Search results for: linguistic information

Authors: Navdeep Singh Randhawa

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Wireless communication system has been proved as the best for any communication. However, there are some undesirable threats of a wireless communication channel on the information transmitted through it, such as attenuation, distortions, delays and phase shifts of the signals arriving at the receiver end which are caused by its band limited and dispersive nature. One of the threat is ISI (Inter Symbol Interference), which has been found as a great obstacle in high speed communication. Thus, there is a need to provide perfect and accurate technique to remove this effect to have an error free communication. Thus, different equalization techniques have been proposed in literature. This paper presents the equalization techniques followed by the concept of adaptive filter equalizer, its algorithms (LMS and RLS) and applications of adaptive equalization technique.

Keywords: channel equalization, adaptive equalizer, least mean square, recursive least square

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Authors: Alemu Moges Belay, Torgeir Welo, Jan Ola Strandhagen

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Lean has long been linked with manufacturing, but its application claimed also by other functions such as product development and services. However, there is a challenge on understanding and defining lean in each function context. This paper aims to investigate the literature that focus mainly on PD process improvement, obtain better understanding and redefine LPD in systematic way. In addition to that, the paper attempts to summarize various proposed transformation strategies, definitions, identifying features of manufacturing and product development that would help to redefining lean in product development context. Finally we redefine LPD in organized way that encompasses different steps such as stage gate, communication and information, events, learning, innovation, knowledge and value creation.

Keywords: lean, lean manufacturing, lean product development, transformation, strategies

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Authors: Gonzalez Carlos, Martinez Fransisco

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There are various types of analysis that allow us to involve seismic phenomena that cause strong requirements for structures that are designed by society; one of them is a probabilistic analysis which works from prediction equations that have been created based on metadata seismic compiled in different regions. These equations form models that are used to describe the 5% damped pseudo spectra response for the various zones considering some easily known input parameters. The biggest problem for the creation of these models requires data with great robust statistics that support the results, and there are several places where this type of information is not available, for which the use of alternative methodologies helps to achieve adjustments to different models of seismic prediction.

Keywords: GMPM, 5% damped pseudo-response spectra, models of seismic prediction, PSHA

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Authors: Ugiri Ejikeme

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In an executive summary, UNESCO describes Framework for Cultural Statistics as a tool for organizing cultural statistics both nationally and internationally. This is based on conceptual foundation and a common understanding of culture that will enable the measurement of a wide range of cultural expressions. This means therefore that cultural expression in whatever guise has the potentiality of contributing reasonably to the development of a given society. The paper looked into the various tangible and intangible cultures in Imo State of Nigeria. Due to government’s insensitivity, there is need to remind ourselves of the need to pay adequate attention to the cultural heritage bequeathed to us by our forefathers for the sake of posterity. Documenting this information in written form therefore becomes imperative. The study concludes that culture if developed, could reasonably contribute to economic and social growth of the society.

Keywords: detonating culture, statistics and development, Imo State, Nigeria

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Authors: Alicja Starczewska, Aleksander Nawrat, Krzysztof Daniec, Jarosław Homa, Kacper Hołda

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Border Gateway Protocol is the main routing protocol that enables routing establishment between all autonomous systems, which are the basic administrative units of the internet. Due to the poor protection of BGP, it is important to use additional BGP security systems. Many solutions to this problem have been proposed over the years, but none of them have been implemented on a global scale. This article describes a system capable of building images of real-time BGP network topology in order to detect BGP anomalies. Our proposal performs a detailed analysis of BGP messages that come into local network cards supplemented by information collected by remote collectors in different localizations.

Keywords: BGP, BGP hijacking, cybersecurity, detection

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Authors: Qian Zhao, Shi-Qi Liu, Bo Guo, Zhi-Jun Cheng, Xiao-Yue Wu

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Cold standby redundancy is considered to be an effective mechanism for improving system reliability and is widely used in industrial engineering. However, because of the complexity of the reliability structure, there is little literature studying on the residual life of cold standby system consisting of complex components. In this paper, a simulation method is presented to predict the residual life of k-out-of-n cold standby system. In practical cases, failure information of a system is either unknown, partly unknown or completely known. Our proposed method is designed to deal with the three scenarios, respectively. Differences between the procedures are analyzed. Finally, numerical examples are used to validate the proposed simulation method.

Keywords: cold standby system, k-out-of-n, residual life, simulation sampling

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Authors: Maria Klitgaard Christensen, Carmen Lim, Sukanta Saha, Danielle Cannon, Finley Prentis, Oleguer Plana-Ripoll, Natalie Momen, Kim Moesgaard Iburg, John J. McGrath

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Introduction: About a third of the world’s population will develop a mental disorder over their lifetime. Having a mental disorder is a huge burden in health loss and cost for the individual, but also for society because of treatment cost, production loss and caregivers’ cost. The objective of this study is to synthesize the international published literature on the economic burden of mental disorders. Methods: Systematic literature searches were conducted in the databases PubMed, Embase, Web of Science, EconLit, NHS York Database and PsychInfo using key terms for cost and mental disorders. Searches were restricted to 1980 until May 2019. The inclusion criteria were: (1) cost-of-illness studies or cost-analyses, (2) diagnosis of at least one mental disorder, (3) samples based on the general population, and (4) outcome in monetary units. 13,640 publications were screened by their title/abstract and 439 articles were full-text screened by at least two independent reviewers. 112 articles were included from the systematic searches and 31 articles from snowball searching, giving a total of 143 included articles. Results: Information about diagnosis, diagnostic criteria, sample size, age, sex, data sources, study perspective, study period, costing approach, cost categories, discount rate and production loss method and cost unit was extracted. The vast majority of the included studies were from Western countries and only a few from Africa and South America. The disorder group most often investigated was mood disorders, followed by schizophrenia and neurotic disorders. The disorder group least examined was intellectual disabilities, followed by eating disorders. The preliminary results show a substantial variety in the used perspective, methodology, costs components and outcomes in the included studies. An online tool is under development enabling the reader to explore the published information on costs by type of mental disorder, subgroups, country, methodology, and study quality. Discussion: This is the first systematic review synthesizing the economic cost of mental disorders worldwide. The paper will provide an important and comprehensive overview over the economic burden of mental disorders, and the output from this review will inform policymaking.

Keywords: cost-of-illness, health economics, mental disorders, systematic review

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Authors: Sarada Nivedita Bhuyan

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Earthquake is the sudden, rapid movement of the earth’s crust and is the natural means of releasing stress. Tectonic plates play a major role for earthquakes as tectonic plates are the crust of the planet. The boundary lines of tectonic plates are usually known as fault lines. To understand an earthquake before its occurrence, different types of earthquake precursors are studied by different researchers. Surface temperature, strange cloud cover, earth’s electric field, geomagnetic phenomena, ground water level, active faults, ionospheric anomalies, tectonic movements are taken as parameters for earthquake study by different researchers. In this paper we tried to gather complete and helpful information of earthquake precursors which have been studied until now.

Keywords: earthquake precursors, earthquake, tectonic plates, fault

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Authors: Mohammad Ghahramani, Fahimeh Saei Manesh

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Winning a soccer game is based on thorough and deep analysis of the ongoing match. On the other hand, giant gambling companies are in vital need of such analysis to reduce their loss against their customers. In this research work, we perform deep, real-time analysis on every soccer match around the world that distinguishes our work from others by focusing on particular seasons, teams and partial analytics. Our contributions are presented in the platform called “Analyst Masters.” First, we introduce various sources of information available for soccer analysis for teams around the world that helped us record live statistical data and information from more than 50,000 soccer matches a year. Our second and main contribution is to introduce our proposed in-play performance evaluation. The third contribution is developing new features from stable soccer matches. The statistics of soccer matches and their odds before and in-play are considered in the image format versus time including the halftime. Local Binary patterns, (LBP) is then employed to extract features from the image. Our analyses reveal incredibly interesting features and rules if a soccer match has reached enough stability. For example, our “8-minute rule” implies if 'Team A' scores a goal and can maintain the result for at least 8 minutes then the match would end in their favor in a stable match. We could also make accurate predictions before the match of scoring less/more than 2.5 goals. We benefit from the Gradient Boosting Trees, GBT, to extract highly related features. Once the features are selected from this pool of data, the Decision trees decide if the match is stable. A stable match is then passed to a post-processing stage to check its properties such as betters’ and punters’ behavior and its statistical data to issue the prediction. The proposed method was trained using 140,000 soccer matches and tested on more than 100,000 samples achieving 98% accuracy to select stable matches. Our database from 240,000 matches shows that one can get over 20% betting profit per month using Analyst Masters. Such consistent profit outperforms human experts and shows the inefficiency of the betting market. Top soccer tipsters achieve 50% accuracy and 8% monthly profit in average only on regional matches. Both our collected database of more than 240,000 soccer matches from 2012 and our algorithm would greatly benefit coaches and punters to get accurate analysis.

Keywords: soccer, analytics, machine learning, database

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Authors: H. Arzani, Z. Arzani

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Every piece of rangeland has a different set of physical and biological characteristics. This requires the manager to synthesis various information for regular monitoring to define changes trend to get wright decision for sustainable management. So range managers need to use computerized technologies to monitor rangeland, and select. The best management practices. There are four examples of computerized technologies that can benefit sustainable management: (1) Photographic method for cover measurement: The method was tested in different vegetation communities in semi humid and arid regions. Interpretation of pictures of quadrats was done using Arc View software. Data analysis was done by SPSS software using paired t test. Based on the results, generally, photographic method can be used to measure ground cover in most vegetation communities. (2) GPS application for corresponding ground samples and satellite pixels: In two provinces of Tehran and Markazi, six reference points were selected and in each point, eight GPS models were tested. Significant relation among GPS model, time and location with accuracy of estimated coordinates was found. After selection of suitable method, in Markazi province coordinates of plots along four transects in each 6 sites of rangelands was recorded. The best time of GPS application was in the morning hours, Etrex Vista had less error than other models, and a significant relation among GPS model, time and location with accuracy of estimated coordinates was found. (3) Application of satellite data for rangeland monitoring: Focusing on the long term variation of vegetation parameters such as vegetation cover and production is essential. Our study in grass and shrub lands showed that there were significant correlations between quantitative vegetation characteristics and satellite data. So it is possible to monitor rangeland vegetation using digital data for sustainable utilization. (4) Rangeland suitability classification with GIS: Range suitability assessment can facilitate sustainable management planning. Three sub-models of sensitivity to erosion, water suitability and forage production out puts were entered to final range suitability classification model. GIS was facilitate classification of range suitability and produced suitability maps for sheep grazing. Generally digital computers assist range managers to interpret, modify, calibrate or integrating information for correct management.

Keywords: computer, GPS, GIS, remote sensing, photographic method, monitoring, rangeland ecosystem, management, suitability, sheep grazing

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Authors: Alvaro Figueira, Bruno Cabral

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Nowadays, students are used to be assessed through an online platform. Educators have stepped up from a period in which they endured the transition from paper to digital. The use of a diversified set of question types that range from quizzes to open questions is currently common in most university courses. In many courses, today, the evaluation methodology also fosters the students’ online participation in forums, the download, and upload of modified files, or even the participation in group activities. At the same time, new pedagogy theories that promote the active participation of students in the learning process, and the systematic use of problem-based learning, are being adopted using an eLearning system for that purpose. However, although there can be a lot of feedback from these activities to student’s, usually it is restricted to the assessments of online well-defined tasks. In this article, we propose an automatic system that informs students of abnormal deviations of a 'correct' learning path in the course. Our approach is based on the fact that by obtaining this information earlier in the semester, may provide students and educators an opportunity to resolve an eventual problem regarding the student’s current online actions towards the course. Our goal is to prevent situations that have a significant probability to lead to a poor grade and, eventually, to failing. In the major learning management systems (LMS) currently available, the interaction between the students and the system itself is registered in log files in the form of registers that mark beginning of actions performed by the user. Our proposed system uses that logged information to derive new one: the time each student spends on each activity, the time and order of the resources used by the student and, finally, the online resource usage pattern. Then, using the grades assigned to the students in previous years, we built a learning dataset that is used to feed a machine learning meta classifier. The produced classification model is then used to predict the grades a learning path is heading to, in the current year. Not only this approach serves the teacher, but also the student to receive automatic feedback on her current situation, having past years as a perspective. Our system can be applied to online courses that integrate the use of an online platform that stores user actions in a log file, and that has access to other student’s evaluations. The system is based on a data mining process on the log files and on a self-feedback machine learning algorithm that works paired with the Moodle LMS.

Keywords: data mining, e-learning, grade prediction, machine learning, student learning path

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Authors: Xiaoyuan Chen

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Background: This study aimed to characterize the epidemiological and clinical features of five rare subtypes of hepatocellular carcinoma (HCC) and to create a competing risk nomogram for predicting cancer-specific survival. Methods: This study used the Surveillance, Epidemiology, and End Results database to analyze the clinicopathological data of 50,218 patients with classic HCC and five rare subtypes (ICD-O-3 Histology Code=8170/3-8175/3) between 2004 and 2018. The annual percent change (APC) was calculated using Joinpoint regression, and a nomogram was developed based on multivariable competing risk survival analyses. The prognostic performance of the nomogram was evaluated using the Akaike information criterion, Bayesian information criterion, C-index, calibration curve, and area under the receiver operating characteristic curve. Decision curve analysis was used to assess the clinical value of the models. Results: The incidence of scirrhous carcinoma showed a decreasing trend (APC=-6.8%, P=0.025), while the morbidity of other rare subtypes remained stable from 2004 to 2018. The incidence-based mortality plateau in all subtypes during the period. Clear cell carcinoma was the most common subtype (n=551, 1.1%), followed by fibrolamellar (n=241, 0.5%), scirrhous (n=82, 0.2%), spindle cell (n=61, 0.1%), and pleomorphic (n=17, ~0%) carcinomas. Patients with fibrolamellar carcinoma were younger and more likely to have non-cirrhotic liver and better prognoses. Scirrhous carcinoma shared almost the same macro clinical characteristics and outcomes as classic HCC. Clear cell carcinoma tended to occur in the Asia-Pacific elderly male population, and more than half of them were large HCC (Size>5cm). Sarcomatoid (including spindle cell and pleomorphic) carcinoma was associated with larger tumor size, poorer differentiation, and more dismal prognoses. The pathological subtype, T stage, M stage, surgery, alpha-fetoprotein, and cancer history were identified as independent predictors in patients with rare subtypes. The nomogram showed good calibration, discrimination, and net benefits in clinical practice. Conclusion: The rare subtypes of HCC had distinct clinicopathological features and biological behaviors compared with classic HCC. Our findings could provide a valuable reference for clinicians. The constructed nomogram could accurately predict prognoses, which is beneficial for individualized management.

Keywords: hepatocellular carcinoma, pathological subtype, fibrolamellar carcinoma, scirrhous carcinoma, clear cell carcinoma, spindle cell carcinoma, pleomorphic carcinoma

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Authors: Fathi Abid, Bilel Kaffel

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The olive tree, the olive harvest during winter season and the production of olive oil better known by professionals under the name of the crushing operation have interested institutional traders such as olive-oil offices and private companies such as food industry refining and extracting pomace olive oil as well as export-import public and private companies specializing in olive oil. The major problem facing producers of olive oil each winter campaign, contrary to what is expected, it is not whether the harvest will be good or not but whether the sale price will allow them to cover production costs and achieve a reasonable margin of profit or not. These questions are entirely legitimate if we judge by the importance of the issue and the heavy complexity of the uncertainty and competition made tougher by a high level of indebtedness and the experience and expertise of speculators and producers whose objectives are sometimes conflicting. The aim of this paper is to study the formation mechanism of olive oil prices in order to learn about speculators’ behavior and expectations in the market, how they contribute by their industry knowledge and their financial alliances and the size the financial challenge that may be involved for them to build private information hoses globally to take advantage. The methodology used in this paper is based on two stages, in the first stage we study econometrically the formation mechanisms of olive oil price in order to understand the market participant behavior by implementing ARMA, SARMA, GARCH and stochastic diffusion processes models, the second stage is devoted to prediction purposes, we use a combined wavelet- ANN approach. Our main findings indicate that olive oil market participants interact with each other in a way that they promote stylized facts formation. The unstable participant’s behaviors create the volatility clustering, non-linearity dependent and cyclicity phenomena. By imitating each other in some periods of the campaign, different participants contribute to the fat tails observed in the olive oil price distribution. The best prediction model for the olive oil price is based on a back propagation artificial neural network approach with input information based on wavelet decomposition and recent past history.

Keywords: olive oil price, stylized facts, ARMA model, SARMA model, GARCH model, combined wavelet-artificial neural network, continuous-time stochastic volatility mode

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Authors: Francesco Pinci

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This research investigates the significance of product packaging as an effective marketing tool. By using commercially available pasta as an example, the study specifically examines the visual components of packaging, including color, shape, packaging material, and logo. The insights gained from studies like this are particularly valuable to food and beverage companies as they provide marketers with a deeper understanding of the factors influencing consumer purchasing decisions. The research analyzes data collected through surveys conducted via Google Forms and visual data obtained using iMotions eye-tracker software. The results affirm the importance of packaging design elements, such as color and product information, in shaping consumer buying behavior.

Keywords: consumer behaviour, eyetracker, food marketing, neuromarketing

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Authors: Bhupinder Singh, Joy Prakash Misra

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This article provides the information regarding machining of hastelloy-X on wire electro spark machining (WEDM). Experimental investigation has been carried out by varying pulse-on time (TON), pulse-off time (TOFF), peak current (IP) and spark gap voltage (SV). Effect of these parameters is studied on material removal rate (MRR). Experiments are designed as per box-behnken design (BBD) technique of response surface methodology (RSM). Analysis of variance (ANOVA) results indicates that TON, TOFF, IP, SV, TON x IP are significant parameters that influenced the MRR, and it is depicted that value of MRR is more at high discharge energy (HDE) and less at low discharge energy (LDE). Furthermore, miniature impeller and miniature gear (OD≤10MM) is fabricated by WEDM at optimized condition.

Keywords: advanced manufacturing, WEDM, super alloy, gear

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Authors: Runlian Miao, Wei Xie, Hong Zhang

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In China, Beijing-Tianjin-Hebei is regarded as a strategically important region because itenjoys highest development in economic development, opening up, innovative capacity and andpopulation. Integrated development of Beijing-Tianjin-Hebei region is increasingly emphasized by the government recently years. In 2014, it has ascended to one of the national great development strategies by Chinese central government. In 2015, Coordinated Development Planning Compendium for Beijing-Tianjin-Hebei Region was approved. Such decisions signify Beijing-Tianjin-Hebei region would lead innovation-driven economic development in China. As an essential factor to achieve national innovation-driven development and significant part of regional industry chain, the optimization of science and technology resources allocation will exert great influence to regional economic transformation and upgrading and innovation-driven development. However, unbalanced distribution, poor sharing of resources and existence of information isolated islands have contributed to different interior innovation capability, vitality and efficiency, which impeded innovation and growth of the whole region. Under such a background, to integrate and vitalize regional science and technology resources and then establish high-end, fast-responding and precise innovation service system basing on regional resources, would be of great significance for integrated development of Beijing-Tianjin-Hebei region and even handling of unbalanced and insufficient development problem in China. This research uses the method of literature review and field investigation and applies related theories prevailing home and abroad, centering service path of science and technology resources for innovation. Based on the status quo and problems of regional development of Beijing-Tianjin-Hebei, theoretically, the author proposed to combine regional economics and new economic geography to explore solution to problem of low resource allocation efficiency. Further, the author puts forward to applying digital map into resource management and building a platform for information co-building and sharing. At last, the author presents the thought to establish a specific service mode of ‘science and technology plus digital map plus intelligence research plus platform service’ and suggestion on co-building and sharing mechanism of 3 (Beijing, Tianjin and Hebei ) plus 11 (important cities in Hebei Province).

Keywords: Beijing-Tianjin-Hebei, science and technology resources, innovation service, digital platform

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Authors: Selvavinayagan Babiharan

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This research aims to develop a deep learning platform to enhance mathematics education for secondary students in Sri Lanka. The platform will be designed to incorporate interactive and user-friendly features to engage students in active learning and promote their mathematical skills. The proposed platform will be developed using TensorFlow and Keras, two widely used deep learning frameworks. The system will be trained on a large dataset of math problems, which will be collected from Sri Lankan school curricula. The results of this research will contribute to the improvement of mathematics education in Sri Lanka and provide a valuable tool for teachers to enhance the learning experience of their students.

Keywords: information technology, education, machine learning, mathematics

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Authors: Andrey Lebedev, Ilya Dmitrenok, JooYoung Lee, Leonard Johard

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Top-k shortest path routing problem is an extension of finding the shortest path in a given network. Shortest path is one of the most essential measures as it reveals the relations between two nodes in a network. However, in many real world networks, whose diameters are small, top-k shortest path is more interesting as it contains more information about the network topology. Many variations to compute top-k shortest paths have been studied. In this paper, we apply an efficient top-k shortest distance routing algorithm to the link prediction problem and test its efficacy. We compare the results with other base line and state-of-the-art methods as well as with the shortest path. Then, we also propose a top-k distance based graph matching algorithm.

Keywords: graph matching, link prediction, shortest path, similarity

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Authors: Peter Pfeifer, Julian Pfeifer, Niko Pfeifer

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Information processing is the focus of brain and cognition research. This work has a different perspective; it starts with behaviors. The detailed analysis of behaviors leads to the discovery that a significant proportion of them are based on only five basic drives. These basic drives are combinable, and the combinations result in the diversity of human behavior and thinking. The key elements are drive memories. They collect memories of drive-related situations and feelings. They contain variations of basic drives in numerous areas of life and build combinations with different meanings depending on the area. Human thinking could be explained with variations on these nested combinations of basic drives.

Keywords: cognition, psycholinguistics, psychology, psychophysiology of cognition

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Authors: Gikunda Aloise, Mjema Saida, Barasa Herbert, Wanyungu John, Kimani Maureen

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Background: Community Health Workforce (CHW) is health care providers at the community level (Level 1) and serves as a bridge between the community and the formal healthcare system. This human resource has enormous potential to extend healthcare services and ensures that the vulnerable, marginalized, and hard-to-reach populations have access to quality healthcare services at the community and primary health facility levels. However, these cadres are neither recognized, remunerated, nor in most instances, registered in a master list. Management and supervision of CHWs is not easy if their individual demographics, training capacity and incentives is not well documented through a centralized registry. Description: In February 2022, Amref supported the Kenya Ministry of Health in developing a community health workforce database called Community Health Workers Master List Registry (CHWML), which is hosted in Kenya Health Information System (KHIS) tracker. CHW registration exercise was through a sensitization meeting conducted by the County Community Health Focal Person for the Sub-County Community Health Focal Person and Community Health Assistants who uploaded information on individual demographics, training undertaken and incentives received by CHVs. Care was taken to ensure compliance with Kenyan laws on the availability and use of personal data as prescribed by the Data Protection Act, 2019 (DPA). Results and lessons learnt: By June 2022, 80,825 CHWs had been registered in the system; 78,174 (96%) CHVs and 2,636 (4%) CHAs. 25,235 (31%) are male, 55,505 (68%) are female & 85 (1%) are transgender. 39,979. (49%) had secondary education and 2500 (3%) had no formal education. Only 27 641 (34%) received a monthly stipend. 68,436 CHVs (85%) had undergone basic training. However, there is a need to validate the data to align with the current situation in the counties. Conclusions/Next steps: The use of CHWML will unlock opportunities for building more resilient and sustainable health systems and inform financial planning, resource allocation, capacity development, and quality service delivery. The MOH will update the CHWML guidelines in adherence to the data protection act which will inform standard procedures for maintaining, updating the registry and integrate Community Health Workforce registry with the HRH system.

Keywords: community health registry, community health volunteers (CHVs), community health workers masters list (CHWML), data protection act

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Authors: Zhen Wu, David Lupien St-Pierre, Georges Abdul-Nour

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In decision making under strict uncertainty, decision makers have to choose a decision without any information about the states of nature. The classic criteria of Laplace, Wald, Savage, Hurwicz and Starr are introduced and compared in a case study of sewer network planning. Furthermore, results from different criteria are discussed and analyzed. Moreover, this paper discusses the idea that decision making under strict uncertainty (DMUSU) can be viewed as a two-player game and thus be solved by a solution concept in game theory: Nash equilibrium.

Keywords: decision criteria, decision making, sewer network planning, decision making, strict uncertainty

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Authors: Nakache Radouane, M. Boukelloul, M. Fredj

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Method room and pillar sizes are key factors for safe mining and their recovery in open-stop mining. This method is advantageous due to its simplicity and requirement of little information to be used. It is probably the most representative method among the total load approach methods although it also remains a safe design method. Using a finite element software (PLAXIS 3D), analyses were carried out with an elasto-plastic model and comparisons were made with methods based on the total load approach. The results were presented as the optimization for improving the ore recovery rate while maintaining a safe working environment.

Keywords: room and pillar, mining, total load approach, elasto-plastic

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Authors: Yirui Wang

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In the past few decades, patient safety has been the focus of much attention in the global medical and health field. As medical standards continue to improve and develop, the demand for patient safety is also growing. As one of the important dimensions in assessing patient safety, the Patient Perception Patient Safety Assessment Tool provides unique and valuable information from the patient's own perspective and plays an important role in promoting patient safety. This article aims to summarize and analyze the assessment content, assessment methods and applications of currently commonly used patient-perceived patient safety assessment tools at home and abroad, with a view to providing a reference for medical staff to select appropriate patient-perceived patient safety assessment tools.

Keywords: patients, patient safety, perception, assessment tools, review

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Authors: Wafa' Slaibi Alsharafat

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Cloud computing is a modern approach in network environment. According to increased number of network users and online systems, there is a need to help these systems to be away from unauthorized resource access and detect any attempts for privacy contravention. For that purpose, Intrusion Detection System is an effective security mechanism to detect any attempts of attacks for cloud resources and their information. In this paper, Cloud Intrusion Detection System has been proposed in term of reducing or eliminating any attacks. This model concerns about achieving high detection rate after conducting a set of experiments using benchmarks dataset called KDD'99.

Keywords: IDS, cloud computing, anticipating classifier system, intrusion detection

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Authors: R. Hurtado-Ortiz, D. Clermont, M. Schüngel, C. Bizet, D. Smith, E. Stackebrandt

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Microbiological resources and their derivatives are the essential raw material for the advancement of human health, agro-food, food security, biotechnology, research and development in all life sciences. Microbial resources, and their genetic and metabolic products, are utilised in many areas such as production of healthy and functional food, identification of new antimicrobials against emerging and resistant pathogens, fighting agricultural disease, identifying novel energy sources on the basis of microbial biomass and screening for new active molecules for the bio-industries. The complexity of public collections, distribution and use of living biological material (not only living but also DNA, services, training, consultation, etc.) and service offer, demands the coordination and sharing of policies, processes and procedures. The Microbial Resource Research Infrastructure (MIRRI) is an initiative within the European Strategy Forum Infrastructures (ESFRI), bring together 16 partners including 13 European public microbial culture collections and biological resource centres (BRCs), supported by several European and non-European associated partners. The objective of MIRRI is to support innovation in microbiology by provision of a one-stop shop for well-characterized microbial resources and high quality services on a not-for-profit basis for biotechnology in support of microbiological research. In addition, MIRRI contributes to the structuring of microbial resources capacity both at the national and European levels. This will facilitate access to microorganisms for biotechnology for the enhancement of the bio-economy in Europe. MIRRI will overcome the fragmentation of access to current resources and services, develop harmonised strategies for delivery of associated information, ensure bio-security and other regulatory conditions to bring access and promote the uptake of these resources into European research. Data mining of the landscape of current information is needed to discover potential and drive innovation, to ensure the uptake of high quality microbial resources into research. MIRRI is in its Preparatory Phase focusing on governance and structure including technical, legal governance and financial issues. MIRRI will help the Biological Resources Centres to work more closely with policy makers, stakeholders, funders and researchers, to deliver resources and services needed for innovation.

Keywords: culture collections, microbiology, infrastructure, microbial resources, biotechnology

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Authors: Marcio Freire Cruz, Naoaki Ono, Shigehiko Kanaya, Carlos Arthur Mattos Teixeira Cavalcante

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The systems that record patient care information, known as Electronic Medical Record (EMR) and those that monitor vital signs of patients, such as heart rate, body temperature, and blood pressure have been extremely valuable for the effectiveness of the patient’s treatment. Several kinds of research have been using data from EMRs and vital signs of patients to predict illnesses. Among them, we highlight those that intend to predict, classify, or, at least identify patterns, of sepsis illness in patients under vital signs monitoring. Sepsis is an organic dysfunction caused by a dysregulated patient's response to an infection that affects millions of people worldwide. Early detection of sepsis is expected to provide a significant improvement in its treatment. Preceding works usually combined medical, statistical, mathematical and computational models to develop detection methods for early prediction, getting higher accuracies, and using the smallest number of variables. Among other techniques, we could find researches using survival analysis, specialist systems, machine learning and deep learning that reached great results. In our research, patients are modeled as points moving each hour in an n-dimensional space where n is the number of vital signs (variables). These points can reach a sepsis target point after some time. For now, the sepsis target point was calculated using the median of all patients’ variables on the sepsis onset. From these points, we calculate for each hour the position vector, the first derivative (velocity vector) and the second derivative (acceleration vector) of the variables to evaluate their behavior. And we construct a prediction model based on a Long Short-Term Memory (LSTM) Network, including these derivatives as explanatory variables. The accuracy of the prediction 6 hours before the time of sepsis, considering only the vital signs reached 83.24% and by including the vectors position, speed, and acceleration, we obtained 94.96%. The data are being collected from Medical Information Mart for Intensive Care (MIMIC) Database, a public database that contains vital signs, laboratory test results, observations, notes, and so on, from more than 60.000 patients.

Keywords: dynamic analysis, long short-term memory, prediction, sepsis

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Authors: N. Burtebayev, Sh. Hamada, Zh. Kerimkulov, D. K. Alimov, A. V. Yushkov, N. Amangeldi, A. N. Bakhtibaev

Abstract:

The main aim of the current work is to examine if 14N is candidate to be clusterized nuclei or not. In order to check this attendance, we have measured the angular distributions for 14N ion beam elastically scattered on 12C target nuclei at different low energies; 17.5, 21, and 24.5MeV which are close to the Coulomb barrier energy for 14N+12C nuclear system. Study of various transfer reactions could provide us with useful information about the attendance of nuclei to be in a composite form (core + valence). The experimental data were analyzed using two approaches; Phenomenological (Optical Potential) and semi-microscopic (Double Folding Potential). The agreement between the experimental data and the theoretical predictions is fairly good in the whole angular range.

Keywords: deuteron transfer, elastic scattering, optical model, double folding, density distribution

Procedia PDF Downloads 327

Authors: M. Olmedilla, M. R. Martinez-Torres, S. L. Toral

Abstract:

Electronic Word-Of-Mouth (eWOM) communities represent today an important source of information in which more and more customers base their purchasing decisions. They include thousands of reviews concerning very different products and services posted by many individuals geographically distributed all over the world. Due to their massive audience, eWOM communities can help users to find the product they are looking for even if they are less popular or rare. This is known as the long tail effect, which leads to a larger number of lower-selling niche products. This paper analyzes the long tail effect in a well-known eWOM community and defines a tool for finding niche products unavailable through conventional channels.

Keywords: eWOM, online user reviews, long tail theory, product categorization, social network analysis

Procedia PDF Downloads 421

Authors: Huinan Zhang, Wenjie Jiang

Abstract:

Accurate estimation of tropical cyclone intensity is of great importance for disaster prevention and mitigation. Existing techniques are largely based on satellite imagery data, and research and utilization of the inner thermal core structure characteristics of tropical cyclones still pose challenges. This paper presents a two-stage tropical cyclone intensity estimation network based on the fusion of coarse and fine-grained features from microwave brightness temperature data. The data used in this network are obtained from the thermal core structure of tropical cyclones through the Advanced Technology Microwave Sounder (ATMS) inversion. Firstly, the thermal core information in the pressure direction is comprehensively expressed through the maximal intensity projection (MIP) method, constructing coarse-grained thermal core images that represent the tropical cyclone. These images provide a coarse-grained feature range wind speed estimation result in the first stage. Then, based on this result, fine-grained features are extracted by combining thermal core information from multiple view profiles with a distributed network and fused with coarse-grained features from the first stage to obtain the final two-stage network wind speed estimation. Furthermore, to better capture the long-tail distribution characteristics of tropical cyclones, focal loss is used in the coarse-grained loss function of the first stage, and ordinal regression loss is adopted in the second stage to replace traditional single-value regression. The selection of tropical cyclones spans from 2012 to 2021, distributed in the North Atlantic (NA) regions. The training set includes 2012 to 2017, the validation set includes 2018 to 2019, and the test set includes 2020 to 2021. Based on the Saffir-Simpson Hurricane Wind Scale (SSHS), this paper categorizes tropical cyclone levels into three major categories: pre-hurricane, minor hurricane, and major hurricane, with a classification accuracy rate of 86.18% and an intensity estimation error of 4.01m/s for NA based on this accuracy. The results indicate that thermal core data can effectively represent the level and intensity of tropical cyclones, warranting further exploration of tropical cyclone attributes under this data.

Keywords: Artificial intelligence, deep learning, data mining, remote sensing

Procedia PDF Downloads 63

Authors: Ali K. M. Al-Nasrawi, Uday A. Al-Hamdany, Sarah M. Hamylton, Brian G. Jones, Yasir M. Alyazichi

Abstract:

Surface elevation dynamics have always responded to disturbance regimes. Creating Digital Elevation Models (DEMs) to detect surface dynamics has led to the development of several methods, devices and data clouds. DEMs can provide accurate and quick results with cost efficiency, in comparison to the inherited geomatics survey techniques. Nowadays, remote sensing datasets have become a primary source to create DEMs, including LiDAR point clouds with GIS analytic tools. However, these data need to be tested for error detection and correction. This paper evaluates various DEMs from different data sources over time for Apple Orchard Island, a coastal site in southeastern Australia, in order to detect surface dynamics. Subsequently, 30 chosen locations were examined in the field to test the error of the DEMs surface detection using high resolution global positioning systems (GPSs). Results show significant surface elevation changes on Apple Orchard Island. Accretion occurred on most of the island while surface elevation loss due to erosion is limited to the northern and southern parts. Concurrently, the projected differential correction and validation method aimed to identify errors in the dataset. The resultant DEMs demonstrated a small error ratio (≤ 3%) from the gathered datasets when compared with the fieldwork survey using RTK-GPS. As modern modelling approaches need to become more effective and accurate, applying several tools to create different DEMs on a multi-temporal scale would allow easy predictions in time-cost-frames with more comprehensive coverage and greater accuracy. With a DEM technique for the eco-geomorphic context, such insights about the ecosystem dynamic detection, at such a coastal intertidal system, would be valuable to assess the accuracy of the predicted eco-geomorphic risk for the conservation management sustainability. Demonstrating this framework to evaluate the historical and current anthropogenic and environmental stressors on coastal surface elevation dynamism could be profitably applied worldwide.

Keywords: DEMs, eco-geomorphic-dynamic processes, geospatial Information Science, remote sensing, surface elevation changes,

Procedia PDF Downloads 267