Search results for: monitoring and modeling

Authors: Intanri Kurniati, Raja Iqbal Mulya Harahap, Agustyas Tjiptaningrum, Reni Zuraida

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Background: Diabetes mellitus is still increasing and has become a health and social burden in the world. It is known that glycation among various proteins is increased in diabetic patients compared with non-diabetic subjects. Some of these glycated proteins are suggested to be involved in the development and progression of chronic diabetic complications. Among these glycated proteins, glycated hemoglobin (HbA1C) is commonly used as the gold standard index of glycemic control in the clinical setting. HbA1C testing has some methods, and the most commonly used is immunoturbidimetry. This research aimed to compare the HbA1c level between immunoturbidimetry and HbA1C level in T2DM patients. Methods: This research involves 77 patients from Abd Muluk Hospital Bandar Lampung; the patient was asked for consent in this research, then underwent phlebotomy to be tested for HbA1C; the sample was then examined for HbA1C with Turbidimetric Inhibition Immunoassay (TINIA) and High-Performance Liquid Chromatography (HPLC) method. Result: Mean± SD of the samples with the TINIA method was 9.2±1,2; meanwhile, the level HbA1C with the HPLC method is 9.6±1,2. The t-test showed no significant difference between the group subjects. (p<0.05). It was proposed that the two methods have high suitability in testing, and both are eligibly used for the patient. Discussion: There was no significant difference among research subjects, indicating that the high conformity of the two methods is suitable to be used for monitoring patients clinically. Conclusion: There is increasing in HbA1C level in a patient with T2DM measured with HPLC and or Turbidimetric Inhibition Immunoassay (TINIA) method, and there were no significant differences among those methods.

Keywords: diabetes mellitus, glycated albumin, HbA1C, HPLC, immunoturbidimetry

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Authors: Mohammad Heidari

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In this paper a method for high strength steel is proposed of residual stresses in autofrettaged tubes by combination of artificial neural networks is presented. Many different thick walled cylinders that were subjected to different conditions were studied. At first, the residual stress is calculated by analytical solution. Then by changing of the parameters that influenced in residual stresses such as percentage of autofrettage, internal pressure, wall ratio of cylinder, material property of cylinder, bauschinger and hardening effect factor, a neural network is created. These parameters are the input of network. The output of network is residual stress. Numerical data, employed for training the network and capabilities of the model in predicting the residual stress has been verified. The output obtained from neural network model is compared with numerical results, and the amount of relative error has been calculated. Based on this verification error, it is shown that the radial basis function of neural network has the average error of 2.75% in predicting residual stress of thick wall cylinder. Further analysis of residual stress of thick wall cylinder under different input conditions has been investigated and comparison results of modeling with numerical considerations shows a good agreement, which also proves the feasibility and effectiveness of the adopted approach.

Keywords: thick walled cylinder, residual stress, radial basis, artificial neural network

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Authors: T. A. Rosy, M. R. I. Talukdar, N. S. Juyena, F. Y. Bari, M. N. Islam

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The ovarian follicular dynamics, vaginal electrical resistance (VER) and progesterone (P4) and estrogen (E2) profiles were investigated during estrus cycle in four indigenous ewes. Daily VER values were recorded with heat detector. The follicles were observed and measured by trans-rectal ultrasonography. Blood was collected daily for hormonal profiles. Results showed a significant variation in VER values (P<0.05) at estrus in regards to ewes and cycles. The day difference between two successive lower values in VER waves ranged from 13-17 days which might indicate the estrus cycle in indigenous ewes. Trans-rectal ultrasonography of ovaries revealed the presence of two to four waves of follicular growth during the study period. Results also showed that follicular diameter was negatively correlated with VER values. Study of hormonal profiles by ELISA revealed a positive correlation between E2 concentration and development of follicle and negative correlation between P4 concentration and development of follicle. The concentrations of estradiol increased at the time of estrus and then fall down in a basal level. Development of follicular size was accompanied by an increase in the concentration of serum estradiol. Inversely, when follicles heed to ovulation concentration of progesterone starts to fall down and after ovulation it turns its way to the zenith and remains at this state until next ovulatory follicle comes to its maximum diameter. This study could help scientists to set up a manipulative reproductive technique for improving genetic values of sheep in Bangladesh.

Keywords: ovarian follicle, hormonal profile, sheep, ultrasonography, vaginal electrical resistance

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Authors: Dennis Okora Amima Ondieki

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Fertility transition has been identified to be affected by numerous factors. This research aimed to investigate the most real factors affecting fertility transition in Kenya. These factors were firstly extracted from the literature convened into demographic features, social, and economic features, social-cultural features, reproductive features and modernization features. All these factors had 23 factors identified for this study. The data for this study was from the Kenya Demographic and Health Surveys (KDHS) conducted in 1999-2003 and 2003-2008/9. The data was continuous, and it involved the mean birth order for the ten periods. Principal component analysis (PCA) was utilized using 23 factors. Principal component analysis conveyed religion, region, education and marital status as the real factors. PC scores were calculated for every point. The identified principal components were utilized as forecasters in the multiple regression model, with the fertility level as the response variable. The four components were found to be affecting fertility transition differently. It was found that fertility is affected positively by factors of region and marital and negatively by factors of religion and education. These four factors can be considered in the planning policy in Kenya and Africa at large.

Keywords: fertility transition, principal component analysis, Kenya demographic health survey, birth order

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Authors: Aylin Mohammadalipour Tofighi

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Marine-based geoengineering methods, proposed to mitigate climate change, operate primarily through two mechanisms: reducing atmospheric carbon dioxide levels and diminishing solar absorption by the oceans. While these approaches promise beneficial outcomes, they are fraught with environmental, legal, ethical, and political challenges, necessitating robust international governance. This paper underscores the critical role of international cooperation within the governance framework, offering a focused analysis of existing international environmental mechanisms applicable to marine-based geoengineering governance. It evaluates the efficacy and limitations of current international legal structures, including treaties and organizations, in managing marine-based geoengineering, noting significant gaps such as the absence of specific regulations, dedicated international entities, and explicit governance mechanisms such as monitoring. To rectify these problems, the paper advocates for concrete steps to bolster international cooperation. These include the formulation of dedicated marine-based geoengineering guidelines within international agreements, the establishment of specialized supervisory entities, and the promotion of transparent, global consensus-building. These recommendations aim to foster governance that is environmentally sustainable, ethically sound, and politically feasible, thereby enhancing knowledge exchange, spurring innovation, and advancing the development of marine-based geoengineering approaches. This study emphasizes the importance of collaborative approaches in managing the complexities of marine-based geoengineering, contributing significantly to the discourse on international environmental governance in the face of rapid climate and technological changes.

Keywords: climate change, environmental law, international cooperation, international governance, international law, marine-based geoengineering, marine law, regulatory frameworks

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Authors: Q. Rzina, M. Lahrouni, S. Rida, N. Saadaoui, Y. Almossaid, K. Oufdou, K. Fares

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Many organic solid wastes are produced in the world. Poultry manure (PM), municipal organic wastes (MOW) and sugar beet lime sludge (LS) are produced in large quantities in Morocco. The co-composting of these organic wastes was investigated. The recycling and the valorization of such wastes is environmentally and economically beneficial especially for PM which is known source of bacterial pathogens. The aerobic biodegradation process was carried out by using three windrows of variable compositions: C1 prepared without LS (only MOW were composted with PM), C2 prepared from MOW plus PM and10% LS; and the last one C3 from MOW plus PM and 20% LS. The main process physico-chemical parameters (temperature, pH, humidity and C/N) and microbiological populations (mesophilic and thermophilic flora, total coliform, fecal coliform, Streptococci, Staphylococcus aureus and mesophilic fungi) were monitored over three months to ascertain the compost maturity and to ensure the compost hygienic aspect. The final products were characterized by their relatively high organic matter content, and low C/N ratio of 10.6-10.9. The organic matter degradation was reached approximately 59% for C2 and C3. In addition, the monitoring of the microbial population showed that the produced composts are mature and hygienic. The agronomic valorization of the final composts was tested on radish plant with tree level of composts and poultry manure without composting. The primary results of field trial showed a growth of radish plant biomass and root development without any phytotoxicity detected which reflects the quality of the composts produced. As for poultry manure it allowed to have a better results than other composts because of its readily available nitrogen.

Keywords: compost, municipal organic wastes, poultry manure, radish crop, sugar beet lime sludge

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Authors: Alvaro Jose Cordova, Hsuan Teh Hu

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The use of concrete masonry constructions in developing countries has become very frequent, especially for domestic purpose. Most of them with asymmetric wall configurations in plan resulting in significant torsional actions when subjected to seismic loads. The study consisted on the finding of a material model for hollow unreinforced concrete masonry and a validation with experimental data found in literature. Numerical simulations were performed to 20 buildings with variations in wall distributions and heights. Results were analyzed by inspection and with a non-linear static method. The findings revealed that eccentricities as well as structure rigidities have a strong influence on the overall response of concrete masonry buildings. In addition, slab rotations depicted more accurate information about the torsional behavior than maximum versus average displacement ratios. The failure modes in low buildings were characterized by high tensile strains in the first floor. Whereas in tall buildings these strains were lowered significantly by higher compression stresses due to a higher self-weight. These tall buildings developed multiple plastic hinges along the height. Finally, the non-linear static analysis exposed a brittle response for all masonry assemblies. This type of behavior is undesired in any construction and the need for a material model for reinforced masonry is pointed out.

Keywords: concrete damaged plasticity, concrete masonry, macro-modeling, nonlinear static analysis, torsional capacity

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Authors: Samuel Nderitu Muchiri, Tracy Wangechi Maina

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Truck drivers face a myriad of challenges in their profession. Enhancements in logistics effectiveness can be pivotal in propelling economic developments. The specific objective of the study was to assess the influence of driver distraction on crash risk. The study is significant as it elucidates best practices that truck drivers can embrace in an effort to enhance road safety. These include amalgamating behaviors that enable drivers to fruitfully execute multifaceted functions such as finding and following routes, evading collisions, monitoring speed, adhering to road regulations, and evaluating vehicle systems’ conditions. The analysis involved an empirical review of ten previous studies related to the research topic. The articles revealed that driver distraction plays a substantial role in road accidents and other crucial road security incidents across the globe. Africa depends immensely on the freight transport sector to facilitate supply chain operations. Several studies indicate that drivers who operate primarily on rural roads, such as those found in Sub-Saharan Africa, have an increased propensity to engage in distracted activities such as cell phone usage while driving. The findings also identified the need for digitalization in truck driving operations, including carrier management techniques such as fatigue management, artificial intelligence, and automating functions like cell phone usage controls. The recommendations can aid policymakers and commercial truck carriers in deepening their understanding of driver distraction and enforcing mitigations to foster road safety.

Keywords: truck drivers, distraction, digitalization, crash risk, road safety

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Authors: Patrick Mueller, Alan Martin, Justin Stockwell, Robert Goldblatt

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Elevated concentrations of inorganic nitrogen (N) compounds (nitrate, nitrite, and ammonia) are a ubiquitous feature to mine-influenced drainages due to the leaching of blasting residues and use of cyanide in the milling of gold ores. For many mines, the management of N is a focus for environmental protection, therefore understanding the factors controlling the speciation and behavior of N is central to effective decision making. In this paper, the passive attenuation of ammonia and nitrite is described for three northern water bodies (two lakes and a tailings pond) influenced by mining activities. In two of the water bodies, inorganic N compounds originate from explosives residues in mine water and waste rock. The third water body is a decommissioned tailings impoundment, with N compounds largely originating from the breakdown of cyanide compounds used in the processing of gold ores. Empirical observations from water quality monitoring indicate nitrification (the oxidation of ammonia to nitrate) occurs in all three waterbodies, where enrichment of nitrate occurs commensurately with ammonia depletion. The N species conversions in these systems occurred more rapidly than chemical oxidation kinetics permit, indicating that microbial mediated conversion was occurring, despite the cool water temperatures. While nitrification of ammonia and nitrite to nitrate was the primary process, in all three waterbodies nitrite was consistently present at approximately 0.5 to 2.0 % of total N, even following ammonia depletion. The persistence of trace amounts of nitrite under these conditions suggests the co-occurrence denitrification processes in the water column and/or underlying substrates. The implications for N management in mine waters are discussed.

Keywords: explosives, mining, nitrification, water

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Authors: Hongbo Zhang, Xinlu Tang, Jiangwei Li, Chi Yan

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Home zone parking pilot (HPP) is a fast-growing segment in low-speed autonomous driving applications. It requires the car automatically cruise around a parking lot and park itself in a range of up to 100 meters inside a recurrent home/office parking lot, which requires precise parking lot mapping and localization solution. Although Lidar is ideal for SLAM, the car OEMs favor a low-cost fish-eye camera based visual SLAM approach. Recent approaches have employed segmentation models to extract semantic features and improve mapping accuracy, but these AI models are memory unfriendly and computationally expensive, making deploying on embedded ADAS systems difficult. To address this issue, we proposed a new method that utilizes object detection models to extract robust and accurate parking lot features. The proposed method could reduce computational costs while maintaining high accuracy. Once combined with vehicles’ wheel-pulse information, the system could construct maps and locate the vehicle in real-time. This article will discuss in detail (1) the fish-eye based Around View Monitoring (AVM) with transparent chassis images as the inputs, (2) an Object Detection (OD) based feature point extraction algorithm to generate point cloud, (3) a low computational parking lot mapping algorithm and (4) the real-time localization algorithm. At last, we will demonstrate the experiment results with an embedded ADAS system installed on a real car in the underground parking lot.

Keywords: ADAS, home zone parking pilot, object detection, visual SLAM

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Authors: Greg Turner, Bin Lu, Cheer-Sun Yang

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As computing technology advances, smartphone applications can assist in student learning in a pervasive way. For example, the idea of using a mobile apps for the PA Common Trees, Pests, Pathogens, in the field as a reference tool allows middle school students to learn about trees and associated pests/pathogens without bringing a textbook. In the past, some researches study the mobile software Mobile Application Software Development Life Cycle (MADLC) including traditional models such as the waterfall model, or more recent Agile Methods. Others study the issues related to the software development process. Very little research is on the development of three heterogenous mobile systems simultaneously in a small university where the availability of developers is an issue. In this paper, we propose to use a hybride model of Waterfall Model and the Agile Model, known as the Relay Race Methodology (RRM) in practice, to reflect the concept of racing and relaying for scheduling. Based on the development project, we observe that the modeling of the transition between any two phases is manifested naturally. Thus, we claim that the RRM model can provide a de fecto rather than a de jure basis for the core concept in the MADLC. In this paper, the background of the project is introduced first. Then, the challenges are pointed out followed by our solutions. Finally, the experiences learned and the future work are presented.

Keywords: agile methods, mobile apps, software process model, waterfall model

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Authors: Antarip Bhattacharya, Dhritiman Maitra

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Primary hyperparathyroidism (PHPT) is the most common cause of hypercalcemia due to autonomous production of parathormone (PTH) and the third most common endocrine disorder. Upto 2% of postmenopausal women could have this condition. Primary hyperparathyroidism is characterized by hypercalcemia with a high or insufficiently suppressed level of parathyroid hormone and is caused by a solitary parathyroid adenoma in 85-90% of patients. PHPT may also be caused by parathyroid hyperplasia (involving multiple glands) or parathyroid carcinoma. Associated morbidities and sequelae include decreased bone mineral density, fractures, kidney stones, hypertension, cardiac comorbidities and psychiatric disorder which entail huge costs for treatment. In the year 2021, by virtue of running a Breast and Endocrine Surgery clinic in a Tier 1 city at a tertiary care hospital, the opportunity to be associated with patients of hyperparathyroidism came our way. Here, we shall describe the spectrum of clinical presentations and customisation of treatment for parathyroid diseases with reference to the above patients. A retrospective analysis of the data of all patients presenting with symptoms of parathyroid diseases was made and classified according to the cause. 13 patients had presented with symptoms of hyperparathyroidism and each case presented with unique symptoms and necessitated detailed evaluation. The treatment or surgery offered to each patient was tailored to his/her individual disease and led to favourable outcomes. Diseases affecting parathyroid are not as rare as we believe. Each case merits detailed clinical evaluation, investigations and tailoring of suitable treatment with regard to medical management and extent of surgery. Intra-operative frozen section/iOPTH monitoring are really useful adjuncts for intra-operative decision making.

Keywords: hyperparathyroidism, parathyroid adenoma, parathyroid surgery, PTH

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Authors: Rômulo Eustáquio Martins de Souza, Paulo Alexandre Rodrigues de Vasconcelos Figueiredo

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In recent years, there has been a surge in interest in applying artificial intelligence techniques, particularly machine learning algorithms. Machine learning is a data-analysis technique that automates the creation of analytical models, making it especially useful for designing complex situations. As a result, this technology aids in reducing human intervention while producing accurate results. This methodology is also extensively used in aerospace engineering since this is a field that encompasses several high-complexity operations, such as rocket propulsion. Rocket propulsion is a high-risk operation in which engine failure could result in the loss of life. As a result, it is critical to use computational methods capable of precisely representing the spacecraft's analytical model to guarantee its security and operation. Thus, this paper describes the use of machine learning algorithms for rocket propulsion to aid the realization that this technique is an efficient way to deal with challenging and restrictive aerospace engineering activities. The paper focuses on three machine-learning-aided rocket propulsion applications: set-point control of an expander-bleed rocket engine, supersonic retro-propulsion of a small-scale rocket, and leak detection and isolation on rocket engine data. This paper describes the data-driven methods used for each implementation in depth and presents the obtained results.

Keywords: data analysis, modeling, machine learning, aerospace, rocket propulsion

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Authors: Olugbade Damola, Adekomi Adebimbo, Sofowora Olaniyi Alaba

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One of the major problems facing education in Nigeria is the provision of quality Science and Technology education. Inadequate teaching facilities, non-usage of innovative teaching strategies, ineffective classroom management, lack of students’ motivation and poor integration of ICT has resulted in the increase in percentage of students who failed Basic Science and Technology in Junior Secondary Certification Examination for National Examination Council in Nigeria. To address these challenges, the Federal Government came up with a road map on education. This was with a view of enhancing quality education through integration of modern technology into teaching and learning, enhancing quality assurance through proper monitoring and introduction of innovative methods of teaching. This led the researcher to investigate how MOODLE LMS could be used to enhance students’ learning outcomes in BST. A sample of 120 students was purposively selected from four secondary schools in Ogbomoso. The experimental group was taught using MOODLE LMS, while the control group was taught using the conventional method. Data obtained were analyzed using mean, standard deviation and t-test. The result showed that MOODLE LMS was an effective learning platform in teaching BST in junior secondary schools (t=4.953, P<0.05). Students’ attitudes towards BST was also enhanced through MOODLE LMS (t=15.632, P<0.05). The use of MOODLE LMS significantly enhanced students’ retention (t=6.640, P<0.05). In conclusion, the Federal Government efforts at enhancing quality assurance through integration of modern technology and e-learning in Secondary schools proved to have yielded good result has students found MOODLE LMS to be motivating and interactive. Attendance was improved.

Keywords: basic science and technology, MOODLE LMS, performance, quality assurance

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Authors: Rui Serôdio, Alexandra Serra, José Albino Lima, Luísa Catita, Paula Lopes

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We will present the outline of the Project SIA (Social Inclusion through Accessibility) focusing in one of its core components: how our applied research model contributes to define community participation as a pillar for strategic and political agenda amongst local authorities. Project ISA, supported by EU regional funding, was design as part of a broader model developed by SIMLab–Social Inclusion Monitoring Laboratory, in which the relation University-Community is a core element. The project illustrates how University of Porto developed a large scale project of applied psychology research in a close partnership with 18 municipalities that cover almost all regions of Portugal, and with a private architecture enterprise, specialized in inclusive accessibility and “design for all”. Three fundamental goals were defined: (1) creation of a model that would promote the effective civic participation of local citizens; (2) the “voice” of such participation should be both individual and collective; (3) the scientific and technical framework should serve as one of the bases for political decision on inclusive accessibility local planning. The two main studies were run in a standardized model across all municipalities and the samples of the three modalities of community participation were the following: individual participation based on 543 semi-structured interviews and 6373 inquiries; collective participation based on group session with 302 local citizens. We present some of the broader findings of Project SIA and discuss how they relate to our applied research model.

Keywords: applied psychology, collective identity, community participation, inclusive accessibility

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Authors: Justine Ugochukwu Osuagwu, Sazali Abd Wahab

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The performance and operation of NNPC have faced series of attacks by all stakeholders as many have observed lots of inefficiency not only on the part of the management but the staff. This has raised questions of whether their operations and performance are being seriously affected by lack of transformational leadership, and the political competition prevalent in the country. The author has applied the administrative leadership theory and institutional theory as a guide to this study and empirically relates such theories to the study. The study also has utilized the quantitative approach where questionnaires were distributed to 370 participants, and the correctly filled and returned questionnaires were used for the analysis using structural equation modeling. The path coefficient of transformational leadership to performance is strong and positive with β = 0.672; t-value = 14.245; p-value = 0.000. Also, the result found that political competition does not mediate the relationship between transformational leadership and performance of NNPC. (β = -0.008; t-value = -0.600; p- value > 0.05). However, the indirect path is all insignificant, meaning that transformational leadership has relationship with corporate performance.The study found that,while political competition does not serve as a mediator in the relationship between transformational leadership and corporate performance, these styles of leadership have a direct and positive impact on corporate performance. The direct relationship between transformational leadership and political competition was not discovered, despite the fact that political competition has a direct and significant impact, both positive and negative, on corporate performance. As a result, both political competition and transformational leadership have the potential to significantly alter corporate performance.

Keywords: performance, transformational leadership, political competition, corporation performance, Nigeria national petroleum corporation

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Authors: Regina Chua

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To reduce the costs of re-fielding a large survey, a Random Forest classifier was applied to measure the accuracy of classifying individuals into their assigned segments with the fewest possible questions. Given a long survey, one needed to determine the most predictive ten or fewer questions that would accurately assign new individuals to custom segments. Furthermore, the solution needed to be quick in its classification and usable in non-Python environments. In this paper, a supervised Random Forest classifier was modeled on a dataset with 7,000 individuals, 60 questions, and 254 features. The Random Forest consisted of an iterative collection of individual decision trees that result in a predicted segment with robust precision and recall scores compared to a single tree. A random 70-30 stratified sampling for training the algorithm was used, and accuracy trade-offs at different depths for each segment were identified. Ultimately, the Random Forest classifier performed at 87% accuracy at a depth of 10 with 20 instead of 254 features and 10 instead of 60 questions. With an acceptable accuracy in prioritizing feature selection, new tools were developed for non-Python environments: a worksheet with a formulaic version of the algorithm and an embedded function to predict the segment of an individual in real-time. Random Forest was determined to be an optimal classification model by its feature selection, performance, processing speed, and flexible application in other environments.

Keywords: machine learning, supervised learning, data science, random forest, classification, prediction, predictive modeling

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Authors: Siham Rahoui, Amr Mahfouz, Amr Arisha

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In a highly competitive environment characterised by uncertainty and disruptions, such as the recent COVID-19 outbreak, supply chains (SC) face the challenge of maintaining their cost at minimum levels while continuing to provide customers with high-quality products and services. More importantly, businesses in such an economic context strive to maintain survival by keeping the cost of undertaken activities (such as logistics) low and in-house. To do so, managers need to understand the costs associated with different products and services in order to have a clear vision of the SC performance, maintain profitability levels, and make strategic decisions. In this context, SC literature explored different costing models that sought to determine the costs of undertaking supply chain-related activities. While some cost accounting techniques have been extensively explored in the SC context, more contributions are needed to explore the potential of time driven activity-based costing (TDABC). More specifically, more applications are needed in the manufacturing context of the SC, where the debate is ongoing. The aim of the study is to assess the capability of the technique to assess the operational performance of the logistics function. Through a case study methodology applied to a manufacturing company operating in the automotive industry, TDABC evaluates the efficiency of the current configuration and its logistics processes. The study shows that monitoring the process efficiency and cost efficiency leads to strategic decisions that contributed to improve the overall efficiency of the logistics processes.

Keywords: efficiency, operational performance, supply chain costing, time driven activity based costing

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Authors: Saba Fahham, Supak Ngamsom, Suchaya Damrongsri

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Objective: The objective is to determine the optimal threshold of Romexis software for the airway volume and minimum cross-section area (MCA) analysis using Image J as a gold standard. Materials and Methods: A total of ten cone-beam computed tomography (CBCT) images were collected. The airway volume and MCA of each patient were analyzed using the automatic airway segmentation function in the CBCT DICOM viewer (Romexis). Airway volume and MCA measurements were conducted on each CBCT sagittal view with fifteen different threshold values from the Romexis software, Ranging from 300 to 1000. Duplicate DICOM files, in axial view, were imported into Image J for concurrent airway volume and MCA analysis as the gold standard. The airway volume and MCA measured from Romexis and Image J were compared using a t-test with Bonferroni correction, and statistical significance was set at p<0.003. Results: Concerning airway volume, thresholds of 600 to 850 as well as 1000, exhibited results that were not significantly distinct from those obtained through Image J. Regarding MCA, employing thresholds from 400 to 850 within Romexis Viewer showed no variance from Image J. Notably, within the threshold range of 600 to 850, there were no statistically significant differences observed in both airway volume and MCA analyses, in comparison to Image J. Conclusion: This study demonstrated that the utilization of Planmeca Romexis Viewer 6.4.3.3 within threshold range of 600 to 850 yields airway volume and MCA measurements that exhibit no statistically significant variance in comparison to measurements obtained through Image J. This outcome holds implications for diagnosing upper airway obstructions and post-orthodontic surgical monitoring.

Keywords: airway analysis, airway segmentation, cone beam computed tomography, threshold

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Authors: George Vernon Byrd, Bhim Bahadur Ghaley, Eri Hayashi

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In urban farming, research and innovation are taking place at an unprecedented pace, and soilless growing technologies are emerging at different rates motivated by different objectives in various parts of the world. Local food production is ultimately a main objective everywhere, but adoption rates and expressions vary with socio-economic drivers. Herein, the status of hydroponics and aquaponics is summarized for four countries with diverse socio-economic settings: Europe (Denmark), Asia (Japan and Nepal) and North America (US). In Denmark, with a strong environmental ethic, soilless growing is increasing in urban agriculture because it is considered environmentally friendly. In Japan, soil-based farming is being replaced with commercial plant factories using advanced technology such as complete environmental control and computer monitoring. In Nepal, where rapid loss of agriculture land is occurring near cities, dozens of hydroponics and aquaponics systems have been built in the past decade, particularly in “non-traditional” sites such as roof tops to supplement family food. In the US, where there is also strong interest in locally grown fresh food, backyard and commercial systems have proliferated. Nevertheless, soilless growing is still in the research and development and early adopter stages, and the broad contribution of hydroponics and aquaponics to food security is yet to be fully determined. Nevertheless, current adoption of these technologies in diverse environments in different socio-economic settings highlights the potential contribution to food security with social and environmental benefits which contribute to several Sustainable Development Goals.

Keywords: aquaponics, hydroponics, soilless agriculture, urban agriculture

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Authors: Bennani Zoubir Nada, El Hiri Abderrazak, El Hajri Aimad

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In context of economic and health crisis, sustainable entrepreneurship has become one of the best solutions to economic recovery. This study is about the third program of the Moroccan national human development initiative in her third phase which began in 2019 and continuous until 2023, and which deals with income improvement and social inclusion of young people, under the high patronage of his majesty the king of Morocco. What is the approach of this program and how entrepreneurship training of young people can be a pillar to generate income and create jobs? Starting on the effectuation theory, we adopted an exploratory qualitative approach through semi-structured interviews with national human development initiative stakeholders in the area of Meknes-Morocco, which allowed us the state of progress of this program. We carried out a survey based on a grid of questions to collect information that we processed using NVIVO software. The most relevant results are that people eligible are jobless young people, who are between 18 and 35 years old, who reside in Meknes and surroundings and who have a project idea. They are trained by experts in entrepreneurship and management through targeted and diversified courses. To ensure the sustainability of projects, the project organisers have provided measures to ensure the sustainability of the companies through continuous monitoring and evaluation as well as support during all phases from the project idea to the realisation and progress.

Keywords: sustainable entrepreneurship, training, social inclusion, national human development initiative in Morocco (INDH), youth entrepreneurship, the effectuation theory

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Authors: Ramandeep Kaur M. Malhi, Prashant K. Srivastava, G.Sandhya Kiran

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Quantitative estimates of foliar biochemicals, namely chlorophyll content (CC), serve as key information for the assessment of plant productivity, stress, and the availability of nutrients. This also plays a critical role in predicting the dynamic response of any vegetation to altering climate conditions. The advent of hyperspectral data with an enhanced number of available wavelengths has increased the possibility of acquiring improved information on CC. Retrieval of CC is extensively carried through well known spectral indices derived from hyperspectral data. In the present study, an attempt is made to develop hyperspectral indices by identifying optimum bands for CC estimation in Butea monosperma (Lam.) Taub growing in forests of Shoolpaneshwar Wildlife Sanctuary, Narmada district, Gujarat State, India. 196 narrow bands of EO-1 Hyperion images were screened, and the best optimum wavelength from blue, green, red, and near infrared (NIR) regions were identified based on the coefficient of determination (R²) between band reflectance and laboratory estimated CC. The identified optimum wavelengths were then employed for developing 12 hyperspectral indices. These spectral index values and CC values were then correlated to investigate the relation between laboratory measured CC and spectral indices. Band 15 of blue range and Band 22 of green range, Band 40 of the red region, and Band 79 of NIR region were found to be optimum bands for estimating CC. The optimum band based combinations on hyperspectral data proved to be the most effective indices for quantifying Butea CC with NDVI and TVI identified as the best (R² > 0.7, p < 0.01). The study demonstrated the significance of band characterization in the development of the best hyperspectral indices for the chlorophyll estimation, which can aid in monitoring the vitality of forests.

Keywords: band, characterization, chlorophyll, hyperspectral, indices

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Authors: Yan Cheng, Yijun Shao, James Rudolph, Charlene R. Weir, Beth Sahlmann, Qing Zeng-Treitler

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Supervised predictive models require labeled data for training purposes. Complete and accurate labeled data, i.e., a ‘gold standard’, is not always available, and imperfectly labeled data may need to serve as an alternative. An important question is if the accuracy of the labeled data creates a performance ceiling for the trained model. In this study, we trained several models to recognize the presence of delirium in clinical documents using data with annotations that are not completely accurate (i.e., weakly-supervised learning). In the external evaluation, the support vector machine model with a linear kernel performed best, achieving an area under the curve of 89.3% and accuracy of 88%, surpassing the 80% accuracy of the training sample. We then generated a set of simulated data and carried out a series of experiments which demonstrated that models trained on imperfect data can (but do not always) outperform the accuracy of the training data, e.g., the area under the curve for some models is higher than 80% when trained on the data with an error rate of 40%. Our experiments also showed that the error resistance of linear modeling is associated with larger sample size, error type, and linearity of the data (all p-values < 0.001). In conclusion, this study sheds light on the usefulness of imperfect data in clinical research via weakly-supervised learning.

Keywords: weakly-supervised learning, support vector machine, prediction, delirium, simulation

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Authors: Chrysovalanto Sofia Karatosidi, Dimitra Iordanoglou

Abstract:

The overlap of leadership skills with personality traits, beliefs, values, and the integration of cognitive abilities, analytical and critical thinking skills into leadership competencies raises the need to segregate further and investigate them. Hence, the domains of cognitive functions that contribute to leadership effectiveness should also be identified. Organizational cognitive neuroscience and neuroleadership can shed light on the study of these critical leadership skills. As the first part of our research, this pilot study aims to explore the relationships between higher-order cognitive functions (executive functions), trait emotional intelligence (EI), personality, and general cognitive ability in leadership. Twenty-six graduate and postgraduate students were assessed on neuropsychological tests that measure important aspects of executive functions (EF) and completed self-reported questionnaires about trait EI, personality, leadership styles, and leadership effectiveness. Specifically, we examined four core EF—fluency (phonemic and semantic), information updating and monitoring, working memory, and inhibition of prepotent responses. Leadership effectiveness was positively associated with phonemic fluency (PF), which involves mental flexibility, in turn, an increasingly important ability for future leaders in this rapidly changing world. Transformational leadership was positively associated with trait EI, extraversion, and openness to experience, a result that is following previous findings. The relationship between specific EF constructs and leadership effectiveness emphasizes the role of higher-order cognitive functions in the field of leadership as an individual difference. EF brings a new perspective into leadership literature by providing a direct, non-invasive, scientifically-valid connection between brain function and leadership behavior.

Keywords: cognitive neuroscience, emotional intelligence, executive functions, leadership

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Authors: M. Mohsin Munir, Taimoor Ahmad, Javed Munir, Usman Rashid

Abstract:

Determination of maximum elevation of a flowing fluid due to sudden rejection of load in a hydropower facility is of great interest to hydraulic engineers to ensure safety of the hydraulic structures. Several mathematical models exist that employ one-dimensional modeling for the determination of surge but none of these perfectly simulate real-time circumstances. The paper envisages investigation of surge inception and propagation for a Low Head Hydropower project using Computational Fluid Dynamics (CFD) analysis on FLOW-3D software package. The fluid dynamic model utilizes its analysis for surge by employing Reynolds’ Averaged Navier-Stokes Equations (RANSE). The CFD model is designed for a case study at Taunsa hydropower Project in Pakistan. Various scenarios have run through the model keeping in view upstream boundary conditions. The prototype results were then compared with the results of physical model testing for the same scenarios. The results of the numerical model proved quite accurate coherence with the physical model testing and offers insight into phenomenon which are not apparent in physical model and shall be adopted in future for the similar low head projects limiting delays and cost incurred in the physical model testing.

Keywords: surge, FLOW-3D, numerical model, Taunsa, RANSE

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Authors: Soheila Sadeghi

Abstract:

Accurate forecasting of project performance metrics is crucial for successfully managing and delivering urban road reconstruction projects. Traditional methods often rely on static baseline plans and fail to consider the dynamic nature of project progress and external factors. This research proposes a machine learning-based approach to forecast project performance metrics, such as cost variance and earned value, for each Work Breakdown Structure (WBS) category in an urban road reconstruction project. The proposed model utilizes time series forecasting techniques, including Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, to predict future performance based on historical data and project progress. The model also incorporates external factors, such as weather patterns and resource availability, as features to enhance the accuracy of forecasts. By applying the predictive power of machine learning, the performance forecasting model enables proactive identification of potential deviations from the baseline plan, which allows project managers to take timely corrective actions. The research aims to validate the effectiveness of the proposed approach using a case study of an urban road reconstruction project, comparing the model's forecasts with actual project performance data. The findings of this research contribute to the advancement of project management practices in the construction industry, offering a data-driven solution for improving project performance monitoring and control.

Keywords: project performance forecasting, machine learning, time series forecasting, cost variance, earned value management

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Authors: Zerzouri Nora, Benalia Nadia, Bensiali Nadia

Abstract:

This paper presents a grid-connected wind power generation scheme using Doubly Fed Induction Generator (DFIG). This can supply power at constant voltage and constant frequency with the rotor speed varying. This makes it suitable for variable speed wind energy application. The DFIG system consists of wind turbine, asynchronous wound rotor induction generator, and inverter with Space Vector Modulation (SVM) controller. In which the stator is connected directly to the grid and the rotor winding is in interface with rotor converter and grid converter. The use of back-to-back SVM converter in the rotor circuit results in low distortion current, reactive power control and operate at variable speed. Mathematical modeling of the DFIG is done in order to analyze the performance of the systems and they are simulated using MATLAB. The simulation results for the system are obtained and hence it shows that the system can operate at variable speed with low harmonic current distortion. The objective is to track and extract maximum power from the wind energy system and transfer it to the grid for useful work.

Keywords: Doubly Fed Induction Generator, Wind Energy Conversion Systems, Space Vector Modulation, distortion harmonics

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Authors: Porntip Ruengtam

Abstract:

This article is an extension of previous research presenting the relevant factors related to environmental perceptions, residential community, and the design of a healing environment, which have effects on the well-being and requirements of Thai elderly. Research methodology began with observations and interviews in three case studies in terms of the management processes and environment design of similar existing projects in Thailand. The interview results were taken to summarize with related theories and literature. A questionnaire survey was designed for data collection to confirm the factors of requirements in a residential community intended for the Thai elderly. A structural equation model (SEM) was formulated to explain the cause-effect factors for the requirements of a residential community for Thai elderly. The research revealed that the requirements of a residential community for Thai elderly were classified into three groups when utilizing a technique for exploratory factor analysis. The factors were comprised of (1) requirements for general facilities and activities, (2) requirements for facilities related to health and security, and (3) requirements for facilities related to physical exercise in the residential community. The results from the SEM showed the background of elderly people had a direct effect on their requirements for a residential community from various aspects. The results should lead to the formulation of policies for design and management of residential communities for the elderly in order to enhance quality of life as well as both the physical and mental health of the Thai elderly.

Keywords: elderly, environmental design, residential community, structural equation modeling

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Authors: Najrullah Khan, Athar Ali Khan

Abstract:

The Topp-Leone distribution was introduced by Topp- Leone in 1955. In this paper, an attempt has been made to fit Topp-Leone Generalized exponential (TPGE) distribution. A real survival data set is used for illustrations. Implementation is done using R and JAGS and appropriate illustrations are made. R and JAGS codes have been provided to implement censoring mechanism using both optimization and simulation tools. The main aim of this paper is to describe and illustrate the Bayesian modelling approach to the analysis of survival data. Emphasis is placed on the modeling of data and the interpretation of the results. Crucial to this is an understanding of the nature of the incomplete or 'censored' data encountered. Analytic approximation and simulation tools are covered here, but most of the emphasis is on Markov chain based Monte Carlo method including independent Metropolis algorithm, which is currently the most popular technique. For analytic approximation, among various optimization algorithms and trust region method is found to be the best. In this paper, TPGE model is also used to analyze the lifetime data in Bayesian paradigm. Results are evaluated from the above mentioned real survival data set. The analytic approximation and simulation methods are implemented using some software packages. It is clear from our findings that simulation tools provide better results as compared to those obtained by asymptotic approximation.

Keywords: Bayesian Inference, JAGS, Laplace Approximation, LaplacesDemon, posterior, R Software, simulation

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Authors: Stephen L. Roberts

Abstract:

This article offers a critical analysis of the rise of syndromic surveillance systems for the advanced detection of pandemic threats within contemporary global health security frameworks. The article traces the iterative evolution and ascendancy of three such novel syndromic surveillance systems for the strengthening of health security initiatives over the past two decades: 1) The Program for Monitoring Emerging Diseases (ProMED-mail); 2) The Global Public Health Intelligence Network (GPHIN); and 3) HealthMap. This article demonstrates how each newly introduced syndromic surveillance system has become increasingly oriented towards the integration of digital algorithms into core surveillance capacities to continually harness and forecast upon infinitely generating sets of digital, open-source data, potentially indicative of forthcoming pandemic threats. This article argues that the increased centrality of the algorithm within these next-generation syndromic surveillance systems produces a new and distinct form of infectious disease surveillance for the governing of emergent pathogenic contingencies. Conceptually, the article also shows how the rise of this algorithmic mode of infectious disease surveillance produces divergences in the governmental rationalities of global health security, leading to the rise of an algorithmic governmentality within contemporary contexts of Big Data and these surveillance systems. Empirically, this article demonstrates how this new form of algorithmic infectious disease surveillance has been rapidly integrated into diplomatic, legal, and political frameworks to strengthen the practice of global health security – producing subtle, yet distinct shifts in the outbreak notification and reporting transparency of states, increasingly scrutinized by the algorithmic gaze of syndromic surveillance.

Keywords: algorithms, global health, pandemic, surveillance

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