Search results for: data storage

Authors: Giulia Fredi, Andrea Dorigato, Luca Fambri, Alessandro Pegoretti

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Thermal energy storage (TES) is the storage of heat for later use, thus filling the gap between energy request and supply. The most widely used materials for TES are the organic solid-liquid phase change materials (PCMs), such as paraffin. These materials store/release a high amount of latent heat thanks to their high specific melting enthalpy, operate in a narrow temperature range and have a tunable working temperature. However, they suffer from a low thermal conductivity and need to be confined to prevent leakage. These two issues can be tackled by confining PCMs with carbon nanotubes (CNTs). TES applications include the buildings industry, solar thermal energy collection and thermal management of electronics. In most cases, TES systems are an additional component to be added to the main structure, but if weight and volume savings are key issues, it would be advantageous to embed the TES functionality directly in the structure. Such multifunctional materials could be employed in the automotive industry, where the diffusion of lightweight structures could complicate the thermal management of the cockpit environment or of other temperature sensitive components. This work aims to produce epoxy/carbon structural laminates containing CNT-stabilized paraffin. CNTs were added to molten paraffin in a fraction of 10 wt%, as this was the minimum amount at which no leakage was detected above the melting temperature (45°C). The paraffin/CNT blend was cryogenically milled to obtain particles with an average size of 50 µm. They were added in various percentages (20, 30 and 40 wt%) to an epoxy/hardener formulation, which was used as a matrix to produce laminates through a wet layup technique, by stacking five plies of a plain carbon fiber fabric. The samples were characterized microstructurally, thermally and mechanically. Differential scanning calorimetry (DSC) tests showed that the paraffin kept its ability to melt and crystallize also in the laminates, and the melting enthalpy was almost proportional to the paraffin weight fraction. These thermal properties were retained after fifty heating/cooling cycles. Laser flash analysis showed that the thermal conductivity through the thickness increased with an increase of the PCM, due to the presence of CNTs. The ability of the developed laminates to contribute to the thermal management was also assessed by monitoring their cooling rates through a thermal camera. Three-point bending tests showed that the flexural modulus was only slightly impaired by the presence of the paraffin/CNT particles, while a more sensible decrease of the stress and strain at break and the interlaminar shear strength was detected. Optical and scanning electron microscope images revealed that these could be attributed to the preferential location of the PCM in the interlaminar region. These results demonstrated the feasibility of multifunctional structural TES composites and highlighted that the PCM size and distribution affect the mechanical properties. In this perspective, this group is working on the encapsulation of paraffin in a sol-gel derived organosilica shell. Submicron spheres have been produced, and the current activity focuses on the optimization of the synthesis parameters to increase the emulsion efficiency.

Keywords: carbon fibers, carbon nanotubes, lightweight materials, multifunctional composites, thermal energy storage

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Authors: Amitha Puranik, V. S. Binu, Seena Biju

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Missing data is a common problem in spatial analysis especially at the aggregate level. Missing can either occur in covariate or in response variable or in both in a given location. Many missing data techniques are available to estimate the missing data values but not all of these methods can be applied on spatial data since the data are autocorrelated. Hence there is a need to develop a method that estimates the missing values in both response variable and covariates in spatial data by taking account of the spatial autocorrelation. The present study aims to develop a model to estimate the missing data points at the aggregate level in spatial data by accounting for (a) Spatial autocorrelation of the response variable (b) Spatial autocorrelation of covariates and (c) Correlation between covariates and the response variable. Estimating the missing values of spatial data requires a model that explicitly account for the spatial autocorrelation. The proposed model not only accounts for spatial autocorrelation but also utilizes the correlation that exists between covariates, within covariates and between a response variable and covariates. The precise estimation of the missing data points in spatial data will result in an increased precision of the estimated effects of independent variables on the response variable in spatial regression analysis.

Keywords: spatial regression, missing data estimation, spatial autocorrelation, simulation analysis

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Authors: Lorenzo Petrucci

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This paper is intended to give insight into the potential use of small-scale off-grid trigeneration systems powered by biogas generated in a dairy farm. The off-grid plant object of analysis comprises a dual-fuel Genset as well as electrical and thermal storage equipment and an adsorption machine. The loads are the different apparatus used in the dairy farm, a household where the workers live and a small electric vehicle whose batteries can also be used as a power source in case of emergency. The insertion in the plant of an adsorption machine is mainly justified by the abundance of thermal energy and the simultaneous high cooling demand associated with the milk-chilling process. In the evaluated operational scenario, our research highlights the importance of prioritizing specific small loads which cannot sustain an interrupted supply of power over time. As a consequence, a photovoltaic and thermal panel is included in the plant and is tasked with providing energy independently of potentially disruptive events such as engine malfunctioning or scarce and unstable supplies of fuels. To efficiently manage the plant an energy dispatch strategy is created in order to control the flow of energy between the power sources and the thermal and electric storages. In this article we elaborate on models of the equipment and from these models, we extract parameters useful to build load-dependent profiles of the prime movers and storage efficiencies. We show that under reasonable assumptions the analysis provides a sensible estimate of the generated energy. The simulations indicate that a Diesel Generator sized to a value 25% higher than the total electrical peak demand operates 65% of the time below the minimum acceptable load threshold. To circumvent such a critical operating mode, dump loads are added through the activation and deactivation of small resistors. In this way, the excess of electric energy generated can be transformed into useful heat. The combination of PVT and electrical storage to support the prioritized load in an emergency scenario is evaluated in two different days of the year having the lowest and highest irradiation values, respectively. The results show that the renewable energy component of the plant can successfully sustain the prioritized loads and only during a day with very low irradiation levels it also needs the support of the EVs’ battery. Finally, we show that the adsorption machine can reduce the ice builder and the air conditioning energy consumption by 40%.

Keywords: hybrid power plants, mathematical modeling, off-grid plants, renewable energy, trigeneration

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Authors: Mirko Ante, Şeniz Sörgel, Andreas Bund

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Li-S- (Lithium-Sulfur-) battery systems provide very high specific gravimetric energy (2600 Wh/kg) and volumetric energy density (2800Wh/l). Hence, Li-S batteries are one of the key technologies for both the upcoming electromobility and stationary applications. Furthermore, the Li-S battery system is potentially cheap and environmentally benign. However, the technical implementation suffers from cycling stability, low charge and discharge rates and incomplete understanding of the complex polysulfide reaction mechanism. The aim of this work is to develop an effective electrocatalyst for the polysulfide reactions so that the electrode kinetics of the sulfur half-cell will be improved. Accordingly, the overvoltage will be decreased, and the efficiency of the cell will be increased. An enhanced electroactive surface additionally improves the charge and discharge rates. To reach this goal, functionalized electrocatalytic coatings are investigated to accelerate the kinetics of the polysulfide reactions. In order to determine a suitable electrocatalyst, apparent exchange current densities of a variety of materials (Ni, Co, Pt, Cr, Al, Cu, ITO, stainless steel) have been evaluated in a polysulfide containing electrolyte by potentiodynamic measurements and a Butler-Volmer fit including diffusion limitation. The samples have been examined by Scanning Electron Microscopy (SEM) after the potentiodynamic measurements. Up to now, our work shows that cobalt is a promising material with good electrocatalytic properties for the polysulfide reactions and good chemical stability in the system. Furthermore, an electrodeposition from a modified Watt’s nickel electrolyte with a sulfur source seems to provide an autocatalytic effect, but the electrocatalytic behavior decreases after several cycles of the current-potential-curve.

Keywords: electrocatalyst, energy storage, lithium sulfur battery, sulfur electrode materials

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Authors: P. Larpsomboonchai

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Agro-industry is one of major industries that has strong impacts on national economic incomes, growth, stability, and sustainable development. Moreover, this industry also has strong influences on social, cultural and political issues. Furthermore, this industry, as producing primary and secondary products, is facing challenges from such diverse factors such as demand inconsistency, intense international competition, technological advancements and new competitors. In order to maintain and to improve industry’s competitiveness in both domestics and international markets, science and technology are key factors. Besides hard sciences and technologies, modern industrial engineering concepts such as Just in Time (JIT) Total Quality Management (TQM), Quick Response (QR), Supply Chain Management (SCM) and Lean can be very effective to supportant to increase efficiency and effectiveness of these agricultural products on world stage. Onion is one of Thailand’s major export products which brings back national incomes. But, it also facing challenges in many ways. This paper focused its interests in onion packing process and its related activities such as storage and shipment from one of major packing plant and storage in Mae Wang District, Chiang Mai, Thailand, by applying Toyota Production System (TPS) or Lean concepts, to improve process capability throughout the entire packing and distribution process which will be profitable for the whole onion supply chain. And it will be beneficial to other related agricultural products in Thailand and other ASEAN countries.

Keywords: packing process, Toyota Production System (TPS), lean concepts, waste reduction, lean in agro-industries activities

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Authors: Sarra Senhadji, Imene Benzeguimi, Zohra Yagoub

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Big Data represents the recent technology of manipulating voluminous and unstructured data sets over multiple sources. Therefore, NOSQL appears to handle the problem of unstructured data. Association rules mining is one of the popular techniques of data mining to extract hidden relationship from transactional databases. The algorithm for finding association dependencies is well-solved with Map Reduce. The goal of our work is to reduce the time of generating of frequent itemsets by using Map Reduce and NOSQL database oriented document. A comparative study is given to evaluate the performances of our algorithm with the classical algorithm Apriori.

Keywords: Apriori, Association rules mining, Big Data, Data Mining, Hadoop, MapReduce, MongoDB, NoSQL

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Authors: Bogoro Audu Gani, Tobi Nzelibe Ajiji Haruna

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Women have both knowledge and expertise, whose recognition can lead to more efficient, effective, sustainable, and fair waste management operations. Studies have shown that the failure to take cognizance of the traditional role of women in the management of urban environments results in a serious loss of efficiency and productivity. However, urban managers in developing countries are yet to identify and integrate those critical roles of women into urban environmental management. This research is motivated not only due the poor solid waste management but also by the total neglect of the role of women in solid waste management in the Bauchi metropolis. Systematic random sampling technique was adopted for the selection of the samples and 4% of the study population was taken as the sample size. The major instruments used for data collection were questionnaires, interviews and direct measurement of household solid waste at source and the data is presented in tables and charts. It is found that over 95% of sweeping, cooking and food preparation are exclusively reserved for women in the study area. Women dominate the generation, storage and collection of household solid waste with 81%, 96% and 91%, respectively, within the study area. It is also discovered that segregation can be 95% effectively carried out by women that have free time. However, urban managers in the Bauchi metropolis are yet to identify the role of women with a view to integrating them into solid waste management in order to achieve a healthy and clean living environment in the Bauchi metropolis. Among other suggestions, the paper recommends that the role of women should be identified and integrated into developing policies and programs for a clean and healthy living urban environment; this will not only improve the environmental quality but would also increase the income base of the family.

Keywords: women, solid waste, integration, segregation

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Authors: Melaku Tsehay

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The Consortium of Christian Relief and Development Associations (CCRDA), and the CORE Group Polio Partners (CGPP) Secretariat have been working with Global Alliance for Vac-cines and Immunization (GAVI) to improve the immunization data quality in Afar and Somali Regional States. The main aim of this study was to compare the quality of immunization data before and after the above interventions in health facilities in the pastoralist communities in Ethiopia. To this end, a comparative-cross-sectional study was conducted on 51 health facilities. The baseline data was collected in May 2019, while the end line data in August 2021. The WHO data quality self-assessment tool (DQS) was used to collect data. A significant improvment was seen in the accuracy of the pentavalent vaccine (PT)1 (p = 0.012) data at the health posts (HP), while PT3 (p = 0.010), and Measles (p = 0.020) at the health centers (HC). Besides, a highly sig-nificant improvment was observed in the accuracy of tetanus toxoid (TT)2 data at HP (p < 0.001). The level of over- or under-reporting was found to be < 8%, at the HP, and < 10% at the HC for PT3. The data completeness was also increased from 72.09% to 88.89% at the HC. Nearly 74% of the health facilities timely reported their respective immunization data, which is much better than the baseline (7.1%) (p < 0.001). These findings may provide some hints for the policies and pro-grams targetting on improving immunization data qaulity in the pastoralist communities.

Keywords: data quality, immunization, verification factor, pastoralist region

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Authors: Thohaku Abdul Hadi, Hadyan Perdana Putra, Nugroho Wicaksono, Adhika Prajna Nandiwardhana, Onang Surya Nugroho, Heri Suryoatmojo, Soedibjo

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Access to electricity is now a basic requirement of mankind. Unfortunately, there are still many places around the world which have no access to electricity, such as small islands, where there could potentially be a factory, a plantation, a residential area, or resorts. Many of these places might have substantial potential for energy generation such us Photovoltaic (PV) and Wind turbine (WT), which can be used to generate electricity independently for themselves. Solar energy and wind power are renewable energy sources which are mostly found in nature and also kinds of alternative energy that are still developing in a rapid speed to help and meet the demand of electricity. PV and Wind has a characteristic of power depend on solar irradiation and wind speed based on geographical these areas. This paper presented a control methodology of hybrid small scale PV/Wind energy system that use a fuzzy logic controller (FLC) to extract the maximum power point tracking (MPPT) in different solar irradiation and wind speed. This paper discusses simulation and analysis of the generation process of hybrid resources in MPP and power conditioning unit (PCU) of Photovoltaic (PV) and Wind Turbine (WT) that is connected to the three-phase low voltage electricity grid system (380V) without battery storage. The capacity of the sources used is 2.2 kWp PV and 2.5 kW PMSG (Permanent Magnet Synchronous Generator) -WT power rating. The Modeling of hybrid PV/Wind, as well as integrated power electronics components in grid connected system, are simulated using MATLAB/Simulink.

Keywords: fuzzy MPPT, grid connected inverter, photovoltaic (PV), PMSG wind turbine

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Authors: Maria Paula Santos, Ana Lucas

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Organizations support their operations and decision making on the data they have at their disposal, so the quality of these data is remarkably important and Data Quality (DQ) is currently a relevant issue, the literature being unanimous in pointing out that poor DQ can result in large costs for organizations. The literature review identified and described 24 Critical Success Factors (CSF) for Data Quality Management (DQM) that were presented to a panel of experts, who ordered them according to their degree of importance, using the Delphi method with the Q-sort technique, based on an online questionnaire. The study shows that the five most important CSF for DQM are: definition of appropriate policies and standards, control of inputs, definition of a strategic plan for DQ, organizational culture focused on quality of the data and obtaining top management commitment and support.

Keywords: critical success factors, data quality, data quality management, Delphi, Q-Sort

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Authors: Muhammad Sajid Arshad, Sadaf Bashir

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In this study, the effects of pomegranate peel based edible coating were determined on safety and quality of chicken nuggets. Four treatment groups were prepared as control (without coating), coating with sodium alginate (SA) (1.5%), pomegranate peel powder (PPP) (1.5%), and combination of SA and PPP. There was a significant variation observed with respect to coating treatments and storage intervals. The chicken nuggets were subjected to refrigerated storage (40C) and were analyzed at regular intervals of 0, 7, 14 1 and 21 days. The microbiological quality was determined by total aerobic and coliform counts. Total aerobic (5.09±0.05 log CFU/g) and coliforms (3.91±0.06 log CFU/g) counts were higher in uncoated chicken nuggets whereas lower was observed in coated chicken nuggets having combination of SA and PPP. Likewise, antioxidants potential of chicken nuggets was observed by assessing total phenolic contents (TPC) and DPPH activity. Higher TPC (135.66 GAE/100g) and DPPH (64.65%) were found in combination with SA and PPP, whereas minimum TPC (91.38) and DPPH (41.48) was observed in uncoated chicken nuggets. Regarding the stability analysis of chicken nuggets, thiobarbituric acid reactive substances (TBARS) and peroxide value (POV) were determined. Higher TBARS (1.62±0.03 MDA/Kg) and POV (0.92±0.03 meq peroxide/kg) were found in uncoated chicken nuggets. Hunter color values were also observed in both uncoated and coated chicken nuggets. Sensorial attributes were also observed by the trained panelists. The higher sensory score for appearance, color, taste, texture and overall acceptability were observed in control (uncoated) while in coated treatments, it was found within acceptable limits. In nutshell, the combination of SA and PPP enhanced the overall quality, antioxidant potential, and stability of chicken nuggets.

Keywords: chicken nuggets, edible coatings, pomegranate peel powder, sodium alginate

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Authors: Daniela Rimnacova, Maryna Vorokhta, Martina Svabova

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CO₂ adsorption capacity of several materials - waste (power fly ash, slag, carbonized sewage sludge), rocks (Czech Silurian shale, black coal), and carbon (synthesized carbon, activated carbon as a reference material) - were measured on dry samples using a unique hand-made manometric sorption apparatus at a temperature of 45 °C and pressures of up to 7 MPa. The main aim was finding utilization of the waste materials and rocks for removal of the air or water pollutants caused by anthropogenic activities, as well as for the carbon dioxide storage. The equilibrium amount of the adsorbate depends on temperature, gas saturation pressure, porosity, surface area and volume of pores, and last but not least, on the composition of the adsorbents. Given experimental conditions can simulate in-situ situations in the rock bed and can be achieved just by a high-pressure apparatus. The CO₂ excess adsorption capacities ranged from 0.018 mmol/g (ash) to 13.55 mmol/g (synthesized carbon). The synthetized carbon had the highest adsorption capacity among all studied materials as well as the highest price. This material is usually used for the adsorption of specific pollutants. The excess adsorption capacity of activated carbon was 9.19 mmol/g. It is used for water and air cleaning. Ash can be used for chemisorption onto ash particle surfaces or capture of special pollutants. Shale is a potential material for enhanced gas recovery or CO₂ sequestration in-situ. Slag is a potential material for capture of gases with a possibility of the underground gas storage after the adsorption process. The carbonized sewage sludge is quite a good adsorbent for the removal and capture of pollutants, as well as shales or black coal which show an interesting relationship between the price and adsorption capacity.

Keywords: adsorption, CO₂, high pressure, porous materials

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Authors: Sreetama Ghosh, Sundara Ramaprabhu

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Carbon dioxide gas has been a major cause for the worldwide increase in green house effect, which leads to climate change and global warming. So CO₂ capture & sequestration has become an effective way to reduce the concentration of CO₂ in the environment. One such way to capture CO₂ in porous materials is by adsorption process. A potential material in this aspect is porous hexagonal boron nitride or 'white graphene' which is a well-known two-dimensional layered material with very high thermal stability. It had been investigated that the sample with hierarchical pore structure and high specific surface area shows excellent performance in capturing carbon dioxide gas and thereby mitigating the problem of environmental pollution to the certain extent. Besides, the presence of sulfur as well as nitrogen in the sample synergistically helps in the increase in adsorption capacity. In this work, a cost effective single step synthesis of highly porous boron nitride nanosheets doped with sulfur had been demonstrated. Besides, the CO₂ adsorption-desorption studies were carried on using a pressure reduction technique. The studies show that the nanosheets exhibit excellent cyclic stability in storage performance. Thermodynamic studies suggest that the adsorption takes place mainly through physisorption. The studies show that the nanosheets exhibit excellent cyclic stability in storage performance. Further, the surface modification of the highly porous nano sheets carried out by incorporating ionic liquids had further enhanced the capturing capability of CO₂ gas in the nanocomposite, revealing that this particular material has the potential to be an excellent adsorbent of carbon dioxide gas.

Keywords: CO₂ capture, hexagonal boron nitride nanosheets, porous network, sulfur doping

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Authors: Nermine A. Harraz, Aliaa Abouali

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The retail stores put up for sale multiple items while the spaces in the backroom and display areas constitute a scarce resource. Availability, volume, and location of the product displayed in the showroom influence the customer’s demand. Managing these operations individually will result in sub-optimal overall retail store’s profit; therefore, a non-linear integer programming model (NLIP) is developed to determine the inventory replenishment and shelf space allocation decisions that together maximize the retailer’s profit under shelf space and backroom storage constraints taking into consideration that the demand rate is positively dependent on the amount and location of items displayed in the showroom. The developed model is solved using LINGO® software. The NLIP model is implemented in a real world case study in a large retail outlet providing a large variety of products. The proposed model is validated and shows logical results when using the experimental data collected from the market.

Keywords: retailing management, inventory replenishment, shelf space allocation, showroom, backroom

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Authors: B. Kędra, R. Małkowski

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This paper presents information on Power System Component Simulator – a device designed for LINTE^2 laboratory owned by Gdansk University of Technology in Poland. In this paper, we first provide an introductory information on the Power System Component Simulator and its capabilities. Then, the concept of the unit is presented. Requirements for the unit are described as well as proposed and introduced functions are listed. Implementation details are given. Hardware structure is presented and described. Information about used communication interface, data maintenance and storage solution, as well as used Simulink real-time features are presented. List and description of all measurements is provided. Potential of laboratory setup modifications is evaluated. Lastly, the results of experiments performed using Power System Component Simulator are presented. This includes simulation of under frequency load shedding, frequency and voltage dependent characteristics of groups of load units, time characteristics of group of different load units in a chosen area.

Keywords: power converter, Simulink Real-Time, Matlab, load, tap controller

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Authors: Djamila Benhaddouche, Abdelkader Benyettou

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In this paper, we used data mining to extract biomedical knowledge. In general, complex biomedical data collected in studies of populations are treated by statistical methods, although they are robust, they are not sufficient in themselves to harness the potential wealth of data. For that you used in step two learning algorithms: the Decision Trees and Support Vector Machine (SVM). These supervised classification methods are used to make the diagnosis of thyroid disease. In this context, we propose to promote the study and use of symbolic data mining techniques.

Keywords: biomedical data, learning, classifier, algorithms decision tree, knowledge extraction

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Authors: Usama Ahmed

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Data mining is the process of analyze data which are used to predict helpful information. It is the field of research which solve various type of problem. In data mining, classification is an important technique to classify different kind of data. Diabetes is most common disease. This paper implements different classification technique using Waikato Environment for Knowledge Analysis (WEKA) on diabetes dataset and find which algorithm is suitable for working. The best classification algorithm based on diabetic data is Naïve Bayes. The accuracy of Naïve Bayes is 76.31% and take 0.06 seconds to build the model.

Keywords: data mining, classification, diabetes, WEKA

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Authors: Agnetha Schuchardt, Jennifer Hartmann, Laura Schulte, Roman Peperhove, Lars Gerhold

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If critical infrastructures fail, even for a short period of time, it can have significant negative consequences for the affected population. This is especially true for the food sector that is strongly interlinked with other sectors like the power supply. A blackout could lead to several cities being without food supply for numerous days, simply because cash register systems do no longer work properly. Following the public opinion, securing the food supply in emergencies is considered a task of the state, however, in the German context, the key players are private enterprises and private households. Both are not aware of their responsibility and both cannot be forced to take any preventive measures prior to an emergency. This problem became evident to officials and politicians so that the law covering food security was revised in order to include private stakeholders into mitigation processes. The paper will present a scientific review of governmental and regulatory literature. The focus is the inclusion of the food industry through a law reform and the challenges that still exist. Together with legal experts, an analysis of regulations will be presented that explains the development of the law reform concerning food security and emergency storage in Germany. The main findings are that the existing public food emergency storage is out-dated, insufficient and too expensive. The state is required to protect food as a critical infrastructure but does not have the capacities to live up to this role. Through a law reform in 2017, new structures should to established. The innovation was to include the private sector into the civil defense concept since it has the required knowledge and experience. But the food industry is still reluctant. Preventive measures do not serve economic purposes – on the contrary, they cost money. The paper will discuss respective examples like equipping supermarkets with emergency power supply or self-sufficient cash register systems and why the state is not willing to cover the costs of these measures, but neither is the economy. The biggest problem with the new law is that private enterprises can only be forced to support food security if the state of emergency has occurred already and not one minute earlier. The paper will cover two main results: the literature review and an expert workshop that will be conducted in summer 2018 with stakeholders from different parts of the food supply chain as well as officials of the public food emergency concept. The results from this participative process will be presented and recommendations will be offered that show how the private economy could be better included into a modern food emergency concept (e. g. tax reductions for stockpiling).

Keywords: critical infrastructure, disaster control, emergency food storage, food security, private economy, resilience

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Authors: Abdulkarim Nasir, Alhassan T. Yahaya, Hauwa T. Abdulkarim, Abdussalam El-Suleiman, Yakubu K. Abubakar

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The need for sustainable and reliable electricity supply in rural communities of Nigeria remains a pressing issue, given the country's vast energy deficit and the significant number of inhabitants lacking access to electricity. This research focuses on the development of a portable hybrid renewable energy system designed to provide a sustainable and efficient electricity supply to these underserved regions. The proposed system integrates multiple renewable energy sources, specifically solar and wind, to harness the abundant natural resources available in Nigeria. The design and development process involves the selection and optimization of components such as photovoltaic panels, wind turbines, energy storage units (batteries), and power management systems. These components are chosen based on their suitability for rural environments, cost-effectiveness, and ease of maintenance. The hybrid system is designed to be portable, allowing for easy transportation and deployment in remote locations with limited infrastructure. Key to the system's effectiveness is its hybrid nature, which ensures continuous power supply by compensating for the intermittent nature of individual renewable sources. Solar energy is harnessed during the day, while wind energy is captured whenever wind conditions are favourable, thus ensuring a more stable and reliable energy output. Energy storage units are critical in this setup, storing excess energy generated during peak production times and supplying power during periods of low renewable generation. These studies include assessing the solar irradiance, wind speed patterns, and energy consumption needs of rural communities. The simulation results inform the optimization of the system's design to maximize energy efficiency and reliability. This paper presents the development and evaluation of a 4 kW standalone hybrid system combining wind and solar power. The portable device measures approximately 8 feet 5 inches in width, 8 inches 4 inches in depth, and around 38 feet in height. It includes four solar panels with a capacity of 120 watts each, a 1.5 kW wind turbine, a solar charge controller, remote power storage, batteries, and battery control mechanisms. Designed to operate independently of the grid, this hybrid device offers versatility for use in highways and various other applications. It also presents a summary and characterization of the device, along with photovoltaic data collected in Nigeria during the month of April. The construction plan for the hybrid energy tower is outlined, which involves combining a vertical-axis wind turbine with solar panels to harness both wind and solar energy. Positioned between the roadway divider and automobiles, the tower takes advantage of the air velocity generated by passing vehicles. The solar panels are strategically mounted to deflect air toward the turbine while generating energy. Generators and gear systems attached to the turbine shaft enable power generation, offering a portable solution to energy challenges in Nigerian communities. The study also addresses the economic feasibility of the system, considering the initial investment costs, maintenance, and potential savings from reduced fossil fuel use. A comparative analysis with traditional energy supply methods highlights the long-term benefits and sustainability of the hybrid system.

Keywords: renewable energy, solar panel, wind turbine, hybrid system, generator

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Authors: Asifa Amara, Prachi Singh, Kanishka, Debargho Pathak, Akshat Kumar, Jayakumar Eravelly

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Today's generation is totally dependent on technology that uses data as its fuel. The present study is all about innovations and developments in data science and gives an idea about how efficiently to use the data provided. This study will help to understand the core concepts of data science. The concept of artificial intelligence was introduced by Alan Turing in which the main principle was to create an artificial system that can run independently of human-given programs and can function with the help of analyzing data to understand the requirements of the users. Data science comprises business understanding, analyzing data, ethical concerns, understanding programming languages, various fields and sources of data, skills, etc. The usage of data science has evolved over the years. In this review article, we have covered a part of data science, i.e., machine learning. Machine learning uses data science for its work. Machines learn through their experience, which helps them to do any work more efficiently. This article includes a comparative study image between human understanding and machine understanding, advantages, applications, and real-time examples of machine learning. Data science is an important game changer in the life of human beings. Since the advent of data science, we have found its benefits and how it leads to a better understanding of people, and how it cherishes individual needs. It has improved business strategies, services provided by them, forecasting, the ability to attend sustainable developments, etc. This study also focuses on a better understanding of data science which will help us to create a better world.

Keywords: data science, machine learning, data analytics, artificial intelligence

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Authors: D. Nkazi, S. E. Iyuke, J. Mulopo

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Increasing global energy demand as well as air quality concerns have in recent years led to the search for alternative clean fuels to replace fossil fuels. One such alternative is the blending of petrol with ethanol, which has numerous advantages such ethanol’s ability to act as oxygenate thus reducing the carbon monoxide emissions from the exhaust of internal combustion engines of vehicles. However, the hygroscopic nature of ethanol is a major concern in obtaining a perfectly homogenized petrol-ethanol fuel. This problem has led to the study of ways of homogenizing the petrol-ethanol mixtures. During the blending process, volumes fraction of ethanol and petrol were studied with respect to the depth within the storage container to confirm homogenization of the blend and time of storage. The results reveal that the density of the mixture was constant. The binodal curve of the ternary diagram shows an increase of homogeneous region, indicating an improved of interaction between water and petrol. The concentration distribution in the reactor showed proof of cavitation formation since in both directions, the variation of concentration with both time and distance was found to be oscillatory. On comparing the profiles in both directions, the concentration gradient, diffusion flux, and energy and diffusion rates were found to be higher in the vertical direction compared to the horizontal direction. It was therefore concluded that ultrasonication creates cavitation in the mixture which enhances mass transfer and mixing of ethanol and petrol. The horizontal direction was found to be the diffusion rate limiting step which proposed that the blender should have a larger height to diameter ratio. It is, however, recommended that further studies be done on the rate-limiting step so as to have actual dimensions of the reactor.

Keywords: ultrasonication, petrol, ethanol, concentration

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Authors: Iftikhar Ahmad, Youssef Elkezza

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Sarir oilfield is in North Africa. It has facilities for oil and gas production. The assets of the Sarir oilfield can be divided into five following categories, namely: (i) well bore and wellheads; (ii) vessels such as separators, desalters, and gas processing facilities; (iii) pipelines including all flow lines, trunk lines, and shipping lines; (iv) storage tanks; (v) other assets such as turbines and compressors, etc. The nature of the petroleum industry recognizes the potential human, environmental and financial consequences that can result from failing to maintain the integrity of wellheads, vessels, tanks, pipelines, and other assets. The importance of effective asset integrity management increases as the industry infrastructure continues to age. The primary objective of assets integrity management (AIM) is to maintain assets in a fit-for-service condition while extending their remaining life in the most reliable, safe, and cost-effective manner. Corrosion management is one of the important aspects of successful asset integrity management. It covers corrosion mitigation, monitoring, inspection, and risk evaluation. External corrosion on pipelines, well bores, buried assets, and bottoms of tanks is controlled with a combination of coatings by cathodic protection, while the external corrosion on surface equipment, wellheads, and storage tanks is controlled by coatings. The periodic cleaning of the pipeline by pigging helps in the prevention of internal corrosion. Further, internal corrosion of pipelines is prevented by chemical treatment and controlled operations. This paper describes the integrity management system used in the Sarir oil field for its oil and gas production facilities based on standard practices of corrosion mitigation and inspection.

Keywords: assets integrity management, corrosion prevention in oilfield assets, corrosion management in oilfield, corrosion prevention, inspection activities

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Authors: N. Valderrama, W. Albarracín, N. Algecira

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It was studied the effect of the inclusion of thyme and rosemary essential oils into chitosan films, as well as the microbiological and physical properties when storing chitosan film with and without the mentioned inclusion. The film forming solution was prepared by dissolving chitosan (2%, w/v), polysorbate 80 (4% w/w CH) and glycerol (16% w/w CH) in aqueous lactic acid solutions (control). The thyme (TEO) and rosemary (REO) essential oils (EOs) were included 1:1 w/w (EOs:CH) on their combination 50/50 (TEO:REO). The films were stored at temperatures of 5, 20, 33°C and a relative humidity of 75% during four weeks. The films with essential oil inclusion did not show an antimicrobial activity against strains. This behavior could be explained because the chitosan only inhibits the growth of microorganisms in direct contact with the active sites. However, the inhibition capacity of TEO was higher than the REO and a synergic effect between TEO:REO was found for S. enteritidis strains in the chitosan solution. Some physical properties were modified by the inclusion of essential oils. The addition of essential oils does not affect the mechanical properties (tensile strength, elongation at break, puncture deformation), the water solubility, the swelling index nor the DSC behavior. However, the essential oil inclusion can significantly decrease the thickness, the moisture content, and the L* value of films whereas the b* value increased due to molecular interactions between the polymeric matrix, the loosing of the structure, and the chemical modifications. On the other hand, the temperature and time of storage changed some physical properties on the chitosan films. This could have occurred because of chemical changes, such as swelling in the presence of high humidity air and the reacetylation of amino groups. In the majority of cases, properties such as moisture content, tensile strength, elongation at break, puncture deformation, a*, b*, chrome, ΔE increased whereas water resistance, swelling index, L*, and hue angle decreased.

Keywords: chitosan, food additives, modified films, polymers

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Authors: Bambang Novianto, Rizal Aditya Herdianto, Raphael Bianco Huwae, Afifah, Alfonso Brolin Sihite, Rudi Lumanto

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The mobile banking application is a type of mobile application that is growing rapidly. This is caused by the ease of service and time savings in making transactions. On the other hand, this certainly provides a challenge in security issues. The use of mobile banking can not be separated from cyberattacks that may occur which can result the theft of sensitive information or financial loss. The financial loss and the theft of sensitive information is the most avoided thing because besides harming the user, it can also cause a loss of customer trust in a bank. Cyberattacks that are often carried out against mobile applications are phishing, hacking, theft, misuse of data, etc. Cyberattack can occur when a vulnerability is successfully exploited. OWASP mobile Top 10 has recorded as many as 10 vulnerabilities that are most commonly found in mobile applications. In the others, android permissions also have the potential to cause vulnerabilities. Therefore, an overview of the profile of the mobile banking application becomes an urgency that needs to be known. So that it is expected to be a consideration of the parties involved for improving security. In this study, an experiment has been conducted to capture the profile of the mobile banking applications in Indonesia based on android permission and OWASP mobile top 10 2016. The results show that there are six basic vulnerabilities based on OWASP Mobile Top 10 that are most commonly found in mobile banking applications in Indonesia, i.e. M1:Improper Platform Usage, M2:Insecure Data Storage, M3:Insecure Communication, M5:Insufficient Cryptography, M7:Client Code Quality, and M9:Reverse Engineering. The most permitted android permissions are the internet, status network access, and telephone read status.

Keywords: mobile banking application, OWASP mobile top 10 2016, android permission, sensitive information, financial loss

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Authors: Azadeh Bashiri

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Introduction: Lack of proper diagnosis and inadequate treatment of asthma leads to physical and financial complications. This study aimed to use data mining techniques and creating a neural network intelligent system for diagnosis of asthma. Methods: The study population is the patients who had visited one of the Lung Clinics in Tehran. Data were analyzed using the SPSS statistical tool and the chi-square Pearson's coefficient was the basis of decision making for data ranking. The considered neural network is trained using back propagation learning technique. Results: According to the analysis performed by means of SPSS to select the top factors, 13 effective factors were selected, in different performances, data was mixed in various forms, so the different models were made for training the data and testing networks and in all different modes, the network was able to predict correctly 100% of all cases. Conclusion: Using data mining methods before the design structure of system, aimed to reduce the data dimension and the optimum choice of the data, will lead to a more accurate system. Therefore, considering the data mining approaches due to the nature of medical data is necessary.

Keywords: asthma, data mining, Artificial Neural Network, intelligent system

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Authors: Christopher Muhawe, Masooda Bashir

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With increased Internet Communication Technology(ICT), the virtual world has become the new normal. At the same time, there is an unprecedented collection of massive amounts of data by both private and public entities. Unfortunately, this increase in data collection has been in tandem with an increase in data misuse and data breach. Regrettably, the majority of data breach and data misuse claims have been unsuccessful in the United States courts for the failure of proof of direct injury to physical or economic interests. The requirement to express data privacy harms from an economic or physical stance negates the fact that not all data harms are physical or economic in nature. The challenge is compounded by the fact that data breach harms and risks do not attach immediately. This research will use a descriptive and normative approach to show that not all data harms can be expressed in economic or physical terms. Expressing privacy harms purely from an economic or physical harm perspective negates the fact that data insecurity may result into harms which run counter the functions of privacy in our lives. The promotion of liberty, selfhood, autonomy, promotion of human social relations and the furtherance of the existence of a free society. There is no economic value that can be placed on these functions of privacy. The proposed approach addresses data harms from a psychological and social perspective.

Keywords: data breach and misuse, economic harms, privacy harms, psychological harms

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Authors: Subhash Nerella, Ziyuan Guan, Azra Bihorac, Parisa Rashidi

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Intensive care units (ICUs) provide care to critically ill patients in severe and life-threatening conditions. However, patient monitoring in the ICU is limited by the time and resource constraints imposed on healthcare providers. Many critical care indices such as mobility are still manually assessed, which can be subjective, prone to human errors, and lack granularity. Other important aspects, such as environmental factors, are not monitored at all. For example, critically ill patients often experience circadian disruptions due to the absence of effective environmental “timekeepers” such as the light/dark cycle and the systemic effect of acute illness on chronobiologic markers. Although the occurrence of delirium is associated with circadian disruption risk factors, these factors are not routinely monitored in the ICU. Hence, there is a critical unmet need to develop systems for precise and real-time assessment through novel enabling technologies. We have developed the mobility and circadian disruption quantification system (Mobi-DiQ) by augmenting biomarker and clinical data with pervasive sensing data to generate mobility and circadian cues related to mobility, nightly disruptions, and light and noise exposure. We hypothesize that Mobi-DiQ can provide accurate mobility and circadian cues that correlate with bedside clinical mobility assessments and circadian biomarkers, ultimately important for delirium risk assessment and prevention. The collected multimodal dataset consists of depth images, Electromyography (EMG) data, patient extremity movement captured by accelerometers, ambient light levels, Sound Pressure Level (SPL), and indoor air quality measured by volatile organic compounds, and the equivalent CO₂ concentration. For delirium risk assessment, the system recognizes mobility cues (axial body movement features and body key points) and circadian cues, including nightly disruptions, ambient SPL, and light intensity, as well as other environmental factors such as indoor air quality. The Mobi-DiQ system consists of three major components: the pervasive sensing system, a data storage and analysis server, and a data annotation system. For data collection, six local pervasive sensing systems were deployed, including a local computer and sensors. A video recording tool with graphical user interface (GUI) developed in python was used to capture depth image frames for analyzing patient mobility. All sensor data is encrypted, then automatically uploaded to the Mobi-DiQ server through a secured VPN connection. Several data pipelines are developed to automate the data transfer, curation, and data preparation for annotation and model training. The data curation and post-processing are performed on the server. A custom secure annotation tool with GUI was developed to annotate depth activity data. The annotation tool is linked to the MongoDB database to record the data annotation and to provide summarization. Docker containers are also utilized to manage services and pipelines running on the server in an isolated manner. The processed clinical data and annotations are used to train and develop real-time pervasive sensing systems to augment clinical decision-making and promote targeted interventions. In the future, we intend to evaluate our system as a clinical implementation trial, as well as to refine and validate it by using other data sources, including neurological data obtained through continuous electroencephalography (EEG).

Keywords: deep learning, delirium, healthcare, pervasive sensing

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Authors: Chandra Prayaga, Aaron Wade, Lakshmi Prayaga, Gopi Shankar Mallu

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This paper presents the use of machine learning algorithms to predict the success of students in an introductory physics course. Data having 140 rows pertaining to the performance of two batches of students was used. The lack of sufficient data to train robust machine learning models was compensated for by generating synthetic data similar to the real data. CTGAN and CTGAN with Gaussian Copula (Gaussian) were used to generate synthetic data, with the real data as input. To check the similarity between the real data and each synthetic dataset, pair plots were made. The synthetic data was used to train machine learning models using the PyCaret package. For the CTGAN data, the Ada Boost Classifier (ADA) was found to be the ML model with the best fit, whereas the CTGAN with Gaussian Copula yielded Logistic Regression (LR) as the best model. Both models were then tested for accuracy with the real data. ROC-AUC analysis was performed for all the ten classes of the target variable (Grades A, A-, B+, B, B-, C+, C, C-, D, F). The ADA model with CTGAN data showed a mean AUC score of 0.4377, but the LR model with the Gaussian data showed a mean AUC score of 0.6149. ROC-AUC plots were obtained for each Grade value separately. The LR model with Gaussian data showed consistently better AUC scores compared to the ADA model with CTGAN data, except in two cases of the Grade value, C- and A-.

Keywords: machine learning, student success, physics course, grades, synthetic data, CTGAN, gaussian copula CTGAN

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Authors: Dasat G. S., Christopher F. Tim, J. Dun C.

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Salt marshes are known to sequester carbon dioxide from the atmosphere into the soil, but natural and anthropogenic activities could trigger the release of large quantities of centuries of buried carbon dioxide, methane and nitrous oxide (CO2, CH4 and N2O) which are the major greenhouse gases (GHGs) implicated with climate change. Therefore, this study investigated the biogeochemical activities by collecting soil samples from low, mid and high zones of the Cefni salt marsh, within the Maltreat estuary, on the island of Anglesey, north Wales, United Kingdom for a consortium of laboratory based experiments using standard operating protocols (POS) to quantify the soil organic matter contents and the rate of microbial decomposition and carbon storage at the Carbon Capture Laboratory of Bangor University Wales. Results of investigations reveals that the mid zone had 56.23% and 9.98% of soil water and soil organic matter (SOM) contents respectively higher than the low and high zones. Phenol oxidase activity (1193.53µmol dicq g-1 h-1) was highest at the low zone in comparison to the high and mid zones (867.60 and 608.74 µmol dicq g-1 h-1) respectively. Soil phenolic concentration was found to be highest in the mid zone (53.25 µg-1 g-1) when compared with those from the high (15.66 µg-1 g-1) and low (4.18 µg-1 g-1) zones respectively. Activities of hydrolase enzymes showed similar trend for the high and low zones and much lower activities in the mid zone. CO2 flux from the mid zone (6.79 ug g-1 h-1) was significantly greater than those from high (-2.29 ug g-1 h-1) and low (1.30 µg g-1 h-1) zones. Since salt marshes provide essential ecosystem services, their degradation or alteration in whatever form could compromise such ecosystem services and could convert them from net sinks into net sources with consequential effects to the global environment.

Keywords: saltmarsh, decomposition, carbon cycling, enzymes

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Authors: Chuping Lo

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This paper presents a simple model demonstrating that ceteris paribus countries with lower barriers to accessing global data tend to earn higher incomes than other countries. Therefore, large countries that inherently have greater data resources tend to have higher incomes than smaller countries, such that the former may be more hesitant than the latter to liberalize cross-border data flows to maintain this advantage. Furthermore, countries with higher artificial intelligence (AI) intensity in production technologies tend to benefit more from economies of scale in data aggregation, leading to higher income and more trade as they are better able to utilize global data.

Keywords: digital intensity, digital divide, international trade, scale of economics

Procedia PDF Downloads 53