Search results for: fifth-generation district heating network

Authors: Muhammad Farhan Saeed, Waheed Aslam Khan, Muhammad Nadeem, Iftikhar Ahmad, Zakir Ali

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Milk is an important and fundamental nutrition source of human diet. In Pakistan, the milk used by the consumer is of low quality and is often contaminated due to the lack of quality controls. Mycotoxins produced from molds which contaminate the agriculture commodities of animal feed. Mycotoxins are poisons which affect the animals when they consume contaminated feeds. Aflatoxin AFM1 is naturally occurring form of mycotoxins in milk which is carcinogenic. To assess public awareness regarding milk Aflatoxin contamination, a population-based survey using a questionnaire was carried out from general public and from farmers of both rural and urban areas. It was revealed from the data that people of rural area were more satisfied about quality of available milk but the awareness level about milk contamination was found lower in both areas. Total 297 samples of milk were collected from rural (n=156) and urban (n=141) areas of district Vehari during June-July 2015. Milk samples were collected from three different point sources; farmer, milkman and milkshop. These point sources had three types of dairy milk including cow milk, buffalo milk and mixed milk. After performing ELISA test 18 samples with positive ELISA results were maintain per source for further analysis for aflatoxin M1 (AFM1) by High Performance Liquid Chromatography (HPLC). Higher percentages of samples were found exceeding the permissible limit for urban area. In rural area about 15% samples and from urban area about 35% samples were exceeded the permissible limit of AFM1 with 0.05µg/kg set by European Union. From urban areas about 55% of buffalo, 33% of cows and 17% of mixed milk samples were exceeded the permissible AFM1 level as compared with 17%, 11% and 17% for milk samples from rural areas respectively. Samples from urban areas 33%, 44% and 28% were exceeded the permissible AFM1 level for farmer, milkman and of milk shop respectively as compared with 28% and 17% of farmer and milkman’s samples from rural areas respectively. The presence of AFM1 in milk samples demands the implementation of strict regulations and also urges the need for continuous monitoring of milk and milk products in order to minimize the health hazards. Regulations regarding aflatoxins contamination and adulteration should be strictly imposed to prevent health problems related to milk quality. Permissible limits for aflatoxin should be enforced strongly in Pakistan so that economic loss due to aflatoxin contamination can be reduced.

Keywords: Vehari, aflatoxins AFM1, milk, HPLC

Procedia PDF Downloads 374

Authors: Uswah Khairuddin, Rubiyah Yusof, Nenny Ruthfalydia Rosli

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

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

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Authors: Muhammad Umar Khan, Mahesh Kumar, Faraz Neakakhtar

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Although industrialization marks to the economy of a country yet it increases the pollution and temperature of the environment. The world is now shifting towards green energy because the utilization of fossil fuels is resulting in global warming. So we need to develop systems that can operate on renewable energy resources and have low heat losses. The combined solar and geothermal multigeneration system can solve this issue. Rather than making rankine cycle purely a solar-driven, heat from solar is used to drive vapour absorption cycle and reheated to generate power using geothermal reservoir. The results are displayed by using Engineering Equation Solver software, where inputs are varied to optimize the energy and exergy efficiencies of the system. The cooling effect is 348.2 KW, while the network output is 23.8 MW and reducing resultant emission of 105553 tons of CO₂ per year. This eco-friendly multigeneration system is capable of eliminating the use of fossil fuels and increasing the geothermal energy efficiency.

Keywords: cooling effect, eco-friendly, green energy, heat loses, multigeneration system, renewable energy, work output

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Authors: Helen S. Joyner (Melito), Mohammad Anvari

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Concentrated emulsions stabilized by proteins are systems of great importance in food, pharmaceutical and cosmetic products. Controlling emulsion rheology is critical for ensuring desired properties during formation, storage, and consumption of emulsion-based products. Studies on concentrated emulsions have focused on rheology of monodispersed systems. However, emulsions used for industrial applications are polydispersed in nature, and this polydispersity is regarded as an important parameter that also governs the rheology of the concentrated emulsions. Therefore, the objective of this study was to characterize rheological (small and large deformation behaviors) and microstructural properties of concentrated emulsions which were not truly monodispersed as usually encountered in food products such as margarines, mayonnaise, creams, spreads, and etc. The concentrated emulsions were prepared at different concentrations of fish gelatin (0.2, 0.4, 0.8% w/v in the whole emulsion system), oil-water ratio 80-20 (w/w), homogenization speed 10000 rpm, and 25oC. Confocal laser scanning microscopy (CLSM) was used to determine the microstructure of the emulsions. To prepare samples for CLSM analysis, FG solutions were stained by Fluorescein isothiocyanate dye. Emulsion viscosity profiles were determined using shear rate sweeps (0.01 to 100 1/s). The linear viscoelastic regions (LVRs) of the emulsions were determined using strain sweeps (0.01 to 100% strain) for each sample. Frequency sweeps were performed in the LVR (0.1% strain) from 0.6 to 100 rad/s. Large amplitude oscillatory shear (LAOS) testing was conducted by collecting raw waveform data at 0.05, 1, 10, and 100% strain at 4 different frequencies (0.5, 1, 10, and 100 rad/s). All measurements were performed in triplicate at 25oC. The CLSM results revealed that increased fish gelatin concentration resulted in more stable oil-in-water emulsions with homogeneous, finely dispersed oil droplets. Furthermore, the protein concentration had a significant effect on emulsion rheological properties. Apparent viscosity and dynamic moduli at small deformations increased with increasing fish gelatin concentration. These results were related to increased inter-droplet network connections caused by increased fish gelatin adsorption at the surface of oil droplets. Nevertheless, all samples showed shear-thinning and weak gel behaviors over shear rate and frequency sweeps, respectively. Lissajous plots, or plots of stress versus strain, and phase lag values were used to determine nonlinear behavior of the emulsions in LAOS testing. Greater distortion in the elliptical shape of the plots followed by higher phase lag values was observed at large strains and frequencies in all samples, indicating increased nonlinear behavior. Shifts from elastic-dominated to viscous dominated behavior were also observed. These shifts were attributed to damage to the sample microstructure (e.g. gel network disruption), which would lead to viscous-type behaviors such as permanent deformation and flow. Unlike the small deformation results, the LAOS behavior of the concentrated emulsions was not dependent on fish gelatin concentration. Systems with different microstructures showed similar nonlinear viscoelastic behaviors. The results of this study provided valuable information that can be used to incorporate concentrated emulsions in emulsion-based food formulations.

Keywords: concentrated emulsion, fish gelatin, microstructure, rheology

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Authors: Abel Mahowe

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Furrow erosion is the major problem menacing sustainability of irrigation in Malawi and polluting water bodies resulting in death of many aquatic animals. Many rivers in Malawi are drying due to some poor practices that are being practiced around these water bodies, furrow erosion is one of the cause of sedimentation in these rivers although it has gradual effect on deteriorating of these rivers hence neglected, but has got long term disastrous effect on water bodies. Many aquatic animals also suffer when these sediments are taken into these water bodies. An assessment of effect of manure treatment on furrow erosion was carried out in Sagawika irrigation scheme located in Kasungu District north part of Malawi. The soil on the field was clay loam and had just been tilled. The average furrow slope of 0.2% and was divided into two blocks, A and B. Each block had 20V-shaped furrow having a length of 10 m. Three different manure were used to construct these furrows by mixing it with soil which was moderately moist and 5 furrows from each block were constructed without manure. In each block 5furrow were made using a specific type of manure, and one set of five furrows in each block was made without manure treatment. The types of manure that were used were goat manure, pig manure, and manure from crop residuals. The manure application late was 5 kg/m. The furrow was constructed at a spacing of 0.6 m. Tomato was planted in the two blocks at spacing of 0.15 m between rows and 0.15 m between planting stations. Irrigation water was led from feeder canal into the irrigation furrows using siphons. The siphons discharge into each furrow was set at 1.86 L/S. The ¾ rule was used to determine the cut-off time for the irrigation cycles in order to reduce the run-off at the tail end. During each irrigation cycle, samples of the runoff water were collected at one-minute intervals and analyzed for total sediment concentration for use in estimating the total soil sediment loss. The results of the study have shown that a significant amount of soil is lost in soils without many organic matters, there was a low level of erosion in furrows that were constructed using manure treatment within the blocks. In addition, the results have shown that manure also differs in their ability to control erosion since pig manure proved to have greater abilities in binding the soil together than other manure since they were reduction in the amount of sediments at the tail end of furrows constructed by this type of manure. The results prove that manure contains organic matters which helps soil particles to bind together hence resisting the erosive force of water. The use of manure when constructing furrows in soil with less organic matter can highly reduce erosion hence reducing also pollution of water bodies and improve the conditions of aquatic animals.

Keywords: aquatic, erosion, furrow, soil

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Authors: Juan Bojórquez, Henry E. Reyes, Edén Bojórquez, Alfredo Reyes-Salazar

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This paper addressed the use of Artificial Intelligence to obtain the structural reliability of reinforced concrete buildings. For this purpose, artificial neuronal networks (ANN) are developed to predict seismic demand hazard curves. In order to have enough input-output data to train the ANN, a set of reinforced concrete buildings (low, mid, and high rise) are designed, then a probabilistic seismic hazard analysis is made to obtain the seismic demand hazard curves. The results are then used as input-output data to train the ANN in a feedforward backpropagation model. The predicted values of the seismic demand hazard curves found by the ANN are then compared. Finally, it is concluded that the computer time analysis is significantly lower and the predictions obtained from the ANN were accurate in comparison to the values obtained from the conventional methods.

Keywords: structural reliability, seismic design, machine learning, artificial neural network, probabilistic seismic hazard analysis, seismic demand hazard curves

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Authors: Kaustubh Chakradeo, Michael Delves, Sofya Titarenko

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Malaria is a serious disease which affects hundreds of millions of people around the world, each year. If not treated in time, it can be fatal. Despite recent developments in malaria diagnostics, the microscopy method to detect malaria remains the most common. Unfortunately, the accuracy of microscopic diagnostics is dependent on the skill of the microscopist and limits the throughput of malaria diagnosis. With the development of Artificial Intelligence tools and Deep Learning techniques in particular, it is possible to lower the cost, while achieving an overall higher accuracy. In this paper, we present a VGG-based model and compare it with previously developed models for identifying infected cells. Our model surpasses most previously developed models in a range of the accuracy metrics. The model has an advantage of being constructed from a relatively small number of layers. This reduces the computer resources and computational time. Moreover, we test our model on two types of datasets and argue that the currently developed deep-learning-based methods cannot efficiently distinguish between infected and contaminated cells. A more precise study of suspicious regions is required.

Keywords: convolution neural network, deep learning, malaria, thin blood smears

Procedia PDF Downloads 130

Authors: Rajagopal Kavitha, Van Lun Low, Mohd Sofian-Azirun, Chee Dhang Chen, Mohd Yusof Farida Zuraina, Mohd Salleh Ahmad Firdaus, Navaratnam Shanti, Abdul Haiyee Zaibunnisa

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This study investigated the hidden genetic lineages in the oriental latrine fly Chrysomya megacephala (Fabricius) across four states (i.e., Johore, Pahang, Perak and Selangor) and a federal territory (i.e., Kuala Lumpur) in Malaysia using Cytochrome b (Cyt b) genetic marker. The Cyt b phylogenetic tree and haplotype network revealed three distinct genetic lineages of Ch. megacephala. Lineage A, the basal clade was restricted to flies that originated from Kuala Lumpur and Selangor, while Lineages B and C, comprised of flies from all studied populations. An overlap of the three genetically divergent groups of Ch. megacephala was observed. However, the flies from both Kuala Lumpur and Selangor populations consisted of three different lineages, indicating that they are genetically diverse compared to those from Pahang, Perak and Johore.

Keywords: forensic entomology, calliphoridae, mitochondrial DNA, cryptic lineage

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Authors: Ronald Mraz, Gary C. Kessler, Ethan Gold, John G. Cline

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The International Ice Patrol (IIP) continually monitors iceberg activity in the North Atlantic by direct observation using ships, aircraft, and satellite imagery. Daily reports detailing navigational boundaries of icebergs have significantly reduced the risk of iceberg contact. What is currently lacking is formatting this data for automatic transmission and display of iceberg navigational boundaries in commercial navigation equipment. This paper describes the methodology and implementation of a system to format iceberg limit information for dissemination through existing radio network communications. This information will then automatically display on commercial navigation equipment. Additionally, this information is reformatted for Google Earth rendering of iceberg track line limits. Having iceberg limit information automatically available in standard navigation equipment will help support full autonomous operation of sailing vessels.

Keywords: iceberg, iceberg risk, iceberg track lines, AIS messaging, international ice patrol, North American ice service, google earth, autonomous surface vessels

Procedia PDF Downloads 137

Authors: Tim Bolle, Franziska Kreimendahl, Thomas Gries, Stefan Jockenhoevel

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The therapeutic treatment of extensive, deep wounds is limited. Autologous split-skin grafts are used as a so-called ‘gold standard’. Most common deficits are the defects at the donor site, the risk of scarring as well as the limited availability and quality of the autologous grafts. The aim of this project is a tissue engineered dermal-epidermal skin replacement to overcome the limitations of the gold standard. A key requirement for the development of such a three-dimensional implant is the formation of a functional capillary-like network inside the implant to ensure a sufficient nutrient and gas supply. Tailored three-dimensional warp knitted spacer fabrics are used to reinforce the mechanically week fibrin gel-based scaffold and further to create a directed in vitro pre-vascularization along the parallel-oriented pile yarns within a co-culture. In this study various three-dimensional warp knitted spacer fabrics were developed in a factorial design to analyze the influence of the machine parameters such as the stitch density and the pattern of the fabric on the scaffold performance and further to determine suitable parameters for a successful fibrin gel-incorporation and a physiological performance of the scaffold. The fabrics were manufactured on a Karl Mayer double-bar raschel machine DR 16 EEC/EAC. A fine machine gauge of E30 was used to ensure a high pile yarn density for sufficient nutrient, gas and waste exchange. In order to ensure a high mechanical stability of the graft, the fabrics were made of biocompatible PVDF yarns. Key parameters such as the pore size, porosity and stress/strain behavior were investigated under standardized, controlled climate conditions. The influence of the input parameters on the mechanical and morphological properties as well as the ability of fibrin gel incorporation into the spacer fabric was analyzed. Subsequently, the pile yarns of the spacer fabrics were colonized with Human Umbilical Vein Endothelial Cells (HUVEC) to analyze the ability of the fabric to further function as a guiding structure for a directed vascularization. The cells were stained with DAPI and investigated using fluorescence microscopy. The analysis revealed that the stitch density and the binding pattern have a strong influence on both the mechanical and morphological properties of the fabric. As expected, the incorporation of the fibrin gel was significantly improved with higher pore sizes and porosities, whereas the mechanical strength decreases. Furthermore, the colonization trials revealed a high cell distribution and density on the pile yarns of the spacer fabrics. For a tailored reinforcing structure, the minimum porosity and pore size needs to be evaluated which still ensures a complete incorporation of the reinforcing structure into the fibrin gel matrix. That will enable a mechanically stable dermal graft with a dense vascular network for a sufficient nutrient and oxygen supply of the cells. The results are promising for subsequent research in the field of reinforcing mechanically weak biological scaffolds and develop functional three-dimensional scaffolds with an oriented pre-vascularization.

Keywords: fibrin-gel, skin replacement, spacer fabric, pre-vascularization

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Authors: Natalia Khvorostianov, Larissa Remennick

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Recent immigrants seldom join the ranks of volunteers for various social causes. This gap reflects both material reasons (immigrants’ lower income and lack of free time) and cultural differences (value systems, religiosity, language barrier, attitudes towards host society, etc.). Immigrants from the former socialist countries are particularly averse to organized forms of volunteering for a host of reasons rooted in their past, including the memories of false or forced forms of collectivism imposed by the state. In this qualitative study, based on 21 semi-structured interviews, we explored the perceptions and practices of volunteer work among FSU immigrants - participants in one volunteering project run by an Israeli NGO for the benefit of elderly ex-Soviet immigrants. Our goal was to understand the motivations of immigrant volunteers and the role of volunteering in the processes of their own social and economic integration in their adopted country – Israel. The results indicate that most volunteers chose causes targeting fellow immigrants, their resettlement and well-being, and were motivated by the wish to build co-ethnic support network and overcome marginalization in the Israeli society. Other volunteers were driven by the need for self-actualization in the context of underemployment and occupational downgrading.

Keywords: FSU immigrants, integration, volunteering, participation, social capital

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Authors: Liana Khandolishvili, Giorgi Dvalashvili

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Karst waters in the world play an important role in the water supply. Among them, the Vaucluse in Chiatura municipality (Georgia) is used as drinking water and is irreplaceable for the local population. Accordingly, it is important to assess their geo-ecological conditions and take care to maintain sustainability. The aim of the paper is to identify the hazards of pollution of underground waters in the karst environment and to develop a scheme for their protection, which will take into consideration both the hydrogeological characteristics and the role of humans. To achieve this goal, the EPIK method was selected using which an epikarst zone of the study area was studied in detail, as well as the protective cover, infiltration conditions and frequency of karst network development, after which the conditions of karst waters in Chiatura municipality was assessed, their main pollutants were identified and the recommendations were prepared for their protection. The results of the study showed that the karst water pollution rate in Chiatura municipality is highest, where karst-fissured layers are represented and intensive extraction works are underway. The EPIK method is innovative in Georgia and was first introduced on the example of karst waters of Chiatura municipality.

Keywords: cave, EPIK method, pollution, Karst waters, geology, geography, ecology

Procedia PDF Downloads 93

Authors: L. Chetibi, D. Hamana, T. O. Busko, M. P. Kulish, S. Achour

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TiO2 nanostructures have attracted much attention due to their optical, dielectric and photocatalytic properties as well as applications including optical coating, photocatalysis and photoelectrochemical solar cells. This work aims to prepare TiO2 nanofibers (NFs) on titanium substrate (Ti) by in situ oxidation of Ti foils in a mixture solution of concentrated H2O2 and NaOH followed by proton exchange and calcinations. Scanning Electron microscopy (SEM) revealed an obvious network of TiO2 nanofibers. The photoluminescence (PL) spectra of these nanostructures revealed a broad intense band in the visible light range with a reduced near edge band emission. The PL bands in the visible region, mainly, results from surface oxygen vacancies and others defects. After irradiation with Ti+ ions (the irradiation energy was E = 140 keV with doses of 1013 ions/cm2), the intensity of the PL spectrum decreased as a consequence of the radiation treatment. The irradiation with Ti+ leads to a reduction of defects and generation of non irradiative defects near to the level of the conduction band as evidenced by the PL results. On the other hand, reducing the surface defects on TiO2 nanostructures may improve photocatalytic and optoelectronic properties of this nanostructure.

Keywords: TiO2, nanofibers, photoluminescence, irradiation

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Authors: Ahmed Emad Ahmed, Zeyad Ahmed Hussein, Mohamed Salama Afifi, Ahmed Mohammed Eid

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Water has a great importance in life. In order to deliver water from resources to the users, many procedures should be taken by the water engineers. One of the main procedures to deliver water to the community is by designing pressurizer pipe networks for water. The main aim of this work is to calculate the water demand of a small town and then design a simple water network to distribute water resources among the town with the smallest losses. Literature has been mentioned to cover the main point related to water distribution. Moreover, the methodology has introduced two approaches to solve the research problem, one by the iterative method of Hardy-cross and the other by water software Pipe Flow. The results have introduced two main designs to satisfy the same research requirements. Finally, the researchers have concluded that the use of water software provides more abilities and options for water engineers.

Keywords: looping pipe networks, hardy cross networks accuracy, relative error of hardy cross method

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Authors: Ksenia Volkova, Artur Petrosyan, Ignatii Dubyshkin, Alexei Ossadtchi

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Brain-computer interfaces are a growing research field producing many implementations that find use in different fields and are used for research and practical purposes. Despite the popularity of the implementations using non-invasive neuroimaging methods, radical improvement of the state channel bandwidth and, thus, decoding accuracy is only possible by using invasive techniques. Electrocorticography (ECoG) is a minimally invasive neuroimaging method that provides highly informative brain activity signals, effective analysis of which requires the use of machine learning methods that are able to learn representations of complex patterns. Deep learning is a family of machine learning algorithms that allow learning representations of data with multiple levels of abstraction. This study explores the potential of deep learning approaches for ECoG processing, decoding movement intentions and the perception of proprioceptive information. To obtain synchronous recording of kinematic movement characteristics and corresponding electrical brain activity, a series of experiments were carried out, during which subjects performed finger movements at their own pace. Finger movements were recorded with a three-axis accelerometer, while ECoG was synchronously registered from the electrode strips that were implanted over the contralateral sensorimotor cortex. Then, multichannel ECoG signals were used to track finger movement trajectory characterized by accelerometer signal. This process was carried out both causally and non-causally, using different position of the ECoG data segment with respect to the accelerometer data stream. The recorded data was split into training and testing sets, containing continuous non-overlapping fragments of the multichannel ECoG. A deep convolutional neural network was implemented and trained, using 1-second segments of ECoG data from the training dataset as input. To assess the decoding accuracy, correlation coefficient r between the output of the model and the accelerometer readings was computed. After optimization of hyperparameters and training, the deep learning model allowed reasonably accurate causal decoding of finger movement with correlation coefficient r = 0.8. In contrast, the classical Wiener-filter like approach was able to achieve only 0.56 in the causal decoding mode. In the noncausal case, the traditional approach reached the accuracy of r = 0.69, which may be due to the presence of additional proprioceptive information. This result demonstrates that the deep neural network was able to effectively find a representation of the complex top-down information related to the actual movement rather than proprioception. The sensitivity analysis shows physiologically plausible pictures of the extent to which individual features (channel, wavelet subband) are utilized during the decoding procedure. In conclusion, the results of this study have demonstrated that a combination of a minimally invasive neuroimaging technique such as ECoG and advanced machine learning approaches allows decoding motion with high accuracy. Such setup provides means for control of devices with a large number of degrees of freedom as well as exploratory studies of the complex neural processes underlying movement execution.

Keywords: brain-computer interface, deep learning, ECoG, movement decoding, sensorimotor cortex

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Authors: Gavin Quibell, Martin Kleynhans, Margot Soler

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The Sendai Framework notes that disaster risk reduction is essential for sustainable development, and Disaster Risk Reduction is included in 3 of the Sustainable Development Goals (SDGs), and 4 of the SDG targets. However, apart from promoting better governance and resourcing of disaster management agencies, little guidance is given how low-income nations can balance investments across the SDGs to achieve sustainable development in an increasingly climate vulnerable world with increasing prevalence of flood and drought disasters. As one of the world’s poorest nations, Malawi must balance investments across all the SDGs. This paper explores how Malawi’s National Guidelines for Community-based Flood Risk Management integrate sustainable development and flood management objectives at different administrative levels. While Malawi periodically suffers from large, widespread flooding, the greatest impacts are felt through the smaller annual floods and flash floods. The Guidelines address this through principles that recognize that while the protection of human life is the most important priority for flood risk management, addressing the impacts of floods on the rural poor and the economy requires different approaches. The National Guidelines are therefore underpinned by the following; 1. In the short-term investments in flood risk management must focus on breaking the poverty – vulnerability cycle; 2. In the long-term investments in the other SDGs will have the greatest flood risk management benefits; 3. If measures are in place to prevent loss of life and protect strategic infrastructure, it is better to protect more people against small and medium size floods than fewer people against larger floods; 4. Flood prevention measures should focus on small (1:5 return period) floods; 5. Flood protection measures should focus on small and medium floods (1:20 return period) while minimizing the risk of failure in larger floods; 6. The impacts of larger floods ( > 1:50) must be addressed through improved preparedness; 7. The impacts of climate change on flood frequencies are best addressed by focusing on growth not overdesign; and 8. Manage floods and droughts conjunctively. The National Guidelines weave these principles into Malawi’s approach to flood risk management through recommendations for planning and implementing flood prevention, protection and preparedness measures at district, traditional authority and village levels.

Keywords: flood risk management in low-income countries, sustainable development, investments in prevention, protection and preparedness, community-based flood risk management, Malawi

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Authors: Sylvie Sossou, Grégoire Gansou, Ildevert Egue

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Introduction: The mission of the family and the school is to educate and train citizens of the city. But the family’s values , parental roles, respect for life collapse in their traditional African form. Indeed laxity with regard to divorce, liberal ideas about child rearing influence the emotional life of the latter. Several causes may contribute to the decline in academic performance. In order to seek a psychological solution to the issue, a study was conducted in 6 schools at the 9th district in Cotonou, cosmopolitan city of Benin. Objective: To evaluate the impact of single parenthood on the psycho child development. Materials and Methods: Questionnaires and interviews were used to gather verbal information. The questionnaires were administered to parents and children (schoolchildren 4, 5 and six form) from 7 to 12 years in lone parenthood. The interview was done with teachers and school leaders. We identified 209 cases of children living with a "single-parent" and 68 single parents. Results: Of the 209 children surveyed the results showed that 116 children are cut relational triangle in early childhood (before 3 years). The psychological effects showed that the separation has caused sadness for 52 children, anger 22, shame 17, crying at 31 children, fear for 14, the silence at 58 children. In front of complete family’s children, these children experience feelings of aggression in 11.48%; sadness in 30.64%; 5.26% the shame, the 6.69% tears; jealousy in 2.39% and 2.87% of indifference. The option to get married in 44.15% of children is a challenge to want to give a happy childhood for their offspring; 22.01% feel rejected, there is uncertainty for 11.48% of cases and 25.36% didn’t give answer. 49, 76% of children want to see their family together; 7.65% are against to avoid disputes and in many cases to save the mother of the father's physical abuse. 27.75% of the ex-partners decline responsibility in the care of the child. Furthermore family difficulties affecting the intellectual capacities of children: 37.32% of children see school difficulties related to family problems despite all the pressure single-parent to see his child succeed. Single parenthood affects inter-family relations: pressure 33.97%; nervousness 24.88%; overprotection 29.18%; backbiting 11.96%, are the lives of these families. Conclusion: At the end of the investigation, results showed that there is a causal relationship between psychological disorders, academic difficulties of children and quality of parental relationships. Other cases may exist, but the lack of resources meant that we have only limited at 6 schools. Early psychological treatment for these children is needed.

Keywords: single-parent, psycho child, school, Cotonou

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Authors: A. Bayat, R. Bello-Mendoza, D. G. Wareham

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Two major categories of green waste are fruit and vegetable (FV) waste and garden and yard (GY) waste. Although, anaerobic digestions (AD) is able to manage FV waste; there is less confidence in the conditions for AD to handle GY wastes (grass, leaves, trees and bush trimmings); mainly because GY contains lignin and other recalcitrant organics. GY in the dry state (TS ≥ 15 %) can be digested at mesophilic temperatures; however, little methane data has been reported under thermophilic conditions, where conceivably better methane yields could be achieved. In addition, it is suspected that at lower solids concentrations, the methane yield could be increased. As such, the aim of this research is to find the temperature and solids concentration conditions that produce the most methane; under two different temperature regimes (mesophilic, thermophilic) and three solids states (i.e. 'dry', 'semi-dry' and 'wet'). Twenty liters of GY waste was collected from a public park located in the northern district in Tehran. The clippings consisted of freshly cut grass as well as dry branches and leaves. The GY waste was chopped before being fed into a mechanical blender that reduced it to a paste-like consistency. An initial TS concentration of approximately 38 % was achieved. Four hundred mL of anaerobic inoculum (average total solids (TS) concentration of 2.03 ± 0.131 % of which 73.4% were volatile solid (VS), soluble chemical oxygen demand (sCOD) of 4.59 ± 0.3 g/L) was mixed with the GY waste substrate paste (along with distilled water) to achieve a TS content of approximately 20 %. For comparative purposes, approximately 20 liters of FV waste was ground in the same manner as the GY waste. Since FV waste has a much higher natural water content than GY, it was dewatered to obtain a starting TS concentration in the dry solid-state range (TS ≥ 15 %). Three samples were dewatered to an average starting TS concentration of 32.71 %. The inoculum was added (along with distilled water) to dilute the initial FV TS concentrations down to semi-dry conditions (10-15 %) and wet conditions (below 10 %). Twelve 1-L batch bioreactors were loaded simultaneously with either GY or FV waste at TS solid concentrations ranging from 3.85 ± 1.22 % to 20.11 ± 1.23 %. The reactors were sealed and were operated for 30 days while being immersed in water baths to maintain a constant temperature of 37 ± 0.5 °C (mesophilic) or 55 ± 0.5 °C (thermophilic). A maximum methane yield of 115.42 (L methane/ kg VS added) was obtained for the GY thermophilic-wet AD combination. Methane yield was enhanced by 240 % compared to the GY waste mesophilic-dry condition. The results confirm that high temperature regimes and small solids concentrations are conditions that enhance methane yield from GY waste. A similar trend was observed for the anaerobic digestion of FV waste. Furthermore, a maximum value of VS (53 %) and sCOD (84 %) reduction was achieved during the AD of GY waste under the thermophilic-wet condition.

Keywords: anaerobic digestion, thermophilic, mesophilic, total solids concentration

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Authors: Francis Padi, Solomon Nunoo, John Kojo Annan

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The paper "Survey on Fiber Optic Deployment for Telecommunications Operators in Ghana: Coverage Gap, Recommendations and Research Directions" presents a comprehensive survey on the deployment of fiber optic networks for telecommunications operators in Ghana. It addresses the challenges encountered by operators using microwave transmission systems for backhauling traffic and emphasizes the advantages of deploying fiber optic networks. The study delves into the coverage gap, provides recommendations, and outlines research directions to enhance the telecommunications infrastructure in Ghana. Additionally, it evaluates next-generation optical access technologies and architectures tailored to operators' needs. The paper also investigates current technological solutions and regulatory, technical, and economical dimensions related to sharing mobile telecommunication networks in emerging countries. Overall, this paper offers valuable insights into fiber optic network deployment for telecommunications operators in Ghana and suggests strategies to meet the increasing demand for data and mobile applications.

Keywords: survey on fiber optic deployment, coverage gap, recommendations, research directions

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Authors: Konstantin S. Vainutis, Mark E. Andreev, Anastasia N. Voronova, Mikhail Yu. Shchelkanov

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Introduction: The trematodes from the family Cyclocoelidae (cyclocoelids) belong to the superfamily Echinostomatoidea infecting air sacs and trachea of wild birds. At present, the family Cyclocoelidae comprises nine valid genera in three subfamilies: Cyclocoelinae (type taxon), Haematotrephinae, and Typhlocoelinae. To our best knowledge, in this study, molecular genetic methods were used for the first time for studying cyclocoelids from the Russian Far East. Here we provide the data on the morphology and phylogeny of cyclocoelids from gadwall from the Russian Far East. The morphological and genetic data obtained for cyclocoelids indicated the necessity to revise the previously proposed classification within the family Cyclocoelidae. Objectives: The first objective was performing the morphological study of cyclocoelids found in M. strepera from the Russian Far East. The second objective is to reconstruct the phylogenetic relationships of the studied trematodes with other cyclocoelids using the 28S gene. Material and methods: During the field studies in the Khasansky district of the Primorsky region, 21 cyclocoelids were recovered from the air sacs of a single gadwall Mareca strepera. Seven samples of cyclocoelids were overstained in alum carmine, dehydrated in a graded ethanol series, cleared in clove oil, and mounted in Canada balsam. Genomic DNA was extracted from four cyclocoelids using the alkaline lysis method HotShot. The 28S rDNA fragment was amplified using the forward primer Digl2 and the reverse primer 1500R. Results: According to morphological features (ovary intratesticular, forming a triangle with the testes), the studied worms belong to the subfamily Cyclocoelinae Stossich, 1902. In particular, the highest morphological similarity was observed in relation to the trematodes of the genus Cyclocoelum Brandes, 1892 – genital pores are pharyngeal. However, the genetic analysis has shown significant discrepancies between the trematodes studied regarding the genus Cyclocoelum. On the phylogenetic tree, these trematodes took the sister position in relation to the genus Morishitium (previously considered in the subfamily Szidatitrematinae). Conclusion: Based on the results of the morphological and genetic studies, cyclocoelids isolated from Mareca strepera are suggested to be described in the previously unknown genus and differentiated from the type genus Cyclocoelum of the type subfamily Cyclocoelinae. Considering the available molecular data, including described cyclocoelids, the family Cyclocoelidae comprises ten valid genera in the three subfamilies mentioned above.

Keywords: new species, trematoda, phylogeny, cyclocoelidae

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Authors: Luther Ollier, Sylvie Thiria, Anastase Charantonis, Carlos E. Mejia, Michel Crépon

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Sea Surface Height Anomaly (SLA) is a signature of the sub-mesoscale dynamics of the upper ocean. Sea Surface Temperature (SST) is driven by these dynamics and can be used to improve the spatial interpolation of SLA fields. In this study, we focused on the temporal evolution of SLA fields. We explored the capacity of deep learning (DL) methods to predict short-term SLA fields using SST fields. We used simulated daily SLA and SST data from the Mercator Global Analysis and Forecasting System, with a resolution of (1/12)◦ in the North Atlantic Ocean (26.5-44.42◦N, -64.25–41.83◦E), covering the period from 1993 to 2019. Using a slightly modified image-to-image convolutional DL architecture, we demonstrated that SST is a relevant variable for controlling the SLA prediction. With a learning process inspired by the teaching-forcing method, we managed to improve the SLA forecast at five days by using the SST fields as additional information. We obtained predictions of a 12 cm (20 cm) error of SLA evolution for scales smaller than mesoscales and at time scales of 5 days (20 days), respectively. Moreover, the information provided by the SST allows us to limit the SLA error to 16 cm at 20 days when learning the trajectory.

Keywords: deep-learning, altimetry, sea surface temperature, forecast

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Authors: Payam Mousavi, Andrew L. Stewart, Huiwen You, Aryeh F. G. Fayerman

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We present a decision-support tool to assist an operator in the detection and tracking of a suspect vehicle traveling to an unknown target destination. Multiple data sources, such as traffic cameras, traffic information, weather, etc., are integrated and processed in real-time to infer a suspect’s intended destination chosen from a list of pre-determined high-value targets. Previously, we presented our work in the detection and tracking of vehicles using traffic and airborne cameras. Here, we focus on the fusion and processing of that information to predict a suspect’s behavior. The network of cameras is represented by a directional graph, where the edges correspond to direct road connections between the nodes and the edge weights are proportional to the average time it takes to travel from one node to another. For our experiments, we construct our graph based on the greater Los Angeles subset of the Caltrans’s “Performance Measurement System” (PeMS) dataset. We propose a Bayesian approach where a posterior probability for each target is continuously updated based on detections of the suspect in the live video feeds. Additionally, we introduce the concept of ‘soft interventions’, inspired by the field of Causal Inference. Soft interventions are herein defined as interventions that do not immediately interfere with the suspect’s movements; rather, a soft intervention may induce the suspect into making a new decision, ultimately making their intent more transparent. For example, a soft intervention could be temporarily closing a road a few blocks from the suspect’s current location, which may require the suspect to change their current course. The objective of these interventions is to gain the maximum amount of information about the suspect’s intent in the shortest possible time. Our system currently operates in a human-on-the-loop mode where at each step, a set of recommendations are presented to the operator to aid in decision-making. In principle, the system could operate autonomously, only prompting the operator for critical decisions, allowing the system to significantly scale up to larger areas and multiple suspects. Once the intended target is identified with sufficient confidence, the vehicle is reported to the authorities to take further action. Other recommendations include a selection of road closures, i.e., soft interventions, or to continue monitoring. We evaluate the performance of the proposed system using simulated scenarios where the suspect, starting at random locations, takes a noisy shortest path to their intended target. In all scenarios, the suspect’s intended target is unknown to our system. The decision thresholds are selected to maximize the chances of determining the suspect’s intended target in the minimum amount of time and with the smallest number of interventions. We conclude by discussing the limitations of our current approach to motivate a machine learning approach, based on reinforcement learning in order to relax some of the current limiting assumptions.

Keywords: autonomous surveillance, Bayesian reasoning, decision support, interventions, patterns of life, predictive analytics, predictive insights

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Authors: N. Drir, L. Barazane, M. Loudini

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It is well known that photovoltaic (PV) cells are an attractive source of energy. Abundant and ubiquitous, this source is one of the important renewable energy sources that have been increasing worldwide year by year. However, in the V-P characteristic curve of GPV, there is a maximum point called the maximum power point (MPP) which depends closely on the variation of atmospheric conditions and the rotation of the earth. In fact, such characteristics outputs are nonlinear and change with variations of temperature and irradiation, so we need a controller named maximum power point tracker MPPT to extract the maximum power at the terminals of photovoltaic generator. In this context, the authors propose here to study the modeling of a photovoltaic system and to find an appropriate method for optimizing the operation of the PV generator using two intelligent controllers respectively to track this point. The first one is based on artificial neural networks and the second on fuzzy logic. After the conception and the integration of each controller in the global process, the performances are examined and compared through a series of simulation. These two controller have prove by their results good tracking of the MPPT compare with the other method which are proposed up to now.

Keywords: maximum power point tracking, neural networks, photovoltaic, P&O

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Authors: Xiao Yihong, Yu Kexin, Burra Venkata Durga Kumar

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With the accelerating process of urbanization, the problem of urban road congestion is becoming more and more serious. Intelligent transportation system combining distributed and artificial intelligence has become a research hotspot. As the core development direction of the intelligent transportation system, Cooperative Intelligent Transportation System (C-ITS) integrates advanced information technology and communication methods and realizes the integration of humans, vehicle, roadside infrastructure, and other elements through the multi-agent distributed system. By analyzing the system architecture and technical characteristics of C-ITS, the report proposes a distributed multi-agent C-ITS. The system consists of Roadside Sub-system, Vehicle Sub-system, and Personal Sub-system. At the same time, we explore the scalability of the C-ITS and put forward incorporating local rewards in the centralized training decentralized execution paradigm, hoping to add a scalable value decomposition method. In addition, we also suggest introducing blockchain to improve the safety of the traffic information transmission process. The system is expected to improve vehicle capacity and traffic safety.

Keywords: distributed system, artificial intelligence, multi-agent, cooperative intelligent transportation system

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Authors: Shathya Duobiene, Gediminas Račiukaitis

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Solar-powered wireless sensor nodes work best when they operate continuously with minimal energy consumption. Wireless Sensor Networks (WSNs) are a new technology opens up wide studies, and advancements are expanding the prevalence of numerous monitoring applications and real-time aid for environments. The Selective Surface Activation Induced by Laser (SSAIL) technology is an exciting development that gives the design of WSNs more flexibility in terms of their shape, dimensions, and materials. This research work proposes a methodology for using SSAIL technology for forest ecosystem monitoring by wireless sensor networks. WSN monitoring the temperature and humidity were deployed, and their architectures are discussed. The paper presents the experimental outcomes of deploying newly built sensor nodes in forested areas. Finally, a practical method is offered to extend the WSN's lifespan and ensure its continued operation. When operational, the node is independent of the base station's power supply and uses only as much energy as necessary to sense and transmit data.

Keywords: internet of things (IoT), wireless sensor network, sensor nodes, SSAIL technology, forest ecosystem

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Authors: Leah Ning

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This paper discusses the development of a machine learning algorithm that accurately detects metastatic breast cancer (cancer has spread elsewhere from its origin part) in selected images that come from pathology scans of lymph node sections. Being able to develop an accurate artificial intelligence (AI) algorithm would help significantly in breast cancer diagnosis since manual examination of lymph node scans is both tedious and oftentimes highly subjective. The usage of AI in the diagnosis process provides a much more straightforward, reliable, and efficient method for medical professionals and would enable faster diagnosis and, therefore, more immediate treatment. The overall approach used was to train a convolution neural network (CNN) based on a set of pathology scan data and use the trained model to binarily classify if a new scan were benign or malignant, outputting a 0 or a 1, respectively. The final model’s prediction accuracy is very high, with 100% for the train set and over 70% for the test set. Being able to have such high accuracy using an AI model is monumental in regard to medical pathology and cancer detection. Having AI as a new tool capable of quick detection will significantly help medical professionals and patients suffering from cancer.

Keywords: breast cancer detection, AI, machine learning, algorithm

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Authors: Rehan Mohammad

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Background: Improved Water, Sanitation and Hygiene (WASH) practices in schools ensure children’s health, well-being and cognitive performance. In India under various WASH interventions in schools, teachers, and other staff make every possible effort to educate children about personal hygiene, sanitation practices and harms of open defecation. However, once children get back to their families, they see other practicing inappropriate WASH behaviors, and they consequently start following them. This show disconnect between school behavior and family behavior, which needs to be bridged to achieve desired WASH outcomes. Aims and Objectives: The aim of this study is to assess the factors causing disconnect of WASH-related behaviors between school and the family of children. It also suggests behavior change interventions to bridge the gap. Methodology: The present study has chosen a mixed- method approach. Both quantitative and qualitative methods of data collection have been used in the present study. The purposive sampling for data collection has been chosen. The data have been collected from 20% children in each age group of 04-08 years and 09-12 years spread over three primary schools and 20% of households to which they belong to which is spread over three slum communities in south district of Delhi. Results: The present study shows that despite of several behavior change interventions at school level, children still practice inappropriate WASH behaviors due to disconnect between school and family behaviors. These behaviors show variation from one age group to another. The inappropriate WASH behaviors being practiced by children include open defecation, wrong disposal of garbage, not keeping personal hygiene, not practicing hand washing practices during critical junctures and not washing fruits and vegetables before eating. The present study has highlighted that 80% of children in the age group of 04-08 years still practice inappropriate WASH behaviors when they go back to their families after school whereas, this percentage has reduced to 40% in case of children in the age group 09-12 years. Present study uncovers association between school and family teaching which creates a huge gap between WASH-related behavioral practices. The study has established that children learn and de-learn the WASH behaviors due to the evident disconnect between behavior change interventions at schools and household level. The study has also made it clear that children understand the significance of appropriate WASH practices but owing to the disconnect the behaviors remain unsettled. The study proposes several behavior change interventions to sync the behaviors of children at school and family level to ensure children’s health, well-being and cognitive performance.

Keywords: behavioral interventions, child health, family behavior, school behavior, WASH

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Authors: Anna Stepien

Abstract:

The paper presents the results of research on modifications of sand-lime bricks, especially using glass additives (glass fiber and glass sand) and other additives (e.g.:basalt&barite aggregate, lithium silicate and microsilica) as well. The main goal of this paper is to answer the question ‘How to use glass additives in the sand-lime mass and get a better bricks?’ The article contains information on modification of sand-lime bricks using glass fiber, glass sand, microsilica (different structure of silica). It also presents the results of the conducted compression tests, which were focused on compressive strength, water absorption, bulk density, and their microstructure. The Scanning Electron Microscope, spectrum EDS, X-ray diffractometry and DTA analysis helped to define the microstructural changes of modified products. The interpretation of the products structure revealed the existence of diversified phases i.e.the C-S-H and tobermorite. CaO-SiO2-H2O system is the object of intensive research due to its meaning in chemistry and technologies of mineral binding materials. Because the blocks are the autoclaving materials, the temperature of hydrothermal treatment of the products is around 200°C, the pressure - 1,6-1,8 MPa and the time - up to 8hours (it means: 1h heating + 6h autoclaving + 1h cooling). The microstructure of the products consists mostly of hydrated calcium silicates with a different level of structural arrangement. The X-ray diffraction indicated that the type of used sand is an important factor in the manufacturing of sand-lime elements. Quartz sand of a high hardness is also a substrate hardly reacting with other possible modifiers, which may cause deterioration of certain physical and mechanical properties. TG and DTA curves show the changes in the weight loss of the sand-lime bricks specimen against time as well as the endo- and exothermic reactions that took place. The endothermic effect with the maximum at T=573°C is related to isomorphic transformation of quartz. This effect is not accompanied by a change of the specimen weight. The next endothermic effect with the maximum at T=730-760°C is related to the decomposition of the calcium carbonates. The bulk density of the brick it is 1,73kg/dm3, the presence of xonotlite in the microstructure and significant weight loss during DTA and TG tests (around 0,6% after 70 minutes) have been noticed. Silicate elements were assessed on the basis of their compressive property. Orthogonal compositional plan type 3k (with k=2), i.e.full two-factor experiment was applied in order to carry out the experiments both, in the compression strength test and bulk density test. Some modification (e.g.products with barite and basalt aggregate) have improved the compressive strength around 41.3 MPa and water absorption due to capillary raising have been limited to 12%. The next modification was adding glass fiber to sand-lime mass, then glass sand. The results show that the compressive strength was higher than in the case of traditional bricks, while modified bricks were lighter.

Keywords: bricks, fiber, glass, microstructure

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Authors: Cinthia Campoverde, Mateo Benavidez, Victor Arias, Milton Torres

Abstract:

This article focuses on the 3D electromagnetic mapping of the intensity of the signal received by a mobile antenna within the open areas of the Department of Livestock of the Escuela Superior Politecnica de Chimborazo (ESPOCH), located in the city of Riobamba, Ecuador. The transmitting antenna belongs to the mobile telephone company ”TUENTI”, and is analyzed in the 2 GHz bands, operating at a frequency of 1940 MHz, using Long Term Evolution (LTE). Power signal strength data in the area were measured empirically using the ”Network Cell Info” application. A total of 170 samples were collected, distributed in 19 concentric circles around the base station. 3 campaigns were carried out at the same time, with similar traffic, and average values were obtained at each point, which varies between -65.33 dBm to -101.67 dBm. Also, the two virtualization software used are Sketchup and Unreal. Finally, the virtualized environment was visualized through virtual reality using Oculus 3D glasses, where the power levels are displayed according to a range of powers.

Keywords: reception power, LTE technology, virtualization, virtual reality, power levels

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Authors: Shayan Mohajer Hamidi

Abstract:

Federated Learning (FL) has emerged as a promising paradigm for training machine learning models on distributed data without the need for centralized data aggregation. In the realm of wireless communications, FL has the potential to leverage the vast amounts of data generated by wireless devices to improve model performance and enable intelligent applications. However, the fairness aspect of FL in wireless communications remains largely unexplored. This abstract presents an idea for fair federated learning in wireless communications, addressing the challenges of imbalanced data distribution, privacy preservation, and resource allocation. Firstly, the proposed approach aims to tackle the issue of imbalanced data distribution in wireless networks. In typical FL scenarios, the distribution of data across wireless devices can be highly skewed, resulting in unfair model updates. To address this, we propose a weighted aggregation strategy that assigns higher importance to devices with fewer samples during the aggregation process. By incorporating fairness-aware weighting mechanisms, the proposed approach ensures that each participating device's contribution is proportional to its data distribution, thereby mitigating the impact of data imbalance on model performance. Secondly, privacy preservation is a critical concern in federated learning, especially in wireless communications where sensitive user data is involved. The proposed approach incorporates privacy-enhancing techniques, such as differential privacy, to protect user privacy during the model training process. By adding carefully calibrated noise to the gradient updates, the proposed approach ensures that the privacy of individual devices is preserved without compromising the overall model accuracy. Moreover, the approach considers the heterogeneity of devices in terms of computational capabilities and energy constraints, allowing devices to adaptively adjust the level of privacy preservation to strike a balance between privacy and utility. Thirdly, efficient resource allocation is crucial for federated learning in wireless communications, as devices operate under limited bandwidth, energy, and computational resources. The proposed approach leverages optimization techniques to allocate resources effectively among the participating devices, considering factors such as data quality, network conditions, and device capabilities. By intelligently distributing the computational load, communication bandwidth, and energy consumption, the proposed approach minimizes resource wastage and ensures a fair and efficient FL process in wireless networks. To evaluate the performance of the proposed fair federated learning approach, extensive simulations and experiments will be conducted. The experiments will involve a diverse set of wireless devices, ranging from smartphones to Internet of Things (IoT) devices, operating in various scenarios with different data distributions and network conditions. The evaluation metrics will include model accuracy, fairness measures, privacy preservation, and resource utilization. The expected outcomes of this research include improved model performance, fair allocation of resources, enhanced privacy preservation, and a better understanding of the challenges and solutions for fair federated learning in wireless communications. The proposed approach has the potential to revolutionize wireless communication systems by enabling intelligent applications while addressing fairness concerns and preserving user privacy.

Keywords: federated learning, wireless communications, fairness, imbalanced data, privacy preservation, resource allocation, differential privacy, optimization

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