Search results for: waste classification

Authors: Raymond Ezenweani, Jeffrey Ogbebor

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The menace of aquatic pollution has become increasingly of great concern and the effects of this pollution as a result of anthropogenic activities cannot be over emphasized. Phycoremediation is the application of algal remediation technology in the removal of harmful products from the environment. Harmful products also known as pollutants are usually introduced into the environment through variety of processes such as industrial discharge, agricultural runoff, flooding, and acid rain. This work has to do with the capability of algae in the efficient removal of different pollutants, ranging from hydrocarbons, eutrophication, agricultural chemicals and wastes, heavy metals, foul smell from septic tanks or dumps through different processes such as bioconversion, biosorption, bioabsorption and biodecomposition. Algae are capable of bioconversion of environmentally persistent compounds to degradable compounds and also capable of putting harmful bacteria growth into check in waste water remediation. Numerous algal organisms such as Nannochloropsis spp, Chlorella spp, Tetraselmis spp, Shpaerocystics spp, cyanobacteria and different macroalgae have been tested by different researchers in laboratory scale and shown to have 100% efficiency in environmental remediation. Algae as a result of their photosynthetic capacity are also efficient in air cleansing and management of global warming by sequestering carbon iv oxide in air and converting it into organic carbon, thereby making food available for the other organisms in the higher trophic level of the aquatic food chain. Algae play major role in the sustenance of the aquatic ecosystem by their virtue of being photosynthetic. They are the primary producers and their role in environmental sustainability is remarkable.

Keywords: Algae , Pollutant, ., Phycoremediation, Aquatic, Sustainability

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Authors: A. Jéglot, F. Plauborg, M. K. Schnorr, R. S. Sørensen, L. Elsgaard

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Artificial wetlands such as woodchip bioreactors are efficient tools to remove nitrate from agricultural wastewater with a minimized environmental impact. However, the temperature dependence of the microbiological nitrate removal prevents the woodchip bioreactors from being an efficient system when the water temperature drops below 8℃. To quantify and describe the temperature effects on nitrate removal efficiency, we studied nitrate-reducing enrichments from a woodchip bioreactor in Denmark based on samples collected in Spring and Fall. Growth was quantified as optical density, and nitrate and nitrous oxide concentrations were measured in time-course experiments to compare the growth of the microbial population and the nitrate conversion efficiencies at different temperatures. Ammonia was measured to indicate the importance of dissimilatory nitrate reduction to ammonia (DNRA) in nitrate conversion for the given denitrifying community. The temperature responses observed followed the increasing trend proposed by the Arrhenius equation, indicating higher nitrate removal efficiencies at higher temperatures. However, the growth and the nitrous oxide production observed at low temperature provided evidence of the psychrotolerance of the microbial community under study. The assays conducted showed higher nitrate removal from the microbial community extracted from the woodchip bioreactor at the cold season compared to the ones extracted during the warmer season. This indicated the ability of the bacterial populations in the bioreactor to evolve and adapt to different seasonal temperatures.

Keywords: agricultural waste water treatment, artificial wetland, denitrification, psychrophilic conditions

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Authors: Monsuru Oyenola Popoola, Oladapo Samson Abiola, Wusamotu Alao Adeniji

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The prevalent and incessant abandonment of road construction projects are alarming that it creates several negative effects to social, economic and environmental values of the project. The purpose of this paper is to investigate and determined the various causes and effects of abandoning road construction projects in Nigeria. Likert Scale questionnaire design was used to administered and analysed the data obtained for the stydy. 135 (Nr) questionnaires were completed and retrieved from the respondents, out of 200 (Nr) questionnaires sent out, representing a response rate of 67.5%. The analysis utilized the Relative Importance Index (R.I.I.) method and the results are presented in tabular form. The findings confirms that at least 20 factors were the causes of road projects abandonment in Nigeria with most including Leadership Instability, Improper Project Planning, Inconsistence in government policies and Design, Contractor Incompetence, Economy Instability and Inflation, Delay in remittance of money, Improper financial analysis, Poor risk management, Climatic Conditions, Improper Project Estimates etc. The findings also show that at least eight (8) effect were identified on the system, and these include; Waste of Financial Resources, Loss of economic value, Environmental degradation, Loss of economic value, Reduction in standard of living, Litigation and Arbitration, etc. The reflection is that allocating reasonable finance, developing appropriate and effective implementation plans and monitoring, evaluation and reporting on development project activities by key actors should enhance in resolving the problem of road projects abandonment.

Keywords: road construction, abandonment of road projects, climatic condition, project planning, contractor

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Authors: Md. Kamrul Hassan, Ari Pappinen

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Wood energy is the single most important form of renewable energy in many parts of the world especially in the least developing countries in South Asia like Bangladesh. The last portion of the national population of this country depends on wood energy for their daily primary energy need. This paper deals with the estimation of wood fuel at the current level and identifies the challenges and strategies related to the development of this resource. Desk research, interactive research and field survey were conducted for gathering and analyzing of data for this study. The study revealed that wood fuel plays a significant role in total primary energy supply in Bangladesh, and the contribution of wood fuel in final energy consumption in 2013 was about 24%. Trees on homestead areas, secondary plantation on off forest lands, and forests are the main sources of supplying wood fuel in the country. Insufficient supply of wood fuel against high upward demand is the main cause of concern for sustainable consumption, which eventually leads deterioration and depletion of the resources. Inadequate afforestation programme, lack of initiatives towards the utilization of set-aside lands for wood energy plantations, and inefficient management of the existing resources have been identified as the major impediments to the development of wood energy in Bangladesh. The study argued that enhancement of public-private-partnership afforestation programmes, intensifying the waste and marginal lands with short-rotation tree species, and formulation of biomass-based rural energy strategies at the regional level are relevant to the promotion of sustainable wood energy in the country.

Keywords: Bangladesh, challenge, supply, wood energy

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Authors: Induja R. Nimma, Anand Reddy Maligireddy, Artur Schneider, Melissa Lyle

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Background: Although heart failure is one of the most common causes of hospitalization in adult patients, there is limited knowledge on outcomes following initial hospitalization for COVID-19 with heart failure (HCF-19). We felt it pertinent to analyze 30-day readmission causes and outcomes among patients with HCF-19 using the United States using real-world big data via the National readmission database. Objective: The aim is to describe the rate and causes of readmissions and morbidity of heart failure with coinciding COVID-19 (HFC-19) in the United States, using the 2020 National Readmission Database (NRD). Methods: A descriptive, retrospective study was conducted on the 2020 NRD, a nationally representative sample of all US hospitalizations. Adult (>18 years) inpatient admissions with COVID-19 with HF and readmissions in 30 days were selected based on the International Classification of Diseases-Tenth Revision, Procedure Code. Results: In 2020, 2,60,372 adult patients were hospitalized with COVID-19 and HF. The median age was 74 (IQR: 64-83), and 47% were female. The median length of stay was 7(4-13) days, and the total cost of stay was 62,025 (31,956 – 130,670) United States dollars, respectively. Among the index hospital admissions, 61,527 (23.6%) died, and 22,794 (11.5%) were readmitted within 30 days. The median age of patients readmitted in 30 days was 73 (63-82), 45% were female, and 1,962 (16%) died. The most common principal diagnosis for readmission in these patients was COVID-19= 34.8%, Sepsis= 16.5%, HF = 7.1%, AKI = 2.2%, respiratory failure with hypoxia =1.7%, and Pneumonia = 1%. Conclusion: The rate of readmission in patients with heart failure exacerbations is increasing yearly. COVID-19 was observed to be the most common principal diagnosis in patients readmitted within 30 days. Complicated hypertension, chronic pulmonary disease, complicated diabetes, renal failure, alcohol use, drug use, and peripheral vascular disorders are risk factors associated with readmission. Familiarity with the most common causes and predictors for readmission helps guide the development of initiatives to minimize adverse outcomes and the cost of medical care.

Keywords: Covid-19, heart failure, national readmission database, readmission outcomes

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Authors: Mathew Wakefield, Matthew Mitchell, Lisa Wise, Christopher McCarthy

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The properties of memory representations in artificial neural networks have cognitive implications. Distributed representations that encode instances as a pattern of activity across layers of nodes afford memory compression and enforce the selection of a single point in instance space. These encoding schemes also appear to distort the representational space, as well as trading off the ability to validate that input information is within the bounds of past experience. In contrast, a localist representation which encodes some meaningful information into individual nodes in a network layer affords less memory compression while retaining the integrity of the representational space. This allows the validity of an input to be determined. The validity (or familiarity) of input along with the capacity of localist representation for multiple instance selections affords a memory sampling approach that dynamically balances the bias-variance trade-off. When the input is familiar, bias may be high by referring only to the most similar instances in memory. When the input is less familiar, variance can be increased by referring to more instances that capture a broader range of features. Using this approach in a localist instance memory network, an experiment demonstrates a relationship between representational conflict, generalization performance, and memorization demand. Relatively small sampling ranges produce the best performance on a classic machine learning dataset of visual objects. Combining memory validity with conflict detection produces a reliable confidence judgement that can separate responses with high and low error rates. Confidence can also be used to signal the need for supervisory input. Using this judgement, the need for supervised learning as well as memory encoding can be substantially reduced with only a trivial detriment to classification performance.

Keywords: artificial neural networks, representation, memory, conflict monitoring, confidence

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Authors: Adel Mohamed El-Baghdady, Walid Sayed Abdulgalil, Ahmad Asran, Ibrahim Nosier

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This paper studies the effectiveness of applying value engineering to actual concrete mixtures. The study was conducted in the State of Qatar on a number of strategic construction projects with international engineering specifications for the 2022 World Cup projects. The study examined the concrete mixtures of Doha Metro project and the development of KAHRAMAA’s (Qatar Electricity and Water Company) Abu Funtas Strategic Desalination Plant, in order to generally improve the quality and productivity of ready-mixed concrete used in construction and hydraulic projects. The application of value engineering to such concrete mixtures resulted in the following: i) improving the quality of concrete mixtures and increasing the durability of buildings in which they are used; ii) reducing the waste of excess materials of concrete mixture, optimizing the use of resources, and enhancing sustainability; iii) reducing the use of cement, thus reducing CO₂ emissions which ensures the protection of environment and public health; iv) reducing actual costs of concrete mixtures and, in turn, reducing the costs of construction projects; and v) increasing the market share and competitiveness of concrete producers. This research shows that applying the methodology of value engineering to ready-mixed concrete is an effective way to save around 5% of the total cost of concrete mixtures supplied to construction and hydraulic projects, improve the quality according to the technical requirements and as per the standards and specifications for ready-mixed concrete, improve the environmental impact, and promote sustainability.

Keywords: value management, cost of concrete, performance, optimization, sustainability, environmental impact

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Authors: Daniel Canales, Fabián Alvarez, Pablo Varela, Marcela Saavedra, Claudio García, Paula Zapata

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Calcium oxide nanoparticles (n-CaO) ca. 8 nm were obtained from eggshell waste. The n-CaO was incorporated into Poly(lactic acid) PLA matrix in 10 and 20 wt.% of filler content by electrospinning process to obtain PLA/n-CaO nanocomposite fibers as a potential use in scaffold for bone tissue regeneration. The fibers morphology and diameter were homogeneity, the PLA had a diameter of 2.2 ± 0.8 µm and, with the nanoparticles incorporation (20wt.%), reached ca. 2.9 ± 0.9 µm. The PLA/n-CaO nanocomposites fibers showed in vitro bioactivity, capable of inducing the precipitation of hydroxyapatite (HA) layer in the fiber surface after 7 days in Simulated Body Solution (SBF). The biocidal and biological properties of PLA/n-Cao with 20 wt.% were evaluated, showing a 30% reduction in bacterial viability against S. aureus and 11% for E. coli after 6 hours of bacterial suspensions exposure. Furthermore, the fibers did not show a cytotoxic effect on the bone marrow ST-2 cell line, permitting the cell adhesion and proliferation in Roswell Park Memorial Institute medium (RPMI). The PLA/n-CaO with 20 wt.% of nanoparticles showed a higher capacity to promote the osteogenic differentiation, significantly increasing the alkaline phosphatase (ALP) expression after 7 days compared to PLA and cell control. The in vivo analysis corroborated the biocompatibility of scaffolds prepared, the presence of n-CaO in PLA reduced the formation of fibrous encapsulation of the material improve the healing process.

Keywords: electrospun scaffolds, PLA based nanocomposites, calcium oxide nanoparticles, bioactive materials, tissue engineering

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Authors: Dalya M. Hassan

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Marketplaces and commercial streets represent some of the most used and lively urban public spaces. Not only do they provide an outlet for commercial exchange, but they also facilitate social and recreational encounters. Such encounters can be influenced by both formal as well as informal vending activities. This paper explores and documents forms of informal vending practices and how they relate to social patterns that occur along the sidewalks of Commercial Streets in Cairo. A qualitative single case study approach of ‘Midan El Gami’ marketplace in Heliopolis, Cairo is adopted. The methodology applied includes direct and walk-by observations for two main commercial streets in the marketplace. Four zoomed-in activity maps are also done for three sidewalk segments that displayed varying vending and social features. Main findings include a documentation and classification of types of informal vending practices as well as a documentation of vendors’ distribution patterns in the urban space. Informal vending activities mainly included informal street vendors and shop spillovers, either as product or seating spillovers. Results indicated that staying and lingering activities were more prevalent in sidewalks that had certain physical features, such as diversity of shops, shaded areas, open frontages, and product or seating spillovers. Moreover, differences in social activity patterns were noted between sidewalks with street vendors and sidewalks with spillovers. While the first displayed more buying, selling, and people watching activities, the latter displayed more social relations and bonds amongst traders’ communities and café patrons. Ultimately, this paper provides a documentation, which suggests that informal vending can have a positive influence on creating a lively commercial street and on resulting patterns of use on the sidewalk space. The results can provide a basis for further investigations and analysis concerning this topic. This could aid in better accommodating informal vending activities within the design of future commercial streets.

Keywords: commercial streets, informal vending practices, sidewalks, social encounters

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Authors: Zachary Pan

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Our general mission is to solve the problem of classifying images into different body part types and deciding if each of them is healthy or not. However, for now, we will determine healthiness for only one-sixth of the body parts, specifically the chest. We will detect pneumonia in X-ray scans of those chest images. With this type of AI, doctors can use it as a second opinion when they are taking CT or X-ray scans of their patients. Another ad-vantage of using this machine learning classifier is that it has no human weaknesses like fatigue. The overall ap-proach to this problem is to split the problem into two parts: first, classify the image, then determine if it is healthy. In order to classify the image into a specific body part class, the body parts dataset must be split into test and training sets. We can then use many models, like neural networks or logistic regression models, and fit them using the training set. Now, using the test set, we can obtain a realistic accuracy the models will have on images in the real world since these testing images have never been seen by the models before. In order to increase this testing accuracy, we can also apply many complex algorithms to the models, like multiplicative weight update. For the second part of the problem, to determine if the body part is healthy, we can have another dataset consisting of healthy and non-healthy images of the specific body part and once again split that into the test and training sets. We then use another neural network to train on those training set images and use the testing set to figure out its accuracy. We will do this process only for the chest images. A major conclusion reached is that convolutional neural networks are the most reliable and accurate at image classification. In classifying the images, the logistic regression model, the neural network, neural networks with multiplicative weight update, neural networks with the black box algorithm, and the convolutional neural network achieved 96.83 percent accuracy, 97.33 percent accuracy, 97.83 percent accuracy, 96.67 percent accuracy, and 98.83 percent accuracy, respectively. On the other hand, the overall accuracy of the model that de-termines if the images are healthy or not is around 78.37 percent accuracy.

Keywords: body part, healthcare, machine learning, neural networks

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Authors: Jang-Hwan Jo

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The supply management of ecosystem services of local forests is an essential issue as it is linked to the ecological welfare of local residents. This study aims to estimate the supply, trade-offs, and synergies of local forest regulating ecosystem services using a land cover classification map (LCCM) and a forest types map (FTM). Rigorous literature reviews and Expert Delphi analysis were conducted using the detailed variables of 1:5,000 LCCM and FTM. Land-use scoring method and Getis-Ord Gi* Analysis were utilized on detailed variables to propose a method for estimating supply, trade-offs, and synergies of the local forest regulating ecosystem services. The analysis revealed that the rank order (1st to 5th) of supply of regulating ecosystem services was Erosion prevention, Air quality regulation, Heat island mitigation, Water quality regulation, and Carbon storage. When analyzing the correlation between defined services of the entire city, almost all services showed a synergistic effect. However, when analyzing locally, trade-off effects (Heat island mitigation – Air quality regulation, Water quality regulation – Air quality regulation) appeared in the eastern and northwestern forest areas. This suggests the need to consider not only the synergy and trade-offs of the entire forest between specific ecosystem services but also the synergy and trade-offs of local areas in managing the regulating ecosystem services of local forests. The study result can provide primary data for the stakeholders to determine the initial conditions of the planning stage when discussing the establishment of policies related to the adjustment of the supply of regulating ecosystem services of the forests with limited access. Moreover, the study result can also help refine the estimation of the supply of the regulating ecosystem services with the availability of other forms of data.

Keywords: ecosystem service, getis ord gi* analysis, land use scoring method, regional forest, regulating service, synergies, trade-offs

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Authors: C. T. Eze, I. N. E. Onwurah

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An important hydrogeologic problem in areas of high faults formations is high environmental health hazard occasioned by microbial and heavy metals contamination of ground waters. Consequently, we examined the microbial load and heavy metals concentration of hospital wastewater discharged into the environment at Park Lane General Hospital Enugu Urban, Nigeria. The microbial counts, characteristics and frequency of occurrences of the isolated microorganisms were determined by cultural, morphological and biochemical characteristics using established procedure while the varying concentrations of the identified heavy metals were determined using the spectrophotometric method. The microbiological analyses showed a mean total aerobic bacteria counts from 13.7 ± 0.65 × 107 to 22.8 ± 1.14 ×1010 CFU/ml, mean total anaerobic bacteria counts from 6.0 ± 1.6 × 103 to 1.7 ± 0.41 ×104 CFU/ml and mean total fungal counts from 0 ± 0 to 2.3 ± 0.16 × 105 CFU/ml. The isolated micro-organisms which included both pathogenic and non-pathogenic organisms were Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Salmonella typhi, Bacillus subtilis, Proteus vulgaris, Klesbsiella pneumonia and bacteriodes sp. The only fungal isolate was Candida albican. The heavy metals identified in the leachate were Arsenic, Cadmium, Lead, Mercury and Chromium and their concentrations ranged from 0.003 ± 0.00082 to 0.14 ± 0.0082 mg/l. These values were above WHO permissible limits while others fall within the limits. Therefore, hospital waste water can pose the environmental health risk when not properly treated before discharge, especially in geologic formations with high fault formations.

Keywords: bacterial isolates, fungal isolates, heavy metals, hospital wastewater, microbial counts

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Authors: M. Thuanthong, W. Youravong, N. Sirinupong

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Nile tilapia (Oreochromis niloticus) is one of fish species cultured in Thailand with a high production volume. A lot of skin is generated during fish processing. In addition, there are many research reported that fish skin contains abundant of collagen. Thus, the use of Nile tilapia skin as collagen source can increase the benefit of industrial waste. In this study, Acid soluble collagen (ASC) was extracted at 5, 15 or 25 ˚C with 0.5 M acetic acid then the acid was removed out and collagen was concentrated by ultrafiltration-diafiltration (UFDF). The triple helix collagen from UFDF process was used as substrate to produce collagen peptides by alcalase hydrolysis in an enzymatic membrane reactor (EMR) coupling with 1 kDa molecular weight cut off (MWCO) polysulfone hollow fiber membrane. The results showed that ASC extracted at high temperature (25 ˚C) with 0.5 M acetic acid for 5 h still preserved triple helix structure. In the UFDF process, the acid removal was higher than 90 % without any effect on ASC properties, particularly triple helix structure as indicated by circular dichroism spectrum. Moreover, Collagen from UFDF was used to produce collagen peptides by EMR. In EMR, collagen was pre-hydrolyzed by alcalase for 60 min before introduced to membrane separation. The EMR operation was operated for 10 h and provided a good of protein conversion stability. The results suggested that there is a successfulness of UF in application for acid removal to produce ASC with desirable preservation of its quality. In addition, the EMR was proven to be an effective process to produce low molecular weight peptides with ACE-inhibitory activity properties.

Keywords: acid soluble collagen, ultrafiltration-diafiltration, enzymatic membrane reactor, ace-inhibitory activity

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Authors: Mikhail Panes, Sergei Pozniak, Nikolai Pozniak

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Purpose: To evaluate the effectiveness of intrastromal donor limbal segments implantation for treatment of progressive keratoconus considering on main characteristics of corneal endothelial cells. Setting: Outpatient ophthalmic clinic. Methods: Twenty patients (20 eyes) with progressive keratoconus II-III of Amsler classification were recruited. The worst eye was treated with the transplantation of donor limbal segments in the recipient corneal stroma, while the fellow eye was left untreated as a control of functional and morphological changes. Furthermore, twenty patients (20 eyes) without progressive keratoconus was used as a control of corneal endothelial cells changes. All patients underwent a complete ocular examination including uncorrected and corrected distance visual acuity (UDVA, CDVA), slit lamp examination fundus examination, corneal topography and pachymetry, auto-keratometry, Anterior Segment Optical Coherence Tomography and Corneal Endothelial Specular Microscopy. Results: After two years, statistically significant improvement in the UDVA and CDVA (on the average on two lines for UDVA and three-four lines for CDVA) were noted. Besides corneal astigmatism decreased from 5.82 ± 2.64 to 1.92 ± 1.4 D. Moreover there were no statistically significant differences in the changes of mean spherical equivalent, keratometry and pachymetry indicators. It should be noted that after two years there were no significant differences in the changes of the number and form of corneal endothelial cells. It can be regarded as a process stabilization. In untreated control eyes, there was a general trend towards worsening of UDVA, CDVA and corneal thickness, while corneal astigmatism was increased. Conclusion: Intrastromal donor segments implantation is a safe technique for keratoconus treatment. Intrastromal donor segments implantation is an efficient procedure to stabilize and improve progressive keratoconus.

Keywords: corneal endothelial cells, intrastromal donor limbal segments, progressive keratoconus, surgical treatment of keratoconus

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Authors: Francesca di Donato, Sara Trucco

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Green accounting is a growing topic of interest. Indeed, nowadays, most firms affect the environment; therefore, companies are seeking the best way to disclose environmental information. Furthermore, companies are increasingly committed to improving the environment, and the topic is gaining more importance to the public, governments, and policymakers. Green accounting is a type of accounting that considers environmental costs and their impact on the financial performance of firms. Thus, the motivation of the current research is to investigate the state-of-the-art literature on the relationship between green accounting and firm performance since the birth of the topic of green accounting and to investigate gaps in the literature that represent fruitful terrain for future research. In doing so, this study provides a bibliometric literature review of existing evidence related to the link between green accounting and firm performance since 2000. The search, based on the most relevant databases for scientific journals (which are Scopus, Emerald, Web of Science, Google Scholar, and Econlit), returned 1917 scientific articles. The articles were manually reviewed in order to identify only the relevant studies in the field by excluding articles with titles and abstracts out of scope. The final sample was composed of 107 articles. A content analysis was carried out on the final sample of articles; in doing so, a classification system has been proposed. Findings show the most relevant environmental costs and issues considered in previous studies and how green accounting may be linked to the financial and non-financial performance of a firm. The study also offers suggestions for future research in this domain. This study has several practical implications. Indeed, the topic of green accounting may be applied to different sectors and different types of companies. Therefore, this study may help managers to better understand the most relevant environmental information to disclose and how environmental issues may be managed to improve the performance of the firms. Moreover, the bibliometric literature review may be of interest to those stakeholders who are interested in the historical evolution of the topic.

Keywords: bibliometric literature review, firm performance, green accounting, literature review

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Authors: Letizia Marchetti, Federica Annunzi, Federico Fiorini, Cristiano Nicolella

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Automotive shredder residue (ASR) is a mixture of waste that makes up 20-25% of end-of-life vehicles. For many years, ASR was commonly disposed of in landfills or incinerated, causing serious environmental problems. Nowadays, thermochemical treatments are a promising alternative, although the heterogeneity of ASR still poses some challenges. One of the emerging thermochemical treatments for ASR is pyrolysis, which promotes the decomposition of long polymeric chains by providing heat in the absence of an oxidizing agent. In this way, pyrolysis promotes the conversion of ASR into solid, liquid, and gaseous phases. This work aims to improve the performance of a two-step pyrolysis process. After the characterization of the analysed ASR, the focus is on determining the effects of residence time on product yields and gas composition. A batch experimental setup that reproduces the entire process was used. The setup consists of three sections: the pyrolysis section (made of two reactors), the separation section, and the analysis section. Two different residence times were investigated to find suitable conditions for the first sample of ASR. These first tests showed that the products obtained were more sensitive to residence time in the second reactor. Indeed, slightly increasing residence time in the second reactor managed to raise the yield of gas and carbon residue and decrease the yield of liquid fraction. Then, to test the versatility of the setup, the same conditions were applied to a different sample of ASR coming from a different chemical plant. The comparison between the two ASR samples shows that similar product yields and compositions are obtained using the same setup.

Keywords: automotive shredder residue, experimental tests, heterogeneity, product yields, two-step pyrolysis

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Authors: Laura Sour

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Government accounting is an essential instrument of transparency and accountability in public administration, which allows connecting internal management with the implementation of policies and their evaluation by third parties through the construction of indicators on the cost of government. Several countries have adopted the International Public Sector Accounting Standards (IPSAS) as part of their modernization strategy. This document will evaluate the quantity and harmonization of the financial information published in the financial statements of 12 Latin American countries based on what is established in IPSAS 1, 2 and 17. For this, seven types of financial statements are analyzed. published during the period from 2015 to 2019. Based on this information, it will be possible to describe the evolution in the government financial publication to carry out a detailed analysis of the items that have been most transparent in these countries. Finally, the level of harmonization of the financial statements will be studied using the Herfindahl-Hirschman index (IHH) to determine the degree of comparability of the information. To date, the results indicate that the public sector has increased the quantity and harmonization of the financial information published during the study period, but in a heterogeneous way: From the data collected, it has been found that the financial statement published with greater frequency and quantity is the Income Statement (classification of expenses by nature). On the other hand, the most complete reports were published by Costa Rica (2017 to 2019) and Mexico (2016 to 2018), periods during which these countries complied with 92.9 percent of the items analyzed. Although 2017 and 2018 are the years in which the most financial statements were reported, it is important to mention that Mexico is the country that has published the most financial information throughout the entire study period. The use of the IHH is expected to provide accurate information on the quality with which countries have adopted IPSAS within their government accounting systems to promote transparency and accountability in the continent.

Keywords: accounting and auditing, government policy and regulation, harmonization, public sector accounting and audits IPSAS

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Authors: Elnaz Abbasian, Mohsen Faizi

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In its thousand-year development, architecture has gained valuable patterns. Iran’s desert regions possess developed patterns of traditional architecture and outstanding skeletal features. Unfortunately increasing population and urbanization growth in the past decade as well as the lack of harmony with environment’s texture has destroyed such permanent concepts in the building’s skeleton, causing a lot of energy waste in the modern architecture. The important question is how cooling patterns of Iran’s traditional architecture can be used in a new way in the modern architecture of residential buildings? This research is library-based and documental that looks at sustainable development, analyzes the features of Iranian architecture in hot and dry climate in terms of sustainability as well as historical patterns, and makes a model for real environment. By methodological analysis of past, it intends to suggest a new pattern for residential buildings’ cooling in Iran’s hot and dry climate which is in full accordance to the ecology of the design and at the same time possesses the architectural indices of the past. In the process of cities’ physical development, ecological measures, in proportion to desert’s natural background and climate conditions, has kept the natural fences, preventing buildings from facing climate adversities. Designing and construction of buildings with this viewpoint can reduce the energy needed for maintaining and regulating environmental conditions and with the use of appropriate building technology help minimizing the consumption of fossil fuels while having permanent patterns of desert buildings’ architecture.

Keywords: sustainability concepts, sustainable development, energy climate architecture, fossil fuel, hot and dry climate, patterns of traditional sustainability for residential buildings, modern pattern of cooling

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Authors: Mirza Waseem Abbas, Syed Danish Raza

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For many countries agriculture is still the major force driving the economy and a critically important socioeconomic sector, despite exceptional industrial development across the globe. In countries like Pakistan, this sector is considered the backbone of the economy, and most of the economic decision making revolves around agricultural outputs and data. Timely and accurate facts and figures about this vital sector hold immense significance and have serious implications for the long-term development of the economy. Therefore, any significant improvements in the statistics and other forms of data regarding agriculture sector are considered important by all policymakers. This is especially true for decision making for the betterment of crops and the agriculture sector in general. Provincial and federal agricultural departments collect data for all cash and non-cash crops and the sector, in general, every year. Traditional data collection for such a large sector i.e. agriculture, being time-consuming, prone to human error and labor-intensive, is slowly but gradually being replaced by remote sensing techniques. For this study, remotely sensed data were used for change detection (machine learning, supervised & unsupervised classification) to assess the increase or decrease in area under agriculture over the last fifteen years due to urbanization. Detailed Landsat Images for the selected agricultural districts were acquired for the year 2000 and compared to images of the same area acquired for the year 2016. Observed differences validated through detailed analysis of the areas show that there was a considerable decrease in vegetation during the last fifteen years in four major agricultural districts of the Punjab province due to urbanization (housing societies).

Keywords: change detection, area estimation, machine learning, urbanization, remote sensing

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Authors: Kuo-Chun Chien, Chia-Hui Chang, Ren-Der Sun

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Legal Judgment Prediction (LJP) is a subtask for legal AI. Its main purpose is to use the facts of a case to predict the judgment result. In Taiwan's criminal procedure, when prosecutors complete the investigation of the case, they will decide whether to prosecute the suspect and which article of criminal law should be used based on the facts and evidence of the case. In this study, we collected 305,240 indictments from the public inquiry system of the procuratorate of the Ministry of Justice, which included 169 charges and 317 articles from 21 laws. We take the crime facts in the indictments as the main input to jointly learn the prediction model for law source, article, and charge simultaneously based on the pre-trained Bert model. For single article cases where the frequency of the charge and article are greater than 50, the prediction performance of law sources, articles, and charges reach 97.66, 92.22, and 60.52 macro-f1, respectively. To understand the big performance gap between articles and charges, we used a bipartite graph to visualize the relationship between the articles and charges, and found that the reason for the poor prediction performance was actually due to the wording precision. Some charges use the simplest words, while others may include the perpetrator or the result to make the charges more specific. For example, Article 284 of the Criminal Law may be indicted as “negligent injury”, "negligent death”, "business injury", "driving business injury", or "non-driving business injury". As another example, Article 10 of the Drug Hazard Control Regulations can be charged as “Drug Control Regulations” or “Drug Hazard Control Regulations”. In order to solve the above problems and more accurately predict the article and charge, we plan to include the article content or charge names in the input, and use the sentence-pair classification method for question-answer problems in the BERT model to improve the performance. We will also consider a sequence-to-sequence approach to charge prediction.

Keywords: legal judgment prediction, deep learning, natural language processing, BERT, data visualization

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Authors: Mahsa Kallhor Mohammadi, Hamideh Reshadatjoo

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This study verified the relationship between in-service training and employees’ feeling of psychological ownership. This research applied a descriptive survey that investigated a correlation between variables. The target population was 140 employees of a Drilling Fluid and Waste Management Service Company, and the sample was 123 employees who were selected randomly and encouraged to complete an electronic questionnaire which was designed based on standard questionnaires for research variables covering 62 questions. The face validity of the questionnaire was supported by an experimental test, and its content validity was approved by the thesis supervisor and consulting advisor. For the descriptive statistics frequency tables and diagrams, measures of central tendency such as mode, median, and mean and measures of variability such as variance, standards deviation, and quartile deviation were used. In the inferential statistics section, the Pearson correlation coefficient was used to verify the relationship between the variables of the research. According to the results, all of the research hypotheses were supported. According to hypothesis 1, there was a positive and significant relationship between training policy-making and employees’ psychological ownership (r=0/408, α=0/05). According to hypothesis 2, there was a positive and significant relationship between training planning and employees’ psychological ownership (r=0/446, α=0/05). According to hypothesis 3, there was a positive and significant relationship between providing the training and employees’ psychological ownership (r=0/512, α=0/05). According to hypothesis 4, there was a positive and significant relationship between training performance management and employees’ psychological ownership (r=0/462, α=0/05). According to hypothesis 5, there was a positive and significant relationship between employees’ motivation and psychological ownership (r=0/694, α=0/05). Therefore, through systematic in-service training, which is in the same line with the strategic goals of an organization and is based on scientific needs analysis, design, implementation, and evaluation, it is possible to improve employees’ sense of psychological ownership toward an organization.

Keywords: in-service training, motivation, organizational behavior, psychological ownership

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Authors: Sophia Shi

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Acute kidney injury (AKI) is the sudden onset of kidney damage in which the kidneys cannot filter waste from the blood, requiring emergency hospitalization. AKI patient mortality rate is high in the ICU and is virtually impossible for doctors to predict because it is so unexpected. Currently, there is no hybrid model predicting AKI that takes advantage of two types of data. De-identified patient data from the MIMIC-III database and de-identified kidney images and corresponding patient records from the Beijing Hospital of the Ministry of Health were collected. Using data features including serum creatinine among others, two numeric models using MIMIC and Beijing Hospital data were built, and with the hospital ultrasounds, an image-only model was built. Convolutional neural networks (CNN) were used, VGG and Resnet for numeric data and Resnet for image data, and they were combined into a hybrid model by concatenating feature maps of both types of models to create a new input. This input enters another CNN block and then two fully connected layers, ending in a binary output after running through Softmax and additional code. The hybrid model successfully predicted AKI and the highest AUROC of the model was 0.953, achieving an accuracy of 90% and F1-score of 0.91. This model can be implemented into urgent clinical settings such as the ICU and aid doctors by assessing the risk of AKI shortly after the patient’s admission to the ICU, so that doctors can take preventative measures and diminish mortality risks and severe kidney damage.

Keywords: Acute kidney injury, Convolutional neural network, Hybrid deep learning, Patient record data, ResNet, Ultrasound kidney images, VGG

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Authors: Mohmd Sarireh

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The geotechnical study can be employed successfully to assess and follow the expected development or delay in the project construction. The development project of city center or downtown was taken as a case study for the investigation of the project conditions that might support progress or cause delay. The project was proposed to build 7447 m2 by reinforced concrete mainly to serve and support the services provided to people in Tafila. The project construction had faced challenges and obstacles such as soil collapse because of excavation of the weak soil that found in the project site. In addition, the topography of the project area showed a high slope from South-West to North. The slope through the project footprint reached to 83.3% which is considered very high slope. One year and a half proposed to finish the project construction since the 1st of March 2013 and it was planned to be finished by the 31th of August 2014, but the project needs more than one year and a half as extension according to the consultant engineer. The collecting of data was conducted through the interviews with the engineers and officials, and by analyzing the soil reports and samples taken during design and excavation. The major findings came out to weak and fractured soil and construction waste that were found at project site. Also, soil was considered very fine according to the plasticity index (PI) values, in addition to the high depths required for foundation that contribute to the collapse of soil and the increase of project cost. The current project aims to present how the unseen conditions can delay the project construction and increase the cost of the project that rises to JD8.305 Million.

Keywords: geotechnical, management, progress, risk, soil unseen conditions management

Procedia PDF Downloads 186

Authors: Sara Alhabli, Eman Elashiry, Osama Felemban, Abdullah Almushayt, Faisal Dardeer, Ahmed Mohammad, Fajr Orri, Nada Bamashmous

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Background: In December of 2019, the coronavirus (SARS-CoV-2) first appeared and quickly spread to become a worldwide pandemic. This study aimed to evaluate the prevalence and types of pediatric dental emergencies during the COVID-19 lockdown in Jeddah, Saudi Arabia, at the University Dental Hospital (UDH) of King Abdulaziz University (KAU) and identified the management provided for these dental emergency visits. Materials and Methods: Data collection was done retrospectively from electronic dental records for children aged 0-18 that attended the UDH emergency clinic during the period from March 1st, 2020, to September 30th, 2020. An electronic form formulated specifically for this study was used to collect the required data from electronic patient records, including demographic data, emergency classification, management, and referrals. Results: A total of 3146 patients were seen at the emergency clinics during this period, of which 661 were children (21%). Types of emergency conditions included 0.8% emergency cases, 34% urgent, and 65.2% non-urgent conditions. Severe dental pain (73.1%) and abscesses (20%) were the most common urgent dental conditions. Most non-urgent conditions presented for initial or periodic visits, recalls, or routine radiographs (74%). Treatments rarely involved restorations, with 8% among urgent conditions and 5.4% among non-urgent conditions. Antibiotics were only prescribed to 6.9% of urgent conditions. Conclusions: The largest group of children presenting at the emergency dental clinics were found to be children with non-urgent conditions. Tele dentistry can be a solution to avoid large numbers of non-urgent patients presenting to emergency clinics. Additionally, dental care for non-urgent conditions during the pandemic should focus more on procedures with less aerosol generation.

Keywords: COVID-19 pandemic, dental emergencies, oral health, pediatric dentistry, children

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Authors: Riccardo Patriarca, Giulio Di Gravio, Francesco Costantino, Massimo Tronci

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An accurate inventory management policy acquires a crucial role in the several high-availability sectors. In these sectors, due to the high-cost of spares and backorders, an (S-1, S) replenishment policy is necessary for high-availability items. The policy enables the shipment of a substitute efficient item anytime the inventory size decreases by one. This policy can be modelled following the Multi-Echelon Technique for Recoverable Item Control (METRIC). The METRIC is a system-based technique that allows defining the optimum stock level in a multi-echelon network, adopting measures in line with the decision-maker’s perspective. The METRIC defines an availability-cost function with inventory costs and required service levels, using as inputs data about the demand trend, the supplying and maintenance characteristics of the network and the budget/availability constraints. The traditional METRIC relies on the hypothesis that a Poisson distribution well represents the demand distribution in case of items with a low failure rate. However, in this research, we will explore the effects of using a Poisson distribution to model the demand of low failure rate items characterized by an irregular demand trend. This characteristic of a demand is not included in the traditional METRIC formulation leading to the need of revising its traditional formulation. Using the CV (Coefficient of Variation) and ADI (Average inter-Demand Interval) classification, we will define the inherent flaws of Poisson-based METRIC for irregular demand items, defining an innovative ad hoc distribution which can better fit the irregular demands. This distribution will allow defining proper stock levels to reduce stocking and backorder costs due to the high irregularities in the demand trend. A case study in the aviation domain will clarify the benefits of this innovative METRIC approach.

Keywords: METRIC, inventory management, irregular demand, spare parts

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Authors: Manish Pandey, Gurinderjit Kaur, Meenu Talwar, Sachin Chauhan, Jagbir Gill

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Health-care management systems are a unit of nice connection as a result of the supply a straightforward and fast management of all aspects relating to a patient, not essentially medical. What is more, there are unit additional and additional cases of pathologies during which diagnosing and treatment may be solely allotted by victimization medical imaging techniques. With associate ever-increasing prevalence, medical pictures area unit directly acquired in or regenerate into digital type, for his or her storage additionally as sequent retrieval and process. Data Mining is the process of extracting information from large data sets through using algorithms and Techniques drawn from the field of Statistics, Machine Learning and Data Base Management Systems. Forecasting may be a prediction of what's going to occur within the future, associated it's an unsure method. Owing to the uncertainty, the accuracy of a forecast is as vital because the outcome foretold by foretelling the freelance variables. A forecast management should be wont to establish if the accuracy of the forecast is within satisfactory limits. Fuzzy regression strategies have normally been wont to develop shopper preferences models that correlate the engineering characteristics with shopper preferences relating to a replacement product; the patron preference models offer a platform, wherever by product developers will decide the engineering characteristics so as to satisfy shopper preferences before developing the merchandise. Recent analysis shows that these fuzzy regression strategies area units normally will not to model client preferences. We tend to propose a Testing the strength of Exponential Regression Model over regression toward the mean Model.

Keywords: health-care management systems, fuzzy regression, data mining, forecasting, fuzzy membership function

Procedia PDF Downloads 245

Authors: Maad Kamal Al-Anni

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This study is about an algorithmic dependence of Artificial Neural Network on Multilayer Perceptron (MPL) pertaining to the classification and clustering presentations for Mobile Adhoc Network vulnerabilities. Moreover, mobile ad hoc network (MANET) is ubiquitous intelligent internetworking devices in which it has the ability to detect their environment using an autonomous system of mobile nodes that are connected via wireless links. Security affairs are the most important subject in MANET due to the easy penetrative scenarios occurred in such an auto configuration network. One of the powerful techniques used for inspecting the network packets is Intrusion Detection System (IDS); in this article, we are going to show the effectiveness of artificial neural networks used as a machine learning along with stochastic approach (information gain) to classify the malicious behaviors in simulated network with respect to different IDS techniques. The monitoring agent is responsible for detection inference engine, the audit data is collected from collecting agent by simulating the node attack and contrasted outputs with normal behaviors of the framework, whenever. In the event that there is any deviation from the ordinary behaviors then the monitoring agent is considered this event as an attack , in this article we are going to demonstrate the  signature-based IDS approach in a MANET by implementing the back propagation algorithm over ensemble-based Traffic Table (TT), thus the signature of malicious behaviors or undesirable activities are often significantly prognosticated and efficiently figured out, by increasing the parametric set-up of Back propagation algorithm during the experimental results which empirically shown its effectiveness  for the ratio of detection index up to 98.6 percentage. Consequently it is proved in empirical results in this article, the performance matrices are also being included in this article with Xgraph screen show by different through puts like Packet Delivery Ratio (PDR), Through Put(TP), and Average Delay(AD).

Keywords: Intrusion Detection System (IDS), Mobile Adhoc Networks (MANET), Back Propagation Algorithm (BPA), Neural Networks (NN)

Procedia PDF Downloads 157

Authors: Wade Ghribi, Abdelmoty M. Ahmed, Ahmed Said Badawy, Belgacem Bouallegue

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In higher education institutions, the most pressing priority is to improve student performance and retention. Large volumes of student data are used in Educational Data Mining techniques to find new hidden information from students' learning behavior, particularly to uncover the early symptom of at-risk pupils. On the other hand, data with noise, outliers, and irrelevant information may provide incorrect conclusions. By identifying features of students' data that have the potential to improve performance prediction results, comparing and identifying the most appropriate ensemble learning technique after preprocessing the data, and optimizing the hyperparameters, this paper aims to develop a reliable students' performance prediction model for Higher Education Institutions. Data was gathered from two different systems: a student information system and an e-learning system for undergraduate students in the College of Computer Science of a Saudi Arabian State University. The cases of 4413 students were used in this article. The process includes data collection, data integration, data preprocessing (such as cleaning, normalization, and transformation), feature selection, pattern extraction, and, finally, model optimization and assessment. Random Forest, Bagging, Stacking, Majority Vote, and two types of Boosting techniques, AdaBoost and XGBoost, are ensemble learning approaches, whereas Decision Tree, Support Vector Machine, and Artificial Neural Network are supervised learning techniques. Hyperparameters for ensemble learning systems will be fine-tuned to provide enhanced performance and optimal output. The findings imply that combining features of students' behavior from e-learning and students' information systems using Majority Vote produced better outcomes than the other ensemble techniques.

Keywords: educational data mining, student performance prediction, e-learning, classification, ensemble learning, higher education

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Authors: Mohammad H. Yarmohammadian, Mehdi Nasr Isfahani, Elham Anbari

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Introduction: The awareness of using chemical, biological, and nuclear agents in everyday industrial and non-industrial incidents has increased recently; release of these materials can be accidental or intentional. Since hospitals are the forefronts of confronting Chemical, Biological, Radiological and Nuclear( CBRN) incidents, the goal of the present research was to provide a generic protocol for CBRN incidents through a comparative review of CBRN protocols and guidelines of different countries and reviewing various books, handbooks and papers. Method: The integrative approach or research synthesis was adopted in this study. First a simple narrative review of programs, books, handbooks, and papers about response to CBRN incidents in different countries was carried out. Then the most important and functional information was discussed in the form of a generic protocol in focus group sessions and subsequently confirmed. Results: Findings indicated that most of the countries had various protocols, guidelines, and handbooks for hazardous materials or CBRN incidents. The final outcome of the research synthesis was a 50 page generic protocol whose main topics included introduction, definition and classification of CBRN agents, four major phases of incident and disaster management cycle, hospital response management plan, equipment, and recommended supplies and antidotes for decontamination (radiological/nuclear, chemical, biological); each of these also had subtopics. Conclusion: In the majority of international protocols, guidelines, handbooks and also international and Iranian books and papers, there is an emphasis on the importance of incident command system, determining the safety degree of decontamination zones, maps of decontamination zones, decontamination process, triage classifications, personal protective equipment, and supplies and antidotes for decontamination; these are the least requirements for such incidents and also consistent with the provided generic protocol.

Keywords: hospital, CBRN, decontamination, generic protocol, CBRN Incidents

Procedia PDF Downloads 264

Authors: O. A. Anjola, M. A. Adejobi, L. A Tijani

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This study was conducted to investigate the effects of brewer spent grain (BSG) on growth performance and serum biochemistry characteristics of blood of broilers chickens. Three hundred and fifteen (4 weeks old) Oba – Marshall Broilers were used for the experiment. Five experimental diets were formulated with diet 1 (T1) containing 100% soya bean meal as the control, Diet 2, 3, 4 and 5 had BSG as replacement for soya bean meal at 0%, 36%, 57%, 76% and 100% respectively. The birds were allocated into each dietary group in a completely randomized design with 63 chicks in 3 replicates of 21 chicks each. The birds were offered these diets ad libitum from four weeks old to nine weeks old (35 days). Feed intake, body weight, weight gain, and feed conversion ratio (FCR) were assessed. Blood samples were also collected to examine the effect of BSG waste on hematology and serum biochemistry of broilers. Result indicated that BSG did not significantly (P>0.05) affect feed intake and weight gain. However, FCR and final weight of finishing broilers differs significantly (P<0.05) among treatments. The blood hematology and serum biochemistry indices did not follow a particular trend. Cholesterol concentration reduced with increasing level of BSG in the diet. Hb, RBC, WBC, neutrophils, lymphocytes, heterophiles and MCHC were significant (P<0.05) while MHC and MVC were not significantly (P>0.05) affected by BSG in diets. serum total protein, albumin, and cholesterol concentration also showed significance (P<0.05) difference. Thus, BSG can replace soya bean meal up to 14% in the broiler finisher diet without deleterious effect on the growth, hematology and the serum biochemistry of broiler chicken.

Keywords: broilers, growth performance, haematology, serum biochemistry

Procedia PDF Downloads 303