Search results for: random forest algorithm

Authors: Pratama R. Yunia, Firmansyah, I., Ariani, Ulfa R. Maharani, Fikri M. Al

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Nowadays, notwithstanding that the role of SMS as a means of communication has been largely replaced by online applications such as WhatsApp, Telegram, and others, the fact that SMS is still used for certain and important communication needs is indisputable. Among them is for sending one time password (OTP) as an authentication media for various online applications ranging from chatting, shopping to online banking applications. However, the usage of SMS does not pretty much guarantee the security of transmitted messages. As a matter of fact, the transmitted messages between BTS is still in the form of plaintext, making it extremely vulnerable to eavesdropping, especially if the message is confidential, for instance, the OTP. One solution to overcome this problem is to use an SMS application which provides security services for each transmitted message. Responding to this problem, in this study, an automatic key SMS encryption scheme was designed as a means to secure SMS communication. The proposed scheme allows SMS sending, which is automatically encrypted with keys that are constantly changing (automatic key update), automatic key exchange, and automatic key generation. In terms of the security method, the proposed scheme applies cryptographic techniques with a hybrid cryptosystem mechanism. Proofing the proposed scheme, a client to client SMS encryption application was developed using Java platform with AES-256 as encryption algorithm, RSA-768 as public and private key generator and SHA-256 for message hashing function. The result of this study is a secure automatic key SMS encryption scheme using hybrid cryptosystem which can guarantee the security of every transmitted message, so as to become a reliable solution in sending confidential messages through SMS although it still has weaknesses in terms of processing time.

Keywords: encryption scheme, hybrid cryptosystem, one time password, SMS security

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Authors: Falana Yetunde Olaitan, Ijah U. J. J, Solebo Shakirat O.

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Genetically modified crops are already phenomenally successful and are grown worldwide in more than eighteen countries on more than 67 million hectares. Nigeria, in October 2018, approved Bacillus thuringiensis (Bt) cotton and maize; therefore, the need to carry out environmental risk assessment studies. A total of 15 4L octagonal ceramic pots were filled with 4kg of soil and placed on the bench in 2 rows of 10 pots each and the 3rd row of 5 pots, 1st-row pots were used to plant GM cotton seeds, while the 2nd-row pots were used for non-GM cotton seeds and the 3rd row of 5 pots served as control, all in the screen house. Soil samples for metagenomic DNA extraction were collected at random and at the monthly interval after planting at a distance of 2mm from the plant’s root and at a depth of 10cm using a sterile spatula. Soil samples for physicochemical analysis were collected before planting and after harvesting the GM and non-GM crops as well as from the control soil. The DNA was extracted, quantified and sequenced; Sample 1A (DNA from GM cotton Soil at 1st interval) gave the lowest sequence read with 0.853M while sample 2B (DNA from GM cotton Soil at 2nd interval) gave the highest with 5.785M, others gave between 1.8M and 4.7M. The samples treatment were grouped into four, Group 1 (GM cotton soil from 1 to 3 intervals) had between 800,000 and 5,700,000 strains of microbes (SOM), Group 2 (non GM cotton soil from 1 to 3 intervals) had between 1,400,600 and 4,200,000 SOM, Group 3 (control soil) had between 900,000 and 3,600,000 SOM and Group 4 (initial soil) had between 3,700,000 and 4,000,000 SOM. The microbes observed were predominantly bacteria (including archaea), fungi, dark matter alongside protists and phages. The predominant bacterial groups were the Terrabacteria (Bacillus funiculus, Bacillus sp.), the Proteobacteria (Microvirga massiliensis, sphingomonas sp.) and the Archaea (Nitrososphaera sp.), while the fungi were Aspergillus fischeri and Fusarium falciforme. The comparative analysis between groups was done using JACCARD PERMANOVA beta diversity analysis at P-value not more than 0.76 and there was no significant pair found. The pH for initial, GM cotton, non-GM cotton and control soil were 6.28, 6.26, 7.25, 8.26 and the percentage moisture was 0.63, 0.78, 0.89 and 0.82, respectively, while the percentage Nitrogen was observed to be 17.79, 1.14, 1.10 and 0.56 respectively. Other parameters include, varying concentrations of Potassium (0.46, 1,284.47, 1,785.48, 1,252.83 mg/kg) and Phosphorus (18.76, 17.76, 16.87, 15.23 mg/kg) were recorded for the four treatments respectively. The soil consisted mainly of silt (32.09 to 34.66%) and clay (58.89 to 60.23%), reflecting the soil texture as silty – clay. The results were then tested with ANOVA at less than 0.05 P-value and no pair was found to be significant as well. The results suggest that the GM crops have no significant effect on microbial ecology and physicochemical properties of the soil and, in turn, no direct or indirect effects on human health.

Keywords: genetically modified crop, microbial ecology, physicochemical properties, metagenomics, DNA, soil

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Authors: Abdul Ganiyu Iddrisu

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Digitization is the process of transforming analog material into digital form, especially for storage and use in a computer. Significant development of information and communication technology (ICT) over the past years has encouraged many researchers to investigate its contribution to promoting economic growth, and reducing poverty. Yet compelling empirical evidence on the effects of digitization on economic growth remains weak, particularly in Africa. This is because extant studies that explicitly evaluate digitization and economic growth nexus are mostly reports and desk reviews. This points out an empirical knowledge gap in the literature. Hypothetically, digitization influences financial sector development which in turn influences economic growth. Digitization has changed the financial sector and its operating environment. Obstacles to access to financing, for instance, physical distance, minimum balance requirements, low-income flows among others can be circumvented. Savings have increased, micro-savers have opened bank accounts, and banks are now able to price short-term loans. This has the potential to develop the financial sector, however, empirical evidence on digitization-financial development nexus is dearth. On the other hand, a number of studies maintained that financial sector development greatly influences growth of economies. We therefore argue that financial sector development is one of the transmission mechanisms through which digitization affects economic growth. Employing macro-country-level data from African countries and using fixed effects, random effects and Hausman-Taylor estimation approaches, this paper contributes to the literature by analysing economic growth in Africa focusing on the role of digitization, and financial sector development. First, we assess how digitization influence financial sector development in Africa. From an economic policy perspective, it is important to identify digitization determinants of financial sector development so that action can be taken to reduce the economic shocks associated with financial sector distortions. This nexus is rarely examined empirically in the literature. Secondly, we examine the effect of domestic credit to private sector and stock market capitalization as a percentage of GDP as used to proxy for financial sector development on 2 economic growth. Digitization is represented by the volume of digital/ICT equipment imported and GDP growth is used to proxy economic growth. Finally, we examine the effect of digitization on economic growth in the light of financial sector development. The following key results were found; first, digitalization propels financial sector development in Africa. Second, financial sector development enhances economic growth. Finally, contrary to our expectation, the results also indicate that digitalization conditioned on financial sector development tends to reduce economic growth in Africa. However, results of the net effects suggest that digitalization, overall, improves economic growth in Africa. We, therefore, conclude that, digitalization in Africa does not only develop the financial sector but unconditionally contributes the growth of the continent’s economies.

Keywords: digitalization, economic growth, financial sector development, Africa

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Authors: Chiranjit Das, Sanjay Jharkharia

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This study is focused on transport mode selection under lead time variability and emissions constraint. In order to reduce the carbon emissions generation due to transportation, organization has often faced a dilemmatic choice of transport mode selection since logistic cost and emissions reduction are complementary with each other. Another important aspect of transportation decision is lead-time variability which is least considered in transport mode selection problem. Thus, in this study, we provide a comprehensive mathematical based analytical model to decide transport mode selection under emissions constraint. We also extend our work through analysing the effect of lead time variability in the transport mode selection by a sensitivity analysis. In order to account lead time variability into the model, two identically normally distributed random variables are incorporated in this study including unit lead time variability and lead time demand variability. Therefore, in this study, we are addressing following questions: How the decisions of transport mode selection will be affected by lead time variability? How lead time variability will impact on total supply chain cost under carbon emissions? To accomplish these objectives, a total transportation cost function is developed including unit purchasing cost, unit transportation cost, emissions cost, holding cost during lead time, and penalty cost for stock out due to lead time variability. A set of modes is available to transport each node, in this paper, we consider only four transport modes such as air, road, rail, and water. Transportation cost, distance, emissions level for each transport mode is considered as deterministic and static in this paper. Each mode is having different emissions level depending on the distance and product characteristics. Emissions cost is indirectly affected by the lead time variability if there is any switching of transport mode from lower emissions prone transport mode to higher emissions prone transport mode in order to reduce penalty cost. We provide a numerical analysis in order to study the effectiveness of the mathematical model. We found that chances of stock out during lead time will be higher due to the higher variability of lead time and lad time demand. Numerical results show that penalty cost of air transport mode is negative that means chances of stock out zero, but, having higher holding and emissions cost. Therefore, air transport mode is only selected when there is any emergency order to reduce penalty cost, otherwise, rail and road transport is the most preferred mode of transportation. Thus, this paper is contributing to the literature by a novel approach to decide transport mode under emissions cost and lead time variability. This model can be extended by studying the effect of lead time variability under some other strategic transportation issues such as modal split option, full truck load strategy, and demand consolidation strategy etc.

Keywords: carbon emissions, inventory theoretic model, lead time variability, transport mode selection

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Authors: Xi Liu, Fan Yuan

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In the evolving landscape of physics-based machine learning (PBML), particularly within fluid dynamics and its applications in electromechanical engineering, robot vision, and robot learning, achieving precision and alignment with researchers' specific needs presents a formidable challenge. In response, this work proposes a methodology that integrates neural transformation with a modified smoothed particle hydrodynamics model for generating transformed 3D fluid simulations. This approach is useful for nanoscale science, where the unique and complex behaviors of viscoelastic medium demand accurate neurally-transformed simulations for materials understanding and manipulation. In electromechanical engineering, the method enhances the design and functionality of fluid-operated systems, particularly microfluidic devices, contributing to advancements in nanomaterial design, drug delivery systems, and more. The proposed approach also aligns with the principles of PBML, offering advantages such as multi-fluid stylization and consistent particle attribute transfer. This capability is valuable in various fields where the interaction of multiple fluid components is significant. Moreover, the application of neurally-transformed hydrodynamical models extends to manufacturing processes, such as the production of microelectromechanical systems, enhancing efficiency and cost-effectiveness. The system's ability to perform neural transfer on 3D fluid scenes using a deep learning algorithm alongside physical models further adds a layer of flexibility, allowing researchers to tailor simulations to specific needs across scientific and engineering disciplines.

Keywords: physics-based machine learning, robot vision, robot learning, hydrodynamics

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Authors: Warda L. Panondi, Norihiro Izumi

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Sedimentation is a significant threat to the sustainability of reservoirs and their watershed. In the Philippines, the Pulangi watershed experienced a high sediment loss mainly due to land conversions and plantations that showed critical erosion rates beyond the tolerable limit of -10 ton/ha/yr in all of its sub-basin. From this event, the prediction of runoff volume and sediment yield is essential to examine using the country's soil conservation techniques realistically. In this research, the Pulangi watershed was modeled using the soil water assessment tool (SWAT) to predict its watershed basin's annual runoff and sediment yield. For the calibration and validation of the model, the SWAT-CUP was utilized. The model was calibrated with monthly discharge data for 1990-1993 and validated for 1994-1997. Simultaneously, the sediment yield was calibrated in 2014 and validated in 2015 because of limited observed datasets. Uncertainty analysis and calculation of efficiency indexes were accomplished through the SUFI-2 algorithm. According to the coefficient of determination (R2), Nash Sutcliffe efficiency (NSE), King-Gupta efficiency (KGE), and PBIAS, the calculation of streamflow indicates a good performance for both calibration and validation periods while the sediment yield resulted in a satisfactory performance for both calibration and validation. Therefore, this study was able to identify the most critical sub-basin and severe needs of soil conservation. Furthermore, this study will provide baseline information to prevent floods and landslides and serve as a useful reference for land-use policies and watershed management and sustainability in the Pulangi watershed.

Keywords: Pulangi watershed, sediment yield, streamflow, SWAT model

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Authors: Yu Chen Su

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Introduction: Gestational diabetes mellitus (GDM) refers to impaired glucose tolerance in pregnant women, impacting both the mother and newborn with short and long-term effects. It increases risks of preeclampsia, hypertension, type 2 diabetes, cesarean section, and preterm birth. GDM is associated with fetal macrosomia, shoulder dystocia, neonatal hypoglycemia, and future type 2 diabetes risk. A study on 6,421 pregnant women found 12% experienced high stress, linked to maladaptive coping and depressive emotions. Women with high-risk pregnancies may experience greater stress and depression. Research suggests GDM increases depression prevalence. A study on 632 Hispanic women with GDM showed severe stress and depression tendencies. Involving 95 women with GDM, 33.4% exhibited depression symptoms. Another study compared 180 GDM women to 186 with normal glucose levels, revealing higher depression levels in GDM women. They found GDM women were 1.85 times more likely to receive antidepressants during pregnancy and 1.69 times more likely to experience postpartum depression. Maternal stress and depressive symptoms during pregnancy are significant factors. Early identification by healthcare professionals can greatly benefit GDM women, their infants, and their families. Objectives: The purpose of this study was to investigate the association between gestational diabetes mellitus (GDM) and the risk of depression. Methods: This study reviewed and analyzed relevant literature on gestational diabetes mellitus (GDM) and depression in 6,876 patients. The literature search followed PRISMA guidelines and included databases like Embase, PubMed, MEDLINE, CINAHL, and Cochrane Library. Prospective or retrospective studies with relevant risk ratios and estimates were included, using a random-effects model for the analysis of depression risk correlation. Studies without depression data or relevant risks were excluded. The search period extended until October 2022. Results: Systematic review of 7 studies (6,876 participants) found a significant association (OR = 8.77, CI: 7.98-9.64, p < 0.05) between gestational diabetes mellitus (GDM) and higher depression risk compared to healthy pregnant women. Conclusions: Pregnancy is a significant life transition involving physiological, psychological, and social changes. Gestational diabetes poses challenges to women's physical and mental well-being. Sensitive healthcare professionals identifying issues early can greatly benefit women, babies, and the family.

Keywords: gestational diabetes, depression, systematic review, neta-analysis

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Authors: Ellis Kofi Akwaa-Sekyi, Jordi Moreno Gené

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Managerial actions which negatively profile banks and impair corporate reputation are addressed through effective internal control systems. Disregard for acceptable standards and procedures for granting credit have affected bank loan portfolios and could be cited for the crises in some European countries. The study intends to determine the effectiveness of internal control systems, investigate whether perceived agency problems exist on the part of board members and to establish the relationship between internal controls and credit risk among listed banks in the European Union. Drawing theoretical support from the behavioural compliance and agency theories, about seventeen internal control variables (drawn from the revised COSO framework), bank-specific, country, stock market and macro-economic variables will be involved in the study. A purely quantitative approach will be employed to model internal control variables covering the control environment, risk management, control activities, information and communication and monitoring. Panel data from 2005-2014 on listed banks from 28 European Union countries will be used for the study. Hypotheses will be tested and the Generalized Least Squares (GLS) regression will be run to establish the relationship between dependent and independent variables. The Hausman test will be used to select whether random or fixed effect model will be used. It is expected that listed banks will have sound internal control systems but their effectiveness cannot be confirmed. A perceived agency problem on the part of the board of directors is expected to be confirmed. The study expects significant effect of internal controls on credit risk. The study will uncover another perspective of internal controls as not only an operational risk issue but credit risk too. Banks will be cautious that observing effective internal control systems is an ethical and socially responsible act since the collapse (crisis) of financial institutions as a result of excessive default is a major contagion. This study deviates from the usual primary data approach to measuring internal control variables and rather models internal control variables in a quantitative approach for the panel data. Thus a grey area in approaching the revised COSO framework for internal controls is opened for further research. Most bank failures and crises could be averted if effective internal control systems are religiously adhered to.

Keywords: agency theory, credit risk, internal controls, revised COSO framework

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Authors: Vallence Ngabo Maniragaba

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This study aimed at examining the variations of undernutrition among children below 5 years of age in Uganda. The approach of spatial and spatiotemporal analysis helped in identifying cluster patterns, hot spots and emerging hot spots. Data from the 6 Uganda Demographic and Health Surveys spanning from 1990 to 2016 were used with the main outcome variable being undernutrition among children <5 years of age. All data that were relevant to this study were retrieved from the survey datasets and combined with the 214 shape files for the districts of Uganda to enable spatial and spatiotemporal analysis. Spatial maps with the spatial distribution of the prevalence of undernutrition, both in space and time, were generated using ArcGIS Pro version 2.8. Moran’s I, an index of spatial autocorrelation, rules out doubts of spatial randomness in order to identify spatially clustered patterns of hot or cold spot areas. Furthermore, space-time cubes were generated to establish the trend in undernutrition as well as to mirror its variations over time and across Uganda. Moreover, emerging hot spot analysis was done to help identify the patterns of undernutrition over time. The results indicate a heterogeneous distribution of undernutrition across Uganda and the same variations were also evident over time. Moran’s I index confirmed spatial clustered patterns as opposed to random distributions of undernutrition prevalence. Four hot spot areas, namely; the Karamoja, the Sebei, the West Nile and the Toro regions were significantly evident, most of the central parts of Uganda were identified as cold spot clusters, while most of Western Uganda, the Acholi and the Lango regions had no statistically significant spatial patterns by the year 2016. The spatio-temporal analysis identified the Karamoja and Sebei regions as clusters of persistent, consecutive and intensifying hot spots, West Nile region was identified as a sporadic hot spot area while the Toro region was identified with both sporadic and emerging hotspots. In conclusion, undernutrition is a silent pandemic that needs to be handled with both hands. At 31.2 percent, the prevalence is still very high and unpleasant. The distribution across the country is nonuniform with some areas such as the Karamoja, the West Nile, the Sebei and the Toro regions being epicenters of undernutrition in Uganda. Over time, the same areas have experienced and exhibited high undernutrition prevalence. Policymakers, as well as the implementers, should bear in mind the spatial variations across the country and prioritize hot spot areas in order to have efficient, timely and region-specific interventions.

Keywords: undernutrition, spatial autocorrelation, hotspots analysis, geographically weighted regressions, emerging hotspots analysis, under-fives, Uganda

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Authors: Biva Samadder, Samita Manna

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India is a melting pot of races, tribes, castes and communities. The population of India can be roughly branched into the huge majority of “Civilized” Indians of the Plains and the minority of Tribal population of the hill area and the forest who constituting almost 16 percent of total population of India. The Kirat community composed of four ethnic tribes: Limbu, Lepcha, Dhimal, and Rai. These Kirat people were found to be rich in indigenous knowledge, skill and practices especially for the use on medicinal plants and livelihood purposes. The “Mundhum" is the oral scripture or the “Bible of the Limbus” which serves as the canon of the codes of the Limbu socialization, their moral values and the very orientation of their lifestyle. From birth till death the Limbus are disciplined in the life with full of religious rituals, traditions and culture governed by community norms with a rich legacy of indigenous knowledge and traditional practices. The present study has been conducted using both secondary as well as primary data by applying social methodology consisting of the social survey, questionnaire, interviews and observations in the Kalimpong Block-I of Darjeeling District of west Bengal of India, which is a heterogeneous zone in terms of its ethnic composition and where the Limbus are pre-dominantly concentrated. Due to their close contact with other caste and communities Limbus are now adjusted with the changing situation by borrowing some cultural traits from the other communities and changes that have taken place in their cultural practices, religious beliefs, economic aspects, languages and in social roles and relationships which is bringing the change in their material culture. Limbu language is placed in the Tibeto- Burman Language category. But due to the political and cultural domination of educationally sound and numerically dominant Bengali race, the different communities in this area forced to come under the one umbrella of the Nepali or Gorkhali nation (nation-people). Their respective identities had to be submerged in order to constitute as a strong force to resist Nepali domination and ensure their common survival. As Nepali is a lingua-franca of the area knowing and speaking Nepali language helps them in procuring economic and occupational facilities. Ironically, present day younger generation does not feel comfortable speaking in their own Limbu tongue. The traditional knowledge about medicinal plants, healing, and health culture is found to be wear away due to the lack of interest of young generation. Not only poverty, along with exclusion due to policies they are in the phase of extinction, but their capabilities are ignored and not documented and preserved especially in the case of Limbus who having a great cultural heritage of an oral tradition. Attempts have been made to discuss the persistence and changes in socioeconomic pattern of life in relation to the social structure, material culture, cultural practices, social relationships, indigenous technology, ethos and their values and belief system.

Keywords: changing social relationship, cultural transition, identity, indigenous knowledge, language

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Authors: Saad M. Darwish, Adel A. El-Zoghabi, Moustafa F. Ashry

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The popularity of the Internet and the availability of powerful computers and high-speed networks as low-cost commodity components are changing the way we use computers today. These technical opportunities have led to the possibility of using geographically distributed and multi-owner resources to solve large-scale problems in science, engineering, and commerce. Recent research on these topics has led to the emergence of a new paradigm known as Grid computing. To achieve the promising potentials of tremendous distributed resources, effective and efficient load balancing algorithms are fundamentally important. Unfortunately, load balancing algorithms in traditional parallel and distributed systems, which usually run on homogeneous and dedicated resources, cannot work well in the new circumstances. In this paper, the concept of a fast fractal transform in heterogeneous grid computing based on R-tree and the domain-range entropy is proposed to improve fault tolerance and load balancing algorithm by improve connectivity, communication delay, network bandwidth, resource availability, and resource unpredictability. A novel two-dimension figure of merit is suggested to describe the network effects on load balance and fault tolerance estimation. Fault tolerance is enhanced by adaptively decrease replication time and message cost while load balance is enhanced by adaptively decrease mean job response time. Experimental results show that the proposed method yields superior performance over other methods.

Keywords: Grid computing, load balancing, fault tolerance, R-tree, heterogeneous systems

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Authors: Li-hsing Shih, Wei-Jen Hsu

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Recently, the sale of electrical vehicles (EVs) has increased dramatically due to maturing technology development and decreasing cost. Governments of many countries have made regulations and policies in favor of EVs due to their long-term commitment to net zero carbon emissions. However, due to uncertain factors such as the future price of EVs, forecasting the future market share of EVs is a challenging subject for both the auto industry and local government. This study tries to forecast the market share of EVs using conjoint models and Monte Carlo simulation. The research is conducted in three phases. (1) A conjoint model is established to represent the customer preference structure on purchasing vehicles while five product attributes of both EV and internal combustion engine vehicles (ICEV) are selected. A questionnaire survey is conducted to collect responses from Taiwanese consumers and estimate the part-worth utility functions of all respondents. The resulting part-worth utility functions can be used to estimate the market share, assuming each respondent will purchase the product with the highest total utility. For example, attribute values of an ICEV and a competing EV are given respectively, two total utilities of the two vehicles of a respondent are calculated and then knowing his/her choice. Once the choices of all respondents are known, an estimate of market share can be obtained. (2) Among the attributes, future price is the key attribute that dominates consumers’ choice. This study adopts the assumption of a learning curve to predict the future price of EVs. Based on the learning curve method and past price data of EVs, a regression model is established and the probability distribution function of the price of EVs in 2030 is obtained. (3) Since the future price is a random variable from the results of phase 2, a Monte Carlo simulation is then conducted to simulate the choices of all respondents by using their part-worth utility functions. For instance, using one thousand generated future prices of an EV together with other forecasted attribute values of the EV and an ICEV, one thousand market shares can be obtained with a Monte Carlo simulation. The resulting probability distribution of the market share of EVs provides more information than a fixed number forecast, reflecting the uncertain nature of the future development of EVs. The research results can help the auto industry and local government make more appropriate decisions and future action plans.

Keywords: conjoint model, electrical vehicle, learning curve, Monte Carlo simulation

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Authors: Saurav S. Rath, Birendra K. David

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Generic pharmaceutical industry has to operate in strict timelines of product development and scale-up from lab to plant. Hence, detailed product & process understanding and implementation of appropriate mechanistic modelling and Quality-by-design (QbD) approaches are imperative in the product life cycle. This work provides example cases of such efforts in topical dosage products. Topical products are typically in the form of emulsions, gels, thick suspensions or even simple solutions. The efficacy of such products is determined by characteristics like rheology and morphology. Defining, and scaling up the right manufacturing process with a given set of ingredients, to achieve the right product characteristics presents as a challenge to the process engineer. For example, the non-Newtonian rheology varies not only with CPPs and CMAs but also is an implicit function of globule size (CQA). Hence, this calls for various mechanistic models, to help predict the product behaviour. This paper focusses on such models obtained from computational fluid dynamics (CFD) coupled with population balance modelling (PBM) and constitutive models (like shear, energy density). In a special case of the use of high shear homogenisers (HSHs) for the manufacture of thick emulsions/gels, this work presents some findings on (i) scale-up algorithm for HSH using shear strain, a novel scale-up parameter for estimating mixing parameters, (ii) non-linear relationship between viscosity and shear imparted into the system, (iii) effect of hold time on rheology of product. Specific examples of how this approach enabled scale-up across 1L, 10L, 200L, 500L and 1000L scales will be discussed.

Keywords: computational fluid dynamics, morphology, quality-by-design, rheology

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Authors: Ahmed ElAzab, Amira M. Idrees, Mahmoud A. Mahmoud, Hesham Hefny

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Social networking sites such as Twitter and Facebook attracts over 500 million users across the world, for those users, their social life, even their practical life, has become interrelated. Their interaction with social networking has affected their life forever. Accordingly, social networking sites have become among the main channels that are responsible for vast dissemination of different kinds of information during real time events. This popularity in Social networking has led to different problems including the possibility of exposing incorrect information to their users through fake accounts which results to the spread of malicious content during life events. This situation can result to a huge damage in the real world to the society in general including citizens, business entities, and others. In this paper, we present a classification method for detecting fake accounts on Twitter. The study determines the minimized set of the main factors that influence the detection of the fake accounts on Twitter, then the determined factors have been applied using different classification techniques, a comparison of the results for these techniques has been performed and the most accurate algorithm is selected according to the accuracy of the results. The study has been compared with different recent research in the same area, this comparison has proved the accuracy of the proposed study. We claim that this study can be continuously applied on Twitter social network to automatically detect the fake accounts, moreover, the study can be applied on different Social network sites such as Facebook with minor changes according to the nature of the social network which are discussed in this paper.

Keywords: fake accounts detection, classification algorithms, twitter accounts analysis, features based techniques

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Authors: Elham Taghizadeh, Mostafa Abedzadeh, Mostafa Setak

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Logistics network is expected that opened facilities work continuously for a long time horizon without any failure; but in real world problems, facilities may face disruptions. This paper studies a reliable joint inventory location problem to optimize cost of facility locations, customers’ assignment, and inventory management decisions when facilities face failure risks and doesn’t work. In our model we assume when a facility is out of work, its customers may be reassigned to other operational facilities otherwise they must endure high penalty costs associated with losing service. For defining the model closer to real world problems, the model is proposed based on p-median problem and the facilities are considered to have limited capacities. We define a new binary variable (Z_is) for showing that customers are not assigned to any facilities. Our problem involve a bi-objective model; the first one minimizes the sum of facility construction costs and expected inventory holding costs, the second one function that mention for the first one is minimizes maximum expected customer costs under normal and failure scenarios. For solving this model we use NSGAII and MOSS algorithms have been applied to find the pareto- archive solution. Also Response Surface Methodology (RSM) is applied for optimizing the NSGAII Algorithm Parameters. We compare performance of two algorithms with three metrics and the results show NSGAII is more suitable for our model.

Keywords: joint inventory-location problem, facility location, NSGAII, MOSS

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Authors: Perpetua Modjadji, Sphiwe Madiba

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Background: The growth patterns of children are good nutritional indicators of their nutritional status, health, and socioeconomic level. However, the maternal factors and the belief system of the society affect the growth of children promoting undernutrition. This study determined the influence of maternal factors on growth patterns of schoolchildren in a rural site. Methods: A convergent mixed method study was conducted among 508 schoolchildren and their mothers in Dikgale Health and Demographic Surveillance System Site, South Africa. Multistage sampling was used to select schools (purposive) and learners (random), who were paired with their mothers. Anthropometry was measured and socio-demographic, obstetrical, household information, maternal influence on children’s nutrition, and growth were assessed using an interviewer administered questionnaire (quantitative). The influence of the cultural beliefs and practices of mothers on the nutrition and growth of their children was explored using focus group discussions (qualitative). Narratives of mothers were used to best understand growth patterns of schoolchildren (mixed method). Data were analyzed using STATA 14 (quantitative) and Nvivo 11 (qualitative). Quantitative and qualitative data were merged for integrated mixed method analysis using a joint display analysis. Results: Mean age of children was 10 ± 2 years, ranging from 6 to 15 years. Substantial percentages of thinness (25%), underweight (24%), and stunting (22%) were observed among the children. Mothers had a mean age of 37 ± 7 years, and 75% were overweight or obese. A depressed socio-economic status indicated by a higher rate of unemployment with no income (82.3%), and dependency on social grants (86.8%) was observed. Determinants of poor growth patterns were child’s age and gender, maternal age, height and BMI, access to water supply, and refrigerator use. The narratives of mothers suggested that the children in most of their households were exposed to poverty and the inadequate intake of quality food. Conclusion: Poor growth patterns were observed among schoolchildren while their mothers were overweight or obese. Child’s gender, school grade, maternal body mass index, and access to water were the main determinants. Congruence was observed between most qualitative themes and quantitative constructs. A need for a multi sectoral approach considering an evidence based and feasible nutrition programs for schoolchildren, especially those in rural settings and educating mothers, cannot be over-emphasized.

Keywords: growth patterns, maternal factors, rural context, schoolchildren, South Africa

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Authors: Abdul Ganiyu Iddrisu, Bei Chen

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Digitization is the process of transforming analog material into digital form, especially for storage and use in a computer. Significant development of information and communication technology (ICT) over the past years has encouraged many researchers to investigate its contribution to promoting economic growth and reducing poverty. Yet the compelling empirical evidence on the effects of digitization on economic growth remains weak, particularly in Africa. This is because extant studies that explicitly evaluate digitization and economic growth nexus are mostly reports and desk reviews. This points out an empirical knowledge gap in the literature. Hypothetically, digitization influences financial sector development which in turn influences economic growth. Digitization has changed the financial sector and its operating environment. Obstacles to access to financing, for instance, physical distance, minimum balance requirements, and low-income flows, among others can be circumvented. Savings have increased, micro-savers have opened bank accounts, and banks are now able to price short-term loans. This has the potential to develop the financial sector. However, empirical evidence on the digitization-financial development nexus is dearth. On the other hand, a number of studies maintained that financial sector development greatly influences growth of economies. We, therefore, argue that financial sector development is one of the transmission mechanisms through which digitization affects economic growth. Employing macro-country-level data from African countries and using fixed effects, random effects and Hausman-Taylor estimation approaches, this paper contributes to the literature by analysing economic growth in Africa, focusing on the role of digitization and financial sector development. First, we assess how digitization influences financial sector development in Africa. From an economic policy perspective, it is important to identify digitization determinants of financial sector development so that action can be taken to reduce the economic shocks associated with financial sector distortions. This nexus is rarely examined empirically in the literature. Secondly, we examine the effect of domestic credit to the private sector and stock market capitalization as a percentage of GDP as used to proxy for financial sector development on economic growth. Digitization is represented by the volume of digital/ICT equipment imported and GDP growth is used to proxy economic growth. Finally, we examine the effect of digitization on economic growth in the light of financial sector development. The following key results were found; first, digitalization propels financial sector development in Africa. Second, financial sector development enhances economic growth. Finally, contrary to our expectation, the results also indicate that digitalization conditioned on financial sector development tends to reduce economic growth in Africa. However, results of the net effects suggest that digitalization, overall, improve economic growth in Africa. We, therefore, conclude that, digitalization in Africa does not only develop the financial sector but unconditionally contributes the growth of the continent’s economies.

Keywords: digitalization, financial sector development, Africa, economic growth

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Authors: Golnoush Rostami, Farzam Kharvari

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In recent years, developments in the field of architectural education have resulted in a shift from conventional teaching methods to alternative state-of-the-art approaches in teaching methods and strategies. Criticism in architecture has been a key player both in the profession and education, but it has been mostly offered by renowned individuals. Hence, not only students or other professionals but also critics themselves may not have the option to experience buildings and rely on available 2D materials, such as images and plans, that may not result in a holistic understanding and evaluation of buildings. On the other hand, immersive environments provide students and professionals the opportunity to experience buildings virtually and reflect their evaluation by experiencing rather than judging based on 2D materials. Therefore, the aim of this study is to compare the effect of experiencing buildings in immersive environments and 2D drawings, including images and plans, on architecture criticism and architectural education. As a result, three buildings that have parametric brick facades were studied through 2D materials and in Unreal Engine v. 24 as an immersive environment among 22 architecture students that were selected using convenient sampling and were divided into two equal groups using simple random sampling. This study used mixed methods, including quantitative and qualitative methods; the quantitative section was carried out by a questionnaire, and deep interviews were used for the qualitative section. A questionnaire was developed for measuring three constructs, including privacy regulation based on Altman’s theory, the sufficiency of illuminance levels in the building, and the visual status of the view (visually appealing views based on obstructions that may have been caused by facades). Furthermore, participants had the opportunity to reflect their understanding and evaluation of the buildings in individual interviews. Accordingly, the collected data from the questionnaires were analyzed using independent t-test and descriptive analyses in IBM SPSS Statistics v. 26, and interviews were analyzed using the content analysis method. The results of the interviews showed that the participants who experienced the buildings in the immersive environment were able to have a thorough and more precise evaluation of the buildings in comparison to those who studied them through 2D materials. Moreover, the analyses of the respondents’ questionnaires revealed that there were statistically significant differences between measured constructs among the two groups. The outcome of this study suggests that integrating immersive environments into the profession and architectural education as an effective and efficient tool for architecture criticism is vital since these environments allow users to have a holistic evaluation of buildings for vigorous and sound criticism.

Keywords: immersive environments, architecture criticism, architectural education, occupant-centric evaluation, pre-occupancy evaluation

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Authors: Kaushal Kishore, Suman Mukhopadhyay, Susovan Das, Manashi Adhikary, Sandip Bhattacharyya

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In integrated steel plants, conveyor belts are widely used for transferring raw materials from one location to another. An output pin bush coupling attached with a conveyor transferring iron ore fines and fluxes failed after two years of service life. This led to an operational delay of approximately 15 hours. This study is focused on failure analysis of the coupling and recommending counter-measures to prevent any such failures in the future. Investigation consisted of careful visual observation, checking of operating parameters, stress calculation and analysis, macro and micro-fractography, material characterizations like chemical and metallurgical analysis and tensile and impact testings. The fracture occurred from an unusually sharp double step. There were multiple corrosion pits near the step that aggravated the situation. Inner contact surface of the coupling revealed differential abrasion that created a macroscopic difference in the height of the component. This pointed towards misalignment of the coupling beyond a threshold limit. In addition to these design and installation issues, material of the coupling did not meet the quality standards. These were made up of grey cast iron having graphite morphology intermediate between random distribution (Type A) and rosette pattern (Type B). This manifested as a marked reduction in impact toughness and tensile strength of the component. These findings corroborated well with the brittle mode of fracture that might have occurred during minor impact loading while loading of conveyor belt with raw materials from height. Simulated study was conducted to examine the effect of corrosion pits on tensile and impact toughness of grey cast iron. It was observed that pitting marginally reduced tensile strength and ductility. However, there was marked (up to 45%) reduction in impact toughness due to pitting. Thus, it became evident that failure of the coupling occurred due to combination of factors like inferior material, misalignment, poor step design and corrosion pitting. Recommendation for life enhancement of coupling included the use of tougher SG 500/7 grade, incorporation of proper fillet radius for the step, correction of alignment and application of corrosion resistant organic coating to prevent pitting.

Keywords: brittle fracture, cast iron, coupling, double step, pitting, simulated impact tests

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Authors: Shariful Islam, Sharun Akter Khushbu, S. M. Shaqib, Shahriar Sultan Ramit

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In the construction sector, ensuring worker safety is of the utmost significance. In this study, a deep learning-based technique is presented for identifying safety gear worn by construction workers, such as helmets, goggles, jackets, gloves, and footwear. The suggested method precisely locates these safety items by using the YOLO v7 (You Only Look Once) object detection algorithm. The dataset utilized in this work consists of labeled images split into training, testing and validation sets. Each image has bounding box labels that indicate where the safety equipment is located within the image. The model is trained to identify and categorize the safety equipment based on the labeled dataset through an iterative training approach. We used custom dataset to train this model. Our trained model performed admirably well, with good precision, recall, and F1-score for safety equipment recognition. Also, the model's evaluation produced encouraging results, with a [email protected] score of 87.7%. The model performs effectively, making it possible to quickly identify safety equipment violations on building sites. A thorough evaluation of the outcomes reveals the model's advantages and points up potential areas for development. By offering an automatic and trustworthy method for safety equipment detection, this research contributes to the fields of computer vision and workplace safety. The proposed deep learning-based approach will increase safety compliance and reduce the risk of accidents in the construction industry.

Keywords: deep learning, safety equipment detection, YOLOv7, computer vision, workplace safety

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Authors: Ahmad Fauzi Makarim, Okkie Puspitorini, Hani'ah Mahmudah, Nur Adi Siswandari, Ari Wijayanti

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As a maritime country, Indonesia needs a solution in maritime connectivity which can assist the maritime communication system which including communication from harbor to the ship or ship to ship. The needs of many application services for maritime communication, whether for safety reasons until voyage service to help the process of voyage activity needs connection with a high bandwith. To support the government efforts in handling that kind of problem, a research is conducted in maritime communication issue by applying the new developed technology in Indonesia, namely IEEE 802.11. In this research, 3 outdoor WiFi devices are used in which have a frequency of 5.8 GHz. Maritime of Tanjung Perak harbor in Surabaya until Karang Jamuang Island are used as the location of the research with defining permission of ship node spreading by Navigation District Class 1. That maritime area formed by state 1 and state 2 areas which are the narrow area with average wave height of 0.7 meter based on the data from BMKG S urabaya. After that, wave height used as one of the parameters which are used in analyzing characteristic of signal propagation at sea surface, so it can be determined on the coverage area of transmitter system. In this research has been used three samples of outdoor wifi, there is the coverage of device A can be determined about 2256 meter, device B 4000 meter, and device C 1174 meter. Then to analyze of network connectivity for the ship to ship is used AODV routing algorithm system based on the value of the power transmit was smallest of all nodes within the transmitter coverage.

Keywords: maritime of Indonesia, maritime communications, outdoor wifi, coverage, AODV

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Authors: Gedefaye Nibret Mihretie

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Background: Transactional sex is casual sex between two people to receive material incentives in exchange for sexual favors. Transactional sex is associated with negative consequences, which increase the risk of sexually transmitted diseases, including HIV/AIDS, unintended pregnancy, unsafe abortion, and physiological trauma. Many primary studies in Sub-Saharan Africa have been conducted to assess the prevalence and associated factors of transactional sex among women. These studies had great discrepancies and inconsistent results. Hence, this systematic review and meta-analysis aimed to synthesize the pooled prevalence of the practice of transactional sex among women and its associated factors in Sub-Saharan Africa. Method: Cross-sectional studies were systematically searched from March 6, 2022, to April 24, 2022, using PubMed, Google Scholar, HINARI, Cochrane Library, and grey literature. The pooled prevalence of transactional sex and associated factors was estimated using DerSemonial-Laird Random Effect Model. Stata (version 16.0) was used to analyze the data. The I-squared statistic was used to assess the studies' heterogeneity. A funnel plot and Egger's test were used to check for publication bias. A subgroup analysis was performed to minimize the underline heterogeneity depending on the study years, source of data, sample sizes and geographical location. Results: Four thousand one hundred thirty articles were extracted from various databases. The final thirty-two studies were included in this systematic review, including 108,075 participants. The pooled prevalence of transactional sex among women in Sub-Saharan Africa was 12.55%, with a confidence interval of 9.59% to 15.52%. Educational status (OR = .48, 95%CI, 0.27, 0.69) was the protective factors of transactional sex whereas, alcohol use (OR = 1.85, 95% CI: 1.19, 2.52), early sex debut (OR = 2.57, 95%CI, 1.17, 3.98), substance abuse (OR = 4.21, 95% CI: 2.05, 6.37), having history of sexual experience abuse (OR = 4.08, 95% CI: 1.38, 6.78), physical violence abuse (OR = 6.59, 95% CI: 1.17, 12.02), and sexual violence abuse (OR = 3.56, 95% CI: 1.15, 8.27) were the risk factors of transactional sex. Conclusion: The prevalence of transactional sex among women in Sub-Saharan Africa was high. Educational status, alcohol use, substance abuse, early sex debut, having a history of sexual experiences, physical violence, and sexual violence were predictors of transaction sex. Governmental and other stakeholders are designed to reduce alcohol utilization, provide health information about the negative consequences of early sex debut, substance abuse, and reduce sexual violence, ensuring gender equality through mass media, which should be included in state policy.

Keywords: women’s health, child health, reproductive health, midwifery

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Authors: Anietie J. Atangwho, Udeme E. Asibong, Item J. Atangwho, Ndifreke E. Udonwa

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Diabesity, a syndrome where diabetes and obesity occur simultaneously in a single patient, has emerged as a recent challenge to the medical world and is already at epidemic proportion in some countries. Therefore, this study aimed to determine the prevalence of diabesity among adult patients attending the General Outpatient clinic of three healthcare facilities in Calabar in a bid to improve healthcare delivery to patients at risk. A cross-sectional descriptive study design was employed using a mixed method approach that comprised quantitative and qualitative components i.e., Focused Group Discussion (FGD) and Key Informant Interview (KII). One hundred and ninety (190) participants aged 18 to 72 years and body mass index (BMI) ≥ 30kg/m2 were recruited as the study population for the quantitative study using systematic random sampling technique and analysed using SPSS version 25. The qualitative component performed 4 FGDs and 3 KIIs. Results of sociodemographic variables showed respondents aged 35 – 44 as highest in number (37.3%). Of this number, 83.7% were females, 76.8% married, and 3.7% earned USD1,110.00 monthly. Whereas majority of the participants (65.8 %) were within class 1 obesity, only 38% considered themselves obese. Diabesity occurrence was found to be 12.6% (i.e. BMI ≥ 30 to 45.2kg/m2 vs FBS ≥ 7.0 – 14.8mmo/l), with 38% of them being previously undiagnosed. About 48.4 % of the respondents ate two meals only per day; with 90.5% eating between meals. Snacking was predominant, mostly pastries (67.9%), with 58.9% taking cola drinks alongside. Sixty-one percent participated in one form of exercise or the other, with walking/trekking as the most common; 34.4 % had no regular exercise schedule. Only about 39.5% of the participants spent less than an hour on devices like phone, television, and laptops. Additionally, previously known and newly diagnosed hypertensive patients were 27.9% and 7.2%, respectively. Qualitative assessment with KII and FGDs showed eating unhealthy diets and lack of exercise as major factors responsible for diabesity. The bivariate analysis revealed significant association between diabesity with marital status and hypertension (p = 0.007 and p = 0.005, respectively). Also, positive association with diabesity were eating snacking (p = 0.017) and number of times a respondent snacks per day (p = 0.035). Overall, the study has revealed the occurrence of diabesity in Calabar at 12.6 % of the study population, with 38 % of them previously undiagnosed; it identified unhealthy diets and lack of exercise as causative factors as well as hypertension as snacking associatory indicators of diabesity.

Keywords: diabesity, obesity, diabetes, unhealthy diet

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Authors: Shabadini Sampath, Jinglang Feng

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With huge kinetic energy, space debris poses a major threat to astronauts’ space activities and spacecraft in orbit if a collision happens. The active removal of space debris is required in order to avoid frequent collisions that would occur. In addition, the amount of space debris will increase uncontrollably, posing a threat to the safety of the entire space system. But the safe and reliable removal of large-scale space debris has been a huge challenge to date. While capturing and deorbiting space debris, the space manipulator has to achieve high control precision. However, due to uncertainties and unknown disturbances, there is difficulty in coordinating the control of the space manipulator. To address this challenge, this paper focuses on developing a robust and intelligent control algorithm that controls joint movement and restricts it on the sliding manifold by reducing uncertainties. A neural network adaptive sliding mode controller (NNASMC) is applied with the objective of finding the control law such that the joint motions of the space manipulator follow the given trajectory. A computed torque control (CTC) is an effective motion control strategy that is used in this paper for computing space manipulator arm torque to generate the required motion. Based on the Lyapunov stability theorem, the proposed intelligent controller NNASMC and CTC guarantees the robustness and global asymptotic stability of the closed-loop control system. Finally, the controllers used in the paper are modeled and simulated using MATLAB Simulink. The results are presented to prove the effectiveness of the proposed controller approach.

Keywords: GNC, active removal of space debris, AI controllers, MatLabSimulink

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Authors: R. L. P. de Lima, F. C. B. Boogaard, D. Setyo Rini, P. Arisandi, R. E. de Graaf-Van Dinther

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Water quality in many Asian countries is very poor due to inefficient solid waste management, high population growth and the lack of sewage and purification systems for households and industry. A consortium of Indonesian and Dutch organizations has begun a large-scale international research project to evaluate and propose solutions to face the surface water pollution challenges in Brantas Basin, Indonesia (East Java: Malang / Surabaya). The first phase of the project consisted in a baseline study to assess the current status of surface water bodies and to determine the ambitions and strategies among local stakeholders. This study was conducted with high participatory / collaborative and knowledge sharing objectives. Several methods such as using mobile sensors (attached to boats or underwater drones), test strips and mobile apps, bio-monitoring (sediments), ecology scans using underwater cameras, or continuous / static measurements, were applied in different locations in the regions of the basin, at multiple locations within the water systems (e.g. spring, upstream / downstream of industry and urban areas, mouth of the Surabaya River, groundwater). Results gave an indication of (reference) values of basic water quality parameters such as turbidity, electrical conductivity, dissolved oxygen or nutrients (ammonium / nitrate). An important outcome was that collecting random samples may not be representative of a body of water, given that water quality parameters can vary widely in space (x, y, and depth) and time (day / night and seasonal). Innovative / dynamic monitoring methods (e.g. underwater drones, sensors on boats) can contribute to better understand the quality of the living environment (water, ecology, sediment) and factors that affect it. The field work activities, in particular, underwater drones, revealed potential as awareness actions as they attracted interest from locals and local press. This baseline study involved the cooperation with local managing organizations with Dutch partners, and their willingness to work together is important to ensure participatory actions and social awareness regarding the process of adaptation and strengthening of regulations, or for the construction of facilities such as sewage.

Keywords: water quality monitoring, pollution, underwater drones, social awareness

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Authors: Filip Buruiana, Alexander Hall, Reimar Hofmann, Thomas Hofmann, Silviu Ganceanu, Alexandru Tudorica

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In-memory column-stores make interactive analysis feasible for many big data scenarios. PowerDrill is a system used internally at Google for exploration in logs data. Even though it is a highly parallelized column-store and uses in memory caching, interactive response times cannot be achieved for all datasets (note that it is common to analyze data with 50 billion records in PowerDrill). In this paper, we investigate two orthogonal approaches to optimize performance at the expense of an acceptable loss of accuracy. Both approaches can be implemented as outer wrappers around existing database engines and so they should be easily applicable to other systems. For the first optimization we show that memory is the limiting factor in executing queries at speed and therefore explore possibilities to improve memory efficiency. We adapt some of the theory behind data sketches to reduce the size of particularly expensive fields in our largest tables by a factor of 4.5 when compared to a standard compression algorithm. This saves 37% of the overall memory in PowerDrill and introduces a 0.4% relative error in the 90th percentile for results of queries with the expensive fields. We additionally evaluate the effects of using sampling on accuracy and propose a simple heuristic for annotating individual result-values as accurate (or not). Based on measurements of user behavior in our real production system, we show that these estimates are essential for interpreting intermediate results before final results are available. For a large set of queries this effectively brings down the 95th latency percentile from 30 to 4 seconds.

Keywords: big data, in-memory column-store, high-performance SQL queries, approximate SQL queries

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Authors: Poojan Vora, Hardik Pancholi, Sanket Tajane, Harsh Shah, Elias Keedy

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The global COVID-19 pandemic has affected the industry drastically in many ways. Even though the vaccine is being distributed quickly and despite the decreasing number of positive cases, testing is projected to remain a key aspect of the ‘new normal’. Improving existing plant layout and improving safety within the facility are of great importance in today’s industries because of the need to ensure productivity optimization and reduce safety risks. In practice, it is essential for any manufacturing plant to reduce nonvalue adding steps such as the movement of materials and rearrange similar processes. In the current pandemic situation, optimized layouts will not only increase safety measures but also decrease the fixed cost per unit manufactured. In our case study, we carefully studied the existing layout and the manufacturing steps of a new Texas start-up company that manufactures COVID testing kits. The effects of production rate are incorporated with the computerized relative allocation of facilities technique (CRAFT) algorithm to improve the plant layout and estimate the optimization parameters. Our work reduces the company’s material handling time and increases their daily production. Real data from the company are used in the case study to highlight the importance of colleges in fostering small business needs and improving the collaboration between college researchers and industries by using existing models to advance best practices.

Keywords: computerized relative allocation of facilities technique, facilities planning, optimization, start-up business

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Authors: Talal Alharigi, Kawther Alshlash, Mariska Weijerman

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The government of Saudi Arabia has developed an ambitious “Vision 2030”, which includes a Green Initiative (i.e., the planting of 10 billion trees) and the establishment of seven Royal Reserves as protected areas that comprise 13% of the total land area. The main objective of the reserves is to restore ecosystem balance and reconnect people with nature. Two royal reserves are managed by The Imam Abdulaziz bin Mohammed Royal Reserve Development Authority, including Imam Abdulaziz bin Mohammed Royal Reserve and King Khalid Royal Reserve. The authority has developed a management plan to enhance the habitat through seed dispersal and the planting of 10 million trees, and to restock wildlife that was once abundant in these arid ecosystems (e.g., oryx, Nubian ibex, gazelles, red-necked ostrich). Expectations are that with the restoration of the native vegetation, soil condition and natural hydrologic processes will improve and lead to further enhancement of vegetation and, over time, an increase in biodiversity of flora and fauna. To evaluate the management strategies in reaching these expectations, a comprehensive monitoring and evaluation program was developed. The main objectives of this program are to (1) monitor the status and trends of indicator species, (2) improve desert ecosystem understanding, (3) assess the effects of human activities, and (4) provide science-based management recommendations. Using a random stratified survey design, a diverse suite of survey methods will be implemented, including belt and quadrant transects, camera traps, GPS tracking devices, and drones. Data will be gathered on biotic parameters (plant and animal diversity, density, and distribution) and abiotic parameters (humidity, temperature, precipitation, wind, air, soil quality, vibrations, and noise levels) to meet the goals of the monitoring program. This case study intends to provide a detailed overview of the management plan and monitoring program of two royal reserves and outlines the types of data gathered which can be made available for future research projects.

Keywords: camera traps, desert ecosystem, enhancement, GPS tracking, management evaluation, monitoring, planting, restocking, restoration

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Authors: Baqir Khawari

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This study critically examines the influence of remittances on household poverty and income inequality in Mazar-i-Sharif, Balkh Province, Afghanistan, utilizing robust OLS and Logit models with a rigorous multi-random sampling method. The empirical findings reveal that a 1% increase in per capita international remittances is associated with a substantial 0.071% and 0.059% rise in per capita income during the fiscal years 2019/20 and 2020/21, respectively. Furthermore, this increase significantly mitigates the per capita depth of poverty by 0.0272% and 0.025% and the severity of poverty by 0.0149% and 0.0145% over the same periods. Notably, the impact of international remittances on poverty alleviation surpasses that of internal remittances. In addressing income inequality, the analysis demonstrates that remittances contribute to a reduction in the Gini coefficient by 2% in 2019/20 and 7% in 2020/21, underscoring their pivotal role in promoting equitable economic distribution. However, the COVID-19 pandemic has posed significant challenges, diminishing remittance flows and, consequently, their positive effects on household welfare. The logistic regression results further corroborate these findings, indicating that increased per capita remittances, both international and internal, markedly decrease the likelihood of households falling below the poverty line. Specifically, a 1% rise in per capita external remittances reduces this likelihood by 4.5% in 2019/20 and by 3.7% in 2020/21, while internal remittances decrease it by 3% and 2.4%, respectively. The study also explores the demographic determinants of poverty. Larger household sizes and older household heads correlate positively with poverty, whereas higher education levels among household heads and members, and a greater proportion of male members, correlate negatively with poverty incidence and severity. Female-headed households are disproportionately affected by poverty, exacerbated by socio-cultural restrictions. Despite these adversities, the data suggest that remittances are a crucial instrument for poverty alleviation and income inequality reduction in Afghanistan. The findings advocate for policy interventions aimed at enhancing formal remittance channels, promoting education, and empowering women. Effective governance and sustained international assistance are essential to harness the full potential of remittances in combating poverty and inequality. This study highlights the need for strategic, multifaceted approaches to foster sustainable economic development in Afghanistan’s challenging socio-political context.

Keywords: migration, remittances, poverty, inequality, COVID-19, Afghanistan

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Authors: Hamidreza Hoshyarmanesh, Hosein Madieh, Sanju Lama, Yaser Maddahi, Garnette R. Sutherland, Kourosh Zareinia

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This paper illustrates the concept of an algorithm to register specified markers on the neuroArm surgical manipulators, an image-guided MR-compatible tele-operated robot for microsurgery and stereotaxy. Two range-finding algorithms, namely time-of-flight and phase-shift, are evaluated for registration and supervisory control. The time-of-flight approach is implemented in a semi-field experiment to determine the precise position of a tiny retro-reflective moving object. The moving object simulates a surgical tool tip. The tool is a target that would be connected to the neuroArm end-effector during surgery inside the magnet bore of the MR imaging system. In order to apply flight approach, a 905-nm pulsed laser diode and an avalanche photodiode are utilized as the transmitter and receiver, respectively. For the experiment, a high frequency time to digital converter was designed using a field-programmable gate arrays. In the phase-shift approach, a continuous green laser beam with a wavelength of 530 nm was used as the transmitter. Results showed that a positioning error of 0.1 mm occurred when the scanner-target point distance was set in the range of 2.5 to 3 meters. The effectiveness of this non-contact approach exhibited that the method could be employed as an alternative for conventional mechanical registration arm. Furthermore, the approach is not limited by physical contact and extension of joint angles.

Keywords: 3D laser scanner, intraoperative MR imaging, neuroArm, real time registration, robot-assisted surgery, supervisory control

Procedia PDF Downloads 280