Search results for: air quality prediction

Authors: Hien Thi Dieu Truong, Mahmoud Al-Sarayreh, Pullanagari Reddy, Marlon M. Reis, Richard Archer

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New Zealand mānuka honey is a honeybee product derived mainly from Leptospermum scoparium nectar. The potent antibacterial activity of mānuka honey derives principally from methylglyoxal (MGO), in addition to the hydrogen peroxide and other lesser activities present in all honey. MGO is formed from dihydroxyacetone (DHA) unique to L. scoparium nectar. Mānuka honey also has an idiosyncratic phenolic profile that is useful as a chemical maker. Authentic mānuka honey is highly valuable, but almost all honey is formed from natural mixtures of nectars harvested by a hive over a time period. Once diluted by other nectars, mānuka honey irrevocably loses value. We aimed to apply hyperspectral imaging to honey frames before bulk extraction to minimise the dilution of genuine mānuka by other honey and ensure authenticity at the source. This technology is non-destructive and suitable for an industrial setting. Chemometrics using linear Partial Least Squares (PLS) and Support Vector Machine (SVM) showed limited efficacy in interpreting chemical footprints due to large non-linear relationships between predictor and predictand in a large sample set, likely due to honey quality variability across geographic regions. Therefore, an advanced modelling approach, one-dimensional convolutional neural networks (1D-CNN), was investigated for analysing hyperspectral data for extraction of biochemical information from honey. The 1D-CNN model showed superior prediction of honey quality (R² = 0.73, RMSE = 2.346, RPD= 2.56) to PLS (R² = 0.66, RMSE = 2.607, RPD= 1.91) and SVM (R² = 0.67, RMSE = 2.559, RPD=1.98). Classification of mono-floral manuka honey from multi-floral and non-manuka honey exceeded 90% accuracy for all models tried. Overall, this study reveals the potential of HSI and deep learning modelling for automating the evaluation of honey quality in frames.

Keywords: mānuka honey, quality, purity, potency, deep learning, 1D-CNN, chemometrics

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Authors: Maria Paula Santos, Ana Lucas

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Organizations support their operations and decision making on the data they have at their disposal, so the quality of these data is remarkably important and Data Quality (DQ) is currently a relevant issue, the literature being unanimous in pointing out that poor DQ can result in large costs for organizations. The literature review identified and described 24 Critical Success Factors (CSF) for Data Quality Management (DQM) that were presented to a panel of experts, who ordered them according to their degree of importance, using the Delphi method with the Q-sort technique, based on an online questionnaire. The study shows that the five most important CSF for DQM are: definition of appropriate policies and standards, control of inputs, definition of a strategic plan for DQ, organizational culture focused on quality of the data and obtaining top management commitment and support.

Keywords: critical success factors, data quality, data quality management, Delphi, Q-Sort

Procedia PDF Downloads 192

Authors: Adesola C. Odole, Michael O. Ogunlana, Nse A. Odunaiya, Olufemi O. Oyewole, Chidozie E. Mbada, Ogochukwu K. Onyeso, Ayomikun F. Ayodeji, Opeyemi M. Adegoke, Iyanuoluwa Odole, Comfort T. Sanuade, Moyosooreoluwa E. Odole, Oluwagbohunmi A. Awosoga

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Purpose: The Nigerian healthcare industry is bedeviled with infrastructural decay, inadequate funding and staffing, and a dysfunctional healthcare system. This study investigated the influence of health professionals’ well-being and quality of work-life (QoWL) on the quality of care (QoC) of patients in Nigeria. Methods: The study was a multicentre cross-sectional survey conducted at four tertiary health institutions in southwest Nigeria. Participants’ demographic information, well-being, quality of work-life, and quality of care were obtained using four standardized questionnaires. Data were summarized using descriptive statistics of frequency (percentage) and mean (standard deviation). Inferential statistics included Chi-square, Pearson’s correlation, and independent samples t-test analyses. Results: Medical practitioners (n=609) and nurses (n=570) constituted 74.6% of all the health professionals, with physiotherapists, pharmacists, and medical laboratory scientists constituting 25.4%. The mean (SD) participants’ well-being = 71.65% (14.65), quality of life = 61.8% (21.31), quality of work-life = 65.73% (10.52) and quality of care = 70.14% (12.77). Participants’ quality of life had a significant negative correlation with the quality of care, while well-being and quality of work-life had a significant positive correlation with the quality of care. Conclusion: We concluded that health professionals’ well-being and quality of work-life are important factors that influence their productivity and, ultimately, the quality of care rendered to patients. The hospital management and policymakers should ensure improved work-related factors to improve the well-being of health professionals. This will enhance the quality of care given to patients and ultimately reduce brain drain and medical tourism.

Keywords: health professionals, quality of care, quality of life, quality of work-life, well-being

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Authors: Muhammad Ahnaf Zahin, Yaw Adu-Gyamfi

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Bus transit is a crucial component of transportation networks, especially in urban areas. Any intelligent transportation system must have accurate real-time information on bus travel times since it minimizes waiting times for passengers at different stations along a route, improves service reliability, and significantly optimizes travel patterns. Bus agencies must enhance the quality of their information service to serve their passengers better and draw in more travelers since people waiting at bus stops are frequently anxious about when the bus will arrive at their starting point and when it will reach their destination. For solving this issue, different models have been developed for predicting bus travel times recently, but most of them are focused on smaller road networks due to their relatively subpar performance in high-density urban areas on a vast network. This paper develops a deep learning-based architecture using a single-step multi-station forecasting approach to predict average bus travel times for numerous routes, stops, and trips on a large-scale network using heterogeneous bus transit data collected from the GTFS database. Over one week, data was gathered from multiple bus routes in Saint Louis, Missouri. In this study, Gated Recurrent Unit (GRU) neural network was followed to predict the mean vehicle travel times for different hours of the day for multiple stations along multiple routes. Historical time steps and prediction horizon were set up to 5 and 1, respectively, which means that five hours of historical average travel time data were used to predict average travel time for the following hour. The spatial and temporal information and the historical average travel times were captured from the dataset for model input parameters. As adjacency matrices for the spatial input parameters, the station distances and sequence numbers were used, and the time of day (hour) was considered for the temporal inputs. Other inputs, including volatility information such as standard deviation and variance of journey durations, were also included in the model to make it more robust. The model's performance was evaluated based on a metric called mean absolute percentage error (MAPE). The observed prediction errors for various routes, trips, and stations remained consistent throughout the day. The results showed that the developed model could predict travel times more accurately during peak traffic hours, having a MAPE of around 14%, and performed less accurately during the latter part of the day. In the context of a complicated transportation network in high-density urban areas, the model showed its applicability for real-time travel time prediction of public transportation and ensured the high quality of the predictions generated by the model.

Keywords: gated recurrent unit, mean absolute percentage error, single-step forecasting, travel time prediction.

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Authors: Chetsada Noknoi

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This research aims to study the quality of life of alien workers in Thailand and to compare the quality of work life of alien workers based on personal factors and work factors. Data analysis is performed using frequencies, percentage, mean standard deviation, t-test and ANOVA. Findings will benefit to the relevant authorities to be aware of the quality of life of alien workers in Thailand. This will help to find ways to enhance the quality of life of alien workers. It also brings awareness to the problems and obstacles that alien workers face in their work and life. It is a strategic approach to improve the management of the country's alien workers to be more efficient and effective. Moreover, the knowledge can be the basis of service to the society in different ways.

Keywords: quality of work life, alien worker, contemporary marketing, management

Procedia PDF Downloads 382

Authors: Gaoussou Doukoure Abdel Kader, Mnkandla Ernest

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This research aims to investigate the usage of a personal software process oriented information system in order to facilitate the promotion of software quality and its improvement in organizations. In this light, at the term of a literature review on software quality and related concepts, the personal software process is discussed, more particularly in terms of software quality. Semi-structured interviews will be conducted with a team of software engineers on the first hand to establish a baseline on their understanding of what quality entails for them. The PSP methodology will then be presented to the engineers in its most basic aspects. The research will then proceed to practical case study where a PSP oriented information system is submitted to engineers for usage throughout their development process. Reports from the PSP information system as well as feedback from the engineers will be used in conjunction with the theoretical foundation to establish a PSP inspired framework for software quality promotion and improvement.

Keywords: information communication technology, personal software process, software quality, process quality, software engineering

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Authors: N. Ravikumar, S. Krishnan, B. Yokeshkumar

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In present days, the power quality issues are increases due to non-linear loads like fridge, AC, washing machines, induction motor, etc. This power quality issues will affects the output voltages, output current, and output power of the total performance of the generator. This paper explains how to test the generator using the Fluke 435 II series power quality analyser. This Fluke 435 II series power quality analyser is used to measure the voltage, current, power, energy, total harmonic distortion (THD), current harmonics, voltage harmonics, power factor, and frequency. The Fluke 435 II series power quality analyser have several advantages. They are i) it will records output in analog and digital format. ii) the fluke analyzer will records at every 0.25 sec. iii) it will also measure all the electrical parameter at a time.

Keywords: THD, harmonics, power quality, TNEB, Fluke 435

Procedia PDF Downloads 157

Authors: Xiangtuo Chen, Paul-Henry Cournéde

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Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches.

Keywords: crop yield prediction, crop model, sensitivity analysis, paramater estimation, particle swarm optimization, random forest

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Authors: Yang Zhou, Heli Sun, Jianbin Huang, Jizhong Zhao, Shaojie Qiao

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Traffic prediction is a fundamental problem in urban environment, facilitating the smart management of various businesses, such as taxi dispatching, bike relocation, and stampede alert. Most earlier methods rely on identifying the intrinsic spatial-temporal correlation to forecast. However, the complex nature of this problem entails a more sophisticated solution that can simultaneously capture the mutual influence of both adjacent and far-flung areas, with the information of time-dimension also incorporated seamlessly. To tackle this difficulty, we propose a new multi-phase architecture, DSTDS (Dual Spatial-Temporal Data Scheme for traffic prediction), that aims to reveal the underlying relationship that determines future traffic trend. First, a graph-based neural network with an attention mechanism is devised to obtain the static features of the road network. Then, a multi-granularity recurrent neural network is built in conjunction with the knowledge from a grid-based model. Subsequently, the preceding output is fed into a spatial-temporal super-resolution module. With this 3-phase structure, we carry out extensive experiments on several real-world datasets to demonstrate the effectiveness of our approach, which surpasses several state-of-the-art methods.

Keywords: traffic prediction, spatial-temporal, recurrent neural network, dual data scheme

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Authors: Volkmar Nolting

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Fatigue crack growth prediction has become an important topic in both engineering and non-destructive evaluation. Crack propagation is influenced by the mechanical properties of the material and is conveniently modelled by the Paris-Erdogan equation. The critical crack size and the total number of load cycles are calculated. From a Larson-Miller plot the maximum operational temperature can for a given stress level be determined so that failure does not occur within a given time interval t. The study is used to determine a reasonable inspection cycle and thus enhances operational safety and reduces costs.

Keywords: fracturemechanics, crack growth prediction, lifetime of a component, structural health monitoring

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Authors: Prajakta Bhadgaonkar

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Forgiveness is taught since birth in Indian tradition. However, delivering a disabled child is a trauma for the parents. They keep on blaming themselves for the fault, which they are not responsible. Hence, due to lack of forgiving oneself the quality of life of both parent and child gets affected. In forgiveness, person tries to relieve oneself from the feeling of hatred towards oneself or other person. Forgiveness helps move ahead in the life. Hence, one can handle problem more efficiently resulting into better quality of life. In this study, the 30 parents of children with intellectual disability were contacted to find out quality of life. They were administered standardized measure of quality of life (QOL). The children were between 6 to 8 years of age. Out of these 30 parents, 12 parents (7 females and 5 males) were given forgiveness therapy for three months span. After every one month, the QOL scale was administered. At the end of three months, the significant difference was observed in quality of life of parents of children with intellectual disability. Genderwise there was no significant difference between male and female on quality of life.

Keywords: children with intellectual disability, forgiveness, parents, quality of life

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Authors: S. Adjiri-Bailiche, S. M. Boudia, H. Daaou, S. Hadouche, A. Benzaoui

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In this work the study of changes in the wind speed depending on the altitude is calculated and described by the model of the neural networks, the use of measured data, the speed and direction of wind, temperature and the humidity at 10 m are used as input data and as data targets at 50m above sea level. Comparing predict wind speeds and extrapolated at 50 m above sea level is performed. The results show that the prediction by the method of artificial neural networks is very accurate.

Keywords: MATLAB, neural network, power low, vertical extrapolation, wind energy, wind speed

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Authors: Sijing Xiong, Ran Su, Lit-Hsin Loo, Daniele Zink

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The kidney is a major target for toxic effects of drugs, industrial and environmental chemicals and other compounds. Typically, nephrotoxicity is detected late during drug development, and regulatory animal models could not solve this problem. Validated or accepted in silico or in vitro methods for the prediction of nephrotoxicity are not available. We have established the first and currently only pre-validated in vitro models for the accurate prediction of nephrotoxicity in humans and the first predictive platforms based on renal cells derived from human pluripotent stem cells. In order to further improve the efficiency of our predictive models, we recently developed a high content screening (HCS) platform. This platform employed automated imaging in combination with automated quantitative phenotypic profiling and machine learning methods. 129 image-based phenotypic features were analyzed with respect to their predictive performance in combination with 44 compounds with different chemical structures that included drugs, environmental and industrial chemicals and herbal and fungal compounds. The nephrotoxicity of these compounds in humans is well characterized. A combination of chromatin and cytoskeletal features resulted in high predictivity with respect to nephrotoxicity in humans. Test balanced accuracies of 82% or 89% were obtained with human primary or immortalized renal proximal tubular cells, respectively. Furthermore, our results revealed that a DNA damage response is commonly induced by different PTC-toxicants with diverse chemical structures and injury mechanisms. Together, the results show that the automated HCS platform allows efficient and accurate nephrotoxicity prediction for compounds with diverse chemical structures.

Keywords: high content screening, in vitro models, nephrotoxicity, toxicity prediction

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Authors: Yingkun Huang, Li Guo, Zekang Lan, Kai Tian

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Hard disk drives (HDD) failure of the exascale supercomputing system may lead to service interruption and invalidate previous calculations, and it will cause permanent data loss. Therefore, initiating corrective actions before hard drive failures materialize is critical to the continued operation of jobs. In this paper, a highly accurate analysis model based on CNN-LSTM and oversampling technique was proposed, which can correctly predict the necessity of a disk replacement even ten days in advance. Generally, the learning-based method performs poorly on a training dataset with long-tail distribution, especially fault prediction is a very classic situation as the scarcity of failure data. To overcome the puzzle, a new oversampling was employed to augment the data, and then, an improved CNN-LSTM with the shortcut was built to learn more effective features. The shortcut transmits the results of the previous layer of CNN and is used as the input of the LSTM model after weighted fusion with the output of the next layer. Finally, a detailed, empirical comparison of 6 prediction methods is presented and discussed on a public dataset for evaluation. The experiments indicate that the proposed method predicts disk failure with 0.91 Precision, 0.91 Recall, 0.91 F-measure, and 0.90 MCC for 10 days prediction horizon. Thus, the proposed algorithm is an efficient algorithm for predicting HDD failure in supercomputing.

Keywords: HDD replacement, failure, CNN-LSTM, oversampling, prediction

Procedia PDF Downloads 56

Authors: Mohammed A. A. Sarhan, Issam A. Al-Khatib

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Complying with the technical specifications of compost production is of high importance not only for environmental protection but also for increasing the productivity and promotion of compost use by farmers in agriculture. This study focuses on the compost quality of the Palestinian market and farmers’ attitudes toward agricultural use of compost. The quality is assessed through selection of 20 compost samples of different suppliers and producers and lab testing for quality parameters, while the farmers’ attitudes to compost use for agriculture are evaluated through survey questionnaire of 321 farmers in the Hebron area. The results showed that the compost in the Palestinian markets is of medium quality due to partial or non-compliance with the quality standards and guidelines. The Palestinian farmers showed a positive attitude since 91.2% of them have the desire to use compost in agriculture. The results also showed that knowledge of difference between compost and chemical fertilizers, perception of compost benefits and previously experiencing problems in compost use, are significant factors affecting the farmers’ attitude toward the use of compost as an organic fertilizer.

Keywords: attitude, compost, compost quality, organic fertilizer, manure

Procedia PDF Downloads 139

Authors: Ichihan Tai, Bill Olson, Paul Blessner

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Traditional early warning systems that alarm against crisis are generally based on structured or numerical data; therefore, a system that can make predictions based on unstructured textual data, an uncorrelated data source, is a great complement to the traditional early warning systems. The Chicago Board Options Exchange (CBOE) Volatility Index (VIX), commonly referred to as the fear index, measures the cost of insurance against market crash, and spikes in the event of crisis. In this study, news data is consumed for prediction of whether there will be a market-wide crisis by predicting the movement of the fear index, and the historical references to similar events are presented in an unsupervised manner. Topic modeling-based prediction and representation are made based on daily news data between 1990 and 2015 from The Wall Street Journal against VIX index data from CBOE.

Keywords: early warning system, knowledge management, market prediction, topic modeling.

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Authors: Maria Komal, Zaineb Khalil, Mehreen Sirshar

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Due to the recent technological advancements, Games have become one of the most demanding applications. Gaming industry is rapidly growing and the key to success in this industry is the development of good quality games, which is a highly competitive issue. The ultimate goal of game developers is to provide player’s satisfaction by developing high-quality games. This research is the comprehensive survey of techniques followed by game industries to ensure games quality. After analysis of various techniques, it has been found that quality simulation according to ISO standards and play test methods are used to ensure games quality. Because game development requires cross-disciplined team, an increasing trend towards distributed game development has been observed. This paper evaluates the strengths and weaknesses of current methodologies used in game industry and draws a conclusion. We have also proposed quality parameters which can be used as a heuristic framework to identify those attributes which have high testing priorities.

Keywords: game development, computer games, video games, gaming industry, quality assurance, playability, user experience

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Authors: Rodrigo S. Fonseca, Antônio C. P. Veiga

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Aiming at helping people with some movement limitation that makes typing and communication difficult, there is a need to customize an assistive tool with a learning environment that helps the user in order to optimize text input, identifying the error and providing the correction and possibilities of choice in the Portuguese language. The work presents an Orthographic and Grammatical System that can be incorporated into writing environments, improving and facilitating the use of an alphanumeric keyboard, using a prototype built using a genetic algorithm in addition to carrying out the prediction, which can occur based on the quantity and position of the inserted letters and even placement in the sentence, ensuring the sequence of ideas using a Long Short Term Memory (LSTM) neural network. The prototype optimizes data entry, being a component of assistive technology for the textual formulation, detecting errors, seeking solutions and informing the user of accurate predictions quickly and effectively through machine learning.

Keywords: genetic algorithm, neural networks, word prediction, machine learning

Procedia PDF Downloads 167

Authors: Manjinder Singh, Anish Sachdeva

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Research on total quality management (TQM), total productive maintenance (TPM), international organization for standardization (ISO) and six sigma generally investigate the implementation and impact of these programs in isolation. However, none of these quality improvement programs is self-sufficient and they may not be powerful enough to deliver the improvements and innovations that are required nowadays to ensure the survival and growth of a firm. They are not mutually exclusive and inconsistent. On the contrary, they need complementary support and may reinforce mutually to make use of their complementarity, inducement of side-effects in favor of other quality improvement program, mutual simulation and exploitation of shared values. In this paper, first of all, the various management activities were identified which are normally under focus when any quality improvement program is implemented in any organization. Then TOPSIS methodology was applied to establish the ranking of various quality improvement programs (total quality management, total productive maintenance, ISO and six sigma which were brought to the corporate boardroom to improve the quality) with respect to different management activities (operations related activities, quality related activities, maintenance related activities, organizational related activities, human related activities and finance related activities).

Keywords: total productive maintenance (TPM), total quality management (TQM), TOPSIS, international organization for standardization (ISO)

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Authors: Albena Ivanova, Jill Kurp, Austin Hampe

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Cereal products in the US contain rich information on the front of the package (FOP) as well as point-of-purchase (POP) summaries provided by the store. These summaries frequently are confusing and misleading to the consumer. This study explores the relationship between perceived quality, objective quality, price, and value in the cold cereal industry. A total of 270 cold cereal products were analyzed and the price, quality and value for different summaries were compared using ANOVA tests. The results provide evidence that the United States Department of Agriculture Organic FOP/POP are related to higher objective quality, higher price, but not to a higher value. Whole grain FOP/POP related to a higher objective quality, lower or similar price, and higher value. Heart-healthy POP related to higher objective quality, similar price, and higher value. Gluten-free FOP/POP related to lower objective quality, higher price, and lower value. Kid's cereals were of lower objective quality, same price, and lower value compared to family and adult markets. The findings point to a disturbing tendency of companies to continue to produce lower quality products for the kids’ market, pricing them the same as high-quality products. The paper outlines strategies that marketers and policymakers can utilize to contribute to the increased objective quality and value of breakfast cereal products in the United States.

Keywords: cereals, certifications, front-of-package claims, consumer health.

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Authors: Kassaye Balkew Workagegn, Gunnar Klemetsdal, Hans Magnus Gjøen

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In this study, the first objective was to investigate whether non-lethal or non-invasive methods, utilizing body measurements, could be used to efficiently predict fillet weight and fillet yield for a complete diallel cross of three Nile tilapia (Oreochromis niloticus) strains collected from three Ethiopian Rift Valley lakes, Lakes Ziway, Koka and Chamo. The second objective was to estimate heritability of body weight, actual and predicted fillet traits, as well as genetic correlations between these traits. A third goal was to estimate additive, reciprocal, and heterosis effects for body weight and the various fillet traits. As in females, early sexual maturation was widespread, only 958 male fish from 81 full-sib families were used, both for the prediction of fillet traits and in genetic analysis. The prediction equations from body measurements were established by forward regression analysis, choosing models with the least predicted residual error sums of squares (PRESS). The results revealed that body measurements on live Nile tilapia is well suited to predict fillet weight but not fillet yield (R²= 0.945 and 0.209, respectively), but both models were seemingly unbiased. The genetic analyses were carried out with bivariate, multibreed models. Body weight, fillet weight, and predicted fillet weight were all estimated with a heritability ranged from 0.23 to 0.28, and with genetic correlations close to one. Contrary, fillet yield was only to a minor degree heritable (0.05), while predicted fillet yield obtained a heritability of 0.19, being a resultant of two body weight variables known to have high heritability. The latter trait was estimated with genetic correlations to body weight and fillet weight traits larger than 0.82. No significant differences among strains were found for their additive genetic, reciprocal, or heterosis effects, while total heterosis effects were estimated as positive and significant (P < 0.05). As a conclusion, prediction of prediction of fillet weight based on body measurements is possible, but not for fillet yield.

Keywords: additive, fillet traits, genetic correlation, heritability, heterosis, prediction, reciprocal

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Authors: Leesa Mohanty, Ashok Banerjee

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Better audit quality signifies the financial statements of the auditee firm reflect true and fair view of their actual state of affairs, which reduces information asymmetry between management and shareholders, as a result, helps protect interests of shareholders. This study examines the impact of joint audit on audit quality. It is motivated by the ongoing debate where The Institute of Chartered Accountants of India (ICAI), the regulatory body governing auditors, has advocated the finance ministry and the Reserve Bank of India (RBI) for the mandatory use of joint audit in private banks to enhance the quality of audit. Earlier, the Government of India had rejected the plea by ICAI for mandatory joint audits in large companies stating it is not a viable option for promoting domestic firms. We introduce a new measure of audit quality. Drawing from the domain of text analytics, we use relevant phrases in audit reports to gauge audit quality and demonstrate that joint audit improves audit quality. We also, for robustness, use prevalent proxy for audit quality (Big N Auditor, ratio of audit fees to total fees) and find negative effect of joint audit on audit quality. We, therefore highlight that different proxy for audit quality show opposite effect of joint audit.

Keywords: audit fees, audit quality, Big N. Auditor, joint audit

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Authors: Marc Dorval, Marie-Hélène Jobin

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This work presents the various perspectives, dimensions, components and definitions given to quality in the operations management (OM) and healthcare services (HCS) literature in time, highlighting gaps and learning opportunities between the two disciplines through a thorough search into their rich and distinct body of knowledge. Greater and new insights about the general nature of quality are obtained with findings such as in OM, quality has been approached in six fairly distinct paradigms (excellence, value, conformity to specifications, attributes, satisfaction and meeting or exceeding customer expectations), whereas in HCS, two approaches are prominent (Donabedian’s structure, process and outcomes model and Lohr and Schroeder’s circumscribed definition). The two disciplines views on quality seem to have progressed much in parallel with little cross-learning from each other. This work then proposes an encompassing definition of quality as a lever and suggests further research and development avenues for a better use of the concept of quality by academics and practitioners alike toward the goals of greater organizational performance and improved management in healthcare and possibly other service domains.

Keywords: healthcare, management, operations, quality, services

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Authors: Khadije Khazaeli, Farzane Saberi

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Spinal cord injury is one of the traumatic events which has a great impact on the quality of life. spirituality has been used to improve many disorders and abnormalities in recent years and positive results have been seen; accordingly, the present study investigated the relationship between spirituality and quality of life in these patients. This study is a cross-sectional study of the correlation type was conducted on 100 people with spinal cord injury in Isfahan province in 2016 by the available sample method. Spirituality was assessed through the Spiritual Attitude Questionnaire and quality of life through the World Health Organization Quality of Life Questionnaire. Pearson correlation and regression tests were used to analyze the data. The results of this study showed that spirituality has a significant relationship with the quality of life of patients with spinal cord injury. It was also proved that all sub-units of spirituality, including attitude and spiritual ability, can affect all components of quality of life. The findings suggest that spirituality, along with other factors, can lead to a significant improvement in quality of life and, ultimately, general health of patients with SCI.

Keywords: spinal cord injury, quality of life, spirituality, patients

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Authors: Chengxuan Geng, Yizhou E

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This paper mainly examines the changes in the relationship between earnings management and audit quality before and after financial crisis in the context of American firms from 2005 to 2010. Based on a sample of 3584 firm year observations, we find that there are changes concerning the relation between accrual-based earnings management and audit quality during the pre-crisis and post-crisis periods. However, the results do not provide enough evidence with regard to the variances in the association between real activities earnings management and audit quality during these two periods.

Keywords: audit quality, earnings management, financial crisis, relationship

Procedia PDF Downloads 316

Authors: Bruno Pinto Vieira, Marco Antonio Calijorne Soares, Armando Sérgio de Aguiar Filho

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Nowadays, a massive amount of information has been produced by different data sources, including mobile devices and transactional systems. In this scenario, concerns arise on how to maintain or establish data quality, which is now treated as a product to be defined, measured, analyzed, and improved to meet consumers' needs, which is the one who uses these data in decision making and companies strategies. Information that reaches low levels of quality can lead to issues that can consume time and money, such as missed business opportunities, inadequate decisions, and bad risk management actions. The step of selecting, identifying, evaluating, and selecting data sources with significant quality according to the need has become a costly task for users since the sources do not provide information about their quality. Traditional data quality control methods are based on user experience or business rules limiting performance and slowing down the process with less than desirable accuracy. Using advanced machine learning algorithms, it is possible to take advantage of computational resources to overcome challenges and add value to companies and users. In this study, machine learning is applied to data quality analysis on different datasets, seeking to compare the performance of the techniques according to the dimensions of quality assessment. As a result, we could create a ranking of approaches used, besides a system that is able to carry out automatically, data quality assessment.

Keywords: machine learning, data quality, quality dimension, quality assessment

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Authors: Nataša Kalajdžija, Strahinja Kovačević, Davor Lončar, Sanja Podunavac Kuzmanović, Lidija Jevrić

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In order to investigate the relationship between retention and structure, a quantitative Structure Retention Relationships (QSRRs) study was applied for the prediction of retention times of a set of 23 secoestrane derivatives in a reversed-phase thin-layer chromatography. After the calculation of molecular descriptors, a suitable set of molecular descriptors was selected by using step-wise multiple linear regressions. Artificial Neural Network (ANN) method was employed to model the nonlinear structure-activity relationships. The ANN technique resulted in 5-6-1 ANN model with the correlation coefficient of 0.98. We found that the following descriptors: Critical pressure, total energy, protease inhibition, distribution coefficient (LogD) and parameter of lipophilicity (miLogP) have a significant effect on the retention times. The prediction results are in very good agreement with the experimental ones. This approach provided a new and effective method for predicting the chromatographic retention index for the secoestrane derivatives investigated.

Keywords: lipophilicity, QSRR, RP TLC retention, secoestranes

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Authors: Mariangela Viviani, Daniele Germano, Simone Colace, Agostino Forestiero, Giuseppe Papuzzo, Sara Laurita

Abstract:

Nowadays, people spend most of their time in closed environments, in offices, or at home. Therefore, secure and highly livable environmental conditions are needed to reduce the probability of aerial viruses spreading. Also, to lower the human impact on the planet, it is important to reduce energy consumption. Heating, Ventilation, and Air Conditioning (HVAC) systems account for the major part of energy consumption in buildings [1]. Devising systems to control and regulate the airflow is, therefore, essential for energy efficiency. Moreover, an optimal setting for thermal comfort and air quality is essential for people’s well-being, at home or in offices, and increases productivity. Thanks to the features of Artificial Intelligence (AI) tools and techniques, it is possible to design innovative systems with: (i) Improved monitoring and prediction accuracy; (ii) Enhanced decision-making and mitigation strategies; (iii) Real-time air quality information; (iv) Increased efficiency in data analysis and processing; (v) Advanced early warning systems for air pollution events; (vi) Automated and cost-effective m onitoring network; and (vii) A better understanding of air quality patterns and trends. We propose AIR SAFE, an IoT-based infrastructure designed to optimize air quality and thermal comfort in indoor environments leveraging AI tools. AIR SAFE employs a network of smart sensors collecting indoor and outdoor data to be analyzed in order to take any corrective measures to ensure the occupants’ wellness. The data are analyzed through AI algorithms able to predict the future levels of temperature, relative humidity, and CO₂ concentration [2]. Based on these predictions, AIR SAFE takes actions, such as opening/closing the window or the air conditioner, to guarantee a high level of thermal comfort and air quality in the environment. In this contribution, we present the results from the AI algorithm we have implemented on the first s et o f d ata c ollected i n a real environment. The results were compared with other models from the literature to validate our approach.

Keywords: air quality, internet of things, artificial intelligence, smart home

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Authors: Daniel Walker, Adam Qureshi, David Marchant, Alex Bahrami Balani

Abstract:

The present study aimed to assess the impact of concussion and physical pain on depression and health-related quality of life. Depressive symptoms were assessed using the Center for Epidemiological Studies' Depression Scale, and scores of health-related quality of life were measured by health-related quality of life short form-12. Data analysis of 67 participants (concussed 32 vs. 35 non-concussed) revealed that (i) 52% were displaying depressive symptoms (concussed 30% vs. non-concussed 22%) (ii) concussion had a significant effect on depressive symptoms when controlling for pain but no effect on the quality of life scores when controlling the same variable (iii) pain had a significant effect on depressive symptoms and quality of life. With this, both concussion and physical pain seem to have a negative impact on mental health; however, individuals may only recognise a reduction in quality of life with increased physical pain, hence a deterioration in mental well-being could be disregarded as a factor of health-related quality of life.

Keywords: depression, quality of life, concussion, physical pain

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Authors: Ilir Rexhepi, Naim Ismajli

Abstract:

Quality and quality management are key factors of success nowadays. Public sector and quality management in this sector contains many challenges and difficulties, most notably in a new country like Kosovo. This study analyses the process of implementation of quality management system in public administration institutions in this country. The main objective is to show how to set up a quality management system and how does the quality management system setup affect the overall public administration services in Kosovo. This study shows how the efficiency and effectiveness of public institution services/performance is rapidly improving through the establishment and functionalization of Quality Management System. The specific impact of established QMC within the organization has resulted with the identification of mission related processes within the entire system including input identification, the person in charge and the way of conversion to the output of each activity though the interference with other service processes within the system. By giving detailed analyses of all steps of implementation of the Quality Management System, its effect and consequences towards the overall public institution service performance, we try to go one step further, by showing it as a very good example or tool of other public institutions for improving their service performance. Interviews with employees, middle and high level managers including the quality manager and general secretaries are also part of analyses in this paper.

Keywords: quality, quality management system, efficiency, public administration institutions

Procedia PDF Downloads 250