Search results for: gradual change detection

Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

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Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

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Authors: M. Błoch, P. Karpiński, P. Gasperowicz, R. Płoski, A. Lebioda, P. Skiba, A. Rozensztrauch, D. Patkowski, R. Śmigiel

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Introduction: The cause of the isolated form of esophageal atresia has been yet unknown. Objectives: The primary objective of this study was to indicate epigenetic factors which may play an important role in the etiopathogenesis of esophageal atresia. Methods: We recruited a group of 6 pairs of twins, among whom one of the twins developed EA. The selection of such a group for testing allows for excluding external factors (e.g., infections, drugs, toxins) as the cause of the birth defect. The analyzes were performed with the use of genetic material isolated from the whole blood and esophagus tissue of a patient with EA. The reduced representation bisulphite sequencing (RRBS) technique was used to study the change in the genomic imprinting -a change in the expression of genes, which may be the epigenetic cause of EA. Results: In the course of the analyzes, significant hypomethylation and hypermethylation regions were identified. 65 genes with probably increased expression and 65 with decreased expression were selected. These genes have not been marked in literature as possibly pathogenic in esophageal atresia. However, their participation in the pathogenesis of esophageal atresia cannot be clearly excluded. Conclusion: We suggest a role of hypomethylation or hypermethylation of selected genes as one of the possible epigenetic factors in EA pathogenesis. The use of the RRBS technique in the search for the cause of EA is pioneer research; therefore, it seems necessary to extend the research group to new patients with EA. Acknowledgment: The work was supported by the National Science Centre, Poland, under research project 2016/21/N/NZ5/01927.

Keywords: esophageal atresia, epigenetics, embryonic development, surgery, genes expression, twins

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Authors: Mohammad Borhani, Ahmad Jamshidzaei, Mehdi Koohsari

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The study of climate has captivated human interest throughout history. In response to this fascination, individuals historically organized their daily activities in alignment with prevailing climatic conditions and seasonal variations. Understanding the elements and specific climatic parameters of each region, such as precipitation, which directly impacts human life, is essential because, in recent years, there has been a significant increase in heavy rainfall in various parts of the world attributed to the effects of climate change. Climate prediction models suggest a future scenario characterized by an increase in severe precipitation events and related floods on a global scale. This is a result of human-induced greenhouse gas emissions causing changes in the natural precipitation patterns. The Intergovernmental Panel on Climate Change reported global warming in 2001. The average global temperature has shown an increasing trend since 1861. In the 20th century, this increase has been between (0/2 ± 0/6) °C. The present study focused on examining the trend of monthly, seasonal, and annual precipitation in Sistan and Baluchestan provinces. The study employed data obtained from 13 precipitation measurement stations managed by the Iran Water Resources Management Company, encompassing daily precipitation records spanning the period from 1997 to 2016. The results indicated that the total monthly precipitation at the studied stations in Sistan and Baluchestan province follows a sinusoidal trend. The highest intense precipitation was observed in January, February, and March, while the lowest occurred in September, October, and then November. The investigation of the trend of seasonal precipitation in this province showed that precipitation follows an upward trend in the autumn season, reaching its peak in winter, and then shows a decreasing trend in spring and summer. Also, the examination of average precipitation indicated that the highest yearly precipitation occurred in 1997 and then in 2004, while the lowest annual precipitation took place between 1999 and 2001. The analysis of the annual precipitation trend demonstrates a decrease in precipitation from 1997 to 2016 in Sistan and Baluchestan province.

Keywords: climate change, extreme precipitation, greenhouse gas, trend analysis

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Authors: Nada Mohamed, Abdel Aziz Mohamed

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Climate change in coastal areas is a global issue that can be felt on local scale and will be around for decades and centuries to come to an end; it also has critical risks on the city’s economy, communities, and the natural environment. One of these changes that cause a huge risk on coastal cities is the sea level rise (SLR). SLR is a result of scarcity and reduction in global environmental system. The main cause of climate change and global warming is the countries with high development index (HDI) as Japan and Germany while the medium and low HDI countries as Egypt does not have enough awareness and advanced tactics to adapt with this changes that destroy urban areas and cause loss in land and economy. This is why Climate Resilience is one of the UN sustainable development goals 2030, which is calling for actions to strengthen climate change resilience through mitigation and adaptation. For many reasons, adaptation has received less attention than mitigation and it is only recently that adaptation has become a focal global point of attention. This adaption can be achieved through some actions such as upgrading the use and the design of the land, adjusting business and activities of people, and increasing community understanding of climate risks. To reach the adaption goals, and we have to apply a strategic pathway to Climate Resilience, which is the Urban Bioregionalism Paradigm. Resiliency has been framed as persistence, adaptation, and transformation. Climate Resilience decision support system includes a visualization platform where ecological, social, and economic information can be viewed alongside with specific geographies that's why Urban Bioregionalism is a socio-ecological system which is defined as a paradigm that has potential to help move social attitudes toward environmental understanding and deepen human-environment connections within ecological development. The research aim is to achieve an adaptive integrated urban development model throughout the analyses of tactics and strategies that can be used to adapt urban areas and coastal communities to the challenges of climate changes especially SLR and also simulation model using advanced technological software for a coastal city corridor to elaborates the suitable strategy to apply.

Keywords: climate resilience, sea level rise, SLR, coastal resilience, adaptive development simulation

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Authors: Julia Gurol

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Why do we observe a shift towards convergence in EU-China security interests? While contradicting attitudes towards key principles of inter-state and region-to-state relations, including state sovereignty, territorial integrity, and intervention policies have ever since hindered EU-China inter-regional cooperation beyond the economic realm, collaboration in peace and security issues is now becoming a key pillar of European-Chinese relations. In addition, the Belt and Road Initiative as most ambitious Chinese foreign policy project explicitly touches upon several European foreign policy and security preferences. Based on these counterintuitive findings, this paper traces the process of convergence of Sino-European security interests. Drawing on qualitative text analysis of official Chinese and European policy papers and documents from the establishment of diplomatic relations in 1975 until today, it assesses the striking change over time. On this basis, the paper uses theories of neo-functionalism, inter-regionalism, and securitization and borrows from constructivist views in International Relations’ theory, to expound possible motives for the change in Chinese and respectively European preferences in the security realm. The results reveal interesting insights into the decisive factors and motives behind both countries’ foreign policies. The paper concludes with a discussion of further potential and difficulties of EU-China security cooperation.

Keywords: belt and road initiative, China, European Union, foreign policy, neo-functionalism, security

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Authors: Ali Ghorbanipour

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The aim of this study is to investigate the drug resistance pattern and the value of the MIC (minimum inhibitory concentration)method to reduce the impact of infectious diseases and the slow development of resistance. Method: The study was conducted on clinical specimens collected between 2018 to 2021. identification of isolates and antibiotic susceptibility testing were performed using conventional biochemical tests. Antibiotic resistance was determined using kibry-Bauer disk diffusion and MIC by E-test methods comparative with microdilution plate elisa method. Results were interpreted according to CLSI. Results: Out of 249600 different clinical specimens, 18720 different pathogenic bacteria by overall detection ratio 7.7% were detected. Among pathogen bacterial were Gram negative bacteria (70%,n=13000) and Gram positive bacteria(30%,n=5720).Medically relevant gram-negative bacteria include a multitude of species such as E.coli , Klebsiella .spp , Pseudomonas .aeroginosa , Acinetobacter .spp , Enterobacterspp ,and gram positive bacteria Staphylococcus.spp , Enterococcus .spp , Streptococcus .spp was isolated . Conclusion: Our results highlighted that the resistance ratio among Gram Negative bacteria and Gram positive bacteria with different infection is high it suggest constant screening and follow-up programs for the detection of antibiotic resistance and the value of MIC drug susceptibility reporting that provide a new way to the usage of resistant antibiotic in combination with other antibiotics or accurate weight of antibiotics that inhibit or kill bacteria. Evaluation of wrong medication in the expansion of resistance and side effects of over usage antibiotics are goals. Ali ghorbanipour presently working as a supervision at the microbiology department of Massoud medical laboratory. Iran. Earlier, he worked as head department of pulmonary infection in firoozgarhospital, Iran. He received master degree in 2012 from Fergusson College. His research prime objective is a biologic wound dressing .to his credit, he has Published10 articles in various international congresses by presenting posters.

Keywords: antimicrobial profile, MIC & MBC Method, microplate antimicrobial assay, E-test

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Authors: Hussien Ali, Mesfin Menza, Fitsum Hagos, Amare Haileslassie

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Climate change has perverse effects on agricultural productivity and natural resource base, negatively affecting the well-being of the households and communities. The government and NGOs promote climate smart agricultural (CSA) practices to help farmers adapt to and mitigate the negative effects of climate change. This study aims to identify widely available CSA practices and examine their impacts on food security and multi-dimensional poverty of rural farm households in the Central Rift Valley, Ethiopia. Using three-stage proportional to size sampling procedure, the study randomly selected 278 households from two kebeles from four districts each. A cross-sectional data of 2020/21 cropping season was collected using structured and pretested survey questionnaire. Food consumption score, dietary diversity score, food insecurity experience scale, and multidimensional poverty index were calculated to measure households’ welfare indicators. Multinomial endogenous switching regression model was used to assess average treatment effects of CSA on these outcome indicators on adopter and non-adopter households. The results indicate that the widely adopted CSA practices in the area are conservation agriculture, soil fertility management, crop diversification, and small-scale irrigation. Adopter households have, on average, statistically higher food consumption score, dietary diversity score and lower food insecurity access scale than non-adopters. Moreover, adopter households, on average, have lower deprivation score in multidimensional poverty compared to non-adopter households. Up scaling the adoption of CSA practices through the improvement of households’ implementation capacity and better information, technical advice, and innovative financing mechanisms is advised. Up scaling CSA practices can further promote achieving global goals such as SDG 1, SDG 2, and SDG 13 targets, aimed to end poverty and hunger and mitigate the adverse impacts of climate change, respectively.

Keywords: climate-smart agriculture, food security, multidimensional poverty, upscaling CSA, Ethiopia

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Authors: Azadeh Safadoust, Ali Akbar Mahboubi, Mohammad Reza Mosaddeghi, Bahram Gharabaghi

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A study was conducted to evaluate hydraulic properties of a sandy loam soil and corn (Zea mays L.) crop production under a short-term tillage and manure combinations field experiment carried out in west of Iran. Treatments included composted cattle manure application rates [0, 30, and 60 Mg (dry weight) ha⁻¹] and tillage systems [no-tillage (NT), chisel plowing (CP), and moldboard plowing (MP)] arranged in a split-plot design. Soil water characteristic curve (SWCC) and saturated hydraulic conductivity (Ks) were significantly affected by manure and tillage treatments. At any matric suction, the soil water content was in the order of MP>CP>NT. At all matric suctions, the amount of water retained by the soil increased as manure application rate increased (i.e. 60>30>0 Mg ha⁻¹). Similar to the tillage effects, at high suctions the differences of water retained due to manure addition were less than that at low suctions. The change of SWCC from tillage methods and manure applications may attribute to the change of pore size and aggregate size distributions. Soil Ks was in the order of CP>MP>NT for the first two layers and in the order of MP>CP and NT for the deeper soil layer. The Ks also increased with increasing rates of manure application (i.e. 60>30>0 Mg ha⁻¹). This was due to the increase in the total pore size and continuity.

Keywords: corn, manure, saturated hydraulic conductivity, soil water characteristic curve, tillage

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Authors: Tso Sheng-Yang, Wang Tien-Ni

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Introduction and Purpose: Previous researchers have documented children with ASD (Autism Spectrum Disorders) prefer to escaping internal privates and external privates when they face tough conditions they can’t control or they don’t like.Especially, when children with ASD need to learn challenging tasks, such us Chinese language, their inappropriate behaviors will occur apparently. Recently, researchers apply positive behavior support strategies for children with ASD to enhance their self-efficacy and therefore to reduce their adverse behaviors. Thus, the purpose of this research was to design a series of lecture based on art therapy and to evaluate its effectiveness on the child’s self-efficacy. Method: This research was the single-case design study that recruited a high school boy with ASD. Whole research can be separated into three conditions. First, baseline condition, before the class started and ended, the researcher collected participant’s competencies of self-efficacy every session. In intervention condition, the research used dramatic conventions to teach the child in Chinese language twice a week.When the data was stable across three documents, the period entered to the maintenance condition. In maintenance condition, the researcher only collected the score of self-efficacynot to do other interventions five times a month to represent the effectiveness of maintenance.The time and frequency of data collection among three conditions are identical. Concerning art therapy, the common approach, e.g., music, drama, or painting is to use art medium as independent variable. Due to visual cues of art medium, the ASD can be easily to gain joint attention with teachers. Besides, the ASD have difficulties in understanding abstract objectives Thus, using the drama convention is helpful for the ASD to construct the environment and understand the context of Classical Chinese. By real operation, it can improve the ASD to understand the context and construct prior knowledge. Result: Bassd on the 10-points Likert scale and research, we product following results. (a) In baseline condition, the average score of self-efficacyis 1.12 points, rangedfrom 1 to 2 points, and the level change is 0 point. (b)In intervention condition, the average score of self-efficacy is 7.66 points rangedfrom 7 to 9 points, and the level change is 1 point. (c)In maintenance condition, the average score of self-efficacy is 6.66 points rangedfrom 6 to 7 points, and the level change is 1 point. Concerning immediacy of change, between baseline and intervention conditions, the difference is 5 points. No overlaps were found between these two conditions. Conclusion: According to the result, we find that it is effective that using dramatic conventions a s teaching strategies to teach children with ASD. The result presents the score of self-efficacyimmediately enhances when the dramatic conventions commences. Thus, we suggest the teacher can use this approach and adjust, based on the student’s trait, to teach the ASD on difficult task.

Keywords: dramatic conventions, autism spectrum disorder, slef-efficacy, teaching strategy

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Authors: Ursula Das, Manavjit Singh Dhindsa, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

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Wildland fires, also known as forest fires or wildfires, are exhibiting an alarming surge in frequency in recent times, further adding to its perennial global concern. Forest fires often lead to devastating consequences ranging from loss of healthy forest foliage and wildlife to substantial economic losses and the tragic loss of human lives. Despite the existence of substantial literature on the detection of active forest fires, numerous potential research avenues in forest fire management, such as preventative measures and ancillary effects of forest fires, remain largely underexplored. This paper undertakes a systematic review of these underexplored areas in forest fire research, meticulously categorizing them into distinct phases, namely pre-fire, during-fire, and post-fire stages. The pre-fire phase encompasses the assessment of fire risk, analysis of fuel properties, and other activities aimed at preventing or reducing the risk of forest fires. The during-fire phase includes activities aimed at reducing the impact of active forest fires, such as the detection and localization of active fires, optimization of wildfire suppression methods, and prediction of the behavior of active fires. The post-fire phase involves analyzing the impact of forest fires on various aspects, such as the extent of damage in forest areas, post-fire regeneration of forests, impact on wildlife, economic losses, and health impacts from byproducts produced during burning. A comprehensive understanding of the three stages is imperative for effective forest fire management and mitigation of the impact of forest fires on both ecological systems and human well-being. Artificial intelligence and machine learning (AI/ML) methods have garnered much attention in the cyber-physical systems domain in recent times leading to their adoption in decision-making in diverse applications including disaster management. This paper explores the current state of AI/ML applications for managing the activities in the aforementioned phases of forest fire. While conventional machine learning and deep learning methods have been extensively explored for the prevention, detection, and management of forest fires, a systematic classification of these methods into distinct AI research domains is conspicuously absent. This paper gives a comprehensive overview of the state of forest fire research across more recent and prominent AI/ML disciplines, including big data, classical machine learning, computer vision, explainable AI, generative AI, natural language processing, optimization algorithms, and time series forecasting. By providing a detailed overview of the potential areas of research and identifying the diverse ways AI/ML can be employed in forest fire research, this paper aims to serve as a roadmap for future investigations in this domain.

Keywords: artificial intelligence, computer vision, deep learning, during-fire activities, forest fire management, machine learning, pre-fire activities, post-fire activities

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Authors: Navah Z. Ratzon, Rachel Shichrur

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Background: Many studies have assessed and identified the risk factors of safe driving, but there is relatively little research-based evidence concerning the ability to improve the driving skills of drivers in general and in particular of bus drivers, who are defined as a population at risk. Accidents involving bus drivers can endanger dozens of passengers and cause high direct and indirect damages. Objective: To examine the effectiveness of a metacognitive-functional intervention program for the reduction of risk factors among professional drivers relative to a control group. Methods: The study examined 77 bus drivers working for a large public company in the center of the country, aged 27-69. Twenty-one drivers continued to the intervention stage; four of them dropped out before the end of the intervention. The intervention program we developed was based on previous driving models and the guiding occupational therapy practice framework model in Israel, while adjusting the model to the professional driving in public transportation and its particular risk factors. Treatment focused on raising awareness to safe driving risk factors identified at prescreening (ergonomic, perceptual-cognitive and on-road driving data), with reference to the difficulties that the driver raises and providing coping strategies. The intervention has been customized for each driver and included three sessions of two hours. The effectiveness of the intervention was tested using objective measures: In-Vehicle Data Recorders (IVDR) for monitoring natural driving data, traffic accident data before and after the intervention, and subjective measures (occupational performance questionnaire for bus drivers). Results: Statistical analysis found a significant difference between the degree of change in the rate of IVDR perilous events (t(17)=2.14, p=0.046), before and after the intervention. There was significant difference in the number of accidents per year before and after the intervention in the intervention group (t(17)=2.11, p=0.05), but no significant change in the control group. Subjective ratings of the level of performance and of satisfaction with performance improved in all areas tested following the intervention. The change in the ‘human factors/person’ field, was significant (performance : t=- 2.30, p=0.04; satisfaction with performance : t=-3.18, p=0.009). The change in the ‘driving occupation/tasks’ field, was not significant but showed a tendency toward significance (t=-1.94, p=0.07,). No significant differences were found in driving environment-related variables. Conclusions: The metacognitive-functional intervention significantly improved the objective and subjective measures of safety of bus drivers’ driving. These novel results highlight the potential contribution of occupational therapists, using metacognitive functional treatment, to preventing car accidents among the healthy drivers population and improving the well-being of these drivers. This study also enables familiarity with advanced technologies of IVDR systems and enriches the knowledge of occupational therapists in regards to using a wide variety of driving assessment tools and making the best practice decisions.

Keywords: bus drivers, IVDR, human risk factors, metacognitive-functional intervention

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Authors: Fabienne Fel, Eric Griette

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Reshoring, whether concerning back-reshoring or near-reshoring, is a quite recent phenomenon. Despite the economic and political interest of this topic, academic research questioning determinants of reshoring remains rare. Our paper aims at contributing to fill this gap. In order to better understand the reasons for reshoring, we conducted a study among 280 French firms during spring 2016, three-quarters of which sourced, or source, in China. 105 firms in the sample have reshored all or part of their Chinese production or supply in recent years, and we aimed to establish a typology of the motives that drove them to this decision. We asked our respondents about the history of their Chinese supplies, their current reshoring strategies, and their motivations. Statistical analysis was performed with SPSS 22 and SPAD 8. Our results show that change in commercial and financial terms with China is the first motive explaining the current reshoring movement from this country (it applies to 54% of our respondents). A change in corporate strategy is the second motive (30% of our respondents); the reshoring decision follows a change in companies’ strategies (upgrading, implementation of a CSR policy, or a 'lean management' strategy). The third motive (14% of our sample) is a mere correction of the initial offshoring decision, considered as a mistake (under-estimation of hidden costs, non-quality and non-responsiveness problems). Some authors emphasize that developing a short supply chain, involving geographic proximity between design and production, gives a competitive advantage to companies wishing to offer innovative products. Admittedly 40% of our respondents indicate that this motive could have played a part in their decision to reshore, but this reason was not enough for any of them and is not an intrinsic motive leading to leaving Chinese suppliers. Having questioned our respondents about the importance given to various problems leading them to reshore, we then performed a Principal Components Analysis (PCA), associated with an Ascending Hierarchical Classification (AHC), based on Ward criterion, so as to point out more specific motivations. Three main classes of companies should be distinguished: -The 'Cost Killers' (23% of the sample), which reshore their supplies from China only because of higher procurement costs and so as to find lower costs elsewhere. -The 'Realists' (50% of the sample), giving equal weight or importance to increasing procurement costs in China and to the quality of their supplies (to a large extend). Companies being part of this class tend to take advantage of this changing environment to change their procurement strategy, seeking suppliers offering better quality and responsiveness. - The 'Voluntarists' (26% of the sample), which choose to reshore their Chinese supplies regardless of higher Chinese costs, to obtain better quality and greater responsiveness. We emphasize that if the main driver for reshoring from China is indeed higher local costs, it is should not be regarded as an exclusive motivation; 77% of the companies in the sample, are also seeking, sometimes exclusively, more reactive suppliers, liable to quality, respect for the environment and intellectual property.

Keywords: China, procurement, reshoring, strategy, supplies

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Authors: Alok Agrawal, Alok Satapathy

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In this paper, an attempt to determine the effective thermal conductivity (keff) of particulate filled polymer composites using finite element method (FEM) a powerful computational technique is made. A commercially available finite element package ANSYS is used for this numerical analysis. Three-dimensional spheres-in-cube lattice array models are constructed to simulate the microstructures of micro-sized particulate filled polymer composites with filler content ranging from 2.35 to 26.8 vol %. Based on the temperature profiles across the composite body, the keff of each composition is estimated theoretically by FEM. Composites with similar filler contents are than fabricated using compression molding technique by reinforcing micro-sized aluminium oxide (Al2O3) in polypropylene (PP) resin. Thermal conductivities of these composite samples are measured according to the ASTM standard E-1530 by using the Unitherm™ Model 2022 tester, which operates on the double guarded heat flow principle. The experimentally measured conductivity values are compared with the numerical values and also with those obtained from existing empirical models. This comparison reveals that the FEM simulated values are found to be in reasonable good agreement with the experimental data. Values obtained from the theoretical model proposed by the authors are also found to be in even closer approximation with the measured values within percolation limit. Further, this study shows that there is gradual enhancement in the conductivity of PP resin with increase in filler percentage and thereby its heat conduction capability is improved. It is noticed that with addition of 26.8 vol % of filler, the keff of composite increases to around 6.3 times that of neat PP. This study validates the proposed model for PP-Al2O3 composite system and proves that finite element analysis can be an excellent methodology for such investigations. With such improved heat conduction ability, these composites can find potential applications in micro-electronics, printed circuit boards, encapsulations etc.

Keywords: analytical modelling, effective thermal conductivity, finite element method, polymer matrix composite

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Authors: Mohieldin Elmashad, Salwa Yassin

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Expansive soils regarded as one of the most problematic unsaturated formations in the Egyptian arid zones and present a great challenge in civil engineering, in general, and geotechnical engineering, in particular. Severe geotechnical complications and consequent structural damages have been arising due to an excessive and differential volumetric change upon wetting and change in water content. Different studies have been carried out concerning the swelling performance of the expansive soils using different additives including phospho-gypsum as an industrial waste product. However, this paper describes the results of a comprehensive testing programme that was carried out to investigate the effect of phospho-gypsum (PG) and sodium chloride (NaCl), as an additive mixture, on the swelling performance of constituent samples of swelling soils. The constituent samples comprise commercial bentonite collected from a natural site, mixed with different percentages of PG-NaCl mixture. The testing programme had been scoped to cover the physical and chemical properties of the constituent samples. In addition, a mineralogical study using x-ray diffraction (XRD) was performed on the collected bentonite and the mixed bentonite with PG-NaCl mixture samples. The obtained results of this study showed significant improvement in the swelling performance of the tested samples with the increase of the proposed PG-NaCl mixture content.

Keywords: expansive soils, industrial waste, mineralogical study, swelling performance, X-ray diffraction

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Authors: Wan-Ting Liao, Angela K. Tzeng

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Empathy has been an important issue in psychology, education, as well as cognitive neuroscience. Empathy has two major components: cognitive and emotional. Cognitive component refers to the ability to understand others’ perspectives, thoughts, and actions, whereas emotional component refers to understand how others feel. Empathy can be induced, attitude can then be changed, and with enough attitude change, helping behavior can occur. This finding leads us to two questions: is attitude change really necessary for prosocial behavior? And, what roles cognitive and affective empathy play? For the second question, participants with different psychopathic personality (PP) traits are critical because high PP people were found to suffer only affective empathy deficit. Their cognitive empathy shows no significant difference from the control group. 132 college students voluntarily participated in the current three-stage study. Stage 1 was to collect basic information including Interpersonal Reactivity Index (IRI), Psychopathic Personality Inventory-Revised (PPI-R), Attitude Scale, Visual Analogue Scale (VAS), and demographic data. Stage two was for empathy induction with three controversial scenarios, namely domestic violence, depression with a suicide attempt, and an ex-offender. Participants read all three stories and then rewrite the stories by one of two perspectives (empathetic vs. objective). They would then complete the VAS and Attitude Scale one more time for their post-attitude and emotional status. Three IVs were introduced for data analysis: PP (High vs. Low), Responsibility (whether or not the character is responsible for what happened), and Perspective-taking (Empathic vs. Objective). Stage 3 was for the action. Participants were instructed to freely use the 17 tokens they received as donations. They were debriefed and interviewed at the end of the experiment. The major findings were people with higher empathy tend to take more action in helping. Attitude change is not necessary for prosocial behavior. The controversy of the scenarios and how familiar participants are towards target groups play very important roles. Finally, people with high PP tend to show more public prosocial behavior due to their affective empathy deficit. Pre-existing value and belief as well as recent dramatic social events seem to have a big impact and possibly reduce the effect of the independent variables (IV) in our paradigm.

Keywords: empathy, cognitive, emotional, psychopathy

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Authors: Parul Jain, Jitendra Saha, L. C. Gupta, Satyabrata Patnaik, Ashok K. Ganguli, Ratnamala Chatterjee

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This study shows how sensitively and drastically multiferroic properties of CaMn₇O₁₂ get modified by isovalent Sr-doping, namely, in Ca₁₋ₓSrₓMn₇O₁₂ for x as small as 0.01 and 0.02. CaMn₇O₁₂ is a type-II multiferroic, wherein polarization is caused by magnetic spin ordering. In this report magnetic and ferroelectric properties of Ca₁₋ₓSrₓMn₇O₁₂ (0 ≤ x ≤ 0.1) are investigated. Samples were prepared by wet sol gel technique using their respective nitrates; powders thus obtained were calcined and sintered in optimized conditions. The X-ray diffraction patterns of all samples doped with Sr concentrations in the range (0 ≤ x ≤ 10%) were found to be free from secondary phases. Magnetization versus temperature and magnetization versus field measurements were carried out using Quantum Design SQUID magnetometer. Pyroelectric current measurements were done for finding the polarization in the samples. Findings of the measurements are: (i) increase of Sr-doping in CaMn₇O₁₂ lattice i.e. for x ≤ 0.02, increases the polarization, whereas decreases the magnetization and the coercivity of the samples; (ii) the material with x = 0.02 exhibits ferroelectric polarization Ps which is more than double the Ps in the un-doped material and the magnetization M is reduced to less than half of that of the pure material; remarkably (iii) the modifications in Ps and M are reversed as x increases beyond x = 0.02 and for x = 0.10, Ps is reduced even below that for the pure sample; (iv) there is no visible change of the two magnetic transitions TN1 (90 K) and TN2 (48 K) of the pure material as a function of x. The strong simultaneous variations of Ps and M for x = 0.02 strongly suggest that either a basic modification of the magnetic structure of the material or a significant change of the coupling of P and M or possibly both.

Keywords: ferroelectric, isovalent, multiferroic, polarization, pyroelectric

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Authors: Kanwal Javid, Shazia Pervaiz, Maria Mumtaz, Muhammad Ameer Nawaz Akram

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Forests and grasslands ecosystems play an important role in the global carbon cycle. Land management activities influence both ecosystems and enable them to absorb and sequester carbon dioxide (CO2). Similarly, in Pakistan, these terrestrial ecosystems are well known to mitigate carbon emissions and have a great source to supply a variety of services such as clean air and water, biodiversity, wood products, wildlife habitat, food, recreation and carbon sequestration. Carbon sequestration is the main agenda of developed and developing nations to reduce the impacts of global warming. But the amount of carbon storage within these ecosystems can be affected by many factors related to air quality such as land management, land-use change, deforestation, over grazing and natural calamities. Moreover, the long-term capacity of forests and grasslands to absorb and sequester CO2 depends on their health, productivity, resilience and ability to adapt to changing conditions. Thus, the main rationale of this study is to monitor the difference in carbon amount of forests and grasslands of Northern Pakistan using MODIS data sets and map results using Geographic Information System. Results of the study conclude that forests ecosystems are more effective in reducing the CO2 level and play a key role in improving the quality of air.

Keywords: carbon sequestration, grasslands, global warming, climate change.

Procedia PDF Downloads 187

Authors: Raed KOUTA, José-Alcebiades-Ernesto HLUNGUANE, Eric Châtele

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The search for and exploitation of new fossil energy resources is taking place in the context of the gradual depletion of existing deposits. Despite the adoption of international targets to combat global warming, the demand for fuels continues to grow, contradicting the movement towards an energy-efficient society. The increase in the share of offshore in global hydrocarbon production tends to compensate for the depletion of terrestrial reserves, thus constituting a major challenge for the players in the sector. Through the economic potential it represents, and the energy independence it provides, offshore exploitation is also a challenge for States such as Mozambique, which have large maritime areas and whose environmental wealth must be considered. The exploitation of new reserves on economically viable terms depends on available technologies. The development of deep and ultra-deep offshore requires significant research and development efforts. Progress has also been made in managing the multiple risks inherent in this activity. Our study proposes a reliability approach to develop products and processes designed to live at sea. Indeed, the context of an offshore platform requires highly reliable solutions to overcome the difficulties of access to the system for regular maintenance and quick repairs and which must resist deterioration and degradation processes. One of the characteristics of failures that we consider is the actual conditions of use that are considered 'extreme.' These conditions depend on time and the interactions between the different causes. These are the two factors that give the degradation process its dynamic character, hence the need to develop dynamic reliability models. Our work highlights mathematical models that can explicitly manage interactions between components and process variables. These models are accompanied by numerical resolution methods that help to structure a dynamic reliability approach in a physical and probabilistic context. The application developed makes it possible to evaluate the reliability, availability, and maintainability of a floating storage and unloading platform for liquefied natural gas production.

Keywords: dynamic reliability, offshore plateform, stochastic process, uncertainties

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: Ali Bahkali, Rayan Yousef Booq, Mohammad Khiyami

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Soil samples were collected from five different regions in the Kingdom of Saudi Arabia. Microbiological methods included dilution methods and pour plates to isolate and purify bacteria soil. The ability of isolates to develop biopolymer was investigated on petri dishes containing elements and substance concentrations stimulating developing biopolymer. Fluorescent stains, Nile red and Nile blue were used to stain the bacterial cells developing biopolymers. In addition, Sudan black was used to detect biopolymers in bacterial cells. The isolates which developed biopolymers were identified based on their gene sequence of 1 6sRNA and their ability to grow and synthesize PHAs on mineral medium supplemented with 1% dates molasses as the only carbon source under nitrogen limitation. During the study 293 bacterial isolates were isolated and detected. Through the initial survey on the petri dishes, 84 isolates showed the ability to develop biopolymers. These bacterial colonies developed a pink color due to accumulation of the biopolymers in the cells. Twenty-three isolates were able to grow on dates molasses, three strains of which showed the ability to accumulate biopolymers. These strains included Bacillus sp., Ralstonia sp. and Microbacterium sp. They were detected by Nile blue A stain with fluorescence microscopy (OLYMPUS IX 51). Among the isolated strains Ralstonia sp. was selected after its ability to grow on molasses dates in the presence of a limited nitrogen source was detected. The optimum conditions for formation of biopolymers by isolated strains were investigated. Conditions studied included, best incubation duration (2 days), temperature (30°C) and pH (7-8). The maximum PHB production was raised by 1% (v1v) when using concentrations of dates molasses 1, 2, 3, 4 and 5% in MSM. The best inoculated with 1% old inoculum (1= OD). The ideal extraction method of PHA and PHB proved to be 0.4% sodium hypochlorite solution, producing a quantity of polymer 98.79% of the cell's dry weight. The maximum PHB production was 1.79 g/L recorded by Ralstonia sp. after 48 h, while it was 1.40 g/L produced by R.eutropha ATCC 17697 after 48 h.

Keywords: bacteria forming polyhydroxyalkanoate, detection, molecular, Saudi Arabia

Procedia PDF Downloads 347

Authors: Ine Huybrechts, Anja Declercq, Emily Verté, Peter Raeymaeckers, Sibyl Anthierens

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Goal-oriented care is a concept gaining increased interest as an approach to go towards more coordinated and integrated primary care. It places patients’ personal life goals at the core of health care support, hereby shifting the focus from “what’s the matter with this patient” to “what matters to this patient.” In Flanders/Belgium, various primary care providers, health and social care organizations and governmental bodies have picked up this concept and have initiated actions to facilitate this approach. The implementation of goal-oriented care not only happens on the micro-level, but it also requires efforts on the meso- and macro-level. Within implementation research, there is a growing recognition that the context in which an intervention takes place strongly relates to its implementation outcomes. However, when investigating contextual variables, the external context and its impact on implementation processes is often overlooked. This study aims to explore how we can better identify and understand the external context and how it relates to the mechanism of change within the implementation process of goal-oriented care in Flanders/Belgium. Results can be used to support and guide initiatives to introduce innovative approaches such as goal-oriented care inside an organization or in the broader primary care landscape. We have conducted qualitative research, performing in-depth interviews with n=23 respondents who have affinity with the implementation of goal-oriented care within their professional function. This lead to in-depth insights from a wide range of actors, with meso-level and/or macro-level perspectives on the implementation of goal-oriented care. This means that we have interviewed actors that are not only involved with initiatives to implement goal-oriented care, but also actors that actively give form to the external context in which goal-oriented care is implemented. Data were collected using a semi-structured interview guide, audio recorded, and analyzed first inductively and then deductively using various theories and concepts that derive from organizational research. Our preliminary findings suggest t Our findings can contribute to further define actions needed for sustainable implementation of goal-oriented primary care. It gives insights in the dynamics between contextual variables and implementation efforts, hereby indicating towards those contextual variables that can be further shaped to facilitate the implementation of an innovation such as goal-oriented care. hat organizational theories can help understand the mechanism of change of implementation processes with a macro-level perspective. Institutional theories, contingency theories, resources dependency theories and others can expose the mechanism of change for an innovation such as goal-oriented care. Our findings can contribute to further define actions needed for sustainable implementation of goal-oriented primary care. It gives insights in the dynamics between contextual variables and implementation efforts, hereby indicating towards those contextual variables that can be further shaped to facilitate the implementation of an innovation such as goal-oriented care.

Keywords: goal-oriented care, implementation processes, organizational theories, person-centered care, implementation research

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Authors: Teresa Kupczyk, Selçuk Mustafa Özcan, Joanna Kubicka

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The paper presents results of specialist literature analysis concerning learning outcomes and student satisfaction as a factor of the necessary change in the Polish higher education system. The objective of the empirical research was to determine students’ assessment of learning outcomes, satisfaction of their expectations, as well as their satisfaction with lectures and practical classes held in the traditional form, e-learning and video-conference. The assessment concerned effectiveness of time spent at classes, usefulness of the delivered knowledge, instructors’ preparation and teaching skills, application of tools, studies curriculum, its adaptation to students’ needs and labour market, as well as studying conditions. Self-assessment of learning outcomes was confronted with assessment by lecturers. The indirect objective of the research was also to identify how students assessed their activity and commitment in acquisition of knowledge and their discipline in achieving education goals. It was analysed how the studies held affected the students’ willingness to improve their skills and assessment of their perspectives at the labour market. To capture the changes underway, the research was held at the beginning, during and after completion of the studies. The study group included 86 students of two editions of full-time studies majoring in Management and specialising in “Mega-event organisation”. The studies were held within the EU-funded project entitled “Responding to challenges of new markets – innovative managerial education”. The results obtained were analysed statistically. Average results and standard deviations were calculated. In order to describe differences between the studied variables present during the process of studies, as well as considering the respondents’ gender, t-Student test for independent samples was performed with the IBM SPSS Statistics 21.0 software package. Correlations between variables were identified by calculation of Pearson and Spearman correlation coefficients. Research results suggest necessity to introduce some changes in the teaching system applied at Polish higher education institutions, not only considering the obtained outcomes, but also impact on students’ willingness to improve their qualifications constantly, improved self-assessment among students and their opportunities at the labour market.

Keywords: higher education, learning outcomes, students, change

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Authors: Reham Banwair, Lana Alshehri, Sara Hadrawi

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The purpose of this project is to identify user satisfaction with the AI functions on Snapchat, in order to generate improvement proposals that allow its development within the app. To achieve this, a qualitative analysis was carried out through interviews to people who usually use the application, revealing their satisfaction or dissatisfaction with the usefulness of the AI. In addition, the background of the company and its introduction in these algorithms were analyzed. Furthermore, the characteristics of the three main functions of AI were explained: identify songs, solve mathematical problems, and recognize plants. As a result, it was obtained that 50% still do not know the characteristics of AI, 50% still believe song recognition is not always correct, 41.7% believe that math problems are usually accurate and 91.7% believes the plant detection tool is working properly.

Keywords: artificial intelligence, scanning, Snapchat, machine learning

Procedia PDF Downloads 134

Authors: Bhojan Dhakal, Naba Raj Devkota, Chet Raj Upreti, Maheshwar Sapkota

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A study was conducted to find out the production potential, nutrient composition, and the variability of the most commonly available fodder trees along with the varying altitude to help optimize the dry matter requirement during winter lean period. The study was carried out from March to June, 2012 in Lho and Prok Village Development Committee of Manaslu Conservation Area (MCA), located in Gorkha district of Nepal. The other objective of the research was to learn the impact of climate change on livestock production linking it with feed availability. The study was conducted in two parts: social and biological. Accordingly, a households (HHs) survey was conducted to collect primary data from 70 HHs, focusing on the perception of respondents on impacts of climatic variability on the feeding management. The next part consisted of understanding yield potential and nutrient composition of the four most commonly available fodder trees (M. azedirach, M. alba, F. roxburghii, F. nemoralis), within two altitudes range: (1500-2000 masl and 2000-2500 masl) by using a RCB design in 2*4 factorial combination of treatments, each replicated four times. Results revealed that majority of the farmers perceived the change in climatic phenomenon more severely within the past five years. Farmers were using different adaptation technologies such as collection of forage from jungle, reducing unproductive animals, fodder trees utilization, and crop by product feeding at feed scarcity period. Ranking of the different fodder trees on the basis of indigenous knowledge and experiences revealed that F. roxburghii was the best-preferred fodder tree species (index value 0.72) in terms overall preferability whereas M. azedirach had highest growth and productivity (index value 0.77), F. roxburghii had highest adoptability (index value 0.69) and palatability (index value 0.69) as well. Similarly, fresh yield and dry matter yield of the each fodder trees was significant (P < 0.01) between the altitude and within species. Fodder trees yield analysis revealed that the highest dry matter (DM) yield (28 kg/tree) was obtained for F. roxburghii but that remained statistically similar (P > 0.05) to the other treatment. On the other hand, most of the parameters: ether extract (EE), acid detergent lignin (ADL), acid detergent fibre (ADF), cell wall digestibility (CWD), relative digestibility (RD), digestible nutrient (TDN), and Calcium (Ca) among the treatments were highly significant (P < 0.01). This indicates the scope of introducing productive and nutritive fodder trees species even at the high altitude to help reduce fodder scarcity problem during winter. The finding also revealed the scope of promoting all available local fodder trees species as crude protein content of these species were similar.

Keywords: fodder trees, yield potential, climate change, nutrient composition

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Authors: Ma Lanting, S. Eguilior, A. Hurtado, Juan F. Llamas Borrajo

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Horizontal hydraulic fracturing is a technology to increase natural gas flow and improve productivity in the low permeability formation. During this drilling operation tons of flowback and produced water which contains many organic compounds return to the surface with a potential risk of influencing the surrounding environment and human health. A mathematical model is urgently needed to represent organic compounds in water transportation process behavior and the concentration change with time throughout the hydraulic fracturing operation life cycle. A comprehensive model combined Organic Matter Transport Dynamic Model with Two-Compartment First-order Model Constant (TFRC) Model has been established to quantify the organic compounds concentration. This algorithm model is composed of two transportation parts based on time factor. For the fast part, the curve fitting technique is applied using flowback water data from the Marcellus shale gas site fracturing and the coefficients of determination (R2) from all analyzed compounds demonstrate a high experimental feasibility of this numerical model. Furthermore, along a decade of drilling the concentration ratio curves have been estimated by the slow part of this model. The result shows that the larger value of Koc in chemicals, the later maximum concentration in water will reach, as well as all the maximum concentrations percentage would reach up to 90% of initial concentration from shale formation within a long sufficient period.

Keywords: model, shale gas, concentration, organic compounds

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Authors: E. Montesinos-Pedro, L. G. Toscano-Flores, N. Domínguez-Ramírez

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Water scarcity is a worldwide issue, coupled with climate change is a relevant problem, that affect not only large cities, but also rural areas. The Municipality of Santiago Tilantongo belongs to the district of Nochixtlán Oaxaca, it’s built up from 14 communities, one of them San Martin de las Palmas. This community was founded in 1900, at that time the inhabitants were supplied with water through rivers of the region which were abundant (they used containers filled in the river for that purpose); However, over the years the level of the rivers began to drop and in 1994 specific wells were located to store water and at the same time make it drinkable, this whit support of the state of Oaxaca and the program Procampo. By the year 2000 the shortage of water in the supply sources was notorious, the community requested support from the Oaxaca State government to solve the problem. The government’s response consisted in the implementation of ferro-cement tanks (2005) and water wells (2010), both for rainwater collection, Hower, it was not enough. Now days the community has a population of 60 inhabitants who have resisted and adapted to water scarcity, not only with the programs implemented by the government, but they also have implemented important structural analysis strategies. The objective of this research is to know the adaptation strategies used by the community to analyze them and propose improvements for water conservation and mitigation of this scarcity.

Keywords: adaptation, climate change, mitigation, resiliencia

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Authors: Japhet N. Mwambusi

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High tropical forests deforestation for solid wood furniture industry is among of climate change contributing agents. This pressure indirectly is caused by furniture joints failure due to poor gluing technology based on improper use of different glues to different wood species which lead to low quality and weak wood-glue joints. This study was carried in order to run performance tests of wood glues on different wood species used in wood workshops: Morogoro Tanzania whereby three popular wood species of C. lusitanica, T. glandis and E. maidenii were tested against five glues of Woodfix, Bullbond, Ponal, Fevicol and Coral found in the market. The findings were necessary on developing a guideline for proper glue selection for a particular wood species joining. Random sampling was employed to interview carpenters while conducting a survey on the background of carpenters like their education level and to determine factors that influence their glues choice. Monsanto Tensiometer was used to determine bonding strength of identified wood glues to different wood species in use under British Standard of testing wood shear strength (BS EN 205) procedures. Data obtained from interviewing carpenters were analyzed through Statistical Package of Social Science software (SPSS) to allow the comparison of different data while laboratory data were compiled, related and compared by the use of MS Excel worksheet software as well as Analysis of Variance (ANOVA). Results revealed that among all five wood glues tested in the laboratory to three different wood species, Coral performed much better with the average shear strength 4.18 N/mm², 3.23 N/mm² and 5.42 N/mm² for Cypress, Teak and Eucalyptus respectively. This displays that for a strong joint to be formed to all tree wood species for soft wood and hard wood, Coral has a first priority in use. The developed table of guideline from this research can be useful to carpenters on proper glue selection to a particular wood species so as to meet glue-bond strength. This will secure furniture market as well as reduce pressure to the forests for furniture production because of the strong existing furniture due to their strong joints. Indeed, this can be a good strategy on reducing climate change speed in tropics which result from high deforestation of trees for furniture production.

Keywords: climate change, deforestation, gluing technology, joint failure, wood-glue, wood species

Procedia PDF Downloads 240

Authors: Mehri Sadat Alavinasab Ashgezari, Gholam Reza Nabi Bidhendi, Fatemeh Sadat Alavinasab Ashkezari

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Atmospheric aerosol loading (AAL) from anthropogenic sources is an evidence in industrial development. The accelerated trends in material consumption at the global scale in recent years demonstrate consumption paradigms sensible to the planetary boundaries (PB). This paper is a statistical approach on recognizing the path of climate-relevant bulk materials production (CBMP) of steel, cement and plastics to AAL via an updated and validated spatiotemporal distribution. The methodology of statistical analysis used the most updated regional or global databases or instrumental technologies. This corresponded to a selection of processes and areas capable for tracking AAL within the last decade, analyzing the most validated data while leading to explore the behavior functions or models. The results also represented a correlation within socio economic metabolism idea between the materials specified as macronutrients of society and AAL as a PB with an unknown threshold. The selected country contributors of China, India, US and the sample country of Iran show comparable cumulative AAL values vs to the bulk materials domestic extraction and production rate in the study period of 2012 to 2022. Generally, there is a tendency towards gradual descend in the worldwide and regional aerosol concentration after 2015. As of our evaluation, a considerable share of human role, equivalent 20% from CBMP, is for the main anthropogenic species of aerosols, including sulfate, black carbon and organic particulate matters too. This study, in an innovative approach, also explores the potential role of AAL control mechanisms from the economy sectors where ordered and smoothing loading trends are accredited through the disordered phenomena of CBMP and aerosol precursor emissions. The equilibrium states envisioned is an approval to the well-established theory of Spin Glasses applicable in physical system like the Earth and here to AAL.

Keywords: atmospheric aeroso loading, material flows, climate bulk materials, industrial ecology

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Authors: Hieu Duy Nguyen, Chitsan Lin Jr., Dung Duc Tran

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Nowadays, drought and salinity intrusion becomes a severe problem in the Lower Mekong region due to climate change, especially in coastal provinces. Freshwater resources are decreased due to sea-level rise and the decline in water flow from upstream in the dry season. The combination of the above issues can lead to many effects on the environment and human health in affected areas such as the pathological of digestive or decreased the immune system. Tidal cycle and upstream flows are the two main factors affecting the saline intrusion process and the former salt-wedge in the estuary. Under suitable conditions, salt-wedge can be going further upstream under the water surface and affected groundwater. In order to have a proper plan for the mitigation of the above adverse effects, we need to understand the characteristics of this process. In this study, 3D model is used to investigate the movement of salt-wedge under different conditions of tidal and flow discharge. The salinity in the vertical profile is also measured in the dry season of 2017 and 2018 for model calibrating. The data has proved that there is the presence of salt-wedge in the study area. The obtained results will help strategic planners to use and preserve water resources more effectively and serve as a basis for new research directions on saline intrusion and human health.

Keywords: salt-wedge, salinity intrusion, human health, 3D model

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Authors: Expedito Pinto De Paula Junior

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Current systems such as artificial satellites, airplanes, automobiles, turbines, power systems and air traffic controls are becoming increasingly more complex and/or highly integrated as defined in SAE-ARP-4754A (Society Automotive Engineering - Certification considerations for highly-integrated or complex aircraft systems standard). Among other processes, the development of such systems requires careful Change and Configuration Management (CCM) to establish and maintain product integrity. Understand the maturity of CCM process based in historical approach is crucial for better implementation in hardware and software lifecycle. The sense of work organization, in all fields of development is directly related to the order and interrelation of the parties, changes in time, and record of these changes. Generally, is observed that engineers, administrators and managers invest more time in technical activities than in organization of work. More these professionals are focused in solving complex problems with a purely technical bias. CCM process is fundamental for development, production and operation of new products specially in the safety critical systems. The objective of this paper is open a discussion about the historical revision based in standards focus of CCM around the world in order to understand and reflect the importance across the years, the contribution of this process for technology evolution, to understand the mature of organizations in the system lifecycle project and the benefits of CCM to avoid errors and mistakes during the Lifecycle Product.

Keywords: changes, configuration management, historical, revision

Procedia PDF Downloads 201