Search results for: soil texture prediction

Authors: Kiran Fatima, Kashif Ali

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Staphylococcus aureus is an opportunistic human as well as animal pathogen that causes a variety of diseases. A total of 70 staphylococci isolates were obtained from soil, water, yogurt, and clinical samples. The likely staphylococci clinical isolates were identified phenotypically by different biochemical tests. Molecular identification was done by PCR using species-specific 16S rRNA primer pairs, and finally, 50 isolates were found to be positive as Staphylococcus aureus, sciuri, xylous and cohnii. Screened isolates were further analyzed by several microbiological diagnostics tests, including gram staining, coagulase, capsule, hemolysis, fermentation of glucose, lactose, maltose, and sucrose tests enzymatic reactions. It was found that 78%, 81%, and 51% of isolates were positive for gelatin hydrolysis, protease, and lipase activities, respectively. Antibiogram analysis of isolated Staphylococcus aureus strains with respect to different antimicrobial agents revealed resistance patterns ranging from 57 to 96%. Our study also shows 70% of strains to be MRSA, 54.3% as VRSA, and 54.3% as both MRSA and VRSA. All the identified isolates were subjected to detection of mecA, nuc, and hlb genes, and 70%, 84%, and 40% were found to harbour mecA, nuc, and hlb genes, respectively. The current investigation is highly important and informative for the high-level multidrug-resistant Staphylococcus aureus infections inclusive also of methicillin and vancomycin.

Keywords: MRSA, VRSA, mecA, MSSA

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Authors: Christoph Opperer

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Space syntax is a theory and method for analyzing the spatial layout of buildings and urban environments to understand how they can influence patterns of human movement, social interaction, and behavior. While direct visibility is a key factor in space syntax research, important visual information such as light, color, texture, etc., are typically not considered, even though psychological studies have shown a strong correlation to the human perceptual experience within physical space – with light and color, for example, playing a crucial role in shaping the perception of spaciousness. Furthermore, these surface properties are often the visual features that are most salient and responsible for drawing attention to certain elements within the environment. This paper explores the potential of integrating these factors into general space syntax methods and visibility-based analysis of space, particularly for architectural spatial layouts. To this end, we use a combination of geometric (isovist) and topological (visibility graph) approaches together with image-based methods, allowing a comprehensive exploration of the relationship between spatial geometry, visual aesthetics, and human experience. Custom-coded ray-tracing techniques are employed to generate spherical panorama images, encoding three-dimensional spatial data in the form of two-dimensional images. These images are then processed through computer vision algorithms to generate saliency-maps, which serve as a visual representation of areas most likely to attract human attention based on their visual properties. The maps are subsequently used to weight the vertices of isovists and the visibility graph, placing greater emphasis on areas with high saliency. Compared to traditional methods, our weighted visibility analysis introduces an additional layer of information density by assigning different weights or importance levels to various aspects within the field of view. This extends general space syntax measures to provide a more nuanced understanding of visibility patterns that better reflect the dynamics of human attention and perception. Furthermore, by drawing parallels to traditional isovist and VGA analysis, our weighted approach emphasizes a crucial distinction, which has been pointed out by Ervin and Steinitz: the difference between what is possible to see and what is likely to be seen. Therefore, this paper emphasizes the importance of including surface properties in visibility-based analysis to gain deeper insights into how people interact with their surroundings and to establish a stronger connection with human attention and perception.

Keywords: space syntax, visibility analysis, isovist, visibility graph, visual features, human perception, saliency detection, raytracing, spherical images

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Authors: Halefom Kidane

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This study aims to assess the wind resource potential and characterize the urban area wind patterns in Hawassa City, Ethiopia. The estimation and characterization of wind resources are crucial for sustainable urban planning, renewable energy development, and climate change mitigation strategies. A secondary data collection method was used to carry out the study. The collected data at 2 meters was analyzed statistically and extrapolated to the standard heights of 10-meter and 30-meter heights using the power law equation. The standard deviation method was used to calculate the value of scale and shape factors. From the analysis presented, the maximum and minimum mean daily wind speed at 2 meters in 2016 was 1.33 m/s and 0.05 m/s in 2017, 1.67 m/s and 0.14 m/s in 2018, 1.61m and 0.07 m/s, respectively. The maximum monthly average wind speed of Hawassa City in 2016 at 2 meters was noticed in the month of December, which is around 0.78 m/s, while in 2017, the maximum wind speed was recorded in the month of January with a wind speed magnitude of 0.80 m/s and in 2018 June was maximum speed which is 0.76 m/s. On the other hand, October was the month with the minimum mean wind speed in all years, with a value of 0.47 m/s in 2016,0.47 in 2017 and 0.34 in 2018. The annual mean wind speed was 0.61 m/s in 2016,0.64, m/s in 2017 and 0.57 m/s in 2018 at a height of 2 meters. From extrapolation, the annual mean wind speeds for the years 2016,2017 and 2018 at 10 heights were 1.17 m/s,1.22 m/s, and 1.11 m/s, and at the height of 30 meters, were 3.34m/s,3.78 m/s, and 3.01 m/s respectively/Thus, the site consists mainly primarily classes-I of wind speed even at the extrapolated heights.

Keywords: artificial neural networks, forecasting, min-max normalization, wind speed

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Authors: Sabyasachi Biswas, Indrajit Ghosh

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The random and heterogeneous behavior of vehicles in India puts up a greater challenge for researchers. Stop-and-go modeling at signalized intersections under heterogeneous traffic conditions has remained one of the most sought-after fields. Vehicles are often caught up in the dilemma zone and are unable to take quick decisions whether to stop or cross the intersection. This hampers the traffic movement and may lead to accidents. The purpose of this work is to develop a stop and go prediction model that depicts the drivers’ decision during the yellow time at signalised intersections. To accomplish this, certain traffic parameters were taken into account to develop surrogate model. This research investigated the Stop and Go behavior of the drivers by collecting data from 4-signalized intersections located in two major Indian cities. Model was developed to predict the drivers’ decision making during the yellow phase of the traffic signal. The parameters used for modeling included distance to stop line, time to stop line, speed, and length of the vehicle. A Kriging base surrogate model has been developed to investigate the drivers’ decision-making behavior in amber phase. It is observed that the proposed approach yields a highly accurate result (97.4 percent) by Gaussian function. It was observed that the accuracy for the crossing probability was 95.45, 90.9 and 86.36.11 percent respectively as predicted by the Kriging models with Gaussian, Exponential and Linear functions.

Keywords: decision-making decision, dilemma zone, surrogate model, Kriging

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Authors: Orsolya Kegyes-Brassai, Zsolt Szilvágyi, Ákos Wolf, Richard P. Ray

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Local ground conditions have a substantial influence on the seismic response of structures. Their inclusion in seismic hazard assessment and structural design can be realized at different levels of sophistication. However, response results based on more advanced calculation methods e.g. nonlinear or equivalent linear site analysis tend to show significant discrepancies when compared to simpler approaches. This project's main objective was to compare results from several 1-D response programs to Eurocode 8 design spectra. Data from in-situ site investigations were used for assessing local ground conditions at several locations in Hungary. After discussion of the in-situ measurements and calculation methods used, a comprehensive evaluation of all major contributing factors for site response is given. While the Eurocode spectra should account for local ground conditions based on soil classification, there is a wide variation in peak ground acceleration determined from 1-D analyses versus Eurocode. Results show that current Eurocode 8 design spectra may not be conservative enough to account for local ground conditions typical for Hungary.

Keywords: 1-D site response analysis, multichannel analysis of surface waves (MASW), seismic CPT, seismic hazard assessment

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Authors: Daniel Rosero, Johan S. Arana, Sebastian Arango, Alejandro Cruz, Isabel Gomez-Gutierrez, Peter Thomson

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Santiago de Cali is located in the southwestern Colombia in a high seismic hazard zone. About 50% of the city is on the banks of the Cauca River, which is the second most important hydric affluent in the country and whose alluvial deposits contain potentially liquefiable sands. Among the methods used to study a site's liquefaction potential is the finite elements method which use constitutive models to simulate the soil response for different load types. Among the different constitutive models, the Modified UBCSand stands out to study the seismic behavior of sands, and especially the liquefaction phenomenon. In this paper, the dynamic behavior of a potentially liquefiable sand of Santiago de Cali is studied by cyclic triaxial and CPTu tests. Subsequently, the behavior of the sand is simulated using the Modified UBCSand constitutive model, whose parameters are calibrated using the results of cyclic triaxial and CPTu tests. The above with the aim of analyze the constitutive model applicability for studying the geotechnical problems associated to liquefaction in the city.

Keywords: constitutive model, cyclic triaxial test, dynamic behavior, liquefiable sand, modified ubcsand

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Authors: Nazanin Nikanmajd

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Microorganisms are essential for influencing environmental processes, such as nutrient cycling, pollutant breakdown, and ecosystem well-being. Recent developments in high-throughput sequencing technologies and metagenomic methods have given us fresh understandings about the range and capabilities of microorganisms in different settings. This research examines how environmental stressors like climate change, pollution, and habitat degradation affect the composition and roles of microbial communities in soil and water ecosystems. We show that human-caused disruptions change the makeup of microbial communities, causing changes in important metabolic pathways for biogeochemical processes. More precisely, we pinpoint important microbial groups that show resistance or susceptibility to certain stress factors, emphasizing their possible uses in bioremediation and ecosystem rehabilitation. The results highlight the importance of adopting a holistic approach to comprehend microbial changes in evolving environments, impacting sustainable environmental conservation and management strategies. This research helps develop new solutions to reduce the impacts of environmental degradation on microbial ecosystem services by understanding the intricate relationships between microorganisms and their surroundings.

Keywords: environmental microbiology, microbial communities, climate change, pollution, bioremediation, metagenomics, ecosystem services, ecosystem restoration

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Authors: Sangram Shamrao Patil, Hara Mohan Jena

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Phthalates are among the most common organic pollutant since they have become widespread in the environment and found in sediments, natural waters, soils, plants, landfill leachates, biota including human tissue and aquatic organisms. Diethyl phthalate (DEP) is a low molecular weight phthalate which has wide applications as plasticizer and become a major cause of environmental pollution. Environmental protection agency (EPA) listed DEP as priority pollutant because of its toxicity and they recommended human health ambient water quality criterion for diethyl phthalate (DEP) as 4 mg/l. Therefore, wastes containing phthalates require proper treatment before being discharged into the environment. Biodegradation is attractive and efficient treatment method as it is cost effective and produces non-toxic end products. In the present study, a DEP degrading aerobic bacterium was isolated from soil contaminated with plastic industry wastewater. Morphological and biochemical characteristics of isolate were performed. 16S rRNA sequencing and phylogenetic analysis of isolate was carried out and it was identified as Empedobacter brevis. Isolate has been found to tolerate up to 1650 mg/l of DEP. This study will be significant for exploring an application of microbes for remediation of phthalates and development of a suitable bioreactor.

Keywords: diethyl phthalate, plasticizer, pollutant, biodegradation

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Authors: Rushva Parihar, Anushka Barua

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The negative repercussions of a warming planet are already visible, with biodiversity loss, water scarcity, and extreme weather events becoming ever so frequent. The agriculture sector is perhaps the most impacted, and modern agriculture has failed to defend farmers from the effects of climate change. This, coupled with the added pressure of higher demands for food production caused due to population growth, has only compounded the impact. Traditional agricultural practices that are routed in indigenous knowledge have long safeguarded the delicate balance of the ecosystem through sustainable production techniques. This paper uses secondary data to explore these traditional processes (like Beejamrita, Jeevamrita, sheep penning, earthen bunding, and others) from around the world that have been developed over centuries and focuses on how they can be used to tackle contemporary issues arising from climate change (such as nutrient and water loss, soil degradation, increased incidences of pests). Finally, the resulting framework has been applied to the context of Indian agriculture as a means to combat climate change and improve food security, all while encouraging documentation and transfer of local knowledge as a shared resource among farmers.

Keywords: sustainable food systems, traditional agricultural practices, climate smart agriculture, climate change, indigenous knowledge

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Authors: Deeman Yousif Mahmood, Mohammed Abdullah Hussein

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The goal of a network-based intrusion detection system is to classify activities of network traffics into two major categories: normal and attack (intrusive) activities. Nowadays, data mining and machine learning plays an important role in many sciences; including intrusion detection system (IDS) using both supervised and unsupervised techniques. However, one of the essential steps of data mining is feature selection that helps in improving the efficiency, performance and prediction rate of proposed approach. This paper applies unsupervised K-means clustering algorithm with information gain (IG) for feature selection and reduction to build a network intrusion detection system. For our experimental analysis, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 60.0% for the training set and the remainder for the testing set, a 2 class classifications have been implemented (Normal, Attack). Weka framework which is a java based open source software consists of a collection of machine learning algorithms for data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with using the full features of the dataset with the same algorithm.

Keywords: information gain (IG), intrusion detection system (IDS), k-means clustering, Weka

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Authors: Hamdi Amroun, Yacine Benziani, Mehdi Ammi

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In this paper, we propose an approach of detecting the behavior of the viewers of a TV program in a non-controlled environment. The experiment we propose is based on the use of three types of connected objects (smartphone, smart watch, and a connected remote control). 23 participants were observed while watching their TV programs during three phases: before, during and after watching a TV program. Their behaviors were detected using an approach based on The Dempster Shafer Theory (DST) in two phases. The first phase is to approximate dynamically the mass functions using an approach based on the correlation coefficient. The second phase is to calculate the approximate mass functions. To approximate the mass functions, two approaches have been tested: the first approach was to divide each features data space into cells; each one has a specific probability distribution over the behaviors. The probability distributions were computed statistically (estimated by empirical distribution). The second approach was to predict the TV-viewing behaviors through the use of classifiers algorithms and add uncertainty to the prediction based on the uncertainty of the model. Results showed that mixing the fusion rule with the computation of the initial approximate mass functions using a classifier led to an overall of 96%, 95% and 96% success rate for the first, second and third TV-viewing phase respectively. The results were also compared to those found in the literature. This study aims to anticipate certain actions in order to maintain the attention of TV viewers towards the proposed TV programs with usual connected objects, taking into account the various uncertainties that can be generated.

Keywords: Iot, TV-viewing behaviors identification, automatic classification, unconstrained environment

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Authors: Gabor Kiraly

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Wine regions are based on equilibria between climate, soil, grape varieties, and farming expertise that define the special character and quality of local vine farming and wine production. Changes in climate conditions may increase risk of destabilizing this equilibrium. Adaptation decisions, including adjusting practices, processes and capitals in response to climate change stresses – may reduce this risk. However, farmers’ adaptive behavior are subject to a wide range of factors and forces such as links between climate change implications and production, farm - scale adaptive capacity and other external forces that might hinder them to make efficient response to climate change challenges. This paper will aim to study climate change adaptation practices and strategies of grape growers in a way of applying a complex and holistic approach involving theories, methods and tools both from environmental and social sciences. It will introduce the field of adaptation studies as an evidence - based discourse by presenting an overview of examples from wine regions where adaptation studies have already reached an advanced stage. This will serve as a theoretical background for a preliminary research with the aim to examine the feasibility and applicability of such a research approach in the Hungarian context.

Keywords: climate change, adaptation, viticulture, Hungary

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Authors: Ali Akyar, Osman Serdar Gedik

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Depth estimation is a challenging computer vision task that involves estimating the distance between objects in a scene and the camera. It predicts how far each pixel in the 2D image is from the capturing point. There are some important Monocular Depth Estimation (MDE) studies that are based on Vision Transformers (ViT). We benchmark three major studies. The first work aims to build a simple and powerful foundation model that deals with any images under any condition. The second work proposes a method by mixing multiple datasets during training and a robust training objective. The third work combines generalization performance and state-of-the-art results on specific datasets. Although there are studies with thermal images too, we wanted to benchmark these three non-thermal, state-of-the-art studies with a hybrid image dataset which is taken by Multi-Spectral Dynamic Imaging (MSX) technology. MSX technology produces detailed thermal images by bringing together the thermal and visual spectrums. Using this technology, our dataset images are not blur and poorly detailed as the normal thermal images. On the other hand, they are not taken at the perfect light conditions as RGB images. We compared three methods under test with our thermal dataset which was not done before. Additionally, we propose an image enhancement deep learning model for thermal data. This model helps extract the features required for monocular depth estimation. The experimental results demonstrate that, after using our proposed model, the performance of these three methods under test increased significantly for thermal image depth prediction.

Keywords: monocular depth estimation, thermal dataset, benchmarking, vision transformers

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Authors: Aya Effat, Ossama A. Hosny, Elkhayam M. Dorra

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Estimating costs is a crucial step in construction management and should be completed at the beginning of every project to establish the project's budget. The precision of the cost estimate plays a significant role in the success of construction projects as it allows project managers to effectively manage the project's costs. Site overhead costs constitute a significant portion of construction project budgets, necessitating accurate prediction and management. These costs are influenced by a multitude of factors, requiring a thorough examination and analysis to understand their relative importance and impact. Thus, the main aim of this research is to enhance the contractor’s ability to predict and manage site overheads by identifying and analyzing the main factors influencing the site overheads costs in the Egyptian construction industry. Through a comprehensive literature review, key factors were first identified and subsequently validated using a thorough comparative analysis of data from 55 real-life construction projects. Through this comparative analysis, the relationship between each factor and site overheads percentage as well as each site overheads subcategory and each project construction phase was identified and examined. Furthermore, correlation analysis was done to check for multicollinearity and identify factors with the highest impact. The findings of this research offer valuable insights into the key drivers of site overhead costs in the Egyptian construction industry. By understanding these factors, construction professionals can make informed decisions regarding the estimation and management of site overhead costs.

Keywords: comparative analysis, cost estimation, construction management, site overheads

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Authors: Mohd Khairezan Rahmat

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Since the impetus of becoming a develop country by the year 2020, the Malaysian government have been proactive in strengthening the integration of ICT into the national educational system. Teacher-education programs have the responsibility to prepare the nation future teachers by instilling in them the desire, confidence, and ability to fully utilized the potential of ICT into their instruction process. In an effort to fulfill this responsibility, teacher-education program are beginning to create alternatives means for preparing cutting-edge teachers. One of the alternatives is the student’s learning portal. In line with this mission, this study investigates the Faculty of Education, University Teknologi MARA (UiTM) pre-service teachers’ perception of usefulness, attitude, and ability toward the usage of the university learning portal, known as iLearn. The study also aimed to predict factors that might hinder the pre-service teachers’ decision to used iLearn as their platform in learning. The Structural Equation Modeling (SEM), was employed in analyzed the survey data. The suggested findings informed that pre-service teacher’s successful integration of the iLearn was highly influenced by their perception of usefulness of the system. The findings also suggested that the more familiar the pre-service teacher with the iLearn, the more possibility they will use the system. In light of similar study, the present findings hope to highlight the important to understand the user’s perception toward any proposed technology.

Keywords: e-learning, prediction factors, pre-service teacher, structural equation modeling (SEM)

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Authors: F. Haddadj, S. Hamdi, M. Khames, A. Kadi, S. Zenia, A. Smai, H. Saadi, B. Doumandji-Mitiche

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The use of a biopesticide based on a microorganism scale requires particular care including safety against the useful auxiliary fauna and mammals among other human beings. Due to its persistence in soil and its apparent human and animal safety, Beauveria bassiana is a cryptogram used for controlling pests organizations, particularly in the locust where its effectiveness has been proven by several highly studies. This fungus is also called for greater respect for biotic communities and the environment. Indeed, biopesticides have several environmental benefits: biodegradability, their activity and selectivity decrease unintended non-target species effects, decreased resistance to some of them. It is in this sense that we contribute by presenting our work on the safety of B. bassiana against mammals. For this we conducted a toxicological study of this fungus strain on Wistar rats Rattus norvegicus, first its effect on weight gain. In a second time were performed histological target organ is the liver. After 20 days of treatment, the results of the toxicological studies have shown that B. bassiana caused no change in the physiological state of rats or weight gain, behavior and diet. On cuts in liver histology revealed no disturbance on the organ.

Keywords: entomopathogenic fungus, B. bassiana, Rattus norvegicus, toxicological study, environment

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Authors: Majid Farsadrooh, Mehran Feizi-Dehnayebi

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Serum albumin is the most abundant protein that is present in the circulatory system of a wide variety of organisms. Although it is a significant macromolecule, it can contribute to osmotic blood pressure and also, plays a superior role in drug disposition and efficiency. Molecular docking simulation can improve in silico drug design and discovery procedures to propound a lead compound and develop it from the discovery step to the clinic. In this study, the molecular docking simulation was applied to select a lead molecule through an investigation of the interaction of the two anticancer drugs (Alitretinoin and Abemaciclib) with Human Serum Albumin (HSA). Then, a series of new compounds (a-e) were suggested using lead molecule modification. Density functional theory (DFT) including MEP map and HOMO-LUMO analysis were used for the newly proposed compounds to predict the reactivity zones on the molecules, stability, and chemical reactivity. DFT calculation illustrated that these new compounds were stable. The estimated binding free energy (ΔG) values for a-e compounds were obtained as -5.78, -5.81, -5.95, -5,98, and -6.11 kcal/mol, respectively. Finally, the pharmaceutical properties and toxicity of these new compounds were estimated through OSIRIS DataWarrior software. The results indicated no risk of tumorigenic, irritant, or reproductive effects and mutagenicity for compounds d and e. As a result, compounds d and e, could be selected for further study as potential therapeutic candidates. Moreover, employing molecular docking simulation with the prediction of pharmaceutical properties helps to discover new potential drug compounds.

Keywords: drug design, anticancer, computational studies, DFT analysis

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Authors: Lima Priscila, Gianotto Raiza, Arruda Leonan, Magalhães Filho Fernando

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Industrialization and especially the accentuated population growth in developing countries and the lack of drainage, public cleaning, water and sanitation services has caused concern about the need for expansion of water treatment units and sewage. However, these units have been generating by-products, such as the sludge. This paper aims to investigate aspects of operation and maintenance of sludge from a wastewater treatment plant (WWTP - 90 L.s-1) and two water treatment plants (WTPs; 1.4 m3.s-1 and 0.5 m3.s-1) for the purpose of proper disposal and reuse, evaluating their qualitative and quantitative characteristics, the Brazilian legislation and standards. It was concluded that the sludge from the water treatment plants is directly related to the quality of raw water collected, and it becomes feasible for use in construction materials, and to dispose it in the sewage system, improving the efficiency of the WWTP regarding precipitation of phosphorus (35% of removal). The WTP Lageado had 55,726 kg/month of sludge production, more than WTP Guariroba (29,336 kg/month), even though the flow of WTP Guariroba is 1,400 L.s-1 and the WTP Lagedo 500 L.s-1, being explained by the quality that influences more than the flow. The WWTP sludge have higher concentrations of organic materials due to their origin and could be used to improve the fertility of the soil, crop production and recovery of degraded areas. The volume of sludge generated at the WWTP was 1,760 ton/month, with 5.6% of solid content in the raw sludge and in the dewatered sludge it increased its content to 23%.

Keywords: disposal, sludge, water treatment, wastewater treatment

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Authors: Naresh Reddy Kolanu

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The stability and collapse behavior of thin-walled composite structures, particularly carbon fiber-reinforced plastic (CFRP) panels, are paramount concerns for structural designers. Accurate prediction of collapse loads necessitates precise modeling of damage evolution in the post-buckling regime. This study conducts a comparative assessment of various finite element (FE) methodologies employed in predicting post-buckling collapse in stiffened CFRP panels. A systematic approach is adopted, wherein FE models with various damage capabilities are constructed and analyzed. The study investigates the influence of interacting intra- and interlaminar damage modes on the post-buckling response and failure behavior of the stiffened CFRP structure. Additionally, the capabilities of shell and brick FE-based models are evaluated and compared to determine their effectiveness in capturing the complex collapse behavior. Conclusions are drawn through quantitative comparison with experimental results, focusing on post-buckling response and collapse load. This comprehensive evaluation provides insights into the most effective FE methodologies for accurately predicting the collapse behavior of stiffened CFRP panels, thereby aiding structural designers in enhancing the stability and safety of composite structures.

Keywords: CFRP stiffened panels, delamination, Hashin’s failure, post-buckling, progressive damage model

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Authors: Alim Asamatdinov

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Superabsorbent polymer hydrogels can swell to absorb huge volumes of water or aqueous solutions. This property has led to many practical applications of these new materials, particularly in agriculture for improving the water retention of soils and the water supply of plants. This article reviews the methods of polymeric hydrogels, measurements and treatments of their properties, as well as their effects on soil and on plant growth. The thermodynamic approach used to describe the swelling behaviour of polymer networks proves to be quite helpful in modelling the hydrogel efficiency of water-absorbing additives. The paper presents the results of a study of the physical and chemical properties of hydrogels based on of the production of "Nitron" (Polyacrylonitrile) wastes fibre and salts of the 3-rd transition metals and formalin. The developed hydrogels HG-Al, HG-Cr and HG-formalin have been tested for water holding the capacity of sand. Such a conclusion was also confirmed by data from the method of determining the wilting point by vegetative thumbnails. In the entering process using a dose of 0.1% of the swelling polymeric hydrogel in sand with a culture of barley the difference between the wilting point in comparison with the control was negligible. This indicates that the moisture which was contained in the hydrogel is involved in moisture availability for plant growth, to the same extent as that in the capillaries.

Keywords: hydrogel, chemical, polymer, sandy, colloid

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Authors: Raju Ranjha, Surbhi Aggarwal

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Background: Inflammation plays an important role in infectious and non-infectious diseases. MiRNA is also reported to play role in inflammation and associated cancers. Chemokine CXCL12 is also known to play role in inflammation and various cancers. CXCL12/CXCR4 chemokine axis was involved in pathogenesis of IBD specially UC. Supplementation of CXCL12 induces homing of dendritic cells to spleen and enhances control of plasmodium parasite in BALB/c mice. We looked at the regulation of CXCL12β by miRNA in UC colitis. Prolonged inflammation of colon in UC patient increases the risk of developing colorectal cancer. We looked at the expression differences of CXCl12β and its targeting miRNA in cancer susceptible area of colon of UC patients. Aim: Aim of this study was to find out the expression regulation of CXCL12β by miRNA in inflammation. Materials and Methods: Biopsy samples and blood samples were collected from UC patients and non-IBD controls. mRNA expression was analyzed using microarray and real-time PCR. CXCL12β targeting miRNA were looked by using online target prediction tools. Expression of CXCL12β in blood samples and cell line supernatant was analyzed using ELISA. miRNA target was validated using dual luciferase assay. Results and conclusion: We found miR-200a regulate the expression of CXCL12β in UC. Expression of CXCL12β was increased in cancer susceptible part of colon and expression of its targeting miRNA was decreased in the same part of colon. miR-200a regulate CXCL12β expression in inflammation and may be an important therapeutic target in inflammation associated cancer.

Keywords: inflammation, miRNA, regulation, CXCL12

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Authors: Leonard D. Agana, Wendell Ace Dela Cruz, Arjan C. Lingaya, Bonifacio T. Doma Jr.

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The purpose of this paper is to study the predict the fuel performance parameters, which include drivability index (DI), vapor lock index (VLI), and vapor lock potential using distillation curve and Reid vapor pressure (RVP) of dual alcohol-gasoline fuel blends. Distillation curve and Reid vapor pressure were predicted using artificial neural networks (ANN) with macroscopic properties such as boiling points, RVP, and molecular weights as the input layers. The ANN consists of 5 hidden layers and was trained using Bayesian regularization. The training mean square error (MSE) and R-value for the ANN of RVP are 91.4113 and 0.9151, respectively, while the training MSE and R-value for the distillation curve are 33.4867 and 0.9927. Fuel performance analysis of the dual alcohol–gasoline blends indicated that highly volatile gasoline blended with dual alcohols results in non-compliant fuel blends with D4814 standard. Mixtures of low-volatile gasoline and 10% methanol or 10% ethanol can still be blended with up to 10% C3 and C4 alcohols. Intermediate volatile gasoline containing 10% methanol or 10% ethanol can still be blended with C3 and C4 alcohols that have low RVPs, such as 1-propanol, 1-butanol, 2-butanol, and i-butanol. Biography: Graduate School of Chemical, Biological, and Materials Engineering and Sciences, Mapua University, Muralla St., Intramuros, Manila, 1002, Philippines

Keywords: dual alcohol-gasoline blends, distillation curve, machine learning, reid vapor pressure

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Authors: Jia Zhang, Fengmei Yao, Yanjing Tan

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The accurate estimation of crop yield is of great importance for the food security. In this study, a process-based mechanism model was modified to estimate yield of C4 crop by modifying the carbon metabolic pathway in the photosynthesis sub-module of the RS-P-YEC (Remote-Sensing-Photosynthesis-Yield estimation for Crops) model. The yield was calculated by multiplying net primary productivity (NPP) and the harvest index (HI) derived from the ratio of grain to stalk yield. The modified RS-P-YEC model was used to simulate maize yield in the Northeast China Plain during the period 2002-2011. The statistical data of maize yield from study area was used to validate the simulated results at county-level. The results showed that the Pearson correlation coefficient (R) was 0.827 (P < 0.01) between the simulated yield and the statistical data, and the root mean square error (RMSE) was 712 kg/ha with a relative error (RE) of 9.3%. From 2002-2011, the yield of maize planting zone in the Northeast China Plain was increasing with smaller coefficient of variation (CV). The spatial pattern of simulated maize yield was consistent with the actual distribution in the Northeast China Plain, with an increasing trend from the northeast to the southwest. Hence the results demonstrated that the modified process-based model coupled with remote sensing data was suitable for yield prediction of maize in the Northeast China Plain at the spatial scale.

Keywords: process-based model, C4 crop, maize yield, remote sensing, Northeast China Plain

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Authors: Anushka Saxena, Rakhi Wahee Pratap

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Skincare textiles, an emerging field at the crossroads of textile technology and wellness, offer groundbreaking innovations that aim to enhance health and well-being through everyday wear. This research paper explores the diverse spectrum of skin-friendly textiles, with a focus on key categories such as cosmetotextiles, skincare finishes, marine collagen fabrics, and nanotextiles. These developments reflect a shift towards a more holistic approach to clothing, where garments not only serve as fashion or protection but also contribute actively to personal care and wellness. Cosmetotextiles represent a transformative fusion of beauty and textile industries, where fabrics are impregnated with active cosmetic ingredients that provide skincare benefits as they are worn. By incorporating substances such as vitamins, moisturizers, essential oils, and antioxidants, these textiles continuously release beneficial ingredients to the skin throughout the day. The result is improved skin hydration, enhanced smoothness, and targeted therapeutic effects that redefine how clothing can interact with the body. Marine collagen fabrics introduce a novel approach to skincare, harnessing collagen derived from marine life to improve skin elasticity, hydration, and overall texture. With their natural bioactive compounds, these textiles can help stimulate skin repair and rejuvenation, positioning them as a promising tool in both beauty and medical applications. Marine collagen offers the dual benefit of contributing to sustainable textiles while delivering scientifically-backed skincare benefits. Nanotextiles, on the other hand, leverage the advancements in nanotechnology by integrating nanoparticles into fabric structures. These textiles exhibit multifunctional properties, such as antimicrobial action, UV protection, and wound-healing capabilities, making them highly suitable for medical textiles. Nanotextiles provide an avenue for creating clothing that not only protects the skin from environmental aggressors but also aids in recovery, regeneration, and long-term health maintenance. This paper provides an in-depth overview of healing textiles, examining their current advancements, practical applications, and the challenges faced in their development. Furthermore, it explores the future prospects of this innovative field, particularly its potential in bridging the gap between cutting-edge technology and traditional textile practices. As consumer demand for multifunctional and health-promoting textiles grows, healing textiles present a promising solution for improving quality of life through fabric innovation.

Keywords: skincare textiles, nanotechnology, cosmetotextiles, nanotextiles, marine collagen textiles and health

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Authors: Allen Yushark Fosu, Ndue Kanari, James Vaughan, Alexandre Changes

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Spodumene is a lithium-bearing mineral of great interest due to increasing demand of lithium in emerging electric and hybrid vehicles. The conventional method of processing the mineral for the metal requires inevitable thermal transformation of α-phase to the β-phase followed by roasting with suitable reagents to produce lithium salts for downstream processes. The selection of appropriate reagent for roasting is key for the success of the process and overall lithium recovery. Several researches have been conducted to identify good reagents for the process efficiency, leading to sulfation, alkaline, chlorination, fluorination, and carbonizing as the methods of lithium recovery from the mineral.HSC Chemistry is a thermochemical software that can be used to model metallurgical process feasibility and predict possible reaction products prior to experimental investigation. The software was employed to investigate and explain the various reagent characteristics as employed in literature during spodumene roasting up to 1200°C. The simulation indicated that all used reagents for sulfation and alkaline were feasible in the direction of lithium salt production. Chlorination was only feasible when Cl2 and CaCl2 were used as chlorination agents but not NaCl nor KCl. Depending on the kind of lithium salt formed during carbonizing and fluorination, the process was either spontaneous or nonspontaneous throughout the temperature range investigated. The HSC software was further used to simulate and predict some promising reagents which may be equally good for roasting the mineral for efficient lithium extraction but have not yet been considered by researchers.

Keywords: thermochemical modelling, HSC chemistry software, lithium, spodumene, roasting

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Authors: Rezvan Ravanfar Haghighi, S. Chatterjee, Pratik Kumar, V. C. Vani, Priya Jagia, Sanjiv Sharma, Susama Rani Mandal, R. Lakshmy

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The dual energy computed tomography (DECT) aims at noting the HU(V) values for the sample at two different voltages V=V1, V2 and thus obtain the electron densities (ρe) and effective atomic number (Zeff) of the substance. In the present paper, we aim to obtain a numerical algorithm by which (ρe, Zeff) can be obtained from the HU(100) and HU(140) data, where V=100, 140 kVp. The idea is to use this inversion method to characterize and distinguish between the lipid and fibrous coronary artery plaques.With the idea to develop the inversion algorithm for low Zeff materials, as is the case with non calcified coronary artery plaque, we prepare aqueous samples whose calculated values of (ρe, Zeff) lie in the range (2.65×1023≤ ρe≤ 3.64×1023 per cc ) and (6.80≤ Zeff ≤ 8.90). We fill the phantom with these known samples and experimentally determine HU(100) and HU(140) for the same pixels. Knowing that the HU(V) values are related to the attenuation coefficient of the system, we present an algorithm by which the (ρe, Zeff) is calibrated with respect to (HU(100), HU(140)). The calibration is done with a known set of 20 samples; its accuracy is checked with a different set of 23 known samples. We find that the calibration gives the ρe with an accuracy of ± 4% while Zeff is found within ±1% of the actual value, the confidence being 95%.In this inversion method (ρe, Zeff) of the scanned sample can be found by eliminating the effects of the CT machine and also by ensuring that the determination of the two unknowns (ρe, Zeff) does not interfere with each other. It is found that this algorithm can be used for prediction of chemical characteristic (ρe, Zeff) of unknown scanned materials with 95% confidence level, by inversion of the DECT data.

Keywords: chemical composition, dual-energy computed tomography, inversion algorithm

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Authors: Pattra Pukdeekiat, Siriluk Ruangrungrote

Abstract:

This work was originated to precisely determine direct solar radiation by using atmospheric physics models since the accurate prediction of solar radiation is necessary and useful for solar energy applications including atmospheric research. The possible models and techniques for a calculation of regional direct solar radiation were challenging and compulsory for the case of unavailable instrumental measurement. The investigation was mathematically governed by six astronomical parameters i.e. declination (δ), hour angle (ω), solar time, solar zenith angle (θz), extraterrestrial radiation (Iso) and eccentricity (E0) along with two atmospheric parameters i.e. air mass (mr) and dew point temperature at Bangna meteorological station (13.67° N, 100.61° E) in Bangkok, Thailand. Analyses of five models of solar radiation determination with the assumption of clear sky were applied accompanied by three statistical tests: Mean Bias Difference (MBD), Root Mean Square Difference (RMSD) and Coefficient of determination (R2) in order to validate the accuracy of obtainable results. The calculated direct solar radiation was in a range of 491-505 Watt/m2 with relative percentage error 8.41% for winter and 532-540 Watt/m2 with relative percentage error 4.89% for summer 2014. Additionally, dataset of seven continuous days, representing both seasons were considered with the MBD, RMSD and R2 of -0.08, 0.25, 0.86 and -0.14, 0.35, 3.29, respectively, which belong to Kumar model for winter and CSR model for summer. In summary, the determination of direct solar radiation based on atmospheric models and empirical equations could advantageously provide immediate and reliable values of the solar components for any site in the region without a constraint of actual measurement.

Keywords: atmospheric physics models, astronomical parameters, atmospheric parameters, clear sky condition

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Authors: João Filipe Papel, Tatsuji Munaka

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With society's aging and the number of elderly with dementia rising, researchers have been actively studying how to support the elderly in the early stages of dementia with the objective of allowing them to have a better life quality and as much as possible independence. To make this possible, most researchers in this field are using the Internet Of Things to monitor the elderly activities and assist them in performing them. The most common sensor used to monitor the elderly activities is the Camera sensor due to its easy installation and configuration. The other commonly used sensor is the sound sensor. However, we need to consider privacy when using these sensors. This research aims to develop a system capable of predicting the early stages of dementia based on monitoring and controlling the elderly activities of daily living. To make this system possible, some issues need to be addressed. First, the issue related to elderly privacy when trying to detect their Activities of Daily Living. Privacy when performing detection and monitoring Activities of Daily Living it's a serious concern. One of the purposes of this research is to achieve this detection and monitoring without putting the privacy of the elderly at risk. To make this possible, the study focuses on using an approach based on using Magnet Sensors to collect binary data. The second is to use the data collected by monitoring Activities of Daily Living to predict the early stages of Dementia. To make this possible, the research team suggests developing a proprietary ontology combined with both data-driven and knowledge-driven.

Keywords: dementia, activity recognition, magnet sensors, ontology, data driven and knowledge driven, IoT, activities of daily living

Procedia PDF Downloads 108

Authors: A. M. John-Otumu, M. M. Rahman, M. C. Onuoha, E. P. Ojonugwa

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A Convolutional Neural Network (CNN) model for predicting Lassa fever was built using Python 3.8.0 programming language, alongside Keras 2.2.4 and TensorFlow 2.6.1 libraries as the development environment in order to reduce the current high risk of Lassa fever in West Africa, particularly in Nigeria. The study was prompted by some major flaws in existing conventional laboratory equipment for diagnosing Lassa fever (RT-PCR), as well as flaws in AI-based techniques that have been used for probing and prognosis of Lassa fever based on literature. There were 15,679 blood smear microscopic image datasets collected in total. The proposed model was trained on 70% of the dataset and tested on 30% of the microscopic images in avoid overfitting. A 3x3x3 convolution filter was also used in the proposed system to extract features from microscopic images. The proposed CNN-based model had a recall value of 96%, a precision value of 93%, an F1 score of 95%, and an accuracy of 94% in predicting and accurately classifying the images into clean or infected samples. Based on empirical evidence from the results of the literature consulted, the proposed model outperformed other existing AI-based techniques evaluated. If properly deployed, the model will assist physicians, medical laboratory scientists, and patients in making accurate diagnoses for Lassa fever cases, allowing the mortality rate due to the Lassa fever virus to be reduced through sound decision-making.

Keywords: artificial intelligence, ANN, blood smear, CNN, deep learning, Lassa fever

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Authors: Ratnakar Mahajan, Matteo Lelli, Kinjal Parmar

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Ground improvement is a very important aspect of infrastructure development, especially when it comes to deep-ground improvement. The use of various geosynthetic applications is very common these days for ground improvement. This paper presents a case study where the combination of two geosynthetic applications was used in order to optimize the design as well as to control the settlements through uniform load distribution. The Agartala-Akaura rail project was made to help increase railway connectivity between India and Bangladesh. Both countries have started the construction of the same. The project requires high railway embankments to be built for the rail link. However, the challenge was to design a proper ground improvement solution as the entire area comprises very soft soil for an average depth of 15m. After due diligence, a combination of two methods was worked out by Maccaferri. PVDs were provided for the consolidation, and on top of that, a layer of high-strength geogrids (Paralink) was proposed as a basal reinforcement. The design approach was followed as described in Indian standards as well as British standards. By introducing a basal reinforcement, the spacing of PVDs could be increased, which allowed quick installation and less material consumption while keeping the consolidation time within the project duration.

Keywords: ground improvement, basal reinforcement, PVDs, high strength geogrids, Paralink

Procedia PDF Downloads 78