Search results for: predictive decision

Authors: Vladimíra Čavojová, Eva Ballová Mikušková

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In this study we tried to replicate the unconscious thought advantage (UTA), which states that complex decisions are better handled by unconscious thinking. We designed an experiment in e-prime using similar material as the original study (choosing between four different apartments, each described by 12 attributes). A total of 73 participants (52 women (71.2%); 18 to 62 age: M=24.63; SD=8.7) took part in the experiment. We did not replicate the results suggested by UTT. However, from the present study we cannot conclude whether this was the case of flaws in the theory or flaws in our experiment and we discuss several ways in which the issue of UTA could be examined further.

Keywords: decision making, unconscious thoughts, UTT, complex decisions

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Authors: Jay L. Fu

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Patients with Alzheimer's disease progressively lose their memory and thinking skills and, eventually, the ability to carry out simple daily tasks. The disease is irreversible, but early detection and treatment can slow down the disease progression. In this research, publicly available MRI data and demographic data from 373 MRI imaging sessions were utilized to build models to predict dementia. Various machine learning models, including logistic regression, k-nearest neighbor, support vector machine, random forest, and neural network, were developed. Data were divided into training and testing sets, where training sets were used to build the predictive model, and testing sets were used to assess the accuracy of prediction. Key risk factors were identified, and various models were compared to come forward with the best prediction model. Among these models, the random forest model appeared to be the best model with an accuracy of 90.34%. MMSE, nWBV, and gender were the three most important contributing factors to the detection of Alzheimer’s. Among all the models used, the percent in which at least 4 of the 5 models shared the same diagnosis for a testing input was 90.42%. These machine learning models allow early detection of Alzheimer’s with good accuracy, which ultimately leads to early treatment of these patients.

Keywords: Alzheimer's disease, clinical diagnosis, magnetic resonance imaging, machine learning prediction

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Authors: Purva Mishra, Rajesh Potlia, Kuljeet Singh Sandhu

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The role of glycyl residues in the protein structure has lingered within the research community for the last several decades. Glycyl residue is the only amino acid that is achiral due to the lack of a side chain and can, therefore, exhibit Ramachandran conformations that are disallowed for L-amino acids. The structural and functional significance of glycyl residues with L-disallowed conformation, however, remains obscure. Through statistical analysis of various datasets, we found that the glycyls with L-disallowed conformations are over-represented at disease-associated sites and tend to be evolutionarily conserved. The mutations of L-disallowed glycyls tend to destabilize the native conformation, reduce protein solubility, and promote inter-molecular aggregations. We uncovered a structural motif referred to as “β-crescent” formed around the L-disallowed glycyl, which prevents β-sheet aggregation by disrupting the alternating pattern of β-pleats. The L-disallowed conformation of glycyls also holds predictive power to infer the pathogenic missense variants. Altogether, our observations highlight that the L-disallowed conformation of glycyls is selected to facilitate native folding and prevent inter-molecular aggregations. The findings may also have implications for designing more stable proteins and prioritizing the genetic lesions implicated in diseases.

Keywords: Ramachandran plot, β-sheet, protein stability, protein aggregation

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Authors: Mohammad Javad Zehtab, S. Alireza Mirghasemi, Ali Majlesara, Parvin Tajik, Babak Siavashi

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Objective: There are different modalities proposed for tennis elbow treatment with few randomized trials comparing them. We designed a study to compare the effectiveness of five different modalities and determine the usefulness of recently proposed extensor grip test (EGT) in predicting the response to treatment. Methods: In a randomized controlled clinical trial 92 of 98 tennis elbow patients in Sina hospital of Tehran, Iran between 2006 and 2007 fulfill trial entry criteria, among these patients 56 (60.9%) had positive EGT result. Stratified on EGT result, patients allocated randomly to 5 treatment groups: Brace (B) group, physiotherapy (P), brace + physiotherapy (BP), injection (I) and injection + physiotherapy (IP). Results: Patients who had positive result of EGT had better response to treatments: less SOC (p = 0.06), less PFFQ and patients’ satisfaction scores (p < 0.001). Among the treatment IP was the most successful, then BP, P and B, respectively; injection was the worst treatment modality. Response to treatment was comparable in all groups between EGT positive and negative patients except bracing; in which positive EGT was correlated with a dramatic response to treatment. Conclusion: In all patients IP and then BP is recommended but in EGT negatives, bracing seems to be of no use. Injection alone is not recommended in either group.

Keywords: tennis elbow, extensor grip test, physiotherapy, tennis elbow treatment

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Authors: Karlijn Van Den Broek, Ian Walker, Christian Klöckner

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Campaigns aiming to induce energy-saving behaviour among householders use a wide range of approaches that address many different drivers thought to underpin this behaviour. However, little research has compared the relative importance of the different factors that influence energy behaviour, meaning campaigns are not informed about where best to focus resources. Therefore, this study applies the Comprehensive Action Determination Model (CADM) to compare the role of normative, intentional, habitual, and situational processes on energy-saving behaviour. An online survey on a sample of households (N = 247) measured the CADM variables and the data was analysed using structural equation modelling. Results showed that situational and habitual processes were best able to account for energy saving behaviour while normative and intentional processes had little predictive power. These findings suggest that policymakers should move away from motivating householders to save energy and should instead focus their efforts on changing energy habits and creating environments that facilitate energy saving behaviour. These findings add to the wider development in social and environmental psychology that emphasizes the importance of extra-personal variables such as the physical environment in shaping behaviour.

Keywords: energy consumption, behavioural modelling, environmental psychology theory, habits, values

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Authors: Scott A. Abbott

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This paper examines the increase in the occurrence of natural disasters worldwide and the need to support vulnerable communities in post-disaster recovery. Preparation and implementation of post-disaster recovery projects need to be improved to allow communities to recover infrastructure, housing, economically and socially following a catastrophe. With the continual rise in catastrophic events worldwide due to climate change, impacts on construction resources affect the ability for post-disaster recovery to be undertaken. This research focuses on case studies of catastrophic events in Queensland, Australia, to contribute to the body of knowledge and gain valuable insights on lessons learned from past events and how they have been managed. The aim of this research is to adopt qualitative data using semi-structured interviews from participants predominantly from the insurance sector to understand barriers that have previously and currently exist in post-disaster recovery. Existing literature was reviewed to reveal gaps in knowledge that needed to be tested. Qualitative data was collected and summarised from field research with the results analysed and discussed. Barriers that impacted post-disaster recovery included time, cost, and resource capability and capacity. Causal themes that impacted time and cost were identified as decision making, pre-planning, and preparedness, as well as effective communication across stakeholders. The research study applied a qualitative approach to the existing literature and case studies across Queensland, Australia, to identify existing and new barriers that impact post-disaster recovery. It was recommended to implement effective procurement strategies to assist in cost control; implement pre-planning and preparedness strategies across funder, contractor, and local governments; more effective and timely decision making to reduce time and cost impacts.

Keywords: construction recovery, cost, disaster recovery, resources, time

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Authors: Levi Corallo

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Typed language made by humans on computers and cell phones has made a significant distinction from previous modes of written language exchanges. While acronyms remain one of the most predominant markings of typed language, another and perhaps more recent revolution in the way humans communicate has been with the use of symbols or glyphs, primarily Emojis—globally introduced on the iPhone keyboard by Apple in 2008. This paper seeks to analyze the use of symbols in typed communication from both a linguistic and machine learning perspective. The Unicode system will be explored and methods of encoding will be juxtaposed with the current machine and human perception. Topics in how typed symbol usage exists in conversation will be explored as well as topics across current research methods dealing with Emojis like sentiment analysis, predictive text models, and so on. This study proposes that sequential analysis is a significant feature for analyzing unicode characters in a corpus with machine learning. Current models that are trying to learn or translate the meaning of Emojis should be starting to learn using bi- and tri-grams of Emoji, as well as observing the relationship between combinations of different Emoji in tandem. The sociolinguistics of an entire new vernacular of language referred to here as ‘typed language’ will also be delineated across my analysis with unicode glyphs from both a semantic and technical perspective.

Keywords: unicode, text symbols, emojis, glyphs, communication

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Authors: Hannes Groeneweg

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Myanmar is considered one of the largest fishing nations in the world. The country’s rapid economic and political reform process since 2011 entails both challenges and opportunities for its marine fishing sector. The development pathway of the sector remains unclear. Which future will eventually materialize is shaped and determined by the various visions and actions of the stakeholders engaging in political debates and decision-making. These visions can be conceptualized through the Science and Technology Studies (STS) concept of sociotechnical imaginaries. The research of this article is guided by the question of which imaginaries are currently relevant, who is propagating these imaginaries, and how are these imaginaries produced and contested. Using qualitative documentary analysis of policy documents, reports, and media articles as well as in-depth interviews with key stakeholders, three archetypical national sociotechnical imaginaries of Myanmar’s future marine fisheries were identified: The industrial scale extractivism imaginary views marine fishing sector as a driver for national economic growth and focuses on the industrial and technological development of the production chain, increasing yield and exports. Sustainable fishing management encompasses the vulnerability of marine ecosystems and views increasing efficient sustainability governance, planning, and management into existing fishing practices. In the traditional sufficiency fishing imaginary, small-scale fishing practices are viewed as an important livelihood practice for millions of coastal dwellers. The need to conserve them through strengthening the self-reliance, autonomy, and resilience of these communities is stressed. In national debates, the first two imaginaries are currently dominant. The imaginaries, as well as their contestations, are also linked to other critical political issues. The paper suggests that participatory decision-making processes are needed to create an inclusive imaginary of the future marine fishing sector.

Keywords: science and technology studies, sociotechnical imaginaries, marine fishing, knowledge coproduction, Myanmar

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Authors: Ali A. Bazzi, Ameer Hamza, Riley O’Hara, Kimberly Kado, Karen H. Hagglund, Lamia Fathallah, Robert T. Morris

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Introduction: Endometrial Cancer is a common gynecologic malignancy primarily treated with complete surgical staging, which may include complete pelvic and para-aortic lymphadenectomy. The role of lymphadenectomy is controversial, especially the intraoperative indications for the procedure. Three factors are important in decision to proceed with lymphadenectomy: Myometrial invasion, maximum tumor dimension, and histology. Many institutions incorporate these criteria in varying degrees in the decision to proceed with lymphadenectomy. This investigation assesses the use of intraoperatively measured MTD with and without pre-operative histologic grade. Methods: This study compared retrospectively EEC patients with intraoperatively measured MTD ≤2 cm to those with MTD >2 cm from January 1, 2002 to August 31, 2017. This assessment compared those with MTD ≤ 2cm with endometrial biopsy (EB) grade 1-2 to patients with MTD > 2cm with EB grade 3. Lymph node metastasis (LNM), recurrence, and survival were compared in these groups. Results: This study reviewed 222 patient cases. In tumors > 2 cm, LNM occurred in 20% cases while in tumors ≤ 2 cm, LNM was found in 6% cases (p=0.04). Recurrence and mean survival based on last follow up visit in these two groups were not statistically different (p=0.78 and 0.36 respectively). Data demonstrated a trend that when combined with preoperative EB International Federation of Gynecology and Obstetrics (FIGO) grade, a higher proportion of patients with EB FIGO Grade 3 and MTD > 2 cm had LNM compared to those with EB FIGO Grade 1-2 and MTD ≤ 2 cm (43% vs, 11%, p=0.06). LNM was found in 15% of cases in which lymphadenectomy was performed based on current practices, whereas if the criteria of EB FIGO 3 and MTD > 2 cm were used the incidence of LNM would have been 44% cases. However, using this criterion, two patients would not have had their nodal metastases detected. Compared to the current practice, the sensitivity and specificity of the proposed criteria would be 60% and 81%, respectively. The PPV and NPV would be 43% and 90%, respectively. Conclusion: The results indicate that MTD combined with EB FIGO grade can detect LNM in a higher proportion of cases when compared to current practice. MTD combined with EB FIGO grade may eliminate the need of frozen section sampling in a substantial number of cases.

Keywords: endometrial cancer, FIGO grade, lymphadenectomy, tumor size

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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: Adil Balla Elkrail

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Visual MODFLOW computer codes were selected to simulate head distribution, calculate the groundwater budgets of the area, and evaluate the effect of external stresses on the groundwater head and to demonstrate how the groundwater model can be used as a comparative technique in order to optimize utilization of the groundwater resource. A conceptual model of the study area, aquifer parameters, boundary, and initial conditions were used to simulate the flow model. The trial-and-error technique was used to calibrate the model. The most important criteria used to check the calibrated model were Root Mean Square error (RMS), Mean Absolute error (AM), Normalized Root Mean Square error (NRMS) and mass balance. The maps of the simulated heads elaborated acceptable model calibration compared to observed heads map. A time length of eight years and the observed heads of the year 2004 were used for model prediction. The predictive simulation showed that the continuation of pumping will cause relatively high changes in head distribution and components of groundwater budget whereas, the low deficit computed (7122 m3/d) between inflows and outflows cannot create a significant drawdown of the potentiometric level. Hence, the area under consideration may represent a high permeability and productive zone and strongly recommended for further groundwater development.

Keywords: aquifers, model simulation, groundwater, calibrations, trail-and- error, prediction

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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: Omar M. Elmabrouk., Roaa Y. Taha., Najat M. Ebrahim, Sabbreen A. Mohammed

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Power transformers are the most crucial part of power electrical system, distribution and transmission grid. This part is maintained using predictive or condition-based maintenance approach. The diagnosis of power transformer condition is performed based on Dissolved Gas Analysis (DGA). There are five main methods utilized for analyzing these gases. These methods are International Electrotechnical Commission (IEC) gas ratio, Key Gas, Roger gas ratio, Doernenburg, and Duval Triangle. Moreover, due to the importance of the transformers, there is a need for an accurate technique to diagnose and hence predict the transformer condition. The main objective of this technique is to avoid the transformer faults and hence to maintain the power electrical system, distribution and transmission grid. In this paper, the DGA was utilized based on the data collected from the transformer records available in the General Electricity Company of Libya (GECOL) which is located in Benghazi-Libya. The Fuzzy Logic (FL) technique was implemented as a diagnostic approach based on IEC gas ratio method. The FL technique gave better results and approved to be used as an accurate prediction technique for power transformer faults. Also, this technique is approved to be a quite interesting for the readers and the concern researchers in the area of FL mathematics and power transformer.

Keywords: dissolved gas-in-oil analysis, fuzzy logic, power transformer, prediction

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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: Florge Francis A. Sy

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Acute pancreatitis (AP) is an inflammatory condition of the pancreas with local and systemic complications. Severe acute pancreatitis (SAP) has a higher mortality rate. Laboratory parameters like the neutrophil-to-lymphocyte ratio (NLR), red cell distribution width (RDW), and mean platelet volume (MPV) have been associated with SAP but with conflicting results. This study aims to determine the predictive value of these parameters on the severity and in-hospital mortality of AP. This retrospective, cross-sectional study was done in a private hospital in Cebu City, Philippines. One-hundred five patients were classified according to severity based on the modified Marshall scoring. The admitting hemogram, including the NLR, RDW, and MPV, was obtained from the complete blood count (CBC). Cut-off values for severity and in-hospital mortality were derived from the ROC. Association between NLR, RDW, and MPV with SAP and mortality were determined with a p-value of < 0.05 considered significant. The mean age for AP was 47.6 years, with 50.5% being male. Most had an unknown cause (49.5%), followed by a biliary cause (37.1%). Of the 105 patients, 23 patients had SAP, and 4 died. Older age, longer in-hospital duration, congestive heart failure, elevated creatinine, urea nitrogen, and white blood cell count were seen in SAP. The NLR was associated with in-hospital mortality using a cut-off of > 10.6 (OR 1.133, 95% CI, p-value 0.003) with 100% sensitivity, 70.3% specificity, 11.76% PPV and 100% NPV (AUC 0.855). The NLR was not associated with SAP. The RDW and MPV were not associated with SAP and mortality. The admitting NLR is, therefore, an easily accessible parameter that can predict in-hospital mortality in acute pancreatitis. Although the present study did not show an association of NLR with SAP nor RDW and MPV with both SAP and mortality, further studies are suggested to establish their clinical value.

Keywords: acute pancreatitis, mean platelet volume, neutrophil-lymphocyte ratio, red cell distribution width

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Authors: Michael Ayodeji Boyede

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Studies have shown that workers’ job performance is very low in Nigeria, especially in the food and beverage industry. This trend had been partially attributed to low workers’ self-efficacy, poor self-knowledge, lack of empathy and poor psychological well-being. The descriptive survey design was adopted. Four factories were purposively selected from three states in Southwestern, Nigeria (Lagos, Ogun and Oyo States). Proportionate random sampling techniques were used in selecting 1,820 junior and supervisory cadre workers in Nestle Plc (369), Coca-Cola Plc (392), Cadbury Plc (443) and Nigeria Breweries (616). The five research instruments used were: Workers’ self-efficacy (r=0.81), Workers’ self-knowledge (r=0.78), Workers’ empathy (r=0.74), Workers’ psychological well-being (r=0.70) and Workers’ performance rating (r=0.72) scales. Quantitative data were analysed using Pearson product moment correlation, Multiple regression at 0.05 level of significance. Findings show that there were significant relationships between Workers’ job performance and self-efficacy (r=.56), self-knowledge (r=.54), Empathy (r=.55) and Psychological Well-being (r=.69) respectively. Self-efficacy, self-knowledge, empathy and psychological well-being jointly predict workers’ job performance (F (4,1815) = 491.05) accounting for 52.0% of its variance. Psychological well-being (B=.52). Self-efficacy (B=.10), self-knowledge (B=.11), empathy (B=. 09) had predictive relative weights on workers’ job performance. Inadequate knowledge and training of the supervisors led to a mismatch of workers thereby reducing workers’ job performance. High self-efficacy, empathy, psychological well-being and good self-knowledge influence workers job performance in the food and beverage industry. Based on the finding employers of labour should provide work environment that would enhance and promote the development of these factors among the workers.

Keywords: self-efficacy, self-knowledge, empathy, psychological well-being, job performance

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Authors: Mirza Mujtaba Baig

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Artificial Intelligence (AI) is evolving rapidly, and one of the areas in which this field has influenced is automation. The automobile, healthcare, education, and robotic industries deploy AI technologies constantly, and the automation of tasks is beneficial to allow time for knowledge-based tasks and also introduce convenience to everyday human endeavors. The paper reviews the challenges faced with the current implementations of autonomous self-driving cars by exploring the machine learning, robotics, and artificial intelligence techniques employed for the development of this innovation. The controversy surrounding the development and deployment of autonomous machines, e.g., vehicles, begs the need for the exploration of the configuration of the programming modules. This paper seeks to add to the body of knowledge of research assisting researchers in decreasing the inconsistencies in current programming modules. Blockchain is a technology of which applications are mostly found within the domains of financial, pharmaceutical, manufacturing, and artificial intelligence. The registering of events in a secured manner as well as applying external algorithms required for the data analytics are especially helpful for integrating, adapting, maintaining, and extending to new domains, especially predictive analytics applications.

Keywords: artificial intelligence, automation, big data, self-driving cars, machine learning, neural networking algorithm, blockchain, business intelligence

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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: Okoh Charity N.

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This study investigated the predictive power of preschoolers’ involvement in indoor and outdoor learning activities on their social learning skills in Niger state, Nigeria. Two research questions and two null hypotheses guided the study. Correlational research design was employed in the study. The population of the study consisted of 8,568 Nursery III preschoolers across the 549 preschools in the five Local Education Authorities in Niger State. A sample of 390 preschoolers drawn through multistage sampling procedure. Two instruments; Preschoolers’ Learning Activities Rating Scale (PLARS) and Preschoolers’ Social Learning Skills Rating Scale (PSLSRS) developed by the researcher were used for data collection. The reliability coefficients obtained for the PLARS and PSLSRS were 0.83 and 0.82, respectively. Data collected were analyzed using simple linear regression. Results showed that 37% of preschoolers’ social learning skills are predicted by their involvement in indoor learning activities, which is statistically significant (p < 0.05). It also shows that 11% of preschoolers’ social learning skills are predicted by their involvement in outdoor learning activities, which is statistically significant (p < 0.05). Therefore, it was recommended among others, that government and school administrators should employ qualified teachers who will stand as role models for preschoolers’ social skills development and provide indoor and outdoor activities and materials for preschoolers in schools.

Keywords: preschooler, social learning, indoor activities, outdoor activities

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Authors: Monica Alzate, Marcos Dono, Jose Manuel Sabucedo

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As long as collective action and its predictors constitute a big body of work in the field of political psychology, context-dependent studies and moral variables are a relatively new issue. The main goal of this presentation is to examine the differences in the predictors of collective action when taking into account two different types of protest, and also focus on the role of moral obligation as a predictor of collective action. To do so, we sampled both protesters and non-protesters from two mobilizations (N=376; N=563) of different nature (catalan Independence, and an 'indignados' march) and performed a logistic regression and a 2x2 MANOVA analysis. Results showed that the predictive variables that were more discriminative between protesters and non-protesters were identity, injustice, efficacy and moral obligation for the catalan Diada and injustice and moral obligation for the 'indignados'. Also while the catalans scored higher in the identification and efficacy variables, the indignados did so in injustice and moral obligation. Differences are evidenced between two types of collective action that coexist within the same protest cycle. The frames of injustice and moral obligation gain strength in the post-2010 mobilizations, a fact probably associated with the combination of materialist and post-materialist values that distinguish the movement. All of this emphasizes the need of studying protest from a contextual point of view. Besides, moral obligation emerges as key predictor of collective action engagement.

Keywords: collective action, identity, moral obligation, protest

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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: Sabah Mohammed, Jinan Fiaidhi, Darien Sawyer

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Data integration of health through connected services can save lives in the event of a medical emergency or provide efficient and effective interventions for the benefit of the patients through the integration of bedside and bench side clinical research. Such integration will support all wind of change in healthcare by being predictive, pre-emptive, personalized, problem-oriented and participatory. Prototyping a healthcare system that enables data integration has been a big challenge for healthcare for a long time. However, an innovative solution started to emerge by focusing on problem lists where everything can connect the problem list forming a growing graph. This notion was introduced by Dr. Lawrence Weed in early 70’s, but the enabling technologies weren’t mature enough to provide a successful implementation prototype. In this article, we are describing our efforts in prototyping Dr. Lawrence Weed's problem-oriented medical record (POMR) and his patient case schema (SOAP) to shape a prototype for connected health. For this, we are using the TypeGraphQL API and our enterprise-based QL4POMR to describe a Web-Based gateway for healthcare services connectivity. Our prototype has reported success in connecting to the HL7 FHIR medical record and the OpenTarget biomedical repositories.

Keywords: connected health, problem-oriented healthcare record, SOAP, QL4POMR, typegraphQL

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Authors: Cheng-yu Hsieh, Bo Zhang, Ahmed Hambaba

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Stock market prediction has long been an area of interest for both expert analysts and investors, driven by its complexity and the noisy, volatile conditions it operates under. This research examines the efficacy of combining the Patch Time Series Transformer (PatchTST) with wavelet transforms, specifically focusing on Haar and Daubechies wavelets, in forecasting the adjusted closing price of the S&P 500 index for the following day. By comparing the performance of the augmented PatchTST models with traditional predictive models such as Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformers, this study highlights significant enhancements in prediction accuracy. The integration of the Daubechies wavelet with PatchTST notably excels, surpassing other configurations and conventional models in terms of Mean Absolute Error (MAE) and Mean Squared Error (MSE). The success of the PatchTST model paired with Daubechies wavelet is attributed to its superior capability in extracting detailed signal information and eliminating irrelevant noise, thus proving to be an effective approach for financial time series forecasting.

Keywords: deep learning, financial forecasting, stock market prediction, patch time series transformer, wavelet transform

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Authors: Philipp Sommer, Amgad Agoub

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The construction sector significantly impacts global CO₂ emissions, particularly through the energy usage of residential buildings. To address this, various governments, including Germany's, are focusing on reducing emissions via sustainable refurbishment initiatives. This study examines the application of machine learning (ML) to estimate energy demands dynamically in residential buildings and enhance the potential for large-scale sustainable refurbishment. A major challenge in Germany is the lack of extensive publicly labeled datasets for energy performance, as energy performance certificates, which provide critical data on building-specific energy requirements and consumption, are not available for all buildings or require on-site inspections. Conversely, England and other countries in the European Union (EU) have rich public datasets, providing a viable alternative for analysis. This research adapts insights from these English datasets to the German context by developing a comprehensive data schema and calibration dataset capable of predicting building energy demand effectively. The study proposes a minimal feature set, determined through feature importance analysis, to optimize the ML model. Findings indicate that ML significantly improves the scalability and accuracy of energy demand forecasts, supporting more effective emissions reduction strategies in the construction industry. Integrating energy performance certificates into municipal heat planning in Germany highlights the transformative impact of data-driven approaches on environmental sustainability. The goal is to identify and utilize key features from open data sources that significantly influence energy demand, creating an efficient forecasting model. Using Extreme Gradient Boosting (XGB) and data from energy performance certificates, effective features such as building type, year of construction, living space, insulation level, and building materials were incorporated. These were supplemented by data derived from descriptions of roofs, walls, windows, and floors, integrated into three datasets. The emphasis was on features accessible via remote sensing, which, along with other correlated characteristics, greatly improved the model's accuracy. The model was further validated using SHapley Additive exPlanations (SHAP) values and aggregated feature importance, which quantified the effects of individual features on the predictions. The refined model using remote sensing data showed a coefficient of determination (R²) of 0.64 and a mean absolute error (MAE) of 4.12, indicating predictions based on efficiency class 1-100 (G-A) may deviate by 4.12 points. This R² increased to 0.84 with the inclusion of more samples, with wall type emerging as the most predictive feature. After optimizing and incorporating related features like estimated primary energy consumption, the R² score for the training and test set reached 0.94, demonstrating good generalization. The study concludes that ML models significantly improve prediction accuracy over traditional methods, illustrating the potential of ML in enhancing energy efficiency analysis and planning. This supports better decision-making for energy optimization and highlights the benefits of developing and refining data schemas using open data to bolster sustainability in the building sector. The study underscores the importance of supporting open data initiatives to collect similar features and support the creation of comparable models in Germany, enhancing the outlook for environmental sustainability.

Keywords: machine learning, remote sensing, residential building, energy performance certificates, data-driven, heat planning

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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

Procedia PDF Downloads 215