Search results for: sequential change detection

Authors: Theo H. G. Moundzounga

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Arsenic and 2-chlorophenol are priority pollutants that pose serious health threats to humans and ecology. An electrochemical sensor, based on graphitic carbon nitride (g-C₃N₄) and carbon dots (CDs), was fabricated and used for the determination of arsenic and 2-chlorophenol. The g-C₃N₄/CDs nanocomposite was prepared via microwave irradiation heating method and was dropped-dried on the surface of the glassy carbon electrode (GCE). Transmission electron microscopy (TEM), X-ray diffraction (XRD), photoluminescence (PL), Fourier transform infrared spectroscopy (FTIR), UV-Vis diffuse reflectance spectroscopy (UV-Vis DRS) were used for the characterization of structure and morphology of the nanocomposite. Electrochemical characterization was done by electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). The electrochemical behaviors of arsenic and 2-chlorophenol on different electrodes (GCE, CDs/GCE, and g-C₃N₄/CDs/GCE) was investigated by differential pulse voltammetry (DPV). The results demonstrated that the g-C₃N₄/CDs/GCE significantly enhanced the oxidation peak current of both analytes. The analytes detection sensitivity was greatly improved, suggesting that this new modified electrode has great potential in the determination of trace level of arsenic and 2-chlorophenol. Experimental conditions which affect the electrochemical response of arsenic and 2-chlorophenol were studied, the oxidation peak currents displayed a good linear relationship to concentration for 2-chlorophenol (R²=0.948, n=5) and arsenic (R²=0.9524, n=5), with a linear range from 0.5 to 2.5μM for 2-CP and arsenic and a detection limit of 2.15μM and 0.39μM respectively. The modified electrode was used to determine arsenic and 2-chlorophenol in spiked tap and effluent water samples by the standard addition method, and the results were satisfying. According to the measurement, the new modified electrode is a good alternative as chemical sensor for determination of other phenols.

Keywords: electrochemistry, electrode, limit of detection, sensor

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Authors: Panagiotis Gkekas, Christos Sougles, Dionysios Kehagias, Dimitrios Tzovaras

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Cooperative intelligent transport systems (C-ITS) can greatly improve safety and efficiency in road transport by enabling communication, not only between vehicles themselves but also between vehicles and infrastructure. For that reason, traffic surveillance systems on the road are of great importance. This paper focuses on the development of an on-road unit comprising several magnetic sensors for real-time vehicle detection, movement direction, and speed measurement calculations. Magnetic sensors can feel and measure changes in the earth’s magnetic field. Vehicles are composed of many parts with ferromagnetic properties. Depending on sensors’ sensitivity, changes in the earth’s magnetic field caused by passing vehicles can be detected and analyzed in order to extract information on the properties of moving vehicles. In this paper, we present a prototype algorithm for real-time, high-accuracy, vehicle detection, and speed measurement, which can be implemented as a portable, low-cost, and non-invasive to existing infrastructure solution with the potential to replace existing high-cost implementations. The paper describes the algorithm and presents results from its preliminary lab testing in a close to real condition environment. Acknowledgments: Work presented in this paper was co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation (call RESEARCH–CREATE–INNOVATE) under contract no. Τ1EDK-03081 (project ODOS2020).

Keywords: magnetic sensors, vehicle detection, speed measurement, traffic surveillance system

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Authors: Michael Heuer, Ayoub Al-Hamadi, Alexander Rain, Marc-Michael Meinecke

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Vulnerable road users, e.g. pedestrians, have a high impact on fatal accident numbers. To reduce these statistics, car manufactures are intensively developing suitable safety systems. Hereby, fast and reliable environment recognition is a major challenge. In this paper we describe a tracking approach that is only based on a 24 GHz radar sensor. While common radar signal processing loses much information, we make use of a track-before-detect filter to incorporate raw measurements. It is explained how the Range-Doppler spectrum can help to indicated pedestrians and stabilize tracking even in occultation scenarios compared to sensors in series.

Keywords: radar, pedestrian detection, active safety, sensor

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Authors: Shamima Akhter, Samira Bagheri, M. Shalauddin, Wan Jefrey Basirun

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In the present study, a nanocomposite of f-MWCNTs-Chitosan was prepared by the immobilization of Co(II) transition metal through self-assembly method and used for the simultaneous voltammetric determination of paracetamol (PA). The composite material was characterized by field emission scanning electron microscopy (FESEM) and energy dispersive X-Ray analysis (EDX). The electroactivity of cobalt immobilized f-MWCNTs with excellent adsorptive polymer chitosan was assessed during the electro-oxidation of paracetamol. The resulting GCE modified f-MWCNTs/CTS-Co showed electrocatalytic activity towards the oxidation of PA. The electrochemical performances were investigated using cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and differential pulse voltammetry (DPV) methods. Under favorable experimental conditions, differential pulse voltammetry showed a linear dynamic range for paracetamol solution in the range of 0.1 to 400µmol L⁻¹ with a detection limit of 0.01 µmol L⁻¹. The proposed sensor exhibited significant selectivity for the paracetamol detection. The proposed method was successfully applied for the determination of paracetamol in commercial tablets and human serum sample.

Keywords: nanomaterials, paracetamol, electrochemical technique, multi-wall carbon nanotube

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Authors: Hameed Rukayat Opeyemi, Pericles Pilidis Pagone Emmanuele, Agbadede Roupa, Allison Isaiah

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Climate change represents one of the single most challenging problems facing the world today. According to the National Oceanic and Administrative Association, Atmospheric temperature rose almost 25% since 1958, Artic sea ice has shrunk 40% since 1959 and global sea levels have risen more than 5.5cm since 1990. Power plants are the major culprits of GHG emission to the atmosphere. Several technologies have been proposed to reduce the amount of GHG emitted to the atmosphere from power plant, one of which is the less researched Advanced zero-emission power plant. The advanced zero emission power plants make use of mixed conductive membrane (MCM) reactor also known as oxygen transfer membrane (OTM) for oxygen transfer. The MCM employs membrane separation process. The membrane separation process was first introduced in 1899 when Walter Hermann Nernst investigated electric current between metals and solutions. He found that when a dense ceramic is heated, the current of oxygen molecules move through it. In the bid to curb the amount of GHG emitted to the atmosphere, the membrane separation process was applied to the field of power engineering in the low carbon cycle known as the Advanced zero emission power plant (AZEP cycle). The AZEP cycle was originally invented by Norsk Hydro, Norway and ABB Alstom power (now known as Demag Delaval Industrial turbomachinery AB), Sweden. The AZEP drew a lot of attention because its ability to capture ~100% CO2 and also boasts of about 30-50% cost reduction compared to other carbon abatement technologies, the penalty in efficiency is also not as much as its counterparts and crowns it with almost zero NOx emissions due to very low nitrogen concentrations in the working fluid. The advanced zero emission power plants differ from a conventional gas turbine in the sense that its combustor is substituted with the mixed conductive membrane (MCM-reactor). The MCM-reactor is made up of the combustor, low-temperature heat exchanger LTHX (referred to by some authors as air preheater the mixed conductive membrane responsible for oxygen transfer and the high-temperature heat exchanger and in some layouts, the bleed gas heat exchanger. Air is taken in by the compressor and compressed to a temperature of about 723 Kelvin and pressure of 2 Mega-Pascals. The membrane area needed for oxygen transfer is reduced by increasing the temperature of 90% of the air using the LTHX; the temperature is also increased to facilitate oxygen transfer through the membrane. The air stream enters the LTHX through the transition duct leading to inlet of the LTHX. The temperature of the air stream is then increased to about 1150 K depending on the design point specification of the plant and the efficiency of the heat exchanging system. The amount of oxygen transported through the membrane is directly proportional to the temperature of air going through the membrane. The AZEP cycle was developed using the Fortran software and economic analysis was conducted using excel and Matlab followed by optimization case study. The Simple bleed gas heat exchange layout (100 % CO2 capture), Bleed gas heat exchanger layout with flue gas turbine (100 % CO2 capture), Pre-expansion reheating layout (Sequential burning layout)–AZEP 85% (85% CO2 capture) and Pre-expansion reheating layout (Sequential burning layout) with flue gas turbine–AZEP 85% (85% CO2 capture). This paper discusses monte carlo risk analysis of four possible layouts of the AZEP cycle.

Keywords: gas turbine, global warming, green house gas, fossil fuel power plants

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Authors: Christine Fräulin, Daniela Schurr, Hamed Shahidi Rad, Gerrit Waters, Günter Rinke, Roland Dittmeyer, Michael Nilles

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The reaction mechanisms of many liquid-phase reactions in organic chemistry have not yet been sufficiently clarified. Process conditions of several hundred degrees celsius and pressures to ten megapascals complicate the sampling and the determination of kinetic data. Space resolved in-situ measurements promises new insights. A non-invasive in-situ measurement technique has the advantages that no sample preparation is necessary, there is no change in sample mixture before analysis and the sampling do no lead to interventions in the flow. Thus, the goal of our research was the development of a contact-free spatially resolved measurement technique for kinetic studies of liquid phase reaction under process conditions. Therefore we used laser Raman spectroscopy combined with an optical transparent microchannel reactor. To show the performance of the system we choose the oxidation of cyclohexane as sample reaction. Cyclohexane oxidation is an economically important process. The products are intermediates for caprolactam and adipic acid, which are starting materials for polyamide 6 and 6.6 production. To maintain high selectivities of 70 to 90 %, the reaction is performed in industry at a low conversion of about six percent. As Raman spectroscopy is usually very selective but not very sensitive the detection of the small product concentration in cyclohexane oxidation is quite challenging. To meet these requirements, an optical experimental setup was optimized to determine the concentrations by laser Raman spectroscopy with respect to good detection sensitivity. With this measurement technique space resolved kinetic studies of uncatalysed and homogeneous catalyzed cyclohexane oxidation were carried out to obtain details about the reaction mechanism.

Keywords: in-situ laser raman spectroscopy, space resolved kinetic measurements, homogeneous catalysis, chemistry

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Authors: Mohmmad A. Obeidat, Amjed Al Fahoum, Ayman M. Mansour

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The recognition of an abnormal activity of the brain functionality is a vital issue. To determine the type of the abnormal activity either a brain image or brain signal are usually considered. Imaging localizes the defect within the brain area and relates this area with somebody functionalities. However, some functions may be disturbed without affecting the brain as in epilepsy. In this case, imaging may not provide the symptoms of the problem. A cheaper yet efficient approach that can be utilized to detect abnormal activity is the measurement and analysis of the electroencephalogram (EEG) signals. The main goal of this work is to come up with a new method to facilitate the classification of the abnormal and disorder activities within the brain directly using EEG signal processing, which makes it possible to be applied in an on-line monitoring system.

Keywords: EEG, wavelet, epilepsy, detection

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Authors: Shreyansh Adesara, Sneha Pandiya

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The mobile ad-hoc network (MANET) is an important and open area research for the examination and determination of the performance evolution. Temporary ordered routing algorithm (TORA) is adaptable and distributed MANET routing algorithm which is totally dependent on internet MANET Encapsulation protocol (IMEP) for the detection of the link and sensing of the link. If IMEP detect the wrong link failure then the network suffer from congestion and unnecessary route maintenance. Thus, the improvement in link detection method of TORA is introduced by various methods on IMEP by different perspective from different person. There are also different reactive routing protocols like AODV, TORA and DSR has been compared for the knowledge of the routing scenario for different parameter and using different model.

Keywords: IMEP, mobile ad-hoc network, protocol, TORA

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Authors: Rajkumar Ghosh

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Climate change has led to decreased water availability in the Gomti Nagar area of Uttar Pradesh, India. Climate change has reduced the amount of precipitation and increased the rate of evaporation. The region is heavily reliant on surface water sources (Gomti river, Sharda Canal) and groundwater. Efficient management of groundwater resources is crucial for addressing water shortages. These may include: Exploring alternative water sources, such as wastewater recycling and desalination, can help augment water supply and reduce dependency on rainfall-dependent sources. Promoting the use of water-efficient technologies in industries, agriculture, and water-efficient infrastructure in urban areas can contribute to reducing water demand and optimizing water use. Incorporating climate change considerations into urban planning and infrastructure development can help ensure water security in the face of future climate uncertainties. Addressing water scarcity in the Gomti Nagar area requires a multi-pronged approach that combines sustainable groundwater management practices, climate change adaptation strategies, and integrated water resource management. By implementing these measures, the region can work towards ensuring a more sustainable and reliable water supply in the context of climate change. Water is the most important natural resource for the existence of living beings in the Earth's ecosystem. On Earth, 1.2 percent of the water is drinkable, but only 0.3 percent is usable by people. Water scarcity is a growing concern in India due to the impact of climate change and over-exploitation of water resources. Excess groundwater withdrawal causes regular declines in groundwater level. Due to city boundary expansion and growing urbanization, the recharge point for groundwater tables is decreasing. Rainwater infiltration into the subsoil is also reduced by unplanned, uneven settlements in urban change.

Keywords: climate change, water scarcity, groundwater, rainfall, water supply

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Authors: Ruchika Malhotra, Megha Khanna

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The development of change prediction models can help the software practitioners in planning testing and inspection resources at early phases of software development. However, a major challenge faced during the training process of any classification model is the imbalanced nature of the software quality data. A data with very few minority outcome categories leads to inefficient learning process and a classification model developed from the imbalanced data generally does not predict these minority categories correctly. Thus, for a given dataset, a minority of classes may be change prone whereas a majority of classes may be non-change prone. This study explores various alternatives for adeptly handling the imbalanced software quality data using different sampling methods and effective MetaCost learners. The study also analyzes and justifies the use of different performance metrics while dealing with the imbalanced data. In order to empirically validate different alternatives, the study uses change data from three application packages of open-source Android data set and evaluates the performance of six different machine learning techniques. The results of the study indicate extensive improvement in the performance of the classification models when using resampling method and robust performance measures.

Keywords: change proneness, empirical validation, imbalanced learning, machine learning techniques, object-oriented metrics

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Authors: May Fadheel Estephan, Richard Perks

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Context: Cancer is a serious health concern that affects millions of people worldwide. Early detection and treatment are essential for improving patient outcomes. However, current methods for cancer detection have limitations, such as low sensitivity and specificity. Research Aim: The aim of this study was to develop an optical sensor for cancer detection using elastic light scattering spectroscopy (ELSS). ELSS is a noninvasive optical technique that can be used to characterize the size and concentration of particles in a solution. Methodology: An optical probe was fabricated with a 100-μm-diameter core and a 132-μm centre-to-centre separation. The probe was used to measure the ELSS spectra of polystyrene spheres with diameters of 2, 0.8, and 0.413 μm. The spectra were then analysed to determine the size and concentration of the spheres. Findings: The results showed that the optical probe was able to differentiate between the three different sizes of polystyrene spheres. The probe was also able to detect the presence of polystyrene spheres in suspension concentrations as low as 0.01%. Theoretical Importance: The results of this study demonstrate the potential of ELSS for cancer detection. ELSS is a noninvasive technique that can be used to characterize the size and concentration of cells in a tissue sample. This information can be used to identify cancer cells and assess the stage of the disease. Data Collection: The data for this study were collected by measuring the ELSS spectra of polystyrene spheres with different diameters. The spectra were collected using a spectrometer and a computer. Analysis Procedures: The ELSS spectra were analysed using a software program to determine the size and concentration of the spheres. The software program used a mathematical algorithm to fit the spectra to a theoretical model. Question Addressed: The question addressed by this study was whether ELSS could be used to detect cancer cells. The results of the study showed that ELSS could be used to differentiate between different sizes of cells, suggesting that it could be used to detect cancer cells. Conclusion: The findings of this research show the utility of ELSS in the early identification of cancer. ELSS is a noninvasive method for characterizing the number and size of cells in a tissue sample. To determine cancer cells and determine the disease's stage, this information can be employed. Further research is needed to evaluate the clinical performance of ELSS for cancer detection.

Keywords: elastic light scattering spectroscopy, polystyrene spheres in suspension, optical probe, fibre optics

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Authors: T. M. Buthelezi, E. O. Olagundoye, R. G. L. Cele

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This article argues that whilst African folklore has mainly been used for entertainment, it also has an educational value that has power to change young people’s attitudes and behavior. The paper is informed by the findings from the data that was generated from 154 university students who were coming from diverse backgrounds. The qualitative data was thematically analysed. Referring to the six steps of the behaviour change model, we found that African Folklore provides relevant cultural knowledge and instills values that enable young people to engage on self-reflection that eventually leads them towards attitudinal changes and behaviour modification. Using the transformative learning theory, we argue that African Folklore in itself is a pedagogical strategy that integrates cultural knowledge, values with entertainment elements concisely enough to take the young people through a transformative phase which encompasses psychological, convictional and life-style adaptation. During data production stage all ethical considerations were observed including obtaining gatekeeper’s permission letter and ethical clearance certificate from the Ethics Committee of the University. The paper recommends that African Folklore approach should be incorporated into the school curriculum particularly in life skills education with aims to change behaviour.

Keywords: African folklore, young people, attitudinal, behavior change, university students

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Authors: Kgopa Bontle, Tholen Sodi

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Climate change poses a major threat to the well-being of both people and the environment, with subsistence farmers most affected as they rely on local supply systems that are sensitive to climate variation. This study documented psychological resilience factors associated with climate change adaptations by subsistence farmers in Maruleng Municipality, Limpopo Province. A qualitative study was conducted to examine the notions of climate change by subsistence farmers, the psychological resilience factors, the strategies to cope with climate change, adaptation methods, and the development of subsistence farmers’ psychological resilience factors model. Data were collected through direct interactions with participants using a grounded theory research design. An open-ended interview was used to collect data with a sample of 15 participants selected through theoretical sampling in Maruleng Municipality. The participants were both Sepedi and Xitsonga speaking from 2 villages, mostly unemployed, pensioners and dependent on social grants. The study included both males and females who were predominately the elderly. The research findings indicate that farmers have limited knowledge of what climate change is and what causes it. Furthermore, the research reflects that although their responses were non-scientific but sensible enough to know what they were dealing with. They mentioned extreme weather, which includes hot days and less rainfall and changes in seasons, as some of the impacts brought by climate change. The results also indicated that participants have learned to adapt through several adaptation strategies, including mulching, changes in irrigation time slots and being innovative. The resilience factors that emerged from the study were a passion for farming, hope, enthusiasm, courage, acceptance/tolerance, livelihood and belief systems. Looking at the socio-economic factors of the current study setting argumentation leads to the conclusion that it is important that government should assist the subsistence farmers as it was observed from the participants that they felt neglected by the government and policymakers as they are small scale farmers and are not included like commercial farmers.

Keywords: climate change, psychological resilience factors, human adaptation, subsistence farmers

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Authors: Jeewan Rai, Kinzang, Yeshi Jigme Choden

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Accumulation of dust on solar panels impacts the overall efficiency and the amount of energy it produces. Detecting and mitigating dust accumulation is, therefore, crucial for optimizing solar energy production. While various techniques exist for detecting dust to schedule cleaning, many of these methods use licensed software like MATLAB, which can be financially burdensome. This study proposes the use of a modified pre-trained ResNet-50 model architecture with an adjusted fully connected layer for binary classification. An experimental setup was installed utilizing a single 75 Wp panel with an inclination maintained at a 30-degree angle. The fine dirt particles were artificially introduced and datasets of images of clean and dusty panels were collected from five different sides were taken, to compensate for the surface reflectance from the PV panel due to camera angles. Those datasets were used to train and test the model, and the accuracy achieved was 90%. The model's ability to detect dust with minimal false positives ensures more efficient maintenance scheduling. This research demonstrates the potential of AI-driven dust detection systems to enhance the operational efficiency of solar PV installations.

Keywords: AI, dust detection, deep learning, image processing, ResNet-50, PV panels

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Authors: S. Curtin, A. Trace

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Introduction: As patients can often be ambivalent about or resistant to any change in their smoking behavior the traditional ‘5 A’ model may be limited as it assumes that patients are ready and motivated to change. However, there is a stage model that is helpful to give guidance for dental students: the Transtheoretical Model (TTM). This model allows students to understand the tasks and goals for the pre-contemplation stage. The TTM was introduced in early stages as a core component of a smoking cessation programme that was integrated into a Behavioral Science programme as applied to dentistry. The aim of the present study is to evaluate and illustrate the students’ current level of knowledge from the questions the students generated in order to engage patients in the tasks and goals of the pre-contemplation stage. Method: N=47 responses of fifth-year undergraduate dental students. These responses were the data set for this study and related to their knowledge base of appropriate questions for a dentist to ask at the pre-contemplation stage of change. A deductive -descriptive analysis was conducted on the data. The goals and tasks of the pre-contemplation stage of the TTM provided a template for this deductive analysis. Results: 51% of students generated relevant, open, exploratory questions for the pre-contemplation stage, whilst 100% of students generated closed questions. With regard to those questions appropriate for the pre-contemplation stage, 19% were open and exploratory, while 66% were closed questions. A deductive analysis of the open exploratory questions revealed that 53% of the questions addressed increased concern about the current pattern of behavior, 38% of the questions concerned increased awareness of a need for change and only 8% of the questions dealt with the envisioning of the possibility of change. Conclusion: All students formulated relevant questions for the pre-contemplation stage, and half of the students generated the open, exploratory questions that increased patients’ awareness of the need to change. More training is required to facilitate a shift in the formulation from closed to open questioning, especially given that, traditionally, smoking cessation was modeled on the ‘5 As’, and that the general training for dentists supports an advisory and directive approach.

Keywords: behaviour change, pre-contemplation stage, trans-theoretical model, undergraduate dentistry students

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Authors: Ali Ilter Akdag, Pratima Ray

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Background:This acute gastroenteritis viruses such as rotavirus, astrovirus, and adenovirus are mainly responsible for diarrhea in children below < 5 years old. Molecular detection of these viruses is crucially important to the understand development of the effective cure. This study aimed to determine the prevalence of common these viruses in children < 5 years old presented with diarrhea from Lala Lajpat Rai Memorial Medical College (LLRM) centre (Meerut) North India, India Methods: Total 312 fecal samples were collected from diarrheal children duration 3 years: in year 2014 (n = 118), 2015 (n = 128) and 2016 (n = 66) ,< 5 years of age who presented with acute diarrhea at the Lala Lajpat Rai Memorial Medical College (LLRM) centre(Meerut) North India, India. All samples were the first detection by EIA/RT-PCR for rotaviruses, adenovirus and astrovirus. Results: In 312 samples from children with acute diarrhea in sample viral agent was found, rotavirus A was the most frequent virus identified (57 cases; 18.2%), followed by Astrovirus in 28 cases (8.9%), adenovirus in 21 cases (6.7%). Mixed infections were found in 14 cases, all of which presented with acute diarrhea (14/312; 4.48%). Conclusions: These viruses are a major cause of diarrhea in children <5 years old in North India. Rotavirus A is the most common etiological agent, follow by astrovirus. This surveillance is important to vaccine development of the entire population. There is variation detection of virus year wise due to differences in the season of sampling, method of sampling, hygiene condition, socioeconomic level of the entire people, enrolment criteria, and virus detection methods. It was found Astrovirus higher then Rotavirus in 2015, but overall three years study Rotavirus A is mainly responsible for causing severe diarrhea in children <5 years old in North India. It emphasizes the required for cost-effective diagnostic assays for Rotaviruses which would help to determine the disease burden.

Keywords: adenovirus, Astrovirus, hospitalized children, Rotavirus

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Authors: Reem El Chakik

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The levels of heavy metals in agricultural lands in Lebanon have been witnessing a noticeable increase in the past few years, due to increased anthropogenic pollution sources. Heavy metals pose a serious threat to the environment for being non-biodegradable and persistent, accumulating thus to dangerous levels in the soil. Besides the traditional laboratory and chemical analysis methods, Hyperspectral Imaging (HSI) has proven its efficiency in the rapid detection of HMs contamination. In Lebanon, a continuous environmental monitoring, including the monitoring of levels of HMs in agricultural soils, is lacking. This is due in part to the high cost of analysis. Hence, this proposed research aims at defining the current national status of HMs contamination in agricultural soil, and to evaluate the effectiveness of using HSI in the detection of HM in contaminated agricultural fields. To achieve the two main objectives of this study, soil samples were collected from different areas throughout the country and were analyzed for HMs using Atomic Absorption Spectrophotometry (AAS). The results were compared to those obtained from the HSI technique that was applied using Hyspex SWIR-384 camera. The results showed that the Lebanese agricultural soils contain high contamination levels of Zn, and that the more clayey the soil is, the lower reflectance it has.

Keywords: agricultural soils in Lebanon, atomic absorption spectrophotometer, hyperspectral imaging., heavy metals contamination

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Authors: Fiona Mwaniki

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Climate change is considered a serious threat to sustainable development globally and as one of the greatest ecological, economic and social challenges of our time. The global demand for food is projected to increase by 60% by 2050. Small holder farmers who are vulnerable to the adverse effects of climate change are expected to contribute to this projected demand. Effective climate change education and communication is therefore required for smallholder and subsistence farmers’ in order to build communities that are more climate change aware, prepared and resilient. In Kenya radio is the most important and dominant mass communication tool for agricultural extension. This study investigated the potential role of radio in influencing farmers’ understanding and use of climate change information. The broad aims of this study were three-fold. Firstly, to identify Kenyan farmers’ perceptions and responses to the impacts of climate change. Secondly, to develop radio programs that communicate climate change information to Kenyan farmers and thirdly, to evaluate the impact of information disseminated through radio on farmers’ understanding and responses to climate change mitigation and adaptation. This study was conducted within the farming community of Kilifi County, located along the Kenyan coast. Education and communication about climate change was undertaken using radio to make available information understandable to different social and cultural groups. A mixed methods pre-and post-intervention design that provided the opportunity for triangulating results from both quantitative and qualitative data was used. Quantitative and qualitative data was collected simultaneously, where quantitative data was collected through semi structured surveys with 421 farmers’ and qualitative data was derived from 11 focus group interviews, six interviews with key informants and nine climate change experts. The climate change knowledge gaps identified in the initial quantitative and qualitative data were used in developing radio programs. Final quantitative and qualitative data collection and analysis enabled an assessment of the impact of climate change messages aired through radio on the farming community in Kilifi County. Results of this study indicate that 32% of the farmers’ listened to the radio programs and 26% implemented technologies aired on the programs that would help them adapt to climate change. The most adopted technologies were planting drought tolerant crops including indigenous crop varieties, planting trees, water harvesting and use of manure. The proportion of farmers who indicated they knew “a fair amount” about climate change increased significantly (Z= -5.1977, p < 0.001) from 33% (at the pre intervention phase of this study) to 64% (post intervention). However, 68% of the farmers felt they needed “a lot more” information on agriculture interventions (43%), access to financial resources (21%) and the effects of climate change (15%). The challenges farmers’ faced when adopting the interventions included lack of access to financial resources (18%), high cost of adaptation measures (17%), and poor access to water (10%). This study concludes that radio effectively complements other agricultural extension methods and has the potential to engage farmers’ on climate change issues and motivate them to take action.

Keywords: climate change, climate change intervention, farmers, radio

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Authors: Suchana Marahatta, Sabina Bhattarai, Bishnu Hari Paudel, Dilip Thakur

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Background: Every year thousands of patients develop nerve damage and disabilities as a result of leprosy which can be prevented by early detection and treatment. So, early detection and treatment of nerve function impairment is of paramount importance in leprosy. Objectives: To assess the electrophysiological pattern of the peripheral nerves in leprosy patients and to compare it with clinical assessment tools. Materials and Methods: In this comparative cross-sectional study, 74 newly diagnosed leprosy patients without reaction were enrolled. They underwent thorough evaluation for peripheral nerve function impairment using clinical tests [i.e. nerve palpation (NP), monofilament (MF) testing, voluntary muscle testing (VMT)] and nerve conduction study (NCS). Clinical findings were compared with that of NCS using SPSS version 11.5. Results: NCS was impaired in 43.24% of leprosy patient at the baseline. Among them, sensory NCS was impaired in more patients (32.4%) in comparison to motor NCS (20.3%). NP, MF, and VMT were impaired in 58.1%, 25.7%, and 9.4% of the patients, respectively. Maximum concordance of monofilament testing and sensory NCS was found for sural nerve (14.7%). Likewise, the concordance of motor NP and motor NCS was the maximum for ulnar nerve (14.9%). When individual parameters of the NCS were considered, amplitude was found to be the most frequently affected parameter for both sensory and motor NCS. It was impaired in 100% of cases with abnormal NCS findings. Conclusion: Since there was no acceptable concordance between NCS findings and clinical findings, we should consider NCS whenever feasible for early detection of neuropathy in leprosy. The amplitude of both sensory nerve action potential (SNAP) and compound nerve action potential (CAMP) could be important determinants of the abnormal NCS if supported by further studies.

Keywords: leprosy, nerve function impairment, neuropathy, nerve conduction study

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Authors: Luziane De Fatima Kirchner, Maria De Jesus Dutra Dos Reis, Francine Nathalie Ferraresi Rodrigues Queluz

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This study evaluated the effect of two components of analytic-behavioral intervention (1-management of conditions of the physical environment, 2-management of the interpersonal relationship) of women with fibromyalgia (FM), besides Clinical Significance and Reliable Change at the end of the intervention. Self-report instruments were used to evaluate stress, anxiety, depression, social skills and disability due to pain and Cortisol Awakening Response (CAR). Four women with a medical diagnosis of FM (mean age 52.7; sd = 6.65), participated of the following procedures: initial evaluation, 10 sessions of component 1, intermediate evaluation, 10 sessions of component 2, and final evaluation. The 20 sessions were effective, with positive changes in the scores of all the self-report instruments, highlighting the results of the stress symptoms that had improvement in the intermediate evaluation. There was, however, no change in the cortisol response on awakening. The Clinical Significance or Reliable Change observed, according to the scores of the stress, anxiety, depression and social skills instruments, corroborated the reports of the participants in the session and the objectives of the treatment. Implications for future studies are discussed, above all, the importance in conducting evaluations with the use of direct measures together with self-report measures.

Keywords: behavioral intervention, clinical significance, fibromyalgia, reliable change

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Authors: Jing Zhang, Daniel Nikovski

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We propose an approximation algorithm called LINKUMP to compute the Pan Matrix Profile (PMP) under the unnormalized l∞ distance (useful for value-based similarity search) using double-ended queue and linear interpolation. The algorithm has comparable time/space complexities as the state-of-the-art algorithm for typical PMP computation under the normalized l₂ distance (useful for shape-based similarity search). We validate its efficiency and effectiveness through extensive numerical experiments and a real-world anomaly detection application.

Keywords: pan matrix profile, unnormalized euclidean distance, double-ended queue, discord discovery, anomaly detection

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Authors: M. Elanchezhian, C. Gunasekaran, A. Agnes Deepa, M. Salahudeen

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In recent years climate change is one of the most emerging threats facing by biodiversity both the animals and plants species. Elevated carbon dioxide and ozone concentrations, extreme temperature, changes in rainfall patterns, insects-plant interaction are the main criteria that affect biodiversity. In the present study, which emphasis the climate change and its effects on terrestrial insect diversity in Mukuruthi National Park a protected areas of Western Ghats in India. Sampling was done seasonally at the three areas using pitfall traps, over the period of January to December 2013. The statistical findings were done by Shannon wiener diversity index (H). A significant seasonal variation pattern was detected for total insect’s diversity at the different study areas. Totally nine orders of insects were recorded. Diversity and abundance of terrestrial insects shows much difference between the Natural, Shoal forest and the Grasslands.

Keywords: biodiversity, climate change, mukuruthi national park, terrestrial invertebrates

Procedia PDF Downloads 512

Authors: A. Dinelka Panagoda, Bathiya Bandara, Chamod Wijetunga, Chathura Malinda, Lakmal Rupasinghe, Chethana Liyanapathirana

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This research takes us forward with the concepts of Federated Learning and Software-Defined Networking (SDN) to introduce an efficient malware detection technique and provide a mitigation mechanism to give birth to a resilient and automated healthcare sector network system by also adding the feature of extended privacy preservation. Due to the daily transformation of new malware attacks on hospital Integrated Clinical Environment (ICEs), the healthcare industry is at an undefinable peak of never knowing its continuity direction. The state of blindness by the array of indispensable opportunities that new medical device inventions and their connected coordination offer daily, a factor that should be focused driven is not yet entirely understood by most healthcare operators and patients. This solution has the involvement of four clients in the form of hospital networks to build up the federated learning experimentation architectural structure with different geographical participation to reach the most reasonable accuracy rate with privacy preservation. While the logistic regression with cross-entropy conveys the detection, SDN comes in handy in the second half of the research to stack up the initial development phases of the system with malware mitigation based on policy implementation. The overall evaluation sums up with a system that proves the accuracy with the added privacy. It is no longer needed to continue with traditional centralized systems that offer almost everything but not privacy.

Keywords: software-defined network, federated learning, privacy, integrated clinical environment, decentralized learning, malware detection, malware mitigation

Procedia PDF Downloads 175

Authors: Aishrith Rao

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COVID 19 is a pandemic which has resulted in thousands of deaths around the world and a huge impact on the global economy. Testing is a huge issue as the test kits have limited availability and are expensive to manufacture. Using deep learning methods on radiology images in the detection of the coronavirus as these images contain information about the spread of the virus in the lungs is extremely economical and time-saving as it can be used in areas with a lack of testing facilities. This paper focuses on binary classification and multi-class classification of COVID 19 and other diseases such as pneumonia, tuberculosis, etc. Different deep learning methods such as VGG-19, COVID-Net, ResNET+ SVM, Deep CNN, DarkCovidnet, etc., have been used, and their accuracy has been compared using the Chest X-Ray dataset.

Keywords: deep learning, computer vision, radiology, COVID-19, ResNet, VGG-19, deep neural networks

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Authors: Ian Omung'a

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Breast cancer remains to be one of the deadliest cancers for women worldwide, with the risk of developing tumors being as high as 50 percent in Sub-Saharan African countries like Kenya. With as many as 42 percent of these cases set to be diagnosed late when cancer has metastasized and or the prognosis has become terminal, Full Field Digital [FFD] Mammography remains an effective screening technique that leads to early detection where in most cases, successful interventions can be made to control or eliminate the tumors altogether. FFD Mammograms have been proven to multiply more effective when used together with Computer-Aided Detection and Diagnosis [CADe] systems, relying on algorithmic implementations of Deep Learning techniques in Computer Vision to carry out deep pattern recognition that is comparable to the level of a human radiologist and decipher whether specific areas of interest in the mammogram scan image portray abnormalities if any and whether these abnormalities are indicative of a benign or malignant tumor. Within this paper, we review emergent Deep Learning techniques that will prove relevant to the development of State-of-The-Art FFD Mammogram CADe systems. These techniques will span self-supervised learning for context-encoded occlusion, self-supervised learning for pre-processing and labeling automation, as well as the creation of a standardized large-scale mammography dataset as a benchmark for CADe systems' evaluation. Finally, comparisons are drawn between existing practices that pre-date these techniques and how the development of CADe systems that incorporate them will be different.

Keywords: breast cancer diagnosis, computer aided detection and diagnosis, deep learning, whole mammogram classfication, ultrasound classification, computer vision

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Authors: K. Khelil, H. Ammar, K. Saouchi

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Fiber Bragg Grating (FBG) structure is an periodically modulated optical fiber. It acts as a selective filter of wavelength whose reflected peak is called Bragg wavelength and it depends on the period of the fiber and the refractive index. The simulation of FBG is based on solving the Coupled Mode Theory equation by using the Transfer Matrix Method which is carried out using MATLAB. It is found that spectral reflectivity is shifted when the change of temperature and strain is uniform. Under non-uniform temperature or strain perturbation, the spectrum is both shifted and destroyed. In case of transverse loading, reflectivity spectrum is split into two peaks, the first is specific to X axis, and the second belongs to Y axis. FBGs are used in civil engineering to detect perturbations applied to buildings.

Keywords: Bragg wavelength, coupled mode theory, optical fiber, temperature measurement

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Authors: Miraç Savaş Turhan, Ali Danışman

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New institutional theorists in organization theory have used institutional logics perspective to explain the contradictory practices in modern western societies. Accordingly, distinct institutional logics are embedded in central institutions such as the market, state, democracy, family, and religion. Individual and organizational actors and their practices are restricted and guided by institutional logics in a particular field. Through this perspective, actors are assumed to have a situated, embedded, boundedly intentional, and adaptive role against the structure in social, cultural and political context. Since the early 1990's, increasing number of studies has attempted to explain the role of actors in creating, maintaining, and changing institutions. Yet, most of these studies have focused on organizational field-level actors, ignoring the role that can be played by individual actors within organizations. As a result, we have much information about what organizational field level actors can do, but relatively little knowledge about the ability of organizational change agents within organization in relation to institutional orders. This study is an attempt to find out how the ability of individual actors who attempt to change their organization is constrained and shaped by institutional logics dominating the field. We examine this issue in a private school in the Turkish Education field. We first describe dominating institutional logics in the Turkish Education field. Then we conducted in-depth interviews and content analysis in the school. The early results indicate that attempts and actions of organizational change agents are remarkably directed and shaped by the dominating institutional logics in the Turkish Education field.

Keywords: Institutional logics, individual actors, organizational change, organizational change agent

Procedia PDF Downloads 385

Authors: Yulan Wu

Abstract:

The spread of fake news on social media has posed significant societal harm to the public and the nation, with its threats spanning various domains, including politics, economics, health, and more. News on social media often covers multiple domains, and existing models studied by researchers and relevant organizations often perform well on datasets from a single domain. However, when these methods are applied to social platforms with news spanning multiple domains, their performance significantly deteriorates. Existing research has attempted to enhance the detection performance of multi-domain datasets by adding single-domain labels to the data. However, these methods overlook the fact that a news article typically belongs to multiple domains, leading to the loss of domain knowledge information contained within the news text. To address this issue, research has found that news records in different domains often use different vocabularies to describe their content. In this paper, we propose a fake news detection framework that combines domain knowledge and expert knowledge. Firstly, it utilizes an unsupervised domain discovery module to generate a low-dimensional vector for each news article, representing domain embeddings, which can retain multi-domain knowledge of the news content. Then, a feature extraction module uses the domain embeddings discovered through unsupervised domain knowledge to guide multiple experts in extracting news knowledge for the total feature representation. Finally, a classifier is used to determine whether the news is fake or not. Experiments show that this approach can improve multi-domain fake news detection performance while reducing the cost of manually labeling domain labels.

Keywords: fake news, deep learning, natural language processing, multiple domains

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Authors: Cherdvong Saengsupavanich

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Human utilizes coastal resources as well as deteriorates them. Coastal tourism may degrade the environment if poorly managed. This research investigated the shoreline change at Koa Toa Island, one of the most famous tourist destinations. Aerial photographs and satellite images from three different periods were collected and analyzed. The results showed that the noticeable shoreline change before and after the tourism on the island had expanded. Between 1995 and 2002 when the tourism on Koh Toa Island was not intensive, sediment deposition occurred along most of the coastline. However, after the tourism had grown during 2002 to 2015, the coast evidently experienced less deposition and more erosion. The erosion resulted from less land-based sediment being provided to the littoral system. If the coastline of Koh Toa Island is not carefully sustained, the tourism will disappear along with the beautiful beach.  

Keywords: coastal engineering and management, coastal erosion, coastal tourism, Koh Toa Island, Thailand

Procedia PDF Downloads 299

Authors: R. S. Yadav, J. Havlica, I. Kuřitka, Z. Kozakova, J. Masilko, L. Kalina, M. Hajdúchová, V. Enev, J. Wasserbauer

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In this work, copper ferrite CuFe2O4 spinel ferrite nanoparticles with different particle size at different annealing temperature were synthesized using the starch-assisted sol-gel auto-combustion method. The synthesized nanoparticles were characterized by conventional powder X-ray diffraction (XRD) spectroscopy, Raman Spectroscopy, Fourier Transform Infrared Spectroscopy, Field-Emission Scanning Electron Microscopy, X-ray Photoelectron Spectroscopy, and Vibrating Sample Magnetometer. The XRD patterns confirmed the formation of CuFe2O4 spinel ferrite nanoparticles. Field-Emission Scanning Electron Microscopy revealed that particles are of spherical morphology with particle size 5-20 nm at lower annealing temperature. An infrared spectroscopy study showed the presence of two principal absorption bands in the frequency range around 530 cm-1 (ν1) and around 360 cm-1 (ν2); which indicate the presence of tetrahedral and octahedral group complexes, respectively, within the spinel ferrite nanoparticles. Raman spectroscopy study also indicated the change in octahedral and tetrahedral site related Raman modes in copper ferrite nanoparticles with change of particle size. This change in magnetic behavior with change of particle size of CuFe2O4 nanoparticles was also observed. The change in magnetic properties with change of particle size is due to cation redistribution, which was confirmed by X-Ray photoelectron study.

Keywords: copper ferrite, nanoparticles, magnetic property, CuFe2O4

Procedia PDF Downloads 449