Search results for: quality data assurance

Authors: M. Derraz, M.Farhaoui

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In the water treatment processes, the determination of the optimal dose of the coagulant is an issue of particular concern. Coagulant dosing is correlated to raw water quality which depends on some parameters (turbidity, ph, temperature, conductivity…). The objective of this study is to provide water treatment operators with a tool that enables to predict and replace, sometimes, the manual method (jar testing) used in this plant to predict the optimum coagulant dose. The model is constructed using actual process data for a water treatment plant located in the middle of Morocco (Meknes).

Keywords: coagulation process, aluminum sulfate, model, coagulant dose

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Authors: Rachel Koshy, Nazrila Hairizan Bt. Nasir, Samsiah Bt. Awang, Kamaliah Bt. Mohamad Noh

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Malaysia collaborated as a ‘trailblazer’ country with PHCPI (Primary Health Care Performance Initiative) to populate the Primary Health Care (PHC) Vital Signs Profile (VSP) for the country. The PHC VSP provides an innovative snapshot of the primary health care system's performance. Four domains were assessed: system financing, system capacity, system performance, and system equity, and completed in 2019. There were two phases using a mixed method study design. The first phase involved a quantitative study, utilising existing secondary data from national and international sources. In the case of unavailability of data for any indicators, comparable alternative indicators were used. The second phase was a mixed quantitative-qualitative approach to measure the functional capacity based on governance and leadership, population health needs, inputs, population health management, and facility organisation and management. PHC spending constituted 35% of overall health spending in Malaysia, with a per capita PHC spending of $152. The capacity domain was strong in the three subdomains of governance and leadership, information system, and funds management. The two subdomains of drugs & supplies and facility organisation & management had low scores, but the lowest score was in empanelment of the population under the population health management. The PHC system performed with an access index of 98%, quality index of 84%, and service coverage of 62%. In the equity domain, there was little fluctuation in the coverage of reproductive, maternal, newborn, and child health services by mother’s level of education and under-five child mortality between urban and rural areas. The public sector was stronger in the capacity domain as compared to the private sector. This is due to the different financing, organisational structures, and service delivery mechanism. The VSP has identified areas for improvement in the effort to provide high-quality PHC for the population. The gaps in PHC can be addressed through the system approach and the positioning of public and private primary health care delivery systems.

Keywords: primary health care, health system, system domains, vital signs profile

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Authors: Suchithra V., Shreedhanya, Kavya Menon, Vidya Niranjan

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Skin metagenomics has a wide range of applications with direct relevance to the health of the organism. It gives us insight to the diverse community of microorganisms (the microbiome) harbored on the skin. In the recent years, it has become increasingly apparent that the interaction between skin microbiome and the human body plays a prominent role in immune system development, cancer development, disease pathology, and many other biological implications. Next Generation Sequencing has led to faster and better understanding of environmental organisms and their mutual interactions. This project is studying the human skin microbiome of different individuals having varied skin conditions. Bacterial 16S rRNA data of skin microbiome is downloaded from SRA toolkit provided by NCBI to perform metagenomics analysis. Twelve samples are selected with two controls, and 3 different categories, i.e., sex (male/female), skin type (moist/intermittently moist/sebaceous) and occlusion (occluded/intermittently occluded/exposed). Quality of the data is increased using Cutadapt, and its analysis is done using FastQC. USearch, a tool used to analyze an NGS data, provides a suitable platform to obtain taxonomy classification and abundance of bacteria from the metagenome data. The statistical tool used for analyzing the USearch result is METAGENassist. The results revealed that the top three abundant organisms found were: Prevotella, Corynebacterium, and Anaerococcus. Prevotella is known to be an infectious bacterium found on wound, tooth cavity, etc. Corynebacterium and Anaerococcus are opportunist bacteria responsible for skin odor. This result infers that Prevotella thrives easily in sebaceous skin conditions. Therefore it is better to undergo intermittently occluded treatment such as applying ointments, creams, etc. to treat wound for sebaceous skin type. Exposing the wound should be avoided as it leads to an increase in Prevotella abundance. Moist skin type individuals can opt for occluded or intermittently occluded treatment as they have shown to decrease the abundance of bacteria during treatment.

Keywords: bacterial 16S rRNA , next generation sequencing, skin metagenomics, skin microbiome, taxonomy

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Authors: Rose Ogbechie, Nicholas Anakwue

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In recent times, research has centered, in the area of Business Ethics, on the issue of human quality treatment (HQT), regarding the way people are dealt with, in organizations, taking into cognizance, respect for the dignity of the human person, as well as, the rights and responsibilities of the corporate individual. As such, the principle of respect is an essential ethical principle that should govern professional relationships in the workplace. There is a prevailing myth in the Nigerian business space, that to drive business success, business leadership must coerce and drive people, oftentimes, beyond comfort to meet work expectations. This has, most times, necessitated abuses and insults on subordinates in the workplace, and instituted a rigid hierarchy of management in business relationships. Nestlé Nigeria, one of the largest foods and beverage companies in Africa, provides a contrast to this myth in their success heuristic. Over the years in Nigeria, the company has registered significant successes in the Nigerian Fast-Moving Consumer Goods (FMCG) Market, with stellar performances year-on-year, and a high-penetration rate of its products in the Nigerian consumer space. At the heart of the FMCG giant’s success and culture is the principle of respect—respect for stakeholders, respect for all peoples, respect for cultures, respect for the environment. Utilizing qualitative research methods, through interviews and focus group discussions with Nestlé’s stakeholders, this paper explores the ethical principle of respect, and how, through it, human quality treatment influences positively organizational growth.

Keywords: human quality treatment, respect, Nestlé Nigeria, FMCG, organizational growth

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Authors: Mahnoor Shoaib

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Sustainable development involves meeting present needs without compromising future generations’ capacity to meet their own while enhancing the quality of life through a healthy and safe environment. In the context of rapid urbanization and globalization, architects and planners bear the responsibility of designing residential buildings that are sustainable and conducive to quality living. Residential buildings serve as multifunctional spaces for personal and family life, making them essential for fostering healthy communities. Therefore, sustainable housing must address not only economic and environmental factors but also social, historical, and cultural dimensions to enhance residents' social lives. This research investigates the socio-cultural aspects of tall residential buildings in Karachi, Pakistan, a developing community characterized by rapid population growth and urbanization. A mixed-methods approach, including qualitative interviews and surveys, was employed to assess residents' perceptions of sustainability in tall buildings, focusing on socio-cultural design constraints and their impact on residential satisfaction. The study finds that socio-cultural elements, such as liveability, social cohesion, and spatial agency, significantly influence residents’ satisfaction with high-rise developments. Moreover, it highlights the need for contextual design solutions that integrate local cultural values into the architecture of tall buildings rather than imposing Western design principles. In conclusion, this research provides valuable insights for architects, designers, and urban planners, emphasizing the importance of understanding community needs and preferences in developing sustainable residential environments. By prioritizing socio-cultural sustainability, we can enhance the overall quality of life for residents in tall buildings, contributing to healthier and more vibrant communities.

Keywords: high-rise residential buildings, quality of life, social cohesion, socio-cultural sustainability

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Authors: Abimbola M. Enitan, J. Adeyemo

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Anaerobic modeling is a useful tool to describe and simulate the condition and behaviour of anaerobic treatment units for better effluent quality and biogas generation. The present investigation deals with the anaerobic treatment of brewery wastewater with varying organic loads. The chemical oxygen demand (COD) and total suspended solids (TSS) of the influent and effluent of the bioreactor were determined at various retention times to generate data for kinetic coefficients. The bio-kinetic coefficients in the modified Stover–Kincannon kinetic and methane generation models were determined to study the performance of anaerobic digestion process. At steady-state, the determination of the kinetic coefficient (K), the endogenous decay coefficient (Kd), the maximum growth rate of microorganisms (µmax), the growth yield coefficient (Y), ultimate methane yield (Bo), maximum utilization rate constant Umax and the saturation constant (KB) in the model were calculated to be 0.046 g/g COD, 0.083 (dˉ¹), 0.117 (d-¹), 0.357 g/g, 0.516 (L CH4/gCODadded), 18.51 (g/L/day) and 13.64 (g/L/day) respectively. The outcome of this study will help in simulation of anaerobic model to predict usable methane and good effluent quality during the treatment of industrial wastewater. Thus, this will protect the environment, conserve natural resources, saves time and reduce cost incur by the industries for the discharge of untreated or partially treated wastewater. It will also contribute to a sustainable long-term clean development mechanism for the optimization of the methane produced from anaerobic degradation of waste in a close system.

Keywords: brewery wastewater, methane generation model, environment, anaerobic modeling

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Authors: Diego Rodrigues, Marcelo Lisboa, Elismar Batista, Marcos Dias

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Based on data on child labor, this work arises questions about how to understand and locate the factors that make up the child labor rates, and which properties are important to analyze these cases. Using data mining techniques to discover valid patterns on Brazilian social databases were evaluated data of child labor in the State of Tocantins (located north of Brazil with a territory of 277000 km2 and comprises 139 counties). This work aims to detect factors that are deterministic for the practice of child labor and their relationships with financial indicators, educational, regional and social, generating information that is not explicit in the government database, thus enabling better monitoring and updating policies for this purpose.

Keywords: social data, government decision making, association of social data, data mining

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Authors: Zhi Zhu, Boquan Zhang, Tian Jing, Jingjing Li, Tao Wang

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Data assimilation is a model and data hybrid-driven method that dynamically fuses new observation data with a numerical model to iteratively approach the real system state. It is widely used in state prediction and parameter inference of continuous systems. Because of the discrete event system’s non-linearity and non-Gaussianity, traditional Kalman Filter based on linear and Gaussian assumptions cannot perform data assimilation for such systems, so particle filter has gradually become a technical approach for discrete event simulation data assimilation. Hence, we proposed a particle filter-based discrete event simulation data assimilation method and took the unmanned aerial vehicle (UAV) maintenance service system as a proof of concept to conduct simulation experiments. The experimental results showed that the filtered state data is closer to the real state of the system, which verifies the effectiveness of the proposed method. This research can provide a reference framework for the data assimilation process of other complex nonlinear systems, such as discrete-time and agent simulation.

Keywords: discrete event simulation, data assimilation, particle filter, model and data-driven

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Authors: Pongsawee Supanonth

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This study aims to investigate the consumer behavior and marketing mixed factor affect on consumer decision making for independent movies presented in Lido cinema. The research method will use quantitative research, data was collected by questionnaires distributed to the audience in the Lido cinema for 400 sample by accidental sampling technique. Data was analyzed by descriptive statistic including percentage, mean, standard deviation and inferential statistic including independent t-test for hypothesis testing. The results showed that marketing mixed factors affecting consumer decision-making for Independent movies presented in Lido cinema by gender as different as less than the 0.05 significance level, it was found that the kind of movie ,quality of theater ,price of ticket, facility of watching movies, staff services and promotion of Lido cinema respectively had a vital influence on their attention and response which makes the advertisement more attractive is in harmony with the research hypotheses also.

Keywords: consumer behavior, marketing mixed factor, resonance, consumer decision making, Lido cinema

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Authors: Rachel M. Lørum, Henrik Eriksson, Frida Smith

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Purpose: In recent years, the use of benchmarks for the improvement of healthcare has become increasingly common. There has been an increasing interest in why improvement initiatives so often fail to eliminate the problems they aspire to solve. Benchmarking comes with its fair share of challenges and problems, such as capturing the dynamics and complexities of the care environments, among others. In this study, we demonstrate how learning-oriented, voluntary benchmarks in the complex environment of public healthcare could be designed. Findings: Our four most important findings were the following: first, important organizational learning (OL) regarding the complexity of the service and implications on how to design a benchmark for learning and improvement occurred during the process. Second, participation by a wide range of professionals and stakeholders was crucial for capturing the complexity of people and organizations and increasing the quality of the template. Third, the continuous dialogue between all organizations involved was an important tool for ongoing organizational learning throughout the process. The last important finding was the impact of the facilitator’s role through supporting progress, coordination, and dialogue. Design: We chose participatory design as the research design. Data were derived from written materials such as e-mails, protocols, observational notes, and reflection notes collected during a period of 1.5 years. Originality: Our main contributions are the identification of important strategies, initiatives, and actors to involve when designing voluntary benchmarks for learning and improvement.

Keywords: organizational learning, quality improvement, learning-oriented benchmark, healthcare, patient safety

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Authors: Sadam Alwadi

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Outlier values become a problem that frequently occurs in the data observation or recording process. Thus, the need for data imputation has become an essential matter. In this work, it will make use of the methods described in the prior work to detect the outlier values based on a collection of stock market data. In order to implement the detection and find some solutions that maybe helpful for investors, real closed price data were obtained from the Amman Stock Exchange (ASE). Tukey and Maximum Overlapping Discrete Wavelet Transform (MODWT) methods will be used to impute the detect the outlier values.

Keywords: outlier values, imputation, stock market data, detecting, estimation

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Authors: Mohiuddin Ahsanul Kabir Chowdhury, Nafisa Lira Huq, Afroza Khanom, Rafiqul Islam, Abdullah Nurus Salam Khan, Farhana Karim, Nabila Zaka, Shams El Arifeen, Sk. Masum Billah

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After having astounding achievements in reducing maternal mortality and achieving the target for Millennium Development Goal (MDG) 5, the Government of Bangladesh has set new target to reduce Maternal Mortality Ratio (MMR) to 70 per 100,000 live births aligning with targets of Sustainable Development Goals (SDGs). Aversion of deaths from maternal complication by ensuring quality health care could be an important path to accelerate the rate of reduction of MMR. This formative research was aimed at exploring the provision of quality maternal health services at different level of health facilities. The study was conducted in 1 district hospital (DH) and 4 Upazila health complexes (UHC) of Kurigram district of Bangladesh, utilizing both quantitative and qualitative research methods. We conducted 14 key informant interviews with facility managers and 20 in-depth interviews with health care providers and support staff. Besides, we observed 387 normal deliveries from which we found 17 cases of post partum haemorrhage (PPH) and 2 cases of eclampsia during the data collection period extended from July-September 2016. The quantitative data were analyzed by using descriptive statistics, and the qualitative component underwent thematic analysis with the broad themes of facility readiness for maternal complication management, and management of complications. Inadequacy in human resources has been identified as the most important bottleneck to provide quality care to manage maternal complications. The DH had a particular paucity of human resources in medical officer cadre where about 61% posts were unfilled. On the other hand, in the UHCs the positions mostly empty were obstetricians (75%, paediatricians (75%), staff nurses (65%), and anaesthetists (100%). The workload on the existing staff is increased because of the persistence of vacant posts. Unavailability of anesthetists and consultants does not permit the health care providers (HCP) of lower cadres to perform emergency operative procedures and forces them to refer the patients although referral system is not well organized in rural Bangladesh. Insufficient bed capacity, inadequate training, shortage of emergency medicines etc. are other hindrance factors for facility readiness. Among the 387 observed delivery case, 17 (4.4%) were identified as PPH cases, and only 2 cases were found as eclampsia/pre-eclampsia. The majority of the patients were treated with uterine message (16 out of 17, 94.1%) and injectable Oxytocin (14 out of 17, 82.4%). The providers of DH mentioned that they can manage the PPH because of having provision for diagnostic and blood transfusion services, although not as 24/7 services. Regarding management of eclampsia/pre-eclampsia, HCPs provided Diazepam, MgSO4, and other anti-hypertensives. The UHCs did not have MgSO4 at stock even, and one facility manager admitted that they treat eclampsia with Diazepam only. The nurses of the UHCs were found to be afraid to handle eclampsia cases. The upcoming interventions must ensure refresher training of service providers, continuous availability of essential medicine and equipment needed for complication management, availability of skilled health workforce, availability of functioning blood transfusion unit and pairing of consultants and anaesthetists to reach the newly set targets altogether.

Keywords: Bangladesh, health facilities, maternal complications, quality of care

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Authors: Basave Gowda, Lokesh K., Prasanth S. M., Bellad S. B., Radha J., Lokesh G. Y., Patil S. B., Vijayakumar D. K., Ganigar B. S., Rakesh C. Mathad

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Quality seed plays an important role in enhancing the crop productivity. It was reported and confirmed by large scale verification research trials that by use of quality seeds alone, the crop yield can be enhanced by 15 to 20 per cent. At present, the quality seed production and distribution through organised sectors comprising both public and private seed sector was only 20-25% of the requirement and the remaining quantity is met through unorganised sector which include the farmer to farmers saved seeds. With an objective of developing an alternative seed system, the University of Agricultural Sciences, Raichur in Karnataka state has implemented Seed Village Programme in more than 100 villages covering around 5000 farmers every year since 2009-10 and in the selected seed villages, a group of 50-150 farmers were supplied the foundation seeds of new varieties to an extent of 0.4 ha at 50 % subsidy. And two to three training programmes were conducted in the targeted villages for quality seed production and the seed produced in the target group was processed locally in the university seed processing units and arranged for distribution in the local villages by the seed growers themselves. By this new innovative and modified seed system, the university can able to replace old varieties of pigeon pea and green gram by producing 1482, 2978, 2729, 2560, and 4581 tonnes of seeds of new varieties on large scale under farmers and scientists participatory seed village programmes respectively during 2009-10, 2010-11, 2011-12, 2012-13 and 2013-14. From this new alternate model of seed system, there should be large scale promotion of regional seed system involving farmers, NGO and voluntary organisation for quick and effective replacement of old, low yielding, disease susceptible varieties with new high yielding, disease resistant for enhanced food production and food security.

Keywords: seed system, seed village, seed replacement, varietal replacement

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Authors: Chiara Cosentino, Carlo Pruneti, Carla Merisio, Domenico Sgromo

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Introduction – Psychoneuroimmunology as approach clearly showed how psychological features can influence health through specific physiological pathways linked to the stress reaction. This can be true also in cancer, in its latter conceptualization seen as a chronic disease. Therefore, it is still not clear how the psychological features can combine with a physiological specific path, for a better adjustment to cancer. The aim of this study is identifying how in Italian survivors, perceived social support, body image, coping and quality of life correlate with or influence Heart Rate Variability (HRV), the physiological parameter that can mirror a condition of chronic stress or a good relaxing capability. Method - The study had an exploratory transversal design. The final sample was made of 38 ovarian cancer survivors aged from 29 to 80 (M= 56,08; SD=12,76) following a program for Ovarian Cancer at the Oncological Clinic, University Hospital of Parma, Italy. Participants were asked to fill: Multidimensional Scale of Perceived Social Support (MSPSS); Derridford Appearance Scale-59 (DAS-59); Mental Adjustment to Cancer (MAC); Quality of Life Questionnaire (EORTC). For each participant was recorded Short-Term HRV (5 minutes) using emWavePro. Results– Data showed many interesting correlations within the psychological features. EORTC scores have a significant correlation with DAS-59 (r =-.327 p <.05), MSPSS (r =.411 p<.05), and MAC scores, in particular with the strategy Fatalism (r =.364 p<.05). A good social support improves HRV (F(1,33)= 4.27 p<.05). Perceiving themselves as effective in their environment, preserving a good role functioning (EORTC), positively affects HRV (F(1,33)=9.810 p<.001). Women admitting concerns towards body image seem prone to emotive disclosure, reducing emotional distress and improving HRV (β=.453); emotional avoidance worsens HRV (β=-.391). Discussion and conclusion - Results showed a strong relationship between body image and Quality of Life. These data suggest that higher concerns on body image, in particular, the negative self-concept linked to appearance, was linked to the worst functioning in everyday life. The relation between the negative self-concept and a reduction in emotional functioning is understandable in terms of possible distress deriving from the perception of body appearance. The relationship between a high perceived social support and a better functioning in everyday life was also confirmed. In this sample fatalism, was associated with a better physical, role and emotional functioning. In these women, the presence of a good support may activate the physiological Social Engagement System improving their HRV. Perceiving themselves effective in their environment, preserving a good role functioning, also positively affects HRV, probably following the same physiological pathway. A higher presence of concerns about appearance contributes to a higher HRV. Probably women admitting more body concerns are prone to a better emotive disclosure. This could reduce emotional distress improving HRV and global health. This study reached preliminary demonstration of an ‘Integrated Model of Defense’ in these cancer survivors. In these model, psychological features interact building a better quality of life and a condition of psychological well-being that is associated and influence HRV, then the physiological condition.

Keywords: cancer survivors, heart rate variability, ovarian cancer, psychophysiological adjustment

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Authors: Yan Zhang

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Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Analytics-driven predictive maintenance is gaining increasing attention in many industries such as manufacturing, utilities, aerospace, etc., along with the emerging demand of Internet of Things (IoT) applications and the maturity of technologies that support Big Data storage and processing. This study aims to build an end-to-end analytics solution that includes both real-time machine condition monitoring and machine learning based predictive analytics capabilities. The goal is to showcase a general predictive maintenance solution architecture, which suggests how the data generated from field machines can be collected, transmitted, stored, and analyzed. We use a publicly available aircraft engine run-to-failure dataset to illustrate the streaming analytics component and the batch failure prediction component. We outline the contributions of this study from four aspects. First, we compare the predictive maintenance problems from the view of the traditional reliability centered maintenance field, and from the view of the IoT applications. When evolving to the IoT era, predictive maintenance has shifted its focus from ensuring reliable machine operations to improve production/maintenance efficiency via any maintenance related tasks. It covers a variety of topics, including but not limited to: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure. Second, we review the state-of-art technologies that enable a machine/device to transmit data all the way through the Cloud for storage and advanced analytics. These technologies vary drastically mainly based on the power source and functionality of the devices. For example, a consumer machine such as an elevator uses completely different data transmission protocols comparing to the sensor units in an environmental sensor network. The former may transfer data into the Cloud via WiFi directly. The latter usually uses radio communication inherent the network, and the data is stored in a staging data node before it can be transmitted into the Cloud when necessary. Third, we illustrate show to formulate a machine learning problem to predict machine fault/failures. By showing a step-by-step process of data labeling, feature engineering, model construction and evaluation, we share following experiences: (1) what are the specific data quality issues that have crucial impact on predictive maintenance use cases; (2) how to train and evaluate a model when training data contains inter-dependent records. Four, we review the tools available to build such a data pipeline that digests the data and produce insights. We show the tools we use including data injection, streaming data processing, machine learning model training, and the tool that coordinates/schedules different jobs. In addition, we show the visualization tool that creates rich data visualizations for both real-time insights and prediction results. To conclude, there are two key takeaways from this study. (1) It summarizes the landscape and challenges of predictive maintenance applications. (2) It takes an example in aerospace with publicly available data to illustrate each component in the proposed data pipeline and showcases how the solution can be deployed as a live demo.

Keywords: Internet of Things, machine learning, predictive maintenance, streaming data

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Authors: Mattias Bergstrom

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Significance of the Study: This research will provide new insights into the risks with digital embedded infrastructure. Through this research, we will analyze each risk and its potential negation strategies, especially for AI and autonomous automation. Moreover, the analysis that is presented in this paper will convey valuable information for future research that can create more stable, secure, and efficient autonomous systems. To learn and understand the risks, a large IoT system was envisioned, and risks with hardware, tampering, and cyberattacks were collected, researched, and evaluated to create a comprehensive understanding of the potential risks. Potential solutions have then been evaluated on an open source IoT hardware setup. This list shows the identified passive and active risks evaluated in the research. Passive Risks: (1) Hardware failures- Critical Systems relying on high rate data and data quality are growing; SCADA systems for infrastructure are good examples of such systems. (2) Hardware delivers erroneous data- Sensors break, and when they do so, they don’t always go silent; they can keep going, just that the data they deliver is garbage, and if that data is not filtered out, it becomes disruptive noise in the system. (3) Bad Hardware injection- Erroneous generated sensor data can be pumped into a system by malicious actors with the intent to create disruptive noise in critical systems. (4) Data gravity- The weight of the data collected will affect Data-Mobility. (5) Cost inhibitors- Running services that need huge centralized computing is cost inhibiting. Large complex AI can be extremely expensive to run. Active Risks: Denial of Service- It is one of the most simple attacks, where an attacker just overloads the system with bogus requests so that valid requests disappear in the noise. Malware- Malware can be anything from simple viruses to complex botnets created with specific goals, where the creator is stealing computer power and bandwidth from you to attack someone else. Ransomware- It is a kind of malware, but it is so different in its implementation that it is worth its own mention. The goal with these pieces of software is to encrypt your system so that it can only be unlocked with a key that is held for ransom. DNS spoofing- By spoofing DNS calls, valid requests and data dumps can be sent to bad destinations, where the data can be extracted for extortion or to corrupt and re-inject into a running system creating a data echo noise loop. After testing multiple potential solutions. We found that the most prominent solution to these risks was to use a Peer 2 Peer consensus algorithm over a blockchain to validate the data and behavior of the devices (sensors, storage, and computing) in the system. By the devices autonomously policing themselves for deviant behavior, all risks listed above can be negated. In conclusion, an Internet middleware that provides these features would be an easy and secure solution to any future autonomous IoT deployments. As it provides separation from the open Internet, at the same time, it is accessible over the blockchain keys.

Keywords: IoT, security, infrastructure, SCADA, blockchain, AI

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Authors: P. Jayashree, S. Rajkumar

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With social networks and smart devices generating a multitude of data, effective data management is the need of the hour for networks and cloud applications. Some applications need effective storage while some other applications need effective communication over networks and data reduction comes as a handy solution to meet out both requirements. Most of the data compression techniques are based on data statistics and may result in either lossy or lossless data reductions. Though lossy reductions produce better compression ratios compared to lossless methods, many applications require data accuracy and miniature details to be preserved. A variety of data compression algorithms does exist in the literature for different forms of data like text, image, and multimedia data. In the proposed work, a generic progressive compression algorithm, based on probabilistic encoding, called PEINS is projected as an enhancement over irrational number stored coding technique to cater to storage issues of increasing data volumes as a cost effective solution, which also offers data security as a secondary outcome to some extent. The proposed work reveals cost effectiveness in terms of better compression ratio with no deterioration in compression time.

Keywords: compression ratio, generic compression, irrational number storage, probabilistic encoding

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Authors: Femi Elegbeleye, Omobayo Esan, Muienge Mbodila, Patrick Bowe

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This paper focused on cost effective storage architecture using fog and cloud data storage gateway and presented the design of the framework for the data privacy model and data analytics framework on a real-time analysis when using machine learning method. The paper began with the system analysis, system architecture and its component design, as well as the overall system operations. The several results obtained from this study on data privacy model shows that when two or more data privacy model is combined we tend to have a more stronger privacy to our data, and when fog storage gateway have several advantages over using the traditional cloud storage, from our result shows fog has reduced latency/delay, low bandwidth consumption, and energy usage when been compare with cloud storage, therefore, fog storage will help to lessen excessive cost. This paper dwelt more on the system descriptions, the researchers focused on the research design and framework design for the data privacy model, data storage, and real-time analytics. This paper also shows the major system components and their framework specification. And lastly, the overall research system architecture was shown, its structure, and its interrelationships.

Keywords: IoT, fog, cloud, data analysis, data privacy

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Authors: Paolo Grasso, Sai Kalyan Yelike, Federico Benzi, Mathieu Le Cam

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The cabin air quality (CAQ) in commercial aircraft is of prime interest, especially in the context of the COVID-19 pandemic. Current Environmental Control Systems (ECS) rely on a prescribed fresh airflow per passenger to dilute contaminants. An adaptive ECS strategy is proposed, leveraging air sensing and filtration technologies to ensure a better CAQ. This paper investigates the CAQ level achieved in commercial aircraft’s cabin during various flight scenarios. The modeling and simulation analysis is performed in a Modelica-based environment describing the dynamic behavior of the system. The model includes the following three main systems: cabin, recirculation loop and air-conditioning pack. The cabin model evaluates the thermo-hygrometric conditions and the air quality in the cabin depending on the number of passengers and crew members, the outdoor conditions and the conditions of the air supplied to the cabin. The recirculation loop includes models of the recirculation fan, ordinary and novel filtration technology, mixing chamber and outflow valve. The air-conditioning pack includes models of heat exchangers and turbomachinery needed to condition the hot pressurized air bled from the engine, as well as selected contaminants originated from the outside or bled from the engine. Different ventilation control strategies are modeled and simulated. Currently, a limited understanding of contaminant concentrations in the cabin and the lack of standardized and systematic methods to collect and record data constitute a challenge in establishing a causal relationship between CAQ and passengers' comfort. As a result, contaminants are neither measured nor filtered during flight, and the current sub-optimal way to avoid their accumulation is their dilution with the fresh air flow. However, the use of a prescribed amount of fresh air comes with a cost, making the ECS the most energy-demanding non-propulsive system within an aircraft. In such a context, this study shows that an ECS based on a reduced and adaptive fresh air flow, and relying on air sensing and filtration technologies, provides promising results in terms of CAQ control. The comparative simulation results demonstrate that the proposed adaptive ECS brings substantial improvements to the CAQ in terms of both controlling the asymptotic values of the concentration of the contaminant and in mitigating hazardous scenarios, such as fume events. Original architectures allowing for adaptive control of the inlet air flow rate based on monitored CAQ will change the requirements for filtration systems and redefine the ECS operation.

Keywords: cabin air quality, commercial aircraft, environmental control system, ventilation

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Authors: Gurjeet Khurana, Surender Singh, Poonam Arora, Praveendra K. Sachan

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Introduction: Post-operative quality of recovery is the key outcome in the perspective of anesthesiologist. It is directly related to patient satisfaction. This is unsurprising, considering most aspects of a poor quality recovery after surgery will impair satisfaction with care. This study was thus undertaken to evaluate effects of Dexamethasone and Lignocaine on Quality of Recovery using QoR- 40 questionnaire and compare their effects. Material and methods: After obtaining the ethical committee approval and written informed consent, 67 patients of 18-60 years, ASA grade I and II scheduled for elective laparoscopic cholecystectomy were randomly allocated into two groups. Group I of 34 patients received 2mg/kg lignocaine diluted to 10ml with normal saline. Group 2 of 33 patients received 0.1 mg/kg I/V Dexamethasone diluted to 10ml with normal saline. QoR-40 was assessed on pre-operative day, and again QoR-40 was assessed at 24 hr post-operative day-1. Postoperative pain scores, nausea and vomiting and shoulder pain were secondary outcomes. Results: The Global QoR-40 was more than 180 at 24 hr in both the groups. The Dexamethasone group had higher Global QoR-40 than lignocaine group 187.94 v/s 182.85. Amongst dimensions of QoR-40 Dexamethasone had statistically better physical comfort, physical independence, and pain relief as compared to Lignocaine. Positive items had excellent responses in Dexamethasone group. Headache, backache and sore throat were also less severe in Dexamethasone group as compared to Lignocaine group. Dexamethasone group had lower VAS compared to lignocaine group. Similarly, there was less fentanyl consumption in dexamethasone group (364.08 ± 127.31) in postoperative period when compared to the lignocaine group (412.31 ± 147.8). Group receiving dexamethasone had 36% increase in appetite compared to lignocaine group (17.6%), which facilitated early oral feeding. Frequency of PONV was less in group-2 at different time interval as compared to group 1. Total episode of PONV were 18 in group 1 and 7 in group 2. Statistically significant difference was seen among two groups (p value= 0.007). Use of antiemetic was more in group 1 as compared to group 2 at all the times, though it was not statistically significant at different time intervals. Antiemetics were administered to 18 patients in group 1 as compared to 5 patients in group 2 postoperatively. Statistically significant difference (p value= 0.011) was seen in total antiemetic consumption. Conclusion: Our study demonstrated that pre-operative administration of a single dose of dexamethasone enhanced the quality of recovery after laparoscopic cholecystectomy as compared to Lignocaine bolus dose.

Keywords: dexamethasone, lignocaine, QoR-40 questionnaire, quality of recovery

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Authors: Ji-Seok Koo, Hee‑Yong Kwon, Hui-Young Yun, Kyung-Hui Wang, Youn-Seo Koo

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This paper presents a forecasting system for PM2.5 levels in Seoul, South Korea, leveraging a combination of chemical transport modeling and ConvLSTM-DNN machine learning technology. Exposure to PM2.5 has known detrimental impacts on public health, making its prediction crucial for establishing preventive measures. Existing forecasting models, like the Community Multiscale Air Quality (CMAQ) and Weather Research and Forecasting (WRF), are hindered by their reliance on uncertain input data, such as anthropogenic emissions and meteorological patterns, as well as certain intrinsic model limitations. The system we've developed specifically addresses these issues by integrating machine learning and using carefully selected input features that account for local and distant sources of PM2.5. In South Korea, the PM2.5 concentration is greatly influenced by both local emissions and long-range transport from China, and our model effectively captures these spatial and temporal dynamics. Our PM2.5 prediction system combines the strengths of advanced hybrid machine learning algorithms, convLSTM and DNN, to improve upon the limitations of the traditional CMAQ model. Data used in the system include forecasted information from CMAQ and WRF models, along with actual PM2.5 concentration and weather variable data from monitoring stations in China and South Korea. The system was implemented specifically for Seoul's PM2.5 forecasting.

Keywords: PM2.5 forecast, machine learning, convLSTM, DNN

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Authors: Azzedine Hamza, Chouaib Chakour, Messaoud Ramdani

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Energy demand prediction plays a crucial role in achieving next-generation power systems for agricultural greenhouses. As a result, high prediction quality is required for efficient smart grid management and therefore low-cost energy consumption. The aim of this paper is to investigate the effectiveness of a hybrid data-driven model in day-ahead energy demand prediction. The proposed model consists of Discrete Wavelet Transform (DWT), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The DWT is employed to decompose the original signal in a set of subseries and then an ANFIS is used to generate the forecast for each subseries. The proposed hybrid method (DWT-ANFIS) was evaluated using a greenhouse energy demand data for a week and compared with ANFIS. The performances of the different models were evaluated by comparing the corresponding values of Mean Absolute Percentage Error (MAPE). It was demonstrated that discret wavelet transform can improve agricultural greenhouse energy demand modeling.

Keywords: wavelet transform, ANFIS, energy consumption prediction, greenhouse

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Authors: Chung-Chih Lin, Chien-Liang Wu

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The detection of the polymer melt state during manufacture process is regarded as an efficient way to control the molded part quality in advance. Online monitoring rheological property of polymer melt during processing procedure provides an approach to understand the melt state immediately. Rheological property reflects the polymer melt state at different processing parameters and is very important in injection molding process especially. An approach that demonstrates how to calculate rheological property of polymer melt through in-process measurement, using injection molding as an example, is proposed in this study. The system consists of two sensors and a data acquisition module can process the measured data, which are used for the calculation of rheological properties of polymer melt. The rheological properties of polymer melt discussed in this study include shear rate and viscosity which are investigated with respect to injection speed and melt temperature. The results show that the effect of injection speed on the rheological properties is apparent, especially for high melt temperature and should be considered for precision molding process.

Keywords: injection molding, melt viscosity, shear rate, monitoring

Procedia PDF Downloads 382

Authors: Murat Tosunoglu, Cigdem Gul, Nurcihan Hacioglu, Nurdan Tepeova

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Amphibian and Reptilian species are influenced by pollution and habitat destruction. Blood parameters of Amphibia species were particularly affected by the negative environmental conditions. Studied frog samples 36 clinically normal Pelophylax ridibundus individuals were captured along the Biga Stream between April–June 2014. When comparing our findings with the Turkish legislation (Water pollution control regulation), the 1. Locality of the Biga stream in terms of total coliform classified as "high quality water" (Coliform: 866.66 MPN/100 mL), while the 2. locality was a "contaminated water" (Coliform: 53266.66 MPN/100 mL). Blood samples of the live specimens were obtained in the laboratory within one day of their capture. The blood samples were taken from the etherized frogs by means of ventriculus punctures, via heparinized hematocrit capillaries. Hematological and biochemical analyses based on high quality water and contaminated water, respectively, are as follows: Red blood cell count (444210.52-426846.15 per cubic millimeter of blood), white blood cell count (4215.78-4684.61 per cubic millimeter of blood), hematocrit value (29.25-29.43 %), hemoglobin concentration (7.76-7.22 g/dl), mean corpuscular volume (637.64-719.99 fl), mean corpuscular hemoglobin (184.78-174.75 pg), mean corpuscular hemoglobin concentration (29.44-24.82 %), glucose (103.74-124.13 mg/dl), urea (87.68-81.72 mg/L), cholesterol (148.20-197.39 mg/dl), creatinine (0.29-0.28 mg/dl), uric acid (10.26-7.55 mg/L), albumin (1.13-1.39 g/dl), calcium (11.45-9.70 mg/dl), triglyceride (135.23-155.85 mg/dl), total protein (4.26-3.73 g/dl), phosphorus (6.83-17.86 mg/dl), and magnesium (0.95-1.06 mg/dl). The some hematological parameters in P. ridibundus specimens are given for the first time in this study. No water quality dependent variation was observed in clinic hematology parameters measured.

Keywords: Pelophylax ridibundus, hematological parameters, biochemistry, freshwater quality

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Authors: Abiola A. Sokoya

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Handheld devices are now in use as educational tools for learning in most of the higher institutions, because of the features and functions which can be used in an academic environment. This study examined the usage and the benefits of handheld devices as learning tools. A structured questionnaire was used to collect data, while the data collected was analyzed using simple percentage. It was, however, observed that handheld devices offer numerous functions and application for learning, which could improve academic performance of students. Students are now highly interested in using handheld devices for mobile learning apart from making and receiving calls. The researchers recommended that seminars be organized for students on functions of some common handheld devices that can aid learning for academic purposes. It is also recommended that management of each higher institution should make appropriate policies in-line with the usage of handheld technologies to enhance mobile learning. Government should ensure that appropriate policies and regulations are put in place for the importation of high quality handheld devices into the country, Nigeria being a market place for the technologies. By this, using handheld devices for mobile learning will be enhanced.

Keywords: handheld devices, educational tools, mobile e- learning, usage, benefits

Procedia PDF Downloads 229

Authors: Nagda Ibrahim Mady, Shimaa Mohamed Atiha

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In light of actual needs of elderly people and the changes that accompany age in eyesight, hearing, dexterity, mobility, and memory which make aged people unable to carry out the simplest living affairs especially clothing demands. These needs are almost neglected in the current clothing market obligate aged peoples to wear the available choices without any consideration to their actual desires and needs. Fashion designer has gained many experiences that can gather between ergonomics and stages of fashion designing process. Fashion designer can determine the actual needs of aged people and reply these needs with designs that can achieve Improvement to the life quality of aged people besides maintaining good appearance. Thus Fashion designer can help elderly people to avoid negative impacts age leaves on them, either it is psychological or kinetic or that of dementia. Ergonomics in clothing is considered the tools and mechanisms that are used to fit aged people satisfactions supporting them to improve their living using the least time and effort. Providing the elderly with comfort besides maintaining good appearance that can make self–confidence besides independence. From this point of view the research is looking forward to improve the life of aged people through addressing functional clothes that can make elderly independent in the wearing process. Providing in these designs comfort, quality, and practicality and economic cost. Suggesting the suitable fabrics and materials and applying it to the designs to help the elderly perform their daily living customs. Reaching the successful designs that can be acceptable to specialists and to consumers whom they confirm: it supplies their clothing needs and provides the atheistic and functional performance and therefore it gives them better life.

Keywords: ergonomic, design garments, elderly people, life quality

Procedia PDF Downloads 567

Authors: Suin Seo, Sung-Il Cho

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Background: The use of mobile apps for smoking cessation has grown exponentially in recent years. Yet, there were limited researches which evaluated the quality of smoking cessation apps to our knowledge. In most cases, a clinical practice guideline which is focused on clinical physician was used as an evaluation tool. Objective: The objective of this study was to develop a user-centered measure for quality of mobile smoking cessation apps. Methods: A literature search was conducted to identify articles containing explicit smoking cessation guideline for smoker published until January 2018. WHO’s guide for tobacco users to quit was adopted for evaluation tool which assesses smoker-oriented contents of smoking cessation apps. Compared to the clinical practice guideline, WHO guideline was designed for smokers (non-specialist). On the basis of existing criteria which was developed based on 2008 clinical practice guideline for Treating Tobacco Use and Dependence, evaluation tool was modified and developed by an expert panel. Results: There were five broad categories of criteria that were identified including five objective quality scales: enhancing motivation, assistance with a planning and making quit attempts, preparation for relapse, self-efficacy, connection to smoking. Enhancing motivation and assistance with planning and making quit attempts were similar to contents of clinical practice guideline, but preparation for relapse, self-efficacy and connection to smoking (environment or habit which reminds of smoking) only existed on WHO guideline. WHO guideline had more user-centered elements than clinical guideline. Especially, self-efficacy is the most important determinant of behavior change in accordance with many health behavior change models. With the WHO guideline, it is now possible to analyze the content of the app in the light of a health participant, not a provider. Conclusion: The WHO guideline evaluation tool is a simple, reliable and smoker-centered tool for assessing the quality of mobile smoking cessation apps. It can also be used to provide a checklist for the development of new high-quality smoking cessation apps.

Keywords: smoking cessation, evaluation, mobile application, WHO, guideline

Procedia PDF Downloads 188

Authors: Inkyu Kim, SangMan Moon

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This IEEE 802.11b protocol provides up to 11Mbps data rate, whereas aerospace industry wants to seek higher data rate COTS data link system in the UAV. The Total Maximum Throughput (TMT) and delay time are studied on many researchers in the past years This paper provides theoretical data throughput performance of UAV formation flight data link using the existing 802.11b performance theory. We operate the UAV formation flight with more than 30 quad copters with 802.11b protocol. We may be predicting that UAV formation flight numbers have to bound data link protocol performance limitations.

Keywords: UAV datalink, UAV formation flight datalink, UAV WLAN datalink application, UAV IEEE 802.11b datalink application

Procedia PDF Downloads 392

Authors: Asma AlQahtani, Jumana AlHadad, Maryam AlQallaf, Shoug AlHasan

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New product development (NPD) is the complete process of bringing a new product to the customer by integrating the two broad divisions; one involving the idea generation, product design and detail engineering; and the other involving market research and marketing analysis. It is a common practice for companies to undertake some of these tasks simultaneously (concurrent engineering) and also consider them as an ongoing process (continuous development). The current study explores the framework and methodology for a new product development process utilizing the Quality Function Deployment (QFD) tool for bringing the customer opinion into the product development process. An elaborate customer survey with focus groups in the region was carried out to ensure that customer requirements are integrated into new products as early as the design stage including identifying the recognition of need for the new product. A QFD Matrix (House of Quality) was prepared that links customer requirements to product engineering requirements and a feasibility study and risk assessment exercise was carried out for a Small and Medium Enterprise (SME) in Kuwait for development of the new product. SMEs in Kuwait, particularly in manufacturing sector are mainly focused on serving the local demand, and often lack of product quality adversely affects the ability of the companies to compete on a regional/global basis. Further, lack of focus on identifying customer requirements often deters SMEs to envisage the idea of a New Product Development. The current study therefore focuses in utilizing QFD Matrix right from the conceptual design to detail design and to some extent, extending the link this to design of the manufacturing system. The outcome of the project resulted in a development of the prototype for a new molded product which can ensure consistency between the customer’s requirements and the measurable characteristics of the product. The Engineering Economics and Cost studies were also undertaken to analyse the viability of the new product, the results of which was also linked to the successful implementation of the initial QFD Matrix.

Keywords: Quality Function Deployment, QFD Matrix, new product development, NPD, Kuwait SMEs, prototype development

Procedia PDF Downloads 414

Authors: Radoslav Židek, Veronika Šidlová, Radovan Kasarda, Birgit Fuerst-Waltl

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Machine learning represents a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. The data can present identification patterns which are used to classify into groups. The result of the analysis is the pattern which can be used for identification of data set without the need to obtain input data used for creation of this pattern. An important requirement in this process is careful data preparation validation of model used and its suitable interpretation. For breeders, it is important to know the origin of animals from the point of the genetic diversity. In case of missing pedigree information, other methods can be used for traceability of animal´s origin. Genetic diversity written in genetic data is holding relatively useful information to identify animals originated from individual countries. We can conclude that the application of data mining for molecular genetic data using supervised learning is an appropriate tool for hypothesis testing and identifying an individual.

Keywords: genetic data, Pinzgau cattle, supervised learning, machine learning

Procedia PDF Downloads 551