Search results for: crop sensitivity

Authors: D. A. S. Gamage, B. F. A Basnayake, W. A. J. M. de Costa

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Rice is one of the world’s most important cereals. Increasing food production both to meet in-country requirements and to help overcome food crises is one of the major issues facing Sri Lanka today. However, productive land is limited and has mostly been utilized either for food crop production or other uses. Agriculture plays an important and strategic role in the performance of Sri Lankan national economy. A variety of modern agricultural inputs have been introduced, namely ploughs and harvesters, pesticides, fertilizers and lime. Besides, there are several agricultural institutions developing and updating the management of agricultural sector. Modern agricultural inputs cooperate as a catalyst in raising the productivity. However, in the eagerness of gaining profits from the efficient and productive techniques, this modern agricultural input has affected the environment and living things especially those which have been blended from various chemical substance. The increased pressure to maintain a high level of rice output for consumption has resulted in increased use of pesticides and inorganic fertilizer on rice fields in Sri Lanka. The application of inorganic fertilizer has become a burdened to the country in many ways. The excessive reuse of the ground water resources with a considerable application of organic and chemical fertilizers will lead to a deterioration of the quality and quantity of water. Biochar is a form of charcoal produced through the heating of natural organic materials. It has received significant attention recently for its potential as a soil conditioner, a fertilizer and as a means of storing carbon in a sustainable manner. It is the best solution for managing the agricultural wastes while providing a useful product for increasing agricultural productivity and protecting the environment. The objective of this study was to evaluate rice husk charcoal coated urea as a slow releasing fertilizer and compare the total N, P, K, organic matter in soil and yield of rice production.

Keywords: biochar, paddy husk, soil conditioner, rice straw compost

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Authors: Olaide Ruth Aderibigbe, Oluwatoyin Oluwole

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Pigeon pea is one of the minor leguminous plants; though underutilised, it is used traditionally by farmers to alleviate hunger and malnutrition. Pigeon pea is cultivated in Nigeria by subsistence farmers. It is rich in protein and minerals, however, its utilisation as food is only common among the poor and rural populace who cannot afford expensive sources of protein. One of the factors contributing to its limited use is the high antinutrient content which makes it indigestible, especially when eaten by children. The development of value-added products that can reduce the antinutrient content and make the nutrients more bioavailable will increase the utilisation of the crop and contribute to reduction of malnutrition. This research, therefore, determined the effects of different roasting temperatures (130 0C, 140 0C, and 150 0C) on the proximate, mineral and antinutrient component of a pigeon pea snack. The brown variety of pigeon pea seeds were purchased from a local market- Otto in Lagos, Nigeria. The seeds were cleaned, washed, and soaked in 50 ml of water containing sugar and salt (4:1) for 15 minutes, and thereafter the seeds were roasted at 130 0C, 140 0C, and 150 0C in an electric oven for 10 minutes. Proximate, minerals, phytate, tannin and alkaloid content analyses were carried out in triplicates following standard procedures. The results of the three replicates were polled and expressed as mean±standard deviation; a one-way analysis of variance (ANOVA) and the Least Significance Difference (LSD) were carried out. The roasting temperatures significantly (P<0.05) affected the protein, ash, fibre and carbohydrate content of the snack. Ready-to-eat snack prepared by roasting at 150 0C significantly had the highest protein (23.42±0.47%) compared the ones roasted at 130 0C and 140 0C (18.38±1.25% and 20.63±0.45%, respectively). The same trend was observed for the ash content (3.91±0.11 for 150 0C, 2.36±0.15 for 140 0C and 2.26±0.25 for 130 0C), while the fibre and carbohydrate contents were highest at roasting temperature of 130 0C. Iron, zinc, and calcium were not significantly (P<0.5) affected by the different roasting temperatures. Antinutrients decreased with increasing temperature. Phytate levels recorded were 0.02±0.00, 0.06±0.00, and 0.07±0.00 mg/g; tannin levels were 0.50±0.00, 0.57±0.00, and 0.68±0.00 mg/g, while alkaloids levels were 0.51±0.01, 0.78±0.01, and 0.82±0.01 mg/g for 150 0C, 140 0C, and 130 0C, respectively. These results show that roasting at high temperature (150 0C) can be utilised as a processing technique for increasing protein and decreasing antinutrient content of pigeon pea.

Keywords: antinutrients, pigeon pea, protein, roasting, underutilised species

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Authors: Sabir Hussain, Muhammad Naeem, Yousif Aldryhim, Susan E. Halbert, Qingjun Wu

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Plant viruses annually cause severe economic losses in crop production and globally, different aphid species are responsible for the transmission of such viruses. Additionally, aphids are also serious pests of trees, and agricultural crops. Al Bahah Province, Kingdom of Saudi Arabia (KSA) has a high native and introduced plant species with a temperate climate that provides ample habitats for aphids. In this study, we surveyed virus-transmitting aphids from the Province to highlight their spatial distributions and hot spot areas for their target control strategies. During our fifteen month's survey in Al Bahah Province, three hundred and seventy samples of aphids were collected using both beating sheets and yellow water pan traps. Consequently, fifty-four aphid species representing 30 genera belonging to four families were recorded from Al Bahah Province. Alarmingly, 35 aphid species from our records are virus transmitting species. The most common virus transmitting aphid species based on number of collecting samples, were Macrosiphum euphorbiae (Thomas, 1878), Brachycaudus rumexicolens (Patch, 1917), Uroleucon sonchi (Linnaeus, 1767), Brachycaudus helichrysi (Kaltenbach, 1843), and Myzus persicae (Sulzer, 1776). The numbers of samples for the forementioned species were 66, 24, 23, 22, and 20, respectively. The widest range of plant hosts were found for M. euphorbiae (39 plant species), B. helichrysi (12 plant species), M. persicae (12 plant species), B. rumexicolens (10 plant species), and U. sonchi (9 plant species). The hottest spot areas were found in Al-Baha, Al Mekhwah and Biljarashi cities of the province on the basis of their abundance. This study indicated that Al Bahah Province has relatively rich aphid diversity due to the relatively high plant diversity in a favorable climatic condition. ArcGIS tools can be helpful for biologists to implement the target control strategies against these pests in the integrated pest management, and ultimately to save money and time.

Keywords: Al Bahah province, aphid-virus interaction, biodiversity, global information system

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Authors: Martins Y. Otache, John J. Musa, Abayomi I. Kuti, Mustapha Mohammed

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The performance of an artificial neural network (ANN) is contingent on a host of factors, for instance, the network optimisation scheme. In view of this, the study examined the general implications of the ANN training optimisation algorithm on its forecast performance. To this end, the Bayesian regularisation (Br), Levenberg-Marquardt (LM), and the adaptive learning gradient descent: GDM (with momentum) algorithms were employed under different ANN structural configurations: (1) single-hidden layer, and (2) double-hidden layer feedforward back propagation network. Results obtained revealed generally that the gradient descent with momentum (GDM) optimisation algorithm, with its adaptive learning capability, used a relatively shorter time in both training and validation phases as compared to the Levenberg- Marquardt (LM) and Bayesian Regularisation (Br) algorithms though learning may not be consummated; i.e., in all instances considering also the prediction of extreme flow conditions for 1-day and 5-day ahead, respectively especially using the ANN model. In specific statistical terms on the average, model performance efficiency using the coefficient of efficiency (CE) statistic were Br: 98%, 94%; LM: 98 %, 95 %, and GDM: 96 %, 96% respectively for training and validation phases. However, on the basis of relative error distribution statistics (MAE, MAPE, and MSRE), GDM performed better than the others overall. Based on the findings, it is imperative to state that the adoption of ANN for real-time forecasting should employ training algorithms that do not have computational overhead like the case of LM that requires the computation of the Hessian matrix, protracted time, and sensitivity to initial conditions; to this end, Br and other forms of the gradient descent with momentum should be adopted considering overall time expenditure and quality of the forecast as well as mitigation of network overfitting. On the whole, it is recommended that evaluation should consider implications of (i) data quality and quantity and (ii) transfer functions on the overall network forecast performance.

Keywords: streamflow, neural network, optimisation, algorithm

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Authors: Sungsoo Na, Chanho Park

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Lung cancer is one of the most common severe diseases driving to the death of a human. Lung cancer can be divided into two cases of small-cell lung cancer (SCLC) and non-SCLC (NSCLC), and about 80% of lung cancers belong to the case of NSCLC. From several studies, the correlation between epidermal growth factor receptor (EGFR) and NSCLCs has been investigated. Therefore, EGFR inhibitor drugs such as gefitinib and erlotinib have been used as lung cancer treatments. However, the treatments result showed low response (10~20%) in clinical trials due to EGFR mutations that cause the drug resistance. Patients with resistance to EGFR inhibitor drugs usually are positive to KRAS mutation. Therefore, assessment of EGFR and KRAS mutation is essential for target therapies of NSCLC patient. In order to overcome the limitation of conventional therapies, overall EGFR and KRAS mutations have to be monitored. In this work, the only detection of EGFR will be presented. A variety of techniques has been presented for the detection of EGFR mutations. The standard detection method of EGFR mutation in ctDNA relies on real-time polymerase chain reaction (PCR). Real-time PCR method provides high sensitive detection performance. However, as the amplification step increases cost effect and complexity increase as well. Other types of technology such as BEAMing, next generation sequencing (NGS), an electrochemical sensor and silicon nanowire field-effect transistor have been presented. However, those technologies have limitations of low sensitivity, high cost and complexity of data analyzation. In this report, we propose a label-free and high-sensitive detection method of lung cancer using quartz crystal microbalance based platform. The proposed platform is able to sense lung cancer mutant DNA with a limit of detection of 1nM.

Keywords: cancer DNA, resonance frequency, quartz crystal microbalance, lung cancer

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Authors: Zaide Montes Ortiz, Jorge Alberto Molina, Alejandro Reyes

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Insects are extremely successful organisms. A sophisticated olfactory system is in part responsible for their survival and reproduction. The detection of volatile organic compounds can positively or negatively affect many behaviors in insects. Compounds such as carbon dioxide (CO2), ammonium, indol, and lactic acid are essential for many species of mosquitoes like Anopheles gambiae in order to locate vertebrate hosts. For instance, in A. gambiae, the olfactory receptor AgOR2 is strongly activated by indol, which accounts for almost 30% of human sweat. On the other hand, in some insects of agricultural importance, the detection and identification of pheromone receptors (PRs) in lepidopteran species has become a promising field for integrated pest management. For example, with the disruption of the pheromone receptor, BmOR1, mediated by transcription activator-like effector nucleases (TALENs), the sensitivity to bombykol was completely removed affecting the pheromone-source searching behavior in male moths. Then, the detection and identification of olfactory receptors in the genomes of insects is fundamental to improve our understanding of the ecological interactions, and to provide alternatives in the integrated pests and vectors management. Hence, the objective of this study is to propose a bioinformatic workflow to enhance the detection and identification of potential olfactory receptors in genomes of relevant insects. Applying Hidden Markov models (Hmms) and different computational tools, potential candidates for pheromone receptors in Tuta absoluta were obtained, as well as potential carbon dioxide receptors in Rhodnius prolixus, the main vector of Chagas disease. This study showed the validity of a bioinformatic workflow with a potential to improve the identification of certain olfactory receptors in different orders of insects.

Keywords: bioinformatic workflow, insects, olfactory receptors, protein prediction

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Authors: Muhammad Ashfaq, Muhammad Saleem Haider, Muhammad Ali, Muhammad Sajjad, Amna Ali, Urooj Mubashar

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Phenotypic diversity was confirmed by measuring different morphological traits i.e. seed traits (seed length, seed width, seed thickness, seed length-width ratio, 1000 grain weight) and root-shoot traits (shoot length, root length, shoot fresh weight, root fresh weight, root-shoot ratio, root numbers and root thickness). Variance and association study of desirable traits determine the genotypic differences among the rice germplasm. All the traits showed significant differences among the genotypes. The traits were studied in Randomized complete block design (RCBD) at different water levels. Some traits showed positive correlation with each other and beneficial for increasing the yield and production of the crop. Seed thickness has positive correlation with seed length and seed width (r= 0.104**, r=0.246**). On the other hand, various root shoot traits showed positive highly significant association at different water levels i.e. root length, fresh root weight, root thickness, shoot thickness and root numbers. Our main focus to study the performance/correlation of root shoots traits under stress condition. Fresh root weight, shoot thickness and root numbers showed positive significant association with shoot length, root length, fresh root and shoot weight (r=0.2530**, r=0.2891**, r=0.4626**, r=0.4515**, r=0.5781**, r=0.7164**, r=0.0603**, r= 0.5570**, r=0.5824**). Long root length genotypes favors and suitable for drought stress conditions and screening of diverse genotypes for the further development of new plant material that performing well under different environmental conditions. After screening genetic diversity of potential rice, lines were studied to check the polymorphism by using some SSR markers. DNA was extracted, and PCR analyses were done to study PIC values and allelic diversity of the genotypes. The main objective of this study is to screen out the genotypes on the basis of various genotypic and phenotypic traits.

Keywords: rice, morphological traits, association, germplasm, genetic diversity, water levels, variation

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Authors: Azeb Degife

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Over the past few years, the volume of international investment in agricultural land has increased globally. In recent times, Ethiopian government uses agricultural investment as one of the most important and effective strategies for economic growth, food security and poverty reduction in rural areas. Since the mid-2000s, government has awarded millions of hectares of most fertile land to rich countries and some of the world's most wealthy people to export various kinds of crop, often in long-term leases and at bargain prices. This study focuses on the pros and cons of large-scale agriculture investment Gambella region, Ethiopia. The main results were generated both from primary and secondary data sources. Primary data are obtained through interview, direct observation and a focus group discussion (FGDs). The secondary data are obtained from published documents, reports from governmental and non-governmental institutions. The findings of the study demonstrated that agriculture investment has advantages on the socio-economic and disadvantages on socio-environmental aspects. The main benefits agriculture investments in the region are infrastructural development and generation employment for the local people. Further, the Ethiopian government also generates foreign currency from the agriculture investment opportunities. On the other hand, Gambella people are strongly tied to the land and the rivers that run through in the region. However, now large-scale agricultural investment by foreign and local investors on an industrial scale results deprives people livelihoods and natural resources of the region. Generally, the negative effects of agriculture investment include increasing food insecurity, and displacement of smallholder farmers and pastoralists. Moreover, agriculture investment has strong adverse environmental impacts on natural resources such as land, water, forests and biodiversity. Therefore, an Ethiopian government strategy needs to focus on integration approach and sustainable agricultural growth.

Keywords: agriculture investment, cons, displacement, Gambella, integration approach, pros, socio-economic, socio-environmental

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Authors: Diptiman Dinda, Shyamal Kumar Saha

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In the 21st century, sensitive and selective detection of trace amounts of explosives has become a serious problem. Generally, nitro compound and its derivatives are being used worldwide to prepare different explosives. Recently, TNP (2, 4, 6 trinitrophenol) is the most commonly used constituent to prepare powerful explosives all over the world. It is even powerful than TNT or RDX. As explosives are electron deficient in nature, it is very difficult to detect one separately from a mixture. Again, due to its tremendous water solubility, detection of TNP in presence of other explosives from water is very challenging. Simple instrumentation, cost-effective, fast and high sensitivity make fluorescence based optical sensing a grand success compared to other techniques. Graphene oxide (GO), with large no of epoxy grps, incorporate localized nonradiative electron-hole centres on its surface to give very weak fluorescence. In this work, GO is functionalized with 2, 6-diamino pyridine to remove those epoxy grps. through SN2 reaction. This makes GO into a bright blue luminescent fluorophore (DAP/rGO) which shows an intense PL spectrum at ∼384 nm when excited at 309 nm wavelength. We have also characterized the material by FTIR, XPS, UV, XRD and Raman measurements. Using this as fluorophore, a large fluorescence quenching (96%) is observed after addition of only 200 µL of 1 mM TNP in water solution. Other nitro explosives give very moderate PL quenching compared to TNP. Such high selectivity is related to the operation of FRET mechanism from fluorophore to TNP during this PL quenching experiment. TCSPC measurement also reveals that the lifetime of DAP/rGO drastically decreases from 3.7 to 1.9 ns after addition of TNP. Our material is also quite sensitive to 125 ppb level of TNP. Finally, we believe that this graphene based luminescent material will emerge a new class of sensing materials to detect trace amounts of explosives from aqueous solution.

Keywords: graphene, functionalization, fluorescence quenching, FRET, nitroexplosive detection

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Authors: Gaurav Bhanjana, Ganga Ram Chaudhary, Sandeep Kumar, Neeraj Dilbaghi

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Main sources of endocrine disrupting compounds in the ecosystem are hormones, pesticides, phthalates, flame retardants, dioxins, personal-care products, coplanar polychlorinated biphenyls (PCBs), bisphenol A, and parabens. These endocrine disrupting compounds are responsible for learning disabilities, brain development problems, deformations of the body, cancer, reproductive abnormalities in females and decreased sperm count in human males. Although discharge of these chemical compounds into the environment cannot be stopped, yet their amount can be retarded through proper evaluation and detection techniques. The available techniques for determination of these endocrine disrupting compounds mainly include high performance liquid chromatography (HPLC), mass spectroscopy (MS) and gas chromatography-mass spectrometry (GC–MS). These techniques are accurate and reliable but have certain limitations like need of skilled personnel, time consuming, interference and requirement of pretreatment steps. Moreover, these techniques are laboratory bound and sample is required in large amount for analysis. In view of above facts, new methods for detection of endocrine disrupting compounds should be devised that promise high specificity, ultra sensitivity, cost effective, efficient and easy-to-operate procedure. Nowadays, electrochemical sensors/biosensors modified with nanomaterials are gaining high attention among researchers. Bioelement present in this system makes the developed sensors selective towards analyte of interest. Nanomaterials provide large surface area, high electron communication feature, enhanced catalytic activity and possibilities of chemical modifications. In most of the cases, nanomaterials also serve as an electron mediator or electrocatalyst for some analytes.

Keywords: electrochemical, endocrine disruptors, microscopy, nanoparticles, sensors

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Authors: Jean-François Pouilly

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This study addresses the growing complexity of embedded systems and the critical need for secure, reliable software. Traditional cybersecurity testing methods, often conducted late in the development cycle, struggle to keep pace. This talk explores how formal methods, integrated with advanced analysis tools, empower C/C++ developers to 1) Proactively address vulnerabilities and bugs, which includes formal methods and abstract interpretation techniques to identify potential weaknesses early in the development process, reducing the reliance on penetration and fuzz testing in later stages. 2) Streamline development by focusing on bugs that matter, with close to no false positives and catching flaws earlier, the need for rework and retesting is minimized, leading to faster development cycles, improved efficiency and cost savings. 3) Enhance software dependability which includes combining static analysis using abstract interpretation with full context sensitivity, with hardware memory awareness allows for a more comprehensive understanding of potential vulnerabilities, leading to more dependable and secure software. This approach aligns with industry best practices (ISO2626 or ISO 21434) and empowers C/C++ developers to deliver robust, secure embedded systems that meet the demands of today's and tomorrow's applications. We will illustrate this approach with the TrustInSoft analyzer to show how it accelerates verification for complex cases, reduces user fatigue, and improves developer efficiency, cost-effectiveness, and software cybersecurity. In summary, integrating formal methods and sound Analyzers enhances software reliability and cybersecurity, streamlining development in an increasingly complex environment.

Keywords: safety, cybersecurity, ISO26262, ISO24434, formal methods

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Authors: Ainouna Bouziane

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The ability of Electron Tomography to recover the 3D structure of catalysts, with spatial resolution in the subnanometer scale, has been widely explored and reviewed in the last decades. A variety of experimental techniques, based either on Transmission Electron Microscopy (TEM) or Scanning Transmission Electron Microscopy (STEM) have been used to reveal different features of nanostructured catalysts in 3D, but High Angle Annular Dark Field imaging in STEM mode (HAADF-STEM) stands out as the most frequently used, given its chemical sensitivity and avoidance of imaging artifacts related to diffraction phenomena when dealing with crystalline materials. In this regard, our group has developed a methodology that combines image denoising by undecimated wavelet transforms (UWT) with automated, advanced segmentation procedures and parameter selection methods using CS-TVM (Compressed Sensing-total variation minimization) algorithms to reveal more reliable quantitative information out of the 3D characterization studies. However, evaluating the accuracy of the magnitudes estimated from the segmented volumes is also an important issue that has not been properly addressed yet, because a perfectly known reference is needed. The problem particularly complicates in the case of multicomponent material systems. To tackle this key question, we have developed a methodology that incorporates volume reconstruction/segmentation methods. In particular, we have established an approach to evaluate, in quantitative terms, the accuracy of TVM reconstructions, which considers the influence of relevant experimental parameters like the range of tilt angles, image noise level or object orientation. The approach is based on the analysis of material-realistic, 3D phantoms, which include the most relevant features of the system under analysis.

Keywords: electron tomography, supported catalysts, nanometrology, error assessment

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Authors: Nkechinyere Joy Olawuyi, Babajide Samuel Afolabi, Bola Ibitoye

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This study collected a preprocessed dataset of chest radiographs and formulated a deep neural network model for detecting abnormalities. It also evaluated the performance of the formulated model and implemented a prototype of the formulated model. This was with the view to developing a deep neural network model to automatically classify abnormalities in chest radiographs. In order to achieve the overall purpose of this research, a large set of chest x-ray images were sourced for and collected from the CheXpert dataset, which is an online repository of annotated chest radiographs compiled by the Machine Learning Research Group, Stanford University. The chest radiographs were preprocessed into a format that can be fed into a deep neural network. The preprocessing techniques used were standardization and normalization. The classification problem was formulated as a multi-label binary classification model, which used convolutional neural network architecture to make a decision on whether an abnormality was present or not in the chest radiographs. The classification model was evaluated using specificity, sensitivity, and Area Under Curve (AUC) score as the parameter. A prototype of the classification model was implemented using Keras Open source deep learning framework in Python Programming Language. The AUC ROC curve of the model was able to classify Atelestasis, Support devices, Pleural effusion, Pneumonia, A normal CXR (no finding), Pneumothorax, and Consolidation. However, Lung opacity and Cardiomegaly had a probability of less than 0.5 and thus were classified as absent. Precision, recall, and F1 score values were 0.78; this implies that the number of False Positive and False Negative is the same, revealing some measure of label imbalance in the dataset. The study concluded that the developed model is sufficient to classify abnormalities present in chest radiographs into present or absent.

Keywords: transfer learning, convolutional neural network, radiograph, classification, multi-label

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Authors: Cesar Augusto Hoil Rosas, José Luis Vázquez Burgos, José Juan Carbajal Hernandez

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White fish (Chirostoma estor estor) is an endemic species that habits in the Patzcuaro Lake, located in Michoacan, Mexico; being an important source of gastronomic and cultural wealth of the area. Actually, it have undergone an immense depopulation of individuals, due to the high fishing, contamination and eutrophication of the lake water, resulting in the possible extinction of this important species. This work proposes a new computational model for monitoring and assessment of critical environmental parameters of the white fish ecosystem. According to an Analytical Hierarchy Process, a mathematical model is built assigning weights to each environmental parameter depending on their water quality importance on the ecosystem. Then, a development of an advanced system for the monitoring, analysis and control of water quality is built using the virtual environment of LabVIEW. As results, we have obtained a global score that indicates the condition level of the water quality in the Chirostoma estor ecosystem (excellent, good, regular and poor), allowing to provide an effective decision making about the environmental parameters that affect the proper culture of the white fish such as temperature, pH and dissolved oxygen. In situ evaluations show regular conditions for a success reproduction and growth rates of this species where the water quality tends to have regular levels. This system emerges as a suitable tool for the water management, where future laws for white fish fishery regulations will result in the reduction of the mortality rate in the early stages of development of the species, which represent the most critical phase. This can guarantees better population sizes than those currently obtained in the aquiculture crop. The main benefit will be seen as a contribution to maintain the cultural and gastronomic wealth of the area and for its inhabitants, since white fish is an important food and economical income of the region, but the species is endangered.

Keywords: Chirostoma estor estor, computational system, lab view, white fish

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Authors: Carol Anne Hargreaves

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A key issue in stock investment is how to select representative features for stock selection. The objective of this paper is to firstly determine whether an automated stock investment system, using machine learning techniques, may be used to identify a portfolio of growth stocks that are highly likely to provide returns better than the stock market index. The second objective is to identify the technical features that best characterize whether a stock’s price is likely to go up and to identify the most important factors and their contribution to predicting the likelihood of the stock price going up. Unsupervised machine learning techniques, such as cluster analysis, were applied to the stock data to identify a cluster of stocks that was likely to go up in price – portfolio 1. Next, the principal component analysis technique was used to select stocks that were rated high on component one and component two – portfolio 2. Thirdly, a supervised machine learning technique, the logistic regression method, was used to select stocks with a high probability of their price going up – portfolio 3. The predictive models were validated with metrics such as, sensitivity (recall), specificity and overall accuracy for all models. All accuracy measures were above 70%. All portfolios outperformed the market by more than eight times. The top three stocks were selected for each of the three stock portfolios and traded in the market for one month. After one month the return for each stock portfolio was computed and compared with the stock market index returns. The returns for all three stock portfolios was 23.87% for the principal component analysis stock portfolio, 11.65% for the logistic regression portfolio and 8.88% for the K-means cluster portfolio while the stock market performance was 0.38%. This study confirms that an automated stock investment system using machine learning techniques can identify top performing stock portfolios that outperform the stock market.

Keywords: machine learning, stock market trading, logistic regression, cluster analysis, factor analysis, decision trees, neural networks, automated stock investment system

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Authors: Saeed Poormoaied

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Perishability and developing an intelligent control policy for perishable items are the major concerns of marketing managers in a supply chain. In this study, we address a two-echelon supply chain problem for perishable items with a single vendor and a single buyer. The buyer adopts an aged-based continuous review policy which works by taking both the stock level and the aging process of items into account. The vendor works under the warehouse framework, where its lot size is determined with respect to the batch size of the buyer. The model holds for a positive and fixed lead time for the buyer, and zero lead time for the vendor. The demand follows a Poisson process and any unmet demand is lost. We provide exact analytic expressions for the operational characteristics of the system by using the renewal reward theorem. Items have a fixed lifetime after which they become unusable and are disposed of from the buyer's system. The age of items starts when they are unpacked and ready for the consumption at the buyer. When items are held by the vendor, there is no aging process which results in no perishing at the vendor's site. The model is developed under the centralized framework, which takes the expected profit of both vendor and buyer into consideration. The goal is to determine the optimal policy parameters under the service level constraint at the retailer's site. A sensitivity analysis is performed to investigate the effect of the key input parameters on the expected profit and order quantity in the supply chain. The efficiency of the proposed age-based policy is also evaluated through a numerical study. Our results show that when the unit perishing cost is negligible, a significant cost saving is achieved.

Keywords: two-echelon supply chain, perishable items, age-based policy, renewal reward theorem

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Authors: Valeriy M. Kondratev, Ekaterina A. Vyacheslavova, Talgat Shugabaev, Alexander S. Gudovskikh, Alexey D. Bolshakov

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Modern sensorics imposes strict requirements on the biosensors characteristics, especially technological feasibility, and selectivity. There is a growing interest in the analysis of human health biological markers, which indirectly testifying the pathological processes in the body. Such markers are acids and alkalis produced by the human, in particular - ammonia and hydrochloric acid, which are found in human sweat, blood, and urine, as well as in gastric juice. Biosensors based on modern nanomaterials, especially low dimensional, can be used for this markers detection. Most classical adsorption sensors based on metal and silicon oxides are considered non-selective, because they identically change their electrical resistance (or impedance) under the action of adsorption of different target analytes. This work demonstrates a feasible frequency-resistive method of electrical impedance spectroscopy data analysis. The approach allows to obtain of selectivity in adsorption sensors of a resistive type. The method potential is demonstrated with analyzis of impedance spectra of silicon nanowires in the presence of NH3 and HCl vapors with concentrations of about 125 mmol/L (2 ppm) and water vapor. We demonstrate the possibility of unambiguous distinction of the sensory signal from NH3 and HCl adsorption. Moreover, the method is found applicable for analysis of the composition of ammonia and hydrochloric acid vapors mixture without water cross-sensitivity. Presented silicon sensor can be used to find diseases of the gastrointestinal tract by the qualitative and quantitative detection of ammonia and hydrochloric acid content in biological samples. The method of data analysis can be directly translated to other nanomaterials to analyze their applicability in the field of biosensory.

Keywords: electrical impedance spectroscopy, spectroscopy data analysis, selective adsorption sensor, nanotechnology

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Authors: Mohammad Karimi, Magnus Heurlin, Lars Samuelson, Magnus Borgstrom, Hakan Pettersson

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Nanowire semiconductors are promising candidates for optoelectronic applications such as solar cells, photodetectors and lasers due to their quasi-1D geometry and large surface to volume ratio. The functional wavelength range of NW-based detectors is typically limited to the visible/near-infrared region. In this work, we present electrical and optical properties of IR photodetectors based on large square millimeter ensembles (>1million) of vertically processed semiconductor heterostructure nanowires (NWs) grown on InP substrates which operate in longer wavelengths. InP NWs comprising single or multiple (20) InAs/InAsP QDics axially embedded in an n-i-n geometry, have been grown on InP substrates using metal organic vapor phase epitaxy (MOVPE). The NWs are contacted in vertical direction by atomic layer deposition (ALD) deposition of 50 nm SiO2 as an insulating layer followed by sputtering of indium tin oxide (ITO) and evaporation of Ti and Au as top contact layer. In order to extend the sensitivity range to the mid-wavelength and long-wavelength regions, the intersubband transition within conduction band of InAsP QDisc is suggested. We present first experimental indications of intersubband photocurrent in NW geometry and discuss important design parameters for realization of intersubband detectors. Key advantages with the proposed design include large degree of freedom in choice of materials compositions, possible enhanced optical resonance effects due to periodically ordered NW arrays and the compatibility with silicon substrates. We believe that the proposed detector design offers the route towards monolithic integration of compact and sensitive III-V NW long wavelength detectors with Si technology.

Keywords: intersubband photodetector, infrared, nanowire, quantum disc

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Authors: Carolina Zambrana, Grover Zurita

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The food loss production that occurs in deficient agricultural production is one of the major problems worldwide. This puts the population's food security and the efficiency of farming investments at risk. It is to be expected that this food security will be achieved with the own and efficient production of each country. It will have an impact on the well-being of its population and, thus, also on food sovereignty. The production losses in quantity and quality occur due to the lack of efficient detection of diseases at an early stage. It is very difficult to solve the agriculture efficiency using traditional methods since it takes a long time to be carried out due to detection imprecision of the main diseases, especially when the production areas are extensive. Therefore, the main objective of this research study is to perform a systematic literature review, of the latest five years, of Precision Agriculture (PA) to be able to understand the state of the art of the set of new technologies, procedures, and optimization processes with Artificial Intelligence (AI). This study will focus on Corns, Potatoes, and Rice diagnostic diseases. The extensive literature review will be performed on Elsevier, Scopus, and IEEE databases. In addition, this research will focus on advanced digital imaging processing and the development of software and hardware for PA. The convolution neural network will be handling special attention due to its outstanding diagnostic results. Moreover, the studied data will be incorporated with artificial intelligence algorithms for the automatic diagnosis of crop quality. Finally, precision agriculture with technology applied to the agricultural sector allows the land to be exploited efficiently. This system requires sensors, drones, data acquisition cards, and global positioning systems. This research seeks to merge different areas of science, control engineering, electronics, digital image processing, and artificial intelligence for the development, in the near future, of a low-cost image measurement system that allows the optimization of crops with AI.

Keywords: precision agriculture, convolutional neural network, deep learning, artificial intelligence

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Authors: Hasna Elalaoui Elabdallaoui, Abdelaziz Elfazziki, Mohamed Sadgal

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Outlines: The social economy is a moral approach to solidarity applied to the projects’ development. To reconcile economic activity and social equity, crowdfunding is as an alternative means of financing social projects. Several collaborative blockchain platforms exist. It eliminates the need for a central authority or an inconsiderate middleman. Also, the costs for a successful crowdfunding campaign are reduced, since there is no commission to be paid to the intermediary. It improves the transparency of record keeping and delegates authority to authorities who may be prone to corruption. Objectives: The objectives are: to define a software infrastructure for projects’ participatory financing within a social and solidarity economy, allowing transparent, secure, and fair management and to have a financial mechanism that improves financial inclusion. Methodology: The proposed methodology is: crowdfunding platforms literature review, financing mechanisms literature review, requirements analysis and project definition, a business plan, Platform development process and implementation technology, and testing an MVP. Contributions: The solution consists of proposing a new approach to crowdfunding based on Islamic financing, which is the principle of Mousharaka inspired by Islamic financing, which presents a financial innovation that integrates ethics and the social dimension into contemporary banking practices. Conclusion: Crowdfunding platforms need to secure projects and allow only quality projects but also offer a wide range of options to funders. Thus, a framework based on blockchain technology and Islamic financing is proposed to manage this arbitration between quality and quantity of options. The proposed financing system, "Musharaka", is a mode of financing that prohibits interests and uncertainties. The implementation is offered on the secure Ethereum platform as investors sign and initiate transactions for contributions using their digital signature wallet managed by a cryptography algorithm and smart contracts. Our proposal is illustrated by a crop irrigation project in the Marrakech region.

Keywords: social economy, Musharaka, blockchain, smart contract, crowdfunding

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Authors: Ali Kassem

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Background: In the last few years, factors such as insulin resistance (IR) and hepatic steatosis have been linked to progression of hepatic fibrosis.Patients with chronic liver disease, and cirrhosis in particular, are known to be prone to IR. However, chronic HCV (hepatitis C) infection may induce IR, regardless of the presence of liver cirrhosis. Our aims are to study insulin resistance (IR) assessed by HOMA-IR (Homeostatic Model Assessment Insulin Resistance) as a possible risk factor in disease progression in cirrhotic patients and to evaluate the role of IR in hepatic fibrosis progression. The correlations of HOMA-IR values to laboratory, virological and histopathological parameters of chronic HCV are also examined. Methods: The study included 50 people divided into 30 adult chronic hepatitis C patients diagnosed by PCR (polymerase chain reaction) within previous 6 months and 20 healthy controls. The functional and morphological status of the liver were evaluated by ultrasonography and laboratory investigations including liver function tests and by liver biopsy. Fasting blood glucose and fasting insulin levels were measured and body mass index and insulin resistance were calculated. Patients having HOMA-IR >2.5 were labeled as insulin resistant. Results: Chronic hepatitis C patients with IR showed significantly higher mean values of BMI (body mass index) and fasting insulin than those without IR (P < 0.000). Patients with IR were more likely to have steatosis (p = 0.006), higher necroinflammatory activity (p = 0.05). No significant differences were found between the two groups regarding hepatic fibrosis. Conclusion: HOMA-IR measurement could represent a novel marker to identify the cirrhotic patients at greater risk for the progression of liver disease. As IR is a potentially modifiable risk factor, these findings may have important prognostic and therapeutic implications. Assessment of IR by HOMA-IR and improving insulin sensitivity are recommended in patients with HCV and related chronic liver disease.

Keywords: hepatic fibrosis, hepatitis C virus infection, hepatic steatosis, insulin resistance

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Authors: Susanne Fridsten, Kristina Hellman, Anders Sundin, Lennart Blomqvist

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Background: Patients diagnosed with locally advanced cervical carcinoma are treated with definitive concomitant chemo-radiotherapy. Treatment failure occurs in 30-50% of patients with very poor prognoses. The treatment is standardized with risk for both over-and undertreatment. Consequently, there is a great need for biomarkers able to predict therapy outcomes to allow for individualized treatment. Aim: To explore the role of T2- and diffusion-weighted magnetic resonance imaging (MRI) for early prediction of therapy outcome and the optimal time point for assessment. Methods: A pilot study including 15 patients with cervical carcinoma stage IIB-IIIB (FIGO 2009) undergoing definitive chemoradiotherapy. All patients underwent MRI four times, at baseline, 3 weeks, 5 weeks, and 12 weeks after treatment started. Tumour size, size change (∆size), visibility on diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) and change of ADC (∆ADC) at the different time points were recorded. Results: 7/15 patients relapsed during the study period, referred to as "poor prognosis", PP, and the remaining eight patients are referred to "good prognosis", GP. The tumor size was larger at all time points for PP than for GP. The ∆size between any of the four-time points was the same for PP and GP patients. The sensitivity and specificity to predict prognostic group depending on a remaining tumor on DWI were highest at 5 weeks and 83% (5/6) and 63% (5/8), respectively. The combination of tumor size at baseline and remaining tumor on DWI at 5 weeks in ROC analysis reached an area under the curve (AUC) of 0.83. After 12 weeks, no remaining tumor was seen on DWI among patients with GP, as opposed to 2/7 PP patients. Adding ADC to the tumor size measurements did not improve the predictive value at any time point. Conclusion: A large tumor at baseline MRI combined with a remaining tumor on DWI at 5 weeks predicted a poor prognosis.

Keywords: chemoradiotherapy, diffusion-weighted imaging, magnetic resonance imaging, uterine cervical carcinoma

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Authors: Benjamin L. Freidman, Sally L. Gras, Ian Snape, Geoff W. Stevens, Kathryn A. Mumford

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Increasing transportation and storage of petroleum hydrocarbons in Antarctic and sub-Antarctic regions have resulted in frequent accidental spills. Migrating petroleum hydrocarbon spills can have a significant impact on terrestrial and marine ecosystems in cold regions, as harsh environmental conditions result in heightened sensitivity to pollution. This migration of contaminants has led to the development of Permeable Reactive Barriers (PRB) for application in cold regions. PRB’s are one of the most practical technologies for on-site or in-situ groundwater remediation in cold regions due to their minimal energy, monitoring and maintenance requirements. The Main Power House site has been used as a fuel storage and power generation area for the Macquarie Island research station since at least 1960. Soil analysis at the site has revealed Total Petroleum Hydrocarbon (TPH) (C9-C28) concentrations as high as 19,000 mg/kg soil. Groundwater TPH concentrations at this site can exceed 350 mg/L TPH. Ongoing migration of petroleum hydrocarbons into the neighbouring marine ecosystem resulted in the installation of a ‘funnel and gate’ PRB in November 2014. The ‘funnel and gate’ design successfully intercepted contaminated groundwater and analysis of TPH retention and biodegradation on PRB media are currently underway. Installation of the PRB facilitates research aimed at better understanding the contribution of particle attached biofilms to the remediation of groundwater systems. Bench-scale PRB system analysis at The University of Melbourne is currently examining the role biofilms play in petroleum hydrocarbon degradation, and how controlled release nutrient media can heighten the metabolic activity of biofilms in cold regions in the presence of low temperatures and low nutrient groundwater.

Keywords: groundwater, petroleum, Macquarie island, funnel and gate

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Authors: Omer Al-Kaabi, Ahmed Nasr, Abdullah Al-Ghafri, Mohammed Abdelfattah

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The Falaj system, derived from natural water sources, is a man-made canal system designed to supply communities of farmers with water for domestic and agricultural purposes. For thousands of years, Falaj has served communities by harnessing the force of gravity; it persists as a vital water management system in numerous regions across the Sultanate of Oman. Remarkably, predates the establishment of many fundamental hydraulic principles used today. Al-Khatmain Falaj, with its accessibility and historical significance spanning over 2000 years, was chosen as the focal point of this study. The research aimed to investigate the efficiency of Al-Khatmain Falaj in meeting specific water demands. The HEC-RAS model was utilized to visualize water flow dynamics within the Falaj channels, accompanied by graphical representations of pertinent variables. The application of HEC-RAS helped to measure different water flow scenarios within the channel, enabling a clear comparison with the demand area catchment. The cultivated land of Al-Khatmain is 723,124 m² and consists of 16,873 palm trees representing 91% of the total area and the remaining 9% is mixed types of trees counted 3,920 trees. The study revealed a total demand of 8,244 m³ is required to irrigate the cultivated land. Through rigorous analysis, the study has proven that the Falaj system in Al-Khatmain operates with high efficiency, as the average annual water supply is 9676.8 m3/day. Additionally, the channel designed at 0.6m width x 0.3m height efficiently holds the optimal water supply, with an average flow depth of 0.21m. Also, the system includes an overflow drainage channel to mitigate floods and prevent crop damage based on seasonal requirements. This research holds promise for examining diverse hydrological conditions and devising effective strategies to manage scenarios of both high and low flow rates.

Keywords: Al-Khatmain, sustainability, Falaj, HEC-RAS, water management system

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Authors: Laura Elena De León Prado, Dora Alicia Cortés Hernández, Javier Sánchez

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Among the different cancer treatments that are currently used, hyperthermia has a promising potential due to the multiple benefits that are obtained by this technique. In general terms, hyperthermia is a method that takes advantage of the sensitivity of cancer cells to heat, in order to damage or destroy them. Within the different ways of supplying heat to cancer cells and achieve their destruction or damage, the use of magnetic nanoparticles has attracted attention due to the capability of these particles to generate heat under the influence of an external magnetic field. In addition, these nanoparticles have a high surface area and sizes similar or even lower than biological entities, which allow their approaching and interaction with a specific region of interest. The most used magnetic nanoparticles for hyperthermia treatment are those based on iron oxides, mainly magnetite and maghemite, due to their biocompatibility, good magnetic properties and chemical stability. However, in order to fulfill more efficiently the requirements that demand the treatment of magnetic hyperthermia, there have been investigations using ferrites that incorporate different metallic ions, such as Mg, Mn, Co, Ca, Ni, Cu, Li, Gd, etc., in their structure. This paper reports the synthesis of nanosized Mg_xMn_1-xFe₂O₄ (x = 0.3 and 0.4) ferrites by sol-gel method and their evaluation in terms of heating capability and in vitro hemolysis to determine the potential use of these nanoparticles as thermoseeds for the treatment of cancer by magnetic hyperthermia. It was possible to obtain ferrites with nanometric sizes, a single crystalline phase with an inverse spinel structure and a behavior near to that of superparamagnetic materials. Additionally, at concentrations of 10 mg of magnetic material per mL of water, it was possible to reach a temperature of approximately 45°C, which is within the range of temperatures used for the treatment of hyperthermia. The results of the in vitro hemolysis assay showed that, at the concentrations tested, these nanoparticles are non-hemolytic, as their percentage of hemolysis is close to zero. Therefore, these materials can be used as thermoseeds for the treatment of cancer by magnetic hyperthermia.

Keywords: ferrites, heating capability, hemolysis, nanoparticles, sol-gel

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Authors: Yuvarani Naidu, Yasmeen Siddiqui, Mohd Yusof Rafii , Abu Seman Idris

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Sustainability of oil palm production in Southeast Asia, especially in Indonesia and Malaysia, is jeopardized by Ganoderma boninense, the fungus which causes basal stem rot (BSR) in oil palm. The root contact with unattended infected debris left in the plantations during replanting is known to be the primary source of inoculum. Abiding by the law, potentially effective technique of managing Ganoderma infected oil palm debris is deemed necessary because of the zero-burning policy in Malaysian oil palm plantations. White-rot hymenomycetes antagonistic to Ganoderma sp were selected to test their efficacy as log treatments in degrading Ganoderma infected oil palm logs and to minimize the survival of Ganoderma inoculum. Decay rate in terms of mass loss was significantly higher after the application of solid-state cultivation (SSC) of Trametes lactinea FBW (64% ±1.2), followed by Pycnoporus sanguineus FBR (55% ±1.7) in infected log block tissues, after 10 months of treatments. The degradation pattern was clearly distinguished between the treated and non-treated log blocks with the developed SSC formulations. The control infected log blocks showed the highest, whereas infected log blocks treated with either P. sanguineus FBR or T. lactinea FBW SSC formulations exhibited statistically lowest number of Ganoderma spp. recovery on Ganoderma Selective Medium (GSM), after 8 months of treatment. Out of that, the lowest recovery of Ganoderma spp. was reported in infected log blocks inoculated with the strain T. lactinea FBW (21% ± 0.9) followed by P. sanguineus FBR (33% ± 2.2), after 8 months, Further, no recovery of Ganoderma was noticeable, 10 months after treatment applications in log blocks treated with both of the formulations. This is the first nursery-base study to substantiate the initial colonization of white-rot hymenomycetes on oil palm log blocks previously infected with BSR pathogen, G. boninense. The present study has indicated that log blocks treatment with white-rot hymenomycetes significantly affected the mass loss of diseased and healthy log block tissues. This study provides a basis of biotechnological approaches inefficient degradation of oil palm-generated crop debris, under natural conditions with an ultimate aim of reducing the Ganoderma inoculum under heavy BSR infection pressure in eco-friendly manner.

Keywords: basal stem rot disease, ganoderma boninense, oil palm, white-rot fungi

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Authors: Shayli Varasteh Moradi, Wayne A. Johnston, Dejan Gagoski, Kirill Alexandrov

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The demand for a rapid and more efficient technique to identify protein-protein interaction particularly in the areas of therapeutics and diagnostics development is growing. The method described here is a rapid in vitro protein-protein interaction analysis approach based on AlphaLISA technology combined with Leishmania tarentolae cell-free protein production (LTE) system. Cell-free protein synthesis allows the rapid production of recombinant proteins in a multiplexed format. Among available in vitro expression systems, LTE offers several advantages over other eukaryotic cell-free systems. It is based on a fast growing fermentable organism that is inexpensive in cultivation and lysate production. High integrity of proteins produced in this system and the ability to co-express multiple proteins makes it a desirable method for screening protein interactions. Following the translation of protein pairs in LTE system, the physical interaction between proteins of interests is analysed by AlphaLISA assay. The assay is performed using unpurified in vitro translation reaction and therefore can be readily multiplexed. This approach can be used in various research applications such as epitope mapping, antigen-antibody analysis and protein interaction network mapping. The intra-viral protein interaction network of Zika virus was studied using the developed technique. The viral proteins were co-expressed pair-wise in LTE and all possible interactions among viral proteins were tested using AlphaLISA. The assay resulted to the identification of 54 intra-viral protein-protein interactions from which 19 binary interactions were found to be novel. The presented technique provides a powerful tool for rapid analysis of protein-protein interaction with high sensitivity and throughput.

Keywords: AlphaLISA technology, cell-free protein expression, epitope mapping, Leishmania tarentolae, protein-protein interaction

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Authors: Mohamud Ali Ibrahim, Ali Bayat, Ali Bolat

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Defoliant spraying is an important link in the mechanized cotton harvest because adequate and uniform spraying can improve defoliation quality and reduce cotton trash content. In defoliant application, application volume and spraying technology are extremely important. In this study, the effectiveness of defoliant application to cotton plant that has come to harvest with two different application volumes and three different types of nozzles with a standard field crop sprayer was determined. Experiments were carried in two phases as field area trials and laboratory analysis. Application rates were 250 l/ha and 400 L/ha, and spraying nozzles were (1) Standard flat fan nozzle (TP8006), (2) Air induction nozzle (AI 11002-VS), and (3) Dual Pattern nozzle (AI307003VP). A tracer (BSF) and defoliant were applied to mature cotton with approximately 60% open bolls and samplings for BSF deposition and spray coverage on the cotton plant were done at two plant height (upper layer, lower layer) of plant. Before and after spraying, bolls open and leaves rate on cotton plants were calculated, and filter papers were used to detect BSF deposition, and water sensitive papers (WSP) were used to measure the coverage rate of spraying methods used. Spectrofluorophotometer was used to detect the amount of tracer deposition on targets, and an image process computer programme was used to measure coverage rate on WSP. In analysis, conclusions showed that air induction nozzle (AI 11002-VS) achieved better results than the dual pattern and standard flat fan nozzles in terms of higher depositions, coverages, and leaf defoliations, and boll opening rates. AI nozzles operating at 250 L/ha application rate provide the highest deposition and coverage rate on applications of the defoliant; in addition, BSF as an indicator of the defoliant used reached on leaf beneath in merely this spray nozzle. After defoliation boll opening rate was 85% on the 7th and 12th days after spraying and falling rate of leaves was 76% at application rate of 250 L/ha with air induction (AI1102) nozzle.

Keywords: cotton defoliant, air induction nozzle, dual pattern nozzle, standard flat fan nozzle, coverage rate, spray deposition, boll opening rate, leaves falling rate

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Authors: Navpreet Kaur, Himkusha Thakur, Nirmal Prabhakar

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The most controversial issue in crop production is the use of Organophosphate insecticides. This is evident in many reports that Organophosphate (OP) insecticides, among the broad range of pesticides are mainly involved in acute and chronic poisoning cases. OPs detection is of crucial importance for health protection, food and environmental safety. In our study, a nanocomposite of poly (3,4 ethylenedioxythiophene) (PEDOT) and multi-walled carbon nanotubes (MWCNTs) has been deposited electrochemically onto the surface of fluorine doped tin oxide sheets (FTO) for the analysis of malathion OP. The -COOH functionalization of MWCNTs has been done for the covalent binding with amino groups of AChE enzyme. The use of PEDOT-MWCNT films exhibited an excellent conductivity, enables fast transfer kinetics and provided a favourable biocompatible microenvironment for AChE, for the significant malathion OP detection. The prepared biosensors were characterized by Fourier transform infrared spectrometry (FTIR), Field emission-scanning electron microscopy (FE-SEM) and electrochemical studies. Various optimization studies were done for different parameters including pH (7.5), AChE concentration (50 mU), substrate concentration (0.3 mM) and inhibition time (10 min). Substrate kinetics has been performed and studied for the determination of Michaelis Menten constant. The detection limit for malathion OP was calculated to be 1 fM within the linear range 1 fM to 1 µM. The activity of inhibited AChE enzyme was restored to 98% of its original value by 2-pyridine aldoxime methiodide (2-PAM) (5 mM) treatment for 11 min. The oxime 2-PAM is able to remove malathion from the active site of AChE by means of trans-esterification reaction. The storage stability and reusability of the prepared biosensor is observed to be 30 days and seven times, respectively. The application of the developed biosensor has also been evaluated for spiked lettuce sample. Recoveries of malathion from the spiked lettuce sample ranged between 96-98%. The low detection limit obtained by the developed biosensor made them reliable, sensitive and a low cost process.

Keywords: PEDOT-MWCNT, malathion, organophosphates, acetylcholinesterase, biosensor, oxime (2-PAM)

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Authors: Gul Rahim Rahimi

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The IoT-based soil moisture monitoring system for indoor plants is designed to address the challenges of maintaining optimal moisture levels in soil for plant growth and health. The system utilizes sensor technology to collect real-time data on soil moisture levels, which is then processed and analyzed using machine learning algorithms. This allows for accurate and timely monitoring of soil moisture levels, ensuring plants receive the appropriate amount of water to thrive. The main objectives of the system are twofold: to keep plants fresh and healthy by preventing water deficiency and to provide users with comprehensive insights into the water content of the soil on a daily and hourly basis. By monitoring soil moisture levels, users can identify patterns and trends in water consumption, allowing for more informed decision-making regarding watering schedules and plant care. The scope of the system extends to the agriculture industry, where it can be utilized to minimize the efforts required by farmers to monitor soil moisture levels manually. By automating the process of soil moisture monitoring, farmers can optimize water usage, improve crop yields, and reduce the risk of plant diseases associated with over or under-watering. Key technologies employed in the system include the Capacitive Soil Moisture Sensor V1.2 for accurate soil moisture measurement, the Node MCU ESP8266-12E Board for data transmission and communication, and the Arduino framework for programming and development. Additionally, machine learning algorithms are utilized to analyze the collected data and provide actionable insights. Cloud storage is utilized to store and manage the data collected from multiple sensors, allowing for easy access and retrieval of information. Overall, the IoT-based soil moisture monitoring system offers a scalable and efficient solution for indoor plant care, with potential applications in agriculture and beyond. By harnessing the power of IoT and machine learning, the system empowers users to make informed decisions about plant watering, leading to healthier and more vibrant indoor environments.

Keywords: IoT-based, soil moisture monitoring, indoor plants, water management

Procedia PDF Downloads 43