Search results for: financial performance indicators

Authors: Mohammad Khazaei, Reza Rostami, Hassan Gharayagh Zandi, Ruhollah Basatnia, Mahboubeh Ghayour Najafabadi

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Introduction: This study delves into the influence of diverse interventions on brain functional connectivity among young male basketball players. Given the significance of understanding how interventions affect cognitive functions in athletes, particularly in the context of basketball, this research contributes to the growing body of knowledge in sports neuroscience. Methods: Three distinct groups were selected for comprehensive investigation: the Motivational Interview Group, Placebo Consumption Group, and Ritalin Consumption Group. The study involved assessing brain functional connectivity using various frequency bands (Delta, Theta, Alpha, Beta1, Beta2, Gamma, and Total Band) before and after the interventions. The participants were subjected to specific interventions corresponding to their assigned groups. Results: The findings revealed substantial differences in brain functional connectivity across the studied groups. The Motivational Interview Group exhibited optimal outcomes in PLI (Total Band) connectivity. The Placebo Consumption Group demonstrated a marked impact on PLV (Alpha) connectivity, and the Ritalin Consumption Group experienced a considerable enhancement in imCoh (Total Band) connectivity. Discussion: The observed variations in brain functional connectivity underscore the nuanced effects of different interventions on young male basketball players. The enhanced connectivity in specific frequency bands suggests potential cognitive and performance improvements. Notably, the Motivational Interview and Placebo Consumption groups displayed unique patterns, emphasizing the multifaceted nature of interventions. These findings contribute to the understanding of tailored interventions for optimizing cognitive functions in young male basketball players. Conclusion: This study provides valuable insights into the intricate relationship between interventions and brain functional connectivity in young male basketball players. Further research with expanded sample sizes and more sophisticated statistical analyses is recommended to corroborate and expand upon these initial findings. The implications of this study extend to the broader field of sports neuroscience, aiding in the development of targeted interventions for athletes in various disciplines.

Keywords: electroencephalography, Ritalin, Placebo effect, motivational interview

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Authors: Jillian Correia, Ali Sakkal

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Lunchtime can play a pivotal role in shaping student eating habits. Studies have previously indicated that eating a healthy meal during the school day can improve students’ well-being and academic performance, and potentially prevent childhood obesity. This study investigated the school lunch program in the United Kingdom in order to gain an understanding of the attitudes and beliefs surrounding school meals and the realities of student food patterns. Using a qualitative research methodology, this study was conducted in three primary and secondary school systems in London, United Kingdom. In depth interviews consisting of 14 headteachers, teachers, staff, and chefs and fieldwork observations of approximately 830 primary and secondary school students in the three schools’ cafeterias provided the data. The results of interview responses and fieldwork observation yielded the following set of themes: (a) school meals are publicly portrayed as healthful and nutritious, yet students’ eating habits do not align with this advertising, (b) the level of importance placed on school lunch varies widely among participants and generates inconsistent views concerning who is responsible (government, families, caterers, or schools) for students’ eating habits, (c) role models (i.e. teachers and chefs) present varying levels of interaction with students and conflicting approaches when monitoring students’ eating habits. The latter finding expanded upon Osowski, Göranzon, and Fjellström’s (2013) concept of teacher roles to formulate three education philosophies – the Removed Authority Role Model, the Accommodating Role Model, and the Social Educational Role Model – concluding that the Social Educational Role Model was the most effective at fostering an environment that encouraged healthy eating habits and positive behavior. For schools looking to cultivate strong relationships between students and teachers and facilitate healthier eating habits, these findings were used to construct three key recommendations: (1) elevate the lunch environment by encouraging proper dining etiquette, (2) get teachers eating at the table with students, and (3) shift the focus from monitoring behavior to a teacher-student dialogue centered on food awareness.

Keywords: food culture, eating habits, school meals, student behavior, education, food patterns, lunchtime

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Authors: Jingyi Li, Laurence Stamford, Alejandro Gallego-Schmid

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Renewable electricity has become an indispensable contributor to achieving net-zero by the mid-century to tackle climate change. Unlike solar, wind, or hydro, geothermal was stagnant in its electricity production development for decades. However, with the significant breakthrough made in recent years, especially the implementation of enhanced geothermal systems (EGS) in various regions globally, geothermal electricity could play a pivotal role in alleviating greenhouse gas emissions. Life cycle assessment has been applied to analyze specific geothermal power generation technologies, which proposed suggestions to optimize its environmental performance. For instance, selecting a high heat gradient region enables a higher flow rate from the production well and extends the technical lifespan. Although such process-level improvements have been made, the significance of geothermal power generation technologies so far has not explicitly displayed its competitiveness on a broader horizon. Therefore, this review-based study integrates a circular economy framework into life cycle assessment, clarifying the underlying added values for geothermal power plants to complete the sustainability profile. The derived results have provided an enlarged platform to discuss geothermal power generation technologies: (i) recover the heat and electricity from the process to reduce the fossil fuel requirements; (ii) recycle the construction materials, such as copper, steel, and aluminum for future projects; (iii) extract the lithium ions from geothermal brine and make geothermal reservoir become a potential supplier of the lithium battery industry; (iv) repurpose the abandoned oil and gas wells to build geothermal power plants; (v) integrate geothermal energy with other available renewable energies (e.g., solar and wind) to provide heat and electricity as a hybrid system at different weather; (vi) rethink the fluids used in stimulation process (EGS only), replace water with CO2 to achieve negative emissions from the system. These results provided a new perspective to the researchers, investors, and policymakers to rethink the role of geothermal in the energy supply network.

Keywords: climate, renewable energy, R strategies, sustainability

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Authors: Marta Moczulska

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One of the most important aspects of human management in an organization is the work engagement. In spite of the different perspectives of engagement, it is possible to see that it is expressed in the activity of the individual involved in the performance of tasks, the functioning of the organization. At the same time is considered not only in behavioural but also cognitive and emotional dimensions. Previous studies were related to sources, predictors of engagement and determinants, including organizational ones. Attention was paid to the importance of needs (including belonging, success, development, sense of work), values (such as trust, honesty, respect, justice) or interpersonal relationships, especially with the supervisor. Taking them into account and theories related to human acting, behaviour in the organization, interactions, it was recognized that engagement can be shaped through cooperation and competition. It was assumed that to shape the work engagement, it is necessary to simultaneously cooperate and compete in order to reduce the weaknesses of each of these activities and strengthen the strengths. Combining cooperation and competition is defined as 'coopetition'. However, research conducted in this field is primarily concerned with relations between companies. Intra-organizational coopetition is mainly considered as competing organizational branches or units (cross-functional coopetition). Less attention is paid to competing groups or individuals. It is worth noting the ambiguity of the concepts of cooperation and rivalry. Taking into account the terms used and their meaning, different levels of cooperation and forms of competition can be distinguished. Thus, several types of intra-organizational coopetition can be identified. The article aims at defining the potential for work engagement through intra-organizational coopetition. The aim of research was to know how levels of cooperation in competition conditions influence engagement. It is assumed that rivalry (positive competition) between teams (the highest level of cooperation) is a type of coopetition that contributes to working engagement. Qualitative research will be carried out among students of the University of Zielona Gora, realizing various types of projects. The first research groups will be students working in groups on one project for three months. The second research group will be composed of students working in groups on several projects in the same period (three months). Work engagement will be determined using the UWES questionnaire. Levels of cooperation will be determined using the author's research tool. Due to the fact that the research is ongoing, results will be presented in the final paper.

Keywords: competition, cooperation, intra-organizational coopetition, work engagement

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Authors: U. K. Hulihalli, Shantveerayya

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Buckwheat (Fagopyrum esculentum Moench) is an annual crop belongs to family Poligonaceae. The cultivated buckwheat species are notable for their exceptional nutritive values. It is an important source of carbohydrates, fibre, macro, and microelements such as K, Ca, Mg, Na and Mn, Zn, Se, and Cu. It also contains rutin, flavonoids, riboflavin, pyridoxine and many amino acids which have beneficial effects on human health, including lowering both blood lipid and sugar levels. Rutin, quercetin and some other polyphenols are potent carcinogens against colon and other cancers. Buckwheat has significant nutritive value and plenty of uses. Cultivation of buckwheat in Sothern part of India is very meager. Hence, a study was planned with an objective to know the performance of buckwheat genotypes to different planting geometries and fertility levels. The field experiment was conducted at Main Agriculture Research Station, University of Agriculture Sciences, Dharwad, India, during 2017 Kharif. The experiment was laid-out in split-plot design with three replications having three planting geometries as main plots, two genotypes as sub plots and three fertility levels as sub-sub plot treatments. The soil of the experimental site was vertisol. The standard procedures are followed to record the observations. The planting geometry of 30*10 cm was recorded significantly higher seed yield (893 kg/ha⁻¹), stover yield (1507 kg ha⁻¹), clusters plant⁻¹ (7.4), seeds clusters⁻¹ (7.9) and 1000 seed weight (26.1 g) as compared to 40*10 cm and 20*10 cm planting geometries. Between the genotypes, significantly higher seed yield (943 kg ha⁻¹) and harvest index (45.1) was observed with genotype IC-79147 as compared to PRB-1 genotype (687 kg ha⁻¹ and 34.2, respectively). However, the genotype PRB-1 recorded significantly higher stover yield (1344 kg ha⁻¹) as compared to genotype IC-79147 (1173 kg ha⁻¹). The genotype IC-79147 was recorded significantly higher clusters plant⁻¹ (7.1), seeds clusters⁻¹ (7.9) and 1000 seed weight (24.5 g) as compared PRB-1 (5.4, 5.8 and 22.3 g, respectively). Among the fertility levels tried, the fertility level of 60:30 NP kg ha⁻¹ recorded significantly higher seed yield (845 kg ha^-1) and stover yield (1359 kg ha⁻¹) as compared to 40:20 NP kg ha^-1 (808 and 1259 kg ha⁻¹ respectively) and 20:10 NP kg ha^-1 (793 and 1144 kg ha⁻¹ respectively). Within the treatment combinations, IC 79147 genotype having 30*10 cm planting geometry with 60:30 NP kg ha⁻¹ recorded significantly higher seed yield (1070 kg ha⁻¹), clusters plant⁻¹ (10.3), seeds clusters⁻¹ (9.9) and 1000 seed weight (27.3 g) compared to other treatment combinations.

Keywords: buckwheat, planting geometry, genotypes, fertility levels

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Authors: Amaia Martinez Martinez, Martin Turek, Carlos Ventura, Jay Drew

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This study aims to assess the seismic performance of arch and dome structural systems made from easy to assemble precast blocks of recycled concrete. These systems have been developed by Lock Block Ltd. Company from Vancouver, Canada, as an extension of their currently used retaining wall system. The characterization of the seismic behavior of these structures is performed by a combination of experimental static and dynamic testing, and analytical modeling. For the experimental testing, several tilt tests, as well as a program of shake table testing were undertaken using small scale arch models. A suite of earthquakes with different characteristics from important past events are chosen and scaled properly for the dynamic testing. Shake table testing applying the ground motions in just one direction (in the weak direction of the arch) and in the three directions were conducted and compared. The models were tested with increasing intensity until collapse occurred; which determines the failure level for each earthquake. Since the failure intensity varied with type of earthquake, a sensitivity analysis of the different parameters was performed, being impulses the dominant factor. For all cases, the arches exhibited the typical four-hinge failure mechanism, which was also shown in the analytical model. Experimental testing was also performed reinforcing the arches using a steel band over the structures anchored at both ends of the arch. The models were tested with different pretension levels. The bands were instrumented with strain gauges to measure the force produced by the shaking. These forces were used to develop engineering guidelines for the design of the reinforcement needed for these systems. In addition, an analytical discrete element model was created using 3DEC software. The blocks were designed as rigid blocks, assigning all the properties to the joints including also the contribution of the interlocking shear key between blocks. The model is calibrated to the experimental static tests and validated with the obtained results from the dynamic tests. Then the model can be used to scale up the results to the full scale structure and expanding it to different configurations and boundary conditions.

Keywords: arch, discrete element model, seismic assessment, shake-table testing

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Authors: Elizabeth B. Katana, Brenda N. Simbwa, Josephine Namayanja, Bob O. Amodan, Edirisa J. Nsubuga, Eva A. O. Laker

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Background: Understanding stakeholders’ perceptions towards the implementation of a mass vaccination campaign is important to ensure the design of better strategies to address challenges. We explored stakeholders’ perceptions of the implementation of a nationwide door-to-door mass vaccination campaign for the oral polio vaccine (nOPV2) in Uganda for the two rounds that occurred in January and November 2022. Methods: A qualitative study was conducted among stakeholders who participated in the campaign implementation from 8 districts in Uganda using random sampling. We conducted 46 In-depth interviews lasting 30 – 40 minutes with 6 national/central supervisors, 12 district, 14 sub-county, and 14 parish-level supervisors. Stakeholders were asked about their experiences in the campaign implementation, including challenges faced and their opinions of the campaign impact and use of the door-to-door strategy. Data were analyzed thematically in line with the major campaign activities. Results: Most of the stakeholders were primarily concerned about poor planning, inadequate training of vaccination teams, community resistance including schools, challenges with recruitment and teaming of vaccinators, poor and delayed payments, lack of logistics and motivation for vaccination teams, the timing of the activities and implementing amidst COVID-19 and Ebola. The stakeholders believed that the first round was not well planned and implemented, while the second round was leveraged in their previous experiences. On the other hand, some positive experiences were noted with regard to communication, advocacy and mobilization, vaccine delivery and distribution, district readiness assessments, and cold chain management. Conclusion: This study identified many challenges that were faced in the implementation of the door-to-door mass campaign for nOPV2 in Uganda. This study identified that more needs to be done to improve door-to-door mass campaigns with a focus on motivating the implementers. These findings highlight the need for conducting performance reviews, improved planning, especially routine updates and verification of target populations and training in microplanning, and adequate mapping of community resistance to inform the implementation of future mass campaigns.

Keywords: mass polio vaccination campaigns, door-to-door strategy, stakeholders' perceptions, implementation challenges

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Authors: Zdzisław Kobos, Robert Jędrys, Zbigniew Wochyński

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Motor activity and good health are sine qua non determinants of a proper practice of the profession, especially aviation. Therefore, candidates to the aviation are selected according their psychomotor ability by both specialist medical commissions. Moreover, they must past an examination of the physical fitness. During the studies in the air force academy, eye-hand coordination is formed in two stages. The future aircraft pilots besides all-purpose physical education must practice specialist training on SAGI. Training includes: looping, aerowheel, and gyroscope. Aim of the training on the above listed apparatuses is to form eye-hand coordination during the tasks in the air. Such coordination is necessary to perform various figures in the real flight. Therefore, during the education of the future pilots, determinants of the effective ways of this important parameter of the human body functioning are sought for. Several studies of the sport psychology indicate an important role of the temperament as a factor determining human behavior during the task performance and acquiring operating skills> Polish psychologist Jan Strelau refers to the basic, relatively constant personality features which manifest themselves in the formal characteristics of the human behavior. Temperament, being initially determined by the inborn physiological mechanisms, changes in the course of maturation and some environmental factors and concentrates on the energetic level and reaction characteristics in time. Objectives. This study aimed at seeking a relationship between temperamental features and eye-hand coordination formation during training on SAGI. Material and Methods: Group of 30 students of pilotage was examined in two situations. The first assessment of the eye-hand coordination level was carried out before the beginning of a 30-hour training on SAGI. The second assessment was carried out after training completion. Training lasted for 2 hours once a week. Temperament was evaluated with The Formal Characteristics of Behavior − Temperament Inventory (FCB-TI) developed by Bogdan Zawadzki and Jan Strelau. Eye-hand coordination was assessed with a computer version of the Warsaw System of Psychological Tests. Results: It was found that the training on SAGI increased the level of eye-hand coordination in the examined students. Conclusions: Higher level of the eye-hand coordination was obtained after completion of the training. Moreover, a relationship between eye-hand coordination level and selected temperamental features was statistically significant.

Keywords: temperament, eye-hand coordination, pilot, SAGI

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Authors: Peter O. Osifo, Hein W. J. P. Neomagus, Hein V. D. Merwe

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Chitosan materials from different sources of raw materials were characterized in order to determine optimal preparation conditions and parameters for membrane production. The membrane parameters such as molecular weight, viscosity, and degree of deacetylation were used to evaluate the membrane performance for zinc ion adsorption. The molecular weight of the chitosan was found to influence the viscosity of the chitosan/acetic acid solution. An increase in molecular weight (60000-400000 kg.kmol-1) of the chitosan resulted in a higher viscosity (0.05-0.65 Pa.s) of the chitosan/acetic acid solution. The effect of the degree of deacetylation on the viscosity is not significant. The effect of the membrane production parameters (chitosan- and acetic acid concentration) on the viscosity is mainly determined by the chitosan concentration. For higher chitosan concentrations, a membrane with a better adsorption capacity was obtained. The membrane adsorption capacity increases from 20-130 mg Zn per gram of wet membrane for an increase in chitosan concentration from 2-7 mass %. Chitosan concentrations below 2 and above 7.5 mass % produced membranes that lack good mechanical properties. The optimum manufacturing conditions including chitosan concentration, acetic acid concentration, sodium hydroxide concentration and crosslinking for chitosan membranes within the workable range were defined by the criteria of adsorption capacity and flux. The adsorption increases (50-120 mg.g-1) as the acetic acid concentration increases (1-7 mass %). The sodium hydroxide concentration seems not to have a large effect on the adsorption characteristics of the membrane however, a maximum was reached at a concentration of 5 mass %. The adsorption capacity per gram of wet membrane strongly increases with the chitosan concentration in the acetic acid solution but remains constant per gram of dry chitosan. The optimum solution for membrane production consists of 7 mass % chitosan and 4 mass % acetic acid in de-ionised water. The sodium hydroxide concentration for phase inversion is at optimum at 5 mass %. The optimum cross-linking time was determined to be 6 hours (Percentage crosslinking of 18%). As the cross-linking time increases the adsorption of the zinc decreases (150-50 mg.g-1) in the time range of 0 to 12 hours. After a crosslinking time of 12 hours, the adsorption capacity remains constant. This trend is comparable to the effect on flux through the membrane. The flux decreases (10-3 L.m-2.hr-1) with an increase in crosslinking time range of 0 to 12 hours and reaches a constant minimum after 12 hours.

Keywords: chitosan, membrane, waste water, heavy metal ions, adsorption

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Authors: Amean A. Yasir, Zainab Kh. A. Al-Mahdi Al-Amean

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In both developed and developing countries, obesity among women is increasing, but in different patterns and at very different speeds. It may have a negative effect on health, leading to reduced life expectancy and/or increased health problems. This research studied the age distribution among obese women, the types of overweight and obesity, and the extent of the problem of overweight/obesity and the obesity etiological factors among women in Hillah city in central Iraq. A total of 322 overweight and obese women were included in the study, those women were randomly selected. The Body Mass Index was used as indicator for overweight/ obesity. The incidence of overweight/obesity among age groups were estimated, the etiology factors included genetic, environmental, genetic/environmental and endocrine disease. The overweight and obese women were screened for incidence of infection and/or diseases. The study found that the prevalence of 322 overweight and obese women in Hillah city in central Iraq was 19.25% and 80.78%, respectively. The obese women types were recorded based on BMI and WHO classification as class-1 obesity (29.81%), class-2 obesity (24.22%) and class-3 obesity (26.70%), the result was discrepancy non-significant, P value < 0.05. The incidence of overweight in women was high among those aged 20-29 years (90.32%), 6.45% aged 30-39 years old and 3.22% among ≥ 60 years old, while the incidence of obesity was 20.38% for those in the age group 20-29 years, 17.30% were 30-39 years, 23.84% were 40-49 years, 16.92% were 50-59 years group and 21.53% were ≥ 60 years age group. These results confirm that the age can be considered as a significant factor for obesity types (P value < 0.0001). The result also showed that the both genetic factors and environmental factors were responsible for incidents of overweight or obesity (84.78%) p value < 0.0001. The results also recorded cases of different repeated infections (skin infection, recurrent UTI and influenza), cancer, gallstones, high blood pressure, type 2 diabetes, and infertility. Weight stigma and bias generally refers to negative attitudes; Obesity can affect quality of life, and the results of this study recorded depression among overweight or obese women. This can lead to sexual problems, shame and guilt, social isolation and reduced work performance. Overweight and Obesity are real problems among women of all age groups and is associated with the risk of diseases and infection and negatively affects quality of life. This result warrants further studies into the prevalence of obesity among women in Hillah City in central Iraq and the immune response of obese women.

Keywords: obesity, overweight, Iraq, body mass index

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Authors: Namasivayam Navaranjan, Eric Dimla

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The world needs more quality food, shelter and transportation to meet the demands of growing population and improving living standard of those who currently live below the poverty line. Materials are essential commodities for various applications including food and pharmaceutical packaging, building and automobile. Petroleum based plastics are widely used materials amongst others for these applications and their demand is expected to increase. Use of plastics has environment related issues because considerable amount of plastic used worldwide is disposed in landfills, where its resources are wasted, the material takes up valuable space and blights communities. Some countries have been implementing regulations and/or legislations to increase reuse, recycle, renew and remanufacture materials as well as to minimise the use of non-environmentally friendly materials such as petroleum plastics. However, issue of material waste is still a concern in the countries who have low environmental regulations. Development of materials, mostly bioplastics from renewable biomass resources has become popular in the last decade. It is widely believed that the potential for up to 90% substitution of total plastics consumption by bioplastics is technically possible. The global demand for bioplastics is estimated to be approximately six times larger than in 2010. Recently, standard polymers like polyethylene (PE), polypropylene (PP), Polyvinyl Chloride (PVC) or Polyethylene terephthalate (PET), but also high-performance polymers such as polyamides or polyesters have been totally or partially substituted by their renewable equivalents. An example is Polylactide (PLA) being used as a substitute in films and injection moulded products made of petroleum plastics, e.g. PET. The starting raw materials for bio-based materials are usually sugars or starches that are mostly derived from food resources, partially also recycled materials from food or wood processing. The risk in lower food availability by increasing price of basic grains as a result of competition with biomass-based product sectors for feedstock also needs to be considered for the future bioplastic production. Manufacturing of bioplastic materials is often still reliant upon petroleum as an energy and materials source. Life Cycle Assessment (LCA) of bioplastic products has being conducted to determine the sustainability of a production route. However, the accuracy of LCA depends on several factors and needs improvement. Low oil price and high production cost may also limit the technically possible growth of these plastics in the coming years.

Keywords: bioplastics, plastics, renewable resources, biomass

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Authors: Arefeh Arabaninezhad, Ali Fakher

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Since the deterministic analysis methods fail to take system uncertainties into account, probabilistic and non-probabilistic methods are suggested. Geotechnical analyses are used to determine the stress and deformation caused by construction; accordingly, many input variables which depend on ground behavior are required for geotechnical analyses. The Random Set approach is an applicable reliability analysis method when comprehensive sources of information are not available. Using Random Set method, with relatively small number of simulations compared to fully probabilistic methods, smooth extremes on system responses are obtained. Therefore random set approach has been proposed for reliability analysis in geotechnical problems. In the present study, the application of random set method in reliability analysis of deep excavations is investigated through three deep excavation projects which were monitored during the excavating process. A finite element code is utilized for numerical modeling. Two expected ranges, from different sources of information, are established for each input variable, and a specific probability assignment is defined for each range. To determine the most influential input variables and subsequently reducing the number of required finite element calculations, sensitivity analysis is carried out. Input data for finite element model are obtained by combining the upper and lower bounds of the input variables. The relevant probability share of each finite element calculation is determined considering the probability assigned to input variables present in these combinations. Horizontal displacement of the top point of excavation is considered as the main response of the system. The result of reliability analysis for each intended deep excavation is presented by constructing the Belief and Plausibility distribution function (i.e. lower and upper bounds) of system response obtained from deterministic finite element calculations. To evaluate the quality of input variables as well as applied reliability analysis method, the range of displacements extracted from models has been compared to the in situ measurements and good agreement is observed. The comparison also showed that Random Set Finite Element Method applies to estimate the horizontal displacement of the top point of deep excavation. Finally, the probability of failure or unsatisfactory performance of the system is evaluated by comparing the threshold displacement with reliability analysis results.

Keywords: deep excavation, random set finite element method, reliability analysis, uncertainty

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Authors: Ali Hamza

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Accurate segmentation of breast ultrasound images is of paramount importance in enhancing the diagnostic capabilities of breast cancer detection. This study presents an approach utilizing the U-Net architecture for segmenting breast ultrasound images aimed at improving the accuracy and reliability of mass identification within the breast tissue. The proposed method encompasses a multi-stage process. Initially, preprocessing techniques are employed to refine image quality and diminish noise interference. Subsequently, the U-Net architecture, a deep learning convolutional neural network (CNN), is employed for pixel-wise segmentation of regions of interest corresponding to potential breast masses. The U-Net's distinctive architecture, characterized by a contracting and expansive pathway, enables accurate boundary delineation and detailed feature extraction. To evaluate the effectiveness of the proposed approach, an extensive dataset of breast ultrasound images is employed, encompassing diverse cases. Quantitative performance metrics such as the Dice coefficient, Jaccard index, sensitivity, specificity, and Hausdorff distance are employed to comprehensively assess the segmentation accuracy. Comparative analyses against traditional segmentation methods showcase the superiority of the U-Net architecture in capturing intricate details and accurately segmenting breast masses. The outcomes of this study emphasize the potential of the U-Net-based segmentation approach in bolstering breast ultrasound image analysis. The method's ability to reliably pinpoint mass boundaries holds promise for aiding radiologists in precise diagnosis and treatment planning. However, further validation and integration within clinical workflows are necessary to ascertain their practical clinical utility and facilitate seamless adoption by healthcare professionals. In conclusion, leveraging the U-Net architecture for breast ultrasound image segmentation showcases a robust framework that can significantly enhance diagnostic accuracy and advance the field of breast cancer detection. This approach represents a pivotal step towards empowering medical professionals with a more potent tool for early and accurate breast cancer diagnosis.

Keywords: mage segmentation, U-Net, deep learning, breast cancer detection, diagnostic accuracy, mass identification, convolutional neural network

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Authors: H. Medjadji, A. Boulahouache, N. Salhi, A. Boudjemaa, M. Trari

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Hydrogen from renewable sources, such as solar, is referred to as green hydrogen. The splitting water process using semiconductors, such as photocatalysts, has attracted significant attention due to its potential application for solving the energy crisis and environmental pollution. Spinel ferrites of the MF₂O₄ type have shown broad interest in diverse energy conversion processes, including fuel cells and photo electrocatalytic water splitting. This work focuses on preparing nano-spinels based on iron AFe₂O₄ (A= Ca, Co, and Cu) as photocatalysts using the nitrate method. These materials were characterized both physically and optically and subsequently tested for hydrogen generation under visible light irradiation. Various techniques were used to investigate the properties of the materials, including TGA-DT, X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), UV-visible spectroscopy, Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy (SEM-EDX) and X-ray Photoelectron Spectroscopy (XPS) was also undertaken. XRD analysis confirmed the formation of pure phases at 850°C, with crystalline sizes of 31 nm for CaFe₂O₄, 27 nm for CoFe₂O₄, and 40 nm for CuFe₂O₄. The energy gaps, calculated from recorded diffuse reflection data, are 1.85 eV for CaFe₂O₄, 1.27 eV for CoFe₂O₄, and 1.64 eV for CuFe₂O₄. SEM micrographs showed homogeneous grains with uniform shapes and medium porosity in all samples. EDX elemental analysis determined the absence of any contaminating elements, highlighting the high purity of the prepared materials via the nitrate route. XPS spectra revealed the presence of Fe3+ and O in all samples. Additionally, XPS analysis revealed the presence of Ca²⁺, Co²⁺, and Cu²⁺ on the surface of CaFe₂O₄ and CoFe₂O₄ spinels, respectively. The photocatalytic activity was successfully evaluated by measuring H₂ evolution through the water-splitting process. The best performance was achieved with CaFe₂O₄ in a neutral medium (pH ~ 7), yielding 189 µmol at an optimal temperature of ~50°C. The highest hydrogen production rates for CoFe₂O₄ and CuFe₂O₄ were obtained at pH ~ 12 with release rates of 65 and 85 µmol, respectively, under visible light irradiation at the same optimal temperature. Various conditions were investigated including the pH of the solution, the hole sensors utilization and recyclability.

Keywords: hydrogen, MFe₂O₄, nitrate route, spinel ferrite

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Authors: Yodai Ashikubo, Toshiaki Suzuki, Satoshi Kouya, Mitsuya Motohashi

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Functional microstructures such as wires, fins, needles, and rolls are currently being applied to variety of high-performance devices. Under these conditions, a roll structure (silicon micro-nanoroll) was formed on the surface of the silicon substrate via fine bubbles during anodization using an extremely diluted hydrofluoric acid (HF + H₂O). The as-formed roll had a microscale length and width of approximately 1 µm. The number of rolls was 3-10 times and the thickness of the film forming the rolls was about 10 nm. Thus, it is promising for applications as a distinct device material. These rolls functioned as capsules and/or pipelines. To date, number of rolls and roll length have been controlled by anodization conditions. In general, controlling the position and roll winding state is required for device applications. However, it has not been discussed. Grooves formed on silicon surface before anodization might be useful control the bubbles. In this study, we investigated the effect of the grooves on the position and shape of the roll. The surfaces of the silicon wafers were anodized. The starting material was p-type (100) single-crystalline silicon wafers. The resistivity of the wafer is 5-20 ∙ cm. Grooves were formed on the surface of the substrate before anodization using sandpaper and diamond pen. The average width and depth of the grooves were approximately 1 µm and 0.1 µm, respectively. The HF concentration {HF/ (HF + C₂H5OH + H₂O)} was 0.001 % by volume. The C2H5OH concentration {C₂H5OH/ (HF + C₂H5OH + H₂O)} was 70 %. A vertical single-tank cell and Pt cathode were used for anodization. The silicon roll was observed by field-emission scanning electron microscopy (FE-SEM; JSM-7100, JEOL). The atomic bonding state of the rolls was evaluated using X-ray photoelectron spectroscopy (XPS; ESCA-3400, Shimadzu). For straight groove, the rolls were formed along the groove. This indicates that the orientation of the rolls can be controlled by the grooves. For lattice-like groove, the rolls formed inside the lattice and along the long sides. In other words, the aspect ratio of the lattice is very important for the roll formation. In addition, many rolls were formed and winding states were not uniform when the lattice size is too large. On the other hand, no rolls were formed for small lattice. These results indicate that there is the optimal size of lattice for roll formation. In the future, we are planning on formation of rolls using groove formed by lithography technique instead of sandpaper and the pen. Furthermore, the rolls included nanoparticles will be formed for nanodevices.

Keywords: silicon roll, anodization, fine bubble, microstructure

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Authors: Kirolos Gerges Yakoub Gerges

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Self-sustaining agricultural machines act in stochastic surroundings and therefore, should be capable of perceive the surroundings in real time. This notion can be done using image sensors blended with superior device learning, mainly Deep mastering. Deep convolutional neural networks excel in labeling and perceiving colour pix and since the fee of RGB-cameras is low, the hardware cost of accurate notion relies upon heavily on memory and computation power. This paper investigates the opportunity of designing lightweight convolutional neural networks for semantic segmentation (pixel clever class) with reduced hardware requirements, to allow for embedded usage in self-reliant agricultural machines. The usage of compression techniques, a lightweight convolutional neural community is designed to carry out actual-time semantic segmentation on an embedded platform. The community is skilled on two big datasets, ImageNet and Pascal Context, to apprehend as much as four hundred man or woman instructions. The 400 training are remapped into agricultural superclasses (e.g. human, animal, sky, road, area, shelterbelt and impediment) and the capacity to provide correct actual-time perception of agricultural environment is studied. The network is carried out to the case of self-sufficient grass mowing the usage of the NVIDIA Tegra X1 embedded platform. Feeding case-unique pics to the community consequences in a fully segmented map of the superclasses within the picture. As the network remains being designed and optimized, handiest a qualitative analysis of the technique is entire on the abstract submission deadline. intending this cut-off date, the finalized layout is quantitatively evaluated on 20 annotated grass mowing pictures. Light-weight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show aggressive performance on the subject of accuracy and speed. It’s miles viable to offer value-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.

Keywords: centrifuge pump, hydraulic energy, agricultural applications, irrigationaxial flux machines, axial flux applications, coreless machines, PM machinesautonomous agricultural machines, deep learning, safety, visual perception

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Authors: S. Lertsakulpasuk, S. Athichanagorn

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As depletion-drive gas reservoirs are abandoned when there is insufficient production rate due to pressure depletion, waterflooding has been proposed to increase the reservoir pressure in order to prolong gas production. Due to high cost, water injection may not be economically feasible. Water dumpflood into gas reservoirs is a new promising approach to increase gas recovery by maintaining reservoir pressure with much cheaper costs than conventional waterflooding. Thus, a simulation study of water dumpflood into multiple nearly abandoned or already abandoned thin-bedded gas reservoirs commonly found in the Gulf of Thailand was conducted to demonstrate the advantage of the proposed method and to determine the most suitable operational parameters for reservoirs having different system parameters. A reservoir simulation model consisting of several thin-layered depletion-drive gas reservoirs and an overlying aquifer was constructed in order to investigate the performance of the proposed method. Two producers were initially used to produce gas from the reservoirs. One of them was later converted to a dumpflood well after gas production rate started to decline due to continuous reduction in reservoir pressure. The dumpflood well was used to flow water from the aquifer to increase pressure of the gas reservoir in order to drive gas towards producer. Two main operational parameters which are wellhead pressure of producer and the time to start water dumpflood were investigated to optimize gas recovery for various systems having different gas reservoir dip angles, well spacings, aquifer sizes, and aquifer depths. This simulation study found that water dumpflood can increase gas recovery up to 12% of OGIP depending on operational conditions and system parameters. For the systems having a large aquifer and large distance between wells, it is best to start water dumpflood when the gas rate is still high since the long distance between the gas producer and dumpflood well helps delay water breakthrough at producer. As long as there is no early water breakthrough, the earlier the energy is supplied to the gas reservoirs, the better the gas recovery. On the other hand, for the systems having a small or moderate aquifer size and short distance between the two wells, performing water dumpflood when the rate is close to the economic rate is better because water is more likely to cause an early breakthrough when the distance is short. Water dumpflood into multiple nearly-depleted or depleted gas reservoirs is a novel study. The idea of using water dumpflood to increase gas recovery has been mentioned in the literature but has never been investigated. This detailed study will help a practicing engineer to understand the benefits of such method and can implement it with minimum cost and risk.

Keywords: dumpflood, increase gas recovery, low-pressure gas reservoir, multiple gas reservoirs

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Authors: Jifeng Zhang , Yongpeng Lei

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Continuous carbon fiber reinforced polyamide 6 (CF/PA6) composites are potential for application in the automotive industry due to their high specific strength and stiffness. However, PA6 resin is sensitive to the moisture in the hydrothermal environment and CF/PA6 composites might undergo several physical and chemical changes, such as plasticization, swelling, and hydrolysis, which induces a reduction of mechanical properties. So far, little research has been reported on the assessment of the effects of hydrothermal aging on the mechanical properties of continuous CF/PA6 composite. This study deals with the effects of hydrothermal aging on moisture absorption and mechanical properties of polyamide 6 (PA6) and polyamide 6 reinforced with continuous carbon fibers composites (CF/PA6) by immersion in distilled water at 30 ℃, 50 ℃, 70 ℃, and 90 ℃. Degradation of mechanical performance has been monitored, depending on the water absorption content and the aging temperature. The experimental results reveal that under the same aging condition, the PA6 resin absorbs more water than the CF/PA6 composite, while the water diffusion coefficient of CF/PA6 composite is higher than that of PA6 resin because of interfacial diffusion channel. In mechanical properties degradation process, an exponential reduction in tensile strength and elastic modulus are observed in PA6 resin as aging temperature and water absorption content increases. The degradation trend of flexural properties of CF/PA6 is the same as that of tensile properties of PA6 resin. Moreover, the water content plays a decisive role in mechanical degradation compared with aging temperature. In contrast, hydrothermal environment has mild effect on the tensile properties of CF/PA6 composites. The elongation at breakage of PA6 resin and CF/PA6 reaches the highest value when their water content reaches 6% and 4%, respectively. Dynamic mechanical analysis (DMA) and scanning electron microscope (SEM) were also used to explain the mechanism of mechanical properties alteration. After exposed to the hydrothermal environment, the Tg (glass transition temperature) of samples decreases dramatically with water content increase. This reduction can be ascribed to the plasticization effect of water. For the unaged specimens, the fibers surface is coated with resin and the main fracture mode is fiber breakage, indicating that a good adhesion between fiber and matrix. However, with absorbed water content increasing, the fracture mode transforms to fiber pullout. Finally, based on Arrhenius methodology, a predictive model with relate to the temperature and water content has been presented to estimate the retention of mechanical properties for PA6 and CF/PA6.

Keywords: continuous carbon fiber reinforced polyamide 6 composite, hydrothermal aging, Arrhenius methodology, interface

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Authors: Marius Sebastian Secula, Mihaela Darie, Gabriela Carja

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Ibuprofen is a non-steroidal anti-inflammatory drug (NSAIDs) and is among the most frequently detected pharmaceuticals in environmental samples and among the most widespread drug in the world. Its concentration in the environment is reported to be between 10 and 160 ng L-1. In order to improve the abatement efficiency of this compound for water source prevention and reclamation, the development of innovative technologies is mandatory. AOPs (advanced oxidation processes) are known as highly efficient towards the oxidation of organic pollutants. Among the promising combined treatments, photo-Fenton processes using layered double hydroxides (LDHs) attracted significant consideration especially due to their composition flexibility, high surface area and tailored redox features. This work presents the self-supported Fe, Mn or Ti on ZnFeAl LDHs obtained by co-precipitation followed by reconstruction method as novel efficient photo-catalysts for Fenton-like catalysis. Fe, Mn or Ti/ZnFeAl LDHs nano-hybrids were tested for the degradation of a model pharmaceutical agent, the anti-inflammatory agent ibuprofen, by photocatalysis and photo-Fenton catalysis, respectively, by means of a lab-scale system consisting of a batch reactor equipped with an UV lamp (17 W). The present study presents comparatively the degradation of Ibuprofen in aqueous solution UV light irradiation using four different types of LDHs. The newly prepared Ti/ZnFeAl 4:1 catalyst results in the best degradation performance. After 60 minutes of light irradiation, the Ibuprofen removal efficiency reaches 95%. The slowest degradation of Ibuprofen solution occurs in case of Fe/ZnFeAl 4:1 LDH, (67% removal efficiency after 60 minutes of process). Evolution of Ibuprofen degradation during the photo Fenton process is also studied using Ti/ZnFeAl 2:1 and 4:1 LDHs in the presence and absence of H2O2. It is found that after 60 min the use of Ti/ZnFeAl 4:1 LDH in presence of 100 mg/L H2O2 leads to the fastest degradation of Ibuprofen molecule. After 120 min, both catalysts Ti/ZnFeAl 4:1 and 2:1 result in the same value of removal efficiency (98%). In the absence of H2O2, Ibuprofen degradation reaches only 73% removal efficiency after 120 min of degradation process. Acknowledgements: This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS - UEFISCDI, project number PN-II-RU-TE-2014-4-0405.

Keywords: layered double hydroxide, advanced oxidation process, micropollutant, heterogeneous Fenton

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Authors: Kari Bjorn

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Team learning has been studied and modeled as double loop model and its variations. Also, metacognition has been suggested as a concept to describe the nature of team learning to be more than a simple sum of individual learning of the team members. Team learning has a positive correlation with both individual motivation of its members, as well as the collective factors within the team. Team learning of previously very independent members of two teaching teams is analyzed. Applied Science Universities are training future professionals with ever more diversified and multidisciplinary skills. The size of the units of teaching and learning are increasingly larger for several reasons. First, multi-disciplinary skill development requires more active learning and richer learning environments and learning experiences. This occurs on students teams. Secondly, teaching of multidisciplinary skills requires a multidisciplinary and team-based teaching from the teachers as well. Team formation phases have been identifies and widely accepted. Team role stress has been analyzed in project teams. Projects typically have a well-defined goal and organization. This paper explores team stress of two teacher teams in a parallel running two course units in engineering education. The first is an Industrial Automation Technology and the second is Development of Medical Devices. The courses have a separate student group, and they are in different campuses. Both are run in parallel within 8 week time. Both of them are taught by a group of four teachers with several years of teaching experience, but individually. The team role stress scale items - the survey is done to both teaching groups at the beginning of the course and at the end of the course. The inventory of questions covers the factors of ambiguity, conflict, quantitative role overload and qualitative role overload. Some comparison to the study on project teams can be drawn. Team development stage of the two teaching groups is different. Relating the team role stress factors to the development stage of the group can reveal the potential of management actions to promote team building and to understand the maturity of functional and well-established teams. Mature teams indicate higher job satisfaction and deliver higher performance. Especially, teaching teams who deliver highly intangible results of learning outcome are sensitive to issues in the job satisfaction and team conflicts. Because team teaching is increasing, the paper provides a review of the relevant theories and initial comparative and longitudinal results of the team role stress factors applied to teaching teams.

Keywords: engineering education, stress, team role, team teaching

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Authors: May Fadheel Estephan, Richard Perks

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Context: Cancer is a serious health concern that affects millions of people worldwide. Early detection and treatment are essential for improving patient outcomes. However, current methods for cancer detection have limitations, such as low sensitivity and specificity. Research Aim: The aim of this study was to develop an optical sensor for cancer detection using elastic light scattering spectroscopy (ELSS). ELSS is a noninvasive optical technique that can be used to characterize the size and concentration of particles in a solution. Methodology: An optical probe was fabricated with a 100-μm-diameter core and a 132-μm centre-to-centre separation. The probe was used to measure the ELSS spectra of polystyrene spheres with diameters of 2, 0.8, and 0.413 μm. The spectra were then analysed to determine the size and concentration of the spheres. Findings: The results showed that the optical probe was able to differentiate between the three different sizes of polystyrene spheres. The probe was also able to detect the presence of polystyrene spheres in suspension concentrations as low as 0.01%. Theoretical Importance: The results of this study demonstrate the potential of ELSS for cancer detection. ELSS is a noninvasive technique that can be used to characterize the size and concentration of cells in a tissue sample. This information can be used to identify cancer cells and assess the stage of the disease. Data Collection: The data for this study were collected by measuring the ELSS spectra of polystyrene spheres with different diameters. The spectra were collected using a spectrometer and a computer. Analysis Procedures: The ELSS spectra were analysed using a software program to determine the size and concentration of the spheres. The software program used a mathematical algorithm to fit the spectra to a theoretical model. Question Addressed: The question addressed by this study was whether ELSS could be used to detect cancer cells. The results of the study showed that ELSS could be used to differentiate between different sizes of cells, suggesting that it could be used to detect cancer cells. Conclusion: The findings of this research show the utility of ELSS in the early identification of cancer. ELSS is a noninvasive method for characterizing the number and size of cells in a tissue sample. To determine cancer cells and determine the disease's stage, this information can be employed. Further research is needed to evaluate the clinical performance of ELSS for cancer detection.

Keywords: elastic light scattering spectroscopy, polystyrene spheres in suspension, optical probe, fibre optics

Procedia PDF Downloads 82

Authors: Albert Amagyei, Julia Carroll, Amanda R. Amorim Adegboye, Laura Strumidlo, Rosie Kneafsey

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Introduction: Classroom-based learning has persisted as the mainstream model of pre-registration nurse education. This model is often rigid, teacher-centered, and unable to support active learning and the practical learning needs of nursing students. Health Education England (HEE), a public body of the Department of Health and Social Care, hypothesises that blended learning (BL) programmes may address health system and nursing profession challenges, such as nursing shortages and lack of digital expertise, by exploring opportunities for providing predominantly online, remote-access study which may increase nursing student recruitment, offering alternate pathways to nursing other than the traditional classroom route. This study will provide evidence for blended learning strategies adopted in nursing education as well as examine nursing student learning experiences concerning the challenges and opportunities related to using blended learning within nursing education. Objective: This review will explore the challenges and opportunities of BL within pre-registration nurse education from the student's perspective. Methods: The search was completed within five databases. Eligible studies were appraised independently by four reviewers. The JBI-convergent segregated approach for mixed methods review was used to assess and synthesize the data. The study’s protocol has been registered with the International Register of Systematic Reviews with registration number// PROSPERO (CRD42023423532). Results: Twenty-seven (27) studies (21 quantitative and 6 qualitative) were included in the review. The study confirmed that BL positively impacts nursing students' learning outcomes, as demonstrated by the findings of the meta-analysis and meta-synthesis. Conclusion: The review compared BL to traditional learning, simulation, laboratory, and online learning on nursing students’ learning and programme outcomes as well as learning behaviour and experience. The results show that BL could effectively improve nursing students’ knowledge, academic achievement, critical skills, and clinical performance as well as enhance learner satisfaction and programme retention. The review findings outline that students’ background characteristics, BL design, and format significantly impact the success of the BL nursing programme.

Keywords: nursing student, blended learning, pre-registration nurse education, online learning

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Authors: Qian Wang, Haibo Gu

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To drive practical interpretations and applications of various policies in building the transnational education joint-ventures, middle managers learn to navigate through uncertainties and ambiguities. However, the current literature views very little about those middle managers’ experiences, perceptions, and practices. This paper takes the empirical approach and aims to uncover the middle managers’ experiences by conducting interviews, campus visits, and document analysis. Following the qualitative research method approach, the researchers gathered information from a mixture of fourteen foreign and Chinese managers. Their perceptions of the China-foreign cooperation in higher education and their perceived roles have offered important, valuable insights to this group of people’s attitudes and management performances. The diverse cultural and demographic backgrounds contributed to the significance of the study. There are four key findings. One, middle managers’ immediate micro-contexts and individual attitudes are the top two influential factors in managers’ performances. Two, the foreign middle managers showed a stronger sense of self-identity in risk-taking. Three, the Chinese middle managers preferred to see difficulties as part of their assigned responsibilities. Four, middle managers in independent universities demonstrated a stronger sense of belonging and fewer frustrations than middle managers in secondary institutes. The researchers propose that training for managers in a transnational educational setting should consider these discoveries when select fitting topics and content. In particular, middle managers should be better prepared to anticipate their everyday jobs in the micro-environment; hence, information concerning sponsor organizations’ working culture is as essential as knowing the national and local regulations, and socio-culture. Different case studies can help the managers to recognize and celebrate the diversity in transnational education. Situational stories can help them to become aware of the diverse and wide range of work contexts so that they will not feel to be left alone when facing challenges without relevant previous experience or training. Though this research is a case study based in the Chinese transnational higher education setting, the implications could be relevant and comparable to other transnational higher education situations and help to continue expanding the potential applications in this field.

Keywords: educational management, middle manager performance, transnational higher education

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Authors: Buruk Kitachew Wossenyeleh

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Groundwater is the largest freshwater reservoir in the world. Like the other reservoirs of the hydrologic cycle, it is a finite resource. This study focused on the groundwater modeling of the Ter Kamerenbos well field to understand the groundwater flow system and the impact of different scenarios. The study area covers 68.9Km2 in the Brussels Capital Region and is situated in two river catchments, i.e., Zenne River and Woluwe Stream. The aquifer system has three layers, but in the modeling, they are considered as one layer due to their hydrogeological properties. The catchment aquifer system is replenished by direct recharge from rainfall. The groundwater recharge of the catchment is determined using the spatially distributed water balance model called WetSpass, and it varies annually from zero to 340mm. This groundwater recharge is used as the top boundary condition for the groundwater modeling of the study area. During the groundwater modeling using Processing MODFLOW, constant head boundary conditions are used in the north and south boundaries of the study area. For the east and west boundaries of the study area, head-dependent flow boundary conditions are used. The groundwater model is calibrated manually and automatically using observed hydraulic heads in 12 observation wells. The model performance evaluation showed that the root means the square error is 1.89m and that the NSE is 0.98. The head contour map of the simulated hydraulic heads indicates the flow direction in the catchment, mainly from the Woluwe to Zenne catchment. The simulated head in the study area varies from 13m to 78m. The higher hydraulic heads are found in the southwest of the study area, which has the forest as a land-use type. This calibrated model was run for the climate change scenario and well operation scenario. Climate change may cause the groundwater recharge to increase by 43% and decrease by 30% in 2100 from current conditions for the high and low climate change scenario, respectively. The groundwater head varies for a high climate change scenario from 13m to 82m, whereas for a low climate change scenario, it varies from 13m to 76m. If doubling of the pumping discharge assumed, the groundwater head varies from 13m to 76.5m. However, if the shutdown of the pumps is assumed, the head varies in the range of 13m to 79m. It is concluded that the groundwater model is done in a satisfactory way with some limitations, and the model output can be used to understand the aquifer system under steady-state conditions. Finally, some recommendations are made for the future use and improvement of the model.

Keywords: Ter Kamerenbos, groundwater modelling, WetSpass, climate change, well operation

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Authors: Asadullah Sheikh, Kerry Barnett, Paul Ayres

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Despite significant progress in access, equity and public examination success, poor student performance in mathematics in secondary schools has become a major concern in Bangladesh. A substantial body of research has emphasised the important contribution of teaching practices to student achievement. However, this has not been investigated in Bangladesh. Therefore, the study sought to find out the effectiveness of mathematics teaching practices as a means of improving secondary school mathematics in Dhaka Municipality City (DMC) area, Bangladesh. The purpose of this study was twofold, first, to identify the 20 highest performing secondary schools in mathematics in DMC, and second, to investigate the teaching practices of mathematics teachers in these schools. A two-phase mixed method approach was adopted. In the first phase, secondary source data were obtained from the Board of Intermediate and Secondary Education (BISE), Dhaka and value-added measures used to identify the 20 highest performing secondary schools in mathematics. In the second phase, a concurrent mixed method design, where qualitative methods were embedded within a dominant quantitative approach was utilised. A purposive sampling strategy was used to select fifteen teachers from the 20 highest performing secondary schools. The main sources of data were classroom teaching observations, and teacher interviews. The data from teacher observations were analysed with descriptive and nonparametric statistics. The interview data were analysed qualitatively. The main findings showed teachers adopt a direct teaching approach which incorporates orientation, structuring, modelling, practice, questioning and teacher-student interaction that creates an individualistic learning environment. The variation in developmental levels of teaching skill indicate that teachers do not necessarily use the qualitative (i.e., focus, stage, quality and differentiation) aspects of teaching behaviours effectively. This is the first study to investigate teaching behaviours of effective secondary mathematics teachers within Dhaka, Bangladesh. It contributes in an international dimension to the field of educational effectiveness and raise questions about existing constructivist approaches. Further, it contributes to important insights about teaching behaviours that can be used to inform the development of evidence-based policy and practice on quality teaching in Bangladesh.

Keywords: effective teaching, mathematics, secondary schools, student achievement, value-added measures

Procedia PDF Downloads 241

Authors: John-Nelson Ekumah, Selorm Yao-Say Solomon Adade, Mingming Zhong, Yufan Sun, Qiufang Liang, Muhammad Safiullah Virk, Xorlali Nunekpeku, Nana Adwoa Nkuma Johnson, Bridget Ama Kwadzokpui, Xiaofeng Ren

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Enhancing starch gel strength and stability is crucial. However, traditional gel property assessment methods are destructive, time-consuming, and resource-intensive. Thus, understanding ethanol treatment effects on kudzu starch gel strength and developing a rapid, nondestructive gel strength assessment method is essential for optimizing the treatment process and ensuring product quality consistency. This study investigated the effects of different ethanol concentrations on the microstructure of kudzu starch gels using a comprehensive microstructural analysis. We also developed a nondestructive method for predicting gel strength and classifying treatment levels using near-infrared (NIR) spectroscopy, and advanced data analytics. Scanning electron microscopy revealed progressive network densification and pore collapse with increasing ethanol concentration, correlating with enhanced mechanical properties. NIR spectroscopy, combined with various variable selection methods (CARS, GA, and UVE) and modeling algorithms (PLS, SVM, and ELM), was employed to develop predictive models for gel strength. The UVE-SVM model demonstrated exceptional performance, with the highest R² values (Rc = 0.9786, Rp = 0.9688) and lowest error rates (RMSEC = 6.1340, RMSEP = 6.0283). Pattern recognition algorithms (PCA, LDA, and KNN) successfully classified gels based on ethanol treatment levels, achieving near-perfect accuracy. This integrated approach provided a multiscale perspective on ethanol-induced starch gel modification, from molecular interactions to macroscopic properties. Our findings demonstrate the potential of NIR spectroscopy, coupled with advanced data analysis, as a powerful tool for rapid, nondestructive quality assessment in starch gel production. This study contributes significantly to the understanding of starch modification processes and opens new avenues for research and industrial applications in food science, pharmaceuticals, and biomaterials.

Keywords: kudzu starch gel, near-infrared spectroscopy, gel strength prediction, support vector machine, pattern recognition algorithms, ethanol treatment

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Authors: Marek Dosbaba

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Within the mining sector, SEM-based Automated Mineralogy (AM) has been the standard application for quickly and efficiently handling mineral processing tasks. Over the last decade, the trend has been to analyze larger numbers of samples, often with a higher level of detail. This has necessitated a shift from interactive sample analysis performed by an operator using a SEM, to an increased reliance on offline processing to analyze and report the data. In response to this trend, TESCAN TIMA Mineral Analyzer is designed to quickly create a virtual copy of the studied samples, thereby preserving all the necessary information. Depending on the selected data acquisition mode, TESCAN TIMA can perform hyperspectral mapping and save an X-ray spectrum for each pixel or segment, respectively. This approach allows the user to browse through elemental distribution maps of all elements detectable by means of energy dispersive spectroscopy. Re-evaluation of the existing data for the presence of previously unconsidered elements is possible without the need to repeat the analysis. Additional tiers of data such as a secondary electron or cathodoluminescence images can also be recorded. To take full advantage of these information-rich datasets, TIMA utilizes a new archiving tool introduced by TESCAN. The dataset size can be reduced for long-term storage and all information can be recovered on-demand in case of renewed interest. TESCAN TIMA is optimized for network storage of its datasets because of the larger data storage capacity of servers compared to local drives, which also allows multiple users to access the data remotely. This goes hand in hand with the support of remote control for the entire data acquisition process. TESCAN also brings a newly extended open-source data format that allows other applications to extract, process and report AM data. This offers the ability to link TIMA data to large databases feeding plant performance dashboards or geometallurgical models. The traditional tabular particle-by-particle or grain-by-grain export process is preserved and can be customized with scripts to include user-defined particle/grain properties.

Keywords: Tescan, electron microscopy, mineralogy, SEM, automated mineralogy, database, TESCAN TIMA, open format, archiving, big data

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Authors: Sudha T., Naveen C.

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Dengue is a serious public health issue that causes significant annual economic and welfare burdens on nations. However, enhanced optimization techniques and quantitative modeling approaches can predict the incidence of dengue. By advocating for a data-driven approach, public health officials can make informed decisions, thereby improving the overall effectiveness of sudden disease outbreak control efforts. The National Oceanic and Atmospheric Administration and the Centers for Disease Control and Prevention are two of the U.S. Federal Government agencies from which this study uses environmental data. Based on environmental data that describe changes in temperature, precipitation, vegetation, and other factors known to affect dengue incidence, many predictive models are constructed that use different machine learning methods to estimate weekly dengue cases. The first step involves preparing the data, which includes handling outliers and missing values to make sure the data is prepared for subsequent processing and the creation of an accurate forecasting model. In the second phase, multiple feature selection procedures are applied using various machine learning models and optimization techniques. During the third phase of the research, machine learning models like the Huber Regressor, Support Vector Machine, Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR) are compared with several optimization techniques for feature selection, such as Harmony Search and Genetic Algorithm. In the fourth stage, the model's performance is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) as assistance. Selecting an optimization strategy with the least number of errors, lowest price, biggest productivity, or maximum potential results is the goal. In a variety of industries, including engineering, science, management, mathematics, finance, and medicine, optimization is widely employed. An effective optimization method based on harmony search and an integrated genetic algorithm is introduced for input feature selection, and it shows an important improvement in the model's predictive accuracy. The predictive models with Huber Regressor as the foundation perform the best for optimization and also prediction.

Keywords: deep learning model, dengue fever, prediction, optimization

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Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

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Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: deep learning, disease prediction, end-to-end machine learning, metagenomics, multiple instance learning, precision medicine

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Authors: Alicia Yanadira Nava Fuentes, Ilse Cervantes Camacho, Amadeo José Argüelles Cruz, Ana María Balboa Verduzco

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Augmented reality promises being present in future driving, with its immersive technology let to show directions and maps to identify important places indicating with graphic elements when the car driver requires the information. On the other side, driving is considered a multitasking activity and, for some people, a complex activity where different situations commonly occur that require the immediate attention of the car driver to make decisions that contribute to avoid accidents; therefore, the main aim of the project is the instrumentation of a platform with biometric sensors that allows evaluating the performance in driving vehicles with the influence of augmented reality devices to detect the level of attention in drivers, since it is important to know the effect that it produces. In this study, the physiological sensors EPOC X (EEG), ECG06 PRO and EMG Myoware are joined in the driving test platform with a Logitech G29 steering wheel and the simulation software City Car Driving in which the level of traffic can be controlled, as well as the number of pedestrians that exist within the simulation obtaining a driver interaction in real mode and through a MSP430 microcontroller achieves the acquisition of data for storage. The sensors bring a continuous analog signal in time that needs signal conditioning, at this point, a signal amplifier is incorporated due to the acquired signals having a sensitive range of 1.25 mm/mV, also filtering that consists in eliminating the frequency bands of the signal in order to be interpretative and without noise to convert it from an analog signal into a digital signal to analyze the physiological signals of the drivers, these values are stored in a database. Based on this compilation, we work on the extraction of signal features and implement K-NN (k-nearest neighbor) classification methods and decision trees (unsupervised learning) that enable the study of data for the identification of patterns and determine by classification methods different effects of augmented reality on drivers. The expected results of this project include are a test platform instrumented with biometric sensors for data acquisition during driving and a database with the required variables to determine the effect caused by augmented reality on people in simulated driving.

Keywords: augmented reality, driving, physiological signals, test platform

Procedia PDF Downloads 142