Search results for: plant disease classification

Authors: Nowbuth Manta Devi, Rasmally Mohammed Hussein

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Flood is one of the most devastating climate impacts that many countries are facing. Many different causes have been associated with the intensity of floods being recorded over time. Unplanned development, low carrying capacity of drains, clogged drains, construction in flood plains or increasing intensity of rainfall events. While a combination of these causes can certainly aggravate the flood conditions, in many cases, increasing drainage capacity has not reduced flood risk to the level that was expected. The present study analyzed the extent to which land use is contributing to aggravating impacts of flooding in a city. Satellite images have been analyzed over a period of 20 years at intervals of 5 years. Both unsupervised and supervised classification methods have been used with the image processing module of ArcGIS. The unsupervised classification was first compared to the basemap available in ArcGIS to get a first overview of the results. These results also aided in guiding data collection on-site for the supervised classification. The island of Mauritius is small, and there are large variations in land use over small areas, both within the built areas and in agricultural zones involving food crops. Larger plots of agricultural land under sugar cane plantations are relatively more easily identified. However, the growth stage and health of plants vary and this had to be verified during ground truthing. The results show that although there have been changes in land use as expected over a span of 20 years, this was not significant enough to cause a major increase in flood risk levels. A digital elevation model was analyzed for further understanding. It could not be noted that overtime, development tampered with natural flow paths in addition to increasing the impermeable areas. This situation results in backwater flows, hence increasing flood risks.

Keywords: climate change, flood, natural flow paths, small islands

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Authors: Ayodele A. Otaiku

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Purpose: Nigeria has become the largest importer of rice in Africa and second in the world, 2015. Investigate interactions of organic rice farming on soil quality and health from bio-waste converted to biofertilizer and its environmental impact on rice crop. Methodology: Bio-wastes, poultry waste, organic agriculture wastes, wood ash mixed with microbial inoculant organisms called OBD-Plus microbes (broad spectrum) composted in anaerobic digester to OBD-biofertilizer (2010 - 2012) uses microbes to build humus and other stable carbons. Two field experiments were carried out at Badeggi, Niger state in 2011 and 2012 to evaluate the response of lowland rice production using biofertilizer. The experimental field was laid out in a strip-plot design with five treatments and three replications and at twenty-one day old seedlings of FARO 44 and FARO 52 rice varieties were transplanted. Plots without fertiliser application served as control. Findings: The highest rice grain yield increase of 4.4 t/ha over the control in 2012 against the Nigeria average of lowland rice grain yields of 1.5 t/ha. The utilization of OBD-Biofertilizer can decrease the use of chemical nitrogen fertilizer, prevent the depletion of soil organic matter and reduce environmental pollution. Increasing the floodwater productivity and optimizing the recycling of nutrients cum grazer populations and disease by biocontrols microbes present in the OBD-Biofertilizer. Organic matter in the soil improves by 58% and C/N 15 (2011) and 13.35 (2012). Implications: OBD- Biofertilizer produce plant growth hormones such as indole acetic acid (IAA), glomalin related soil protein and extracellular enzymes as phosphatases that promote soil health and quality. Conclusion: Microorganisms can enhance nutrients use efficiency by increasing root surface area e.g., mycorrhizal, fungi, promoting other beneficial symbioses of the host plant and microbial interactions resulting to increase in soil organic matter. By 2030, climate change is projected to depress cereal production in Africa by 2 to 3 percent. Improved seeds and increased fertilizer use should more than compensate, but this factor will still weigh heavily on efforts to make progress.

Keywords: OBD-plus microbial consortia, OBD-biofertilizer, rice production, soil quality, sustainable agriculture

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Authors: Mst. Tuhina-Khatun, Mohamed Hanafi Musa, Mohd Rafii Yosup, Wong Mui Yun, Md. Aktar-Uz-Zaman, Mahbod Sahebi

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Rice is a staple crop of economic importance of most Asian people, and blast is the major constraints for its higher yield. Heritability of plants traits helps plant breeders to make an appropriate selection and to assess the magnitude of genetic improvement through hybridization. Diversity of crop plants is necessary to manage the continuing genetic erosion and address the issues of genetic conservation for successfully meet the future food requirements. Therefore, an experiment was conducted to estimate heritability and to determine the diversity of 27 blast resistant upland rice genotypes based on 18 quantitative traits using randomized complete block design. Heritability value was found to vary from 38 to 93%. The lowest heritability belonged to the character total number of tillers/plant (38%). In contrast, number of filled grains/panicle, and yield/plant (g) was recorded for their highest heritability value viz. 93 and 91% correspondingly. Cluster analysis based on 18 traits grouped 27 rice genotypes into six clusters. Cluster I was the biggest, which comprised 17 genotypes, accounted for about 62.96% of total population. The multivariate analysis suggested that the genotype ‘Chokoto 14’ could be hybridized with ‘IR 5533-55-1-11’ and ‘IR 5533-PP 854-1’ for broadening the gene pool of blast resistant upland rice germplasms for yield and other favorable characters.

Keywords: blast resistant, diversity analysis, heritability, upland rice

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Authors: Aisulu Tileukulova, Zhexebay Dauren

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Radar plays an important role because it is widely used in civil and military fields. Target detection is one of the most important radar applications. The accuracy of detecting inconspicuous aerial objects in radar facilities is lower against the background of noise. Convolutional neural networks can be used to improve the recognition of this type of aerial object. The purpose of this work is to develop an algorithm for recognizing aerial objects using convolutional neural networks, as well as training a neural network. In this paper, the structure of a convolutional neural network (CNN) consists of different types of layers: 8 convolutional layers and 3 layers of a fully connected perceptron. ReLU is used as an activation function in convolutional layers, while the last layer uses softmax. It is necessary to form a data set for training a neural network in order to detect a target. We built a Confusion Matrix of the CNN model to measure the effectiveness of our model. The results showed that the accuracy when testing the model was 95.7%. Classification of echo signals using CNN shows high accuracy and significantly speeds up the process of predicting the target.

Keywords: radar, neural network, convolutional neural network, echo signals

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Authors: Uswah Khairuddin, Rubiyah Yusof, Nenny Ruthfalydia Rosli

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This paper presents a comparative study between k-Nearest Neighbour (k-NN) and Multi-Layer Perceptron Neural Network (MLP-NN) classifier using Genetic Algorithm (GA) as feature selector for wood recognition system. The features have been extracted from the images using Grey Level Co-Occurrence Matrix (GLCM). The use of GA based feature selection is mainly to ensure that the database used for training the features for the wood species pattern classifier consists of only optimized features. The feature selection process is aimed at selecting only the most discriminating features of the wood species to reduce the confusion for the pattern classifier. This feature selection approach maintains the ‘good’ features that minimizes the inter-class distance and maximizes the intra-class distance. Wrapper GA is used with k-NN classifier as fitness evaluator (GA-kNN). The results shows that k-NN is the best choice of classifier because it uses a very simple distance calculation algorithm and classification tasks can be done in a short time with good classification accuracy.

Keywords: feature selection, genetic algorithm, optimization, wood recognition system

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Authors: G. Dadashova, A. Bakhshaliyev

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Aim: Determine gender differences in the etiology and clinical outcomes, as well as in the remodeling of the left ventricle (LV) in patients with chronic heart failure (CHF), suffering from arterial hypertension (AH) and coronary heart disease (CHD). Material and methods: The study included 112 patients of both sexes; aged 45 to 60 years with postinfarction cardiosclerosis had functional class (FC) heart failure II-IV of NYHA which were examined on the basis of Azerbaijan Scientific Research Institute of Cardiology. The patients were divided into 2 groups: 1st c. 60 males, mean age 54,8 ± 3,3 years, and 2nd gr 52 women, mean age 55,8 ± 3,1 years. To assess cardiac hemodynamic all patients underwent echocardiography (B-M-modes) using ‘Vivid 3’. Thus on the basis of indicators such as the index of the relative thickness of the left ventricle wall and the index of left ventricular mass (LVMI) was identified the architectonic model of the left ventricle. Results: According to our research leading cause of heart failure in women is 50.5% of cases of hypertension, ischemic heart disease 23.7% (with 79.5% of the cases developed in patients with chronic heart failure who did not have a history of myocardial infarction). While in men is the undisputed leader of CHD, forming 78.3% of CHF (80.3% in men with CHF occurred after myocardial infarction). According to our research in women more often than men CHF develops a type of diastolic dysfunction (DD, and left ventricular ejection fraction remained unchanged. Since DD occurs in men at 65,8% vs. 76,4% of women when p < 0,05. In the group of women was more common prognostic neblagopryatnye remodeling - eccentric hypertrophy of the left ventricle: 68% vs. 54.5% among men (p < 0,05), concentric left ventricular hypertrophy: 21% in women vs 19,1% (p > 0,05 ). Conclusions: Patients with heart failure are a number of gender-specific: the prevalence of hypertension in women, and coronary heart disease in men. While in women with heart failure often recorded diastolic dysfunction and characterized by the development of prognostically unfavorable remodeling types: eccentric and concentric LV hypertrophy.

Keywords: chronic heart failure, arterial hypertension, remodeling, diastolic dysfunction, men, women, ischemic heart disease

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Authors: Dessie Fisseha, Derbew Belew, Ambecha Olika

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Potato is a high-potential food security crop in Ethiopia. However, the yield and productivity of the crop have been far below the world average. This is due to several factors, including appropriate agronomic practices, such as time of earthing-up and inflorescence management. A field experiment was conducted at Jimma, Southwest Ethiopia, during 2016/17 under irrigation to determine the effect of time of earthing-up and inflorescence removal on the growth, yield, and quality of potatoes. The treatments consisted of a time of earthing-up (no earthing-up, earthing-up at 15, 30, and 45 days after complete plant emergence) and inflorescence removal (inflorescence removed and not removed). Potato variety (Belete) was used for this experiment. A 2x4 factorial experiment was laid out with three replications. Data collected on the growth, yield, and quality components of potatoes were analyzed using SAS Version 9.3 statistical software. Inflorescence removal affected the majority of the growth and yield parameters, while the time of earthing-up affected all growth, yield, and quality (green tuber number) parameters. Earthing-up at 15 days in combination with inflorescence removal (at 60 days after complete plant emergence) gave better plant growth and maximum tuber yield of the Belete potato variety under irrigated conditions. Since the current research was conducted at one location, in one season, and with one potato cultivar (Belete), it would be advisable to repeat the experiment so as to arrive at a final conclusion and subsequent recommendation.

Keywords: Belete, earthing-up, inflorescence, yield

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Authors: A. Majeed, M.K. Abbasi, S. Hameed, A. Imran, T. Naqqash, M. K. Hanif

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Plant growth-promoting rhizobacteria (PGPR) are free-living soil bacteria that aggressively colonize the rhizosphere/plant roots, and enhance the growth and yield of plants when applied to seed or crops. Root associated (endophytic and rhizospheric) PGPR were isolated from Sunflower (Helianthus anus) grown in soils collected from 16 different sites of sub division Dhirkot, Poonch, Azad Jammu & Kashmir, Pakistan. A total of 150 bacterial isolates were isolated, purified, screened in vitro for their plant growth promoting (PGP) characteristics. 11 most effective isolates were selected on the basis of biochemical assays (nitrogen fixation, phosphate solubilization, growth hormone production, biocontrol assay, and carbon substrates utilization assay through gas chromatography (GCMS), spectrophotometry, high performance liquid chromatography HPLC, fungal and bacterial dual plate assay and BIOLOG GN2/GP2 microplate assay respectively) and were tested on the crop under controlled and field conditions. From the inoculation assay, the most promising 4 strains (on the basis of increased root/shoot weight, root/shoot length, seed oil content, and seed yield) were than selected for colonization studies through confocal laser scanning and transmission electron microscope. 16Sr RNA gene analysis showed that these bacterial isolates belong to Pseudononas, Enterobacter, Azospirrilum, and Citobacter genera. This study is the clear evident that such isolates have the potential for application as inoculants adapted to poor soils and local crops to minimize the chemical fertilizers harmful for soil and environment

Keywords: PGPR, nitrogen fixation, phosphate solubilization, colonization

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Authors: Yunwei Zhang, Na Li, Yuhong Niu

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Bone tissue engineering scaffold was regarded as an important clinical technology of curing bony defect. The patent protection of bone tissue engineering scaffold had been paid more attention and strengthened all over the world. This study analyzed the future development trends of international technologies in the field of bone tissue engineering scaffold and its patent protection. This study used the methods of data classification and classification indexing to analyze 2718 patents retrieved in the patent database. Results showed that the patents coming from United States had a competitive advantage over other countiries in the field of bone tissue engineering scaffold. The number of patent applications by a single company in U.S. was a quarter of that of the world. However, the capability of R&D in China was obviously weaker than global level, patents mainly coming from universities and scientific research institutions. Moreover, it would be predicted that synthetic organic materials as new materials would be gradually replaced by composite materials. The patent technology protections of composite materials would be more strengthened in the future.

Keywords: bone tissue engineering, patent analysis, Scaffold material, patent protection

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Authors: Chia-Pei Chen, Yu-Ju Chen, Yu-Juei Hsu

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The prevalence and incidence of end-stage renal disease in Taiwan ranks the highest in the world. According to the statistical survey of the Ministry of Health and Welfare in 2019, kidney disease is the ninth leading cause of death in Taiwan. It leads to autonomic dysfunction, inflammatory response and symptom distress, and further increases the damage to the structure and function of the kidneys, leading to increased demand for renal replacement therapy and risks of cardiovascular disease, which also has medical costs for the society. If we can intervene in a feasible manual to effectively regulate the autonomic nerve function of CKD patients, reduce the inflammatory response and symptom distress. To prolong the progression of the disease, it will be the main goal of caring for CKD patients. This study aims to test the effect of heart rate variability biofeedback (HRVBF) on improving autonomic nerve function (Heart Rate Variability, HRV), inflammatory response (Interleukin-6 [IL-6], C reaction protein [CRP] ), symptom distress (Piper fatigue scale, Pittsburgh Sleep Quality Index [PSQI], and Beck Depression Inventory-II [BDI-II] ) in patients with chronic kidney disease. This study was experimental research, with a convenience sampling. Participants were recruited from the nephrology clinic at a medical center in northern Taiwan. With signed informed consent, participants were randomly assigned to the HRVBF or control group by using the Excel BINOMDIST function. The HRVBF group received four weekly hospital-based HRVBF training, and 8 weeks of home-based self-practice was done with StressEraser. The control group received usual care. We followed all participants for 3 months, in which we repeatedly measured their autonomic nerve function (HRV), inflammatory response (IL-6, CRP), and symptom distress (Piper fatigue scale, PSQI, and BDI-II) on their first day of study participation (baselines), 1 month, and 3 months after the intervention to test the effects of HRVBF. The results were analyzed by SPSS version 23.0 statistical software. The data of demographics, HRV, IL-6, CRP, Piper fatigue scale, PSQI, and BDI-II were analyzed by descriptive statistics. To test for differences between and within groups in all outcome variables, it was used by paired sample t-test, independent sample t-test, Wilcoxon Signed-Rank test and Mann-Whitney U test. Results: Thirty-four patients with chronic kidney disease were enrolled, but three of them were lost to follow-up. The remaining 31 patients completed the study, including 15 in the HRVBF group and 16 in the control group. The characteristics of the two groups were not significantly different. The four-week hospital-based HRVBF training combined with eight-week home-based self-practice can effectively enhance the parasympathetic nerve performance for patients with chronic kidney disease, which may against the disease-related parasympathetic nerve inhibition. In the inflammatory response, IL-6 and CRP in the HRVBF group could not achieve significant improvement when compared with the control group. Self-reported fatigue and depression significantly decreased in the HRVBF group, but they still failed to achieve a significant difference between the two groups. HRVBF has no significant effect on improving the sleep quality for CKD patients.

Keywords: heart rate variability biofeedback, autonomic nerve function, inflammatory response, symptom distress, chronic kidney disease

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Authors: Jacques M. Berner, Monja Gerber, Gillian L. Maggs-Kolling, Stuart J. Piketh

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The enigmatic melon species, Acanthosicyos horridus Welw. ex Hook. f., locally known as !nara, is endemic to the hyper-arid Namib Desert, where it thrives in sandy dune areas and dry river banks. The Namib Desert is characterized by extreme weather conditions which include high temperatures, very low rainfall, and extremely dry air. Plant and animals that have made the Namib Dessert their home are dependent on non-rainfall water inputs, like fog, dew and water vapor, for survival. Fog is believed to be the most important non-rainfall water input for most of the coastal Namib Desert and is a life line to many Namib plants and animals. It is commonly assumed that the !nara plant is adapted and dependent upon coastal fog events. The !nara plant shares many comparable adaptive features with other organisms that are known to exploit fog as a source of moisture. These include groove-like structures on the stems and the cone-like structures of thorns. These structures are believed to be the driving forces behind directional water flow that allow plants to take advantage of fog events. The !nara-fog interaction was investigated in this study to determine the dependence of !nara on these fog events, as it would illustrate strategies to benefit from non-rainfall water inputs. The direct water uptake capacity of !nara shoots was investigated through absorption tests. Furthermore, the movement and behavior of fluorescent water droplets on a !nara stem were investigated through time-lapse macrophotography. The shoot water potential was measured to investigate the effect of fog on the water status of !nara stems. These tests were used to determine whether the morphology of !nara has evolved to exploit fog as a non-rainfall water input and whether the !nara plant has adapted physiologically in response to fog. Chlorophyll a fluorescence was used to compare the photochemical efficiency of !nara plants on days with fog events to that on non-foggy days. The results indicate that !nara plants do have the ability to take advantage of fog events as commonly believed. However, the !nara plant did not exhibit visible signs of drought stress and this, together with the strong shoot water potential, indicates that these plants are reliant on permanent underground water sources. Chlorophyll a fluorescence data indicated that temperature stress and wind were some of the main abiotic factors influencing the plants’ overall vitality.

Keywords: Acanthosicyos horridus, chlorophyll a fluorescence, fog, foliar absorption, !nara

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Authors: Apostolos Kalatzis, Ashish Teotia, Vishnunarayan Girishan Prabhu, Laura Stanley

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Emotions are functional behaviors influenced by thoughts, stimuli, and other factors that induce neurophysiological changes in the human body. Understanding and classifying emotions are challenging as individuals have varying perceptions of their environments. Therefore, it is crucial that there are publicly available databases and virtual reality (VR) based environments that have been scientifically validated for assessing emotional classification. This study utilized two commercially available VR applications (Guided Meditation VR™ and Richie’s Plank Experience™) to induce acute stress and calm state among participants. Subjective and objective measures were collected to create a validated multimodal dataset and classification scheme for affective state classification. Participants’ subjective measures included the use of the Self-Assessment Manikin, emotional cards and 9 point Visual Analogue Scale for perceived stress, collected using a Virtual Reality Assessment Tool developed by our team. Participants’ objective measures included Electrocardiogram and Respiration data that were collected from 25 participants (15 M, 10 F, Mean = 22.28  4.92). The features extracted from these data included heart rate variability components and respiration rate, both of which were used to train two machine learning models. Subjective responses validated the efficacy of the VR applications in eliciting the two desired affective states; for classifying the affective states, a logistic regression (LR) and a support vector machine (SVM) with a linear kernel algorithm were developed. The LR outperformed the SVM and achieved 93.8%, 96.2%, 93.8% leave one subject out cross-validation accuracy, precision and recall, respectively. The VR assessment tool and data collected in this study are publicly available for other researchers.

Keywords: affective computing, biosignals, machine learning, stress database

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Authors: J. Gonzalez, P. Jimenez, C. Isaza

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Palms have different characteristics that make them vulnerable; that is the case of the Chamaedorea linearis, with the presence of solitary stems of small diameter and medium leaves, culturally harvested, and in religious festivities used. Additionally, they present a weak apical meristem as the only emergency point, slow development and growth, and an affectation due to the high rate of deforestation in Colombia. Propagation of this species can improve the pressure on wild populations and help their survival in the environment. In this study was used in 177 plants biosolids humus from the Wastewater Treatment Plant (WWTP), located at the UMNG Campus Cajica (Cundinamarca, Colombia). The experiment used a control and two treatments with 10% and 20% of humus. During the process, the variables evaluated were number of leaves, percentage of chlorophyll, stem length, and estimated leaf area. The data set were taking during 14 weeks before the reproductive maturity, evidencing that the most representative development of the palms was in the treatment of 20%, plants in this treatment presented major number of leaves, larger stems, a high quantity of chlorophyll, and was a first treatment that present pinnate leaves them represent an important point in maturity process. The research gives an opportunity to improve times of growth in another species of palms and plants (Product result from INV ING 2986 UMNG).

Keywords: biosolids, humus, growth, palms, wastewater treatment plant, WWTP

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Authors: Nilda Tri Putri, Muhammad Taufik

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Tools and agricultural machinery (Alsintan) is a tool used in agribusiness activities. Alsintan used to change the traditional farming systems generally use manual equipment into modern agriculture with mechanization. CV Nugraha Chakti Consultant make an action plan for industrial development Alsintan West Sumatra in 2012 to develop medium industries of Alsintan become a major industry of Alsintan, one of efforts made is increase the production capacity of the industry Alsintan. Production capacity for superior products as hydrotiller and threshers set each for 2.000 units per year. CV Citra Dragon as one of the medium industry alsintan in West Sumatra has a plan to relocate the existing plant to meet growing consumer demand each year. Increased production capacity and plant relocation plan has led to a change in the layout; therefore need to design the layout of the plant facility CV Citra Dragon. First step the to design of plant layout is design the layout of the production floor. The design of the production floor layout is done by applying group technology layout. The initial step is to do a machine grouping and part family using the Average Linkage Clustering (ALC) and Rank Order Clustering (ROC). Furthermore done independent work station design and layout design using the Modified Spanning Tree (MST). Alternative selection layout is done to select the best production floor layout between ALC and ROC cell grouping. Furthermore, to design the layout of warehouses, offices and other production support facilities. Activity Relationship Chart methods used to organize the placement of factory facilities has been designed. After structuring plan facilities, calculated cost manufacturing facility plant establishment. Type of layout is used on the production floor layout technology group. The production floor is composed of four cell machinery, assembly area and painting area. The total distance of the displacement of material in a single production amounted to 1120.16 m which means need 18,7minutes of transportation time for one time production. Alsintan Factory has designed a circular flow pattern with 11 facilities. The facilities were designed consisting of 10 rooms and 1 parking space. The measure of factory building is 84 m x 52 m.

Keywords: Average Linkage Clustering (ALC), Rank Order Clustering (ROC), Modified Spanning Tree (MST), Activity Relationship Chart (ARC)

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Authors: Elida Tafupenji Kamanga

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Cancer of the cervix remains one of the high causes of death among Malawian women. Despite the government introduction of free screening services throughout the country, patronage still remains low and lack of knowledge high. Given the critical role mass media plays in relaying different information to the public including health and its influence on health behaviours, the study sought to analyse Malawi media coverage of the disease and its effectiveness. The findings of the study will help inform media advocacy directed at changing any coverage impeding the effective dissemination of cervical cancer message which consequently will help increase awareness and accessing of screening behaviours among women. A content analysis of 29 newspapers and promotional messages on cervical from a local radio station was conducted for the period from 2012 to 2015. Overall the results showed media coverage in terms of content and frequency increased for the four-year period. However, of concern was the quality of information both media presented to the public. The lapse in information provided means there is little education taking place through the media which could be contributing to the knowledge gap the women have thereby affecting their decision to screen. Also lack of adequate funding to media institutions and lack of collaboration between media institutions and stakeholders involved in the fight against the disease were noted as other contributing factors to low coverage of the disease. Designing messages that are not only informative and educative but also innovative may help increase awareness; improve the knowledge gap and potential adoption of preventive screening behaviour by Malawian women. Conversely, good communication between the media institutions and researchers involved in the fight against the disease through the channelling of new findings back to the public as well as increasing funding towards similar cause should be considered.

Keywords: cervical cancer, effectiveness, media coverage, screening

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Authors: Siddharth Soni, Gourav Kumar Pandey, Sneha Kumari, Dev Mani Pandey, Koel Mukherjee

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The silkworm Bombyx mori is a commercially important insect, but a major roadblock in silk production are silkworm diseases. Flacherie is one of the diseases of the silkworm, that affects the midgut of the 4th and 5th instar larvae and eventually makes them lethargic, stop feeding and finally result in their death. The concerned disease is a result of bacterial and viral infection and in some instances a combination of both. The present study aims to identify and study the expression level of genes in the flacherie condition. For the said work, total RNA was isolated from the infected larvae at their most probable infectious instar and cDNA was synthesized using Reverse Transcriptase PCR (RT-PCR). This cDNA was then used to amplify disease relalted genes whose expression levels were checked using quantitaive PCR (qPCR) using the double delta Ct method. Cry toxin receptors like APN and BtR-175, ROS mediator Dual Oxidase are few proteins whose genes were overexpressed. Interestingly, pattern recognition receptors (PRRs) C-type lectins' genes were found to be downregulated. The results explain about the strong expression of genes that can distinguish the concerned protein in the midgut of diseased silkworm and thereby aiding knowledge in the field of inhibitor designing research.

Keywords: Bombyx mori, flacherie disease, inhibitor designing, up and down regulation

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Authors: Maritsa Kurashvili, George Adamia, Tamar Ananiashvili, Lia Amiranasvili, Tamar Varazi, Marina Pruidze, Marlen Gordeziani, Gia Khatisashvili

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The goal of presented work is the development phytoremediation method targeted to cleaning environment polluted with organochlorine pesticides, based on joint application of plants and microorganisms. For this aim the selection of plants and microorganisms with corresponding capabilities towards three organochlorine pesticides (Lindane, DDT and PCP) has been carried out. The tolerance of plants to tested pesticides and induction degree of plant detoxification enzymes by these compounds have been used as main criteria for estimating the applicability of plants in proposed technology. Obtained results show that alfalfa, maize and soybean among tested six plant species have highest tolerance to pesticides. As a result of screening, more than 30 strains from genera Pseudomonas have been selected. As a result of GC analysis of incubation area, 11 active cultures for investigated pesticides are carefully chosen.

Keywords: DDT, Lindane, organochlorine pesticides, PCP, phytoremediation

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Authors: Y. Atibi, A. Boutaleb Joutei, T. Slimani

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Insecticidal properties of Alliaceae are widely known, they are plant with varied biological properties. Garlic and onion are known for their positive effect on health, including the prevention of cardiovascular disease and some digestive cancers. These health benefits molecules are also responsible for pest potential control of Alliaceae. With these properties, we can consider using Alliaceae as acaricides. The purpose of this study was to compare the effect of chemical and biopesticides on citrus mites, especially Tetranychus urticae, Panonychus citri and Eutetranychus orientalis. Chemical treatment (Dicofol) and biopesticides (Garlic juice + Alcohol) applied on this study to control the various stages of mites, have reduced the proliferation of mobile forms and reducing the number of eggs to acceptable levels. Garlic juice + alcohol revealed efficiency from 50 to 57.69 % against the mobile forms of T. urticae, however, it was effective against the mobile forms of P. citri and E. orientalis with an efficiency of 85.71 % and 100 % respectively, its action has also reduced the number of eggs of T. urticae and E. orientalis at low levels. Therefore, this biopesticide is conceivable viewpoint technical and economic as the infestation by mite is low.

Keywords: Garlic juice, acaricide, biopesticide, mites, alcohol, Tetranychus urticae, Panonychus citri, Eutetranychus orientalis.

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Authors: Tariq Ahmad Dar, Moinuddin, M. Masroor A. Khan

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There is considerable need in enhancing the content and yield of active constituents of medicinal plants keeping in view their massive demand worldwide. Different strategies have been employed to enhance the active constituents of medicinal plants and the use of phytohormones has been proved effective in this regard. Gamma-irradiated Sodium alginate (ISA) is known to elicit an array of plant defense responses and biological activities in plants. Considering the medicinal importance, a pot experiment was conducted to explore the effect of ISA and phosphorus on growth, yield and quality of fenugreek (Trigonella foenum-graecum L.). ISA spray treatments (0, 40, 80 and 120 mg L-1) were applied alone and in combination with 40 kg P ha-1 (P40). Crop performance was assessed in terms of plant growth characteristics, physiological attributes, seed yield and the content of seed trigonelline. Of the ten-treatments, P40 + 80 mg L−1 of ISA proved the best. The results showed that foliar spray of ISA alone or in combination with P40 augmented the plant vegetative growth, enzymatic activities, trigonelline content, trigonelline yield and economic yield of fenugreek. Application of 80 mg L−1 of ISA applied with P40 gave the best results for almost all the parameters studied compared to control or to 80 mg L−1 of ISA applied alone. This treatment increased the total content of chlorophyll, carotenoids, leaf -N, -P and -K and trigonelline compared to the control by 24.85 and 27.40%, 15 and 23.52%, 18.70 and 16.84%, 15.88 and 18.92%, 12 and 14.44%, at 60 and 90 DAS respectively. The combined application of 80 mg L−1 of ISA along with P40 resulted in the maximum increase in seed yield, trigonelline content and trigonelline yield by146, 34 and 232.41%, respectively, over the control. Gel permeation chromatography revealed the formation of low molecular weight fractions in ISA samples, containing even less than 20,000 molecular weight oligomers, which might be responsible for plant growth promotion in this study. Trigonelline content was determined by reverse phase high performance liquid chromatography (HPLC) with C-18 column.

Keywords: gamma-irradiated sodium alginate, phosphorus, gel permeation chromatography, HPLC, trigonelline content, yield

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Authors: Huafeng Wei

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Dementia in Alzheimer’s Disease (AD) is the number one cause of dementia internationally, without effective treatments. Increasing evidence suggest that disruption of intracellular calcium homeostasis, primarily pathological elevation of cytosol and mitochondria but reduction of endoplasmic reticulum (ER) calcium concentrations, play critical upstream roles on multiple pathologies and associated neurodegeneration, impaired neurogenesis, synapse, and cognitive dysfunction in various AD preclinical studies. The last federal drug agency (FDA) approved drug for AD dementia treatment, memantine, exert its therapeutic effects by ameliorating N-methyl-D-aspartate (NMDA) glutamate receptor overactivation and subsequent calcium dysregulation. More research works are needed to develop other drugs targeting calcium dysregulation at multiple pharmacological acting sites for future effective AD dementia treatment. Particularly, calcium channel blockers for the treatment of hypertension and dantrolene for the treatment of muscle spasm and malignant hyperthermia can be repurposed for this purpose. In our own research work, intranasal administration of dantrolene significantly increased its brain concentrations and durations, rendering it a more effective therapeutic drug with less side effects for chronic AD dementia treatment. This review summarizesthe progress of various studies repurposing drugs targeting calcium dysregulation for future effective AD dementia treatment as potentially disease-modifying drugs.

Keywords: alzheimer, calcium, cognitive dysfunction, dementia, neurodegeneration, neurogenesis

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Authors: Mariem Kchaou, Wiem Khlif, Faiez Gargouri

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Modeling business processes is an essential task when evaluating, improving, or documenting existing business processes. To be efficient in such tasks, a business process model (BPM) must have high structural quality and high performance. Evidently, evaluating the performance of a business process model is a necessary step to reduce time, cost, while assessing the structural quality aims to improve the understandability and the modifiability of the BPMN model. To achieve these objectives, a set of structural and performance measures have been proposed. Since the diversity of measures, we propose a framework that integrates both structural and performance aspects for classifying them. Our measure classification is based on business process model perspectives (e.g., informational, functional, organizational, behavioral, and temporal), and the elements (activity, event, actor, etc.) involved in computing the measures. Then, we implement this framework in a tool assisting the structural quality and the performance of a business process. The tool helps the designers to select an appropriate subset of measures associated with the corresponding perspective and to calculate and interpret their values in order to improve the structural quality and the performance of the model.

Keywords: performance, structural quality, perspectives, tool, classification framework, measures

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Authors: Nagui A. Abdel-Khalek, Khaled A. Selim, Ahmed Hamdy

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Kaolin is naturally occurring ore predominantly containing kaolinite mineral in addition to some gangue minerals. Typical impurities present in kaolin ore are quartz, iron oxides, titanoferrous minerals, mica, feldspar, organic matter, etc. The main coloring impurity, particularly in the ultrafine size range, is titanoferrous minerals. Kaolin is used in many industrial applications such as sanitary ware, table ware, ceramic, paint, and paper industries, each of which should be of certain specifications. For most industrial applications, kaolin should be processed to obtain refined clay so as to match with standard specifications. For example, kaolin used in paper and paint industries need to be of high brightness and low yellowness. Egyptian kaolin is not subjected to any beneficiation process and the Egyptian companies apply selective mining followed by, in some localities, crushing and size reduction only. Such low quality kaolin can be used in refractory and pottery production but not in white ware and paper industries. This paper aims to study the amenability of beneficiation of an Egyptian kaolin ore of El-Teih locality, Sinai, to be suitable for different industrial applications. Attrition scrubbing and classification followed by magnetic separation are applied to remove the associated impurities. Attrition scrubbing and classification are used to separate the coarse silica and feldspars. Wet high intensity magnetic separation was applied to remove colored contaminants such as iron oxide and titanium oxide. Different variables affecting of magnetic separation process such as solid percent, magnetic field, matrix loading capacity, and retention time are studied. The results indicated that substantial decrease in iron oxide (from 1.69% to 0.61% ) and TiO2 (from 3.1% to 0.83%) contents as well as improving iso-brightness (from 63.76% to 75.21% and whiteness (from 79.85% to 86.72%) of the product can be achieved.

Keywords: Kaolin, titanoferrous minerals, beneficiation, magnetic separation, attrition scrubbing, classification

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Authors: Pawan Kumar Gupta, Sumana Ghosh

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Parkinson's Disease (PD) is a long-term neurodegenerative movement disorder of the central nervous system with vast symptoms related to the motor system. The common symptoms of PD are tremor, rigidity, bradykinesia/akinesia, and postural instability, but the clinical symptom includes other motor and non‐motor issues. The motor symptoms of the disease are consequence of death of the neurons in a region of the midbrain known as substantia nigra pars compacta, leading to decreased level of a neurotransmitter known as dopamine. The cause of this neuron death is not clearly known but involves formation of Lewy bodies, an abnormal aggregation or clumping of the protein alpha-synuclein in the neurons. Unfortunately, there is no cure for PD, and the management of this disease is challenging. Therefore, it is critical for a patient to be diagnosed at early stages. A limited choice of drugs is available to improve the symptoms, but those become less and less effective over time. Apart from that, with rapid growth in the field of science and technology, other methods such as multi-area brain stimulation are used to treat patients. In order to develop advanced techniques and to support drug development for treating PD patients, an accurate mathematical model is needed to explain the underlying relationship of dopamine secretion in the brain with the hand tremors. There has been a lot of effort in the past few decades on modeling PD tremors and treatment effects from a computational point of view. These models can effectively save time as well as the cost of drug development for the pharmaceutical industry and be helpful for selecting appropriate treatment mechanisms among all possible options. In this review paper, an effort is made to investigate studies on PD modeling and analysis and to highlight some of the key advances in the field over the past centuries with discussion on the current challenges.

Keywords: Parkinson's disease, deep brain stimulation, tremor, modeling

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Authors: Rekha Vijayvergia

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Around seven percent of tribal population of India lives in Rajasthan. Rajasthan has rich cultural diversity and biodiversity. Ethno-botany can be defined as the total natural and traditional relationship and the interactions between man and his surrounding plant wealth from times immemorial, due to sheer, necessity, intuition, observation, and experimentation. Medicinal plants are valuable and are used for the production of various drugs. These plants produce a high diversity of natural products or secondary metabolites like Mahanimbicine, Andrographine, murrayaline, lupeol, and limonin etc. with a prominent function in the protection against diseases like diabetes, kidney stones, osteoporosis, tumours, opthalmia, leucorrhoea, bronchial asthma, diarrhea, cancer, etc. The present report gives an account of traditional medicinal uses of common medicinal plants of Rajasthan. A total of 18 plant species belonging to 13 families are reported, that are being used for various purposes.

Keywords: ethno botany, Rajasthan, secondary metabolites, traditional medicines

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Authors: Taha SamadSoltani, Peyman Rezaei Hachesu, Marjan GhaziSaeedi, Maryam Zolnoori

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Introduction: Data mining defined as a process to find patterns and relationships along data in the database to build predictive models. Application of data mining extended in vast sectors such as the healthcare services. Medical data mining aims to solve real-world problems in the diagnosis and treatment of diseases. This method applies various techniques and algorithms which have different accuracy and precision. The purpose of this study was to apply knowledge discovery and data mining techniques for the diagnosis of asthma based on patient symptoms and history. Method: Data mining includes several steps and decisions should be made by the user which starts by creation of an understanding of the scope and application of previous knowledge in this area and identifying KD process from the point of view of the stakeholders and finished by acting on discovered knowledge using knowledge conducting, integrating knowledge with other systems and knowledge documenting and reporting.in this study a stepwise methodology followed to achieve a logical outcome. Results: Sensitivity, Specifity and Accuracy of KNN, SVM, Naïve bayes, NN, Classification tree and CN2 algorithms and related similar studies was evaluated and ROC curves were plotted to show the performance of the system. Conclusion: The results show that we can accurately diagnose asthma, approximately ninety percent, based on the demographical and clinical data. The study also showed that the methods based on pattern discovery and data mining have a higher sensitivity compared to expert and knowledge-based systems. On the other hand, medical guidelines and evidence-based medicine should be base of diagnostics methods, therefore recommended to machine learning algorithms used in combination with knowledge-based algorithms.

Keywords: asthma, datamining, classification, machine learning

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Authors: Ataa A. Said, Elsayed A. Abuotabl, Gehan F. Abdel Raoof, Khaled Y. Mohamed

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The aim of this study is to evaluate antihyperglycaemic and antihyperlipidemic activities of Pleiogynium timorense (DC.) Leenh (Anacardiaceae) seeds as well as to isolate and identify the bioactive compounds. Antihyperglycaemic effect was evaluated by measuring the effect of two dose levels (150 and 300 mg/kg) of 70% methanol extract of Pleiogynium timorense seeds on blood glucose level when administered 45 minutes before glucose loading. In addition, the effect of the plant extract on the lipid profile was determined by measuring serum total lipids (TL), total cholesterol (TC), triglycerides (TG), high density lipoprotein cholesterol (HDL-C) and low density lipoprotein cholesterol (LDL-C). Furthermore, the bioactive compounds were isolated and identified by chromatographic and spectrometric methods.The results showed that the methanolic extract of the seeds significantly reduced the levels of blood glucose,(TL), (TC), (TG) and (LDL-C) but no significant effect on (HDL-C) comparing with control group. Furthermore, four phenolic compound were isolated which were identified as; catechin, gallic acid, para methoxy benzaldehyde and pyrogallol which were isolated for the first time from the plant. In addition sulphur -containing compound (sulpholane) was isolated for the first time from the plant and from the family. To our knowledge, this is the first study about antihyperglycaemicand antihyperlipidemic activities of the seeds of Pleiogyniumtimorense and its bioactive compounds. So, the methanolic extract of the seeds of Pleiogynium timorense could be a step towards the development of new antihyperglycaemic and antihyperlipidemic drugs.

Keywords: antihyperglycaemic, bioactive compounds, phenolic, Pleiogynium timorense, seeds

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Authors: Rodrigo Arnaldo Scarpel

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Trend following is a widely accepted investment strategy that adopts a rule-based trading mechanism that rather than striving to predict market direction or on information gathering to decide when to buy and when to sell a stock. Thus, in trend following one must respond to market’s movements that has recently happen and what is currently happening, rather than on what will happen. Optimally, in trend following strategy, is to catch a bull market at its early stage, ride the trend, and liquidate the position at the first evidence of the subsequent bear market. For applying the trend following strategy one needs to find the trend and identify trade signals. In order to avoid false signals, i.e., identify fluctuations of short, mid and long terms and to separate noise from real changes in the trend, most academic works rely on moving averages and other technical analysis indicators, such as the moving average convergence divergence (MACD) and the relative strength index (RSI) to uncover intelligible stock trading rules following trend following strategy philosophy. Recently, some works has applied machine learning techniques for trade rules discovery. In those works, the process of rule construction is based on evolutionary learning which aims to adapt the rules to the current environment and searches for the global optimum rules in the search space. In this work, instead of focusing on the usage of machine learning techniques for creating trading rules, a time series trend classification employing a semi-supervised approach was used to early identify both the beginning and the end of upward and downward trends. Such classification model can be employed to identify trade signals and the decision-making procedure is that if an up-trend (down-trend) is identified, a buy (sell) signal is generated. Semi-supervised learning is used for model training when only part of the data is labeled and Semi-supervised classification aims to train a classifier from both the labeled and unlabeled data, such that it is better than the supervised classifier trained only on the labeled data. For illustrating the proposed approach, it was employed daily trade information, including the open, high, low and closing values and volume from January 1, 2000 to December 31, 2022, of the São Paulo Exchange Composite index (IBOVESPA). Through this time period it was visually identified consistent changes in price, upwards or downwards, for assigning labels and leaving the rest of the days (when there is not a consistent change in price) unlabeled. For training the classification model, a pseudo-label semi-supervised learning strategy was used employing different technical analysis indicators. In this learning strategy, the core is to use unlabeled data to generate a pseudo-label for supervised training. For evaluating the achieved results, it was considered the annualized return and excess return, the Sortino and the Sharpe indicators. Through the evaluated time period, the obtained results were very consistent and can be considered promising for generating the intended trading signals.

Keywords: evolutionary learning, semi-supervised classification, time series data, trading signals generation

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Authors: Osama Shakeel

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The objective of the paper is to study the clinic-pathological factors, survival analyses, recurrence rate, metastatic rate, risk factors and the management of cutaneous malignant melanoma at Shaukat Khanum Memorial Cancer Hospital and Research Center. Methodology: From 2014 to 2017, all patients with a diagnosis of cutaneous malignant melanoma (CMM) were included in the study. Demographic variables were collected. Short and long term oncological outcomes were recorded. All data were entered and analyzed in SPSS version 21. Results: A total of 28 patients were included in the study. Median age was 46.5 +/-15.9 years. There were 16 male and 12 female patients. The family history of melanoma was present in 7.1% (n=2) of the patients. All patients had a mean survival of 13.43+/- 9.09 months. Lower limb was the commonest site among all which constitutes 46.4%(n=13). On histopathological analyses, ulceration was seen in 53.6% (n=15) patients. Unclassified tumor type was present in 75%(n=21) of the patients followed by nodular 21.4% (n=6) and superficial spreading 3.5%(n=1). Clark level IV was the commonest presentation constituting 46.4%(n=13). Metastases were seen in 50%(n=14) of the patients. Local recurrence was observed in 60.7%(n=17). 64.3%(n=18) lived after one year of treatment. Conclusion: CMM is a fatal disease. Although its disease of fair skin individuals, however, the incidence of CMM is also rising in this part of the world. Management includes early diagnoses and prompt management. However, mortality associated with this disease is still not favorable.

Keywords: malignant cancer of skin, cutaneous malignant melanoma, skin cancer, survival analyses

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Authors: Muzaffar Shaikh, Shoaib Shaikh, Mark Moyou, Gaby Hawat

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This paper presents the use of Analytic Hierarchy Process (AHP) in evaluating the site selection of a new plant by a corporation. Due to intense competition at a global level, multinational corporations are continuously striving to minimize production and shipping costs of their products. One key factor that plays significant role in cost minimization is where the production plant is located. In the U.S. for example, labor and land costs continue to be very high while they are much cheaper in countries such as India, China, Indonesia, etc. This is why many multinational U.S. corporations (e.g. General Electric, Caterpillar Inc., Ford, General Motors, etc.), have shifted their manufacturing plants outside. The continued expansion of the Internet and its availability along with technological advances in computer hardware and software all around the globe have facilitated U.S. corporations to expand abroad as they seek to reduce production cost. In particular, management of multinational corporations is constantly engaged in concentrating on countries at a broad level, or cities within specific countries where certain or all parts of their end products or the end products themselves can be manufactured cheaper than in the U.S. AHP is based on preference ratings of a specific decision maker who can be the Chief Operating Officer of a company or his/her designated data analytics engineer. It serves as a tool to first evaluate the plant site selection criteria and second, alternate plant sites themselves against these criteria in a systematic manner. Examples of site selection criteria are: Transportation Modes, Taxes, Energy Modes, Labor Force Availability, Labor Rates, Raw Material Availability, Political Stability, Land Costs, etc. As a necessary first step under AHP, evaluation criteria and alternate plant site countries are identified. Depending upon the fidelity of analysis, specific cities within a country can also be chosen as alternative facility locations. AHP experience in this type of analysis indicates that the initial analysis can be performed at the Country-level. Once a specific country is chosen via AHP, secondary analyses can be performed by selecting specific cities or counties within a country. AHP analysis is usually based on preferred ratings of a decision-maker (e.g., 1 to 5, 1 to 7, or 1 to 9, etc., where 1 means least preferred and a 5 means most preferred). The decision-maker assigns preferred ratings first, criterion vs. criterion and creates a Criteria Matrix. Next, he/she assigns preference ratings by alternative vs. alternative against each criterion. Once this data is collected, AHP is applied to first get the rank-ordering of criteria. Next, rank-ordering of alternatives is done against each criterion resulting in an Alternative Matrix. Finally, overall rank ordering of alternative facility locations is obtained by matrix multiplication of Alternative Matrix and Criteria Matrix. The most practical aspect of AHP is the ‘what if’ analysis that the decision-maker can conduct after the initial results to provide valuable sensitivity information of specific criteria to other criteria and alternatives.

Keywords: analytic hierarchy process, multinational corporations, plant site selection, preference ratings

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Authors: Srinidhi. A, Naveed Ahmed, Twinkle Hareendran, Vriksha Prakash

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This work reviews some of the advanced image processing techniques for deep learning applications. Object detection by template matching, image denoising, edge detection, and super-resolution modelling are but a few of the tasks. The paper looks in into great detail, given that such tasks are crucial preprocessing steps that increase the quality and usability of image datasets in subsequent deep learning tasks. We review some of the methods for the assessment of image quality, more specifically sharpness, which is crucial to ensure a robust performance of models. Further, we will discuss the development of deep learning models specific to facial emotion detection, age classification, and gender classification, which essentially includes the preprocessing techniques interrelated with model performance. Conclusions from this study pinpoint the best practices in the preparation of image datasets, targeting the best trade-off between computational efficiency and retaining important image features critical for effective training of deep learning models.

Keywords: image processing, machine learning applications, template matching, emotion detection

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