Search results for: plant detection

Authors: Chao Yu, Qing-Guo Wang, Dan Zhang

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The outlier detection problem for dynamic systems is formulated as a matrix decomposition problem with low-rank, sparse matrices and further recast as a semidefinite programming (SDP) problem. A fast algorithm is presented to solve the resulting problem while keeping the solution matrix structure and it can greatly reduce the computational cost over the standard interior-point method. The computational burden is further reduced by proper construction of subsets of the raw data without violating low rank property of the involved matrix. The proposed method can make exact detection of outliers in case of no or little noise in output observations. In case of significant noise, a novel approach based on under-sampling with averaging is developed to denoise while retaining the saliency of outliers and so-filtered data enables successful outlier detection with the proposed method while the existing filtering methods fail. Use of recovered “clean” data from the proposed method can give much better parameter estimation compared with that based on the raw data.

Keywords: outlier detection, system identification, matrix decomposition, low-rank matrix, sparsity, semidefinite programming, interior-point methods, denoising

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Authors: Tahar Ghnaya, Majda Mnasri, Hanen Zaier, Rim Ghabriche, Chedly Abdelly

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It is known that the symbiotic association between plant and microorganisms are beneficial for plant growth and resistance to metal stress. Hence, it was demonstrated that Arbuscular mycorrhizal fungi have a positive effect on host plants growing in metal polluted soils. Legume plants are those which normally associate to rhizobacteria in order to fix atmospheric nitrogen. The aim of this work was to evaluate the effect this type of symbiosis on the tolerance and the accumulation of Cd. We chose Medicago sativa, as a modal for host legume plants and Rhizobium mililoti 2011 as rhizobial strain. Inoculated and non-inoculated plants of M. sativa were submitted during three month to 0, 50, and 100 mgCd/kg dry soil. Results showed that the presence of Cd in the medium induced, in both inoculated and non-inoculated plants, a chlorosis and necrosis. However, these symptoms were more pronounced in non-inoculated plants. The beneficial effect of inoculation of M. sativa with R. meliloti, on plant growth was confirmed by the measurement of biomass production which showed that the symbiotic association between host plant and rhizobacteria alleviates significantly Cd effect on biomass production, so inoculated plants produced more dry weight as compared to non-inoculated ones in the presence of all Cd tretments. On the other hand, under symbiosis conditions, Cd was more accumulated in different plant organs. Hence, in these plants, shoot Cd concentration reached 425 and it was 280 µg/gDW in non-inoculated ones in the presence of 100 ppm Cd. This result suggests that symbiosis enhances the absorption and translocation of Cd in this plant. In nodules and roots, we detected the highest Cd concentrations, demonstrating that these organs are able to concentrate Cd in their tissues. These data confirm that M. sataiva, cultivated in symbiosis with Rhizobium mililoti could be used in phytoextraction of Cd from contaminated soils.

Keywords: Cd, phytoremediation, Medicago sativa, Arbuscular mycorrhizal

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Authors: Jorge Martinez-Garcia, Ingrid Stelzner, Joerg Stelzner, Damian Gwerder, Philipp Schuetz

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Tree-ring analysis is an important part of the quality assessment and the dating of (archaeological) wood samples. It provides quantitative data about the whole anatomical ring structure, which can be used, for example, to measure the impact of the fluctuating environment on the tree growth, for the dendrochronological analysis of archaeological wooden artefacts and to estimate the wood mechanical properties. Despite advances in computer vision and edge recognition algorithms, detection and counting of annual rings are still limited to 2D datasets and performed in most cases manually, which is a time consuming, tedious task and depends strongly on the operator’s experience. This work presents an image processing approach to detect the whole 3D tree-ring structure directly from X-ray computed tomography imaging data. The approach relies on a modified Canny edge detection algorithm, which captures fully connected tree-ring edges throughout the measured image stack and is validated on X-ray computed tomography data taken from six wood species.

Keywords: ring recognition, edge detection, X-ray computed tomography, dendrochronology

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Authors: Seyed Sadegh Naseralavi

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This paper presents a Kernel parallelization equation for damage identification in structures under unknown periodic excitations. Herein, the dynamic differential equation of the motion of structure is viewed as a mapping from displacements to external forces. Utilizing this viewpoint, a new method for damage detection in structures under periodic loads is presented. The developed method requires only two periods of load. The method detects the damages without finding the input loads. The method is based on the fact that structural displacements under free and forced vibrations are associated with two parallel subspaces in the displacement space. Considering the concept, kernel parallelization equation (KPE) is derived for damage detection under unknown periodic loads. The method is verified for a case study under periodic loads.

Keywords: Kernel, unknown periodic load, damage detection, Kernel parallelization equation

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Authors: Shohreh Azizi, Wag Nel

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The rapid population growth in South Africa has increased the requirement of waste water treatment facilities. The aim of this study is to assess the potential use of Fluidized bed Bio Reactor for Mmabatho sewage treatment plant. The samples were collected from the Inlet and Outlet of reactor daily to analysis the pH, Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), Total Suspended Solid (TSS) as per standard method APHA 2005. The studies were undertaken on a continue laboratory scale, and analytical data was collected before and after treatment. The reduction of 87.22 % COD, 89.80 BOD % was achieved. Fluidized Bed Bio Reactor remove Bod/COD removal as well as nutrient removal. The efforts also made to study the impact of the biological system if the domestic wastewater gets contaminated with any industrial contamination and the result shows that the biological system can tolerate high Total dissolved solids up to 6000 mg/L as well as high heavy metal concentration up to 4 mg/L. The data obtained through the experimental research are demonstrated that the FBBR may be used (<3 h total Hydraulic Retention Time) for secondary treatment in Mmabatho wastewater treatment plant.

Keywords: fluidized bed bioreactor, wastewater treatment plant, biological system, high TDS, heavy metal

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Authors: N. Nalini, Lokesh B. Bhajantri

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In Distributed Sensor Networks, the sensor nodes are prone to failure due to energy depletion and some other reasons. In this regard, fault tolerance of network is essential in distributed sensor environment. Energy efficiency, network or topology control and fault-tolerance are the most important issues in the development of next-generation Distributed Sensor Networks (DSNs). This paper proposes a node fault detection and recovery using Genetic Algorithm (GA) in DSN when some of the sensor nodes are faulty. The main objective of this work is to provide fault tolerance mechanism which is energy efficient and responsive to network using GA, which is used to detect the faulty nodes in the network based on the energy depletion of node and link failure between nodes. The proposed fault detection model is used to detect faults at node level and network level faults (link failure and packet error). Finally, the performance parameters for the proposed scheme are evaluated.

Keywords: distributed sensor networks, genetic algorithm, fault detection and recovery, information technology

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Authors: C. P. Providakis, G. M. Angeli, M. J. Favvata, N. A. Papadopoulos, C. E. Chalioris, C. G. Karayannis

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An effort for the detection of damages in the reinforcement bars of reinforced concrete members using PZTs is presented. The damage can be the result of excessive elongation of the steel bar due to steel yielding or due to local steel corrosion. In both cases the damage is simulated by considering reduced diameter of the rebar along the damaged part of its length. An integration approach based on both electromechanical admittance methodology and guided wave propagation technique is used to evaluate the artificial damage on the examined longitudinal steel bar. Two actuator PZTs and a sensor PZT are considered to be bonded on the examined steel bar. The admittance of the Sensor PZT is calculated using COMSOL 3.4a. Fast Furrier Transformation for a better evaluation of the results is employed. An effort for the quantification of the damage detection using the root mean square deviation (RMSD) between the healthy condition and damage state of the sensor PZT is attempted. The numerical value of the RSMD yields a level for the difference between the healthy and the damaged admittance computation indicating this way the presence of damage in the structure. Experimental measurements are also presented.

Keywords: concrete reinforcement, damage detection, electromechanical admittance, experimental measurements, finite element method, guided waves, PZT

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Authors: J. Naalamle Amissah, Dorcas Osei‐Safo, C. M. Asare, Benjamin Missah‐Assihene, Eric. Y. Danquah, Ivan Addae‐Mensah

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Medicinal plants serve as a reservoir of active ingredients for the treatment of common ailments such as cancer, malaria and diabetes. With the recent wave of health consciousness and reliance on plant based medicines, the demand for medicinal plants has increased considerably. This surge in medicinal plant use has raised great concern amongst key players (herbalist, collectors, conservationist and researchers) along the value chain about the sustainability of the raw material. The over reliance on wild crafting as a means to obtain the raw material spells doom for several of Africa’s native medicinal plant species. In this study domestication protocols for the cultivation of Cryptolepis sanguinolenta (CS), a medicinal plant used in the treatment of malaria were developed. Initial surveys were conducted, using questionnaires comprising of open and close ended questions, to gather information that would inform the domestication and cultivation of the species. A field study was then conducted to determine the plant’s cropping cycle and the effect of staking and plant age on the active ingredient (cryptolepine) concentration in its roots. Results of the survey confirmed the demand for the raw material and threw more light on the harvesting methods and intensity of CS collection from the wild. Cryptolepine concentration was found to be highest (~1.84 mg/100 mg of root material) at 289 days after planting (DAP) which coincided with the peak of root dry weight (52.8 g), signifying the best time for root harvest. Staking was found to be important for seed production. The first 105 DAP were characterized by low yields of root dry weight (13.5 g), followed by a period of rapid growth in which the root dry weight increased almost linearly until 289 DAP. Although dry matter partitioned to the vines increased towards the end of the experimental period (60%), dry matter partitioned to the roots remained fairly constant (30%) throughout the experimental period. Cryptolepine was found to increase as the plant aged and the practice of staking CS promoted pod formation. A suitable cropping cycle for the cultivation of CS was also developed.

Keywords: domestication, staking, conservation, wild harvesting

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Authors: C. P. Providakis, G. M. Angeli, M. J. Favvata, N. A. Papadopoulos, C. E. Chalioris, C. G. Karayannis

Abstract:

An effort for the detection of damages in the reinforcement bars of reinforced concrete members using PZTs is presented. The damage can be the result of excessive elongation of the steel bar due to steel yielding or due to local steel corrosion. In both cases the damage is simulated by considering reduced diameter of the rebar along the damaged part of its length. An integration approach based on both electro-mechanical admittance methodology and guided wave propagation technique is used to evaluate the artificial damage on the examined longitudinal steel bar. Two actuator PZTs and a sensor PZT are considered to be bonded on the examined steel bar. The admittance of the Sensor PZT is calculated using COMSOL 3.4a. Fast Furrier Transformation for a better evaluation of the results is employed. An effort for the quantification of the damage detection using the root mean square deviation (RMSD) between the healthy condition and damage state of the sensor PZT is attempted. The numerical value of the RSMD yields a level for the difference between the healthy and the damaged admittance computation indicating this way the presence of damage in the structure. Experimental measurements are also presented.

Keywords: concrete reinforcement, damage detection, electromechanical admittance, experimental measurements, finite element method, guided waves, PZT

Procedia PDF Downloads 280

Authors: Chia-Yen Lee, Hao-Jen Wang, Jhih-Hao Lai

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In recent years, infrared (IR) imaging has been considered as a potential tool to assess the efficacy of chemotherapy and early detection of breast cancer. Regions of tumor growth with high metabolic rate and angiogenesis phenomenon lead to the high temperatures. Observation of differences between the heat maps in long term is useful to help assess the growth of breast cancer cells and detect breast cancer earlier, wherein the multi-time infrared image alignment technology is a necessary step. Representative feature points detection and matching are essential steps toward the good performance of image registration and quantitative analysis. However, there is no clear boundary on the infrared images and the subject's posture are different for each shot. It cannot adhesive markers on a body surface for a very long period, and it is hard to find anatomic fiducial markers on a body surface. In other words, it’s difficult to detect and match features in an IR sequence images. In this study, automated feature detection and matching algorithms with two type of automatic feature points (i.e., vascular branch points and modified Harris corner) are developed respectively. The preliminary results show that the proposed method could identify the representative feature points on the IR breast images successfully of 98% accuracy and the matching results of 93% accuracy.

Keywords: Harris corner, infrared image, feature detection, registration, matching

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Authors: Ahmed Y. Kutbi, C. Russell. J. Baird, M. McNaughtan, Francis Wayman

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Wastewaters from antibiotic production facilities are characterized with high concentrations of dissolved organic substances. Subsequently, it challenges wastewater treatment plant operator to achieve successful biological treatment and to meet regulatory emission levels. Of the dissolved organic substances, this research is investigating the fate of organic nitrogenous compounds (i.e., Chitin) in an antibiotic production wastewater treatment plant located in Irvine, Scotland and its impact on the WWTP removal performance. Dissolved organic nitrogen (DON) in WWTP effluents are of significance because 1) its potential to cause eutrophication in receiving waters, 2) the formation of nitrogenous disinfection by products in drinking waters and 3) limits WWTPs ability to achieve very low total nitrogen (TN) emissions limits (5 – 25 mg/l). The latter point is where the knowledge gap lays between the operator and the regulator in setting viable TN emission levels. The samples collected from Irvine site at the different stages of the treatment were analyzed for TN and DON. Results showed that the average TN in the WWTP influents and effluents are 798 and 261 mg/l respectively, in other words, the plant achieved 67 % removal of TN. DON Represented 51% of the influents TN, while the effluents accounted 26 % of the TN concentrations. Therefore, an ongoing investigation is carried out to identify DON constituents in WWTP effluent and evaluate its impact on the WWTP performance and its potential bioavailability for algae in receiving waters, which is, in this case, Irvine Bay.

Keywords: biological wastewater treatment plant, dissolved organic nitrogen, bio-availability, Irvine Bay

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Authors: Mehrshad Khosraviani, Faramarz Abbaspour Leyl Abadi

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The fundamental component of the computer network of modern information society will be considered. These networks are connected to the network of the internet generally. Due to the fact that the primary purpose of the Internet is not designed for, in recent decades, none of these networks in many of the attacks has been very important. Today, for the provision of security, different security tools and systems, including intrusion detection systems are used in the network. A common diagnosis system based on artificial immunity, the designer, the Adhasaz Foundation has been evaluated. The idea of using artificial safety methods in the diagnosis of abnormalities in computer networks it has been stimulated in the direction of their specificity, there are safety systems are similar to the common needs of m, that is non-diagnostic. For example, such methods can be used to detect any abnormalities, a variety of attacks, being memory, learning ability, and Khodtnzimi method of artificial immune algorithm pointed out. Diagnosis of the common system of education offered in this paper using only the normal samples is required for network and any additional data about the type of attacks is not. In the proposed system of positive selection and negative selection processes, selection of samples to create a distinction between the colony of normal attack is used. Copa real data collection on the evaluation of ij indicates the proposed system in the false alarm rate is often low compared to other ir methods and the detection rate is in the variations.

Keywords: artificial immune system, abnormality detection, intrusion detection, computer networks

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Authors: Seyed Habib Shojaei, Reza Eivazi, Mir Sajad Shojaei, Alireza Akbari, Pooria Mazloom, Seyede Mitra Sadati, Mir Zeinalabedin Shojaei, Farnaz Farbakhsh

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To study the genetic diversity in rapeseeds and agronomic traits, an experiment was conducted using multivariate statistical methods at Agricultural Research Station of Kashmar in 2012-2013.In this experiment, ten genotypes of rapeseed in a Randomized Complete Block designs with three replications were evaluated. The following traits were studied: seed yield, number of days to the fifty percent of flowering, plant height, number of pods on main stem, length of the pod, seed yield per plant, number of seed in pod, harvest index, weight of 100 seeds, number of pods on lateral branch, number of lateral branches. In analyzing the variance, differences between cultivars were significant. The average comparative revealed that the most valuable variety was Licord regarding to the traits while the least valuable variety was Opera. In stepwise regression, harvest index, grain yield per plant and number of pods per lateral branches were entering to model. Correlation analysis showed that the grain yield with the number of pods per lateral branches and seed yield per plant have positive and significant correlation. In the factor analysis, the first five components explained more than 83% of the variance in the data. In the first factor, seed yield and the number of pods per lateral branches were of the highest importance. The traits, seed yield per plant, and pod per main stem were of a great significance in the second factor. Moreover, in the third factor, plant height and the number of lateral branches were more important. In the fourth factor, plant height and one hundred seeds weight were of the highest variance. Finally, days to fifty percent of flowering and one hundred seeds weight were more important in fifth factor.

Keywords: rapeseed, variance analysis, regression, factor analysis

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Authors: Atefeh Heydari, Mohammadali Tavakoli, Naomie Salim

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In recent years, we have witnessed that online reviews are the most important source of customers’ opinion. They are progressively more used by individuals and organisations to make purchase and business decisions. Unfortunately, for the reason of profit or fame, frauds produce deceptive reviews to hoodwink potential customers. Their activities mislead not only potential customers to make appropriate purchasing decisions and organisations to reshape their business, but also opinion mining techniques by preventing them from reaching accurate results. Spam reviews could be divided into two main groups, i.e. multiple and singleton spam reviews. Detecting a singleton spam review that is the only review written by a user ID is extremely challenging due to lack of clue for detection purposes. Singleton spam reviews are very harmful and various features and proofs used in multiple spam reviews detection are not applicable in this case. Current research aims to propose a novel supervised technique to detect singleton spam reviews. To achieve this, various features are proposed in this study and are to be combined with the most appropriate features extracted from literature and employed in a classifier. In order to compare the performance of different classifiers, SVM and naive Bayes classification algorithms were used for model building. The results revealed that SVM was more accurate than naive Bayes and our proposed technique is capable to detect singleton spam reviews effectively.

Keywords: classification algorithms, Naïve Bayes, opinion review spam detection, singleton review spam detection, support vector machine

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Authors: Hadj Abd El Kader Benghenia, Fethi Bereksi Reguig

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In clinical medicine, blood pressure, raised blood hemodynamic monitoring is rich pathophysiological information of cardiovascular system, of course described through factors such as: blood volume, arterial compliance and peripheral resistance. In this work, we are interested in analyzing these signals to propose a detection algorithm to delineate the different sequences and especially systolic blood pressure (SBP), diastolic blood pressure (DBP), and the wave and dicrotic to do their analysis in order to extract the cardiovascular parameters.

Keywords: blood pressure, SBP, DBP, detection algorithm

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Authors: Sunday Paul Bako, Augustine Uwanekwu Ezealor, Yahuza Tanimu

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In a study to evaluate the response of indigenous ruderal plants to the metal deposition regime imposed by anthropogenic modification in the Southern Guinea Savanna of north Western Nigeria during the dry and wet seasons, herbaceous plants and samples of soils were collected in three 5m by 5m quadrats laid around the environs of the Kaduna Refinery and Petrochemical Company and the banks of River Kaduna. Heavy metal concentration (Cd, Ni, Cr, Cu, Fe, Mn and Zn) in soil and plant samples was determined using Energy Dispersive X-ray Fluorescence. Concentrations of heavy metals in soils were generally observed to be higher during the wet season in both locations although the differences were not statistically significant (P > 0.05). Concentrations of Cd, Zn, Cr, Cu and Ni in all the plants observed were found to be below levels described as phytotoxic to plants. However, above ‘normal’ concentrations of Cr was observed in most of the plant species sampled. The concentrations of Cr, Cu, Ni and Zn in soils around the KRPC and RKB were found to be above the acceptable limits. Although no hyper accumulator plant species was encountered in this study, twenty (20) plant species were identified to have high bioconcentration (BCF > 1.0) of Cd and Cu, which indicated tolerance of these plants to excessive or phytotoxic concentrations of these metals. In addition, they generally produce high above ground biomass, due to rapid vegetative growth. These are likely species for phytoextraction. Elevated concentration of metals in both soil and plant materials may cause a decrease in biodiversity due to direct toxicity. There are also risks to humans and other animals due to bioaccumulation across the food chain. There are further possibilities of further evaluating and genetically improving metal tolerance traits in some of these plant species in relation to their potential use in phytoremediation programmes in metal polluted sites.

Keywords: bioprospecting, phytoremediation, heavy metals, Nigeria

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Authors: Mohit Gupta, Chialing Wei, Thomas Czerniawski

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In this paper, we have built a neural network that can detect walls on 2D sheets and subsequently create a 3D model in Revit using Dynamo. The training set includes 3500 labeled images, and the detection algorithm used is YOLO. Typically, engineers/designers make concentrated efforts to convert 2D cad drawings to 3D models. This costs a considerable amount of time and human effort. This paper makes a contribution in automating the task of 3D walls modeling. 1. Detecting Walls in 2D cad and generating 3D pop-ups in Revit. 2. Saving designer his/her modeling time in drafting elements like walls from 2D cad to 3D representation. An object detection algorithm YOLO is used for wall detection and localization. The neural network is trained over 3500 labeled images of size 256x256x3. Then, Dynamo is interfaced with the output of the neural network to pop-up 3D walls in Revit. The research uses modern technological tools like deep learning and artificial intelligence to automate the process of generating 3D walls without needing humans to manually model them. Thus, contributes to saving time, human effort, and money.

Keywords: neural networks, Yolo, 2D to 3D transformation, CAD object detection

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Authors: Shreyas Gambhirrao, Aditya Vichare, Aniket Tembhurne, Shahuraj Bhosale

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In today's rapidly evolving environment, stress has emerged as a significant health concern across different age groups. Stress that isn't controlled, whether it comes from job responsibilities, health issues, or the never-ending news cycle, can have a negative effect on our well-being. The problem is further aggravated by the ongoing connection to technology. In this high-tech age, identifying and controlling stress is vital. In order to solve this health issue, the study focuses on three key metrics for stress detection: body temperature, heart rate, and galvanic skin response (GSR). These parameters along with the Support Vector Machine classifier assist the system to categorize stress into three groups: 1) Stressed, 2) Not stressed, and 3) Moderate stress. Proposed training model, a NodeMCU combined with particular sensors collects data in real-time and rapidly categorizes individuals based on their stress levels. Real-time stress detection is made possible by this creative combination of hardware and software.

Keywords: real time stress detection, NodeMCU, sensors, heart-rate, body temperature, galvanic skin response (GSR), support vector machine

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Authors: Funmilola O. Omoya, Abdul O. Momoh

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Malaria constitutes one of the major health problems in Nigeria. One of the reasons attributed for the upsurge was the development of resistance of Plasmodium falciparum and the emergence of multi-resistant strains of the parasite to anti-malaria drugs. A continued search for other effective, safe and cheap plant-based anti-malaria agents thus becomes imperative in the face of these difficulties. The objective of this study is therefore to evaluate the in vivo anti-malarial efficacy of ethanolic extracts of Chromolaena odorata and Androgaphis paniculata leaves. The two plants were evaluated for their anti-malaria efficacy in vivo in a 4-day curative test assay against Plasmodium berghei strain in mice. The group treated with 500mg/ml dose of ethanolic extract of A. paniculata plant showed parasite suppression with increase in Packed Cell Volume (PCV) value except day 3 which showed a slight decrease in PCV value. During the 4-day curative test, an increase in the PCV values, weight measurement and zero count of Plasmodium berghei parasite values was recorded after day 3 of drug administration. These results obtained in group treated with A. paniculata extract showed anti-malarial efficacy with higher mortality rate in parasitaemia count when compared with Chromolaena odorata group. These results justify the use of ethanolic extracts of A. paniculata plant as medicinal herb used in folklore medicine in the treatment of malaria.

Keywords: anti-malaria, curative, plant-based anti-malaria agents, biology

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Authors: Amna Shoaib, Sidrah Hanif, Rashid Mehmood

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Current research work was carried out to check influence of farmyard manure (FYM) in Lycopersicon esculentum L. against Fusarium oxysporum f. sp. lycopersici (FO) in copper polluted soil. Silt-loam soil naturally enriched with 70 ppm of Cu was inoculated with 1 x 106 spore suspensions of FO and incorporated with 0%, 1%, 1.5% or 2% FYM. The multilateral interaction of host-pathogen-metal-organic amendment was assessed in terms of morphology, growth, yield, physiology, biochemistry and metal uptake in tomato plant after 30 and 60 days of sowing. When soil was inoculated with FO, plant growth and biomass were significantly increased during vegetative stage, while declining during flowering stage with substantial increase in productivity over control. Infected plants exhibited late wilting and disease severity was found on 26-50% of plant during reproductive stage. Incorporation of up to 1% FYM suppressed disease severity, improved plant growth and biomass, while it decreased yield. Rest of manure doses was found ineffective in suppressing disease. Content of total chlorophyll, sugar and protein were significantly declined in FO inoculated plants and incorporation of FYM caused significant reduction or no influence on sugar and chlorophyll content, and no pronounced difference among different FYM doses were observed. On the other hand, proline, peroxidase, catalase and nitrate reductase activity were found to be increased in infected plants and incorporation of 1-2% FYM further enhanced the activity of these enzymes. Tomato plant uptake of 30-40% of copper naturally present in the soil and incorporation of 1-2% FYM markedly decreased plant uptake of metal by 15-30%, while increased Cu retention in soil. Present study concludes that lower dose (1%) of FYM could be used to manage disease, increase growth and biomass, while being ineffective for yield and productivity in Cu-polluted soil. Altered physiology/biochemistry of plant in response to any treatment could be served as basis for resistant against pathogen and metal homeostasis in plants.

Keywords: Lycopersicon esculentum, copper, Fusarium wilt, farm yard manure

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Authors: Khaled Sekkoum

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The traditional pharmacopoeia of Algerian Sahara is very rich on vegetable drugs. The great resources and biodiversity of Algerian Sahara flora seem responsible. A survey of medicinal plants used by the local population of the south west of Algeria for the urinary disorders is reported. Sixty-three plant species belonging to thirty-three families were identified. Their botanical and local names, plant part used, mode of use and ailment treated are given.

Keywords: medicinal plants, urinary diseases, Sahara, Algeria

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Authors: Benjamin Osae Agyei, Hypolite Bayor

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This present experiment was carried out during the 2012 cropping season, at the Farming for the Future Experimental Field of the University for Development Studies, Nyankpala Campus in the Northern Region of Ghana. The objective of the experiment was to determine the carrot growth and yield responses to poultry manure and N.P.K (15:15:15). Six treatments (Control (no amendment), 20 t/ha poultry manure (PM), 40 t/ha PM, 70 t/ha PM, 35 t/ha PM + 0.11t/ha N.P.K and 0.23 t/ha N.P.K) with three replications for each were laid in a Randomized Complete Block Design (RCBD). Data were collected on plant height, number of leaves per plant, canopy spread, root diameter, root weight, and root length. Microsoft Excel and Genstat Statistical Package (9^th edition) were used for the data analysis. The treatment means were compared by using Least Significant Difference at 10%. Generally, the results showed that there were no significant differences (P>0.1) among the treatments with respect to number of leaves per plant, root diameter, root weight, and root length. However, significant differences occurred among plant heights and canopy spreads. Plant height treated with 40 t/ha PM at the fourth week after planting and canopy spread at eight weeks after planting and ten weeks after planting by 70 t/ha PM and 20 t/ha PM respectively showed significant difference (P<0.1). The study recommended that any of the amended treatments can be applied at their recommended rates to plots for carrot production, since there were no significant differences among the treatments.

Keywords: poultry manure, N.P.K., soil amendment, growth, yield, carrot

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Authors: Serhat Tüzün, Tufan Demirel

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The objective of the present study is to select the most suitable location for a wind power plant station through Choquet integral method. The problem of selecting the location for a wind power station was considered as a multi-criteria decision-making problem. The essential and sub-criteria were specified and location selection was expressed in a hierarchic structure. Among the main criteria taken into account in this paper are wind potential, technical factors, social factors, transportation, and costs. The problem was solved by using different approaches of Choquet integral and the best location for a wind power station was determined. Then, the priority weights obtained from different Choquet integral approaches are compared and commented on.

Keywords: multi-criteria decision making, choquet integral, fuzzy sets, location of a wind power plant

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Authors: Mmbulaheni Ramulondi, Sandy Van Vuuren, Helene De Wet

Abstract:

The use of plant combinations to treat various medical conditions is not a new concept, and it is known that traditional people do not only rely on a single plant extract for efficacy but often combine various plant species for treatment. The knowledge of plant combinations is transferred from one generation to the other in the belief that combination therapy may enhance efficacy, reduce toxicity, decreases adverse effects, increase bioavailability and result in lower dosages. However, combination therapy may also be harmful when the interaction is antagonistic, since it may result in increasing toxicity. Although a fair amount of research has been done on the toxicity of medicinal plants, there is very little done on the toxicity of medicinal plants in combination. The aim of the study was to assess the toxicity potential of 19 plant combinations which have been documented as treatments of hypertension in northern KwaZulu-Natal by lay people. The aqueous extracts were assessed using two assays; the Brine shrimp assay (Artemia franciscana) and the Ames test (Mutagenicity). Only one plant combination (Aloe marlothii with Hypoxis hemerocallidea) in the current study has been previously assessed for toxicity. With the Brine shrimp assay, the plant combinations were tested in two concentrations (2 and 4 mg/ml), while for mutagenicity tests, they were tested at 5 mg/ml. The results showed that in the Brine shrimp assay, six combinations were toxic at 4 mg/ml. The combinations were Albertisia delagoensis with Senecio serratuloides (57%), Aloe marlothii with Catharanthus roseus (98%), Catharanthus roseus with Hypoxis hemerocallidea (66%), Catharanthus roseus with Musa acuminata (89%), Catharanthus roseus with Momordica balsamina (99%) and Aloe marlothii with Trichilia emetica and Hyphaene coriacea (50%). However when the concentration was reduced to 2 mg/ml, only three combinations were toxic which were Aloe marlothii with Catharanthus roseus (76%), Catharanthus roseus with Musa acuminata (66%) and Catharanthus roseus with Momordica balsamina (73%). For the mutagenicity assay, only the combinations between Catharanthus roseus with Hypoxis hemerocallidea and Catharanthus roseus with Momordica balsamina were mutagenic towards the Salmonella typhimurium strains TA98 and TA100. Most of the combinations which were toxic involve C. roseus which was also toxic when tested singularly. It is worth noting that C. roseus was one of the most frequently used plant species both to treat hypertension singularly and in combination and some of the individuals have been using this for the last 20 years. The mortality percentage of the Brine shrimp showed a significant correlation between dosage and toxicity thus toxicity was dosage dependant. A combination which is worth noting is the combination between A. delagoensis and S. serratuloides. Singularly these plants were non-toxic towards Brine shrimp, however their combination resulted in antagonism with the mortality rate of 57% at the total concentration of 4 mg/ml. Low toxicity was mostly observed, giving some validity to combined use, however the few combinations showing increased toxicity demonstrate the importance of analysing plant combinations.

Keywords: dosage, hypertension, plant combinations, toxicity

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Authors: Asghar Ebrahimzadeh, Sattar Malekian, Mohammad Ali Aazami, Mohammad Bagher Hassanpouraghdam

Abstract:

Roses are of main ornamental flowers worldwide. Rosa damascena Mill., besides being an ornamental plant, has major pharmaceutical, cosmetic and fragrance applications. Traditional propagation methods of the plant are using suckers, cutting and grafting. In the present experiment, we used the different explants (leaf section, petioles and nodal cutting) for the optimization of this high-valued ornamental from a native clonal plant. Diverse explants were acquired from mature plants during the growing season and were planted on MS medium supplemented with different hormonal combinations. 70% alcohol and sodium hypochloride were utilized for the surface sterilization. For proliferation, BAP and BA (1-5 mg L-1) and NAA (1-2 mg L-1) were tested. The highest proliferation rate was afforded from MS medium supplemented with 1.5 mg L-1 BA and 5 mg L-1 BAP. Callogenesis from leaf samples and petioles was the best with 1/2 MS medium enriched with 1mg L-1 BAP and 4 mg L-1 2,4-D. Rooting was occurred with the highest frequency in a medium containing 0.1 mg L-1 IBA.

Keywords: Rosa damascene, micropropagation, petiole, IBA, BAP

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Authors: Yuanqing Li, Changyi Lei, Zhaopeng Xue, Zhuo Zheng, Yanbo Long

Abstract:

In agricultural production and breeding, implementing automatic picking robot in orchard farming to reduce human labour and error is challenging. The core function of it is automatic identification based on machine vision. This paper focuses on apple detection and counting in orchards and implements several deep learning methods. Extensive datasets are used and a semi-automatic annotation method is proposed. The proposed deep learning models are in state-of-the-art YOLO family. In view of the essence of the models with various backbones, a multi-dimensional comparison in details is made in terms of counting accuracy, mAP and model memory, laying the foundation for realising automatic precision agriculture.

Keywords: agricultural object detection, deep learning, machine vision, YOLO family

Procedia PDF Downloads 187

Authors: Mohammad Arabi

Abstract:

The development of advanced technologies in the field of signal processing and vibration analysis has enabled more accurate analysis and fault detection in electrical systems. This research investigates the application of temporal and frequency features in detecting faults in electric motor bearings, aiming to enhance fault detection accuracy and prevent unexpected failures. The use of methods such as deep learning algorithms and neural networks in this process can yield better results. The main objective of this research is to evaluate the efficiency and accuracy of methods based on temporal and frequency features in identifying faults in electric motor bearings to prevent sudden breakdowns and operational issues. Additionally, the feasibility of using techniques such as machine learning and optimization algorithms to improve the fault detection process is also considered. This research employed an experimental method and random sampling. Vibration signals were collected from electric motors under normal and faulty conditions. After standardizing the data, temporal and frequency features were extracted. These features were then analyzed using statistical methods such as analysis of variance (ANOVA) and t-tests, as well as machine learning algorithms like artificial neural networks and support vector machines (SVM). The results showed that using temporal and frequency features significantly improves the accuracy of fault detection in electric motor bearings. ANOVA indicated significant differences between normal and faulty signals. Additionally, t-tests confirmed statistically significant differences between the features extracted from normal and faulty signals. Machine learning algorithms such as neural networks and SVM also significantly increased detection accuracy, demonstrating high effectiveness in timely and accurate fault detection. This study demonstrates that using temporal and frequency features combined with machine learning algorithms can serve as an effective tool for detecting faults in electric motor bearings. This approach not only enhances fault detection accuracy but also simplifies and streamlines the detection process. However, challenges such as data standardization and the cost of implementing advanced monitoring systems must also be considered. Utilizing temporal and frequency features in fault detection of electric motor bearings, along with advanced machine learning methods, offers an effective solution for preventing failures and ensuring the operational health of electric motors. Given the promising results of this research, it is recommended that this technology be more widely adopted in industrial maintenance processes.

Keywords: electric motor, fault detection, frequency features, temporal features

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Authors: Pontsho E. Tseke, Phatu W. Mashela

Abstract:

Climate-smart agriculture dictates that underusilised indigenous plant, which served as food for local marginalized communities, be assessed for introduction into mainstream agriculture. Most of the underutilised indigenous plants had survived adverse conditions in the wild; with limited information on how the interact with most abiotic and biotic factors. Cucumis myriocarpus leafy vegetable has nutritional, pharmacological and industrial applications, with limited information on how it interacts with effective microorganisms. The objective of this study was to determine the effects vesicular arbuscular mycorrhiza (VAM) on the growth of C. myriocarpus indigenous leafy vegetable under greenhouse conditions. Four-weeks-old seedlings of C. myriocarpus were transplanted into 20-cm-diameter plastic pots. Two weeks after transplanting, VAM was applied at 0, 10, 20, 30, 40, 50, 60 and 70 g Biocult-VAM plant. At 56 days after treatments, plant growth variables of C. myriocarpus with increase Biocult-VAM levels exhibited positive quadratic relations. Plant variables and increasing concentrations of salinity exhibited positive quadric relations, with 95 to 99% associations. Inclusion, Biocult-VAM can be used in sustainable production of C. myriocarpus for functional food security.

Keywords: abiotic, biotic, rhizasphere, sustainable agriculture

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Authors: Aysun Sezer

Abstract:

Magnetic Resonance Imaging (MRI) is one of the advanced diagnostic tools for evaluating shoulder pathologies. Proton Density (PD)-weighted MRI sequences prove highly effective in detecting edema. However, they are deficient in the anatomical identification of bones due to a trauma-induced decrease in signal-to-noise ratio and blur in the traumatized cortices. Computer-based diagnostic systems require precise segmentation, identification, and localization of anatomical regions in medical imagery. Deep learning-based object detection algorithms exhibit remarkable proficiency in real-time object identification and localization. In this study, the YOLOv8 model was employed to detect humeral head and scapular regions in 665 axial PD-weighted MR images. The YOLOv8 configuration achieved an overall success rate of 99.60% and 89.90% for detecting the humeral head and scapula, respectively, with an intersection over union (IoU) of 0.5. Our findings indicate a significant promise of employing YOLOv8-based detection for the humerus and scapula regions, particularly in the context of PD-weighted images affected by both noise and intensity inhomogeneity.

Keywords: YOLOv8, object detection, humerus, scapula, IRM

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Authors: Yufeng Li, Yinan Ma, Jing Wu, Chengnian Long

Abstract:

Infrared object detection aims at separating small and dim target from clutter background and its capabilities extend beyond the limits of visible light, making it invaluable in a wide range of applications such as improving safety, security, efficiency, and functionality. However, existing methods are usually sensitive to the noise of the input infrared image, leading to a decrease in target detection accuracy and an increase in the false alarm rate in high-noise environments. To address this issue, an infrared small target detection algorithm called YOLO-IR is proposed in this paper to improve the robustness to high infrared noise. To address the problem that high noise significantly reduces the clarity and reliability of target features in infrared images, we design a soft-threshold coordinate attention mechanism to improve the model’s ability to extract target features and its robustness to noise. Since the noise may overwhelm the local details of the target, resulting in the loss of small target features during depth down-sampling, we propose a deep and shallow feature fusion neck to improve the detection accuracy. In addition, because the generalized Intersection over Union (IoU)-based loss functions may be sensitive to noise and lead to unstable training in high-noise environments, we introduce a Wasserstein-distance based loss function to improve the training of the model. The experimental results show that YOLO-IR achieves a 5.0% improvement in recall and a 6.6% improvement in F1-score over existing state-of-art model.

Keywords: infrared small target detection, high noise, robustness, soft-threshold coordinate attention, feature fusion

Procedia PDF Downloads 46