Search results for: covering machine

Authors: Hamed Najafi, Ghasem Nikjou

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Nowadays, there is an increasing attention to the resources scarcity issues. Because of failure in present patterns in the field of the allocation of exhaustible resources between generations and the challenges related to economic justice supply, it is supposed, to present a pattern from the Islamic perspective in this essay. By using content analysis of religious texts, we conclude that governments should remove the gap which is exists between the per capita income of the poor and their minimum consumption (necessary consumption). In order to preserve the exhaustible resources for poor people) not for all), between all generations, government should invest exhaustible resources on endless resources according to Hartwick’s criteria and should spend these benefits for poor people. But, if benefits did not cover the gap between minimum consumption and per capita income of poor levels in one generation, in this case, the government is responsible for covering this gap through the direct consumption of exhaustible resources. For an exact answer to this question, ‘how much of exhaustible resources should expense to maintain justice between generations?’ The theoretical and mathematical modeling has been used and proper function has been provided. The consumption pattern is presented for economic policy makers in Muslim countries, and non-Muslim even, it can be useful.

Keywords: exhaustible resources, Islamic justice, intergenerational justice, distribution of resources, Hartwick criteria

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Authors: Surbhi Mathur, J. M. Vyas

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Although the rapid development of forensic speaker recognition technology has been conducted, there are still many problems to be solved. The biggest problem arises when the cases involving disguised voice samples come across for the purpose of examination and identification. Such type of voice samples of anonymous callers is frequently encountered in crimes involving kidnapping, blackmailing, hoax extortion and many more, where the speaker makes a deliberate effort to manipulate their natural voice in order to conceal their identity due to the fear of being caught. Voice disguise causes serious damage to the natural vocal parameters of the speakers and thus complicates the process of identification. The sole objective of this doctoral project is to find out the possibility of rendering definite opinions in cases involving disguised speech by experimentally determining the effects of different disguise forms on personal identification and percentage rate of speaker recognition for various voice disguise techniques such as raised pitch, lower pitch, increased nasality, covering the mouth, constricting tract, obstacle in mouth etc by analyzing and comparing the amount of phonetic and acoustic variation in of artificial (disguised) and natural sample of an individual, by auditory as well as spectrographic analysis.

Keywords: forensic, speaker recognition, voice, speech, disguise, identification

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Authors: Hussain Ali Bekhet, Tuan Ab. Rashid Bin Tuan Abdullah, Tahira Yasmin

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The strong relationship between energy consumption and economic growth is widely recognised. Most countries’ energy demand declined during the economic depression known as the Global Financial Crisis (GFC) of 2008–2009. The objective of the current study is to investigate the energy consumption and performance of Malaysia’s manufacturing sectors during the GFC. We applied the output multiplier approach, which is based on the input-output model. Two input-output tables of Malaysia covering 2005 and 2010 were used. The results indicate significant changes in the output multipliers of the manufacturing sectors between 2005 and 2010. Moreover, the energy-to-manufacturing sectors’ output multipliers also decreased during the GFC due to a decline in export-oriented industries during the crisis. The increasing importance of the manufacturing sector to the development of Malaysian trade resulted in a noticeable decrease in the consumption of each energy sector’s output, especially the electricity and gas sector. Based on the research findings, the Malaysian government released several policy implementations in the form of stimulus packages to enhance these sectors’ performance and generally improve the Malaysian economy.

Keywords: global financial crisis, input-output model, manufacturing, output multipliers, energy, Malaysia

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Authors: Anders Thorsén, Behrooz Sangchoolie, Peter Folkesson, Ted Strandberg

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As more parts of the power grid become connected to the internet, the risk of cyberattacks increases. To identify the cybersecurity threats and subsequently reduce vulnerabilities, the common practice is to carry out a cybersecurity risk assessment. For safety classified systems and products, there is also a need for safety risk assessments in addition to the cybersecurity risk assessment in order to identify and reduce safety risks. These two risk assessments are usually done separately, but since cybersecurity and functional safety are often related, a more comprehensive method covering both aspects is needed. Some work addressing this has been done for specific domains like the automotive domain, but more general methods suitable for, e.g., intelligent distributed grids, are still missing. One such method from the automotive domain is the Security-Aware Hazard Analysis and Risk Assessment (SAHARA) method that combines safety and cybersecurity risk assessments. This paper presents an approach where the SAHARA method has been modified in order to be more suitable for larger distributed systems. The adapted SAHARA method has a more general risk assessment approach than the original SAHARA. The proposed method has been successfully applied on two use cases of an intelligent distributed grid.

Keywords: intelligent distribution grids, threat analysis, risk assessment, safety, cybersecurity

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Authors: Minghui Zhang, Sirine Fkaier, Sabri Fernana, Svetlana Kiseleva, Denis Kiselev

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The real-time identification of bacteria and fungi is difficult because they emit much weaker signals than pollen. In 2020, Plair developed Rapid-E+, which extends abilities of Rapid-E to detect smaller bioaerosols such as bacteria and fungal spores with diameters down to 0.3 µm, while keeping the similar or even better capability for measurements of large bioaerosols like pollen. Rapid-E+ enables simultaneous measurements of (1) time-resolved, polarization and angle dependent Mie scattering patterns, (2) fluorescence spectra resolved in 16 channels, and (3) fluorescence lifetime of individual particles. Moreover, (4) it provides 2D Mie scattering images which give the full information on particle morphology. The parameters of every single bioaerosol aspired into the instrument are subsequently analysed by machine learning. Firstly, pure species of microbes, e.g., Bacillus subtilis (a species of bacteria), and Penicillium chrysogenum (a species of fungal spores), were aerosolized in a bioaerosol chamber for Rapid-E+ training. Afterwards, we tested microbes under different concentrations. We used several steps of data analysis to classify and identify microbes. All single particles were analysed by the parameters of light scattering and fluorescence in the following steps. (1) They were treated with a smart filter block to get rid of non-microbes. (2) By classification algorithm, we verified the filtered particles were microbes based on the calibration data. (3) The probability threshold (defined by the user) step provides the probability of being microbes ranging from 0 to 100%. We demonstrate how Rapid-E+ identified simultaneously microbes based on the results of Bacillus subtilis (bacteria) and Penicillium chrysogenum (fungal spores). By using machine learning, Rapid-E+ achieved identification precision of 99% against the background. The further classification suggests the precision of 87% and 89% for Bacillus subtilis and Penicillium chrysogenum, respectively. The developed algorithm was subsequently used to evaluate the performance of microbe classification and quantification in real-time. The bacteria and fungi were aerosolized again in the chamber with different concentrations. Rapid-E+ can classify different types of microbes and then quantify them in real-time. Rapid-E+ enables classifying different types of microbes and quantifying them in real-time. Rapid-E+ can identify pollen down to species with similar or even better performance than the previous version (Rapid-E). Therefore, Rapid-E+ is an all-in-one instrument which classifies and quantifies not only pollen, but also bacteria and fungi. Based on the machine learning platform, the user can further develop proprietary algorithms for specific microbes (e.g., virus aerosols) and other aerosols (e.g., combustion-related particles that contain polycyclic aromatic hydrocarbons).

Keywords: bioaerosols, laser-induced fluorescence, Mie-scattering, microorganisms

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Authors: Faiza Belbachir, Thibault Schienhinski

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The massive development of online social networks allows users to post and share their opinions on various topics. With this huge volume of opinion, it is interesting to extract and interpret these information for different domains, e.g., product and service benchmarking, politic, system of recommendation. This is why opinion detection is one of the most important research tasks. It consists on differentiating between opinion data and factual data. The difficulty of this task is to determine an approach which returns opinionated document. Generally, there are two approaches used for opinion detection i.e. Lexical based approaches and Machine Learning based approaches. In Lexical based approaches, a dictionary of sentimental words is used, words are associated with weights. The opinion score of document is derived by the occurrence of words from this dictionary. In Machine learning approaches, usually a classifier is trained using a set of annotated document containing sentiment, and features such as n-grams of words, part-of-speech tags, and logical forms. Majority of these works are based on documents text to determine opinion score but dont take into account if these texts are really correct. Thus, it is interesting to exploit other information to improve opinion detection. In our work, we will develop a new way to consider the opinion score. We introduce the notion of trust score. We determine opinionated documents but also if these opinions are really trustable information in relation with topics. For that we use lexical SentiWordNet to calculate opinion and trust scores, we compute different features about users like (numbers of their comments, numbers of their useful comments, Average useful review). After that, we combine opinion score and trust score to obtain a final score. We applied our method to detect trust opinions in TRIPADVISOR collection. Our experimental results report that the combination between opinion score and trust score improves opinion detection.

Keywords: Tripadvisor, opinion detection, SentiWordNet, trust score

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Authors: Neha Singh

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The incidence of skin cancer has been rising worldwide due to excessive exposure to sun light. Climatic changes and depletion of ozone layer allow the easy entry of UV rays on earth, resulting skin damages such as sunburn, premature skin ageing, allergies and skin cancer. Researches have suggested many modes for protection of human skin against ultraviolet radiation; avoidance to outdoor activities, using textiles for covering the skin, sunscreen and sun glasses. However, this paper gives an insight about how textile material specially woven and knitted cotton can be efficiently utilized for protecting human skin from the harmful ultraviolet radiations by combining reactive dyes with Aloe Vera. Selection of the fabric was based on their utility and suitability as per the climate condition of the country for the upper and lower garment. A standard dyeing process was used, and Aloe Vera molecules were applied by in-micro encapsulation technique. After combining vat dyes with Aloe Vera excellent UPF (Ultra violet Protective Factor) was observed. There is a significant change in the UPF of vat dyed cotton fabric after treatment with Aloe Vera.

Keywords: UV protection, aloe vera, protective clothing, reactive dyes, cotton, woven and knits

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Authors: Nico Rosamilia

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The study evaluates the importance of non-financial disclosure practices for regulators, investors, businesses, and markets. It aims to create a sector-specific set of indicators for environmental, social, and governance (ESG) performances alternative to the ratings of the agencies. The existing literature extensively studies the implementation of ESG rating systems. Conversely, this study has a twofold outcome. Firstly, it should generalize incentive systems and governance policies for ESG and sustainable principles. Therefore, it should contribute to the EU Sustainable Finance Disclosure Regulation. Secondly, it concerns the market and the investors by highlighting successful sustainable investing. Indeed, the study contemplates the effect of ESG adoption practices on corporate value. The research explores the asset pricing angle in order to shed light on the fragmented argument on the finance of ESG. Investors may be misguided about the positive or negative effects of ESG on performances. The paper proposes a different method to evaluate ESG performances. By comparing the results of a traditional econometric approach (Lasso) with a machine learning algorithm (Random Forest), the study establishes a set of indicators for ESG performance. Therefore, the research also empirically contributes to the theoretical strands of literature regarding model selection and variable importance in a finance framework. The algorithms will spit out sector-specific indicators. This set of indicators defines an alternative to the compounded scores of ESG rating agencies and avoids the possible offsetting effect of scores. With this approach, the paper defines a sector-specific set of indicators to standardize ESG disclosure. Additionally, it tries to shed light on the absence of a clear understanding of the direction of the ESG effect on corporate value (the problem of endogeneity).

Keywords: ESG ratings, non-financial information, value of firms, sustainable finance

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Authors: Hyunsun Lee, Yi Zhu

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Wireless ad-hoc network is a decentralized type of temporary machine-to-machine connection that is spontaneous or impromptu so that it does not rely on any fixed infrastructure and centralized administration. As unmanned aerial vehicles (UAVs), also called drones, have recently become more accessible and widely utilized in military and civilian domains such as surveillance, search and detection missions, traffic monitoring, remote filming, product delivery, to name a few. The communication between these UAVs become possible and materialized through Flying Ad-hoc Networks (FANETs). However, due to the high mobility of UAVs that may cause different types of transmission interference, it is vital to design robust routing protocols for FANETs. In this talk, the multicast routing method based on a modified stochastic branching process is proposed. The stochastic branching process is often used to describe an early stage of an infectious disease outbreak, and the reproductive number in the process is used to classify the outbreak into a major or minor outbreak. The reproductive number to regulate the local transmission rate is adapted and modified for flying ad-hoc network communication. The performance of the proposed routing method is compared with other well-known methods such as flooding method and gossip method based on three measures; average reachability, average node usage and average branching factor. The proposed routing method achieves average reachability very closer to flooding method, average node usage closer to gossip method, and outstanding average branching factor among methods. It can be concluded that the proposed multicast routing scheme is more efficient than well-known routing schemes such as flooding and gossip while it maintains high performance.

Keywords: Flying Ad-hoc Networks, Multicast Routing, Stochastic Branching Process, Unmanned Aerial Vehicles

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Authors: Joeri N. Aulman

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In the remote Indian region of Gulbarga a case study of lean airport infrastructure development is getting ‘cast in stone’; In April the first turbo-props will land, and the optimized terminal building will process its first passengers, using minimal square meters in a facility that is based on a complete dress-down of the core operational processes. Yet the solution that resulted from this case study has such elegance in its simplicity that it has emboldened the local administration to invest in its construction and thus secure this remote region’s connectivity to India’s growth story. This paper aims to provide further background to the Gulbarga case study and its relevance to remote region connectivity, covering the demand that was identified, its practical application and its regulatory context and relevance for today’s airport manager and local administrators. This embodies the scope of the paper. In summary, the paper will give airport managers and regional authorities an overview and background to innovative case studies of lean airport infrastructure developments which combine both optimized CAPEX and running costs/OPEX without losing sight of the aspirational nature of up and coming remote regions; a truly sustainable model.

Keywords: airport, CAPEX, lean, sustainable, air connectivity, remote regions

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Authors: Muhammad Cheema, Tahir Shah, Subhash Anand

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The cost of making apparel fabrics for garment manufacturing is very high because of their conventional manufacturing processes and new methods/processes are being constantly developed for making fabrics by unconventional methods. With the advancements in technology and the availability of the innovative fibres, durable nonwoven fabrics by using the hydroentanglement process that can compete with the woven fabrics in terms of their aesthetic and tensile properties are being developed. In the work reported here, the hydroentangled nonwoven fabrics were developed through a hybrid nonwoven manufacturing processes by using fibrillated Tencel® and bi-component (sheath/core) polyethylene/polyester (PE/PET) fibres, in which the initial nonwoven fabrics were prepared by the needle-punching method followed by hydroentanglement process carried out at optimal pressures of 50 to 250bars. The prepared fabrics were characterized according to the British Standards (BS 3356:1990, BS 9237:1995, BS 13934-1:1999) and the attained results were compared with those for a standard plain-weave cotton, polyester woven fabric and commercially available nonwoven fabric (Evolon®). The developed hydroentangled fabrics showed better drape properties owing to their flexural rigidity of 252 mg.cm in the machine direction, while the corresponding commercial hydroentangled fabric displayed a value of 1340 mg.cm in the machine direction. The tensile strength of the developed hydroentangled fabrics showed an approximately 200% increase than the commercial hydroentangled fabrics. Similarly, the developed hydroentangled fabrics showed higher properties in term of air permeability, such as the developed hydroentangled fabric exhibited 448 mm/sec and Evolon fabric exhibited 69 mm/sec at 100 Pa pressure. Thus for apparel fabrics, the work combining the existing methods of nonwoven production, provides additional benefits in terms of cost, time and also helps in reducing the carbon footprint for the apparel fabric manufacture.

Keywords: hydroentanglement, nonwoven apparel, durable nonwoven, wearable nonwoven

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Authors: Marina Nosik, Irina Rymanova, Sergei Sevostyanihin, Natalya Sergeeva, Alexander Sobkin

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This paper presents results of the survey regarding the awareness about HIV/AIDS among HIV-infected individuals. A questionnaire covering various aspects of HIV-infection was conducted among 110 HIV-infected individuals who attended the G.A. Zaharyan Moscow Tuberculosis Clinic, Department for the treatment of TB patients with HIV. The questionnaire included questions about modes of HIV transmission and preventive measures against HIV/AIDS, as well as questions about age, gender, education, and employment status. The survey revealed that the respondents in the whole had a good knowledge regarding modes of HIV transmission and preventive measures against HIV/AIDS: about 83,6% male respondents and 85,7% female respondents gave accurate answers regarding the HIV-infection. However, the overwhelming majority of the study participants, that is, 88,5% men and 98% women, was quite ignorant about the risk of acquiring HIV through saliva and toothbrush of HIV-infected individual. Though that risk is rather insignificant, it is still biologically possible. And this gap in knowledge needs to be filled. As the study showed another point of concern was the fact, that despite the knowledge of HIV transmission risk through unprotected sex about 40% percent of HIV-positive men and 25% of HIV-positive women did not insist on using condoms with their sexual partners. These findings indicate that there are still some aspects about HIV-infection which needed to be clarified and explained through more detailed and specific educational programmes.

Keywords: AIDS, HIV transmission risks, HIV misconceptions, risk behavior

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Authors: Marina Kapsali, John S. Anagnostopoulos

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On the basis of comparative analysis of alternative “development scenarios” for electricity generation, the main objective of the present study is to investigate the techno-economic viability of high wind energy (WE) use at the local (island) level. An integrated theoretical model is developed based on first principles assuming two main possible scenarios for covering future electrification needs of a medium–sized Greek island, i.e. Lesbos. The first scenario (S1), assumes that the island will keep using oil products as the main source for electricity generation. The second scenario (S2) involves the interconnection of the island with the mainland grid to satisfy part of the electricity demand, while remarkable WE penetration is also achieved. The economic feasibility of the above solutions is investigated in terms of determining their Levelized Cost of Energy (LCOE) for the time-period 2020-2045, including also a sensitivity analysis on the worst/reference/best Cases. According to the results obtained, interconnection of Lesbos Island with the mainland grid (S2) presents considerable economic interest in comparison to autonomous development (S1) with WE having a prominent role to this effect.

Keywords: electricity generation cost, levelized cost of energy, mainland, wind energy surplus

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Authors: Milan Joshi, Harsh Agrawal, Pallaw Mishra, Sanand Sule

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Predicting electricity usage is a crucial aspect of organizing and controlling sustainable energy systems. The task of forecasting electricity load is intricate and requires a lot of effort due to the combined impact of social, economic, technical, environmental, and cultural factors on power consumption in communities. As a result, it is important to create strong models that can handle the significant non-linear and complex nature of the task. The objective of this study is to create and compare three machine learning techniques for predicting electricity load for both the day ahead and intraday, taking into account various factors such as meteorological data and social events including holidays and festivals. The proposed methods include a LightGBM, FBProphet, combination of FBProphet and LightGBM for day ahead and Motifs( Stumpy) based on Mueens algorithm for similarity search for intraday. We utilize these techniques to predict electricity usage during normal days and social events in the Himachal Region. We then assess their performance by measuring the MSE, RMSE, and MAPE values. The outcomes demonstrate that the combination of FBProphet and LightGBM method is the most accurate for day ahead and Motifs for intraday forecasting of electricity usage, surpassing other models in terms of MAPE, RMSE, and MSE. Moreover, the FBProphet - LightGBM approach proves to be highly effective in forecasting electricity load during social events, exhibiting precise day ahead predictions. In summary, our proposed electricity forecasting techniques display excellent performance in predicting electricity usage during normal days and special events in the Himachal Region.

Keywords: feature engineering, FBProphet, LightGBM, MASS, Motifs, MAPE

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Authors: Cristina Diaz, Lucia Perez-Gandarillas, Gonzalo Garcia-Fuentes, Simone Visigalli, Roberto Canziani, Giuseppe Di Florio, Paolo Gronchi

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During their lifetime, industrial machines are often subjected to chemical, mechanical and thermal extreme conditions. In some cases, the loss of efficiency comes from the degradation of the surface as a result of its exposition to abrasive environments that can cause wear. This is a common problem to be solved in industries of diverse nature such as food, paper or concrete industries, among others. For this reason, a good selection of the material is of high importance. In the machine design context, stainless steels such as AISI 304 and 316 are widely used. However, the severity of the external conditions can require additional protection for the steel and sometimes coating solutions are demanded in order to extend the lifespan of these materials. Therefore, the development of effective coatings with high wear resistance is of utmost technological relevance. In this research, bilayer coatings made of Titanium-Tantalum, Titanium-Niobium, Titanium-Hafnium, and Titanium-Zirconium have been developed using magnetron sputtering configuration by PVD (Physical Vapor Deposition) technology. Their tribological behavior has been measured and evaluated under different environmental conditions. Two kinds of steels were used as substrates: AISI 304, AISI 316. For the comparison with these materials, titanium alloy substrate was also employed. Regarding the characterization, wear rate and friction coefficient were evaluated by a tribo-tester, using a pin-on-ball configuration with different lubricants such as tomato sauce, wine, olive oil, wet compost, a mix of sand and concrete with water and NaCl to approximate the results to real extreme conditions. In addition, topographical images of the wear tracks were obtained in order to get more insight of the wear behavior and scanning electron microscope (SEM) images were taken to evaluate the adhesion and quality of the coating. The characterization was completed with the measurement of nanoindentation hardness and elastic modulus. Concerning the results, thicknesses of the samples varied from 100 nm (Ti-Zr layer) to 1.4 µm (Ti-Hf layer) and SEM images confirmed that the addition of the Ti layer improved the adhesion of the coatings. Moreover, results have pointed out that these coatings have increased the wear resistance in comparison with the original substrates under environments of different severity. Furthermore, nanoindentation hardness results showed an improvement of the elastic strain to failure and a high modulus of elasticity (approximately 200 GPa). As a conclusion, Ti-Ta, Ti-Zr, Ti-Nb, and Ti-Hf are very promising and effective coatings in terms of tribological behavior, improving considerably the wear resistance and friction coefficient of typically used machine materials.

Keywords: coating, stainless steel, tribology, wear

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Authors: Ahmed Kiani

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The United Arab Emirates is clearly facing a multitude of challenges in curbing its greenhouse gas emissions to meet its pre-allotted framework of Kyoto protocol and COP21 targets due to its hunger for modernization, industrialization, infrastructure growth, soaring population and oil and gas activity. In this work, we focus on the bonafide zero emission electric vehicles market penetration in the country’s transport industry for emission reduction. We study the global electric vehicle market trends, the complementary battery technologies and the trends by manufacturers, emission standards across borders and prioritized advancements which will ultimately dictate the terms of future conditions for the United Arab Emirate transport industry. Based on our findings and analysis at every stage of current viability and state-of-transport-affairs, we postulate policy recommendations to local governmental entities from a supply and demand perspective covering aspects of technology, infrastructure requirements, change in power dynamics, end user incentives program, market regulators behavior and communications amongst key stakeholders. 

Keywords: electric vehicles, greenhouse gas emission reductions, market analysis, policy recommendations

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Authors: Roman Kulikov, Svetlana Kolesnikova

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Last decade Cloud Computing technologies have been rapidly becoming ubiquitous. Each year more and more organizations, corporations, internet services and social networks trust their business sensitive information to Public Cloud. The data storage in Public Cloud is protected by security mechanisms such as firewalls, cryptography algorithms, backups, etc.. In this way, however, only outsider attacks can be prevented, whereas virtualization tools can be easily compromised by insider. The protection of Public Cloud’s critical elements from internal intruder remains extremely challenging. A hypervisor, also called a virtual machine manager, is a program that allows multiple operating systems (OS) to share a single hardware processor in Cloud Computing. One of the hypervisor's functions is to enforce access control policies. Furthermore, it prevents guest OS from disrupting each other and from accessing each other's memory or disk space. Hypervisor is the one of the most critical and vulnerable elements in Cloud Computing infrastructure. Nevertheless, it has been poorly protected from being compromised by insider. By exploiting certain vulnerabilities, privilege escalation can be easily achieved in insider attacks on hypervisor. In this way, an internal intruder, who has compromised one process, is able to gain control of the entire virtual machine. Thereafter, the consequences of insider attacks in Public Cloud might be more catastrophic and significant to virtual tools and sensitive data than of outsider attacks. So far, almost no preventive security countermeasures have been developed. There has been little attention paid for developing models to assist risks mitigation strategies. In this paper formal model of insider attacks on hypervisor is designed. Our analysis identifies critical hypervisor`s vulnerabilities that can be easily compromised by internal intruder. Consequently, possible conditions for successful attacks implementation are uncovered. Hence, development of preventive security countermeasures can be improved on the basis of the proposed model.

Keywords: insider attack, public cloud, cloud computing, hypervisor

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Authors: Kwestan Salimi, Jenna Jacoby, Michelle Basham, Amy Cerato

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The extreme annual weather patterns of the central United States have increased the need for underground shelters for protection from destructive tornadic activity. However, very few residential homes have basements due to the added construction expense and the prevalence of expansive soils covering the central portion of the United States. These expansive soils shrink and swell, increasing earth pressure on basement walls. To mitigate the effect of expansive soils on basement walls, this study performed bench-scale tests using a common natural expansive soil mitigated with a backfill layer of crumb rubber. The results revealed that at 80% soil compaction, a 1:6 backfill height to total height ratio produced a 66% reduction in swell pressure. However, this percent reduction decreased to 27% for 90% soil compaction. It was also found that there is a strong linear correlation between compaction percentage and reduction in swell pressure when using the same backfill height to total height ratio. Using this correlation and extrapolating to 95% compaction, the percent reduction in swell pressure was approximately 12%.

Keywords: expansive soils, swell/shrink, swell pressure, stabilization, crumb rubber

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Authors: Daira Radouane, Naim Boudmagh, Hamada Adel

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The but of this handling is to use the technique of the shearing with a mechanism lapping machine of image: a prism of Wollaston. We want to characterize this prism in order to be able to employ it later on in an analysis by shearing. A prism of Wollaston is a prism produced in a birefringent material i.e. having two indexes of refraction. This prism is cleaved so as to present the directions associated with these indices in its face with entry. It should be noted that these directions are perpendicular between them.

Keywords: non destructive control, aluminium, interferometry, treatment of image

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Authors: Lanchi Xie, Zhihui Li, Zhigang Li, Guiqiang Wang, Lei Xu, Yuwen Yan

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Image-based facial features can be classified into category recognition features and individual recognition features. Current automated face recognition systems extract a specific feature vector of different dimensions from a facial image according to their pre-trained neural network. However, to improve the efficiency of parameter calculation, an algorithm generally reduces the image details by pooling. The operation will overlook the details concerned much by forensic experts. In our experiment, we adopted a variety of face recognition algorithms based on deep learning, compared a large number of naturally collected face images with the known data of the same person's frontal ID photos. Downscaling and manual handling were performed on the testing images. The results supported that the facial recognition algorithms based on deep learning detected structural and morphological information and rarely focused on specific markers such as stains and moles. Overall performance, distribution of genuine scores and impostor scores, and likelihood ratios were tested to evaluate the accuracy of biometric systems and forensic experts. Experiments showed that the biometric systems were skilled in distinguishing category features, and forensic experts were better at discovering the individual features of human faces. In the proposed approach, a fusion was performed at the score level. At the specified false accept rate, the framework achieved a lower false reject rate. This paper contributes to improving the interpretability of the objective method of facial comparison and provides a novel method for human-machine collaboration in this field.

Keywords: likelihood ratio, automated facial recognition, facial comparison, biometrics

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Authors: Karima Afif, Jacinthe Clouthier, Marie-Ève Gaboury-Bonhomme, Véronique Provencher, Morgane Leclercq

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This paper investigates how food banks have secured and improved their supply chain resilience to pursue their mission during COVID-19. More specifically, the implications of the COVID-19 outbreak on the food aid needs, donations, operations, and mission of food banks are explored. To develop an in-depth understanding of the reactions and actions that they have been taken, a qualitative approach has been adopted using a multiple case study design. Data from two focus groups, 12 semi-structured interviews with key informants covering all supply chain levels, and field notes from 7 workplace observations in donation points, food bank facilities, and community-based organizations in Québec (Canada) are triangulated. The results highlight that the pandemic has significantly and unpredictably increased the number of food aid demands, causing significant operational challenges for the food banks supply chain, as well as an unprecedented shortage of donations to food banks. Besides, the sanitary measures have required several adaptative strategies. These implications have caused food banks to enhance their operational flexibility, optimize their logistics operations, enhance their human resources management, and expand collaboration within their supply chain.

Keywords: supply chain resilience, food banks, food donations, food aid, COVID-19

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Authors: Anat Achiron

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Background: Multiple sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system in young adults. It involves the immune system attacking the protective covering of nerve fibers (myelin), leading to inflammation and damage. MS can result in various neurological symptoms, such as muscle weakness, coordination problems, and sensory disturbances. Seizures are not common in MS, and the frequency is estimated between 0.4 to 6.4% over the disease course. Objective: Investigate the frequency of seizures in individuals with multiple sclerosis and to identify associated risk factors. Methods: We evaluated the frequency of seizures in a large cohort of 5686 MS patients followed at the Sheba Multiple Sclerosis Center and studied associated risk factors and comorbidities. Our research was based on data collection using a cohort study design. We applied logistic regression analysis to assess the strength of associations. Results: We found that younger age at onset, longer disease duration, and prolonged time to immunomodulatory treatment initiation were associated with increased risk for seizures. Conclusions: Our findings suggest that seizures in people with MS are directly related to the demyelination process and not associated with other factors like medication side effects or comorbid conditions. Therefore, initiating immunomodulatory treatment early in the disease course could reduce not only disease activity but also decrease seizure risk.

Keywords: epilepsy, seizures, multiple sclerosis, white matter, age

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Authors: Atbin Mahabbati, Jason Beringer, Matthias Leopold

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To address the global challenges of climate and environmental changes, there is a need for quantifying and reducing uncertainties in environmental data, including observations of carbon, water, and energy. Global eddy covariance flux tower networks (FLUXNET), and their regional counterparts (i.e., OzFlux, AmeriFlux, China Flux, etc.) were established in the late 1990s and early 2000s to address the demand. Despite the capability of eddy covariance in validating process modelling analyses, field surveys and remote sensing assessments, there are some serious concerns regarding the challenges associated with the technique, e.g. data gaps and uncertainties. To address these concerns, this research has developed an ensemble model to fill the data gaps of CO₂ flux to avoid the limitations of using a single algorithm, and therefore, provide less error and decline the uncertainties associated with the gap-filling process. In this study, the data of five towers in the OzFlux Network (Alice Springs Mulga, Calperum, Gingin, Howard Springs and Tumbarumba) during 2013 were used to develop an ensemble machine learning model, using five feedforward neural networks (FFNN) with different structures combined with an eXtreme Gradient Boosting (XGB) algorithm. The former methods, FFNN, provided the primary estimations in the first layer, while the later, XGB, used the outputs of the first layer as its input to provide the final estimations of CO₂ flux. The introduced model showed slight superiority over each single FFNN and the XGB, while each of these two methods was used individually, overall RMSE: 2.64, 2.91, and 3.54 g C m⁻² yr⁻¹ respectively (3.54 provided by the best FFNN). The most significant improvement happened to the estimation of the extreme diurnal values (during midday and sunrise), as well as nocturnal estimations, which is generally considered as one of the most challenging parts of CO₂ flux gap-filling. The towers, as well as seasonality, showed different levels of sensitivity to improvements provided by the ensemble model. For instance, Tumbarumba showed more sensitivity compared to Calperum, where the differences between the Ensemble model on the one hand and the FFNNs and XGB, on the other hand, were the least of all 5 sites. Besides, the performance difference between the ensemble model and its components individually were more significant during the warm season (Jan, Feb, Mar, Oct, Nov, and Dec) compared to the cold season (Apr, May, Jun, Jul, Aug, and Sep) due to the higher amount of photosynthesis of plants, which led to a larger range of CO₂ exchange. In conclusion, the introduced ensemble model slightly improved the accuracy of CO₂ flux gap-filling and robustness of the model. Therefore, using ensemble machine learning models is potentially capable of improving data estimation and regression outcome when it seems to be no more room for improvement while using a single algorithm.

Keywords: carbon flux, Eddy covariance, extreme gradient boosting, gap-filling comparison, hybrid model, OzFlux network

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Authors: R. Shrestha, D. P. Subedi, R. B. Tyata, C. S. Wong,

Abstract:

In this paper, a technique for the determination of electron temperatures and electron densities in atmospheric pressure Argon/air discharge by the analysis of optical emission spectra (OES) is reported. The discharge was produced using a high voltage (0-20) kV power supply operating at a frequency of 27 kHz in parallel electrode system, with glass as dielectric. The dielectric layers covering the electrodes act as current limiters and prevent the transition to an arc discharge. Optical emission spectra in the range of (300nm-850nm) were recorded for the discharge with different inter electrode gap keeping electric field constant. Electron temperature (Te) and electron density (ne) are estimated from electrical and optical methods. Electron density was calculated using power balance method. The optical methods are related with line intensity ratio from the relative intensities of Ar-I and Ar-II lines in Argon plasma. The electron density calculated by using line intensity ratio method was compared with the electron density calculated by stark broadening method. The effect of dielectric thickness on plasma parameters (Te and ne) have also been studied and found that Te and ne increases as thickness of dielectric decrease for same inter electrode distance and applied voltage.

Keywords: electron density, electron temperature, optical emission spectra,

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Authors: Jefferson Hernandez, Juan Padilla

Abstract:

Estimation of price elasticity of demand is a valuable tool for the task of price settling. Given its relevance, it is an active field for microeconomic and statistical research. Price elasticity in the industry of oil and gas, in particular for fuels sold in gas stations, has shown to be a challenging topic given the market and state restrictions, and underlying correlations structures between the types of fuels sold by the same gas station. This paper explores the Lotka-Volterra model for the problem for price elasticity estimation in the context of fuels; in addition, it is introduced multivariate random effects with the purpose of dealing with errors, e.g., measurement or missing data errors. In order to model the underlying correlation structures, the Inverse-Wishart, Hierarchical Half-t and LKJ distributions are studied. Here, the Bayesian paradigm through Markov Chain Monte Carlo (MCMC) algorithms for model estimation is considered. Simulation studies covering a wide range of situations were performed in order to evaluate parameter recovery for the proposed models and algorithms. Results revealed that the proposed algorithms recovered quite well all model parameters. Also, a real data set analysis was performed in order to illustrate the proposed approach.

Keywords: price elasticity, volume, correlation structures, Bayesian models

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Authors: Ezeh Chukwunonso Peter Excel, Akruti Vg

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Aim/Background: To show that even after undergoing 1-5 courses of chemotherapy for Gestational Trophoblastic Disease (GTD) reproductive health of women is intact and they conceive successfully after it. Method: Retrospective cohort analysis using data from the Lugansk regional maternity hospital database of years 1993-2013, which shows n=18 females had GTD and underwent 1-5 courses of chemotherapy. Results and Discussion: Frequency of GTD was rare. All 18 patients (pts) belong to age group of 17-39 years, covering wide range of reproductive age. Out of 18 pts, 15 had hydatidiform mole (HM) while other 3 had choriocarcinoma (CC). In anamnesis, among CC pts, 1 had early pre-eclampsia at 24 weeks and 1 had 4th week of late postpartum (PP) bleeding, while all HM pts had genital inflammatory diseases, 1 pt of HM during follow-up had High hCG and 3 times curettage in 5 months. 18 women became pregnant for 25 times after chemotherapy. Chemotherapy was given under indication of either high level of HCG, luteal cyst >6cm or path-morphological results of curettage. CC 3 pts had (2 spontaneous abortions (SA), 2 term cesarean section (CS), 1 preterm CS). HM 15 pts had (3 artificial abortion, 2 SA, 7CS (5 term and 2 preterm), 8 vaginal deliveries (7 term and 1 preterm)). Conclusion: During our research we got 22.2% preterm deliveries and 55.6% CS which is higher than the normal cases, but still all the 18 women were able to have kids successfully after chemotherapy. So we can conclude that chemotherapy for GTD was successful in keeping the reproductive health of women intact.

Keywords: reproductive health, chemotherapy, gestational trophoblastic disease, women

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Authors: Mohammad R. Aghakashkooli, Milutin G. Jovanovic

Abstract:

The brushless doubly-fed reluctance generator (BDFRG) is an emerging, medium-speed alternative to a conventional wound rotor slip-ring doubly-fed induction generator (DFIG) in wind energy conversion systems (WECS). It can provide competitive overall performance and similar low failure rates of a typically 30% rated back-to-back power electronics converter in 2:1 speed ranges but with the following important reliability and cost advantages over DFIG: the maintenance-free operation afforded by its brushless structure, 50% synchronous speed with the same number of rotor poles (allowing the use of a more compact, and more efficient two-stage gearbox instead of a vulnerable three-stage one), and superior grid integration properties including simpler protection for the low voltage ride through compliance of the fractional converter due to the comparatively higher leakage inductances and lower fault currents. Vector controlled pulse-width-modulated converters generally feature a much lower total harmonic distortion relative to hysteresis counterparts with variable switching rates and as such have been a predominant choice for BDFRG (and DFIG) wind turbines. Eliminating a shaft position sensor, which is often required for control implementation in this case, would be desirable to address the associated reliability issues. This fact has largely motivated the recent growing research of sensorless methods and developments of various rotor position and/or speed estimation techniques for this purpose. The main limitation of all the observer-based control approaches for grid-connected wind power applications of the BDFRG reported in the open literature is the requirement for pre-commissioning procedures and prior knowledge of the machine inductances, which are usually difficult to accurately identify by off-line testing. A model reference adaptive system (MRAS) based sensor-less vector control scheme to be presented will overcome this shortcoming. The true machine parameter independence of the proposed field-oriented algorithm, offering robust, inherently decoupled real and reactive power control of the grid-connected winding, is achieved by on-line estimation of the inductance ratio, the underlying rotor angular velocity and position MRAS observer being reliant upon. Such an observer configuration will be more practical to implement and clearly preferable to the existing machine parameter dependent solutions, and especially bearing in mind that with very little modifications it can be adapted for commercial DFIGs with immediately obvious further industrial benefits and prospects of this work. The excellent encoder-less controller performance with maximum power point tracking in the base speed region will be demonstrated by realistic simulation studies using large-scale BDFRG design data and verified by experimental results on a small laboratory prototype of the WECS emulation facility.

Keywords: brushless doubly fed reluctance generator, model reference adaptive system, sensorless vector control, wind energy conversion

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Authors: Abdullah S. AlKhalifa, Mohammad Nasim Uddin, Michael Atkinson

Abstract:

The primary objective of this investigation is to explore the aerodynamic impact of adding trailing edge serrations to a Wells turbine. The baseline turbine consists of eight blades with NACA 0015 airfoils. The blade chord length was 0.125 m, and the span was 0.100 m. Two modified NACA 0015 serrated configurations were studied: 1) full-span and 2) partial span serrations covering the trailing edge from hub to tip. Numerical simulations were carried out by solving the three-dimensional, incompressible steady-state Reynolds Averaged Navier-Stokes (RANS) equations using the k-ω SST turbulence model in ANSYS™ (CFX). The aerodynamic performance of the modified Wells turbine to the baseline was made by comparing non-dimensional parameters of torque coefficient, pressure drop coefficient, and turbine efficiency. A comparison of the surface limiting streamlines was performed to analyze the flow topology of the turbine blades. The trailing edge serrations generated a substantial change in surface pressure and effectively reduced the separated flow region, thus improving efficiency in most cases. As a result, the average efficiency increased across the range of simulated flow coefficients.

Keywords: renewable energy, trailing edge serrations, Wells turbine, partial serration

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Authors: Ilhan Keskin, Hasan Bulent Kantarci

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In this study, one of the tools of Islamic financing known as “Sukuk” a non-interest bearing investment which has started to be implemented in Turkey and the world as a whole is discussed. In order to increase the vitality and efficiency of the economy, by taking lessons from the recent economic crisis new developments in the banking and investment sector are being expanded. The purpose of all investors is to obtain more revenue through the use of capital. The inability of traditional investment tools to meet the expectations of investors and the interest based financial system where one investor benefits at the expense of another there has been the need for a different, reliable and non-interest bearing financial market that is consistent with the Islamic rule. As a result an alternative and more reliable interest free financing tool “Sukuk” rental certificates covering people who are sensitive to Islamic rules, appeal to all segments, hidden remaining capital that contributes to the economy, reduce disparities in income distribution, common risk sharing system of profit and loss sharing has emerged. Today, for the structural countries by examining the state of the world market economy the applicability, enactment and future issues associated with this attractive kind of Islamic finance namely the “Sukuk” market has been explained.

Keywords: Islamic finance, islamic markets, non-interest bearing, rental certificates

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Authors: Ezeoke Callistus Obiora, Ezeoke Nneka Angela

Abstract:

The study investigated the effect of deficit financing on macroeconomic variables in Nigeria. The specific objectives included to find out the relationship between deficit financing and GDP, interest rate, inflation rate, money supply, exchange rate and private investment respectively on a time series covering a period of 44 years (1970 – 2013). The Ordinary Least Square multiple regression produced statistics for the coefficient of determination (R2), F-test, t-test used for the interpretation of the study. The findings revealed that Deficit financing has significant positive effect on GDP and exchange rate. Again, deficit financing has a positive and insignificant relationship inflation, money supply and investment. Only interest rate recorded negative yet insignificant relationship with deficit financing. The implications of the findings are that deficit financing can be a veritable tool for boosting economic development in Nigeria, but the influential positively rising exchange rate implies that deficit financing devalues the Naira exchange rate to other currencies indicating that deficit financing can affect Nigerians competitive advantage at the world market. Thus, the study concludes that deficit financing has not encouraged economic growth in Nigeria.

Keywords: deficit financing, money supply, exchange rate, inflation, GDP, investment, Nigeria

Procedia PDF Downloads 482