Search results for: nonprofit organizations-national data maturity index (NDI)

Authors: Yunfeng Shan, Xiaoliang Wang, Youjun Zhou

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Whole-genome data from two lungfish species, along with other species, present a valuable opportunity to re-investigate the longstanding debate regarding the evolutionary relationships among tetrapods, lungfishes, and coelacanths. However, the use of bootstrap support has become outdated for large-scale phylogenomic data. Without robust phylogenetic support, the phylogenetic trees become meaningless. Therefore, it is necessary to re-evaluate the phylogenies of tetrapods, lungfishes, and coelacanths using novel measures of phylogenetic support specifically designed for phylogenomic data, as the previous phylogenies were based on 100% bootstrap support. Our findings consistently provide strong evidence favoring lungfish as the closest living relative of tetrapods. This conclusion is based on high internode certainty, relative gene support, and high gene concordance factor. The evidence stems from five previous datasets derived from lungfish transcriptomes. These results yield fresh insights into the three hypotheses regarding the phylogenies of tetrapods, lungfishes, and coelacanths. Importantly, these hypotheses are not mere conjectures but are substantiated by a significant number of genes. Analyzing real biological data further demonstrates that the inclusion of additional taxa leads to more diverse tree topologies. Consequently, gene trees and species trees may not be identical even when whole-genome sequencing data is utilized. However, it is worth noting that many gene trees can accurately reflect the species tree if an appropriate number of taxa, typically ranging from six to ten, are sampled. Therefore, it is crucial to carefully select the number of taxa and an appropriate outgroup, such as slow-evolving species, while excluding fast-evolving taxa as outgroups to mitigate the adverse effects of long-branch attraction and achieve an accurate reconstruction of the species tree. This is particularly important as more whole-genome sequencing data becomes available.

Keywords: novel measures of phylogenetic support for phylogenomic data, gene concordance factor confidence, relative gene support, internode certainty, origin of tetrapods

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Authors: Shreya Goyal

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The study aims to build user-friendly software to understand patient arrival patterns and compute the number of potential patients who will visit a particular health facility for a given period by using a machine learning algorithm. The underlying machine learning algorithm used in this study is the Support Vector Machine (SVM). Accurate prediction of patient arrival allows hospitals to operate more effectively, providing timely and efficient care while optimizing resources and improving patient experience. It allows for better allocation of staff, equipment, and other resources. If there's a projected surge in patients, additional staff or resources can be allocated to handle the influx, preventing bottlenecks or delays in care. Understanding patient arrival patterns can also help streamline processes to minimize waiting times for patients and ensure timely access to care for patients in need. Another big advantage of using this software is adhering to strict data protection regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States as the hospital will not have to share the data with any third party or upload it to the cloud because the software can read data locally from the machine. The data needs to be arranged in. a particular format and the software will be able to read the data and provide meaningful output. Using software that operates locally can facilitate compliance with these regulations by minimizing data exposure. Keeping patient data within the hospital's local systems reduces the risk of unauthorized access or breaches associated with transmitting data over networks or storing it in external servers. This can help maintain the confidentiality and integrity of sensitive patient information. Historical patient data is used in this study. The input variables used to train the model include patient age, time of day, day of the week, seasonal variations, and local events. The algorithm uses a Supervised learning method to optimize the objective function and find the global minima. The algorithm stores the values of the local minima after each iteration and at the end compares all the local minima to find the global minima. The strength of this study is the transfer function used to calculate the number of patients. The model has an output accuracy of >95%. The method proposed in this study could be used for better management planning of personnel and medical resources.

Keywords: machine learning, SVM, HIPAA, data

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Authors: Thiago M. R. Dias, Patrícia M. Dias, Gray F. Moita

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The production and publication of scientific works have increased significantly in the last years, being the Internet the main factor of access and distribution of these works. Faced with this, there is a growing interest in understanding how scientific research has evolved, in order to explore this knowledge to encourage research groups to become more productive. Therefore, the objective of this work is to explore repositories containing data from scientific publications and to characterize keyword networks of these publications, in order to identify the most relevant keywords, and to highlight those that have the greatest impact on the network. To do this, each article in the study repository has its keywords extracted and in this way the network is  characterized, after which several metrics for social network analysis are applied for the identification of the highlighted keywords.

Keywords: bibliometrics, data analysis, extraction and data integration, scientometrics

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Authors: Chiun-C.R. Wang, Po-Wen Yen, Chien-Chun Huang

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Banana is produced in large quantities in tropical and subtropical areas. Banana is one of the important fruits which constitute a valuable source of energy, vitamins and minerals. The ripening and maturity standards of banana vary from country to country depending on the expected shelf life of market. The compositions of bananas change dramatically during ethylene-induced ripening that are categorized as nutritive values and commercial utilization. Nevertheless, there is few study reporting the changes of physicochemical properties of banana starch during ethylene-induced ripening of green banana. The objectives of this study were to investigate the changes of chemical composition and enzyme activity of banana and physicochemical properties of banana starch during ethylene-induced ripening. Green bananas were harvested and ripened by ethylene gas at low temperature (15℃) for seven stages. At each stage, banana was sliced and freeze-dried for banana flour preparation. The changes of total starch, resistant starch, chemical compositions, physicochemical properties, activity of amylase, polyphenolic oxidase (PPO) and phenylalanine ammonia lyase (PAL) of banana were analyzed each stage during ripening. The banana starch was isolated and analyzed for gelatinization properties, pasting properties and microscopic appearance each stage of ripening. The results indicated that the highest total starch and resistant starch content of green banana were 76.2% and 34.6%, respectively at the harvest stage. Both total starch and resistant starch content were significantly declined to 25.3% and 8.8%, respectively at the seventh stage. Soluble sugars content of banana increased from 1.21% at harvest stage to 37.72% at seventh stage during ethylene-induced ripening. Swelling power of banana flour decreased with the progress of ripening stage, but solubility increased. These results strongly related with the decreases of starch content of banana flour during ethylene-induced ripening. Both water insoluble and alcohol insoluble solids of banana flour decreased with the progress of ripening stage. Both activity of PPO and PAL increased, but the total free phenolics content decreased, with the increases of ripening stages. As ripening stage extended, the gelatinization enthalpy of banana starch significantly decreased from 15.31 J/g at the harvest stage to 10.55 J/g at the seventh stage. The peak viscosity and setback increased with the progress of ripening stages in the pasting properties of banana starch. The highest final viscosity, 5701 RVU, of banana starch slurry was found at the seventh stage. The scanning electron micrograph of banana starch showed the shapes of banana starch appeared to be round and elongated forms, ranging in 10-50 μm at the harvest stage. As the banana closed to ripe status, some parallel striations were observed on the surface of banana starch granular which could be caused by enzyme reaction during ripening. These results inferred that the highest resistant starch was found in the green banana at the harvest stage could be considered as a potential application of healthy foods. The changes of chemical composition and physicochemical properties of banana could be caused by the hydrolysis of enzymes during the ethylene-induced ripening treatment.

Keywords: ethylene-induced ripening, banana starch, resistant starch, soluble sugars, physicochemical properties, gelatinization enthalpy, pasting characteristics, microscopic appearance

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Authors: Yusri Yusof, Kamran Latif

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Computer Numeric Control (CNC) machine has been widely used in the industries since its inception. Currently, in CNC technology has been used for various operations like milling, drilling, packing and welding etc. with the rapid growth in the manufacturing world the demand of flexibility in the CNC machines has rapidly increased. Previously, the commercial CNC failed to provide flexibility because its structure was of closed nature that does not provide access to the inner features of CNC. Also CNC’s operating ISO data interface model was found to be limited. Therefore, to overcome that problem, Open Architecture Control (OAC) technology and STEP-NC data interface model are introduced. At present the Personal Computer (PC) has been the best platform for the development of open-CNC systems. In this paper, both ISO data interface model interpretation, its verification and execution has been highlighted with the introduction of the new techniques. The proposed is composed of ISO data interpretation, 3D simulation and machine motion control modules. The system is tested on an old 3 axis CNC milling machine. The results are found to be satisfactory in performance. This implementation has successfully enabled sustainable manufacturing environment.

Keywords: CNC, ISO 6983, ISO 14649, LabVIEW, open architecture control, reconfigurable manufacturing systems, sustainable manufacturing, Soft-CNC

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Authors: Kyeong-Ri Ko, Seong Bong Bae, Jang Sik Choi, Sung Bum Pan

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A simple method for testing the posture imbalance of the human body is to check for differences in the bilateral shoulder and pelvic height of the target. In this paper, to check for spinal disorders the authors have studied ways to establish a motion capture system to obtain and express motions of 4-joints, and to acquire data based on this system. The 4 sensors are attached to the both shoulders and pelvis. To verify the established system, the normal and abnormal postures of the targets listening to a lecture were obtained using the established 4-joint based motion capture system. From the results, it was confirmed that the motions taken by the target was identical to the 3-dimensional simulation.

Keywords: inertial sensor, motion capture, motion data acquisition, posture imbalance

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Authors: Suchitra Chnadrashekhar

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Earlier looked as a support function in an organization information technology has now become a critical utility to manage their daily operations. Organizations are processing huge amount of data which was unimaginable few decades before. This has opened the opportunity for IT sector to help industries across domains to handle the data in the most intelligent manner. Presence of IT has been a leverage for the Oil & Gas industry to store, manage and process the data in most efficient way possible thus deriving the economic value in their day-to-day operations. Proper synchronization between Operational data system and Information Technology system is the need of the hour. Predictive analytics supports oil and gas companies by addressing the challenge of critical equipment performance, life cycle, integrity, security, and increase their utilization. Predictive analytics go beyond early warning by providing insights into the roots of problems. To reach their full potential, oil and gas companies need to take a holistic or systems approach towards asset optimization and thus have the functional information at all levels of the organization in order to make the right decisions. This paper discusses how the use of predictive analysis in oil and gas industry is redefining the dynamics of this sector. Also, the paper will be supported by real time data and evaluation of the data for a given oil production asset on an application tool, SAS. The reason for using SAS as an application for our analysis is that SAS provides an analytics-based framework to improve uptimes, performance and availability of crucial assets while reducing the amount of unscheduled maintenance, thus minimizing maintenance-related costs and operation disruptions. With state-of-the-art analytics and reporting, we can predict maintenance problems before they happen and determine root causes in order to update processes for future prevention.

Keywords: hydrocarbon, information technology, SAS, predictive analytics

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Authors: Alia Al Rayssi, Mahra Al Marar, Alyazia Alkhaili, Reem Al Dhaheri, Shayma Alkobaisi, Hoda Amer

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We present in this paper a web-based system called “HelpMeBreathe” for managing asthma. The proposed system provides analytical tools, which allow better understanding of environmental triggers of asthma, hence better support of data-driven decision making. The developed system provides warning messages to a specific asthma patient if the weather in his/her area might cause any difficulty in breathing or could trigger an asthma attack. HelpMeBreathe collects, stores, and analyzes individuals’ moving trajectories and health conditions as well as environmental data. It then processes and displays the patients’ data through an analytical tool that leads to an effective decision making by physicians and other decision makers.

Keywords: asthma, environmental triggers, map interface, web-based systems

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Authors: Arbolino R., Boffardi R., L. De Simone

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The success of a development policy requires the definition of a proper investment planning on behalf of policymakers. Such planning should consider both tangible and intangible features characterizing a territory and, moreover, evaluate both state of place and an ideal situation to be achieved, that represents the final goal of the policy. The aim of this research is to propose a methodological approach to implement this ideal solution or the best solution appliable to the Italian regions. It consists of two steps: the first one is a measure of regional inequality through building a composite indicator for analyzing the level of development and compare the differences among the regions. It is constructed by means of a principal component analysis. Ranking regions according to the scores achieved is useful as benchmark, to identify a best solution towards which other regions should strive. Thus, this distance is measured through a revised Sen index that allows to assign a weight on which calculate the financing resource programming. The results show that this approach is a good instrument to fairly and efficiently allocate public funds, in order to reduce regional inequalities.

Keywords: public economics, inequalities, growth, development

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Authors: Bakr Ali Al-Gamrh, Ku Nor Izah B. Ku Ismail

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We investigate the relationship between corporate governance, leverage, risk, and firm performance. We use a firm level panel that spans the period 2008 to 2012 of all listed firms on Abu Dhabi Stock Exchange and Dubai Financial Market. After constructing an index of corporate governance strength, we find a negative effect of corporate governance on firm performance. We, however, discover that corporate governance strength indirectly improves the negative influence of leverage on firm performance in normal times. On the contrary, the results completely reversed when there is a black swan event. Corporate governance strength plays a significantly negative role in moderating the relationship between leverage and firm performance during the financial crisis. We also reveal that corporate governance strength increases firms’ risk and deteriorates performance during crisis. Results provide evidence that corporate governance indirectly plays a completely different role in different time periods.

Keywords: corporate governance, firm performance, risk, leverage, the UAE

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Authors: Huanhuan Zhao, Bryony J. James

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The relationship between the non-fat solid ratio and bloom formation in untempered chocolate was investigated using two types of chocolate: model chocolate made of varying cocoa powder ratios (46, 49.5 and 53%) and cocoa butter, and commercial Lindt chocolate with varying cocoa content (70, 85 and 90%). X-ray diffraction and colour measurement techniques were used to examine the polymorphism of cocoa butter and the surface whiteness index (WI), respectively. The polymorphic transformation of cocoa butter was highly correlated with the changes of WI during 30 days of storage since it led to the redistribution of fat within the chocolate matrix and resulted in a bloomed surface. The change in WI indicated a similar bloom rate in the chocolates, but the model chocolates with a higher cocoa powder ratio had more pronounced total bloom. This is due to a higher ratio of non-fat solid particles on the surface resulting in microscopic changes in morphology. The ratio of non-fat solids is an important factor in determining the extent of bloom but not the bloom rate.

Keywords: untempered chocolate, microstructure of bloom, polymorphic transformation, surface whiteness

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Authors: Yelin Zhao, Xinxiu Li, Martin Smelik, Oleg Sysoev, Firoj Mahmud, Dina Mansour Aly, Mikael Benson

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Background: Early diagnosis of pancreatic cancer is clinically challenging due to vague, or no symptoms, and lack of biomarkers. Polygenic risk score (PRS) scores may provide a valuable tool to assess increased or decreased risk of PC. This study aimed to develop such PRS by filtering genetic variants identified by GWAS using transcriptional programs identified by single-cell RNA sequencing (scRNA-seq). Methods: ScRNA-seq data from 24 pancreatic ductal adenocarcinoma (PDAC) tumor samples and 11 normal pancreases were analyzed to identify differentially expressed genes (DEGs) in in tumor and microenvironment cell types compared to healthy tissues. Pathway analysis showed that the DEGs were enriched for hundreds of significant pathways. These were clustered into 40 “programs” based on gene similarity, using the Jaccard index. Published genetic variants associated with PDAC were mapped to each program to generate program PRSs (pPRSs). These pPRSs, along with five previously published PRSs (PGS000083, PGS000725, PGS000663, PGS000159, and PGS002264), were evaluated in a European-origin population from the UK Biobank, consisting of 1,310 PDAC participants and 407,473 non-pancreatic cancer participants. Stepwise Cox regression analysis was performed to determine associations between pPRSs with the development of PC, with adjustments of sex and principal components of genetic ancestry. Results: The PDAC genetic variants were mapped to 23 programs and were used to generate pPRSs for these programs. Four distinct pPRSs (P1, P6, P11, and P16) and two published PRSs (PGS000663 and PGS002264) were significantly associated with an increased risk of developing PC. Among these, P6 exhibited the greatest hazard ratio (adjusted HR[95% CI] = 1.67[1.14-2.45], p = 0.008). In contrast, P10 and P4 were associated with lower risk of developing PC (adjusted HR[95% CI] = 0.58[0.42-0.81], p = 0.001, and adjusted HR[95% CI] = 0.75[0.59-0.96], p = 0.019). By comparison, two of the five published PRS exhibited an association with PDAC onset with HR (PGS000663: adjusted HR[95% CI] = 1.24[1.14-1.35], p < 0.001 and PGS002264: adjusted HR[95% CI] = 1.14[1.07-1.22], p < 0.001). Conclusion: Compared to published PRSs, scRNA-seq-based pPRSs may be used not only to assess increased but also decreased risk of PDAC.

Keywords: cox regression, pancreatic cancer, polygenic risk score, scRNA-seq, UK biobank

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Authors: A. Cittadino, P. Gantes, C. Coviella, M. Casset, A. Sanchez Caro

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Three currently active landfills receive the waste from Buenos Aires city and the Great Buenos Aires suburbs. One of the first landfills to receive solid waste from this area was located in Villa Dominico, some 7 km south from Buenos Aires City. With an area of some 750 ha, including riparian habitats, divided into 14 cells, it received solid wastes from June 1979 through February 2004. In December 2010, a biological monitoring program was set up by CEAMSE and Universidad Nacional de Lujan, still operational to date. The aim of the monitoring program is to assess the state of several biological groups within the landfill and to follow their dynamics overtime in order to identify if any, early signs of damage the landfill activities might have over the biota present. Bird and rodent populations, aquatic and terrestrial invertebrates’ populations, cells vegetation coverage, and surrounding areas vegetation coverage and main composition are followed by quarterly samplings. Bird species richness and abundance were estimated by observation over walk transects on each environment. A total of 74 different species of birds were identified. Species richness and diversity were high for both riparian surrounding areas and within the landfill. Several grassland -typical of the 'Pampa'- bird species were found within the landfill, as well as some migratory and endangered bird species. Sherman and Tomahawk traps are set overnight for small mammal sampling. Rodent populations are just above detection limits, and the few specimens captured belong mainly to species common to rural areas, instead of city-dwelling species. The two marsupial species present in the region were captured on occasions. Aquatic macroinvertebrates were sampled on a watercourse upstream and downstream the outlet of the landfill’s wastewater treatment plant and are used to follow water quality using biological indices. Water quality ranged between weak and severe pollution; benthic invertebrates sampled before and after the landfill, show no significant differences in water quality using the IBMWP index. Insect biota from yellow sticky cards and pitfall traps showed over 90 different morphospecies, with Shannon diversity index running from 1.9 to 3.9, strongly affected by the season. An easy-to-perform non-expert demandant method was used to assess vegetation coverage. Two scales of determination are utilized: field observation (1 m resolution), and Google Earth images (that allow for a better than 5 m resolution). Over the eight year period of the study, vegetation coverage over the landfill cells run from a low 83% to 100% on different cells, with an average between 95 to 99% for the entire landfill depending on seasonality. Surrounding area vegetation showed almost 100% coverage during the entire period, with an average density from 2 to 6 species per sq meter and no signs of leachate damaged vegetation.

Keywords: biological indicators, biota monitoring, landfill species diversity, waste management

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Authors: Liqiang Chen

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This study investigates the effects of political connections on the association between firms’ business strategy and their tax aggressiveness in an emerging economy such as China. By studying all public Chinese firms in the period from 2011 to 2017, we find that firms adopting innovative business strategy are more tax aggressive overall, but innovative firms with political connections are less tax aggressive compared to those without political connections. Moreover, we document several channels through which political connections affect the association between innovative business strategy and tax aggressiveness. In particular, we show that the mitigation effect of political connections on tax aggressiveness is stronger for innovative firms located in areas with a lower marketization index and for innovative firms with a lower leverage level or with less earnings management. Our results are robust to an instrumental variable approach to account for possible endogenous bias. Our study contributes to the understanding of firms’ tax behaviors in an emerging economy setting and suggests that there are costs associated with political connections, such as foregone tax saving opportunities, which are understudied in the prior literature.

Keywords: tax aggressiveness, business strategy, political connections, emerging economy

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Authors: Ellen Nhedzi Gozo

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Unavailability of adequate hydro-meteorological data has always limited the analysis and understanding of hydrological behaviour of several dam catchments including Mazowe Dam in Zimbabwe. The problem of insufficient data for Mazowe Dam catchment analysis was solved by extracting catchment characteristics and aerial hydro-meteorological data from ASTER, LANDSAT, Shuttle Radar Topographic Mission SRTM remote sensing (RS) images using ILWIS, ArcGIS and ERDAS Imagine geographic information systems (GIS) software. Available observed hydrological as well as meteorological data complemented the use of the remotely sensed information. Ground truth land cover was mapped using a Garmin Etrex global positioning system (GPS) system. This information was then used to validate land cover classification detail that was obtained from remote sensing images. A bathymetry survey was conducted using a SONAR system connected to GPS. Hydrological modelling using the HBV model was then performed to simulate the hydrological process of the catchment in an effort to verify the reliability of the derived parameters. The model output shows a high Nash-Sutcliffe Coefficient that is close to 1 indicating that the parameters derived from remote sensing and GIS can be applied with confidence in the analysis of Mazowe Dam catchment.

Keywords: geographic information systems, hydrological modelling, remote sensing, water resources management

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Authors: Eva L. Sanjuán, Jacinto Martín, M. Isabel Parra, Mario M. Pizarro

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In Extreme Value Theory, inference estimation for the parameters of the distribution is made employing a small part of the observation values. When block maxima values are taken, many data are discarded. We developed a new Bayesian inference model to seize all the information provided by the data, introducing informative priors and using the relations between baseline and limit parameters. Firstly, we studied the accuracy of the new model for three baseline distributions that lead to a Gumbel extreme distribution: Exponential, Normal and Gumbel. Secondly, we considered mixtures of Normal variables, to simulate practical situations when data do not adjust to pure distributions, because of perturbations (noise).

Keywords: bayesian inference, extreme value theory, Gumbel distribution, highly informative prior

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Authors: Rowan P. Martnishn

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During the execution of a National Endowment of the Arts grant, approximately 55 interviews were collected from professionals across various fields. These interviews were used to create deliverables – historical connections for creations that began as art and evolved entirely into computing technology. With dozens of hours’ worth of transcripts to be analyzed by qualitative coders, a quantitative methodology was created to sift through the documents. The initial step was to both clean and format all the data. First, a basic spelling and grammar check was applied, as well as a Python script for normalized formatting which used an open-source grammatical formatter to make the data as coherent as possible. 10 documents were randomly selected to manually review, where words often incorrectly translated during the transcription were recorded and replaced throughout all other documents. Then, to remove all banter and side comments, the transcripts were spliced into paragraphs (separated by change in speaker) and all paragraphs with less than 300 characters were removed. Secondly, a keyword extractor, a form of natural language processing where significant words in a document are selected, was run on each paragraph for all interviews. Every proper noun was put into a data structure corresponding to that respective interview. From there, a Bidirectional and Auto-Regressive Transformer (B.A.R.T.) summary model was then applied to each paragraph that included any of the proper nouns selected from the interview. At this stage the information to review had been sent from about 60 hours’ worth of data to 20. The data was further processed through light, manual observation – any summaries which proved to fit the criteria of the proposed deliverable were selected, as well their locations within the document. This narrowed that data down to about 5 hours’ worth of processing. The qualitative researchers were then able to find 8 more connections in addition to our previous 4, exceeding our minimum quota of 3 to satisfy the grant. Major findings of the study and subsequent curation of this methodology raised a conceptual finding crucial to working with qualitative data of this magnitude. In the use of artificial intelligence there is a general trade off in a model between breadth of knowledge and specificity. If the model has too much knowledge, the user risks leaving out important data (too general). If the tool is too specific, it has not seen enough data to be useful. Thus, this methodology proposes a solution to this tradeoff. The data is never altered outside of grammatical and spelling checks. Instead, the important information is marked, creating an indicator of where the significant data is without compromising the purity of it. Secondly, the data is chunked into smaller paragraphs, giving specificity, and then cross-referenced with the keywords (allowing generalization over the whole document). This way, no data is harmed, and qualitative experts can go over the raw data instead of using highly manipulated results. Given the success in deliverable creation as well as the circumvention of this tradeoff, this methodology should stand as a model for synthesizing qualitative data while maintaining its original form.

Keywords: B.A.R.T.model, keyword extractor, natural language processing, qualitative coding

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Authors: Aruna Bhat

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Culture can be placed within the social structure that embodies both the creation of social groups, and the manner in which they interact with each other. As many critics have pointed out, culture in the Postmodern context has often been considered a commodity, and indeed it shares many attributes with commercial products. Popular culture follows many patterns of behavior derived from Economics, from the simple principle of supply and demand, to the creation of marketable demographics which fit certain criterion. This trend is exemplary visible in contemporary fiction, especially in contemporary science fiction; Cyberpunk fiction in particular which is an off shoot of pure science fiction. William Gibson is one such author who in his works portrays such a scenario, and in his The Bridge Trilogy he adds another level of interpretation to this state of affairs, by describing a world that is centered on industrialization of a new kind – that focuses around data in the cyberspace. In this new world, data has become the most important commodity, and man has become nothing but a nodal point in a vast ocean of raw data resulting into commodification of each thing including Culture. This paper will attempt to study the presence of above mentioned elements in William Gibson’s The Bridge Trilogy. The theories applied will be Postmodernism and Cultural studies.

Keywords: culture, commodity, cyberpunk, data, postmodern

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Authors: Saif Ul Haq

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The current study intends to examine the safety and quality considerations of clients of housing projects and their impact on the adoption of Quality Function Deployment (QFD) by the construction firm. Mixed method research technique has been used to collect and analyze the data wherein a survey was conducted to collect the data from 220 clients of housing projects in Saudi Arabia. Then, the telephonic and Skype interviews were conducted to collect data of 15 professionals working in the top ten real estate companies of Saudi Arabia. Data were analyzed by using partial least square (PLS) and thematic analysis techniques. Findings reveal that today’s customer prioritizes the safety and quality requirements of their houses and as a result, construction firms adopt QFD to address the needs of customers. The findings are of great importance for the clients of housing projects as well as for the construction firms as they could apply QFD in housing projects to address the safety and quality concerns of their clients.

Keywords: construction industry, quality considerations, quality function deployment, safety considerations

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Authors: Tahira Imtiaz, Karim Ullah

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This paper aims to incorporate the banks employees’ perspective on acceptability of Islamic banking by the customers of Peshawar. A qualitative approach is adopted for which six in-depth interviews with employees of Islamic banks are conducted. The employees were asked to share their experience regarding customers’ acceptance attitude towards acceptability of Islamic banking. Collected data was analyzed through thematic analysis technique and its synthesis with the current literature. Through data analysis a theoretical framework is developed, which highlights the factors which drive customers towards Islamic banking, as witnessed by the employees. The practical implication of analyzed data evident that a new model could be developed on the basis of four determinants of human preference namely: inner satisfaction, time, faith and market forces.

Keywords: customers’ attraction, employees’ perspective, Islamic banking, Riba

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Authors: Yinghui Shang, Ziqing Zhou, Rong Han, Hang Wang, Xiaodi Liu, Yong Yang

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The design of advanced amorphous solids, such as metallic glasses, with targeted properties through artificial intelligence signifies a paradigmatic shift in physical metallurgy and materials technology. Here, we developed a machine-learning architecture that facilitates the generation of metallic glasses with targeted multifunctional properties. Our architecture integrates the state-of-the-art unsupervised generative adversarial network model with supervised models, allowing the incorporation of general prior knowledge derived from thousands of data points across a vast range of alloy compositions, into the creation of data points for a specific type of composition, which overcame the common issue of data scarcity typically encountered in the design of a given type of metallic glasses. Using our generative model, we have successfully designed copper-based metallic glasses, which display exceptionally high hardness or a remarkably low modulus. Notably, our architecture can not only explore uncharted regions in the targeted compositional space but also permits self-improvement after experimentally validated data points are added to the initial dataset for subsequent cycles of data generation, hence paving the way for the customized design of amorphous solids without human intervention.

Keywords: metallic glass, artificial intelligence, mechanical property, automated generation

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Authors: A. T. Siacara, A. T. Beck, M. M. Futai

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This paper shows how accurately and efficiently reliability analyses of geotechnical installations can be performed by directly coupling geotechnical software with a reliability solver. An earth slope is used as the study object. The limit equilibrium method of Morgenstern-Price is used to calculate factors of safety and find the critical slip surface. The deterministic software package Seep/W and Slope/W is coupled with the StRAnD reliability software. Reliability indexes of critical probabilistic surfaces are evaluated by the first-order reliability methods (FORM). By means of sensitivity analysis, the effective cohesion (c') is found to be the most relevant uncertain geotechnical parameter for slope equilibrium. The slope was tested using different geometries, taking into account unsaturated soil properties. Finally, a critical slip surface, identified in terms of minimum factor of safety, is shown here not to be the critical surface in terms of reliability index.

Keywords: slope, unsaturated, reliability, safety, seepage

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Authors: Sunghae Jun

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Technology management (TM) is important issue in a company improving the competitiveness. Among many activities of TM, technology analysis (TA) is important factor, because most decisions for management of technology are decided by the results of TA. TA is to analyze the developed results of target technology using statistics or Delphi. TA based on Delphi is depended on the experts’ domain knowledge, in comparison, TA by statistics and machine learning algorithms use objective data such as patent or paper instead of the experts’ knowledge. Many quantitative TA methods based on statistics and machine learning have been studied, and these have been used for technology forecasting, technological innovation, and management of technology. They applied diverse computing tools and many analytical methods case by case. It is not easy to select the suitable software and statistical method for given TA work. So, in this paper, we propose a methodology for quantitative TA using statistical computing software called R and data science to construct a general framework of TA. From the result of case study, we also show how our methodology is applied to real field. This research contributes to R&D planning and technology valuation in TM areas.

Keywords: technology management, R system, R data science, statistics, machine learning

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Authors: Mohd Asrul Affendi Bi Abdullah, Nyi Nyi Naing

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The purpose of this study was to identify comparable manner between negative binomial death rate (NBDR) and zero inflated negative binomial death rate (ZINBDR) with died patients with (HIV + T B+) and (HIV + T B−). HIV and TB is a serious world wide problem in the developing country. Data were analyzed with applying NBDR and ZINBDR to make comparison which a favorable model is better to used. The ZINBDR model is able to account for the disproportionately large number of zero within the data and is shown to be a consistently better fit than the NBDR model. Hence, as a results ZINBDR model is a superior fit to the data than the NBDR model and provides additional information regarding the died mechanisms HIV+TB. The ZINBDR model is shown to be a use tool for analysis death rate according age categorical.

Keywords: zero inflated negative binomial death rate, HIV and TB, AIC and BIC, death rate

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Authors: Guoliang Fan, Aiping Li, Xuemei Liu, Liyun Xu

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The performance of the tightening equipment will decline with the working process in manufacturing system. The main manifestations are the randomness and discretization degree increasing of the tightening performance. To evaluate the degradation tendency of the tightening performance accurately, a complexity measurement approach based on Kolmogorov entropy is presented. At first, the states of performance index are divided for calibrating the discrete degree. Then the complexity measurement model based on Kolmogorov entropy is built. The model describes the performance degradation tendency of tightening equipment quantitatively. At last, a study case is applied for verifying the efficiency and validity of the approach. The research achievement shows that the presented complexity measurement can effectively evaluate the degradation tendency of the tightening equipment. It can provide theoretical basis for preventive maintenance and life prediction of equipment.

Keywords: complexity measurement, Kolmogorov entropy, manufacturing system, performance evaluation, tightening equipment

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Authors: Chen Wang, Jared Evans, Yan Asmann

Abstract:

With the quick evolvement of next-generation sequencing techniques, whole-exome or exome-panel data have become a cost-effective way for detection of small exonic mutations, but there has been a growing desire to accurately detect copy number variations (CNVs) as well. In order to address this research and clinical needs, we developed a sequencing coverage pattern-based method not only for copy number detections, data integrity checks, CNV calling, and visualization reports. The developed methodologies include complete automation to increase usability, genome content-coverage bias correction, CNV segmentation, data quality reports, and publication quality images. Automatic identification and removal of poor quality outlier samples were made automatically. Multiple experimental batches were routinely detected and further reduced for a clean subset of samples before analysis. Algorithm improvements were also made to improve somatic CNV detection as well as germline CNV detection in trio family. Additionally, a set of utilities was included to facilitate users for producing CNV plots in focused genes of interest. We demonstrate the somatic CNV enhancements by accurately detecting CNVs in whole exome-wide data from the cancer genome atlas cancer samples and a lymphoma case study with paired tumor and normal samples. We also showed our efficient reuses of existing exome sequencing data, for improved germline CNV calling in a family of the trio from the phase-III study of 1000 Genome to detect CNVs with various modes of inheritance. The performance of the developed method is evaluated by comparing CNV calling results with results from other orthogonal copy number platforms. Through our case studies, reuses of exome sequencing data for calling CNVs have several noticeable functionalities, including a better quality control for exome sequencing data, improved joint analysis with single nucleotide variant calls, and novel genomic discovery of under-utilized existing whole exome and custom exome panel data.

Keywords: bioinformatics, computational genetics, copy number variations, data reuse, exome sequencing, next generation sequencing

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Authors: Steffira Anjani, Agnes Dessyana, Luciyana Lesmana

Abstract:

Employee well-being has been a major factor for an employee to deliver optimal performance in the workplace. During the COVID-19 pandemic, there has been a major concern for organizations to develop Employee Assistance Program as an approach to maintain employees’ well-being. However, there is little published evidence assessing the effectiveness of Employee Assistance Program for the employee’s well-being. The purpose of this paper is to advance theory and practice by understanding how the Employee Assistance Program (EAP) impacts to new hire well-being and turnover, especially in private organization. This paper provides an intervention framework used for new employees. The intervention program (onboarding and support group) is carried out to improve new hire well-being and to make them stay at the organization. The intervention is delivered to 36 new hire employees that were recruited from January 2021 to still ongoing 2022. The result of level 1 evaluation shows that new hire employees give a good rating to the intervention program. Next, the result of level 2 evaluation shows that the intervention has a significant difference in new hire well-being before and after the intervention program (Z=-2,11, p<0.05) and increases the percentage of recruitment quality index (RQI = 10%).

Keywords: Employee Assistance Program, well-being, turnover, intervention program

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Authors: Yi Jen Wang, Yu Ju Chen

Abstract:

Background: Heart rate variability (HRV) biofeedback can promote autonomic nervous function, sleep quality and reduce psychological stress. In HRV biofeedback training, it is hoped that through the guidance of machine video or audio, the patient can breathe slowly according to his own heart rate changes so that the heart and lungs can achieve resonance, thereby promoting the related effects of autonomic nerve function; while, it is also pointed out that if slow breathing of 6 times per minute can also guide the case to achieve the effect of cardiopulmonary resonance. However, there is no relevant research to explore the comparison of the effectiveness of cardiopulmonary resonance by using video or audio HRV biofeedback training and metronome-guided slow breathing. Purpose: To compare the promotion of autonomic nervous function performance between using HRV biofeedback and slow breathing guided by a metronome. Method: This research is a kind of experimental design with convenient sampling; the cases are randomly divided into the heart rate variability biofeedback training group and the slow breathing training group. The HRV biofeedback training group will conduct HRV biofeedback training in a four-week laboratory and use the home training device for autonomous training; while the slow breathing training group will conduct slow breathing training in the four-week laboratory using the mobile phone APP breathing metronome to guide the slow breathing training, and use the mobile phone APP for autonomous training at home. After two groups were enrolled and four weeks after the intervention, the autonomic nervous function-related performance was repeatedly measured. Using the chi-square test, student’s t-test and other statistical methods to analyze the results, and use p <0.05 as the basis for statistical significance. Results: A total of 27 subjects were included in the analysis. After four weeks of training, the HRV biofeedback training group showed significant improvement in the HRV indexes (SDNN, RMSSD, HF, TP) and sleep quality. Although the stress index also decreased, it did not reach statistical significance; the slow breathing training group was not statistically significant after four weeks of training, only sleep quality improved significantly, while the HRV indexes (SDNN, RMSSD, TP) all increased. Although HF and stress indexes decreased, they were not statistically significant. Comparing the difference between the two groups after training, it was found that the HF index improved significantly and reached statistical significance in the HRV biofeedback training group. Although the sleep quality of the two groups improved, it did not reach that level in a statistically significant difference. Conclusion: HRV biofeedback training is more effective in promoting autonomic nervous function than slow breathing training, but the effects of reducing stress and promoting sleep quality need to be explored after increasing the number of samples. The results of this study can provide a reference for clinical or community health promotion. In the future, it can also be further designed to integrate heart rate variability biological feedback training into the development of AI artificial intelligence wearable devices, which can make it more convenient for people to train independently and get effective feedback in time.

Keywords: autonomic nervous function, HRV biofeedback, heart rate variability, slow breathing

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Authors: Qiying Zhang, Panpan Xu, Hui Qian

Abstract:

Groundwater is an important water resource in the world, especially in arid and semi-arid regions. In the present study, 141 groundwater samples were collected and analyzed for various physicochemical parameters to assess the irrigation water quality using six indicators (sodium percentage (Na%), sodium adsorption ratio (SAR), magnesium hazard (MH), residual sodium carbonate (RSC), permeability index (PI), and potential salinity (PS)). The results show that the patterns for the average cation and anion concentrations were in decreasing orders of Na^＋ > Mg^2＋ > Ca^2＋ > K^＋and SO₄^2－ > HCO₃^－ > Cl^－ > NO₃^－ > CO₃^2－ > F^－, respectively. The values of Na%, MH, and PS show that most of the groundwater samples are not suitable for irrigation. The same conclusion is drawn from the USSL and Wilcox diagrams. PS values indicate that Cl^－and SO₄^2－have a great influence on irrigation water in Jiaokou Irrigation District. RSC and PI values indicate that more than half of groundwater samples are suitable for irrigation. The finding is beneficial for the policymakers for future water management schemes to achieve a sustainable development goal.

Keywords: groundwater chemistry, Guanzhong Basin, irrigation water quality evaluation, Jiaokou Irrigation District

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Authors: Michael Okeke, Andrew Blyth

Abstract:

Supervisory Control And Data Acquisition (SCADA) as the state of the art Industrial Control Systems (ICS) are used in many different critical infrastructures, from smart home to energy systems and from locomotives train system to planes. Security of SCADA systems is vital since many lives depend on it for daily activities and deviation from normal operation could be disastrous to the environment as well as lives. This paper describes how No-Trust-Zone (NTZ) architecture could be incorporated into SCADA Systems in order to reduce the chances of malicious intent. The architecture is made up of two distinctive parts which are; the field devices such as; sensors, PLCs pumps, and actuators. The second part of the architecture is designed following lambda architecture, which is made up of a detection algorithm based on Particle Swarm Optimization (PSO) and Hadoop framework for data processing and storage. Apache Spark will be a part of the lambda architecture for real-time analysis of packets for anomalies detection.

Keywords: industrial control system (ics, no-trust-zone (ntz), particle swarm optimisation (pso), supervisory control and data acquisition (scada), swarm intelligence (SI)

Procedia PDF Downloads 345