Search results for: supervised decision tree

Authors: Maria Teresa Signes Pont, Jose Juan Cortes Plana

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Our proposal provides suitable modelling to the spread of plant pest and particularly to the propagation of Xylella fastidiosa in the almond trees. We compared the impact of temperature and humidity on the propagation of Xylella fastidiosa in various subspecies. Comparison between Balearic Islands and Alicante (Spain). Most sharpshooter and spittlebug species showed peaks in population density during the month of higher mean temperature and relative humidity (April-October), except for the splittlebug Clastoptera sp.1, whose adult population peaked from September-October (late summer and early autumn). The critical season is from when they hatch from the eggs until they are in the pre-reproductive season (January -April) to expand. We focused on winters in the egg state, which normally hatches in early March. The nymphs secrete a foam (mucilage) in which they live and that protects them from natural enemies of temperature changes and prevents dry as long as the humidity is above 75%. The interaction between the life cycles of vectors and vegetation influences the food preferences of vectors and is responsible for the general seasonal shift of the population from vegetation to trees and vice versa, In addition to the temperature maps, we have observed humidity as it affects the spread of the pest Xylella fastidiosa (Xf).

Keywords: xylella fastidiosa, almod tree, temperature, humidity, environmental model

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Authors: Mathew Wakefield, Matthew Mitchell, Lisa Wise, Christopher McCarthy

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The properties of memory representations in artificial neural networks have cognitive implications. Distributed representations that encode instances as a pattern of activity across layers of nodes afford memory compression and enforce the selection of a single point in instance space. These encoding schemes also appear to distort the representational space, as well as trading off the ability to validate that input information is within the bounds of past experience. In contrast, a localist representation which encodes some meaningful information into individual nodes in a network layer affords less memory compression while retaining the integrity of the representational space. This allows the validity of an input to be determined. The validity (or familiarity) of input along with the capacity of localist representation for multiple instance selections affords a memory sampling approach that dynamically balances the bias-variance trade-off. When the input is familiar, bias may be high by referring only to the most similar instances in memory. When the input is less familiar, variance can be increased by referring to more instances that capture a broader range of features. Using this approach in a localist instance memory network, an experiment demonstrates a relationship between representational conflict, generalization performance, and memorization demand. Relatively small sampling ranges produce the best performance on a classic machine learning dataset of visual objects. Combining memory validity with conflict detection produces a reliable confidence judgement that can separate responses with high and low error rates. Confidence can also be used to signal the need for supervisory input. Using this judgement, the need for supervised learning as well as memory encoding can be substantially reduced with only a trivial detriment to classification performance.

Keywords: artificial neural networks, representation, memory, conflict monitoring, confidence

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Authors: Eric Lee G. Escobedo-Wu, Nidhi Rohatgi, Fouzel Dhebar

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It is challenging for patients to navigate through healthcare systems after-hours. This leads to delays in care, patient/provider dissatisfaction, inappropriate resource utilization, readmissions, and higher costs. It is important to provide patients and providers with effective clinical decision-making tools to allow seamless connectivity and coordinated care. In August 2015, patient-centric Stanford Health Care established Clinical Advice Services (CAS) to provide clinical decision support after-hours. CAS is founded on key Lean principles: Value stream mapping, empathy mapping, waste walk, takt time calculations, standard work, plan-do-check-act cycles, and active daily management. At CAS, Clinical Assistants take the initial call and manage all non-clinical calls (e.g., appointments, directions, general information). If the patient has a clinical symptom, the CAS nurses take the call and utilize standardized clinical algorithms to triage the patient to home, clinic, urgent care, emergency department, or 911. Nurses may also contact the on-call physician based on the clinical algorithm for further direction and consultation. Since August 2015, CAS has managed 228,990 calls from 26 clinical specialties. Reporting is built into the electronic health record for analysis and data collection. 65.3% of the after-hours calls are clinically related. Average clinical algorithm adherence rate has been 92%. An average of 9% of calls was escalated by CAS nurses to the physician on call. An average of 5% of patients was triaged to the Emergency Department by CAS. Key learnings indicate that a seamless connectivity vision, cascading, multidisciplinary ownership of the problem, and synergistic enterprise improvements have contributed to this success while striving for continuous improvement.

Keywords: after hours phone calls, clinical advice services, nurse triage, Stanford Health Care

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Authors: Yin Ma, Xun Wang, Kelsey Austin

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Purpose – Building upon career construction theory and the conservation of resources theory, we developed a moderated mediation model to examine how the perceived university support impact students’ career adapting responses, namely, crystallization, exploration, decision and preparation, via the mediator career adaptability and moderator perceived parental support. Design/methodology/approach – The multi-stage sampling strategy was employed and survey data were collected. Structural equation modeling was used to perform the analysis. Findings – Perceived university support could directly promote students’ career adaptability, and promote three career adapting responses, namely, exploration, decision and preparation. It could also impact four career adapting responses via mediation effect of career adaptability. Its impact on students’ career adaptability can greatly increase when students’ receive parental related career support. Research limitations/implications – The cross-sectional design limits causal inference. Conducted in China, our findings should be cautiously interpreted in other countries due to cultural differences. Practical implications – University support is vital to students’ career adaptability and supports from parents can enhance this process. University-home collaboration is necessary to promote students’ career adapting responses. For students, seeking and utilizing as much supporting resources as possible is vital for their human resources development. On an organizational level, universities could benefit from our findings by introducing the practices which ask students to rate the career-related courses and encourage them to chat with parents regularly. Originality/ value – Using recently developed scale, current work contributes to the literature by investigating the impact of multiple contextual factors on students’ career adapting response. It also provide the empirical support for the role of human intervention in fostering career adapting responses.

Keywords: career adapability, university and parental support, China studies, sociology of education

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Authors: Mustafa Al Sukar, Azzam Sleit, Abdullatif Abu-Dalhoum, Bassam Al-Kasasbeh

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Use and abuse of drugs by teens is very common and can have dangerous consequences. The drugs contribute to physical and sexual aggression such as assault or rape. Some teenagers regularly use drugs to compensate for depression, anxiety or a lack of positive social skills. Teen resort to smoking should not be minimized because it can be "gateway drugs" for other drugs (marijuana, cocaine, hallucinogens, inhalants, and heroin). The combination of teenagers' curiosity, risk taking behavior, and social pressure make it very difficult to say no. This leads most teenagers to the questions: "Will it hurt to try once?" Nowadays, technological advances are changing our lives very rapidly and adding a lot of technologies that help us to track the risk of drug abuse such as smart phones, Wireless Sensor Networks (WSNs), Internet of Things (IoT), etc. This technique may help us to early discovery of drug abuse in order to prevent an aggravation of the influence of drugs on the abuser. In this paper, we have developed a Decision Support System (DSS) for detecting the drug abuse using Artificial Neural Network (ANN); we used a Multilayer Perceptron (MLP) feed-forward neural network in developing the system. The input layer includes 50 variables while the output layer contains one neuron which indicates whether the person is a drug addict. An iterative process is used to determine the number of hidden layers and the number of neurons in each one. We used multiple experiment models that have been completed with Log-Sigmoid transfer function. Particularly, 10-fold cross validation schemes are used to access the generalization of the proposed system. The experiment results have obtained 98.42% classification accuracy for correct diagnosis in our system. The data had been taken from 184 cases in Jordan according to a set of questions compiled from Specialists, and data have been obtained through the families of drug abusers.

Keywords: drug addiction, artificial neural networks, multilayer perceptron (MLP), decision support system

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Authors: Shereif. H. Mahmoud

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Egypt has limited water resources, and it will be under water stress by the year 2030. Therefore, Egypt should consider natural and non-conventional water resources to overcome such a problem. Rain harvesting is one solution. This Paper presents a geographic information system (GIS) methodology - based on decision support system (DSS) that uses remote sensing data, filed survey, and GIS to identify potential RWH areas. The input into the DSS includes a map of rainfall surplus, slope, potential runoff coefficient (PRC), land cover/use, soil texture. In addition, the outputs are map showing potential sites for RWH. Identifying suitable RWH sites implemented in the ArcGIS model environment using the model builder of ArcGIS 10.1. Based on Analytical hierarchy process (AHP) analysis taking into account five layers, the spatial extents of RWH suitability areas identified using Multi-Criteria Evaluation (MCE). The suitability model generated a suitability map for RWH with four suitability classes, i.e. Excellent, Moderate, Poor, and unsuitable. The spatial distribution of the suitability map showed that the excellent suitable areas for RWH concentrated in the northern part of Egypt. According to their averages, 3.24% of the total area have excellent and good suitability for RWH, while 45.04 % and 51.48 % of the total area are moderate and unsuitable suitability, respectively. The majority of the areas with excellent suitability have slopes between 2 and 8% and with an intensively cultivated area. The major soil type in the excellent suitable area is loam and the rainfall range from 100 up to 200 mm. Validation of the used technique depends on comparing existing RWH structures locations with the generated suitability map using proximity analysis tool of ArcGIS 10.1. The result shows that most of exiting RWH structures categorized as successful.

Keywords: rainwater harvesting (RWH), geographic information system (GIS), analytical hierarchy process (AHP), multi-criteria evaluation (MCE), decision support system (DSS)

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Authors: William Casto

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Studies that consider the actual impact of international law upon foreign affairs crises are flawed by an unrealistic model of decision making. The common, unexamined assumption is that a nation has a unitary executive or ruler who considers a wide variety of considerations, including international law, in attempting to resolve a crisis. To the extent that negotiation theory is considered, the focus is on negotiations between or among nations. The unsettling result is a shallow focus that concentrates on each country’s public posturing about international law. The country-to-country model ignores governments’ internal negotiations that lead to their formal position in a crisis. The model for foreign policy crises needs to be supplemented to include a model of internal negotiations. Important foreign policy decisions come from groups within a government committee, advisers, etc. Within these groups, participants may have differing agendas and resort to international law to bolster their positions. To understand the influence of international law in international crises, these internal negotiations must be considered. These negotiations are crucial to creating a foreign policy agenda or recommendations. External negotiations between the two nations are significant, but the internal negotiations provide a better understanding of the actual influence of international law upon international crises. Discovering the details of specific internal negotiations is quite difficult but not necessarily impossible. The present proposal will use a specific crisis to illustrate the role of international law. In 1861 during the American Civil War, a United States navy captain stopped a British mail ship and removed two ambassadors of the rebelling southern states. The result was what is commonly called the Trent Affair. In the wake of the captain’s unauthorized and rash action, Great Britain seriously considered going to war against the United States. A detailed analysis of the Trent Affair is possible using the available and extensive internal British correspondence and memoranda to reach an understanding of the effect of international law upon decision making. The extensive trove of internal British documents is particularly valuable because in 1861, the only effective means of communication was face-to-face or through letters. Telephones did not exist, and travel by horse and carriage was tedious. The British documents tell us how individual participants viewed the process. We can approach an accurate understanding of what actually happened as the British government strove to resolve the crisis. For example, British law officers initially concluded that the American captain’s rash act was permissible under international law. Later, the law officers revised their opinion. A model of internal negotiation is particularly valuable because it strips away nations’ public posturing about disputed international law principles. In internal decision making, there is room for meaningful debate over the relevant principles. This fluid debate tells how international law is used to develop a hard, public bargaining position. The Trent Affair indicates that international law had an actual influence upon the crisis and that law was not mere window dressing for the government’s public position.

Keywords: foreign affairs crises, negotiation, international law, Trent affair

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Authors: Lejla Sunagic

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The aim of the manuscript is to further the understanding of risky decision-making in the context of forced and irregular migration. The empirical evidence is collected through interviews with Syrian refugees who arrived in Europe via irregular pathways. Analytically, it has been approached through the juxtaposition between risk perception and the notion of desire. As different frameworks have been developed to address differences in risk perception, the common thread was the understanding that individual risk-taking has been addressed in terms of benefits outweighing risks. However, this framework cannot explain a big risk an individual takes because of an underprivileged position and due to a lack of positive alternatives, termed as risk-taking from vulnerability. The accounts of the field members of this study that crossed the sea in rubber boats to arrive in Europe make an empirical fit to such a postulate by reporting that the risk they have taken was not the choice but the only coping strategy. However, the vulnerability argument falls short of explaining why the interviewees, thinking retrospectively, find the risky journey they have taken to be worth it, while they would strongly advise others to restrain from taking such a huge risk. This inconsistency has been addressed by adding the notion of desire to migrate to the elements of risk perception. Desire, as a subjective experience, was what made the risk appear smaller in cost-benefit analysis at the time of decision-making of those who have realized migration. However, when they reflect on others in the context of potential migration via the same pathway, the interviewees addressed the others’ lack of capacity to avoid the same obstacles that they themselves were able to circumvent while omitting to reflect on others’ desire to migrate. Thus, in the risk-benefit analysis performed for others, the risk remains unblurred and tips over the benefits, given the inability to take into account the desire of others. If desire, as the transformative potential of migration, is taken out of the cost-benefit analysis of irregular migration, refugees might not have taken the risky journey. By casting the theoretical argument in the language of configuration, the study is filling in the gap of knowledge on the combination of migration drivers and the way they interact and produce migration outcomes.

Keywords: refugees, risk perception, desire, irregular migration

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Authors: Cristian Matti, Valerie Fowles, Eva Enyedi, Piotr Pogorzelski

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The system mapping process can be defined as a knowledge service where a team of facilitators, experts and practitioners facilitate a guided conversation, enable the exchange of information and support an iterative curation process. System mapping processes rely on science-based tools to introduce and simplify a variety of components and concepts of socio-technical systems through metaphors while facilitating an interactive dialogue process to enable the design of co-created maps. System maps work then as “artifacts” to provide information and focus the conversation into specific areas around the defined challenge and related decision-making process. Knowledge management facilitates the curation of that data gathered during the system mapping sessions through practices of documentation and subsequent knowledge co-production for which common practices from data science are applied to identify new patterns, hidden insights, recurrent loops and unexpected elements. This study presents empirical evidence on the application of these techniques to explore mechanisms by which visual tools provide guiding principles to portray system components, key variables and types of data through the lens of climate change. In addition, data science facilitates the structuring of elements that allow the analysis of layers of information through affinity and clustering analysis and, therefore, develop simple indicators for supporting the decision-making process. This paper addresses methodological and empirical elements on the horizontal learning process that integrate system mapping through visual tools, interpretation, cognitive transformation and analysis. The process is designed to introduce practitioners to simple iterative and inclusive processes that create actionable knowledge and enable a shared understanding of the system in which they are embedded.

Keywords: indicators, knowledge management, system mapping, visual tools

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Saloni Khanderia

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The World Trade Organisation’s (WTO) Information Technology Agreement (ITA), was concluded at the Singapore Ministerial Conference in 1996. The ITA is considered to be one of the biggest tariff-cutting deals because it eliminates all customs-related duties on the exportation of specific categories of information technology products to the territory of any other signatory to the Agreement. Over time, innovations in the information and communication technology (ICT) sector mandated the consideration of expanding the list of products covered by the ITA, which took place in the form of ITA-II negotiations during the WTO’s Nairobi Ministerial Conference. India, which was an original Member of the ITA-I, however, decided to opt-out of the negotiations to expand the list of products covered by the agreement. Instead, it preferred to give priority to its national policy initiative, namely the ‘Make-in-India’ programme [the MiI programme], which embarks upon fostering the domestic production of, inter alia, the ICT sector. India claims to have abstained from the ITA-II negotiations by stating that the zero-tariff regime created by the ITA-I debilitated its electronics-manufacturing sectors and on the contrary resulted in an over-reliance on imported electronic inputs. The author undertakes doctrinal research to examine India’s decision to opt-out of ITA-II negotiations, against the backdrop of the MiI Programme, which endeavours to improve productivity across-the-board. This paper accordingly scrutinises the tariff-cutting strategies of India to weigh the better alternative for India. Apropos, it examines whether initiatives like the MiI programme could plausibly resuscitate the ailing domestic electronics-manufacturing sector. The author opines that the country’s present decision to opt-out of ITA-II negotiations should be perceived as a welcome step. Thus, market-oriented reforms such as the MiI Programme, which focuses on indigenous innovation to improve domestic manufacturing in the ICT sector, should instead, in the present circumstances gain priority. Consequently, the MiI Programme would aid in moulding the country’s current tariff policy in a manner that will concurrently assist the promotion and sustenance of domestic manufacturing in the IT sector.

Keywords: electronics-manufacturing sector, information technology agreement, make in india programme, world trade organisation

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Authors: Christos Chaschatzis, Chrysoula Karaiskou, Pantelis Angelidis, Sotirios K. Goudos, Igor Kotsiuba, Panagiotis Sarigiannidis

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Over the past few decades, the rapid growth of the global population has led to the need to increase agricultural production and improve the quality of agricultural goods. There is a growing focus on environmentally eco-friendly solutions, sustainable production, and biologically minimally fertilized products in contemporary society. Precision agriculture has the potential to incorporate a wide range of innovative solutions with the development of machine learning algorithms. YOLOv5 and YOLOv8 are two of the most advanced object detection algorithms capable of accurately recognizing objects in real time. Detecting tree diseases is crucial for improving the food production rate and ensuring sustainability. This research aims to evaluate the efficacy of YOLOv5 and YOLOv8 in detecting the symptoms of Armillaria spp. in sweet cherry trees and determining their health status, with the goal of enhancing the robustness of precision agriculture. Additionally, this study will explore Computer Vision (CV) techniques with machine learning algorithms to improve the detection process’s efficiency.

Keywords: Armillaria spp., machine learning, precision agriculture, smart farming, sweet cherries trees, YOLOv5, YOLOv8

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Authors: Amèdédjihundé H. J. Hounnou, Frédéric Dubas, François-Xavier Fifatin, Didier Chamagne, Antoine Vianou

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This paper presents the techno-economic evaluation of run-of-river small-hydropower plants. In this regard, a multi-objective optimization procedure is proposed for the optimal sizing of the hydropower plants, and NSGAII is employed as the optimization algorithm. Annual generated energy and investment cost are considered as the objective functions, and number of generator units (n) and nominal turbine flow rate (Q_T) constitute the decision variables. Site of Yeripao in Benin is considered as the case study. We have categorized the river of this site using its environmental characteristics: gross head, and first quartile, median, third quartile and mean of flow. Effects of each decision variable on the objective functions are analysed. The results gave Pareto Front which represents the trade-offs between annual energy generation and the investment cost of hydropower plants, as well as the recommended optimal solutions. We noted that with the increase of the annual energy generation, the investment cost rises. Thus, maximizing energy generation is contradictory with minimizing the investment cost. Moreover, we have noted that the solutions of Pareto Front are grouped according to the number of generator units (n). The results also illustrate that the costs per kWh are grouped according to the n and rise with the increase of the nominal turbine flow rate. The lowest investment costs per kWh are obtained for n equal to one and are between 0.065 and 0.180 €/kWh. Following the values of n (equal to 1, 2, 3 or 4), the investment cost and investment cost per kWh increase almost linearly with increasing the nominal turbine flowrate while annual generated. Energy increases logarithmically with increasing of the nominal turbine flowrate. This study made for the Yeripao river can be applied to other rivers with their own characteristics.

Keywords: hydropower plant, investment cost, multi-objective optimization, number of generator units

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Authors: Pei Yi Lin

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Objective: The objective of this study is to use machine learning methods to build an early prediction classifier model for acute delirium to improve the quality of medical care for intensive care patients. Background: Delirium is a common acute and sudden disturbance of consciousness in critically ill patients. After the occurrence, it is easy to prolong the length of hospital stay and increase medical costs and mortality. In 2021, the incidence of delirium in the intensive care unit of internal medicine was as high as 59.78%, which indirectly prolonged the average length of hospital stay by 8.28 days, and the mortality rate is about 2.22% in the past three years. Therefore, it is expected to build a delirium prediction classifier through big data analysis and machine learning methods to detect delirium early. Method: This study is a retrospective study, using the artificial intelligence big data database to extract the characteristic factors related to delirium in intensive care unit patients and let the machine learn. The study included patients aged over 20 years old who were admitted to the intensive care unit between May 1, 2022, and December 31, 2022, excluding GCS assessment <4 points, admission to ICU for less than 24 hours, and CAM-ICU evaluation. The CAMICU delirium assessment results every 8 hours within 30 days of hospitalization are regarded as an event, and the cumulative data from ICU admission to the prediction time point are extracted to predict the possibility of delirium occurring in the next 8 hours, and collect a total of 63,754 research case data, extract 12 feature selections to train the model, including age, sex, average ICU stay hours, visual and auditory abnormalities, RASS assessment score, APACHE-II Score score, number of invasive catheters indwelling, restraint and sedative and hypnotic drugs. Through feature data cleaning, processing and KNN interpolation method supplementation, a total of 54595 research case events were extracted to provide machine learning model analysis, using the research events from May 01 to November 30, 2022, as the model training data, 80% of which is the training set for model training, and 20% for the internal verification of the verification set, and then from December 01 to December 2022 The CU research event on the 31st is an external verification set data, and finally the model inference and performance evaluation are performed, and then the model has trained again by adjusting the model parameters. Results: In this study, XG Boost, Random Forest, Logistic Regression, and Decision Tree were used to analyze and compare four machine learning models. The average accuracy rate of internal verification was highest in Random Forest (AUC=0.86), and the average accuracy rate of external verification was in Random Forest and XG Boost was the highest, AUC was 0.86, and the average accuracy of cross-validation was the highest in Random Forest (ACC=0.77). Conclusion: Clinically, medical staff usually conduct CAM-ICU assessments at the bedside of critically ill patients in clinical practice, but there is a lack of machine learning classification methods to assist ICU patients in real-time assessment, resulting in the inability to provide more objective and continuous monitoring data to assist Clinical staff can more accurately identify and predict the occurrence of delirium in patients. It is hoped that the development and construction of predictive models through machine learning can predict delirium early and immediately, make clinical decisions at the best time, and cooperate with PADIS delirium care measures to provide individualized non-drug interventional care measures to maintain patient safety, and then Improve the quality of care.

Keywords: critically ill patients, machine learning methods, delirium prediction, classifier model

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Authors: Anwar Kashgari

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COVID-19 has severe impacts on business all over the world. The great lockdown of many business owners requires a sage deal with this pandemic. This paper seeks to support business leaders with a standpoint about the COVID-19 situation and provides implications for the (Small and Medium Enterprises) SMEs and companies. The paper reflects the author's view about the impact of COVID-19 on business activities. We discussed the impact of COVID-19 upon three aspects, namely, startups, SMEs, and e-commerce. The KSA is an example of the developing countries about which we present the current situation. Finally, recommendations to policy and decision-makers are given.

Keywords: COVID 19, business networking, globalization

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Authors: Getaneh Berie Tarekegn

Abstract:

A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Seifoddini Hamid, Mardikoraeem Mahsa, Ghorayshi Roya

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Manufacturing facility location selection is an important strategic decision for many industrial corporations. In this paper, a new approach to the manufacturing location selection problem is proposed. In this approach, cluster analysis is employed to identify suitable manufacturing locations based on economic, social, environmental, and political factors. These factors are quantified using the existing real world data.

Keywords: manufacturing facility, manufacturing sites, real world data

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Authors: Laurie U. deBettencourt

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During pre-service field experiences teacher candidates are often asked to reflect as part of their training and in this investigation candidates’ reflective journal entries were reviewed, coded and analyzed with results suggesting teacher candidates need more direct instruction on how to describe, analyze, and make judgements on their instructional practices so that their practices improve over time. Teacher education programs often incorporate reflective-based activities during field experiences. The purpose of this investigation was to determine if special education teacher candidate’s reflective practices changed as they completed their two supervised field experiences and to determine what topics the candidates focused on in their reflections. The six females graduate students were completing two field experiences in special education classrooms within one academic year as part of their coursework leading to a master’s degree and special education teacher state certification. Each candidate wrote 15 reflection journal entries (approximately 200 words each) per field experience. Each of the journal entries were reviewed sentence by sentence to determine a reflective practice score and to determine the topics discussed. The reflective practice score was calculated using four dimensions of reflection (describe, analyze, judge, and apply) in order to create a continuous variable representing their reflective practice across four points of time. A One-way Repeated Measures Analysis of Variance (ANOVA) suggested that special education teacher candidates did not change their reflective practices over time (i.e., at time-point one the practitioner’s mean score was 56.0 out of 100 (SD = 7.6), 53.8 (SD = 4.3) at time-point two, 51.2 (SD = 4.5) at time-point three, and 57.7 (SD = 8.2) at time-point four). Qualitative findings suggest candidates focused mostly on themselves in their reflections. Conclusions suggest the need for teacher preparation programs to provide more direct instruction on how a teacher should reflect. Specific implications are provided for teacher training and future research.

Keywords: field experiences, reflective practices, special educators, teacher preparation

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Authors: Nasser Karami

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The Urmia grapes have been famous for centuries and have been among the most desirable in the production of wine. Interestingly, evidence shows that the Urmia region was the first place in the world where wine was produced and consumed. In fact, the grapes known as “Shiraz” and made popular by “Shiraz Wine” are the grapes cultivated as a local species especially in the West Azerbaijan watershed basin and exported to Europe. But after the Islamic Revolution, because the production, usage, and sale of wine were unlawful (under Islamic rule), they decided to cultivate apples instead of grapes. Before Islamic revolution, about 50 percent of the gardens were producing grapes, but the apple groves took up less than 1.5 percent (100 hectares). Three years after the revolution, in 1982, people were swept up in the revolutionary excitement and grape cultivation decreased, using less than 10 percent of the garden area. Important is the fact that an apple tree needs 12 times more water than a grapevine, it should be noted that in terms of water usage in the area, the agricultural area has not been increased by 2 or 4 times but rather by 12 times. Evaluation of this study showed that contrary to official reports, climate change isn’t major cause of drying up Urmia Lake and 65 percent of this environmental crisis happened due to spreading unsustainable agricultural in basin of this lake.

Keywords: cultivation pattern, unsustainable agriculture, urmia lake drying, water managment

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Authors: Anum Obaid, Bushra Noor, Zoshia Zainab

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In South Asian countries, Pakistan faces significant public health challenges regarding maternal and neonatal health (MNH). Despite global efforts to improve maternal, newborn, child, and health (MNCH) outcomes through initiatives like the Millennium Development Goals (MDGs) and Sustainable Development Goals (SDGs), high maternal and neonatal mortality rates persist. In patriarchal societies, cultural norms, family dynamics, and gender roles heavily influence healthcare accessibility and decision-making processes, often leading to delayed and inadequate maternal care. Addressing these socio-cultural barriers and enhancing healthcare resources is crucial to improving maternal health outcomes in areas like Faisalabad. A qualitative study was conducted involving two groups of informants: gynecologists practicing in private clinics and first-time pregnant women receiving care in government hospitals. Data collection included obtaining institutional permission, conducting semi-structured in-depth interviews, and using non-probability sampling techniques. A proactive strategy to overcome maternal health challenges involves using aversion therapy and disseminating knowledge among family members. This approach aims to foster a deep understanding within the family unit regarding the importance of maternal well-being, thereby creating a supportive environment and facilitating informed decision-making related to healthcare access and lifestyle choices. The findings indicate that maternal health is compromised both physiologically and psychologically, with significant implications for the baby's health. Mental well-being is profoundly affected, largely due to familial behavior and entrenched cultural taboos.

Keywords: maternal health, neonatal health, socio-cultural norms, primigravida women, gynecologist, familial conduct, cultural taboos

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Authors: John Tosin Aladejana

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Wood behave differently under different environmental conditions. The knowledge of the hygroscopic nature of wood becomes a key factor in selecting wood for use and required treatment. This study assessed the hygroscopic behaviour of Hevea brasiliensis (Rubber) wood. Void volume, volumetric swelling in the tangential, radial and longitudinal directions and volumetric shrinkage were used to assess the response of the wood when loosing or taking up moisture. Hevea brasiliensis wood samples cut into 20 × 20 × 60 mm taken longitudinally and transversely were used for the study and dried in the oven at 103 ± 2⁰C. The mean values for moisture content in green Hevea brasiliensis wood were 49.74 %, 51.14 % and 54.36 % for top, middle and bottom portion respectively while 51.77 %, 50.02 % and 53.45 % were recorded for outer, middle and inner portions respectively for the tree. The values obtained for volumetric shrinkage and swelling indicated that shrinkage and swelling were higher at the top part of H. brasiliensis. It was also observed that the longitudinal shrinkage was negligible while tangential direction showed the highest shrinkage among the wood direction. The values of the void volume obtained were 43.0 %, 39.0 % and 38.0 % at the top, middle and bottom respectively. The result obtained showed clarification on the wood density of hevea brasiliensis based on the position and portion of the wood species and the variation in moisture content, void volume, volumetric shrinkage and swelling were also revealed. This will provide information in the process of drying hevea brasiliensis wood to ensure better wood quality devoid of defects.

Keywords: moisture content, shrinkage, swelling, void volume

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Authors: Elizabeth Malebogo Mosepele

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Road transport has become increasingly common in the world; people rely on road networks for transportation purpose on a daily basis. However, environmental impact of roads on surrounding landscapes extends their potential effects even further. This study investigates the impact of road network on natural vegetation. The study will provide baseline knowledge regarding roadside vegetation and would be helpful in future for conservation of biodiversity along the road verges and improvements of road verges. The general hypothesis of this study is that the amount and condition of road side vegetation could be explained by road network conditions. Remote sensing techniques were used to analyze vegetation conditions. Landsat 8 OLI image was used to assess vegetation cover condition. NDVI image was generated and used as a base from which land cover classes were extracted, comprising four categories viz. healthy vegetation, degraded vegetation, bare surface, and water. The classification of the image was achieved using the supervised classification technique. Road networks were digitized from Google Earth. For observed data, transect based quadrats of 50*50 m were conducted next to road segments for vegetation assessment. Vegetation condition was related to road network, with the multinomial logistic regression confirming a significant relationship between vegetation condition and road network. The null hypothesis formulated was that 'there is no variation in vegetation condition as we move away from the road.' Analysis of vegetation condition revealed degraded vegetation within close proximity of a road segment and healthy vegetation as the distance increase away from the road. The Chi Squared value was compared with critical value of 3.84, at the significance level of 0.05 to determine the significance of relationship. Given that the Chi squared value was 395, 5004, the null hypothesis was therefore rejected; there is significant variation in vegetation the distance increases away from the road. The conclusion is that the road network plays an important role in the condition of vegetation.

Keywords: Chi squared, geographic information system, multinomial logistic regression, remote sensing, road side vegetation

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Authors: Dovile Petkeviciute-Barysiene

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Legal technologies not only create new ways for accessing and providing legal services but also transform the role of legal practitioners. Both lawyers and users of legal services expect automated solutions to outperform people with objectivity and impartiality. Although fairness of the automated decisions is crucial, research on assessing various characteristics of automated processes related to the perceived fairness has only begun. One of the major obstacles to this research is the lack of comprehensive understanding of what legal actions are automated and could be meaningfully automated, and to what extent. Neither public nor legal practitioners oftentimes cannot envision technological input due to the lack of general without illustrative examples. The aim of this study is to map decision making stages and automation levels which are and/or could be achieved in legal actions related to pre-trial and trial processes. Major legal decisions and judgments are identified during the consultations with legal practitioners. The dual-process model of information processing is used to describe cognitive processes taking place while making legal decisions and judgments during pre-trial and trial action. Some of the existing legal technologies are incorporated into the analysis as well. Several published automation level taxonomies are considered because none of them fit well into the legal context, as they were all created for avionics, teleoperation, unmanned aerial vehicles, etc. From the information processing perspective, analysis of the legal decisions and judgments expose situations that are most sensitive to cognitive bias, among others, also help to identify areas that would benefit from the automation the most. Automation level analysis, in turn, provides a systematic approach to interaction and cooperation between humans and algorithms. Moreover, an integrated map of legal decisions and judgments, information processing characteristics, and automation levels all together provide some groundwork for the research of legal technology perceived fairness and acceptance. Acknowledgment: This project has received funding from European Social Fund (project No 09.3.3-LMT-K-712-19-0116) under grant agreement with the Research Council of Lithuania (LMTLT).

Keywords: automation levels, information processing, legal judgment and decision making, legal technology

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Authors: A. Pascual Ramsay

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Accounts of the management of the economic and euro crisis in Spain have been dominated by an emphasis on external constraints. However, this approach leaves unanswered important questions about the role of domestic political factors. Using systematic qualitative primary research and employing elite interviewing and process tracing, this paper aims to fill this gap for the period of the Partido Socialista Obrero Español (PSOE) administration. The paper shows that domestic politics played a crucial role in the management of the crisis, most importantly by determining the shape of the measures undertaken. In its three distinct stages – downplaying/inaction, reaction/stimulus, and austerity/reform – the PSOE's response was certainly constrained by external factors, most notably EMU membership and the actions of sovereign-bond investors, the ECB and Germany. Yet while these external constraints forced the government to act, domestic political factors fundamentally shaped the content of key measures: the fiscal stimulus, the labour, financial and pension reforms, the refusal to accept a bailout or the reform of the Constitution. Seven factors were particularly influential: i) electoral and political cost, ii) party and partisanship, iii) organised interests, iv) domestic institutions, v) ideological preferences, vi) ineffective decision-making, and vii) judgement and personal characteristics of decision-makers. In conclusion, domestic politics played an important role in the management of the crisis, a role that has been underestimated by dominant approaches focusing on external constraints and weak domestic policy autonomy. The findings provide empirical evidence to support research agendas that identify significant state discretion in the face of international economic integration and an important role for domestic political factors such as institutions, material interests, partisanship and ideology in shaping economic outcomes.

Keywords: economic crisis, Euro, PSOE, Spain

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Authors: Nadir Ali Rind, Özlem Aksoy, Muhammad Umar Dahot, Salih Dikilitaş, Muhammad Rafiq, Burçak Tütünoğlu

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Melia azedarach L. is freshly fruited small to medium sized tree native to China and North western India. It is growing in Pakistan and Turkey in various areas facing great environmental changes to maintain its survival. The species is valued for its high quality wood, medicinal, ornamental and shade purposes. The present work was aimed to estimate the genetic variation among the populations of Melia azedarach L. leaf samples that were collected from five different locations of Turkey and three different areas of Pakistan. These populations were chosen on the random bases by applying RAPD primers in order to construct a dendogram using UPGMA method to show genetic diversity. After that appropriate conservation strategies were suggested. 14 primers producing polymorphic and monomorphic bands were analyzed. Genetic distances were calculated for all the species studied by RAPD-PCR methods. According to the results the lowest genetic identity values and the highest genetic polymorphic values were determined. It is observed that there was a clear split among populations from different areas in Turkey and Pakistan. These differences may be due to eco-geographical association with genetic variation and should be conserved to retain the genetic variation of the species.

Keywords: melia azedarach L., genetic diversity, conservation, RAPD-PCR, medicinal plant

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Authors: Christina Zourna, Ioanna Papavassiliou-Alexiou

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Drama in Education (DiE) is a dynamic experiential method that can be used in many interdisciplinary contexts. In the Educational and Social Policy Department, University of Macedonia, Thessaloniki, Greece, DiE is being used as a core method for developing professional competences in pre- and postgraduate courses as well as adult education training programs. In this presentation, an innovative DiE application will be described concerning the development of educational leaders’ skills necessary to meet unprecedented, unexpected challenges in the 21st century schools. In a non-threatening risk-taking no-penalty environment, future educational leaders live-in-role problems, challenges, and dilemmas before having to face similar ones in their profession. Through personal involvement, emotional engagement, and reflection, via individual and group activities, they experience the behaviour, dilemmas, decision-making processes, and informed choices of a recognized leader and are able to make connections with their own life. As pretext serves the life of Alexander the Great, the Macedonian King who defeated the vast Persian empire in the 4th century BC and, by uniting all Greeks, conquered the up-to-date known eastern world thanks to his authentic leadership skills and exceptional personality traits. Since the early years of his education mastered by the famous Greek philosopher Aristotle, Alexander proved his unique qualities by providing the world with the example of an undeniably genuine, inspirational, effective, and most recognizable authentic leader. Through questionnaires and individual interviews, participants in these workshops revealed how they developed active listening, empathy, creativity, imagination, critical strategic and out-of-the-box thinking, cooperation and own vision communicating, crisis management skills, self-efficacy, self-awareness, self-exposure, information management, negotiation and inspiration skills, enhanced sense of responsibility and commitment, and decision-making skills.

Keywords: drama in education method, educational leadership, professional competences, skills’ development

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Authors: Wang Yichen, Haruka Yamashita

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In recent years, in the field of sports, decision making such as member in the game and strategy of the game based on then analysis of the accumulated sports data are widely attempted. In fact, in the NBA basketball league where the world's highest level players gather, to win the games, teams analyze the data using various statistical techniques. However, it is difficult to analyze the game data for each play such as the ball tracking or motion of the players in the game, because the situation of the game changes rapidly, and the structure of the data should be complicated. Therefore, it is considered that the analysis method for real time game play data is proposed. In this research, we propose an analytical model for "determining the optimal lineup composition" using the real time play data, which is considered to be difficult for all coaches. In this study, because replacing the entire lineup is too complicated, and the actual question for the replacement of players is "whether or not the lineup should be changed", and “whether or not Small Ball lineup is adopted”. Therefore, we propose an analytical model for the optimal player selection problem based on Small Ball lineups. In basketball, we can accumulate scoring data for each play, which indicates a player's contribution to the game, and the scoring data can be considered as a time series data. In order to compare the importance of players in different situations and lineups, we combine RNN (Recurrent Neural Network) model, which can analyze time series data, and NN (Neural Network) model, which can analyze the situation on the field, to build the prediction model of score. This model is capable to identify the current optimal lineup for different situations. In this research, we collected all the data of accumulated data of NBA from 2019-2020. Then we apply the method to the actual basketball play data to verify the reliability of the proposed model.

Keywords: recurrent neural network, players lineup, basketball data, decision making model

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Authors: M. Miljkovic, A. Urakpo, M. Simic-Koumoutsaris

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Benefit-risk assessment (BRA) is a valuable tool that takes place in multiple stages during a medicine's lifecycle, and this assessment can be conducted in a variety of ways. The aim was to summarize current BRA methods used during approval decisions and in post-approval settings and to see possible future directions. Relevant reviews, recommendations, and guidelines published in medical literature and through regulatory agencies over the past five years have been examined. BRA implies the review of two dimensions: the dimension of benefits (determined mainly by the therapeutic efficacy) and the dimension of risks (comprises the safety profile of a drug). Regulators, industry, and academia have developed various approaches, ranging from descriptive textual (qualitative) to decision-analytic (quantitative) models, to facilitate the BRA of medicines during the product lifecycle (from Phase I trials, to authorization procedure, post-marketing surveillance and health technology assessment for inclusion in public formularies). These approaches can be classified into the following categories: stepwise structured approaches (frameworks); measures for benefits and risks that are usually endpoint specific (metrics), simulation techniques and meta-analysis (estimation techniques), and utility survey techniques to elicit stakeholders’ preferences (utilities). All these approaches share the following two common goals: to assist this analysis and to improve the communication of decisions, but each is subject to its own specific strengths and limitations. Before using any method, its utility, complexity, the extent to which it is established, and the ease of results interpretation should be considered. Despite widespread and long-time use, BRA is subject to debate, suffers from a number of limitations, and currently is still under development. The use of formal, systematic structured approaches to BRA for regulatory decision-making and quantitative methods to support BRA during the product lifecycle is a standard practice in medicine that is subject to continuous improvement and modernization, not only in methodology but also in cooperation between organizations.

Keywords: benefit-risk assessment, benefit-risk profile, product lifecycle, quantitative methods, structured approaches

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Authors: Zahid Hussain

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Provincial Disaster Management Authority (PDMA) Punjab was established under NDM Act 2010 and now working under Senior Member Board of Revenue, deals with the whole spectrum of disasters including preparedness, mitigation, early warning, response, relief, rescue, recovery and rehabilitation. The District Disaster Management Authorities (DDMA) are acting as implementing arms of PDMA in the districts to respond any disaster. DDMAs' role is very important in disaster mitigation, response and recovery as they are the first responder and closest tier to the community. Keeping in view the significant role of DDMAs, technical and human resource capacity are need to be checked. For calculating the technical efficiencies of District Disaster Management Authority (DDMA) in Punjab, three inputs like number of labour, the number of transportation and number of equipment, two outputs like relief assistance and the number of rescue and 25 districts as decision making unit have been selected. For this purpose, 8 years secondary data from 2005 to 2012 has been used. Data Envelopment Analysis technique has been applied. DEA estimates the relative efficiency of peer entities or entities performing the similar tasks. The findings show that all decision making unit (DMU) (districts) are inefficient on techonological and scale efficiency scale while technically efficient on pure and total factor productivity efficiency scale. All DMU are found technically inefficient only in the year 2006. Labour and equipment were not efficiently used in the year 2005, 2007, 2008, 2009 and 2012. Furthermore, only three years 2006, 2010 and 2011 show that districts could not efficiently use transportation in a disaster situation. This study suggests that all districts should curtail labour, transportation and equipment to be efficient. Similarly, overall all districts are not required to achieve number of rescue and relief assistant, these should be reduced.

Keywords: DEA, DMU, PDMA, DDMA

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Authors: Hadia Rafi

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To make any decision in any work/task/project it involves many factors that needed to be looked. The analytic Hierarchy process (AHP) is based on the judgments of experts to derive the required results this technique measures the intangibles and then by the help of judgment and software analysis the comparisons are made which shows how much a certain element/unit leads another. AHP includes how an inconsistent judgment should be made consistent and how the judgment should be improved when possible. The Priority scales are obtained by multiplying them with the priority of their parent node and after that they are added.

Keywords: AHP, priority scales, parent node, software analysis

Procedia PDF Downloads 406