Search results for: learning strategies

Authors: Rosa Buxeda, Lorenzo Saliceti-Piazza, Rodolfo J. Romañach, Luis Ríos, Sandra L. Maldonado-Ramírez

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Biopharmaceuticals manufacturing is one of the major economic activities worldwide. Ninety-three percent of the workforce in a biomanufacturing environment concentrates in production-related areas. As a result, strategic collaborations between industry and academia are crucial to ensure the availability of knowledgeable workforce needed in an economic region to become competitive in biomanufacturing. In the past decade, our institution has been a key strategic partner with multinational biotechnology companies in supplying science and engineering graduates in the field of industrial biotechnology. Initiatives addressing all levels of the educational pipeline, from K-12 to college to continued education for company employees have been established along a ten-year span. The Amgen BioTalents Program was designed to provide undergraduate science and engineering students with training in biomanufacturing. The areas targeted by this educational program enhance their academic development, since these topics are not part of their traditional science and engineering curricula. The educational curriculum involved the process of producing a biomolecule from the genetic engineering of cells to the production of an especially targeted polypeptide, protein expression and purification, to quality control, and validation. This paper will report and describe the implementation details and outcomes of the first sessions of the program.

Keywords: biomanufacturing curriculum, interdisciplinary learning, workforce development, industry-academia partnering

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Authors: Fae Princess Bermudez

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Background: One of the least significant leads on a 12-lead electrocardiogram is the augmented right lead (aVR), as it is not as specific compared to the other leads. In this case series, the value of lead aVR, which is more often than not ignored, is highlighted. Three cases of aVR ST segment elevation on 12-lead electrocardiogram are described, with the end outcome of demise of all three patients. The importance of immediate revascularization is described to improve prognosis in this group of patients. Objectives: This case series aims to primarily present under-reported cases of isolated aVR ST-segrment elevation myocardial infarction (STEMI), their course and outcome. More specific aims are to identify the criteria in determination of isolated aVR STEMI, know its clinical significance, and determine appropriate management for patients with this ECG finding. Method: A short review of previous studies, case reports, articles and guidelines from 2011-2016 was done. The author reviewed available literature, sorted out those that proved to be significant for the presented cases, and described them in conjunction with the aforementioned cases. Findings: Based on the limited information on these rare or under-reported cases, it was found that isolated aVR STEMI had a poorer prognosis that led to significant mortality and morbidity of patients. The significance of aVR ST-elevation was that of an occlusion of the left coronary artery or a severe three-vessel disease in the presence of an Acute Coronary Syndrome. Guidelines from American Heart Association/American College of Cardiology Foundation in 2013 already recognized ST-elevation of lead aVR in isolation as a STEMI; hence, recommended that patients with this particular ECG finding should undergo reperfusion strategies to improve prognosis. Conclusion: The indispensability of isolated aVR ST-segment elevation on ECG should alert physicians, especially Emergency physicians, to the high probability of Acute Coronary Syndrome with a very poor prognosis. If this group of patients is not promptly managed, demise may ensue, with cardiogenic shock as the most probable cause. With this electrocardiogram finding, physicians must be quick to make clinical decisions to increase chances of survival of this group of patients.

Keywords: AVR ST-elevation, diffuse ST-segment depression, left coronary artery infarction, myocardial infarction

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Authors: Abhimanyu Pati, Krishna Kumar Veluri

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The paper intends to bring out a real life experience encountered during actual implementation of a large scale Tier-1 Enterprise Resource Planning (ERP) system in a multi-location, discrete manufacturing organization in India, involved in manufacturing of auto components and aggregates. The business complexities, prior to the implementation of ERP, include multi-product with hierarchical product structures, geographically distributed multiple plant locations with disparate business practices, lack of inter-plant broadband connectivity, existence of disparate legacy applications for different business functions, and non-standardized codifications of products, machines, employees, and accounts apart from others. On the other hand, the manufacturing environment consisted of processes like Assemble-to-Order (ATO), Make-to-Stock (MTS), and Engineer-to-Order (ETO) with a mix of discrete and process operations. The paper has highlighted various business plan areas and concerns, prior to the implementation, with specific focus on strategic issues and objectives. Subsequently, it has dealt with the complete process of ERP implementation, starting from strategic planning, project planning, resource mobilization, and finally, the program execution. The step-by-step process provides a very good learning opportunity about the implementation methodology. At the end, various organizational challenges and lessons emerged, which will act as guidelines and checklist for organizations to successfully align and implement ERP and achieve their business objectives.

Keywords: ERP, ATO, MTS, ETO, discrete manufacturing, strategic planning

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Authors: Christopher A. Igene, Jonathan O. Ighodalo

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Youths play a critical role in the agricultural transformation of any developing nation such as Nigeria. Hence, the preparation of any nation for productive life depends on the policies and programmes designed for its youths. Studies have shown that children and youths contribute significantly in agricultural activities. Youths have vigour and prone to physical work, they constitute a great percentage of labour force in the country. It is of necessity that every policy on national development must of necessity take cognizance of the youths. Hence, the focus on youths in agricultural extension outreaches most especially, the young farmers club. It is an out-of-school education in agriculture and home economics for rural youth through learning by doing. Young farmers club in schools enables the young to learn and acquire those attributes that will enable them grown into useful and mature adult. There appears to be numerous constrains in the use of youths in extension, they are inadequate personnel, poor funding of agricultural sector, poor marketing channels, lack of good roads, others are poor input and lack of information. However, there is a need for Agricultural Development Programme (ADP) to organize workshop for secondary students and agricultural science teachers, schools to organize seminars and workshops for secondary schools who are members of Young Farmers Club (YFC). ADP should also organize agricultural show to encourage students to be members of Young Farmers Club (YFC).

Keywords: agricultural extension, agricultural role, students, youths, young farmers club (YFC)

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Authors: Lintle Mohase, Ninikoe Lebusa, Mpho Stephen Mafa

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Wheat is an economically important cereal crop. However, the changing climatic conditions that intensify drought in production areas, and additional pest infestation, such as the Russian wheat aphid (RWA, Diuraphis noxia), severely hamper its production. Drought and pest management require an additional water supply through irrigation and applying inorganic nutrients (including silicon) as alternative strategies to mitigate the stress effects. Therefore, other approaches are needed to enhance wheat productivity during drought stress and aphid abundance. Two wheat cultivars were raised under greenhouse conditions, exposed to drought stress, and treated with silicon before infestation with the South African RWA biotype 2 (RWASA2). The morphological evaluations showed that severe drought or a combination of drought and infestation significantly reduced the plant height of wheat cultivars. Silicon treatment did not alleviate the growth reduction. The biochemical responses were measured using spectrophotometric assays with specific substrates. An evaluation of the enzyme activities associated with oxidative stress and defence responses indicated that drought stress increased NADPH oxidase activity, while silicon treatment significantly reduced it in drought-stressed and infested plants. At 48 and 72 hours sampling periods, a combination of silicon, drought and infestation treatment significantly increased peroxidase activity compared to drought and infestation treatment. The treatment also increased β-1,3-glucanase activity 72 hours after infestation. In addition, silicon and drought treatment increased glucose but reduced sucrose accumulation. Furthermore, silicon, drought, and infestation treatment combinations reduced the sucrose content. Finally, silicon significantly increased the trehalose content under severe drought and infestation, evident at 48 and 72-hour sampling periods. Our findings shed light on silicon’s ability to induce protective biochemical responses during drought and aphid infestation.

Keywords: drought, enzyme activity, silicon, soluble sugars, Russian wheat aphid, wheat

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Authors: Yaser E. Greish, Emad F. Hindawy, Maryam S. Al Nehayan

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Introductory courses such as General Chemistry I, General Physics I and General Biology need special attention as students taking these courses are usually at their first year of the university. In addition to the language barrier for most of them, they also face other difficulties if these elementary courses are taught in the traditional way. Changing the routine method of teaching of these courses is therefore mandated. In this regard, podcasting of chemistry lectures was used as an add-on to the traditional and non-traditional methods of teaching chemistry to science and non-science students. Podcasts refer to video files that are distributed in a digital format through the Internet using personal computers or mobile devices. Pedagogical strategy is another way of identifying podcasts. Three distinct teaching approaches are evident in the current literature and include receptive viewing, problem-solving, and created video podcasts. The digital format and dispensing of video podcasts have stabilized over the past eight years, the type of podcasts vary considerably according to their purpose, degree of segmentation, pedagogical strategy, and academic focus. In this regard, the whole syllabus of 'General Chemistry I' course was developed as podcasts and were delivered to students throughout the semester. Students used the podcasted files extensively during their studies, especially as part of their preparations for exams. Feedback of students strongly supported the idea of using podcasting as it reflected its effect on the overall understanding of the subject, and a consequent improvement of their grades.

Keywords: podcasting, introductory course, interactivity, flipped classroom

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Authors: Colin Smith, Linsey S Passarella

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Due to the proliferation and decentralized nature of academic publication, no widely accepted scheme exists for organizing papers by their scientific field of study (FoS) to the author’s best knowledge. While many academic journals require author provided keywords for papers, these keywords range wildly in scope and are not consistent across papers, journals, or field domains, necessitating alternative approaches to paper classification. Past attempts to perform field-of-study (FoS) classification on scientific texts have largely used a-hierarchical FoS schemas or ignored the schema’s inherently hierarchical structure, e.g. by compressing the structure into a single layer for multi-label classification. In this paper, we introduce an application of a Layered Neural Network (LNN) to the problem of performing supervised hierarchical classification of scientific fields of study (FoS) on research papers. In this approach, paper embeddings from a pretrained language model are fed into a top-down LNN. Beginning with a single neural network (NN) for the highest layer of the class hierarchy, each node uses a separate local NN to classify the subsequent subfield child node(s) for an input embedding of concatenated paper titles and abstracts. We compare our LNN-FOS method to other recent machine learning methods using the Microsoft Academic Graph (MAG) FoS hierarchy and find that the LNN-FOS offers increased classification accuracy at each FoS hierarchical level.

Keywords: hierarchical classification, layer neural network, scientific field of study, scientific taxonomy

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Authors: Naijin Wu, Peizhong Li, Haijian Wang, Wenxia Wei, Yun Song

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Chlorinated aliphatic hydrocarbons (CAHs) pollution poses a severe threat to human health and is persistent in groundwater. Although chemical reduction or bioremediation is effective, it is still hard to achieve their complete and rapid dechlorination. Recently, the combination of zero-valent iron and biostimulation has been considered to be one of the most promising strategies, but field studies of this technology are scarce. In a typical site contaminated by various types of CAHs, basic physicochemical parameters of groundwater, CAHs and their product concentrations, and microbial abundance and diversity were monitored after a remediation slurry containing both micron zero-valent iron (mZVI) and biostimulation components were directly injected into the aquifer. Results showed that groundwater could form and keep low oxidation-reduction potential (ORP), a neutral pH, and anoxic conditions after different degrees of fluctuations, which was benefit for the reductive dechlorination of CAHs. The injection also caused an obvious increase in the total organic carbon (TOC) concentration and sulfate reduction. After 253 days post-injection, the mean concentration of total chlorinated ethylene (CEE) from two monitoring wells decreased from 304 μg/L to 8 μg/L, and total chlorinated ethane (CEA) decreased from 548 μg/L to 108 μg/L. Occurrence of chloroethane (CA) suggested that hydrogenolysis dechlorination was one of the main degradation pathways for CEA, and also hints that biological dechlorination was activated. A significant increase of ethylene at day 67 post-injection indicated that dechlorination was complete. Additionally, the total bacterial counts increased by 2-3 orders of magnitude after 253 days post-injection. And the microbial species richness decreased and gradually changed to anaerobic/fermentative bacteria. The relative abundance of potential degradation bacteria increased corresponding to the degradation of CAHs. This work demonstrates that mZVI and biostimulation can be combined to achieve the efficient removal of various CAHs from contaminated groundwater sources.

Keywords: chlorinated aliphatic hydrocarbons, groundwater, field study, zero-valent iron, biostimulation

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Authors: Radwa Elshorbagy, Alaa Elden Balbaa, Khaled Ayad, Waleed Reda

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Introduction: Ankle sprain is a common lower limb injury that is complicated by high recurrence rate. The cause of recurrence is not clear; however, changes in motor control have been postulated. Objective: to determine the contribution of proximal hip strategy to dynamic postural control in patients with recurrent ankle sprain. Methods: Fifteen subjects with recurrent ankle sprain (group A) and fifteen healthy control subjects (group B) participated in this study. Abductor-adductors as well as flexor-extensor hip musculatures control was abolished by fatigue using the Biodex Isokinetic System. Dynamic postural control was measured before and after fatigue by the Biodex Balance System. Results: Repeated measures MANOVA was used to compare between and within group differences, in group A fatiguing of hip muscles (flexors-extensors and abductors-adductors) increased overall stability index (OASI), anteroposterior stability index (APSI) and mediolateral stability index (MLSI) significantly (p=0.00) whereas; in group B fatiguing of hip flexors-extensors increased significantly OASI and APSI only (p= 0.017, 0.010; respectively) while fatiguing of hip abductors-adductors has no significant effect on these variables. Moreover, patients with ankle sprain had significantly lower dynamic balance after hip muscles fatigue compared to the control group. Specifically, after hip flexor-extensor fatigue, the OASI, APSI and MLSI were increased significantly than those of the control values (p= 0.002, 0.011, and 0.003, respectively) whereas fatiguing of hip abductors-adductors increased significantly in OASI and APSI only (p=0.012, 0.026, respectively). Conclusion: To maintain dynamic balance, patients with recurrent ankle sprain seem to rely more on the hip strategy. This means that those patients depend on a top to down instead of down to top strategy clinical relevance: patients with recurrent ankle sprain less efficient in maintaining the dynamic postural control due to the change in motor strategies. Indicating that health care providers and rehabilitation specialists should treat CAI as a global/central and not just as a simple local or peripheral injury.

Keywords: hip strategy, ankle strategy, postural control, dynamic balance

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Authors: S. Cobos-Guzman

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This paper presents the results of a mechatronic design based on a 4-wheel omnidirectional mobile robot that can be used in indoor logistic applications. The low-level control has been selected using two open-source hardware (Raspberry Pi 3 Model B+ and Arduino Mega 2560) that control four industrial motors, four ultrasound sensors, four optical encoders, a vision system of two cameras, and a Hokuyo URG-04LX-UG01 laser scanner. Moreover, the system is powered with a lithium battery that can supply 24 V DC and a maximum current-hour of 20Ah.The Robot Operating System (ROS) has been implemented in the Raspberry Pi and the performance is evaluated with the selection of the sensors and hardware selected. The mechatronic system is evaluated and proposed safe modes of power distribution for controlling all the electronic devices based on different tests. Therefore, based on different performance results, some recommendations are indicated for using the Raspberry Pi and Arduino in terms of power, communication, and distribution of control for different devices. According to these recommendations, the selection of sensors is distributed in both real-time controllers (Arduino and Raspberry Pi). On the other hand, the drivers of the cameras have been implemented in Linux and a python program has been implemented to access the cameras. These cameras will be used for implementing a deep learning algorithm to recognize people and objects. In this way, the level of intelligence can be increased in combination with the maps that can be obtained from the laser scanner.

Keywords: autonomous, indoor robot, mechatronic, omnidirectional robot

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Authors: Zahow Muoftah

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Traditional manual visual grading of fruits has been one of the agricultural industry’s major challenges due to its laborious nature as well as inconsistency in the inspection and classification process. The main requirements for computer vision and visual processing are some effective techniques for identifying defects and estimating defect areas. Automated defect detection using computer vision and machine learning has emerged as a promising area of research with a high and direct impact on the visual inspection domain. Grading, sorting, and disease detection are important factors in determining the quality of fruits after harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have been conducted to identify diseases and pests that affect the fruits of agricultural crops. However, most previous studies concentrated solely on the diagnosis of a lesion or disease. This study focused on a comprehensive study to identify pests and diseases of apple and banana fruits using detection and classification defects on Banana and Apples by Computer Vision. As a result, the current article includes research from these domains as well. Finally, various pattern recognition techniques for detecting apple and banana defects are discussed.

Keywords: computer vision, banana, apple, detection, classification

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Authors: Oliver Ikechukwu Inyiama, Boniface Uche Ugwuanyi

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The aim of the research is to evaluate the key financial performance indicators that help both managers and their shareholders of Nigerian Banks to determine the appropriate dividend payout to their ordinary shareholders in an accounting year. Profitability, total asset, and earnings of commercial banks were selected as key performance indicators in Nigeria Banking Sector. They represent the independent variables of the study while dividend per share is the proxy for the dividend paid on ordinary shares which represent the dependent variable. The effect of profitability, total asset and earnings on dividend per share were evaluated through the ordinary least square method of multiple regression analysis. Test for normality of frequency distribution was conducted through descriptive statistics such as Jacque Bera Statistic, skewness and kurtosis. Rate of dividend payout was subsequently applied as an alternate dependent variable to test for robustness of the earlier results. The 64% adjusted R-squared of the pooled data indicates that profitability, total asset, and earnings explain the variation in dividend per share during the period under research while the remaining 36% variation in dividend per share could be explained by changes in other variables not captured by this study as well as the error term. The study concentrated on four leading Nigeria Commercial Banks namely; First Bank of Nigeria Plc, GTBank Plc, United Bank for Africa Plc and Zenith International Bank Plc. Dividend per share was found to be positively affected by total assets and earnings of the commercial banks. However, profitability which was proxied by profit after tax had a negative effect on dividend per share. The implication of the findings is that commercial banks in Nigeria pay more dividend when they are having a dwindling fortune in order to retain the confidence of the shareholders provided their gross earnings and size is on the increase. Therefore, the management and board of directors of Nigeria commercial banks should apply decent marketing strategies to enhance earnings through investment in profitable ventures for an improved dividend payout rate.

Keywords: assets, banks, indicators, performance, profitability, shares

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Authors: Misbah Javaid, Vladimir Estivill-Castro, Rene Hexel

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The field of robotics is advancing rapidly to the point where robots have become an integral part of the modern society. These robots collaborate and contribute productively with humans and compensate some shortcomings from human abilities and complement them with their skills. Effective teamwork of humans and robots demands to investigate the critical issue of trust. The field of human-computer interaction (HCI) has already examined trust humans place in technical systems mostly on issues like reliability and accuracy of performance. Early work in the area of expert systems suggested that automatic generation of explanations improved trust and acceptability of these systems. In this work, we augmented a robot with the user-invoked explanation generation proficiency. To measure explanations effect on human’s level of trust, we collected subjective survey measures and behavioral data in a human-robot team task into an interactive, adversarial and partial information environment. The results showed that with the explanation capability humans not only understand and recognize robot as an expert team partner. But, it was also observed that human's learning and human-robot team performance also significantly improved because of the meaningful interaction with the robot in the human-robot team. Moreover, by observing distinctive outcomes, we expect our research outcomes will also provide insights into further improvement of human-robot trustworthy relationships.

Keywords: explanation interface, adversaries, partial observability, trust building

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Authors: Rodolfo Lorbieski, Silvia Modesto Nassar

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Many supervised machine learning tasks require decision making across numerous different classes. Multi-class classification has several applications, such as face recognition, text recognition and medical diagnostics. The objective of this article is to analyze an adapted method of Stacking in multi-class problems, which combines ensembles within the ensemble itself. For this purpose, a training similar to Stacking was used, but with three levels, where the final decision-maker (level 2) performs its training by combining outputs from the tree-based pair of meta-classifiers (level 1) from Bayesian families. These are in turn trained by pairs of base classifiers (level 0) of the same family. This strategy seeks to promote diversity among the ensembles forming the meta-classifier level 2. Three performance measures were used: (1) accuracy, (2) area under the ROC curve, and (3) time for three factors: (a) datasets, (b) experiments and (c) levels. To compare the factors, ANOVA three-way test was executed for each performance measure, considering 5 datasets by 25 experiments by 3 levels. A triple interaction between factors was observed only in time. The accuracy and area under the ROC curve presented similar results, showing a double interaction between level and experiment, as well as for the dataset factor. It was concluded that level 2 had an average performance above the other levels and that the proposed method is especially efficient for multi-class problems when compared to binary problems.

Keywords: stacking, multi-layers, ensemble, multi-class

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Authors: Emily Injete Amondo, Alisher Mirzabaev, Emmanuel Rukundo

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Children in rural farming households are often vulnerable to a multitude of risks, including health risks associated with climate change and variability. Cognizant of this, this study empirically traced the relationship between climate variability and nutritional health outcomes in rural children while identifying the cause-and-effect transmission mechanisms. We combined four waves of the rich Uganda National Panel Survey (UNPS), part of the World Bank Living Standards Measurement Studies (LSMS) for the period 2009-2014, with long-term and high-frequency rainfall and temperature datasets. Self-reported drought and flood shock variables were further used in separate regressions for triangulation purposes and robustness checks. Panel fixed effects regressions were applied in the empirical analysis, accounting for a variety of causal identification issues. The results showed significant negative outcomes for children’s anthropometric measurements due to the impacts of moderate and extreme droughts, extreme wet spells, and heatwaves. On the contrary, moderate wet spells were positively linked with nutritional measures. Agricultural production and child diarrhea were the main transmission channels, with heatwaves, droughts, and high rainfall variability negatively affecting crop output. The probability of diarrhea was positively related to increases in temperature and dry spells. Results further revealed that children in households who engaged in ex-ante or anticipatory risk-reducing strategies such as savings had better health outcomes as opposed to those engaged in ex-post coping such as involuntary change of diet. These results highlight the importance of adaptation in smoothing the harmful effects of climate variability on the health of rural households and children in Uganda.

Keywords: extreme weather events, undernutrition, diarrhea, agricultural production, gridded weather data

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Authors: Muftahu Jibirin Salihu, Hazri Jamil

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The persisting challenge of access and enrolment to higher education in commonwealth countries has been reported in several studies, including reports of the international organization such as World Bank, UNESCO among others however from the macro perspective. The overarching aim of this study is to examine alternative policy practices towards improving access to university education in Nigeria at meso level of policy practice from evidence base policy studies using one university as a case. The study adopted a qualitative approach to gain insightful understanding on the issue of the study employing a semi-structure interview and policy documents as the means for obtaining the data and other relevant information for the study. The participants of the study were purposively chosen which comprise of a number of individuals from the selected university and other related organization which responsible for the policies development and implementation of Nigerian higher education system. From the findings of the study, several initiatives have been taken at meso level to address this challenge including the introduction of the University Matriculation Program as an alternative route for enhancing to access to the university education. However, the study further provided a number of recommendations which aimed at improving access to university education such as improving the entry requirements, society orientation on university education and the issue of ranking of certificate among the Nigerian higher institutions of learning.

Keywords: policy practice, access, enrolment, university, education, Nigeria

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Authors: Crispin Rakibu Mbamba

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Twenty-two years after the adoption of the United Nation Trafficking Protocol, evidence suggest that child trafficking continues to rise. Community level factors, like poverty which creates the conditions for children’s vulnerability is key to the rise in trafficking cases in Ghana. Albeit, growing evidence suggestthat despite the vulnerabilities, communities have the capacity to prevent and address child trafficking issues. This study contributes to this positive agenda by exploring the ways in which communities (and the key actors) in Ghana contribute to child trafficking interventions.The study objective is explored through in-depth interviews with practitioners (including social workers) from an organization working in trafficking hotspots in Ghana. Interviews wereanalyzed thematically with the help of HyperRESEARCH software. From the in-depth interviews, three themes were identified as the ways in which communities are involved in child trafficking interventions: 1) engagement of community leaders, 2) community-led anti-trafficking committees and 3) knowledge about trafficking. Albeit the cultural differences, evidence on the instrumental role of community chiefs and leaders provide important learning on how to harness trafficking intervention measures and ensure better child protection practices. Based on the findings, we recommend the need to intensify trafficking awareness campaigns in rural communities where education is lacking to contribute to United Nations (UN) promoting Just, Peaceful and Inclusive societies’ mandate.

Keywords: child trafficking, community interventions, knowledge on trafficking, human trafficking intervention

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Authors: Eyasu Markos Woldesemayat, Paolo Vincenzo Genovese

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In recent years with an increase in the concentration of Green House Gases (GHG), Climate Change (CC) externalities are mounting, regardless of governments, are scrambling to implement mitigation and adaptation measures. With multiple social, economic and environmental benefits, Green Infrastructure (GI) has evolved as a highly valuable policy tool to promote sustainable development and smart growth by meeting multiple objectives towards quality of life. However, despite the wide range of benefits, it's uptake in African cities such as Addis Ababa is very low due to several constraining factors. This study, through content analysis and key informant interviews, examined barriers for the uptake of GI among spatial planners in Addis Ababa. Added to this, the study has revealed that the spatial planners had insufficient knowledge about GI planning principles such as multi-functionality, integration, and connectivity, and multiscale. The practice of implementing these holistic principles in urban spatial planning is phenomenally nonexistent. The findings also revealed 20 barriers categorized under four themes, i.e., institutional, socio-cultural, resource, and cognitive barriers. Similarly, it was identified that institutional barriers (0.756), socio-cultural barriers (0.730), cognitive barriers (0.700) and resource barriers (0.642), respectively, are the foremost impending factors for the promotion of GI in Addis Ababa. It was realized that resource barriers were the least constraining factor for enshrining the GI uptake in the city. Strategies to hasten the adoption of GI in the city mainly focus on improving political will, harmonization sectorial plans, improve spatial planning and implementation practice, prioritization of GI in all planning activities, enforcement of environmental laws, introducing collaborative GI governance, creating strong and stable institutions and raising awareness on the need to conserve environment and CC externalities through education and outreach mechanisms.

Keywords: Addis Ababa, climate change, green infrastructure, spatial planning, spatial planners

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Authors: Shohei Maruyama, Yasuo Matsuyama, Sachiyo Aburatani

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The development of the method to annotate unknown gene functions is an important task in bioinformatics. One of the approaches for the annotation is The identification of the metabolic pathway that genes are involved in. Gene expression data have been utilized for the identification, since gene expression data reflect various intracellular phenomena. However, it has been difficult to estimate the gene function with high accuracy. It is considered that the low accuracy of the estimation is caused by the difficulty of accurately measuring a gene expression. Even though they are measured under the same condition, the gene expressions will vary usually. In this study, we proposed a feature extraction method focusing on the variability of gene expressions to estimate the genes' metabolic pathway accurately. First, we estimated the distribution of each gene expression from replicate data. Next, we calculated the similarity between all gene pairs by KL divergence, which is a method for calculating the similarity between distributions. Finally, we utilized the similarity vectors as feature vectors and trained the multiclass SVM for identifying the genes' metabolic pathway. To evaluate our developed method, we applied the method to budding yeast and trained the multiclass SVM for identifying the seven metabolic pathways. As a result, the accuracy that calculated by our developed method was higher than the one that calculated from the raw gene expression data. Thus, our developed method combined with KL divergence is useful for identifying the genes' metabolic pathway.

Keywords: metabolic pathways, gene expression data, microarray, Kullback–Leibler divergence, KL divergence, support vector machines, SVM, machine learning

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Authors: Lun Lou, Jintu Fan

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While personal protective equipment (PPE) prevents the healthcare personnel from exposing to harmful surroundings, it creates a barrier to the dissipation of body heat and perspiration, leading to severe heat stress during prolonged exposure, especially in hot environments. It has been found that most of the existed personal cooling strategies have limitations in achieving effective cooling performance with long duration and lightweight. This work aimed to develop a lightweight (<1.0 kg) and less expensive wearable air cooling and dehumidifying system (WCDS) that can be applied underneath the protective clothing and provide 50W mean cooling power for more than 5 hours at 35°C environmental temperature without compromising the protection of PPE. For the WCDS, blowers will be used to activate an internal air circulation inside the clothing microclimate, which doesn't interfere with the protection of PPE. An air cooling and dehumidifying chamber (ACMR) with a specific design will be developed to reduce the air temperature and humidity inside the protective clothing. Then the cooled and dried air will be supplied to upper chest and back areas through a branching tubing system for personal cooling. A detachable ice cooling unit will be applied from the outside of the PPE to extract heat from the clothing microclimate. This combination allows for convenient replacement of the cooling unit to refresh the cooling effect, which can realize a continuous cooling function without taking off the PPE or adding too much weight. A preliminary thermal manikin test showed that the WCDS was able to reduce the microclimate temperature inside the PPE averagely by about 8°C for 60 minutes when the environmental temperature was 28.0 °C and 33.5 °C, respectively. Replacing the ice cooling unit every hour can maintain this cooling effect, while the longest operation duration is determined by the battery of the blowers, which can last for about 6 hours. This unique design is especially helpful for the PPE users, such as health care workers in infectious and hot environments when continuous cooling and dehumidifying are needed, but the change of protective clothing may increase the risk of infection. The new WCDS will not only improve the thermal comfort of PPE users but can also extend their safe working duration.

Keywords: personal thermal management, heat stress, ppe, health care workers, wearable device

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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In recent years, there has been a desire to forecast student academic achievement prior to graduation. This is to help them improve their grades, particularly for individuals with poor performance. The goal of this study is to employ supervised learning techniques to construct a predictive model for student academic achievement. Many academics have already constructed models that predict student academic achievement based on factors such as smoking, demography, culture, social media, parent educational background, parent finances, and family background, to name a few. This feature and the model employed may not have correctly classified the students in terms of their academic performance. This model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester as a prerequisite to predict if the student will perform well in future on related courses. The model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost, returning a 96.7% accuracy. This model is available as a desktop application, allowing both instructors and students to benefit from user-friendly interfaces for predicting student academic achievement. As a result, it is recommended that both students and professors use this tool to better forecast outcomes.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Sai Sankalp Vemavarapu

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This paper discusses the application of machine learning in the field of transportation engineering for predicting engineering properties of pavement more accurately and efficiently. Predicting the elastic properties aid us in assessing the current road conditions and taking appropriate measures to avoid any inconvenience to commuters. This improves the longevity and sustainability of the pavement layer while reducing its overall life-cycle cost. As an example, we have implemented differential evolution (DE) in the back-calculation of the elastic modulus of multi-layered pavement. The proposed DE global optimization back-calculation approach is integrated with a forward response model. This approach treats back-calculation as a global optimization problem where the cost function to be minimized is defined as the root mean square error in measured and computed deflections. The optimal solution which is elastic modulus, in this case, is searched for in the solution space by the DE algorithm. The best DE parameter combinations and the most optimum value is predicted so that the results are reproducible whenever the need arises. The algorithm’s performance in varied scenarios was analyzed by changing the input parameters. The prediction was well within the permissible error, establishing the supremacy of DE.

Keywords: cost function, differential evolution, falling weight deflectometer, genetic algorithm, global optimization, metaheuristic algorithm, multilayered pavement, pavement condition assessment, pavement layer moduli back calculation

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Authors: Ali Mohammed Yimer, Ayalew Assen, Youssef Belmabkhout

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Environmental remediation is a pressing global concern, necessitating innovative strategies to address the challenges posed by industrial waste and pollution. This study aims to advance environmental remediation by developing cutting-edge functional porous materials from phosphorite residue tailings. Phosphorite mining activities generate vast amounts of waste, which pose significant environmental risks due to their contaminants. The proposed approach involved transforming these phosphorite residue tailings into valuable porous materials through a series of physico-chemical processes including milling, acid-base leaching, designing or templating as well as formation processes. The key components of the tailings were extracted and processed to produce porous arrays with high surface area and porosity. These materials were engineered to possess specific properties suitable for environmental remediation applications, such as enhanced adsorption capacity and selectivity for target contaminants. The synthesized porous materials were thoroughly characterized using advanced analytical techniques (XRD, SEM-EDX, N2 sorption, TGA, FTIR) to assess their structural, morphological, and chemical properties. The performance of the materials in removing various pollutants, including heavy metals and organic compounds, were evaluated through batch adsorption experiments. Additionally, the potential for material regeneration and reusability was investigated to enhance the sustainability of the proposed remediation approach. The outdoors of this research holds significant promise for addressing the environmental challenges associated with phosphorite residue tailings. By valorizing these waste materials into porous materials with exceptional remediation capabilities, this study contributes to the development of sustainable and cost-effective solutions for environmental cleanup. Furthermore, the utilization of phosphorite residue tailings in this manner offers a potential avenue for the remediation of other contaminated sites, thereby fostering a circular economy approach to waste management.

Keywords: functional porous materials, phosphorite residue tailings, adsorption, environmental remediation, sustainable solutions

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Authors: Aditya Jayadas

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During a pandemic like the one we are in with COVID-19, older adults, especially those who live in a senior retirement facility, experience even bigger challenges as they are often dependent on other individuals for care. Many older adults are dependent on caregivers to assist with their instrumented activities of daily living (IADL). With travel restrictions imposed during a pandemic, there is a critical need to ensure that older adults who are homebound continue to be able to participate in physical exercise, cognitive exercise, and social interaction programs. The objective of this study was to better understand the challenges that older adults faced during the pandemic and what they were doing specifically to cope with the pandemic physically, mentally, and through social interaction. A focus group was conducted with ten older adults (age: 82.70 ± 7.81 years; nine female and one male) who resided in a senior retirement facility. During the course of one hour, seven open-ended questions were posed to the participants: a) What has changed in your life since the start of the pandemic, b) What has been most challenging for you, c) What are you doing to take care of yourself, d) Are you doing anything specifically as it relates to your physical health, e) Are you doing anything specifically as it relates to your mental health, f) What did you do for social interaction during the pandemic, g) Is there anything else you would like to share as it relates to your experience during the pandemic. The focus group session was audio-taped, and verbatim transcripts were created to evaluate the responses of the participants. The transcript consisted of 4,698 words and 293 lines of text. The data was analyzed using content analysis. The unit of analysis was the text from the audio recordings that were transcribed. From the review of the transcribed text, themes and sub-themes were identified, along with salient quotes under each sub-theme. The major themes that emerged from the data were: having a routine, engaging in activities, attending exercise classes, use of technology, family, community, and prayer. The quotes under the sub-themes provided compelling evidence of how older adults coped during the pandemic while addressing the challenges they faced and developing strategies to address their physical and mental health while interacting with others. Lessons learned from this focus group can be used to develop specific physical exercise, cognitive exercise, and social interaction programs that benefit the health and well-being of older adults.

Keywords: cognitive exercise, pandemic, physical exercise, social interaction

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Authors: Raymond Xu, Cindy Jingru Wang

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Social media has become a globally connected and influencing platform. Social media data, such as tweets, can help predict the spread of pandemics and provide individuals and healthcare providers early warnings. Public psychological reactions and opinions can be efficiently monitored by AI models on the progression of dominant topics on Twitter. However, statistics show that as the coronavirus spreads, so does an infodemic of misinformation due to pandemic-related factors such as unemployment and lockdowns. Social media algorithms are often biased toward outrage by promoting content that people have an emotional reaction to and are likely to engage with. This can influence users’ attitudes and cause confusion. Therefore, social media is a double-edged sword. Combating fake news and biased content has become one of the essential tasks. This research analyzes the variety of methods used for fake news detection covering random forest, logistic regression, support vector machines, decision tree, naive Bayes, BoW, TF-IDF, LDA, CNN, RNN, LSTM, DeepFake, and hierarchical attention network. The performance of each method is analyzed. Based on these models’ achievements and limitations, a multi-dimensional AI framework is proposed to achieve higher accuracy in infodemic detection, especially pandemic-related news. The model is trained on contextual content, images, and news metadata.

Keywords: artificial intelligence, fake news detection, infodemic detection, image recognition, sentiment analysis

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Authors: Valentine C. Eze, Adam P. Harvey

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Contamination of groundwater and soil by heavy metals and metalloids through anthropogenic activities and natural occurrence poses serious environmental challenges globally. A possible solution to this problem is through phytoremediation of the contaminants using hyper-accumulating plants. Conventional phytoremediation treats the contaminated hyper-accumulator biomass as a waste stream which adds no value to the heavy metal(loid)s decontamination process. This study investigates strategies for remediation of soil contaminated with arsenic and the extractive chemical routes for recovery of arsenic and phosphorus from the hyper-accumulator biomass. Pteris cretica ferns species were investigated for their uptake of arsenic from soil containing 200 ± 3ppm of arsenic. The Pteris cretica ferns were shown to be capable of hyper-accumulation of arsenic, with maximum accumulations of about 4427 ± 79mg to 4875 ± 96mg of As per kg of the dry ferns. The arsenic in the Pteris cretica fronds was extracted into various solvents, with extraction efficiencies of 94.3 ± 2.1% for ethanol-water (1:1 v/v), 81.5 ± 3.2% for 1:1(v/v) methanol-water, and 70.8 ± 2.9% for water alone. The recovery efficiency of arsenic from the molybdic acid complex process 90.8 ± 5.3%. Phosphorus was also recovered from the molybdic acid complex process at 95.1 ± 4.6% efficiency. Quantitative precipitation of Mg₃(AsO₄)₂ and Mg₃(PO₄)₂ occurred in the treatment of the aqueous solutions of arsenic and phosphorus after stripping at pH of 8 – 10. The amounts of Mg₃(AsO₄)₂ and Mg₃(PO₄)₂ obtained were 96 ± 7.2% for arsenic and 94 ± 3.4% for phosphorus. The arsenic nanoparticles produced from the Mg₃(AsO₄)₂ recovered from the biomass have the average particles diameter of 45.5 ± 11.3nm. A two-stage reduction process – a first step pre-reduction of As(V) to As(III) with L-cysteine, followed by NaBH₄ reduction of the As(III) to As(0), was required to produced arsenic nanoparticles from the Mg₃(AsO₄)₂. The arsenic nanoparticles obtained are potentially valuable for medical applications, while the Mg₃(AsO₄)₂ could be used as an insecticide. The phosphorus contents of the Pteris cretica biomass was recovered as phosphomolybdic acid complex and converted to Mg₃(PO₄)₂, which could be useful in productions of fertilizer. Recovery of these valuable products from phytoremediation biomass would incentivize and drive commercial industries’ participation in remediation of contaminated lands.

Keywords: phytoremediation, Pteris cretica, hyper-accumulator, solvent extraction, molybdic acid process, arsenic nanoparticles

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Authors: Lazhar Belkhiri, Ammar Tiri, Lotfi Mouni, Fatma Elhadj Lakouas

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This research aims to present a surface water quality assessment of hydrochemical parameters in the Kebir Rhumel Basin, Algeria. The water quality index (WQI), Mann–Kendall (MK) test, and hierarchical cluster analysis (HCA) were used in oder to understand the spatio-temporal distribution of the surface water quality in the study area. Eleven hydrochemical parameters were measured monthly at eight stations from January 2016 to December 2020. The dominant cation in the surface water was found to be calcium, followed by sodium, and the dominant anion was sulfate, followed by chloride. In terms of WQI, a significant percentage of surface water samples at stations Ain Smara (AS), Beni Haroune (BH), Grarem (GR), and Sidi Khlifa (SK) exhibited poor water quality, with approximately 89.5%, 90.6%, 78.2%, and 62.7%, respectively, falling into this category. Mann–Kendall trend analysis revealed a significantly increasing trend in WQI values at stations Oued Boumerzoug (ON) and SK, indicating that the temporal variation of WQI in these stations is significant. Hierarchical clustering analysis classified the data into three clusters. The first cluster contained approximately 22% of the total number of months, the second cluster included about 30%, and the third cluster had the highest representation, approximately 48% of the total number of months. Within these clusters, certain stations exhibited higher WQI values. In the first cluster, stations GR and ON had the highest WQI values. In the second cluster, stations Oued Boumerzoug (OB) and SK showed the highest WQI values, while in the last cluster, stations AS, BH, El Milia (EM), and Hammam Grouz (HG) had the highest mean WQI values. Also, approximately 38%, 41%, and 38% of the total water samples in the first, second, and third clusters, respectively, were classified as having poor water quality. The findings of this study can serve as a scientific basis for decision-makers to formulate strategies for surface water quality restoration and management in the region.

Keywords: surface water, water quality index (WQI), Mann Kendall (MK) test, hierarchical cluster analysis (HCA), spatial-temporal distribution, Kebir Rhumel Basin

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Authors: Silvia Ximena Barrios, Diego Armando Villamizar Mantilla, Raquel Elvira Ocazionez, , Elena E. Stashenko, María Pilar Vinardell, Jorge Luis Fuentes

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Introduction: Skin overexposure to solar radiation has been a serious public health concern, because of its potential carcinogenicity. Therefore, preventive protection strategies using photoprotective agents are critical to counteract the harmful effect of solar radiation. Plants may be a source of photoprotective compounds that inhibit cellular mutations involved in skin cancer initiation. This work evaluated the photoprotective and antigenotoxic effects against ultraviolet B (UVB) radiation of a mixture of Posoqueria latifolia flower extract and Kaempferol (MixPoKa). Methods: The photoprotective efficacy of MixPoka (Posoqueria latifolia flower extract 250 μg/ml and Kaempferol 349.5 μM) was evaluated using in vitro indices such as sun protection factor SPFᵢₙ ᵥᵢₜᵣₒ and critical wavelength (λc). The MixPoKa photostability (Eff) at human minimal erythema doses (MED), according to the Fitzpatrick skin scale, was also estimated. Cytotoxicity and genotoxicity/antigenotoxicity were studied in MRC5 human fibroblasts using the trypan blue exclusion and Comet assays, respectively. Kinetics of the genetic damage repair post irradiation in the presence and absence of the MixPoka, was also evaluated. Results: The MixPoka -UV absorbance spectrum was high across the spectral bands between 200 and 400 nm. The UVB photoprotection efficacy of MixPoka was high (SPFᵢₙ ᵥᵢₜᵣₒ = 25.70 ± 0.06), showed wide photoprotection spectrum (λc = 380 ± 0), and resulted photostable (Eff = 92.3–100.0%). The MixPoka was neither cytotoxic nor genotoxic in MRC5 human fibroblasts; but presented significant antigenotoxic effect against UVB radiation. Additionally, MixPoka stimulate DNA repair post-irradiation. The potential of this phytochemical mixture as sunscreen ingredients was discussed. Conclusion: MixPoka showed a significant antigenotoxic effect against UVB radiation and stimulated DNA repair after irradiation. MixPoka could be used as an ingredient in a sunscreen cream.

Keywords: flower extract, photoprotection, antigenotoxicity, cytotoxicity, genotoxicit

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Authors: Poteet Frances, Glovinski Ira

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INTRODUCTION: The interoceptive nervous system continuously senses chemical and anatomical changes and helps you recognize, understand, and feel what’s going on inside your body so it is important for energy regulation, memory, affect, and sense of self. A newborn needs predictable routines rather than confusion/chaos to make connections between internal experiences and emotions. AIM: Current legal protocols of removing babies from drug-addicted mothers impact the critical window of bonding. The newborn’s brain is social and the attachment process influences a child’s development which begins immediately after birth through nourishment, comfort, and protection. DESCRIPTION: Our project aims to educate drug-addicted mothers, and medical, nursing, and social work professionals on interoceptive concepts and practices to sustain the mother/newborn relationship. A mother’s interoceptive knowledge predicts children’s emotion regulation and social skills in middle childhood. CONCLUSION: When mothers develop an awareness of their inner bodily sensations, they can self-regulate and be emotionally available to co-regulate (support their newborn during distressing emotions and sensations). Our project has enhanced relationship preservation (mothers understand how their presence matters) and the overall mother/newborn connection.

Keywords: drug-addiction, interoception, legal, mothers, newborn, self-regulation

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Authors: Paolo Fantozzi, Luigi Laura, Umberto Nanni

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The problem of Entity relation discovery in structured data, a well covered topic in literature, consists in searching within unstructured sources (typically, text) in order to find connections among entities. These can be a whole dictionary, or a specific collection of named items. In many cases machine learning and/or text mining techniques are used for this goal. These approaches might be unfeasible in computationally challenging problems, such as processing massive data streams. A faster approach consists in collecting the cooccurrences of any two words (entities) in order to create a graph of relations - a cooccurrence graph. Indeed each cooccurrence highlights some grade of semantic correlation between the words because it is more common to have related words close each other than having them in the opposite sides of the text. Some authors have used sliding windows for such problem: they count all the occurrences within a sliding windows running over the whole text. In this paper we generalise such technique, coming up to a Weighted-Distance Sliding Window, where each occurrence of two named items within the window is accounted with a weight depending on the distance between items: a closer distance implies a stronger evidence of a relationship. We develop an experiment in order to support this intuition, by applying this technique to a data set consisting in the text of the Bible, split into verses.

Keywords: cooccurrence graph, entity relation graph, unstructured text, weighted distance

Procedia PDF Downloads 149