Search results for: geospatial data management

Authors: Shola Haruna Adeosun, Ajoke F. Adebiyi

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Business organizations in Nigeria have failed to realize the role of a good corporate image policy in business dealings. This is probably because they do not understand the concept of corporate image and the necessary tools for promoting it. Corporate image goes beyond attractive products or rendering quality services, advertising and paying good salary. It pervades every aspect of business concern, from the least worker’s personality to the dealings within the organization and with the large society. In the face of the societal dynamics, especially in the business world, brought by technology, companies are faced with stiff competition that maintaining a competitive edge requires aggressive strategies. One of such strategies in effective corporate image management is promotion. This study investigates the strategies that could be deployed in order to build and promote the effective corporate image, as well as enhance profit margins of an organization, using Phinomar Nigeria Limited, Ngwo as case study. The study reveals that Phinomar Nigeria Limited has a laid down corporate image policy but not effectively managed; and that, strategies deployed to promote corporate image are limited; while responses to Phinomar products are fairly high. It, therefore, suggests profitable products but requires periodical improvement in the employee's welfare and work environment; as well as, the need to increase the scope of Phinomar’s social responsibility.

Keywords: corporate image, effective, enhancing, management, profitability, strategy

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Authors: Ubaka Chukwuemeka Michael, Ukwe Chinwe Victoria

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Background: The use of antipsychotic monotherapy remains the standard for schizophrenic disorders so also a prescription switch from older typical to newer atypical classes of antipsychotics on the basis of better efficacy and tolerability. However, surveys on the quality of antipsychotic drug use and substitution in developing countries are very scarce. This study was intended to evaluate quality and factors that drive the prescription and substitution of antipsychotic drugs among schizophrenic patients visiting a regional psychiatric hospital. Methods: Case files of patients visiting a federal government funded Neuropsychiatric Hospital between July 2012 and July 2014 were systematically retrieved. Patient demographic characteristics, clinical details and drug management data were collected and subjected to descriptive and inferential data analysis to determine quality and predictors of utilization. Results: Of the 600 case files used, there were more male patients (55.3%) with an overall mean age of 33.7±14.4 years. Typical antipsychotic agents accounted for over 85% of prescriptions, with majority of the patients receiving more than 2 drugs in at least a visit (80.9%). Fluphenazine (25.2%) and Haloperidol (18.8%) were mostly given as antipsychotics for treatment initiation while Olazenpine (23.0%) and Benzhexol (18.3%) were the most currently prescribed antipsychotics. Nearly half (42%, 252/600) of these patients were switched from one class to another, with 34.5% (207/600) of them switched from typical to atypical drug classes. No demographic or clinical factors influenced drug substitutions but a younger age and being married influenced being prescribed a polypharmacy regimen (more than 2 drugs) and an injectable antipsychotic agent. Conclusion: The prevalence of antipsychotic polypharmacy and use of typical agents among these patients was high. However, only age and marital status affected the quality of antipsychotic prescriptions among these patients.

Keywords: antipsychotics, drug substitution, pharmacoepidemiology, polypharmacy

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Authors: Tengku Nilam Baizura, Noor Zalina Mahmood

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In Malaysia, landfilling is the most preferred method and most of it does not have the proper leachate treatment system which can cause environmental problems. Leachate is the major factor to river water pollution since most landfills are located near the river which is the main water resource for the country. The study aimed to estimate leachate production from landfills in Selangor. A simple mathematical modelling was used for the calculation of annual leachate volume. The estimate of identified landfill area (A) using Google Earth was multiplied by the annual rainfall (R). The product is expressed as volume (V). The data indicate that the leachate production is high even it is fully closed. It is important to design the efficient landfill and proper leachate treatment processes especially for the old/closed landfill. Extensive monitoring will be required to predict future impact.

Keywords: landfill, leachate, municipal solid waste management, waste disposal

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Authors: N. Ali, S. Nakayama, H. Yamaguchi, M. Nadeem

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Traffic congestion is a matter of prime concern in developing countries. This can be primarily attributed due to poor design practices and biased allocation of resources based on political will neglecting the technical feasibilities in infrastructure design. During the last decade, Lahore has expanded at an unprecedented rate as compared to surrounding cities due to more funding and resource allocation by the previous governments. As a result of this, people from surrounding cities and areas moved to the Lahore city for better opportunities and quality of life. This migration inflow inherited the city with an increased population yielding the inefficiency of the existing infrastructure to accommodate enhanced traffic demand. This leads to traffic congestion on major arterial roads of the city. In this simulation study, a major arterial road was selected to evaluate the performance of the five intersections by changing the geometry of the intersections or signal control type. Simulations were done in two software; Highway Capacity Software (HCS) and Synchro Studio and Sim Traffic Software. Some of the traffic management strategies that were employed include actuated-signal control, semi-actuated signal control, fixed-time signal control, and roundabout. The most feasible solution for each intersection in the above-mentioned traffic management techniques was selected with the least delay time (seconds) and improved Level of Service (LOS). The results showed that Jinnah Hospital Intersection and Akbar Chowk Intersection improved 92.97% and 92.67% in delay time reduction, respectively. These results can be used by traffic planners and policy makers for decision making for the expansion of these intersections keeping in mind the traffic demand in future years.

Keywords: traffic congestion, traffic simulation, traffic management, congestion problems

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Authors: Swarnadip Ghosh

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Sentiment Analysis is the process of detecting the contextual polarity of text. In other words, it determines whether a piece of writing is positive, negative or neutral.Sentiment analysis of documents holds great importance in today's world, when numerous information is stored in databases and in the world wide web. An efficient algorithm to illicit such information, would be beneficial for social, economic as well as medical purposes. In this project, we have developed an algorithm to classify a document into positive or negative. Using our algorithm, we obtained a feature set from the data, and classified the documents based on this feature set. It is important to note that, in the classification, we have not used the independence assumption, which is considered by many procedures like the Naive Bayes. This makes the algorithm more general in scope. Moreover, because of the sparsity and high dimensionality of such data, we did not use empirical distribution for estimation, but developed a method by finding degree of close clustering of the data points. We have applied our algorithm on a movie review data set obtained from IMDb and obtained satisfactory results.

Keywords: sentiment, Run's Test, cross validation, higher dimensional pmf estimation

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Authors: Ayodele Ajayi, John Ajayi

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This paper investigates the effect of corporate governance with a view to determining the relationship between board size and bank performance. Data for the study were obtained from the audited financial statements of five sampled banks listed on the Nigerian Stock Exchange. Panel data technique was adopted and analysis was carried out with the use of multiple regression and pooled ordinary least square. Results from the study show that the larger the board size, the greater the profit implying that corporate governance is positively correlated with bank performance.

Keywords: corporate governance, banks performance, board size, pooled data

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Authors: Sadia Nasrin Tisha, Mushfika Sharmin Rahman, Javier Orduz

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Machine learning is applied in a variety of fields throughout the world. The healthcare sector has benefited enormously from it. One of the most effective approaches for predicting human heart diseases is to use machine learning applications to classify data and predict the outcome as a classification. However, with the rapid advancement of quantum technology, quantum computing has emerged as a potential game-changer for many applications. Quantum algorithms have the potential to execute substantially faster than their classical equivalents, which can lead to significant improvements in computational performance and efficiency. In this study, we applied quantum machine learning concepts to predict coronary heart diseases from text data. We experimented thrice with three different features; and three feature sets. The data set consisted of 100 data points. We pursue to do a comparative analysis of the two approaches, highlighting the potential benefits of quantum machine learning for predicting heart diseases.

Keywords: quantum machine learning, SVM, QSVM, matrix product state

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Authors: Kirstin Griffin

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During a period of increased anxiety and stress, communication became the essential tool to help the public stay informed and feel prepared during the Covid-19 pandemic. However, certain groups of patients were not feeling as reassured. The news and media blasted the message that patients with diabetes were “high-risk" in regards to contracting the Covid-19 infection. Routine clinics were being cancelled, GP practices were closing their doors, and patients with type 1 diabetes were understandably scared. The influx of calls to diabetes specialists nurses from concerned patients highlighted the need for better and more specialised information. An Application specifically for patients with type 1 diabetes was created to deliver this information, and it proved to be the essential communication tool that was desperately needed. The Application for patients with type 1 diabetes aimed to deliver specialist information to patients in regards to their diagnosis, management, and ongoing follow-up commitments. The Application gives practical advice on multiple areas of diabetes management, including sick-day rules and diabetic emergencies, as well as up-to-date information on technology, including setting up Libre devices and downloading glucose meters to facilitate attending virtual clinics. Delivery of this information in an easy-to-understand and comprehensive way is intended to improve patient engagement with diabetes services and ultimately empower patients in the control of their own disease. The application also offers a messaging service to allow the diabetes team to send out alerts to patient groups on specific issues, such as changes to clinics, or respond to recent news updates regarding Covid-19. The App was launched in NHS Fife in June 2020 and has amassed 800 active users so far. There is growing engagement with the App since its launch, with over 1000 user interactions in the last month alone. Feedback shows that 100% of users like the App and have found it useful in the management of their diabetes. The App has proven to be an essential tool in communication with one of the most vulnerable groups during the Covid-19 pandemic, and its ongoing development will continue to increase patient engagement and improve glycaemic control for patients with type 1 diabetes. The future of chronic disease management should involve digital solutions such as apps to further empower patients in their healthcare.

Keywords: diabetes, endocrinology, digital healthcare, medical apps

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Authors: Wei-Kuang Liu, Ya-Hsu Chiang

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In the coming of an aging society, the issues of living quality, health care, and social security for the elderly have been gradually taken seriously. In order to maintain favorable living condition, urban societies are also facing the challenge of disasters caused by extreme climate change. However, in the practice of disaster prevention, elderly people are always weak due to their physiological conditions. That is to say, in the planning of resilient urbanism, the aging society is relatively in need of more care. Thus, this research aims to map areas where have high-density elderly population and fragile environmental condition in Taiwan, and to understand the actual situation of disaster prevention management in these areas, so as to provide suggestions for the development of intellectual resilient urban management. The research takes the cities of Taoyuan and Taichung as examples for explorations. According to GIS mapping of areas with high aging index, high-density population and high flooding potential, the communities of Sihai and Fuyuan in Taoyuan and the communities of Taichang and Nanshih in Taichung are highlighted. In these communities, it can be found that there are more elderly population and less labor population with high-density living condition. In addition, they are located in the areas where they have experienced severe flooding in the recent past. Based on a series of interviews with community organizations, there is only one community out of the four using flood information mobile app and Line messages for the management of disaster prevention, and the others still rely on the traditional approaches that manage the works of disaster prevention by their community security patrol teams and community volunteers. The interview outcome shows that most elderly people are not interested in learning the use of intellectual devices. Therefore, this research suggests to keep doing the GIS mapping of areas with high aging index, high-density population and high flooding potential for grasping the high-risk communities and to help develop smart monitor and forecast systems for disaster prevention practice in these areas. Based on case-study explorations, the research also advises that it is important to develop easy-to-use bottom-up and two-way immediate communication mechanism for the management of aging society’s disaster prevention.

Keywords: aging society, disaster prevention, GIS, resilient, Taiwan

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Authors: Trapti Sharma, Devesh Kumar Srivastava

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This paper is basically a analysis study of above MapReduce implementation and also to verify and validate the MapReduce solution model for Parallel K-Mediod algorithm on Hadoop Cluster. MapReduce is a programming model which authorize the managing of huge amounts of data in parallel, on a large number of devices. It is specially well suited to constant or moderate changing set of data since the implementation point of a position is usually high. MapReduce has slowly become the framework of choice for “big data”. The MapReduce model authorizes for systematic and instant organizing of large scale data with a cluster of evaluate nodes. One of the primary affect in Hadoop is how to minimize the completion length (i.e. makespan) of a set of MapReduce duty. In this paper, we have verified and validated various MapReduce applications like wordcount, grep, terasort and parallel K-Mediod clustering algorithm. We have found that as the amount of nodes increases the completion time decreases.

Keywords: hadoop, mapreduce, k-mediod, validation, verification

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Authors: Ahmed Al-Adaileh

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Renewable energy sources, domestic storage systems, controllable loads and machine learning technologies will be key components of future smart homes management systems. An energy management scheme that uses a Deep Learning (DL) approach to support the smart home management systems, which consist of a standalone photovoltaic system, storage unit, heating ventilation air-conditioning system and a set of conventional and smart appliances, is presented. The objective of the proposed scheme is to apply DL-based machine learning to predict various running parameters within a smart home's environment to achieve maximum comfort levels for occupants, reduced electricity bills, and less dependency on the public grid. The problem is using Reinforcement learning, where decisions are taken based on applying the Continuous-time Markov Decision Process. The main contribution of this research is the proposed framework that applies DL to enhance the system's supervised dataset to offer unlimited chances to effectively support smart home systems. A case study involving a set of conventional and smart appliances with dedicated processing units in an inhabited building can demonstrate the validity of the proposed framework. A visualization graph can show "before" and "after" results.

Keywords: smart homes systems, machine learning, deep learning, Markov Decision Process

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Authors: Burak M. Gonultas

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Criminal investigation provides a practical contribution to this process while criminology provides a theoretical background in the apprehension of criminals arrest and clarification of crimes. However, studies on criminal investigation, which is a practical aspect of this process, are not sufficient. Every crime involves different dynamics in terms of investigation. But investigations of serious crimes are versatile and contains complex processes because of cases they are conducted. Therefore, professionals who conduct serious crime investigations differ in some aspects from others in the field. The most fundamental element of this differentiation is skills and abilities of these professionals. According to Eurostat data, Turkey is in an important position in terms of homicide rates. Therefore, in Turkey practice of serious crime investigation is specialized. The present study aims to research the skills and abilities expected from professionals in conducting an effective serious criminal investigation in Turkey and so aims to offer a number of suggestions. 25 emerged ability and skills collected from literature were asked to professionals (n=289) with semi-structured form according to 5 provinces with the highest and 2 provinces with the lowest number of serious crime cases. Three data categories were collected during experience: 1- Five most important skills and abilities, 2- The most important skills for knowledge and inquiry management and 3- Ability and skills that stand out for five stages of serious criminal investigation. The most rated skills and abilities are investigative skill (13%, n=134), planning/designing (9,2%, n=95) and interpersonal relations/communication (8,8%, n=91) in 1010 skills and abilities. While the 1st and 2nd suggest elections of these professionals, the 3rd also suggests how and what type of training will be given to these professionals. This practice differs from other studies in the area in terms of separately addressing the skills and abilities expected in stages of investigation and in terms of selected methodology.

Keywords: ability, criminal investigation, criminology, homicide, serious crimes, skill, Turkey

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Authors: Katerina Krizova, Inigo Molina

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The ongoing climate change affects various natural processes resulting in significant changes in human life. Since there is still a growing human population on the planet with more or less limited resources, agricultural production became an issue and a satisfactory amount of food has to be reassured. To achieve this, agriculture is being studied in a very wide context. The main aim here is to increase primary production on a spatial unit while consuming as low amounts of resources as possible. In Europe, nowadays, the staple issue comes from significantly changing the spatial and temporal distribution of precipitation. Recent growing seasons have been considerably affected by long drought periods that have led to quantitative as well as qualitative yield losses. To cope with such kind of conditions, new techniques and technologies are being implemented in current practices. However, behind assessing the right management, there is always a set of the necessary information about plot properties that need to be acquired. Remotely sensed data had gained attention in recent decades since they provide spatial information about the studied surface based on its spectral behavior. A number of space platforms have been launched carrying various types of sensors. Spectral indices based on calculations with reflectance in visible and NIR bands are nowadays quite commonly used to describe the crop status. However, there is still the staple limit by this kind of data - cloudiness. Relatively frequent revisit of modern satellites cannot be fully utilized since the information is hidden under the clouds. Therefore, microwave remote sensing, which can penetrate the atmosphere, is on its rise today. The scientific literature describes the potential of radar data to estimate staple soil (roughness, moisture) and vegetation (LAI, biomass, height) properties. Although all of these are highly demanded in terms of agricultural monitoring, the crop moisture content is the utmost important parameter in terms of agricultural drought monitoring. The idea behind this study was to exploit the unique combination of SAR (Sentinel-1) and optical (Sentinel-2) data from one provider (ESA) to describe potential crop water stress during dry cropping season of 2019 at six winter wheat plots in the central Czech Republic. For the period of January to August, Sentinel-1 and Sentinel-2 images were obtained and processed. Sentinel-1 imagery carries information about C-band backscatter in two polarisations (VV, VH). Sentinel-2 was used to derive vegetation properties (LAI, FCV, NDWI, and SAVI) as support for Sentinel-1 results. For each term and plot, summary statistics were performed, including precipitation data and soil moisture content obtained through data loggers. Results were presented as summary layouts of VV and VH polarisations and related plots describing other properties. All plots performed along with the principle of the basic SAR backscatter equation. Considering the needs of practical applications, the vegetation moisture content may be assessed using SAR data to predict the drought impact on the final product quality and yields independently of cloud cover over the studied scene.

Keywords: precision agriculture, remote sensing, Sentinel-1, SAR, water content

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Authors: Billel Kenidra, Mohamed Benmohammed

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Data mining technique used in the field of clustering is a subject of active research and assists in biological pattern recognition and extraction of new knowledge from raw data. Clustering means the act of partitioning an unlabeled dataset into groups of similar objects. Each group, called a cluster, consists of objects that are similar between themselves and dissimilar to objects of other groups. Several clustering methods are based on partitional clustering. This category attempts to directly decompose the dataset into a set of disjoint clusters leading to an integer number of clusters that optimizes a given criterion function. The criterion function may emphasize a local or a global structure of the data, and its optimization is an iterative relocation procedure. The K-Means algorithm is one of the most widely used partitional clustering techniques. Since K-Means is extremely sensitive to the initial choice of centers and a poor choice of centers may lead to a local optimum that is quite inferior to the global optimum, we propose a strategy to initiate K-Means centers. The improved K-Means algorithm is compared with the original K-Means, and the results prove how the efficiency has been significantly improved.

Keywords: microarray data mining, biological pattern recognition, partitional clustering, k-means algorithm, centroid initialization

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Authors: Toleen Alaqqad, Kadi Alshabramiy, Suad Zaafarany, Basma Musallam

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CADmapper is a significant tool of software for transforming geographic data into realistic CAD drawings. It speeds up and simplifies the conversion process by automating it. This allows architects, urban planners, engineers, and geographic information system (GIS) experts to solely concentrate on the imaginative and scientific parts of their projects. While the future incorporation of AI has the potential for further improvements, CADmapper's current capabilities make it an indispensable asset in the business. It covers a combination of 2D and 3D city and urban area models. The user can select a specific square section of the map to view, and the fee is based on the dimensions of the area being viewed. The procedure is straightforward: you choose the area you want, then pick whether or not to include topography. 3D architectural data (if available), followed by selecting whatever design program or CAD style you want to publish the document which contains more than 200 free broad town plans in DXF format. If you desire to specify a bespoke area, it's free up to 1 km2.

Keywords: cadmaper, gdata, 2d and 3d data conversion, automated cad drawing, urban planning software

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Authors: Habib Ur Rahman Habib

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Renewable energy resources are being realized as suitable options in hybrid energy planning for on-grid and micro grid. In this paper, operation, planning and optimal design of on-grid distributed energy resources based hybrid system are investigated. The aim is to minimize the cost of the overall energy system keeping in view the environmental emission and minimum penetration of conventional energy resources. Seven grid connected different case studies including diesel only, diesel-renewable based, and renewable based only are designed to perform economic analysis, operational planning and emission. Sensitivity analysis is implemented to investigate the impact of different parameters on the performance of energy resources.

Keywords: data management, renewable energy, distributed energy, smart grid, micro-grid, modeling, energy planning, design optimization

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Authors: Babatunde Akeem Lawal

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In any organization, the primary aim is to maximize profit, but the major challenges facing them is the increase in cost of operation because of this there is increase in cost of production that could lead to inevitable cost control and cost reduction scheme which make it difficult for most organizations to operate at the cost efficient frontier. The study aims to critically examine and evaluate the application of cost control and cost reduction in organization performance and also to review budget as an effective tool of cost control and cost reduction. A descriptive survey research was adopted. A total number of 40 respondent retrieved were used for the study. The analysis of data collected was undertaken by applying appropriate statistical tools. Regression analysis was used to test the hypothesis with the use of SPSS. Based on the findings; it was evident that cost control has a positive impact on organizational performance and also the style of management has a positive impact on organizational performance.

Keywords: organization, cost reduction, cost control, performance, budget, profit

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Authors: Àidan Higgins

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Higher education will be crucial in the coming decades in helping to make Ireland a nation is known for innovation, competitive enterprise, and ongoing academic success, as well as a desirable location to live and work with a high quality of life, vibrant culture, and inclusive social structures. Higher education institutions will actively connect with each student community, society, and business; they will help students develop a sense of place and identity in Ireland and provide the tools they need to contribute significantly to the global community. It will also serve as a catalyst for novel ideas through research, many of which will become the foundation for long-lasting inventive businesses in the future as part of the 2030 National Strategy on Education focuses on change and developing our education system with a focus on how we carry out Research. The emphasis is central to knowledge transfer and a consistent research framework with exploiting opportunities and having the necessary expertise. The newly formed Technological Universities (TU) in Ireland are based on a government initiative to create a new type of higher education institution that focuses on applied and industry-focused research and education. The basis of the TU is to bring together two or more existing institutes of technology to create a larger and more comprehensive institution that offers a wider range of programs and services to students and industry partners. The TU model aims to promote collaboration between academia, industry, and community organizations to foster innovation, research, and economic development. The TU model also aims to enhance the student experience by providing a more seamless pathway from undergraduate to postgraduate studies, as well as greater opportunities for work placements and engagement with industry partners. Additionally, the TUs are designed to provide a greater emphasis on applied research, technology transfer, and entrepreneurship, with the goal of fostering innovation and contributing to economic growth. A project is a collection of organised tasks carried out precisely to produce a singular output (product or service) within a given time frame. Project management is a set of activities that facilitates the successful implementation of a project. The significant differences between research and development projects are the (lack of) precise requirements and (the inability to) plan an outcome from the beginning of the project. The evaluation criteria for a research project must consider these and other "particularities" in works; for instance, proving something cannot be done may be a successful outcome. This study intends to explore how a newly established multi-campus technological university manages research projects effectively. The study will identify the potential and difficulties of managing research projects, the tools, resources and processes available in a multi-campus Technological University context and the methods and approaches employed to deal with these difficulties. Key stakeholders like project managers, academics, and administrators will be surveyed as part of the study, which will also involve an explorative investigation of current literature and data. The findings of this study will contribute significantly to creating best practices for project management in this setting and offer insightful information about the efficient management of research projects within a multi-campus technological university.

Keywords: project management, research and research-related projects, multi-campus technology university, processes

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Authors: Prashansa Singh, Rohit Bajaj, Manjot Kaur

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In the modern agricultural landscape, precision farming has emerged as a pivotal strategy for enhancing crop yield and optimizing resource utilization. This paper introduces an innovative Crop Recommendation System (CRS) that leverages the Internet of Things (IoT) technology and the Message Queuing Telemetry Transport (MQTT) protocol to collect critical environmental and soil data via sensors deployed across agricultural fields. The system is designed to address the challenges of real-time data acquisition, efficient data transmission, and dynamic crop recommendation through the application of advanced Artificial Intelligence (AI) and Machine Learning (ML) models. The CRS architecture encompasses a network of sensors that continuously monitor environmental parameters such as temperature, humidity, soil moisture, and nutrient levels. This sensor data is then transmitted to a central MQTT server, ensuring reliable and low-latency communication even in bandwidth-constrained scenarios typical of rural agricultural settings. Upon reaching the server, the data is processed and analyzed by AI/ML models trained to correlate specific environmental conditions with optimal crop choices and cultivation practices. These models consider historical crop performance data, current agricultural research, and real-time field conditions to generate tailored crop recommendations. This implementation gets 99% accuracy.

Keywords: Iot, MQTT protocol, machine learning, sensor, publish, subscriber, agriculture, humidity

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Authors: Paul Saah, Charles Mbohwa, Nelson Sizwe Madonsela

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In the context of developing countries, this study explores a crucial component of economic development by examining the reasons behind the failure of small and medium-sized enterprises (SMEs). SMEs are acknowledged as essential drivers of economic expansion, job creation, and poverty alleviation in emerging countries. This research uses South Africa as a case study to evaluate the reasons why SMEs fail in developing nations. This study explores a quantitative research methodology to investigate the complex causes of SME failures using statistical tools and reliability tests. To ensure the viability of data collection, a sample size of 400 small business owners was chosen using a non-probability selection technique. A closed-ended questionnaire was the primary technique used to obtain detailed information from the participants. Data was analysed and interpreted using computer software packages such as the Statistical Package for the Social Sciences (SPSS). According to the findings, the main reasons why SMEs fail in developing nations are a lack of strategic business planning, a lack of funding, poor management, a lack of innovation, a lack of business research and a low level of education and training. The results of this study show that SMEs can be sustainable and successful as long as they comprehend and use the suggested small business success determining variables into their daily operations. This implies that the more SMEs in developing countries implement the proposed determinant factors of small business success in their business operations the more the businesses are likely to succeed and vice versa.

Keywords: failure, developing countries, SMEs, economic development, South Africa

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Authors: Abdulfatah Al-Jaradi, Marzoq Ali Odhah, Abdulnasser A. Haza’a

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Background: Antenatal care can be defined as the care provided by skilled healthcare professionals to pregnant women and adolescent girls to ensure the best health conditions for both mother and baby during pregnancy. The components of ANC include risk identification; prevention and management of pregnancy-related or concurrent diseases; and health education and health promotion. The aim of this study: to assess the knowledge, attitude, and practice of pregnant women regarding antenatal care. Methodology: A descriptive KAP study was conducting in public hospitals in Sana'a City-Yemen. The study population was included all pregnant women that intended to the prenatal department and clinical outpatient department, the final sample size was 371 pregnant women, a self-administered questionnaire was used to collect the data, statistical package for social sciences SPSS was used to data analysis. The results: Most (79%) of pregnant women were had correct answers in total knowledge regarding antenatal care, and about two-thirds (67%) of pregnant women were had performance practice regarding antenatal care and two-third (68%) of pregnant women were had a positive attitude. Conclusions & Recommendations: We concluded that a significant association between overall knowledge and practice level toward antenatal care and demographic characteristics of pregnant women, women (residence place, level of education, did your husband support you in attending antenatal care and place of delivery of the last baby), at (P-value ≤ 0.05). We recommended more education and training courses, lecturers and education sessions in clinical facilitators focused ANC, which relies on evidence-based interventions provided to women during pregnancy by skilled healthcare providers such as midwives, doctors, and nurses.

Keywords: antenatal care, knowledge, practice, attitude, pregnant women

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Authors: Sirus Mohammadi, Rohala Moghimi

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The main advantages of microgrids are high energy efficiency through the application of Combined Heat and Power (CHP), high quality and reliability of the delivered electric energy and environmental and economic advantages. This study presents an energy management system (EMS) to optimize the operation of the microgrid (MG). In this paper an Adaptive Modified Firefly Algorithm (AMFA) is presented for optimal operation of a typical MG with renewable energy sources (RESs) accompanied by a back-up Micro-Turbine/Fuel Cell/Battery hybrid power source to level the power mismatch or to store the energy surplus when it’s needed. The problem is formulated as a nonlinear constraint problem to minimize the total operating cost. The management of Energy storage system (ESS), economic load dispatch and operation optimization of distributed generation (DG) are simplified into a single-object optimization problem in the EMS. The proposed algorithm is tested on a typical grid-connected MG including WT/PV/Micro Turbine/Fuel Cell and Energy Storage Devices (ESDs) then its superior performance is compared with those from other evolutionary algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Fuzzy Self Adaptive PSO (FSAPSO), Chaotic Particle PSO (CPSO), Adaptive Modified PSO (AMPSO), and Firefly Algorithm (FA).

Keywords: microgrid, operation management, optimization, firefly algorithm (AMFA)

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Authors: A. Duncan, A. Blake, A. O'Gara, J. Fitzgerald

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Introduction: Acute traumatic rib fractures have significant morbidity and mortality and are a commonly seen injury in trauma patients. Rib fracture pain can often be acute and can prove challenging to manage. We performed an audit on patients with acute traumatic rib fractures with the aim of composing a referral and treatment pathway for such patients. Methods: From January 2021 to January 2022, the pain medicine service encouraged early referral of all traumatic rib fractures to the pain service for a multi-modal management approach. A retrospective audit of analgesic management was performed on a select cohort of 24 patients, with a mean age of 67, of which 19 had unilateral rib fractures. Results: 17 of 24 patients (71%) underwent local, regional block as part of a multi-modal analgesia regime. Only one regional complication was observed, seen with hypotension occurring in one patient with a thoracic epidural. The group who did not undergo regional block had a length of stay (LOS) 17 days longer than those who did (27 vs. 10) and higher rates of pneumonia (29% vs. 18%). Conclusion: Early referral to pain specialists is an important component of the effective management of acute traumatic rib fractures. From our audit, it is evident that regional blocks can be effectively used in these cases as part of a multi-modal analgesia regime and may confer benefits in terms of respiratory complications and length of stay.

Keywords: rib fractures, regional blocks, thoracic epidural, erector spina block

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Authors: Mona Heydari, Ehsan Motamedian, Seyed Abbas Shojaosadati

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Prediction of perturbations after genetic manipulation (especially gene knockout) is one of the important challenges in systems biology. In this paper, a new algorithm is introduced that integrates microarray data into the metabolic model. The algorithm was used to study the change in the cell phenotype after knockout of Gss gene in Escherichia coli BW25113. Algorithm implementation indicated that gene deletion resulted in more activation of the metabolic network. Growth yield was more and less regulating gene were identified for mutant in comparison with the wild-type strain.

Keywords: metabolic network, gene knockout, flux balance analysis, microarray data, integration

Procedia PDF Downloads 579

Authors: Veronica S. Moertini, Vinsensius Kevin, Gede Karya

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Customer reviews have been collected by many kinds of e-commerce websites selling products, services, hotel rooms, tickets and so on. Each website collects its own customer reviews. The reviews can be crawled, collected from those websites and stored as big data. Text analysis techniques can be used to analyze that data to produce summarized information, such as customer opinions. Then, these opinions can be published by independent service provider websites and used to help customers in choosing the most suitable products or services. As the opinions are analyzed from big data of reviews originated from many websites, it is expected that the results are more trusted and accurate. Indonesian customers write reviews in Indonesian language, which comes with its own structures and uniqueness. We found that most of the reviews are expressed with “daily language”, which is informal, do not follow the correct grammar, have many abbreviations and slangs or non-formal words. Hadoop is an emerging platform aimed for storing and analyzing big data in distributed systems. A Hadoop cluster consists of master and slave nodes/computers operated in a network. Hadoop comes with distributed file system (HDFS) and MapReduce framework for supporting parallel computation. However, MapReduce has weakness (i.e. inefficient) for iterative computations, specifically, the cost of reading/writing data (I/O cost) is high. Given this fact, we conclude that MapReduce function is best adapted for “one-pass” computation. In this research, we develop an efficient technique for extracting or mining opinions from big data of Indonesian reviews, which is based on MapReduce with one-pass computation. In designing the algorithm, we avoid iterative computation and instead adopt a “look up table” technique. The stages of the proposed technique are: (1) Crawling the data reviews from websites; (2) cleaning and finding root words from the raw reviews; (3) computing the frequency of the meaningful opinion words; (4) analyzing customers sentiments towards defined objects. The experiments for evaluating the performance of the technique were conducted on a Hadoop cluster with 14 slave nodes. The results show that the proposed technique (stage 2 to 4) discovers useful opinions, is capable of processing big data efficiently and scalable.

Keywords: big data analysis, Hadoop MapReduce, analyzing text data, mining Indonesian reviews

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Authors: Muhammad Saad, AzherBaig, Anwar Maqsood, Muhammad Waseem

Abstract:

The grey wolf has been ranked endangered and Asiatic jackal as near threatened in Pakistan. Scientific data on population and threats to these species are not available in Pakistan, which is required for their proper management and conservation. The present study was conducted to collect data on distribution range, population status and threats to both of these Canis species in Lehri Nature Park. The data were collected using direct observations and indirect signs in the field. The population of grey wolf and Asiatic jackal were scattered into pocket of the study area and its surroundings. The current population of grey wolf was estimated 06 individuals and that of Asiatic jackal 28 individuals in the study area. The present study showed that grey wolf and Asiatic jackal were distributed in the northern and southern part of the study area having dense vegetation cover of tress and shrub between the altitudes of 330 m and 515 m. The research finding revealed that the scrub forest is the most preferred habitat of both the species but due to anthropogenic pressure the scrub forest is under severe threat. The dominant trees species were Acacia modesta, Zizyphus nummularia, and Prosopis juliflora and shrubs species of Dodonea-viscosa, Calotropis procera and Adhatoda vasica. Urial is one of the natural prey species: their population is low due to a number of reasons and therefore the maximum dependence of the wolves was on the livestock of the local and nomadic shepherds. The main prey species in the livestock was goats and sheep. The interviews were conducted with the eye witnesses of wolf attacks including livestock being killed by 5-6 numbers of wolves in different hamlets in the study area. The killing rate of the livestock by the wolves was greater when the nomadic shepherds were present in the area and decreased when they left the area. Presence of nomadic shepherds and killing rate has relation with the shifting of the wolves from the study area. It is further concluded that the population of the grey wolf and Asiatic jackal has decreased over time due to less availability of the natural prey species and habitat destruction.

Keywords: wildlife ecology, population conservation, rehabilitation, conservation

Procedia PDF Downloads 501

Authors: M. Novak, E. Munoz, A. Jana, M. Nelemans

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Cities and local governments the world over are interested to employ circular strategies as a means to bring about food security, create employment and increase resilience. The selection and implementation of circular strategies is facilitated by modeling the effects of strategies locally and understanding the impacts such strategies have had in other (comparable) cities and how that would translate locally. Urban areas are heterogeneous because of their geographic, economic, social characteristics, governance, and culture. In order to better understand the effect of circular strategies on urban systems, we create a dataset for over 300 cities around the world designed to facilitate circular strategy scenario modeling. This new dataset integrates data from over 20 prominent global national and urban data sources, such as the Global Human Settlements layer and International Labour Organisation, as well as incorporating employment data from over 150 cities collected bottom up from local departments and data providers. The dataset is made to be reproducible. Various clustering techniques are explored in the paper. The result is sets of clusters of cities, which can be used for further research, analysis, and support comparative, regional, and national policy making on circular cities.

Keywords: data integration, urban innovation, cluster analysis, circular economy, city profiles, scenario modelling

Procedia PDF Downloads 180

Authors: Nora Alomar, Bianca Alexandra Stefa, Saleh Bazi

Abstract:

This research uncovers the determinants that drive millennial consumers to adhere to product return of fast-fashion products purchases via e-commerce and what effects it has on consumer experience and satisfaction. Online consumption has skyrocketed, with e-commerce being the only, most reliable, and safe method of shopping during and post Covid-19. It has been noted customers are demanding a wide variety of product characteristics and a generous optimal return policy. The authors have selected to examine millennial consumers as they are digital natives and have an affinity for researching, reading product reviews, and shopping online, with a great spending power due to a higher disposable income in comparison to other generations. A multi-study approach is adopted, where study one (interviews, sample of 20 respondents) investigates the factors that drive product return, and study two (PLS-SEM, sample of 250 respondents) looks into the relationships of product return management against behavioral outcomes by having the generated factors (from study one) as moderators. Five themes are generated from study one (return policies, product characteristics, delivery lead time, seasonality, product trial & overspending). The authors identify that two out of the five factors (seasonality, product trial & overspending) have not been highlighted by the literature. The paper examines 11 hypotheses, where 10 are supported. Findings highlight the quality of the product return management influences the overall millennial customer experience and satisfaction. Findings also indicate that product return management was identified to have a significant negative effect on customer experience. Additionally, seasonality has a significant but negative moderation, which means increasing seasonality decreases the relationship between product return management and customer experience and satisfaction. Results highlight that return policies have a significant negative influence on the relationship between returning a product and customer experience and satisfaction. Moreover, product characteristics are also identified to have a significant negative influence on the relationship between returning a product and customer experience and satisfaction. This study further examines the influence of the factors on direct e-commerce websites and third-party e-commerce websites. Findings showcase a strong statistical significance for the increased rate of return of fast-fashion products on third-party websites. This paper aids practitioners in taking strategic decisions related to return management, to improve the quality of logistical services and, in turn, increase profitability.

Keywords: customer experience, customer satisfaction, e-commerce, fast-fashion, product returns

Procedia PDF Downloads 109

Authors: Semeh Ben Salem, Sami Naouali, Moetez Sallami

Abstract:

Clustering is a well known data mining technique used in pattern recognition and information retrieval. The initial dataset to be clustered can either contain categorical or numeric data. Each type of data has its own specific clustering algorithm. In this context, two algorithms are proposed: the k-means for clustering numeric datasets and the k-modes for categorical datasets. The main encountered problem in data mining applications is clustering categorical dataset so relevant in the datasets. One main issue to achieve the clustering process on categorical values is to transform the categorical attributes into numeric measures and directly apply the k-means algorithm instead the k-modes. In this paper, it is proposed to experiment an approach based on the previous issue by transforming the categorical values into numeric ones using the relative frequency of each modality in the attributes. The proposed approach is compared with a previously method based on transforming the categorical datasets into binary values. The scalability and accuracy of the two methods are experimented. The obtained results show that our proposed method outperforms the binary method in all cases.

Keywords: clustering, unsupervised learning, pattern recognition, categorical datasets, knowledge discovery, k-means

Procedia PDF Downloads 260

Authors: Philip A. Szlosek, M. Susan Guyer, Mary G. Barnum, Elizabeth M. Mullin

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In the United States, athletic training is a healthcare profession that encompasses the prevention, examination, diagnosis, treatment, and rehabilitation of injuries and medical conditions. Athletic trainers work under the direction of or in collaboration with a physician and are recognized by the American Medical Association as allied healthcare professionals. Internationally, this profession is often known as athletic therapy. As healthcare professionals, athletic trainers must be prepared for autonomous practice immediately after graduation. However, new athletic trainers have been shown to have clinical areas of strength and weakness.To better assess professional readiness and improve the preparedness of new athletic trainers, the factors of athletic training professional readiness must be defined. Limited research exists defining the holistic aspects of professional readiness needed for athletic trainers. Confirming the factors of professional readiness in athletic training could enhance the professional preparation of athletic trainers and result in more highly prepared new professionals. The objective of this study was to further explore and confirm the factors of professional readiness in athletic training. Authors useda qualitative design based in grounded theory. Participants included athletic trainers with greater than 24 months of experience from a variety of work settings from each district of the National Athletic Trainer’s Association. Participants took the demographic questionnaire electronically using Qualtrics Survey Software (Provo UT). After completing the demographic questionnaire, 20 participants were selected to complete one-on-one interviews using GoToMeeting audiovisual web conferencing software. IBM Statistical Package for the Social Sciences (SPSS, v. 21.0) was used to calculate descriptive statistics for participant demographics. The first author transcribed all interviews verbatim and utilized a grounded theory approach during qualitative data analysis. Data were analyzed using a constant comparative analysis and open and axial coding. Trustworthiness was established using reflexivity, member checks, and peer reviews. Analysis revealed four overarching themes, including management, interpersonal relations, clinical decision-making, and confidence. Management was categorized as athletic training services not involving direct patient care and was divided into three subthemes, including administration skills, advocacy, and time management. Interpersonal Relations was categorized as the need and ability of the athletic trainer to properly interact with others. Interpersonal relations was divided into three subthemes, including personality traits, communication, and collaborative practice. Clinical decision-making was categorized as the skills and attributes required by the athletic trainer whenmaking clinical decisions related to patient care. Clinical decision-making was divided into three subthemes including clinical skills, continuing education, and reflective practice. The final theme was confidence. Participants discussed the importance of confidence regarding relationships building, clinical and administrative duties, and clinical decision-making. Overall, participants explained the value of a well-rounded athletic trainer and emphasized that athletic trainers need communication and organizational skills, the ability to collaborate, and must value self-reflection and continuing education in addition to having clinical expertise. Future research should finalize a comprehensive model of professional readiness for athletic training, develop a holistic assessment instrument for athletic training professional readiness, and explore the preparedness of new athletic trainers.

Keywords: autonomous practice, newly certified athletic trainer, preparedness for professional practice, transition to practice skills

Procedia PDF Downloads 149