Search results for: vetiver root extract

Authors: M. Varin, A. M. Dubois, R. Gadbois-Langevin, B. Chalghaf

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Good knowledge of the physical environment is essential for an integrated forest planning. This information enables better forecasting of operating costs, determination of cutting volumes, and preservation of ecologically sensitive areas. The use of satellite images in stereoscopic pairs gives the capacity to generate high precision 3D models, which are scale-adapted for harvesting operations. These models could represent an alternative to 3D LiDAR data, thanks to their advantageous cost of acquisition. The objective of the study was to assess the quality of stereo-derived canopy height models (CHM) in comparison to a traditional LiDAR CHM and ground tree-height samples. Two study sites harboring two different forest stand types (broadleaf and conifer) were analyzed using stereo pairs and tri-stereo images from the WorldView-3 satellite to calculate CHM. Acquisition of multispectral images from an Unmanned Aerial Vehicle (UAV) was also realized on a smaller part of the broadleaf study site. Different algorithms using two softwares (PCI Geomatica and Correlator3D) with various spatial resolutions and band selections were tested to select the 3D modeling technique, which offered the best performance when compared with LiDAR. In the conifer study site, the CHM produced with Corelator3D using only the 50-cm resolution panchromatic band was the one with the smallest Root-mean-square deviation (RMSE: 1.31 m). In the broadleaf study site, the tri-stereo model provided slightly better performance, with an RMSE of 1.2 m. The tri-stereo model was also compared to the UAV, which resulted in an RMSE of 1.3 m. At individual tree level, when ground samples were compared to satellite, lidar, and UAV CHM, RMSE were 2.8, 2.0, and 2.0 m, respectively. Advanced analysis was done for all of these cases, and it has been noted that RMSE is reduced when the canopy cover is higher when shadow and slopes are lower and when clouds are distant from the analyzed site.

Keywords: very high spatial resolution, satellite imagery, WorlView-3, canopy height models, CHM, LiDAR, unmanned aerial vehicle, UAV

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Authors: K. H. Teng, A. Shaw, M. Ateeq, A. Al-Shamma'a, S. Wylie, S. N. Kazi, B. T. Chew

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Numerical and experimental of using novel electromagnetic wave technique to detect water hardness concentration has been presented in this paper. Simulation is powerful and efficient engineering methods which allow for a quick and accurate prediction of various engineering problems. The RF module is used in this research to predict and design electromagnetic wave propagation and resonance effect of a guided wave to detect water hardness concentration in term of frequency domain, eigenfrequency, and mode analysis. A cylindrical cavity resonator is simulated and designed in the electric field of fundamental mode (TM010). With the finite volume method, the three-dimensional governing equations were discretized. Boundary conditions for the simulation were the cavity materials like aluminum, two ports which include transmitting and receiving port, and assumption of vacuum inside the cavity. The design model was success to simulate a fundamental mode and extract S21 transmission signal within 2.1 – 2.8 GHz regions. The signal spectrum under effect of port selection technique and dielectric properties of different water concentration were studied. It is observed that the linear increment of magnitude in frequency domain when concentration increase. The numerical results were validated closely by the experimentally available data. Hence, conclusion for the available COMSOL simulation package is capable of providing acceptable data for microwave research.

Keywords: electromagnetic wave technique, frequency domain, signal spectrum, water hardness concentration

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Authors: Azita Ramezani, Ghazal Mashhadiagha, Bahareh Sanabakhsh

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This study researches the combination of Random. Forest classifiers with large language models (LLMs) and natural language processing (NLP) to improve diagnostic accuracy in chest X-ray analysis using the Indiana University dataset. Utilizing advanced NLP techniques, the research preprocesses textual data from radiological reports to extract key features, which are then merged with image-derived data. This improved dataset is analyzed with Random Forest classifiers to predict specific clinical results, focusing on the identification of health issues and the estimation of case urgency. The findings reveal that the combination of NLP, LLMs, and machine learning not only increases diagnostic precision but also reliability, especially in quickly identifying critical conditions. Achieving an accuracy of 99.35%, the model shows significant advancements over conventional diagnostic techniques. The results emphasize the large potential of machine learning in medical imaging, suggesting that these technologies could greatly enhance clinician judgment and patient outcomes by offering quicker and more precise diagnostic approximations.

Keywords: natural language processing (NLP), large language models (LLMs), random forest classifier, chest x-ray analysis, medical imaging, diagnostic accuracy, indiana university dataset, machine learning in healthcare, predictive modeling, clinical decision support systems

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Authors: Sherly M. A., Subhankar Karmakar, Terence Chan, Christian Rau

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Floods are one of the most frequent natural disasters, causing widespread devastation, economic damage and threat to human lives. Hydrologic impacts of climate change and intensification of urbanization are two root causes of increased flood occurrences, and recent research trends are oriented towards understanding these aspects. Due to rapid urbanization, population of cities across the world has increased exponentially leading to improperly planned developments. Climate change due to natural and anthropogenic activities on our environment has resulted in spatiotemporal changes in rainfall patterns. The combined effect of both aggravates the vulnerability of urban populations to floods. In this context, an efficient and effective flood risk management with its core component as flood risk mapping is essential in prevention and mitigation of flood disasters. Urban flood risk mapping involves zoning of an urban region based on its flood risk, which depicts the spatiotemporal pattern of frequency and severity of hazards, exposure to hazards, and degree of vulnerability of the population in terms of socio-economic, environmental and infrastructural aspects. Although vulnerability is a key component of risk, its assessment and mapping is often less advanced than hazard mapping and quantification. A synergic effort from technical experts and social scientists is vital for the effectiveness of flood risk management programs. Despite an increasing volume of quality research conducted on urban flood risk, a comprehensive multidisciplinary approach towards flood risk mapping still remains neglected due to which many of the input parameters and definitions of flood risk concepts are imprecise. Thus, the objectives of this review are to introduce and precisely define the relevant input parameters, concepts and terms in urban flood risk mapping, along with its methodology, current status and limitations. The review also aims at providing thought-provoking insights to potential future researchers and flood management professionals.

Keywords: flood risk, flood hazard, flood vulnerability, flood modeling, urban flooding, urban flood risk mapping

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Authors: Mohammad A. Bani-Khaled

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In this work we use the Discrete Proper Orthogonal Decomposition transform to characterize the properties of coupled dynamics in thin-walled beams by exploiting numerical simulations obtained from finite element simulations. The outcomes of the will improve our understanding of the linear and nonlinear coupled behavior of thin-walled beams structures. Thin-walled beams have widespread usage in modern engineering application in both large scale structures (aeronautical structures), as well as in nano-structures (nano-tubes). Therefore, detailed knowledge in regard to the properties of coupled vibrations and buckling in these structures are of great interest in the research community. Due to the geometric complexity in the overall structure and in particular in the cross-sections it is necessary to involve computational mechanics to numerically simulate the dynamics. In using numerical computational techniques, it is not necessary to over simplify a model in order to solve the equations of motions. Computational dynamics methods produce databases of controlled resolution in time and space. These numerical databases contain information on the properties of the coupled dynamics. In order to extract the system dynamic properties and strength of coupling among the various fields of the motion, processing techniques are required. Time- Proper Orthogonal Decomposition transform is a powerful tool for processing databases for the dynamics. It will be used to study the coupled dynamics of thin-walled basic structures. These structures are ideal to form a basis for a systematic study of coupled dynamics in structures of complex geometry.

Keywords: coupled dynamics, geometric complexity, proper orthogonal decomposition (POD), thin walled beams

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Authors: Zhifei Hu, Feng Xia

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In recent years, Graph neural network has been widely used in knowledge graph recommendation. The existing recommendation methods based on graph neural network extract information from knowledge graph through entity and relation, which may not be efficient in the way of information extraction. In order to better propose useful entity information for the current recommendation task in the knowledge graph, we propose an end-to-end Neural network Model based on multi-stream graph attentional Mechanism (MSGAT), which can effectively integrate the knowledge graph into the recommendation system by evaluating the importance of entities from both users and items. Specifically, we use the attention mechanism from the user's perspective to distil the domain nodes information of the predicted item in the knowledge graph, to enhance the user's information on items, and generate the feature representation of the predicted item. Due to user history, click items can reflect the user's interest distribution, we propose a multi-stream attention mechanism, based on the user's preference for entities and relationships, and the similarity between items to be predicted and entities, aggregate user history click item's neighborhood entity information in the knowledge graph and generate the user's feature representation. We evaluate our model on three real recommendation datasets: Movielens-1M (ML-1M), LFM-1B 2015 (LFM-1B), and Amazon-Book (AZ-book). Experimental results show that compared with the most advanced models, our proposed model can better capture the entity information in the knowledge graph, which proves the validity and accuracy of the model.

Keywords: graph attention network, knowledge graph, recommendation, information propagation

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Authors: J. K. D. M. P. Madara, R. B. L. Dharmawickreme, Linu John, Ivee Boiss

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Flemingia vestita, commonly known as ‘Sohphlang’ is an important medicinal plant found in the North-Eastern region of India, which is traditionally recognized for its anthelmintic properties. This study was aimed to evaluate the phytochemical constituents and antibacterial activity of the tuber skin extracts of the plant species. Methanol, acetone, and water were used to obtain the solvent extractions of the skin peel extracts. Concentrated extracts of skin peel were tested using previously established qualitative phytochemical assays. The antibacterial efficacy of methanol tuber skin extract was tested against Gram-negative and positive microorganisms, namely, Klebsiella pneumonia, Escherichia coli, Pseudomonas aeruginosa, Bacillus subtilis, and Mycobacterium tuberculosis strains. Agar well diffusion method was employed to determine the zone of inhibition of the plant extracts. Obtained data were statistically analyzed. Methanol extracts of Flemingia vestita were found to be effective against Bacillus subtilis and Mycobacterium tuberculosis at concentrations of 0.5 mg/ml. The reported zone of inhibition for the two strains was 13.3mm ± 0.57 and 16.3mm ± 4.9, respectively. However Klebsiella pneumoniae, Pseudomonas aeruginosa and Escherichia coli were resistant to the plant extracts with no zone of inhibition. Alkaloids, glycosides, and phenols were found to be present in aqueous, methanol, and acetone extracts of the plant in qualitative phytochemical analysis.

Keywords: flemingia vestita, antibacterial activity, phytochemical screening, well diffusion method

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Authors: Babak Safaei, A. M. Fattahi

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Polymer/carbon nanotube nanocomposites have a wide range of promising applications Due to their enhanced properties. In this work, free vibration analysis of single-walled carbon nanotube-reinforced composite plates is conducted in which carbon nanotubes are embedded in an amorphous polyethylene. The rule of mixture based on various types of plate model namely classical plate theory (CLPT), first-order shear deformation theory (FSDT), and higher-order shear deformation theory (HSDT) was employed to obtain fundamental frequencies of the nanocomposite plates. Generalized differential quadrature (GDQ) method was used to discretize the governing differential equations along with the simply supported and clamped boundary conditions. The material properties of the nanocomposite plates were evaluated using molecular dynamic (MD) simulation corresponding to both short-(10,10) SWCNT and long-(10,10) SWCNT composites. Then the results obtained directly from MD simulations were fitted with those calculated by the rule of mixture to extract appropriate values of carbon nanotube efficiency parameters accounting for the scale-dependent material properties. The selected numerical results are presented to address the influences of nanotube volume fraction and edge supports on the value of fundamental frequency of carbon nanotube-reinforced composite plates corresponding to both long- and short-nanotube composites.

Keywords: nanocomposites, molecular dynamics simulation, free vibration, generalized, differential quadrature (GDQ) method

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Authors: Adebola Adedugbe

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The challenge of post-harvest losses poses serious threats for food security in Nigeria and the north-eastern part with the country losing about $9billion annually due to postharvest losses in the sector. Post-harvest loss (PHL) is the quantitative and qualitative loss of food in various post-harvest operations. In Nigeria, post-harvest losses (PHL) have been a major challenge to food security and improved farmer’s income. In 2022, the Nigerian government had said over 30 percent of food produced by Nigerian farmers perish during post-harvest. For many in northeast Nigeria, agriculture is the predominant source of livelihood and income. The persistent communal conflicts, flood, decade-old attacks by boko haram and insurgency in this region have disrupted farming activities drastically, with farmlands becoming insecure and inaccessible as communities are forced to abandon ancestral homes, The impact of climate change is also affecting agricultural and fishing activities, leading to shortage of food supplies, acute hunger and loss of livelihood. This has continued to impact negatively on the region and country’s food production and availability making it loose billions of US dollars annually in income in this sector. The root cause of postharvest losses among others in crops, livestock and fisheries are lack of modern post-harvest equipment, chemical and lack of technologies used for combating losses. The 2019 Global Hunger Index showed Nigeria’s case was progressing from a ‘serious to alarming level’. As part of measures to address the problem of post-harvest losses experienced by farmers, the federal government of Nigeria concessioned 17 silos with 6000 metric tonne storage space to private sector to enable farmers to have access to storage facilities. This paper discusses the causes, effects and solutions in handling post-harvest losses and optimize returns on food security in northeast Nigeria.

Keywords: farmers, food security, northeast Nigeria, postharvest loss

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Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life of the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or Covidi-19 induced pneumonia. The early prediction and classification of such lung diseases help to reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans have pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publically available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scan, Covid-19, deep learning, image processing, lung disease classification

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Authors: Kothuri Sriraman, Mattupalli Komal Teja

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In this paper, a novel algorithm is proposed for the recognition of hulls in a hand written images that might be irregular or digit or character shape. Identification of objects and internal objects is quite difficult to extract, when the structure of the image is having bulk of clusters. The estimation results are easily obtained while going through identifying the sub-regional objects by using the SASK algorithm. Focusing mainly to recognize the number of internal objects exist in a given image, so as it is shadow-free and error-free. The hard clustering and density clustering process of obtained image rough set is used to recognize the differentiated internal objects, if any. In order to find out the internal hull regions it involves three steps pre-processing, Boundary Extraction and finally, apply the Hull Detection system. By detecting the sub-regional hulls it can increase the machine learning capability in detection of characters and it can also be extend in order to get the hull recognition even in irregular shape objects like wise black holes in the space exploration with their intensities. Layered hulls are those having the structured layers inside while it is useful in the Military Services and Traffic to identify the number of vehicles or persons. This proposed SASK algorithm is helpful in making of that kind of identifying the regions and can useful in undergo for the decision process (to clear the traffic, to identify the number of persons in the opponent’s in the war).

Keywords: chain code, Hull regions, Hough transform, Hull recognition, Layered Outline Extraction, SASK algorithm

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Authors: Riyadh Jamegh, Alla Eldin Kassam, Sawsan Sabih

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In order to overcome uncertainty conditions and inability to meet customers' requests due to these conditions, organizations tend to reserve a certain safety stock level (SSL). This level must be chosen carefully in order to avoid the increase in holding cost due to excess in SSL or shortage cost due to too low SSL. This paper used soft computing fuzzy logic to identify optimal SSL; this fuzzy model uses the dynamic concept to cope with high complexity environment status. The proposed model can deal with three input variables, i.e., demand stability level, raw material availability level, and on hand inventory level by using dynamic fuzzy logic to obtain the best SSL as an output. In this model, demand stability, raw material, and on hand inventory levels are described linguistically and then treated by inference rules of the fuzzy model to extract the best level of safety stock. The aim of this research is to provide dynamic approach which is used to identify safety stock level, and it can be implanted in different industries. Numerical case study in the dairy industry with Yogurt 200 gm cup product is explained to approve the validity of the proposed model. The obtained results are compared with the current level of safety stock which is calculated by using the traditional approach. The importance of the proposed model has been demonstrated by the significant reduction in safety stock level.

Keywords: inventory optimization, soft computing, safety stock optimization, dairy industries inventory optimization

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Authors: Neha Parekh, Divya Ladha, Poonam Wadhwani, Nisha Shah

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Nano scaled materials have attracted tremendous interest as corrosion inhibitor due to their high surface area on the metal surfaces. It is well known that the zinc oxide nanoparticles have higher reactivity towards aqueous acidic solution. This work presents a new method to incorporate zinc oxide nanoparticles with white sesame seeds extract (nano-green inhibitor) for corrosion protection of zinc in acidic medium. The morphology of the zinc oxide nanoparticles was investigated by TEM and DLS. The corrosion inhibition efficiency of the green inhibitor and nano-green inhibitor was determined by Gravimetric and electrochemical impedance spectroscopy (EIS) methods. Gravimetric measurements suggested that nano-green inhibitor is more effective than green inhibitor. Furthermore, with the increasing temperature, inhibition efficiency increases for both the inhibitors. In addition, it was established the Temkin adsorption isotherm fits well with the experimental data for both the inhibitors. The effect of temperature and Temkin adsorption isotherm revealed Chemisorption mechanism occurring in the system. The activation energy (Ea) and other thermodynamic parameters for inhibition process were calculated. The data of EIS showed that the charge transfer controls the corrosion process. The surface morphology of zinc metal (specimen) in absence and presence of green inhibitor and nano-green inhibitor were performed using Scanning Electron Microscopy (SEM) and Atomic Force Microscopy (AFM) techniques. The outcomes indicated a formation of a protective layer over zinc metal (specimen).

Keywords: corrosion, green inhibitor, nanoparticles, zinc

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Authors: Hui Zhang, Ye Tian, Fang Ye, Ziming Guo

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Communication signal modulation recognition technology is one of the key technologies in the field of modern information warfare. At present, communication signal automatic modulation recognition methods are mainly divided into two major categories. One is the maximum likelihood hypothesis testing method based on decision theory, the other is a statistical pattern recognition method based on feature extraction. Now, the most commonly used is a statistical pattern recognition method, which includes feature extraction and classifier design. With the increasingly complex electromagnetic environment of communications, how to effectively extract the features of various signals at low signal-to-noise ratio (SNR) is a hot topic for scholars in various countries. To solve this problem, this paper proposes a feature extraction algorithm for the communication signal based on the improved Holder cloud feature. And the extreme learning machine (ELM) is used which aims at the problem of the real-time in the modern warfare to classify the extracted features. The algorithm extracts the digital features of the improved cloud model without deterministic information in a low SNR environment, and uses the improved cloud model to obtain more stable Holder cloud features and the performance of the algorithm is improved. This algorithm addresses the problem that a simple feature extraction algorithm based on Holder coefficient feature is difficult to recognize at low SNR, and it also has a better recognition accuracy. The results of simulations show that the approach in this paper still has a good classification result at low SNR, even when the SNR is -15dB, the recognition accuracy still reaches 76%.

Keywords: communication signal, feature extraction, Holder coefficient, improved cloud model

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Authors: Jesryl B. Paulite, Eixer Niel V. Enesco

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Lettuce is not well known in the lowland locality in the tropical countries like Philippines. Farmers thought that this crop is not adaptable to the climate that we have in lowland. But some new varieties can tolerate warmer conditions. The massive use of pesticides in lettuce production might chronically affect human health and environment. The move of the Philippine government is toward organic. One of the organic material is vermicompost. It is an organic fertilizer that serves as soil conditioner and enhances soil fertility and promotes vigorous and healthy crop growth and Supplementation of 19-19-19+M.E. will make it better since it contains N-P-K and selected microelements to meet the nutritive requirements of the crop. The experiment was conducted at Purok 3, Brgy. Tiburcia, Kapalong, Davao del Norte from February 6, 2014 to March 4, 2014. The study was conducted to determine the effect of varying frequency application of vermicast as supplemented with 19-19-19+M.E. in lettuce. Specifically, this aimed to 1.) Identify the agronomic performance of lettuce as affected by varying frequency application of vermicast as supplemented with 19-19-19+M.E.; 2.) Assess the economic profitability of lettuce as applied with vermicast as supplemented with 19-19-19+M.E. The study was laid out in Randomized Complete Block Design (RCBD) with four treatments and three replications. The treatments were as follow: T1 – Untreated, T2 - Weekly Application, T3- Bi-weekly Application, and T4- Monthly Application. The data on percent (%) mortality were transformed using square root of transformation before Analysis of Variance (ANOVA). Results revealed not significant in terms of percent mortality in weekly and monthly application of the treatment having a mean of 1.76 % and 3.09 %. However, Significant differences were observed in agronomic performances such as; plant height with a mean of 10.63 cm in weekly application and 6.40 cm for the untreated, leaf width with a mean of 10.80 cm for the weekly application and 6.03 for the untreated, fresh weight with a mean of 25.67 g for the weekly application and 6.83 g for the untreated, and yield with a mean of 1,208.33 kg/ha for the weekly application and 327.08 kg/ha for the untreated, respectively. Results further exposed that profitability of lettuce in terms of Return of Production Cost (RPC) were; bi-weekly with 91.01 %, monthly with 68.20 %, weekly with 25.34 % and untreated (control) with 16.69 %.

Keywords: agronomic performance, economic profitability, vermicast, percent mortality, 19-19-19+ME

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Authors: Zhiyong Li, Hiroshi Katayama

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Since 1990s, e-commerce and internet business have been grown gradually over the word and customers tend to express their demand attributes in terms of specification requirement on parts, component, product structure etc. This paper deals with designing effective decoupling points for build to order systems under e-commerce environment, which can be realized through tradeoff relation analysis between two major criteria, customer order lead time and value of work in progress. These KPIs are critical for successful BTO business, namely time-based service effectiveness on coping with customer requirements for the first issue and cost effective ness with risk aversive operations for the second issue. Approach of this paper consists of investigation of successful business standing for BTO scheme, manufacturing model development of this scheme, quantitative evaluation of proposed models by calculation of two KPI values under various decoupling point distributions and discussion of the results brought by pattern of decoupling point distribution, where some cases provide the pareto optimum performances. To extract the relevant trade-off relation between considered KPIs among 2-dimensional resultant performance, useful logic developed by former research work, i.e. Katayama and Fonseca, is applied. Obtained characteristics are evaluated as effective information for managing BTO manufacturing businesses.

Keywords: build-to-order (BTO), decoupling point, e-commerce, order-to-delivery lead time (ODLT), work in progress (WIP)

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Authors: S. Ben-Shabat, T. Turgeman, O. Leubinski, N. Roth-Bejerano, V. Kagan-Zur, Y. Sitrit

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The ectomycorrhizal (ECM) desert truffle Terfezia boudieri produces edible fruit bodies and forms symbiosis with its host plant Helianthemum sessiliflorum (Cistaceae) in the Negev desert of Israel. The symbiosis is vital for both partners' survival under desert conditions. Under desert habitat conditions, ECMs must form symbiosis before entering the dry season. To secure a successful encounter, in the course of evolution, both partners have responded by evolving special signals exchange that facilitates recognition. Members of the Cistaceae family serve as host plants for many important truffles. Conceivably, during evolution a common molecule present in Cistaceae plants was recruited to facilitate successful encounter with ectomycorrhizas. Arbuscular vesicular fungi (AM) are promiscuous in host preferences, in contrast, ECM fungi show specificity to host plants. Accordingly, we hypothesize that H. sessiliflorum secretes a chemotrophic-signaling, which is common to plants hosting ECM fungi belonging to the Pezizales. However, thus far no signaling molecules have been identified in ECM fungi. We developed a bioassay for chemotrophic activity. Fractionation of root exudates revealed a substance with chemotrophic activity and molecular mass of 534. Following the above concept, screening the transcriptome of Terfezia, grown under chemoattraction, discovered genes showing high homology to G proteins-coupled receptors of plant pathogens involved in positive chemotaxis and chemotaxis suppression. This study aimed to identify the active molecule using analytical methods (LC-MS, NMR etc.). This should contribute to our understanding of how ECM fungi communicate with their hosts in the rhizosphere. In line with the ability of Terfezia to form also endomycorrhizal symbiosis like AM fungi, analysis of the mechanisms may likewise be applicable to AM fungi. Developing methods to manipulate fungal growth by the chemoattractant can open new ways to improve inoculation of plants.

Keywords: chemotrophic signal, Helianthemum sessiliflorum, Terfezia boudieri, ECM

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Authors: Yazan Al-Kofahi, Jamal Alqawasmi.

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In this study, a comprehensive literature review (SLR) was conducted, with the main goal of assessing the existing literature about how artificial intelligence (AI), machine learning (ML), deep learning (DL) models are used in sustainable architecture applications and issues including thermal comfort satisfaction, energy efficiency, cost prediction and many others issues. For this reason, the search strategy was initiated by using different databases, including Scopus, Springer and Google Scholar. The inclusion criteria were used by two research strings related to DL, ML and sustainable architecture. Moreover, the timeframe for the inclusion of the papers was open, even though most of the papers were conducted in the previous four years. As a paper filtration strategy, conferences and books were excluded from database search results. Using these inclusion and exclusion criteria, the search was conducted, and a sample of 59 papers was selected as the final included papers in the analysis. The data extraction phase was basically to extract the needed data from these papers, which were analyzed and correlated. The results of this SLR showed that there are many applications of ML and DL in Sustainable buildings, and that this topic is currently trendy. It was found that most of the papers focused their discussions on addressing Environmental Sustainability issues and factors using machine learning predictive models, with a particular emphasis on the use of Decision Tree algorithms. Moreover, it was found that the Random Forest repressor demonstrates strong performance across all feature selection groups in terms of cost prediction of the building as a machine-learning predictive model.

Keywords: machine learning, deep learning, artificial intelligence, sustainable building

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Authors: David Guillermo Piedrahita Marquez, Hector Suarez Mahecha, Jairo Humberto Lopez

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There is a need to preserve aquaculture matrices due to their high nutritional value, and its broad consumption, one of those species is the white cachama (Piaractus brachypomus), this fish is located in the rivers of eastern Colombia, and the previously mentioned species needs more study. Therefore, in a paper the effects of an alternative method of preservation of shell life were investigated, the method used is the application of an edible coating made from chitosan and ethanolic extract of propolis (EEP) encapsulated in maltodextrin. The coating was applied by immersion, and after that, we investigated the post mortem quality changes of the fish performing physicochemical and microbiological analysis. pH, volatile bases, test thiobarbituric acid and peroxide value were tested; finally, we studied the effect of the coating on mesophilic strains, coliforms and other microorganisms such as Staphylococcus, and Salmonella. Finally, we concluded that the coating prolongs the shelf life because it acts as a barrier to oxygen and moisture, the bioactive compounds trap free radicals and the coatings changes the metabolism and cause the cell lysis of the microorganisms. It was determined that the concentration of malonaldehyde, the volatile basic nitrogen content and pH are the variables that distinguish more clearly between the samples with the treatment and the control samples.

Keywords: antimicrobial activity, lipid oxidation, texture profile analysis (TPA), sensorial analysis, peroxide value, thiobarbituric acid assay (TBA), total volatile basic nitrogen (TVB-N)

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Authors: Zhi Zhang, Liling Cao, Seyedbabak Momenzadeh, Lisa Davey

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Reinforced concrete (RC) flat slab-column systems are commonly used in residential or office buildings, as the flat slab provides efficient clearance resulting in more stories at a given height than regular reinforced concrete beam-slab system. Punching shear of slab-column joints is a critical component of two-way reinforced concrete flat slab design. The unbalanced moment at the joint is transferred via slab moment and shear forces. ACI 318 provides an equation to evaluate the punching shear under the design load. It is important to note that the design code considers gravity and environmental load when considering the design load combinations, while it does not consider the effect from differential foundation settlement, which may be a governing load condition for the slab design. This paper describes how prestressed reinforced concrete slab punching shear is evaluated based on ACI 318 provisions and finite element analysis. A prestressed reinforced concrete slab under differential settlements is studied using the finite element modeling methodology. The punching shear check equation is explained. The methodology to extract data for punching shear check from the finite element model is described and correlated with the corresponding code provisions. The study indicates that the finite element analysis results should be carefully reviewed and processed in order to perform accurate punching shear evaluation. Conclusions are made based on the case studies to help engineers understand the punching shear behavior in prestressed and non-prestressed reinforced concrete slabs.

Keywords: differential settlement, finite element model, prestressed reinforced concrete slab, punching shear

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Authors: Lista Andriyati, Nurpudji A Taslim

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The human immunodeficiency virus (HIV) patients have potential nutritional and metabolic problems. HIV is a virus that attacks cells T helper and impairs the function of immune cells. Infected individuals gradually become immunodeficient, results in increased susceptibility to a wide range of infections such as tuberculosis (TB). Malnutrition has destructive effects on the immune system and host defense mechanisms. Effective and proper nutritional therapies are important to improve medical outcomes and quality of life, which is associated with functional improvement. A case of 38-years old man admitted to hospital with loss of consciousness and was diagnosed HIV infection and relapse lung TB with severe malnutrition, fever, oral candidiasis, anemia (6.3 g/dL), severe hypoalbuminemia (1.9 g/dL), severe hypokalemia (2.2 mmol/L), immune depletion (1085 /µL) and elevated liver enzyme (ALT 1198/AST 375 U/L). Nutritional intervention by giving 2300 kcal of energy, protein 2 g/IBW/day, carbohydrate 350 g, fat 104 g through enteral and parenteral nutrition. Supplementations administered are zinc, vitamin A, vitamin B1, vitamin B6, vitamin B12, vitamin C, vitamin D, and snakehead fish extract high content of protein albumin (Pujimin®). After 46 days, there are clinical and metabolic improvement in Hb (6.3 to 11.2 g/dL), potassium (2.2 to 3.4 mmol/L), albumin (1.9 to 2.3 g/dL), ALT 1198 to 47/AST 375 to 68 U/L) and improved awareness. In conclusion, nutritional therapy in HIV infection with adequate macronutrients and micronutrients fulfillment and immunonutrition is very important to avoid cachexia and to improve nutritional status and immune disfunction.

Keywords: HIV, hypoalbuminemia, malnutrition, tuberculosis

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Authors: Biniam Tedros Kahsay, Yohannes Yebabe Tesfay

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Eritrea is a country in the East of Africa. The country is a neighbor of Djibouti, Ethiopia, and Sudan and is bordered by the Red Sea. The country declared its independence from Ethiopia in 1993. Thus, Eritrea has a lot of commonalities with the Northern Part of Ethiopia's tradition, religion, and languages. Many economists suggested that Eritrea is in a very strategic position for world trade roots and has an impact on geopolitics. This study focused on identifying the most important factor in boosting the Eritrean Economy. The paper collected big secondary data from the World Bank, International Trade and Tariff Data (WTO), East African Community (EAC), Ethiopian Statistical Agency (ESA), and the National Statistics Office (Eritrea). Economists consider economic and population growth in determining trade belts in East Africa. One of the most important Trade Belt that will potentially boost the Eritrean economy is the root of Eritrea (Massawa)->Eritea, (Asmara)->Tigray, (Humora)->Tigray, (Dansha)-> Gondar-> Gojjam-> Benshangual Gumuz => {Oromia, South Sudan}->Uganda. The estimate showed that this is one of the biggest trade roots in East Africa and has a participation of more than 150 million people. We employed various econometric analyses to predict the GDP of Eritrea, considering the future trade belts in East Africa. The result showed that the economy of Eritrea from the Trade Belt will have an elasticity estimate of 65.87% of the GDP of Ethiopia, 3.32% of the GDP of South Sudan, and 0.09% of the GDP of Uganda. The result showed that the existence of war has an elasticity of -93% to the GDP of the country. Thus, if Eritrea wants to strengthen its economy from the East African Trade Belt, the country needs to permanently avoid war in the region. Essentially, the country needs to establish a collaborative platform with the Northern part of Ethiopia (Tigray). Thus, establishing a mutual relationship with Tigray will boost the Eritrean economy. In that regard, Eritrean scholars and policymakers need to work on establishing the East African Trade Belt to boost their economy.

Keywords: Eritrea, east Africa trade belt, GDP, cointegration analysis, critical path analysis

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Authors: Manal M. Hamed, Mosad A. Ghareeb, Abdel-Aleem H. Abdel-Aleem, Amal M. Saad, Mohamed S. Abdel-Aziz, Asmaa H. Hadad

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Natural products derived from medicinal plants provide unlimited opportunities for a new medication leads because of the unmatched accessibility of chemical variation. Six compounds were isolated from the n-butanol extract of Callistemon citrinus (Family Myrtaceae), they were identified as; nepetolide (1), callislignan A (2), 6,8-dimethoxy-4,5-dimethyl-3-methyleneisochroman-1-one (3), 3-methyl-7-O-benzoyl-β-D-glucopyranoside (4), 5, 7, 3', 5'-tetrahydroxy-6, 8-di-C-methyl flavanone (5), and (2R,3R,4S,5S)-2,4-bis(4-hydroxyphenyl)-3,5-dihydroxy-tetrahydropyran (6). The isolated compounds were evaluated as antioxidant and antimicrobial agents. The antioxidant activities of the compounds were determined using DPPH-radical scavenging and total antioxidant capacity (TAC) assays. The results indicated that compound (5) was most active in its capacity to scavenge free radicals in the DPPH assay [SC50 value, 4.65 ± 0.74μg/mL] compared to the standard ascorbic acid and exhibited the highest activity in the TAC assay (610.45 ± 1.67mg AAE/g compound). The pure isolates were tested for their antimicrobial activity against four pathogenic microbial strains including Staphylococcus aureus, Methicillin-resistant Staphylococcus aureus (MRSA), Pseudomonas aeruginosa and Candida albicans. Also, the GC/MS analysis of its leaves essential oil presented nine identified compounds representing 91% of the total oil constituents. The outcomes got from this study give a reasonable justification for the medicinal uses of Callistemon citrinus plant.

Keywords: Callistemon citrinus, flavanone, antioxidant activity, antimicrobial activity, essential oil, Myrtaceae

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Authors: H. Khomri, A. Bentellis

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Since century of the Development Activities extraction and a dispersed mineral processing Toxic metals and much more contaminated vast areas occupied by what they natural outcrops. New types of metalliferous habitats are so appeared. A species that is Lygeum spartum attracted our curiosity because apart from its valuable role in desertification, it is apparently able to exclude antimony and other metals can be. This species, green leaf blades which are provided as cattle feed, would be a good subject for phytoremediation of mineral soils. The study of absorption and translocation of chromium by the Lygeum spartum in the mining region of Djebel Hamimat and the interaction soil-plant, revealed that soils of this species living in this region are alkaline, calcareous majority in their fine texture medium and saline in their minority. They have normal levels of organic matter. They are moderately rich in nitrogen. They contain total chromium content reaches a maximum of 66,80 mg Kg^(-1) and a total absence of soluble chromium. The results of the analysis of variance of the difference between bare soils and soils appear Lygeum spartum made a significant difference only for the silt and organic matter. But for the other variables analyzed this difference is not significant. Thus, this plant has only one action on the amendment, only the levels of silt and organic matter in soils. The results of the multiple regression of the chromium content of the roots according to all soil variables studied did appear that among the studied variables included in the model, only the electrical conductivity and clay occur in the explanation of contents chromium in roots. The chromium content of the aerial parts analyzed by regression based on all studied soil variables allows us to see only the variables: electrical conductivity and content of chromium in the root portion involved in the explanation of the content chromium in the aerial part.

Keywords: absorption, translocation, analysis of variance, chrome, Lygeum spartum, multiple regression, the soil variables

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Authors: Amadou Fall Dia, Maurras Ulbricht Togbe, Aliou Boly, Zakia Kazi Aoul, Elisabeth Metais

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Existing RDF (Resource Description Framework) Stream Processing (RSP) systems allow continuous processing of RDF data issued from different application domains such as weather station measuring phenomena, geolocation, IoT applications, drinking water distribution management, and so on. However, processing window phase often expires before finishing the entire session and RSP systems immediately delete data streams after each processed window. Such mechanism does not allow optimized exploitation of the RDF data streams as the most relevant and pertinent information of the data is often not used in a due time and almost impossible to be exploited for further analyzes. It should be better to keep the most informative part of data within streams while minimizing the memory storage space. In this work, we propose an RDF graph summarization system based on an explicit and implicit expressed needs through three main approaches: (1) an approach for user queries (SPARQL) in order to extract their needs and group them into a more global query, (2) an extension of the closeness centrality measure issued from Social Network Analysis (SNA) to determine the most informative parts of the graph and (3) an RDF graph summarization technique combining extracted user query needs and the extended centrality measure. Experiments and evaluations show efficient results in terms of memory space storage and the most expected approximate query results on summarized graphs compared to the source ones.

Keywords: centrality measures, RDF graphs summary, RDF graphs stream, SPARQL query

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Authors: Eleni Stefanidou, Nikolaos Katsenios, Ioanna Karamichali, Aspasia Efthimiadou, Panagiotis Madesis

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The excessive use of pesticides and fertilizers has significant environmental and human health-related negative effects. In the frame of the development of sustainable agriculture practices, especially in the context of extreme environmental changes (climate change), it is important to develop alternative practices to increase productivity and biotic and abiotic stress tolerance. Beneficial bacteria, such as symbiotic bacteria in legumes (rhizobia) and symbiotic or free-living Plant Growth Promoting Rhizobacteria (PGPR), which could act as "plant probiotics", can promote plant growth and significantly increase the resistance of crops under adverse environmental conditions. In this study, we explored the symbiotic relationships between Faba bean (Vicia faba L.) cultivars with different PGPR bacteria, aiming to identify the possible influence on yield and biotic-abiotic phytoprotection benefits. Transcriptomic analysis of root and whole plant samples was executed for two Vicia faba L. cultivars (Polikarpi and Solon) treated with selected PGPR bacteria (6 treatments: B. subtilis + Rhizobium-mixture, A. chroococcum + Rhizobium-mixture, B. subtilis, A. chroococcum and Rhizobium-mixture). Preliminary results indicate a significant yield (Seed weight and Total number of pods) increase in both varieties, ranging around 25%, in comparison to the control, especially for the Solon cultivar. The increase was observed for all treatments, with the B. subtilis + Rhizobium-mixture treatment being the highest performing. The correlation of the physiological and morphological data with the transcriptome analysis revealed molecular mechanisms and molecular targets underlying the observed yield increase, opening perspectives for the use of nitrogen-fixing bacteria as a natural, more ecological enhancer of legume crop productivity.

Keywords: plant probiotics, PGPR, legumes, sustainable agriculture

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Authors: Somit Dutta, Pallab Kar, Arnab Kumar Chakraborty, Arnab Sen, Tapas Kumar Chaudhuri

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In the present study three species of Clerodendrum; Clerodendrum indicum, Volkameria inermis and Clerodendrum colebrookianum were used to investigate the possible activity against oxidative stress. A detailed in-vivo and in-vitro antioxidant profiling, directly associated with inflammation-related carcinogenesis, has been executed with a motive to evaluate the free radical scavenging activity of Clerodendrum extract. Measurement of cell viability and ROS generation in HEK-293 (Human Embryonic Kidney Cell Line) cells was also estimated. The immune cell proliferative properties (MTT) and in-vitro assay for evaluation of their antioxidant activities including hydroxyl radical, nitric oxide, singlet oxygen, peroxinitrate and hydrogen peroxide, etc. were investigated. GC-MS and FTIR analyses have been performed to identify the active biological compounds. These active biological compounds were further studied to assess their potential medicinal properties, aided by molecular docking and interaction analysis between the active compounds and different proteins related to oxidative stress leading to progression of carcinogenesis. The research article clearly demonstrates the role of ROS in various phases of carcinogenesis. Therefore, the antioxidant and free radical scavenging capacity of all the Clerodendrum species might prove beneficial for the immune system. It might be concluded that this plant species offers great promise for cancer prevention and therapy due to the presence of several bioactive compounds and potent antioxidant capacity of C. colebrookianum.

Keywords: antioxidant, cancer, oxidative stress, reactive oxygen species (ROS)

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Authors: Kana Matsuyanagi

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Anorexia Nervosa (AN) is a psychiatric disorder characterized by a relentless pursuit of thinness and strict restriction of food. The current therapeutic approaches for AN predominantly revolve around outpatient psychotherapies, which create significant financial barriers for the majority of affected patients, hindering their access to treatment. Nonetheless, AN exhibit one of the highest mortality and relapse rates among psychological disorders, underscoring the urgent need to provide patients with an affordable self-treatment tool, enabling those unable to access conventional medical intervention to address their condition autonomously. To this end, a neurofeedback software, termed RelaxNeuron, was developed with the objective of providing an economical and portable means to aid individuals in self-managing AN. Electroencephalography (EEG) was chosen as the preferred modality for RelaxNeuron, as it aligns with the study's goal of supplying a cost-effective and convenient solution for addressing AN. The primary aim of the software is to ameliorate the negative emotional responses towards food stimuli and the accompanying aberrant eye-tracking patterns observed in AN patient, ultimately alleviating the profound fear towards food an elemental symptom and, conceivably, the fundamental etiology of AN. The core functionality of RelaxNeuron hinges on the acquisition and analysis of EEG signals, alongside an electrocardiogram (ECG) signal, to infer the user's emotional state while viewing dynamic food-related imagery on the screen. Moreover, the software quantifies the user's performance in accurately tracking the moving food image. Subsequently, these two parameters undergo further processing in the subsequent algorithm, informing the delivery of either negative or positive feedback to the user. Preliminary test results have exhibited promising outcomes, suggesting the potential advantages of employing RelaxNeuron in the treatment of AN, as evidenced by its capacity to enhance emotional regulation and attentional processing through repetitive and persistent therapeutic interventions.

Keywords: Anorexia Nervosa, fear conditioning, neurofeedback, BCI

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Authors: Theresa Campbell, Camille Bowen-Forbes, William Aalbersberg

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Differences in the sensory properties of two subtly distinct varieties of Rubus rosifolius lead to the examination of their anthocyanin, essential oil and polyphenol profiles. In both cases, notable differences were identified. Pelargonidin-3-rhutinoside (17.2 mg/100 g FW) and Cyanidin-3-glucoside (66.2 mg/100g FW) proved to be the dominant anthocyanins in the red and wine red varieties respectively. Linalool and terpineol were the major constituents of the essential oil from the red variety; however, those of the wine red variety are unidentified. In regard to phenolic compounds, caffeic acid and quercetin were in a higher concentration in the red variety (1.85 and 0.73 mg/100g FW respectively, compared to 1.22 and 0.34 mg/100g FW respectively in the wine red fruits); while ellagic acid and ferulic acid were of a higher concentration in the wine red variety (0.92 and 0.84mg/100g FW respectively, compared to 0.15 and 0.48 mg/100g FW respectively in the red variety). The methanol extract of both fruit varieties showed great antioxidant activity. Analysis of the antimicrobial activity of the fruit extracts against the growth of drug resistant pathogens revealed that they are active against methicillin resistant S. aureus (MRSA), rifampicin resistant S. aureus (RRSA), wild-type S. aureus (WTSA) and vancomycin-resistant Enterococcus faecium (VREF). Activity was also reported against several food-borne pathogens including two strains of E. coli, L. monocytogenes and Enterobacter aerogenes. The cytotoxicity of the various extracts was assessed and the essential oil extracts exhibited superior activity. The phenolic composition and biological activity of the fruits indicate that their consumption is beneficial to health and also that their incorporation into functional foods and nutraceuticals should be considered.

Keywords: phytochemicals, antimicrobial, cytotoxic, Rubus rosifolius

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Authors: K. Khaerunnisa, D. Ryangga, S. A. Pawiro

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Over the past few decades, film dosimetry has become a tool which is used in various radiotherapy modalities, either for clinical quality assurance (QA) or dose verification. The response of the film to irradiation is usually expressed in optical density (OD) or net optical density (netOD). While the film's response to radiation is not linear, then the use of film as a dosimeter must go through a calibration process. This study aimed to compare the function of the calibration curve of various measurement methods with various densitometer, using a flat bad scanner, point densitometer and spectrophotometer. For every response function, a radichromic film calibration curve is generated from each method by performing accuracy, precision and sensitivity analysis. netOD is obtained by measuring changes in the optical density (OD) of the film before irradiation and after irradiation when using a film scanner if it uses ImageJ to extract the pixel value of the film on the red channel of three channels (RGB), calculate the change in OD before and after irradiation when using a point densitometer, and calculate changes in absorbance before and after irradiation when using a spectrophotometer. the results showed that the three calibration methods gave readings with a netOD precision of doses below 3% for the uncertainty value of 1σ (one sigma). while the sensitivity of all three methods has the same trend in responding to film readings against radiation, it has a different magnitude of sensitivity. while the accuracy of the three methods provides readings below 3% for doses above 100 cGy and 200 cGy, but for doses below 100 cGy found above 3% when using point densitometers and spectrophotometers. when all three methods are used for clinical implementation, the results of the study show accuracy and precision below 2% for the use of scanners and spectrophotometers and above 3% for precision and accuracy when using point densitometers.

Keywords: Callibration Methods, Film Dosimetry EBT3, Flat Bad Scanner, Densitomete, Spectrophotometer

Procedia PDF Downloads 135