Search results for: clinical prediction rule

Authors: Torkan Jabbariraad

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This study explores the role of private regulation as a mode of governance that engages non-state actors in establishing and implementing rules or standards for public goods or services. It examines the various forms, functions, and effects of private regulation on the rule of law and human rights and considers the role and duties of public authorities in monitoring and supporting private regulation. It contends that private regulation should be regarded as a beneficial supplement to public regulation rather than a substitute or rival and that public authorities should find a balance between acknowledging the independence and variety of private actors and securing their accountability and legitimacy. It also recommends that applying the principles and values of good governance to private regulation can improve its quality and efficiency. The study relies on various sources and viewpoints from the literature on governance theory, public law, and human rights and suggests further research and discussion on the topic of private regulation and its consequences for society.

Keywords: private regulation, public authority, governance theory, rule of law, human rights

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Authors: Dimitrios Triantakonstantis, Demetris Stathakis

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Urban areas have been expanded throughout the globe. Monitoring and modeling urban growth have become a necessity for a sustainable urban planning and decision making. Urban prediction models are important tools for analyzing the causes and consequences of urban land use dynamics. The objective of this research paper is to analyze and model the urban change, which has been occurred from 1990 to 2000 using CORINE land cover maps. The model was developed using drivers of urban changes (such as road distance, slope, etc.) under an Artificial Neural Network modeling approach. Validation was achieved using a prediction map for 2006 which was compared with a real map of Urban Atlas of 2006. The accuracy produced a Kappa index of agreement of 0,639 and a value of Cramer's V of 0,648. These encouraging results indicate the importance of the developed urban growth prediction model which using a set of available common biophysical drivers could serve as a management tool for the assessment of urban change.

Keywords: artificial neural networks, CORINE, urban atlas, urban growth prediction

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Authors: P. S. Jagadeesh Kumar, S. Meenakshi Sundaram, Wenli Hu, Yang Yung

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In 3D video games, the dominance of production is unceasingly growing with a protruding level of affordability in terms of budget. Afterward, the automation of speech-lip synchronization technique is customarily onerous and has advanced a critical research subject in virtual reality based 3D video games. This paper presents one of these automatic tools, precisely riveted on the synchronization of the speech and the lip movement of the game characters. A robust and precise speech recognition segment that systematized with Algebraic Code Excited Linear Prediction method is developed which unconventionally delivers lip sync results. The Algebraic Code Excited Linear Prediction algorithm is constructed on that used in code-excited linear prediction, but Algebraic Code Excited Linear Prediction codebooks have an explicit algebraic structure levied upon them. This affords a quicker substitute to the software enactments of lip sync algorithms and thus advances the superiority of service factors abridged production cost.

Keywords: algebraic code excited linear prediction, speech-lip synchronization, video games, virtual reality

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Authors: Pradeep Singh, Shrish Verma

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Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. The earlier we predict the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven data sets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: software metrics, fault prediction, cross project, within project.

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Authors: Manduth Ramchander

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Evaluating expressions involving multiplication and division can give rise to the fallacy that brackets can be arbitrarily inserted into expressions involving multiplication and division. The aim of this article was to draw upon mathematical theory to prove that brackets cannot be arbitrarily inserted into expressions involving multiplication and division and in particular in expressions where division precedes multiplication. In doing so, it demonstrates that the notion that two different answers are possible, when evaluating expressions involving multiplication and division, is indeed a false one. Searches conducted in a number of scholarly databases unearthed the rules to be applied when removing brackets from expressions, which revealed that consideration needs to be given to sign changes when brackets are removed. The rule pertaining to expressions involving multiplication and division was then extended upon, in its reverse format, to prove that brackets cannot be arbitrarily inserted into expressions involving multiplication and division. The application of the rule demonstrates that an expression involving multiplication and division can have only one correct answer. It is recommended that both the rule and its reverse be included in the curriculum, preferably at the juncture when manipulation with brackets is introduced.

Keywords: brackets, multiplications and division, operations, order

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Authors: Haozhe Xiang

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With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results.

Keywords: deep learning, graph convolutional network, attention mechanism, LSTM

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Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

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To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

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Authors: Yong-Il Lee, Do-Yeon Hwang, Won-Seog Jeong, Duckshin Park

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Recently, because of the high-speed trains forced ventilation, it is important to control the ventilation. The ventilation is for controlling various contaminants, temperature, and humidity. The high-speed train route is straight to a destination having a high speed. And there are many mountainous areas in Korea. So, tunnel rate is higher then other country. KTX HVAC block off the outdoor air, when entering tunnel. So the high tunnel rate is an effect of ventilation in the KTX cabin. It is important to reduction rate in CO2 concentration prediction. To meet the air quality of the public transport vehicles recommend standards, the KTX cabin of CO2 concentration should be managed. In this study, the concentration change was predicted by CO2 prediction simulation in route to be opened.

Keywords: CO2 prediction, KTX, ventilation, infrastructure and transportation engineering

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Authors: Mao Ju

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The system of general counsel (GC) is an important governance structure designed for the construction of state-owned enterprises (SOEs) under the rule of law. This article is based on the setting of mergers and acquisitions (M&As) and takes the efficiency of M&As to examine the implementation effect of the system of GC for SOEs. Research has found that: (1) companies implementing the system of GC for SOEs have higher efficiency in M&As, manifested in better operational and market performance, and this effect depends on the professional ability and power of the GC. This indicates that the GC of SOEs has played a positive role in the decision-making process of M&As, which helps to improve the efficiency of M&As. (2) The impact of the GC of SOEs on the efficiency of M&As is heterogeneous, and this positive effect is mainly reflected in local and commercial SOEs. (3) The path of this impact is that the GC of SOEs can help reduce ineffective M&As in advance, enhance the ability to integrate M&As after the fact and reduce the risk of goodwill impairment and bankruptcy. This article reveals the impact of the construction of SOEs under the rule of law with the system of GC as the core of M&As activities, providing intuitive evidence for the implementation effect of the GC of SOEs. The research conclusion has important practical guiding value for comprehensively deepening the construction of the rule of SOEs under the rule of law and writing a good chapter on the Chinese path to modernization of SOEs.

Keywords: the system of general counsel, merger and acquisition efficiency, state-owned enterprises, mergers and acquisitions

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Authors: Katawut Kaewbanjong

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We analyzed a volume of data and found significant user satisfaction in software project factors. A statistical significance analysis (logistic regression) and collinearity analysis determined the significance factors from a group of 71 pre-defined factors from 191 software projects in ISBSG Release 12. The eight prediction models used for testing the prediction potential of these factors were Neural network, k-NN, Naïve Bayes, Random forest, Decision tree, Gradient boosted tree, linear regression and logistic regression prediction model. Fifteen pre-defined factors were truly significant in predicting user satisfaction, and they provided 82.71% prediction accuracy when used with a neural network prediction model. These factors were client-server, personnel changes, total defects delivered, project inactive time, industry sector, application type, development type, how methodology was acquired, development techniques, decision making process, intended market, size estimate approach, size estimate method, cost recording method, and effort estimate method. These findings may benefit software development managers considerably.

Keywords: prediction model, statistical analysis, software project, user satisfaction factor

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Authors: Ronelle Prinsloo

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Rule 41A was introduced to the Uniform Rules of Court with the intention of promoting alternative dispute resolution (ADR), specifically mediation, as a means of resolving disputes; its voluntary nature allows parties to explore mediation willingly without the imposition of a mandatory requirement. Defamation suits, often notorious for their protracted litigation timelines, could benefit from the streamlined efficiency offered by mandatory rule 41A processes. Mediation, when mandated, could serve as a swift alternative, alleviating the burden on the court system and providing expedited relief to aggrieved parties. By incorporating a mandatory mediation step, parties might be encouraged to engage in a more constructive dialogue at an earlier stage, potentially fostering resolutions that might be elusive within the confines of protracted courtroom battles. This expedited resolution could not only benefit the litigants involved but also contribute to the broader efficiency and efficacy of the legal system. However, the application of rule 41A in defamation cases raises intriguing questions about its effectiveness in balancing the scales between freedom of expression and the protection of reputation. In considering the potential merits of making rule 41A mandatory in defamation cases, a key consideration is the prospect of expeditious and cost-effective resolution.

Keywords: constitution of South Africa, defamation, litigation, mandatory, mediation

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Authors: K. Petcharaporn, S. Kumchoo

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The acidity (citric acid) is one of the chemical contents that can refer to the internal quality and the maturity index of tomato. The titratable acidity (%TA) can be predicted by a non-destructive method prediction by using the transmittance short wavelength (SW-NIR). Spectroscopy in the wavelength range between 665-955 nm. The set of 167 tomato samples divided into groups of 117 tomatoes sample for training set and 50 tomatoes sample for test set were used to establish the calibration model to predict and measure %TA by partial least squares regression (PLSR) technique. The spectra were pretreated with MSC pretreatment and it gave the optimal result for calibration model as (R = 0.92, RMSEC = 0.03%) and this model obtained high accuracy result to use for %TA prediction in test set as (R = 0.81, RMSEP = 0.05%). From the result of prediction in test set shown that the transmittance SW-NIR spectroscopy technique can be used for a non-destructive method for %TA prediction of tomatoes.

Keywords: tomato, quality, prediction, transmittance, titratable acidity, citric acid

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Authors: Venkat S. Somayajula

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Ground surface temperature history prediction model plays a vital role in determining standards for international nuclear waste management. International standards for borehole based nuclear waste disposal require paleoclimate cycle predictions on scale of a million forward years for the place of waste disposal. This research focuses on developing a paleoclimate cycle prediction model using Bayesian long-short term memory (LSTM) neural architecture operated on accumulated borehole temperature history data. Bayesian models have been previously used for paleoclimate cycle prediction based on Monte-Carlo weight method, but due to limitations pertaining model coupling with certain other prediction networks, Bayesian models in past couldn’t accommodate prediction cycle’s over 1000 years. LSTM has provided frontier to couple developed models with other prediction networks with ease. Paleoclimate cycle developed using this process will be trained on existing borehole data and then will be coupled to surface temperature history prediction networks which give endpoints for backpropagation of LSTM network and optimize the cycle of prediction for larger prediction time scales. Trained LSTM will be tested on past data for validation and then propagated for forward prediction of temperatures at borehole locations. This research will be beneficial for study pertaining to nuclear waste management, anthropological cycle predictions and geophysical features

Keywords: Bayesian long-short term memory neural network, borehole temperature, ground surface temperature history, paleoclimate cycle

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Authors: Sayantika Biswas, Dipanshu Sur, Amitoj Athwal, Ratnabali Chakravorty

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Title: Impact of serum estrogen and progesterone levels in the outcome pregnancy rate in frozen embryo transfer cycles. A prospective cohort study Objective: The aim of the current study was to evaluate the effect of serum estradiol (E2) and progesterone (P4) levels at different time points on pregnancy outcomes in frozen embryo transfer (FET) cycles. Materials & Method: A prospective cohort study was performed in patients undergoing frozen embryo transfer. Patients under age 37 years of age with at least one good blastocyst or three good day 3 embryos were included in the study. For endometrial preparation, 14 days of oral estradiol use (2X2 mg for 5 days. 3X2 mg for 4 days, and 4X2 mg for 5 days) was followed by vaginal progesterone twice a day and 50 mg intramuscular progesterone twice a day. Embryo transfer was scheduled 72-76 hrs or 116-120hrs after the initiation of progesterone. Serum E2 and P4 levels were examined at 4 times a) at the start of the menstrual cycle prior to the hormone supplementation. b) on the day of P4 start. c) on the day of ET. d) on the third day after ET. Result: A total 41 women were included in this study (mean age 31.8; SD 2.8). Clinical pregnancy rate was 65.55%. Serum E2 levels on at the start of the menstrual cycle prior to the hormone supplementation and on the day of P4 start were high in patients who achieved pregnancy compared to who did not (P=0.005 and P=0.019 respectively). P4 levels on on the day of ET were also high in patients with clinical pregnancy. On the day of P4 start, a serum E2 threshold of 186.4 pg/ml had a sensitivity of 82%, and P4 had a sensitivity of 71% for the prediction of clinical pregnancy at the threshold value 16.00 ng/ml. Conclusion: In women undergoing FET with hormone replacement, serum E2 level >186.4 pg/ml on the day of the start of progesterone and serum P4 levels >16.00 ng/ml on embryo transfer day are associated with clinical pregnancy.

Keywords: serum estradiol, serum progesterone, clinical pregnancy, frozen embryo transfer

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Authors: Emma Roberts

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The Rome II Regulation has established a uniform regime of conflict of law rules across the European Union (except for Denmark) which determines the law applicable in non-contractual obligations disputes. It marks a significant development towards the Europeanization of private international law and aims to provide the most appropriate connecting factors to achieve both legal certainty and justice in individual cases. Many non-contractual obligations are recognised to present such distinct factors that, to achieve these aims, a special rule is provided for determining the applicable law in cases in respect of product liability and environmental torts, for example. Throughout the legislative process, the European Parliament sought to establish a separate rule for road traffic accidents, recognising that these cases too present such novel situations that a blanket application of a lex loci damni approach would not provide an appropriate answer. Such attempts were rejected and, as a result, cases arising out of road traffic accidents are subject to the Regulation’s general lex loci damni rule along with its escape clause and limited exception. This paper offers a critique of the Regulation’s response to cross-border road traffic accident cases. In England and Wales, there have been few cases that have applied the Regulation’s provisions to date, but significantly the majority of such cases are in respect of road traffic accidents. This paper examines the decisions in those cases and challenges the legislators’ decision not to provide a special rule for such incidences. Owing to the diversity in compensation systems globally, applying the Regulation’s general rule to cases of road traffic accidents – given the breadth of matters that are to be subject to the lex cause – cannot ensure an outcome that provides ‘justice in individual cases’ as is assured by the Regulation's recitals. Not only does this paper suggest that the absence of a special rule for road traffic accidents means that the Regulation fails to achieve one of its principal aims, but it further makes out a compelling case for the legislative body of the European Union to implement a corrective instrument.

Keywords: accidents abroad, applicable law, cross-border torts, non-contractual obligations, road traffic accidents

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Authors: Golyasamin Khanehzaei, Mohd. Bakri Ishak, Ahmad Makmom Hj Abdullah, Latifah Abd Manaf

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Abstract—Management of clinical waste is a critical problem worldwide. Immediate attention is required to manage the clinical waste in an appropriate way in newly developing economy country such as Malaysia. The increasing amount of clinical waste generated is resulted from rapid urbanization and growing number of private health care facilities in developing countries such as Malaysia. In order to develop a sensible clinical waste management system and improvement of the management, information on factors affecting clinical waste generation has the crucial role. This paper is the study of management characteristics of clinical waste and the level of efficiency of clinical waste management systems operating in private clinics located in Selangor, Malaysia. Are they following the proper international standards? By taking all of this in consideration the aim of this paper is to identify and discuss the current trend, current challenges and also the present opportunities among the challenges of clinical waste management in private clinics of Selangor, Malaysia. The SWOT analysis was characterized for the evaluation of strengths, weaknesses, opportunities and threats. The methodology for this study was constituted of direct observation, Informal interviews, Conducting SWOT analysis, conduction of one sustainability dimensions analysis and application. The results show that clinical waste management in private clinics is far from an ideal model.

Keywords: clinical waste, SWOT analysis, Selangor, Malaysia

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Authors: Soha A. Bahanshal, Byung G. Kim

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Identification of patients at high risk for hospital readmission is of crucial importance for quality health care and cost reduction. Predicting hospital readmissions among diabetic patients has been of great interest to many researchers and health decision makers. We build a prediction model to predict hospital readmission for diabetic patients within 30 days of discharge. The core of the prediction model is a modified k Nearest Neighbor called Hybrid Fuzzy Weighted k Nearest Neighbor algorithm. The prediction is performed on a patient dataset which consists of more than 70,000 patients with 50 attributes. We applied data preprocessing using different techniques in order to handle data imbalance and to fuzzify the data to suit the prediction algorithm. The model so far achieved classification accuracy of 80% compared to other models that only use k Nearest Neighbor.

Keywords: machine learning, prediction, classification, hybrid fuzzy weighted k-nearest neighbor, diabetic hospital readmission

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Authors: Tambe Daniel Atem

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Background: Clinical Research is an organized research on human beings intended to provide adequate information on the drug use as a therapeutic agent on its safety and efficacy. The significance of the study is to educate the global health and life science graduates in Clinical Research in depth to perform better as it involves testing drugs on human beings. Objectives: to provide an overall understanding of the scientific approach to the evaluation of new and existing medical interventions and to apply ethical and regulatory principles appropriate to any individual research. Methodology: It is based on – Primary data analysis and Secondary data analysis. Primary data analysis: means the collection of data from journals, the internet, and other online sources. Secondary data analysis: a survey was conducted with a questionnaire to interview the Clinical Research Professionals to understand the need of training to perform clinical trials globally. The questionnaire consisted details of the professionals working with the expertise. It also included the areas of clinical research which needed intense training before entering into hardcore clinical research domain. Results: The Clinical Trials market worldwide worth over USD 26 billion and the industry has employed an estimated 2,10,000 people in the US and over 70,000 in the U.K, and they form one-third of the total research and development staff. There are more than 2,50,000 vacant positions globally with salary variations in the regions for a Clinical Research Coordinator. R&D cost on new drug development is estimated at US$ 70-85 billion. The cost of doing clinical trials for a new drug is US$ 200-250 million. Due to an increase trained Clinical Research Professionals India has emerged as a global hub for clinical research. The Global Clinical Trial outsourcing opportunity in India in the pharmaceutical industry increased to more than $2 billion in 2014 due to increased outsourcing from U.S and Europe to India. Conclusion: Assessment of training need is recommended for newer Clinical Research Professionals and trial sites, especially prior the conduct of larger confirmatory clinical trials.

Keywords: clinical research, clinical trials, clinical research professionals

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Authors: Stepan Kuchar, Martin Golasowski, Radim Vavrik, Michal Podhoranyi, Boris Sir, Jan Martinovic

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The main goal of this article is to describe the online flood monitoring and prediction system Floreon+ primarily developed for the Moravian-Silesian region in the Czech Republic and the basic process it uses for running automatic rainfall-runoff and hydrodynamic simulations along with their calibration and uncertainty modeling. It takes a long time to execute such process sequentially, which is not acceptable in the online scenario, so the use of high-performance computing environment is proposed for all parts of the process to shorten their duration. Finally, a case study on the Ostravice river catchment is presented that shows actual durations and their gain from the parallel implementation.

Keywords: flood prediction process, high performance computing, online flood prediction system, parallelization

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Authors: David Gibbs-Kneller

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Only parties to a contract can enforce it. This is the privity of the contract. Carriage contracts frequently involve intermediated relationships. While the carrier and cargo-owner will agree on a contract for carriage, there is no privity or consideration between the cargo-owner and third parties. To overcome this, the contract utilizes ‘bailment on terms’ or the rule in Morris. Morris v C W Martin & Sons Ltd is authority for the following: A sub-bailee and bailor may rely on terms of a bailment where the bailor has consented to sub-bailment “on terms”. Bailment on terms can play a significant part in making litigation decisions and determining liability. It is used in standard form contracts and courts have also strived to find consent to bailment on terms in agreements so as to avoid the consequences of privity of contract. However, what this paper exposes is the false legal basis for this model. Lord Denning gave an account adumbrated of the law of bailments to justify the rule in Morris. What Lord Denning was really doing was objecting to the doctrine of privity. To do so, he wrongly asserted there was a lacuna in law that meant third parties could not avail themselves upon terms of a contract. Next, he provided a false analogy between purely contractual rights and possessory liens. Finally, he gave accounts of authorities to say they supported the rule in Morris when they did not. Surprisingly, subsequent case law on the point has not properly engaged with this reasoning. The Pioneer Container held that since the rule in Morris lay in bailments, the decision is not dependent on the doctrine of privity. Yet the basis for this statement was Morris. Once these reasons have been discounted, all bailment on terms rests on is the claim that the law of bailments is an independent source of law. Bailment on terms should not be retained, for it is contrary to established principles in the law of property, tort, and contract. That undermines the certainty of those principles by risking their collapse because there is nothing that keeps bailment on terms within the confines of bailments only. As such, bailment on terms is not good law and should not be used in standard form contracts or by the courts as a means of determining liability. If bailment on terms is a pragmatic rule to retain, it is recommended that rules governing carriage contracts should be amended.

Keywords: bailment, carriage of goods, contract law, privity

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Authors: Suriya

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Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models.

Keywords: deep learning, AOD, PM2.5, prediction, Ulaanbaatar

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Authors: Meera Tandan, Mohan Timilsina, Martin Cormican, Akke Vellinga

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Background: In general practice, many infections are treated empirically without microbiological confirmation. Understanding susceptibility of antimicrobials during empirical prescribing can be helpful to reduce inappropriate prescribing. This study aims to apply a prediction model using a decision tree approach to predict the antimicrobial resistance (AMR) of urinary tract infections (UTI) based on non-clinical features of patients over 65 years. Decision tree models are a novel idea to predict the outcome of AMR at an initial stage. Method: Data was extracted from the database of the microbiological laboratory of the University Hospitals Galway on all antimicrobial susceptibility testing (AST) of urine specimens from patients over the age of 65 from January 2011 to December 2014. The primary endpoint was resistance to common antimicrobials (Nitrofurantoin, trimethoprim, ciprofloxacin, co-amoxiclav and amoxicillin) used to treat UTI. A classification and regression tree (CART) model was generated with the outcome ‘resistant infection’. The importance of each predictor (the number of previous samples, age, gender, location (nursing home, hospital, community) and causative agent) on antimicrobial resistance was estimated. Sensitivity, specificity, negative predictive (NPV) and positive predictive (PPV) values were used to evaluate the performance of the model. Seventy-five percent (75%) of the data were used as a training set and validation of the model was performed with the remaining 25% of the dataset. Results: A total of 9805 UTI patients over 65 years had their urine sample submitted for AST at least once over the four years. E.coli, Klebsiella, Proteus species were the most commonly identified pathogens among the UTI patients without catheter whereas Sertia, Staphylococcus aureus; Enterobacter was common with the catheter. The validated CART model shows slight differences in the sensitivity, specificity, PPV and NPV in between the models with and without the causative organisms. The sensitivity, specificity, PPV and NPV for the model with non-clinical predictors was between 74% and 88% depending on the antimicrobial. Conclusion: The CART models developed using non-clinical predictors have good performance when predicting antimicrobial resistance. These models predict which antimicrobial may be the most appropriate based on non-clinical factors. Other CART models, prospective data collection and validation and an increasing number of non-clinical factors will improve model performance. The presented model provides an alternative approach to decision making on antimicrobial prescribing for UTIs in older patients.

Keywords: antimicrobial resistance, urinary tract infection, prediction, decision tree

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Authors: Idahosa Osagie Ojo

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This research is on colonial racism and the Benin bronze artefacts between 1862 and 1960. It analyses the British racial sentiments against the Benin people that heralded colonial rule and how they influenced the perceptions of the artworks during the period. The aim is to contribute to the knowledge of colonial rule in Benin by bringing to the fore its impacts on the perception and interpretation of the Benin bronze artefacts during the period. Primary and secondary sources were utilised and the historical method was adopted. The findings reveal that the first British racial propaganda against the Benin people started in 1862 and that it was consciously orchestrated to manoeuvre public opinion for the ill-conceived colonial project. The research also reveals that the Benin people were not alone in this, as other peoples of Africa that were targeted for British colonial domination suffered the same fate. Findings also show that racial propaganda was actually used to rationalised colonial rule in Benin and that it later influenced the interpretations and perception of the Benin bronze artefacts throughout the colonial period and beyond.

Keywords: Benin, Bronzes, colonial, racism

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Authors: A. Majidian

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The life prediction of thermal power plant components is necessary to prevent the unexpected outages, optimize maintenance tasks in periodic overhauls and plan inspection tasks with their schedules. One of the main critical components in a power plant is condenser because its failure can affect many other components which are positioned in downstream of condenser. This paper deals with factors affecting life of condenser. Failure rates dependency vs. these factors has been investigated using Artificial Neural Network (ANN) and fuzzy logic algorithms. These algorithms have shown their capabilities as dynamic tools to evaluate life prediction of power plant equipments.

Keywords: life prediction, condenser tube, neural network, fuzzy logic

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Authors: Tarek Aboueldahab, Amin Mohamed Nassar

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Wind energy is a fluctuating energy source unlike conventional power plants, thus, it is necessary to accurately predict short term wind speed to integrate wind energy in the electricity supply structure. To do so, we present a hybrid artificial intelligence model of short term wind speed prediction based on passive aggregation of the particle swarm optimization and neural networks. As a result, improvement of the prediction accuracy is obviously obtained compared to the standard artificial intelligence method.

Keywords: artificial intelligence, neural networks, particle swarm optimization, passive aggregation, wind speed prediction

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Authors: Sedigheh Salemi, Mahnaz Sanjari, Maryam Aalaa, Mohammad Mirzabeigi

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Clinical governance is the framework within which the health service provider is required to ongoing accountability and improvement of the quality of their services. This cross-sectional study was conducted in 661 nurses who work in government hospitals from 35 hospitals of 9 provinces in Iran. The study was approved by the Nursing Council and was carried out with the authorization of the Research Ethics Committee. The questionnaire included 24 questions in which 4 questions focused on clinical governance defining from the nurses' perspective. The reliability was evaluated by Cronbach's alpha (α=0/83). Statistical analyzes were performed, using SPSS version 16. Approximately 40% of nurses correctly answered that clinical governance is not "system of punishment and rewards for the staff". The most nurses believed that "clinical efficacy" is one of the main components of clinical governance. A few of nurses correctly responded that "Evidence Based Practice" and "management" is not part of clinical governance. The small number of nurses correctly answered that the "maintenance of patient records" and "to recognize the adverse effects" is not the role of nurse in clinical governance. Most "do not know" answer was to the "maintenance of patient records". The most nurses unanimously believed that the implementation of clinical governance led to "promoting the quality of care". About a third of nurses correctly stated that the implementation of clinical governance will not lead to "an increase in salaries and benefits of the medical team". As a member of the health team, nurses are responsible in terms of participation in quality improvement and it is necessary to create an environment in which clinical care will flourish and serve to preserve the high standards.

Keywords: clinical governance, nurses, salary, health team

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Authors: Thinh Ngo, Paul Rad, Brian Kelley

Abstract:

Noise estimation is essential in today wireless systems for power control, adaptive modulation, interference suppression and quality of service. Deep learning (DL) has already been applied in the physical layer for modulation and signal classifications. Unacceptably low accuracy of less than 50% is found to undermine traditional application of DL classification for SNR prediction. In this paper, we use divide-and-conquer algorithm and classifier fusion method to simplify SNR classification and therefore enhances DL learning and prediction. Specifically, multiple CNNs are used for classification rather than a single CNN. Each CNN performs a binary classification of a single SNR with two labels: less than, greater than or equal. Together, multiple CNNs are combined to effectively classify over a range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained CNNs to predict SNR over a wide range of joint channel parameters including multiple Doppler shifts (0, 60, 120 Hz), power-delay profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The approach achieves individual SNR prediction accuracy of 92%, composite accuracy of 70% and prediction convergence one order of magnitude faster than that of traditional estimation.

Keywords: classification, CNN, deep learning, prediction, SNR

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Authors: Isabel Mousinho de Figueiredo

Abstract:

Traditionally it was held that the king can do no wrong. History has proved otherwise, and both the rule of law and the open society call for a diversification of checks and balances, including civil liability in tort. Most jurisdictions are right to fear the excessive cost of such liability for the innocent taxpayer. There are notwithstanding extreme instances where refusing compensation is perceived to be outrageous. Many public bodies end up handing out on a voluntary basis, which leaves room to question its legality and merit. Instead, some criteria can shed light on the fairness of an underlying rationale of such compensation and cordon it off within reasonable limits.

Keywords: comparative law, liability of legislators, public bodies, tort law

Procedia PDF Downloads 113

Authors: Pengwei Qiao, Sucai Yang, Wenxia Wei

Abstract:

Soil pollution has become an important issue in China. Accurate spatial distribution prediction of pollutants with interpolation methods is the basis for soil remediation in the site. However, a relatively strong variability of pollutants would decrease the prediction accuracy. Theoretically, partition interpolation can result in accurate prediction results. In order to verify the applicability of partition interpolation for a site, benzo (b) fluoranthene (BbF) in four soil layers was adopted as the research object in this paper. IDW (inverse distance weighting)-, RBF (radial basis function)-and OK (ordinary kriging)-based partition interpolation accuracies were evaluated, and their influential factors were analyzed; then, the uncertainty and applicability of partition interpolation were determined. Three conclusions were drawn. (1) The prediction error of partitioned interpolation decreased by 70% compared to unpartitioned interpolation. (2) Partition interpolation reduced the impact of high CV (coefficient of variation) and high concentration value on the prediction accuracy. (3) The prediction accuracy of IDW-based partition interpolation was higher than that of RBF- and OK-based partition interpolation, and it was suitable for the identification of highly polluted areas at a contaminated site. These results provide a useful method to obtain relatively accurate spatial distribution information of pollutants and to identify highly polluted areas, which is important for soil pollution remediation in the site.

Keywords: accuracy, applicability, partition interpolation, site, soil pollution, uncertainty

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Authors: Reza Moosavi Mohseni, Wenjun Zhang, Jiling Cao

Abstract:

The aim of the present study is to detect the chaotic behavior in monetary economic relevant dynamical system. The study employs three different forms of Taylor rules: current, forward, and backward looking. The result suggests the existence of the chaotic behavior in all three systems. In addition, the results strongly represent that using expectations especially rational expectation hypothesis can increase complexity of the system and leads to more chaotic behavior.

Keywords: taylor rule, monetary system, chaos theory, lyapunov exponent, GMM estimator

Procedia PDF Downloads 528