using Nvivo ver.11 software. Data was meticulously analyzed using Miles and Huberman inductive approach of positivistic style grounded theory, whereby key themes and categories emerged from the rich data collected. The data was precisely coded and classified into case studies (Robert Yin); with a main case study, sub cases (4 classes of MM) and embedded cases (12 individual MMs). Major Findings: An interim conceptual model emerged from analyzing the data with main concepts that included; key performance indicators (KPI’s), HEI effectiveness and outlook, practices, processes and procedures, support mechanisms, student events, rules, regulations and policies, career progression, reporting/accountability, changes and challenges, and lastly skills and attributes. Conclusion: Dynamic elements affecting MM includes; increase in government pressures, student numbers, irrelevant development programmes, bureaucratic structures, transparency and accountability, organization policies, skills sets… can only be confronted by employing structured development programmes originated by HR that are not provided generically. Future Work: Stage 2 (Quantitative method) of the study plans to validate the interim conceptual model externally through fully completed online survey questionnaire (Bram Oppenheim) from external HEIs (n=150). The total sample targeted is 1500 MM. Author contribution focuses on enhancing management theory and narrow the gap between by HR and MM development programme provision.

Keywords: development needs (DN), higher education institutions (HEIs), human resources (HR), middle managers (MM)

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