Search results for: waste classification

Authors: Yi-Guang Li, Suresh Sampath

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Gas turbine performance degrades over time, and the degradation is greatly affected by environmental, ambient, and operating conditions. The engines may degrade slowly under favorable conditions and result in a waste of engine life if a scheduled maintenance scheme is followed. They may also degrade fast and fail before a scheduled overhaul if the conditions are unfavorable, resulting in serious secondary damage, loss of engine availability, and increased maintenance costs. To overcome these problems, gas turbine owners are gradually moving from scheduled maintenance to condition-based maintenance, where condition monitoring is one of the key supporting technologies. This paper presents an integrated adaptive GPA diagnostics and performance monitoring system developed at Cranfield University for gas turbine gas path condition monitoring. It has the capability to predict the performance degradation of major gas path components of gas turbine engines, such as compressors, combustors, and turbines, using gas path measurement data. It is also able to predict engine key performance parameters for condition monitoring, such as turbine entry temperature that cannot be directly measured. The developed technology has been implemented into digital twin computer Software, Pythia, to support the condition monitoring of gas turbine engines. The capabilities of the integrated GPA condition monitoring system are demonstrated in three test cases using a model gas turbine engine similar to the GE aero-derivative LM2500 engine widely used in power generation and marine propulsion. It shows that when the compressor of the model engine degrades, the Adaptive GPA is able to predict the degradation and the changing engine performance accurately using gas path measurements. Such a presented technology and software are generic, can be applied to different types of gas turbine engines, and provide crucial engine health and performance parameters to support condition monitoring and condition-based maintenance.

Keywords: gas turbine, adaptive GPA, performance, diagnostics, condition monitoring

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Authors: Hsiao-Tien Pao, Yi-Ying Li, Hsin-Chia Fu

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This paper addressed the impacts of energy consumption, economic growth, financial development, and population size on environmental degradation using grey relational analysis (GRA) for China, where foreign direct investment (FDI) inflows is the proxy variable for financial development. The more recent historical data during the period 2004–2011 are used, because the use of very old data for data analysis may not be suitable for rapidly developing countries. The results of the GRA indicate that the linkage effects of energy consumption–emissions and GDP–emissions are ranked first and second, respectively. These reveal that energy consumption and economic growth are strongly correlated with emissions. Higher economic growth requires more energy consumption and increasing environmental pollution. Likewise, more efficient energy use needs a higher level of economic development. Therefore, policies to improve energy efficiency and create a low-carbon economy can reduce emissions without hurting economic growth. The finding of FDI–emissions linkage is ranked third. This indicates that China do not apply weak environmental regulations to attract inward FDI. Furthermore, China’s government in attracting inward FDI should strengthen environmental policy. The finding of population–emissions linkage effect is ranked fourth, implying that population size does not directly affect CO₂ emissions, even though China has the world’s largest population, and Chinese people are very economical use of energy-related products. Overall, the energy conservation, improving efficiency, managing demand, and financial development, which aim at curtailing waste of energy, reducing both energy consumption and emissions, and without loss of the country’s competitiveness, can be adopted for developing economies. The GRA is one of the best way to use a lower data to build a dynamic analysis model.

Keywords: China, CO₂ emissions, foreign direct investment, grey relational analysis

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Authors: Jerzy Merkisz, Pawel Fuc, Piotr Lijewski, Andrzej Ziolkowski, Pawel Czarkowski

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The paper presents preliminary results of the development of an automotive exhaust gas energy recovery module. The aim of the performed analyses was to select the geometry of the heat exchanger that would ensure the highest possible transfer of heat at minimum heat flow losses. The starting point for the analyses was a straight portion of a pipe, from which the exhaust system of the tested vehicle was made. The design of the heat exchanger had a cylindrical cross-section, was 300 mm long and was fitted with a diffuser and a confusor. The model works were performed for the mentioned geometry utilizing the finite volume method based on the Ansys CFX v12.1 and v14 software. This method consisted in dividing of the system into small control volumes for which the exhaust gas velocity and pressure calculations were performed using the Navier-Stockes equations. The heat exchange in the system was modeled based on the enthalpy balance. The temperature growth resulting from the acting viscosity was not taken into account. The heat transfer on the fluid/solid boundary in the wall layer with the turbulent flow was done based on an arbitrarily adopted dimensionless temperature. The boundary conditions adopted in the analyses included the convective condition of heat transfer on the outer surface of the heat exchanger and the mass flow and temperature of the exhaust gas at the inlet. The mass flow and temperature of the exhaust gas were assumed based on the measurements performed in actual traffic using portable PEMS analyzers. The research object was a passenger vehicle fitted with a 1.9 dm3 85 kW diesel engine. The tests were performed in city traffic conditions.

Keywords: waste heat recovery, heat exchanger, CFD simulation, pems

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Authors: Mandeep Kaur, Jitendra K. Bera

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The search for atom-economical and green synthetic methods for the synthesis of functionalized molecules has attracted much attention. Metal ligand cooperation (MLC) plays a pivotal role in organometallic catalysis to activate C−H, H−H, O−H, N−H and B−H bonds through reversible bond breaking and bond making process. Towards this goal, a bifunctional N─heterocyclic carbene (NHC) based pyridyl-functionalized amide ligand precursor, and corresponding dearomatized iridium complex was synthesized. The NMR and UV/Vis acid titration study have been done to prove the proton response nature of the iridium complex. Further, the dearomatized iridium complex explored as a catalyst on the platform of MLC via dearomatzation/aromatization mode of action towards atom economical α and β─alkylation of ketones and secondary alcohols by using primary alcohols through hydrogen borrowing methodology. The key features of the catalysis are high turnover frequency (TOF) values, low catalyst loading, low base loading and no waste product. The greener syntheses of quinoline, lactone derivatives and selective alkylation of drug molecules like pregnenolone and testosterone were also achieved successfully. Another structurally similar iridium complex was also synthesized with modified ligand precursor where a pendant amide unit was absent. The inactivity of this analogue iridium complex towards catalysis authenticated the participation of proton responsive imido sidearm of the ligand to accelerate the catalytic reaction. The mechanistic investigation through control experiments, NMR and deuterated labeling study, authenticate the borrowing hydrogen strategy.

Keywords: C-C bond formation, hydrogen borrowing, metal ligand cooperation (MLC), n-heterocyclic carbene

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Authors: Alok Kalra

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Consumers and practitioners (medical herbalists, pharmacists, and aromatherapists) with strong and increased awareness about health and environment demand organically grown medicinal and aromatic plants (MAPs) to offer a valued product. As the system does not permit the use of synthetic fertilizers the use of nutrient rich organic manures is extremely important. CSIR-CIMAP has developed a complete recycling package for managing distillation and agro-waste of medicinal and aromatic plants for production of superior quality vermicompost involving microbes capable of producing high amounts of humic acid. The major benefits being faster composting period and nutrient rich vermicompost; a nutrient advantage of about 100-150% over the most commonly used organic manure (FYM). At CSIR-CIMAP, strains of microbial inoculants with multiple activities especially strains useful both as biofertilizers and biofungicide and consortia of microbes possessing diverse functional activities have been developed. CSIR-CIMAP has also initiated a program where a large number of accessions are being screened for identifying organic proficient genotypes in mints, ashwagandha, geranium and safed musli. Some of the natural plant growth promoters like calliterpenones from the plant Callicarpa macrophylla has been tested successfully for induction of rooting in stem cuttings and improving growth and yield of various crops. Some of the microbes especially the endophytes have even been identified improving the active constituents of medicinal and aromatic plants. The above said scientific interventions making organic farming a charming proposition would be discussed in details.

Keywords: organic agriculture, microbial inoculants, organic fertilizers, natural plant growth promoters

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Authors: H. M. B. N Jayasinghe

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The water is found almost everywhere on Earth. Water resources contain a lot of pollution. Some diseases can be spread through the water to the living beings. So to be clean water it should undergo a number of treatments necessary to make it drinkable. So it is must to have purification technology for the wastewater. So the waste water treatment plants act a major role in these issues. When considering the procedures taken after the water treatment process was always based on manual calculations and recordings. Water purification plants may interact with lots of manual processes. It means the process taking much time consuming. So the final evaluation and chemical, biological treatment process get delayed. So to prevent those types of drawbacks there are some computerized programmable calculation and analytical techniques going to be introduced to the laboratory staff. To solve this problem automated system will be a solution in which guarantees the rational selection. A decision support system is a way to model data and make quality decisions based upon it. It is widely used in the world for the various kind of process automation. Decision support systems that just collect data and organize it effectively are usually called passive models where they do not suggest a specific decision but only reveal information. This web base system is based on global positioning data adding facility with map location. Most worth feature is SMS and E-mail alert service to inform the appropriate person on a critical issue. The technological influence to the system is HTML, MySQL, PHP, and some other web developing technologies. Current issues in the computerized water chemistry analysis are not much deep in progress. For an example the swimming pool water quality calculator. The validity of the system has been verified by test running and comparison with an existing plant data. Automated system will make the life easier in productively and qualitatively.

Keywords: automated system, wastewater, purification technology, map location

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Authors: Xiaoxian Zhang

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Guidelines of ecological risk assessment (ERA) and food safety assessment (FSA) used nowadays, based on an S-shaped threshold dose-response curve (SDR), fail to consider hormesis, a reproducible biphasic dose-response model represented as a J-shaped or an inverted U-shaped curve, that occurs in the real-life environment across multitudinous compounds on cells, organisms, populations, and even the ecosystem. Specifically, in SDR-based ERA and FSA practice, predicted no effect concentration (PNEC) is calculated separately for individual substances from no observed effect concentration (NOEC, usually equivalent to 10% effect concentration (EC10) of a contaminant or food condiment) over an assessment coefficient that is bigger than 1. Experienced researchers doubted that hormesis in the real-life environment might lead to a waste of limited human and material resources in ERA and FSA practice, but related data are scarce. In this study, hormetic effects on bioluminescence of Aliivibrio fischeri (A. f) induced by sulfachloropyridazine (SCP) under 40 conditions to simulate the real-life scenario were investigated, and hormetic effects on growth of human MCF-7 cells caused by brown sugar and mascavado sugar were found likewise. After comparison of related parameters, it has for the first time been proved that there is a 50% probability for safe concentration (SC) of contaminants and food condiments to fall within the hormetic-stimulatory range (HSR) or left to HSR, revealing the unreliability of traditional parameters in standardized (eco)toxicological studies, and supporting qualitatively and quantitatively the over-strictness of ERA and FSA resulted from misuse of SDR. This study provides a novel perspective for ERA and FSA practitioners that hormesis should dominate and conditions where SDR works should only be singled out on a specific basis.

Keywords: dose-response relationship, food safety, ecological risk assessment, hormesis

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Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

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Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should properly evaluate their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, Neural Networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable to offer an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

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Authors: Fatemeh Rezaei, Mohammad H. Yarmohammadian, Masoud Ferdosi, Abbas Haghshnas

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Background: Failure Modes and Effect Analysis is now having known as the main methods of risk assessment and the accreditation requirements for many organizations. The Risk Priority Number (RPN) approach is generally preferred, especially for its easiness of use. Indeed it does not require statistical data, but it is based on subjective evaluations given by the experts about the Occurrence (O i), the Severity (Si) and the Detectability (D i) of each cause of failure. Methods: This study is a quantitative – qualitative research. In terms of qualitative dimension, method of focus groups with inductive approach is used. To evaluate the results of the qualitative study, quantitative assessment was conducted to calculate RPN score. Results; We have studied patient’s journey process in surgery ward and the most important phase of the process determined Transport of the patient from the holding area to the operating room. Failures of the phase with the highest priority determined by defining inclusion criteria included severity (clinical effect, claim consequence, waste of time and financial loss), occurrence (time- unit occurrence and degree of exposure to risk) and preventability (degree of preventability and defensive barriers) and quantifying risks priority criteria in the context of RPN index. Ability of improved RPN reassess by root cause (RCA) analysis showed some variations. Conclusions: Finally, It could be concluded that understandable criteria should have been developed according to personnel specialized language and communication field. Therefore, participation of both technical and clinical groups is necessary to modify and apply these models.

Keywords: failure mode, effects analysis, risk priority number(RPN), health system, risk assessment

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Authors: Mumtaz Khan, Adeel Shahid, Waqas Khan

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Heavy metals presence in water streams is a big danger for aquatic life and ultimately effects human health. Removal of copper (Cu) by ispaghula husk, maize fibre, and maize oil cake from synthetic solution in batch conditions was studied. Different experimental parameters such as contact time, initial solution pH, agitation rate, initial Cu concentration, biosorbent concentration, and biosorbent particle size has been studied to quantify the Cu biosorption. The rate of adsorption of metal ions was very fast at the beginning and became slow after reaching the saturation point, followed by a slower active metabolic uptake of metal ions into the cells. Up to a certain point, (pH=4, concentration of Cu = ~ 640 mg/l, agitation rate = ~ 400 rpm, biosorbent concentration = ~ 0.5g, 3g, 3g for ispaghula husk, maize fiber and maize oil cake, respectively) increasing the pH, concentration of Cu, agitation rate, and biosorbent concentration, increased the biosorption rate; however the sorption capacity increased by decreasing the particle size. At optimized experimental parameters, the maximum Cu biosorption by ispaghula husk, maize fibre and maize oil cake were 86.7%, 59.6% and 71.3%, respectively. Moreover, the results of the kinetics studies demonstrated that the biosorption of copper on ispaghula husk, maize fibre, and maize oil cake followed pseudo-second order kinetics. The results of adsorption were fitted to both the Langmuir and Freundlich models. The Langmuir model represented the sorption process better than Freundlich, and R² value ~ 0.978. Optimizations of physical and environmental parameters revealed, ispaghula husk as more potent copper biosorbent than maize fibre, and maize oil cake. The sorbent is cheap and available easily, so this study can be applied to remove Cu impurities on pilot and industrial scale after certain modifications.

Keywords: biosorption, copper, ispaghula husk, maize fibre, maize oil cake, purification

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Authors: Wanderson Goncalves e Goncalves, José Alberto Silva de Sá

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Human actions are a threat to the balance and conservation of the Amazon forest. Therefore the environmental monitoring services play an important role as the preservation and maintenance of this environment. This study classified forest types using data from a forest inventory provided by the 'Florestal e da Biodiversidade do Estado do Pará' (IDEFLOR-BIO), located between the municipalities of Santarém, Juruti and Aveiro, in the state of Pará, Brazil, covering an area approximately of 600,000 hectares, Bands 3, 4 and 5 of the TM-Landsat satellite image, and Self - Organizing Maps. The information from the satellite images was extracted using QGIS software 2.8.1 Wien and was used as a database for training the neural network. The midpoints of each sample of forest inventory have been linked to images. Later the Digital Numbers of the pixels have been extracted, composing the database that fed the training process and testing of the classifier. The neural network was trained to classify two forest types: Rain Forest of Lowland Emerging Canopy (Dbe) and Rain Forest of Lowland Emerging Canopy plus Open with palm trees (Dbe + Abp) in the Mamuru Arapiuns glebes of Pará State, and the number of examples in the training data set was 400, 200 examples for each class (Dbe and Dbe + Abp), and the size of the test data set was 100, with 50 examples for each class (Dbe and Dbe + Abp). Therefore, total mass of data consisted of 500 examples. The classifier was compiled in Orange Data Mining 2.7 Software and was evaluated in terms of the confusion matrix indicators. The results of the classifier were considered satisfactory, and being obtained values of the global accuracy equal to 89% and Kappa coefficient equal to 78% and F1 score equal to 0,88. It evaluated also the efficiency of the classifier by the ROC plot (receiver operating characteristics), obtaining results close to ideal ratings, showing it to be a very good classifier, and demonstrating the potential of this methodology to provide ecosystem services, particularly in anthropogenic areas in the Amazon.

Keywords: artificial neural network, computational intelligence, pattern recognition, unsupervised learning

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Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

Abstract:

Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should evaluate properly their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, neural networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable of offering an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

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Authors: Mulubrhan Berihu, Supratic Gupta, Zena Gebriel

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Due to the design versatility, availability, and cost efficiency, concrete is continuing to be the most used construction material on earth. However, the production of Portland cement, the primary component of concrete mix is causing to have a serious effect on environmental and economic impacts. This shows there is a need to study using of supplementary cementitious materials (SCMs). The most commonly used supplementary cementitious materials are wastes and the use of these industrial waste products has technical, economical and environmental benefits besides the reduction of CO2 emission from cement production. The study aims to document the effect on strength property of concrete due to use of low cement by maximizing supplementary cementitious materials like fly ash or marble powder. Based on the different mix proportion of pozzolana and marble powder a range of mix design was formulated. The first part of the project is to study the strength of low cement concrete using fly ash replacement experimentally. The test results showed that using up to 85 kg/m3 of cement is possible for plain concrete works like hollow block concrete to achieve 9.8 Mpa and the experimental results indicates that strength is a function of w/b. In the second part a new set of mix design has been carried out with fly ash and marble powder to study the strength of both binary and ternary mixes. In this experimental study, three groups of mix design (c+FA, c+FA+m and c+m), four sets of mixes for each group were taken up. Experimental results show that c+FA has maintained the best strength and impermeability whereas c+m obtained less compressive strength, poorer permeability and split tensile strength. c+FA shows a big difference in gaining of compressive strength from 7 days to 28 days compression strength compared to others and this obviously shows the slow rate of hydration of fly ash concrete. As the w/b ratio increases the strength decreases significantly. At the same time higher permeability has been seen in the specimens which were tested for three hours than one hour.

Keywords: efficiency factor, cement content, compressive strength, mix proportion, w/c ratio, water permeability, SCMs

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Authors: Paschal A. Ubi, Salawu Abdul Rahman Asipita

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The use of suitable engineering materials which poses less harm to ,an and the environment is sort for in recent times, thus giving rise to polymer composites filled with natural organic reinforcement which are biodegradable. Treatment of natural fibres is essential in improving matrix to filler adhesion, hence improving its mechanical properties. In this study, investigations were carried out to determine the effect of sodium hydroxide treatment on the tensile, flexural, impact and hardness properties of crushed and uncrushed luffa cylindrica fibre reinforced recycled low density polyethylene composites. The LC (Luffa Cylindrica) fibres were treated with 0%, 2%, 4%, 6%, 8%, and 10% wt. NaOH concentrations for a period of 24 hours under room temperature conditions. The compounding of the waste LDPE was done using a two roll mill at a temperature of 150 oC and cured in a hydraulic press at a temperature of 150oC for 3 minutes at 3 metric tonnes. A formulation of 20/80g (reinforcement to matrix ratio in grams) was maintained for all fabricated samples. Analysis of the results showed that the uncrushed luffa fibre samples gave better mechanical properties compared with the crushed luffa fibre samples. The uncrushed luffa fibre composites had optimum tensile and flexural strengths of 7.65MPa and 17.08Mpa respectively corresponding to a young modulus and flexural modulus of 21.08MPa and 232.22MPa for the 8% and 4%wt. NaOH concentration respectively. Results obtained in the research showed that NaOH treatment with the 8% NaOH concentration improves the mechanical properties of the LC fibre reinforced composites when compared with other NaOH treatment concentration values.

Keywords: LC fibres, NaOH concentration, LC/rLDPE composite, tensile strength, flexural strength

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Authors: Junaid Asimiyu Mohammed, Olakunle Docas Tosin

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The housing environment in many developing countries is fraught with risks that have potential negative impacts on the lives of the residents. The study examined the risk factors in residential areas of two neighborhoods in Bosso Local Government Areas of Minna in Nigeria with a view to determining the level of their potential impacts. A sample of 378 households was drawn from the estimated population of 22,751 household heads. The questionnaire and direct observation were used as instruments for data collection. The data collected were analyzed using the Relative Importance Index (RII) rule to determine the level of the potential impact of the risk factors while ArcGIS was used for mapping the spatial distribution of the risks. The study established that the housing environment of Angwan Biri and El-Waziri areas of Bosso is poor and vulnerable as 26% of the houses were not habitable and 57% were only fairly habitable. The risks of epidemics, building collapse and rainstorms were evident in the area as 53% of the houses had poor ventilation; 20% of residents had no access to toilets; 47% practiced open waste dumping; 46% of the houses had cracked walls while 52% of the roofs were weak and sagging. The results of the analysis of the potential impact of the risk factors indicate a RII score of 0.528 for building collapse, 0.758 for rainstorms and 0.830 for epidemics, indicating a moderate to very high level of potential impacts. The mean RII score of 0.639 shows a significant potential impact of the risk factors. The study recommends the implementation of sanitation measures, provision of basic urban facilities and neighborhood revitalization through housing infrastructure retrofitting as measures to mitigate the risks of disasters and improve the living conditions of the residents of the study area.

Keywords: assessment, risk, residential, Nigeria

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Authors: Yude Wu

Abstract:

Mongolia, grappling with significant socio-economic challenges, faces pressing issues of inequality and poverty, as evidenced by a high Gini coefficient and the highest poverty rate among the top 20 largest Asian countries. Despite government efforts, Mongolia's poverty rate experienced only a slight reduction from 29.6 percent in 2016 to 27.8 percent in 2020. PEER countries, such as South Africa, Botswana, Kazakhstan, and Peru, share characteristics with Mongolia, including reliance on the mining industry and classification as lower middle-income countries. Successful transitions of these countries to upper middle-income status between 1994 and the 2010s provide valuable insights. Drawing on secondary analyses of existing research and PEER country profiles, the study evaluates past policies, identifies gaps in current approaches, and proposes recommendations to combat poverty sustainably. The hypothesis includes a reliance on the mining industry and a transition from lower to upper middle-income status. Policies from these countries, such as the GEAR policy in South Africa and economic diversification in Botswana, offer insights into Mongolia's development. This essay aims to illuminate the multidimensional nature of underdevelopment in Mongolia through a secondary analysis of existing research and PEER country profiles, evaluating past policies, identifying gaps in current approaches, and providing recommendations for sustainable progress. Drawing inspiration from PEER countries, Mongolia can implement policies such as economic diversification to reduce vulnerability and create stable job opportunities. Emphasis on infrastructure, human capital, and strategic partnerships for Foreign Direct Investment (FDI) aligns with successful strategies implemented by PEER countries, providing a roadmap for Mongolia's development objectives.

Keywords: inequality, PEER countries, comparative analysis, nomadic animal husbandry, sustainable growth

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Authors: Usama Mohamed, Sam Booth, Aliysn J. Nedoma

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As part of the transition to electric vehicles, there has been a recent increase in demand for battery manufacturing. Cathodes typically account for approximately 50% of the total lithium-ion battery cell cost and are a pivotal factor in determining the viability of new industrial infrastructure. Cathodes which offer lower costs whilst maintaining or increasing performance, such as nickel-rich layered cathodes, have a significant competitive advantage when scaling up the manufacturing process. This project evaluates the techno-economic value proposition of an integrated industrial scale cathode active material (CAM) production process, closing the mass and energy balances, and optimizing the operation conditions using a sensitivity analysis. This is done by developing a process model of a co-precipitation synthesis route using Aspen Plus software and validated based on experimental data. The mechanism chemistry and equilibrium conditions were established based on previous literature and HSC-Chemistry software. This is then followed by integrating the energy streams, adding waste recovery and treatment processes, as well as testing the effect of key parameters (temperature, pH, reaction time, etc.) on CAM production yield and emissions. Finally, an economic analysis estimating the fixed and variable costs (including capital expenditure, labor costs, raw materials, etc.) to calculate the cost of CAM ($/kg and $/kWh), total plant cost ($) and net present value (NPV). This work sets the foundational blueprint for future research into sustainable industrial scale processes for CAM manufacturing.

Keywords: cathodes, industrial production, nickel-rich layered cathodes, process modelling, techno-economic analysis

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Authors: Irene Yi

Abstract:

Machine learning, natural language processing, and neural network models of language are becoming more and more prevalent in the fields of technology and linguistics today. Training data for machines are at best, large corpora of human literature and at worst, a reflection of the ugliness in society. Computational linguistics is a growing field dealing with such issues of data collection for technological development. Machines have been trained on millions of human books, only to find that in the course of human history, derogatory and sexist adjectives are used significantly more frequently when describing females in history and literature than when describing males. This is extremely problematic, both as training data, and as the outcome of natural language processing. As machines start to handle more responsibilities, it is crucial to ensure that they do not take with them historical sexist and misogynistic notions. This paper gathers data and algorithms from neural network models of language having to deal with syntax, semantics, sociolinguistics, and text classification. Computational analysis on such linguistic data is used to find patterns of misogyny. Results are significant in showing the existing intentional and unintentional misogynistic notions used to train machines, as well as in developing better technologies that take into account the semantics and syntax of text to be more mindful and reflect gender equality. Further, this paper deals with the idea of non-binary gender pronouns and how machines can process these pronouns correctly, given its semantic and syntactic context. This paper also delves into the implications of gendered grammar and its effect, cross-linguistically, on natural language processing. Languages such as French or Spanish not only have rigid gendered grammar rules, but also historically patriarchal societies. The progression of society comes hand in hand with not only its language, but how machines process those natural languages. These ideas are all extremely vital to the development of natural language models in technology, and they must be taken into account immediately.

Keywords: computational analysis, gendered grammar, misogynistic language, neural networks

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Authors: Aawag Mohsen Al-Awag, Wesam Salah Alaloul, M. S. Liew

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Industrialized building system (IBS) is a method of construction with concentrated practices consisting of techniques, products, and a set of linked elements which operate collectively to accomplish objectives. The Industrialised Building System (IBS) has been recognised as a viable method for improving overall construction performance in terms of quality, cost, safety and health, waste reduction, and productivity. The Malaysian construction industry is considered one of the contributors to the development of the country. The acceptance level of IBS is still below government expectations. Thus, the Malaysian government has been continuously encouraging the industry to use and implement IBS. Conventional systems have several drawbacks, including project delays, low economic efficiency, excess inventory, and poor product quality. When it comes to implementing IBS, construction companies still face several obstacles and problems, notably in terms of contractual and procurement concerns, which leads to the low adoption of IBS in Malaysia. There are barriers to the acceptance of IBS technology, focused on awareness of historical failure and risks connected to IBS practices to provide enhanced performance. Therefore, the transformation from the existing conventional building systems to the industrialized building systems (IBS) is needed more than ever. The flexibility of IBS in Malaysia’s construction industry is very low due to numerous shortcomings and obstacles. Due to its environmental, economic, and social benefits, IBS could play a significant role in the Malaysian construction industry in the future. This paper concentrates on the potential role of IBS in sustainable construction practices in Malaysia. It also highlights the awareness, barriers, advantages, and disadvantages of IBS in the construction sector. The study concludes with recommendations for Malaysian construction stakeholders to encourage and increase the utilization of industrialised building systems.

Keywords: construction industry, industrialized building system, barriers, advantages and disadvantages, construction, sustainability, Malaysia

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Authors: Noah G. Akhimien, Eshrar Latif

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The natural environment is in need for an urgent rescue due to dilapidation and recession of resources. Passive design strategies have proven to be one of the effective ways to reduce CO₂ emissions and to improve building performance. On the other hand, there is a huge drop in material availability due to poor recycling culture. Consequently, building waste pose environmental hazard due to unrecycled building materials from construction and deconstruction. Buildings are seen to be material banks for a circular economy, therefore incorporating circular economy into passive housing will not only safe guide the climate but also improve resource efficiency. The study focuses on incorporating a circular economy in passive design strategies for an affordable energy and resource efficient residential building in Nigeria. Carbon dioxide (CO₂) concentration is still on the increase as buildings are responsible for a significant amount of this emission globally. Therefore, prompt measures need to be taken to combat the effect of global warming and associated threats. Nigeria is rapidly growing in human population, resources on the other hand have receded greatly, and there is an abrupt need for recycling even in the built environment. It is necessary that Nigeria responds to these challenges effectively and efficiently considering building resource and energy. Passive design strategies were assessed using simulations to obtain qualitative and quantitative data which were inferred to case studies as it relates to the Nigeria climate. Building materials were analysed using the ReSOLVE model in order to explore possible recycling phase. This provided relevant information and strategies to illustrate the possibility of circular economy in passive buildings. The study offers an alternative approach, as it is the general principle for the reworking of an economy on ecological lines in passive housing and by closing material loops in circular economy.

Keywords: building, circular, efficiency, environment, sustainability

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Authors: Forouzan Salehi Fergeni

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Converting the movement intents of a person into commands for action employing brain signals like electroencephalogram signals is a brain-computer interface (BCI) system. When left or right-hand motions are imagined, different patterns of brain activity appear, which can be employed as BCI signals for control. To make better the brain-computer interface (BCI) structures, effective and accurate techniques for increasing the classifying precision of motor imagery (MI) based on electroencephalography (EEG) are greatly needed. Subject dependency and non-stationary are two features of EEG signals. So, EEG signals must be effectively processed before being used in BCI applications. In the present study, after applying an 8 to 30 band-pass filter, a car spatial filter is rendered for the purpose of denoising, and then, a method of analysis of variance is used to select more appropriate and informative channels from a category of a large number of different channels. After ordering channels based on their efficiencies, a sequential forward channel selection is employed to choose just a few reliable ones. Features from two domains of time and wavelet are extracted and shortlisted with the help of a statistical technique, namely the t-test. Finally, the selected features are classified with different machine learning and neural network classifiers being k-nearest neighbor, Probabilistic neural network, support-vector-machine, Extreme learning machine, decision tree, Multi-layer perceptron, and linear discriminant analysis with the purpose of comparing their performance in this application. Utilizing a ten-fold cross-validation approach, tests are performed on a motor imagery dataset found in the BCI competition III. Outcomes demonstrated that the SVM classifier got the greatest classification precision of 97% when compared to the other available approaches. The entire investigative findings confirm that the suggested framework is reliable and computationally effective for the construction of BCI systems and surpasses the existing methods.

Keywords: brain-computer interface, channel selection, motor imagery, support-vector-machine

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Authors: Ashmita Mukherjee, Yogesh Harishchandra Kabutare, Suritra Bandyopadhyay, Paulomi Ghosh

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Uncontrolled bleeding in the battlefield and the operation rooms can lead to serious injuries, trauma and even be lethal. Keratin was reported to be a haemostatic material which rapidly activates thrombin followed by activation of fibrinogen leading to the formation of insoluble fibrin. Also platelets, the main initiator of haemostasis are reported to adhere to keratin. However, the major limitation of pure keratin as a biomaterial is its poor physical property and corresponding low mechanical strength. To overcome this problem, keratin was cross-linked with alginate to increase its mechanical stability. In our study, Keratin extracted from feather waste showed yield of 80.5% and protein content of 8.05 ± 0.43 mg/mL (n=3). FTIR and CD spectroscopy confirmed the presence of the essential functional groups and preservation of the secondary structures of keratin. The keratin was then cross-linked with alginate to make a dope. The dope was used to draw fibers of desired diameters in a suitable coagulation bath using a customized wet spinning setup. The resultant morphology of keratin fibers was observed under a brightfield microscope. The FT-IR analysis implied that there was a presence of both keratin and alginate peaks in the fibers. The cross-linking was confirmed in the keratin alginate fibers by a shift of the amide A and amide B peaks towards the right and disappearance of the peak for N-H stretching (1534.68 cm-1). Blood was drawn in citrate vacutainers for whole blood clotting test and blood clotting kinetics, which showed that the keratin fibers could accelerate blood coagulation compared to that of alginate fibers and tissue culture plate. Additionally, cross-linked keratin-alginate fiber was found to have lower haemolytic potential compared to alginate fiber. Thus, keratin cross-linked fibers can have potential applications to combat unrestrained bleeding.

Keywords: biomaterial, biowaste, fiber, keratin

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Authors: Pinel Sebastien, Bourrin François, De Madron Du Rieu Xavier, Ludwig Wolfgang, Arnau Pedro

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Heavy precipitation constitutes a major meteorological threat in the western Mediterranean. Research has investigated the relationship between the states of the Mediterranean Sea and the atmosphere with the precipitation for short temporal windows. However, at a larger temporal scale, the precursor signals of heavy rainfall in the sea and atmosphere have drawn little attention. Moreover, despite ongoing improvements in numerical weather prediction, the medium-term forecasting of rainfall events remains a difficult task. Here, we aim to investigate the influence of early-spring environmental parameters on the following autumnal heavy precipitations. Hence, we develop a machine learning model to predict extreme autumnal rainfall with a 6-month lead time over the Spanish Catalan coastal area, based on i) the sea pattern (main current-LPC and Sea Surface Temperature-SST) at the mesoscale scale, ii) 4 European weather teleconnection patterns (NAO, WeMo, SCAND, MO) at synoptic scale, and iii) the hydrological regime of the main local river (Rhône River). The accuracy of the developed model classifier is evaluated via statistical analysis based on classification accuracy, logarithmic and confusion matrix by comparing with rainfall estimates from rain gauges and satellite observations (CHIRPS-2.0). Sensitivity tests are carried out by changing the model configuration, such as sea SST, sea LPC, river regime, and synoptic atmosphere configuration. The sensitivity analysis suggests a negligible influence from the hydrological regime, unlike SST, LPC, and specific teleconnection weather patterns. At last, this study illustrates how public datasets can be integrated into a machine learning model for heavy rainfall prediction and can interest local policies for management purposes.

Keywords: extreme hazards, sensitivity analysis, heavy rainfall, machine learning, sea-atmosphere modeling, precipitation forecasting

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Authors: Nwogiji Cletus Olando, Oselebe Happiness Ogba, Enoch Achigan-Dako

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Digitaria iburua, commonly known as black fonio, is a cereal crop native to Africa and extensively cultivated by smallholder farmers in Northern Benin, Togo, and Nigeria. This crop holds immense nutritional and socio-cultural value. Unfortunately, limited knowledge about its genetic diversity exists due to a lack of scientific attention. As a result, its potential for improvement in food and agriculture remains largely untapped. To address this gap, a study was conducted using 41 accessions of D. iburua stored in the genebank of the Laboratory of Genetics, Biotechnology, and Seed Science at Abomey-Calavi University, Benin. The study employed both morphological and simple sequence repeat (SSR) markers to evaluate the genetic variability of the accessions. Agro-morphological assessments were carried out during the 2020 cropping season, utilizing an alpha lattice design with three replications. The collected data encompassed qualitative and quantitative traits. Additionally, molecular variability was assessed using eleven SSR markers. The results revealed significant phenotypic variability among the evaluated accessions, leading to their classification into three main clusters. Furthermore, the eleven SSR markers identified a total of 50 alleles, averaging 4.55 alleles per locus. The primers exhibited an average polymorphic information content value of 0.43, with the DE-ARC019 primer displaying the highest value (0.59). These findings suggest a substantial degree of genetic heterogeneity within the evaluated accessions, and the SSR markers employed in the study proved highly effective in detecting and characterizing this genetic variability. In conclusion, this study highlights the presence of significant genetic diversity in black fonio and provides valuable insights for future efforts aimed at its genetic improvement and conservation.

Keywords: genetic diversity, digitaria iburua, genetic improvement, simple sequence repeat markers, Nigeria, conservation

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Authors: Noorulaine Maqsood, Saeed Khan

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Background: Concern regarding incident reporting and bio-safety training in clinical laboratories in Pakistan has increased remarkably in the last few years due to rapid increase in diagnosis and research on infectious organisms. In order to ensure the safety of employees, this issue needs to be addressed immediately. Bio-safety training sessions and lectures are necessary for the protection of laboratory workers in order to ensure safe practices and minimize the count of incident reporting in the lab. Objective: To carry out an inter-departmental survey in lab regarding the awareness of bio-safety practices among lab employees before and after conducting bio-safety training sessions. Methodology: We conducted a 30 questions survey of laboratory workers in June 2013 (before training session) to gather information related to bio-safety awareness. Afterwards, we conducted another survey after training sessions and workshops related to bio-safety. Result: The survey regarding bio-safety level showed that before the training session 32% of the participants were aware of bio-safety level being used in their lab whereas after the session this percentage increased to 72%. 48% of the participants had information about the proper usage of PPE which increased to 76%. Awareness regarding proper management of hazardous waste increased from 32% to 64%. The incident reporting practice, sample handling and hand hygiene awareness was previously reported to be 40%, 65%, and 52% that increased to 80%, 85% and 88% respectively after the training session was completed. Conclusion: The first survey results showed lack of awareness that suggest nearly all senior scientists, faculty, medical technologist, lab attendant and housekeeping staff working in laboratories are required to have bio-safety training, and required inspection at least twice a year by a bio-safety officer and also required to renew their bio-safety training. After the training session, significant changes in awareness level and attitude of the participants regarding biosafety practices were observed. Therefore, such bio-safety sessions should be carried out regularly in clinical laboratories.

Keywords: biosafety practices, clinical laboratory, Pakistan, survey

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Authors: Nyamela M. ‘Masekhohola, Khanare P. Fumane

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The Lesotho downward trend in mathematics attainment at all levels is compounded by the absence of innovative approaches to teaching and learning in Early Childhood. However, studies have shown that play pedagogy can be used to mitigate the challenges of mathematics education. Despite the benefits of play pedagogy to rural learners, its full potential has not been realized in early childhood care and education classrooms to improve children’s performance in mathematics because the adoption of play pedagogy depends on a strength-based approach. The study explores the potential of play pedagogy to improve mathematics education in early childhood care and education in Lesotho. Strength-based approach is known for its advocacy of recognizing and utilizing children’s strengths, capacities and interests. However, this approach and its promisingattributes is not well-known in Lesotho. In particular, little is known about the attributes of play pedagogy that are essential to improve mathematic education in ECCE programs in Lesotho. To identify such attributes and strengthen mathematics education, this systematic review examines evidence published on the strengths of play pedagogy that supports the teaching and learning of mathematics education in ECCE. The purpose of this review is, therefore, to identify and define the strengths of play pedagogy that supports mathematics education. Moreover, the study intends to understand the rural teachers’ perceived intention of using play in ECCE math classrooms through a strength-based approach. Eight key strengths were found (cues for reflection, edutainment, mathematics language development, creativity and imagination, cognitive promotion, exploration, classification, and skills development). This study is the first to identify and define the strength-based attributes of play pedagogy to improve the teaching and learning of mathematics in ECCE centers in Lesotho. The findings reveal which opportunities teachers find important for improving the teaching of mathematics as early as in ECCE programs. We conclude by discussing the implications of the literature for stimulating dialogues towards formulating strength-based approaches to teaching mathematics, as well as reflecting on the broader contributions of play pedagogy as an asset to improve mathematics in Lesotho and beyond.

Keywords: early childhood education, mathematics education, lesotho, play pedagogy, strength-based approach.

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Authors: Michael D. Vinod, Hadi Khabbaz

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Construction of infrastructure above or adjacent to landfills is becoming more common to capitalize on the limited space available within urban areas. However, development above landfills is a challenging task due to large voids, the presence of organic matter, heterogeneous nature of waste and ambiguity surrounding landfill settlement prediction. Prior to construction of infrastructure above landfills, ground improvement techniques are being employed to improve the geotechnical properties of landfill material. Although the ground improvement techniques have little impact on long term biodegradation and creep related landfill settlement, they have shown some notable short term success with a variety of techniques, including methods for verifying the level of effectiveness of ground improvement techniques. This paper provides geotechnical and landfill engineers a guideline for selection of landfill ground improvement techniques and their suitability to project-specific sites. Ground improvement methods assessed and compared in this paper include concrete injected columns (CIC), dynamic compaction, rapid impact compaction (RIC), preloading, high energy impact compaction (HEIC), vibro compaction, vibro replacement, chemical stabilization and the inclusion of geosynthetics such as geocells. For each ground improvement technique a summary of the existing theory, benefits, limitations, suitable modern ground improvement monitoring methods, the applicability of ground improvement techniques for landfills and supporting case studies are provided. The authors highlight the importance of implementing cost-effective monitoring techniques to allow observation and necessary remediation of the subsidence effects associated with long term landfill settlement. These ground improvement techniques are primarily for the purpose of construction above closed landfills to cater for transport infrastructure loading.

Keywords: closed landfills, ground improvement, monitoring, settlement, transport infrastructure

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Authors: Nida Rizvi, Deeksha Katyal, Varun Joshi

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River Hindon is an important river catering the demand of highly populated rural and industrial cluster of western Uttar Pradesh, India. Water quality of river Hindon is deteriorating at an alarming rate due to various industrial, municipal and agricultural activities. The present study aimed at identifying the pollution sources and quantifying the degree to which these sources are responsible for the deteriorating water quality of the river. Various water quality parameters, like pH, temperature, electrical conductivity, total dissolved solids, total hardness, calcium, chloride, nitrate, sulphate, biological oxygen demand, chemical oxygen demand and total alkalinity were assessed. Water quality data obtained from eight study sites for one year has been subjected to the two multivariate techniques, namely, principal component analysis and cluster analysis. Principal component analysis was applied with the aim to find out spatial variability and to identify the sources responsible for the water quality of the river. Three Varifactors were obtained after varimax rotation of initial principal components using principal component analysis. Cluster analysis was carried out to classify sampling stations of certain similarity, which grouped eight different sites into two clusters. The study reveals that the anthropogenic influence (municipal, industrial, waste water and agricultural runoff) was the major source of river water pollution. Thus, this study illustrates the utility of multivariate statistical techniques for analysis and elucidation of multifaceted data sets, recognition of pollution sources/factors and understanding temporal/spatial variations in water quality for effective river water quality management.

Keywords: cluster analysis, multivariate statistical techniques, river Hindon, water quality

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Authors: Adam Y. Sulaiman, Donal F. Cotter, Ming J. Huang, Neil J. Hewitt

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Suitable low global warming potential (GWP) refrigerants that conform to F-gas regulations are required to extend the operational envelope of high-temperature heat pumps (HTHPs) used for industrial waste heat recovery processes. The thermophysical properties and characteristics of these working fluids need to be assessed to provide a comprehensive understanding of operational effectiveness in HTHP applications. This paper presents the results of a theoretical simulation to investigate a range of low-GWP refrigerants and their suitability to supersede refrigerants HFC-245fa and HFC-365mfc. A steady-state thermodynamic model of a single-stage HTHP with an internal heat exchanger (IHX) was developed to assess system cycle characteristics at temperature ranges between 50 to 80 °C heat source and 90 to 150 °C heat sink. A practical approach to maximize the operational efficiency was examined to determine the effects of regulating minimum superheat within the process and subsequent influence on energetic and exergetic efficiencies. A comprehensive map of minimum superheat across the HTHP operating variables were used to assess specific tipping points in performance at 30 and 70 K temperature lifts. Based on initial results, the refrigerants HCFO-1233zd(E) and HFO-1336mzz(Z) were found to be closely aligned matches for refrigerants HFC-245fa and HFC-365mfc. The overall results show effective performance for HCFO-1233zd(E) occurs between 5-7 K minimum superheat, and HFO-1336mzz(Z) between 18-21 K dependant on temperature lift. This work provides a method to optimize refrigerant selection based on operational indicators to maximize overall HTHPs system performance.

Keywords: high-temperature heat pump, minimum superheat, energy & exergy efficiency, low GWP refrigerants

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Authors: Anargha A. Dhorde, Deshpande Gauri, Amit G. Dhorde

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Mula-Mutha basin is one of the speedily urbanizing watersheds, wherein two major urban centers, Pune and Pimpri-Chinchwad, have developed at a shocking rate in the last two decades. Such changing land use/land cover (LULC) is prone to hydrological problems and flash floods are a frequent, eventuality in the lower reaches of the basin. The present research brings out the impact of varying LULC, impervious surfaces on urban surface hydrology and generates storm-runoff scenarios for the hydrological units. The two multi-temporal satellite images were processed and supervised classification is performed with > 75% accuracy. The built-up has increased from 14.4% to 34.37% in the 28 years span, which is concentrated in and around the Pune-PCMC region. Impervious surfaces that were obtained by population calibrated multiple regression models. Almost 50% area of the watershed is impervious, which attribute to increase surface runoff and flash floods. The SCS-CN method was employed to calculate surface runoff of the watershed. The comparison between calculated and measured values of runoff was performed in a statistically precise way which shows no significant difference. Increasing built-up areas, as well as impervious surface areas due to rapid urbanization and industrialization, may lead to generating high runoff volumes in the basin especially in the urbanized areas of the watershed and along the major transportation arteries. Simulations generated with 50 mm and 100 mm rainstorm depth conspicuously noted that most of the changes in terms of increased runoff are constricted to the highly urbanized areas. Considering whole watershed area, the runoff values 39 m³ generated with 1'' rainfall whereas only urbanized areas of the basin (Pune and Pimpari-Chinchwad) were generated 11154 m³ runoff. Such analysis is crucial in providing information regarding their intensity and location, which proves instrumental in their analysis in order to formulate proper mitigation measures and rehabilitation strategies.

Keywords: land use/land cover, LULC, impervious surfaces, surface hydrology, storm-runoff scenarios

Procedia PDF Downloads 197