Search results for: negative data

Authors: Shobha Kumari Yadav, Yubaraj Satyal, Ajaya Dixit

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In addressing groundwater depletion, it is important to develop evidence base so to be used in assessing the state of its degradation. Groundwater data is limited compared to meteorological data, which impedes the groundwater use and management plan. Monitoring of groundwater levels provides information base to assess the condition of aquifers, their responses to water extraction, land-use change, and climatic variability. It is important to maintain a network of spatially distributed, long-term monitoring wells to support groundwater management plan. Monitoring involving local community is a cost effective approach that generates real time data to effectively manage groundwater use. This paper presents the relationship between rainfall and spring flow, which are the main source of freshwater for drinking, household consumptions and agriculture in hills of Nepal. The supply and withdrawal of water from springs depends upon local hydrology and the meteorological characteristics- such as rainfall, evapotranspiration and interflow. The study offers evidence of the use of scientific method and community based initiative for managing groundwater and springshed. The approach presents a method to replicate similar initiative in other parts of the country for maintaining integrity of springs.

Keywords: citizen science, groundwater, water resource management, Nepal

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Authors: Jose Funes, Jeff Sauer, Laixiang Sun

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This research examines determinants of multifamily housing development and spillovers in the District of Columbia. A range of socioeconomic factors related to income distribution, productivity, and land use policies are thought to influence the development in contemporary U.S. multifamily housing markets. The analysis leverages data from the American Community Survey to construct panel datasets spanning from 2010 to 2019. Using spatial regression, we identify several socioeconomic measures and land use policies both positively and negatively associated with new housing supply. We contextualize housing estimates related to race in relation to uneven development in the contemporary D.C. housing supply.

Keywords: neighborhood effect, sorting, spatial spillovers, multifamily housing

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Authors: Ikram Mohamed Eltayeb Elsiddig, Abdel Khalig Muddather, Hiba Abdel Rahman Ali, Saad Mohamed Hussein Ayoub

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Anogeissusleiocarpus (Combretaceae) is well known for its medicinal uses in African traditional medicine, for treating many human diseases mainly skin diseases and infections.Mycetoma disease is a fungal and/ or bacterial skin infection, mainly cause by Madurella mycetomatis fungus.This study was carried out in vitro to investigate the antifungal activity of Anogeissusleiocarpus leaf extracts against the isolated pathogenicMadurellamycetomatis, by using the NCCLS modified method compared to Ketoconazole standard drug and MTT assay. The bioactive fraction was subjected to chemical analysis implementing different chromatographic analytical methods (TLC, HPLC, and LC-MS/MS). The results showed significance antifungal activity of A. leiocarpus leaf extractsagainst the isolated pathogenicM. mycetomatis, compared to negative and positive controls. The chloroform fraction showed the highest antifungal activity.The chromatographic analysis of the chloroform fraction with the highest activity showed the presence of important bioactive compounds such as ellagic and flavellagic acids derivatives, flavonoids and stilbenoid, which are well known for their antifungal activity.

Keywords: Anogeissus leiocarpus, crude extracts and fractions of Anogeissus leiocarpus, in vitrosusceptibility of Madurella mycetomatis, Madurella mycetomatis

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Authors: Pilly Joseph Kagosi

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The study aims at assessing challenges observed during implementation of participatory irrigation management (PIM) approach for food security in semi-arid areas of Tanzania. Data were collected through questionnaire, PRA tools, key informants discussion, Focus Group Discussion (FGD), participant observation and literature review. Data collected from questionnaire was analyzed using SPSS while PRA data was analyzed with the help of local communities during PRA exercise. Data from other methods were analyzed using content analysis. The study revealed that PIM approach has contribution in improved food security at household level due to involvement of communities in water management activities and decision making which enhanced availability of water for irrigation and increased crop production. However there were challenges observed during implementation of the approach including; minimum participation of beneficiaries in decision making during planning and designing stages, meaning inadequate devolution of power among scheme owners; Inadequate and lack of transparency on income expenditure in Water Utilization Associations’ (WUAs), water conflict among WUAs members, conflict between farmers and livestock keepers and conflict between WUAs leaders and village government regarding training opportunities and status; WUAs rules and regulation are not legally recognized by the National court and few farmers involved in planting trees around water sources. However it was realized that some of the mentioned challenges were rectified by farmers themselves facilitated by government officials. The study recommends that, the identified challenges need to be rectified for farmers to realize impotence of PIM approach as it was realized by other Asian countries.

Keywords: potentials of implementing participatory approach, challenges of participatory approach, irrigation management, Tanzania

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Authors: Rahmayanti, Theresia Simatupang, Ronald H. Sianturi

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Corruption criminal act develops rapidly since in the transition era there is weakness in law. Consequently, there is an opportunity for a few people to do fraud and illegal acts and to misuse their positions and formal functions in order to make them rich, and the criminal acts are done systematically and sophisticatedly. Some people believe that legal provisions which specifically regulate the corruption criminal act; namely, Law No. 31/1999 in conjunction with Law No. 20/2001 on the Eradication of Corruption Criminal Act are not effective any more, especially in onus probandi (the burden of proof) on corruptors. The research was a descriptive analysis, a research method which is used to obtain description on a certain situation or condition by explaining the data, and the conclusion is drawn through some analyses. The research used judicial normative approach since it used secondary data as the main data by conducting library research. The system of the burden of proof, which follows the principles of reversal of the burden of proof stipulated in Article 12B, paragraph 1 a and b, Article 37A, and Article 38B of Law No. 20/2001 on the Amendment of Law No. 31/1999, is used only as supporting evidence when the principal case is proved. Meanwhile, how to maximize the implementation of the burden of proof on the perpetrators of corruption criminal act in which the public prosecutor brings a corruption case to Court, depends upon the nature of the case and the type of indictment. The system of burden of proof can be used to eradicate corruption in the Court if some policies and general principles of justice such as independency, impartiality, and legal certainty, are applied.

Keywords: burden of proof, perpetrator, corruption criminal act

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Authors: Gautier Viaud, Paul-Henry Cournède

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Plant growth models have been used extensively for the prediction of the phenotypic performance of plants. However, they remain most often calibrated for a given genotype and therefore do not take into account genotype by environment interactions. One way of achieving such an objective is to consider Bayesian hierarchical models. Three levels can be identified in such models: The first level describes how a given growth model describes the phenotype of the plant as a function of individual parameters, the second level describes how these individual parameters are distributed within a plant population, the third level corresponds to the attribution of priors on population parameters. Thanks to the Bayesian framework, choosing appropriate priors for the population parameters permits to derive analytical expressions for the full conditional distributions of these population parameters. As plant growth models are of a nonlinear nature, individual parameters cannot be sampled explicitly, and a Metropolis step must be performed. This allows for the use of a hybrid Gibbs--Metropolis sampler. A generic approach was devised for the implementation of both general state space models and estimation algorithms within a programming platform. It was designed using the Julia language, which combines an elegant syntax, metaprogramming capabilities and exhibits high efficiency. Results were obtained for Arabidopsis thaliana on both simulated and real data. An organ-scale Greenlab model for the latter is thus presented, where the surface areas of each individual leaf can be simulated. It is assumed that the error made on the measurement of leaf areas is proportional to the leaf area itself; multiplicative normal noises for the observations are therefore used. Real data were obtained via image analysis of zenithal images of Arabidopsis thaliana over a period of 21 days using a two-step segmentation and tracking algorithm which notably takes advantage of the Arabidopsis thaliana phyllotaxy. Since the model formulation is rather flexible, there is no need that the data for a single individual be available at all times, nor that the times at which data is available be the same for all the different individuals. This allows to discard data from image analysis when it is not considered reliable enough, thereby providing low-biased data in large quantity for leaf areas. The proposed model precisely reproduces the dynamics of Arabidopsis thaliana’s growth while accounting for the variability between genotypes. In addition to the estimation of the population parameters, the level of variability is an interesting indicator of the genotypic stability of model parameters. A promising perspective is to test whether some of the latter should be considered as fixed effects.

Keywords: bayesian, genotypic differentiation, hierarchical models, plant growth models

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Authors: Samuel Munaf, Kevin Swingler, Franz Brülisauer, Anthony O’Hare, George Gunn, Aaron Reeves

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Background: Avian influenza, more commonly known as Bird flu, is a viral zoonotic respiratory disease stemming from various species of poultry, including pets and migratory birds. Researchers have purported that the accessibility of health information online, in addition to the low-cost data collection methods the internet provides, has revolutionized the methods in which epidemiological and disease surveillance data is utilized. This paper examines the feasibility of using internet data sources, such as Twitter and livestock forums, for the early detection of the avian flu outbreak, through the use of text mining algorithms and social network analysis. Methods: Social media mining was conducted on Twitter between the period of 01/01/2021 to 31/12/2021 via the Twitter API in Python. The results were filtered firstly by hashtags (#avianflu, #birdflu), word occurrences (avian flu, bird flu, H5N1), and then refined further by location to include only those results from within the UK. Analysis was conducted on this text in a time-series manner to determine keyword frequencies and topic modeling to uncover insights in the text prior to a confirmed outbreak. Further analysis was performed by examining clinical signs (e.g., swollen head, blue comb, dullness) within the time series prior to the confirmed avian flu outbreak by the Animal and Plant Health Agency (APHA). Results: The increased search results in Google and avian flu-related tweets showed a correlation in time with the confirmed cases. Topic modeling uncovered clusters of word occurrences relating to livestock biosecurity, disposal of dead birds, and prevention measures. Conclusions: Text mining social media data can prove to be useful in relation to analysing discussed topics for epidemiological surveillance purposes, especially given the lack of applied research in the veterinary domain. The small sample size of tweets for certain weekly time periods makes it difficult to provide statistically plausible results, in addition to a great amount of textual noise in the data.

Keywords: veterinary epidemiology, disease surveillance, infodemiology, infoveillance, avian influenza, social media

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Authors: Mohamed Bagues, Ikbel Souli, Feiza Boussora, Kamel Nagaz

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In southern Tunisia, two local barley accessions CV. Ardhaoui; 'Bengardeni' and 'Karkeni' were cultivated in the field under deficit drip irrigation with saline water. Three treatments were used: control or full irrigation T0 (100%ETc) and stressed T1 (75%ETc), T2 (50%ETc). Proline and soluble sugars contents increase significantly under drought between accessions compared to control and varies between growth stages. Moreover, the increasing of Ca2+ concentration enhances the absorption of Na+ ion, consequently K+/Na+ decrease significantly between accessions, these results suggest that a high tolerance of Bengardeni accession to drought stress. Therefore, drought tolerance indices (STI, SSI, MP, GMP, YSI and TOL) were used to identify high yielding and drought tolerant between accessions. MP explained the variation of GYi. GMP and STI explained the variation of GYs. The high values of MP, STI and GMP were associated with higher yielding accession. Higher TOL value is associated with significant grain yield reduction in stressed environment suggesting higher stress responses of accessions. Significant positive correlations between MP, STI and GMP and negative between YSI and SSI. MP, STI, GMP and YSI, TOL, SSI are not correlated with each other.

Keywords: drought, proline, soluble sugars, minerals, yield, drought tolerance indices, barley

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Authors: Elnur Abbasov

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Energy security and climate change comprise some of the most important concerns facing humankind today and probably in the future if they are not addressed appropriately. In order to stabilize the global climate, there is the need for the world to lessen its use of fossil energy, which requires enhancement of current energy efficiency as well as the development of novel energy sources, such as energy obtained from renewable sources. There is no doubt that the steady transition towards a solar-based economy is likely to result in the development of completely new sectors, behaviours, and jobs that are pro-environmental. Azerbaijan Republic as the largest nation state in the South Caucasus Region has the potential for using and developing the renewable sources of energy in order to support the environmental challenge resolution associated with the climate change, improving the environmental situation in the country. Solar PV comprises one of the direct usages of solar energy. In this paper, sustainable PV usage scenario in residential houses was introduced to reduce negative environmental effects of land use, water consumption, air pollution etc. It was recommended by an author that, PV systems can be part of function and design of residential building components: such as roofs, walls, windows.

Keywords: energy efficiency, environmentally friendly, photovoltaic engineering, sustainable energy usage scenario

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Authors: Rajkumar Ghosh

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Earthquakes are natural disasters that pose a significant risk to human life and infrastructure. Effective earthquake mitigation measures require a thorough understanding of the dynamics of seismic occurrences, including thrust movement. Traditionally, estimating thrust movement has relied on typical techniques that may not capture the full complexity of these events. Therefore, investigating alternative approaches, such as incorporating out-of-sequence thrust movement data, could enhance earthquake mitigation strategies. This review aims to provide an overview of the applications of out-of-sequence thrust movement in earthquake mitigation. By examining existing research and studies, the objective is to understand how precise estimation of thrust movement can contribute to improving structural design, analyzing infrastructure risk, and developing early warning systems. The study demonstrates how to estimate out-of-sequence thrust movement using multiple data sources, including GPS measurements, satellite imagery, and seismic recordings. By analyzing and synthesizing these diverse datasets, researchers can gain a more comprehensive understanding of thrust movement dynamics during seismic occurrences. The review identifies potential advantages of incorporating out-of-sequence data in earthquake mitigation techniques. These include improving the efficiency of structural design, enhancing infrastructure risk analysis, and developing more accurate early warning systems. By considering out-of-sequence thrust movement estimates, researchers and policymakers can make informed decisions to mitigate the impact of earthquakes. This study contributes to the field of seismic monitoring and earthquake risk assessment by highlighting the benefits of incorporating out-of-sequence thrust movement data. By broadening the scope of analysis beyond traditional techniques, researchers can enhance their knowledge of earthquake dynamics and improve the effectiveness of mitigation measures. The study collects data from various sources, including GPS measurements, satellite imagery, and seismic recordings. These datasets are then analyzed using appropriate statistical and computational techniques to estimate out-of-sequence thrust movement. The review integrates findings from multiple studies to provide a comprehensive assessment of the topic. The study concludes that incorporating out-of-sequence thrust movement data can significantly enhance earthquake mitigation measures. By utilizing diverse data sources, researchers and policymakers can gain a more comprehensive understanding of seismic dynamics and make informed decisions. However, challenges exist, such as data quality difficulties, modelling uncertainties, and computational complications. To address these obstacles and improve the accuracy of estimates, further research and advancements in methodology are recommended. Overall, this review serves as a valuable resource for researchers, engineers, and policymakers involved in earthquake mitigation, as it encourages the development of innovative strategies based on a better understanding of thrust movement dynamics.

Keywords: earthquake, out-of-sequence thrust, disaster, human life

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Authors: Wu Hong, Pan Cuilian, Wu Panzifan

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The student’s attention during the class is one of the most important indicators for the learning evaluation; the level of attention is directly related to the results of primary education. In recent years, extensive research has been conducted across China on improving primary school students’ attention during class. During the specific teaching activities in primary school, students have the characteristics of short concentration periods, high probability of distraction, and difficulty in long-term immersive learning. In physical education teaching, where there are mostly outdoor activities, this characteristic is particularly prominent due to the large changes in the environment and the strong sense of freshness among students. It is imperative to overcome this characteristic in a targeted manner, improve the student’s experience in the course, and raise the degree of systematization. There are many ways to improve the systematization of teaching and learning, but most of them lack quantitative indicators, which makes it difficult to evaluate the improvements before and after changing the teaching methods. Based on the situation above, we use the case analysis method, combined with a literature review, to study the negative impact of low systematization levels in primary school physical education teaching, put forward targeted improvement suggestions, and make a quantitative evaluation of the method change.

Keywords: attention, adolescent, evaluation, systematism, training-method

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Authors: Mohamed Amine Helali, Walid Oueslati, Ayed Added

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Sedimentation rates and total fluxes of heavy metals (Fe, Mn, Pb, Zn and Cu) was measured in three different depths (10m, 20m and 40m) during March and August 2012, offshore of the Mejerda River outlet (Gulf of Tunis, Tunisia). The sedimentation rates are estimated from the fluxes of the suspended particulate matter at 7.32, 5.45 and 4.39 mm y⁻¹ respectively at 10m, 20m and 40m depth. Heavy metals sequestration in sediments was determined by chemical speciation and the total metal contents in each core collected from 10, 20 and 40m depth. Heavy metals intake to the sediment was measured also from the suspended particulate matter, while the fluxes from the sediment to the water column was determined using the benthic chambers technique and from the diffusive fluxes in the pore water. Results shown that iron is the only metal for which the balance transfer between intake/uptake (45 to 117 / 1.8 to 5.8 g m² y⁻¹) and sequestration (277 to 378 g m² y⁻¹) was negative, at the opposite of the Lead which intake fluxes (360 to 480 mg m² y⁻¹) are more than sequestration fluxes (50 to 92 mg m² y⁻¹). The balance transfer is neutral for Mn, Zn, and Cu. These clearly indicate that the contributions of Mejerda have consistently varied over time, probably due to the migration of the River mouth and to the changes in the mining activity in the Mejerda catchment and the recent human activities which affect the delta area.

Keywords: delta, fluxes, heavy metals, sediments, sedimentation rates

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Authors: Nursel Koyuncu, Nihal Ata Tutkun

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Frailty model is a survival model that takes into account the unobserved heterogeneity for exploring the relationship between the survival of an individual and several covariates. In the recent years, proposed survival models become more complex and this feature causes convergence problems especially in large data sets. Therefore selection of sample from these big data sets is very important for estimation of parameters. In sampling literature, some authors have defined new sampling schemes to predict the parameters correctly. For this aim, we try to see the effect of sampling design in semi-parametric frailty model. We conducted a simulation study in R programme to estimate the parameters of semi-parametric frailty model for different sample sizes, censoring rates under classical simple random sampling and ranked set sampling schemes. In the simulation study, we used data set recording 17260 male Civil Servants aged 40–64 years with complete 10-year follow-up as population. Time to death from coronary heart disease is treated as a survival-time and age, systolic blood pressure are used as covariates. We select the 1000 samples from population using different sampling schemes and estimate the parameters. From the simulation study, we concluded that ranked set sampling design performs better than simple random sampling for each scenario.

Keywords: frailty model, ranked set sampling, efficiency, simple random sampling

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Authors: Fatemeh Fahimifar, Rouhollah Nazari, Seyed Mohammad Reza Hosseini

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Achieving an adaptable rate of economic growth has always been at the forefront of Iran development programs. In order to increase welfare level of the people in the society, all economic and social indices should be improved which is possible just in case of country's economic development and growth. While developing countries has realized the gap between developed countries and developing countries in today's world, a massive movement has been emerged in less developed countries to eliminate this economic gap. Hence this study investigates the effect of financial development, foreign direct investment and intellectual property on Iran's economic growth and taking into account other variables on economic growth such as impact of the share of foreign direct investment on GDP, government consumptive expenditure share of GDP has been paid. Period used in this study is related to the years 1974 to 2009. Also, in this research we have used Generalized Method of Moments (GMM) to examine relationship between variables. The results of this study indicate a meaningful and negative impact of financial development, the share of government consumptive expenditure to GDP and similarly, the initial GDP on economic growth. Also, the degree of economy openness, foreign direct investment and intellectual property has a meaningful positive impact on economic growth.

Keywords: financial development, FDI, intellectual property, economic growth, Iran

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Authors: Ezeoke Callistus Obiora, Ezeoke Nneka Angela

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

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

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Authors: Jacek Turski

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The visual space geometries are constructed in the Riemannian geometry framework from simulated iso-disparity conics in the horizontalvisual plane of the binocular system with the asymmetric eyes (AEs). For the eyes fixating at the abathic distance, which depends on the AE’s parameters, the iso-disparity conics are frontal straight lines in physical space. For allother fixations, the iso-disparity conics consist of families of the ellipses or hyperbolas depending on both the AE’s parameters and the bifoveal fixation. However, the iso-disparity conic’s arcs are perceived in the gaze direction asthe frontal lines and are referred to as visual geodesics. Thus, geometriesof physical and visual spaces are different. A simple postulate that combines simulated iso-disparity conics with basic anatomy od the human visual system gives the relative depth for the fixation at the abathic distance that establishes the Riemann matric tensor. The resulting geodesics are incomplete in the gaze direction and, therefore, give thefinite distances to the horizon that depend on the AE’s parameters. Moreover, the curvature vanishes in this eyes posture such that visual space is flat. For all other fixations, only the sign of the curvature canbe inferred from the global behavior of the simulated iso-disparity conics: the curvature is positive for the elliptic iso-disparity curves and negative for the hyperbolic iso-disparity curves.

Keywords: asymmetric eye model, iso-disparity conics, metric tensor, geodesics, curvature

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Authors: Bassem Nassar, Wessam Hussein, Medhat Mokhtar

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The crucial concern of satellite operations is to ensure the health and safety of satellites. The worst case in this perspective is probably the loss of a mission but the more common interruption of satellite functionality can result in compromised mission objectives. All the data acquiring from the spacecraft are known as Telemetry (TM), which contains the wealth information related to the health of all its subsystems. Each single item of information is contained in a telemetry parameter, which represents a time-variant property (i.e. a status or a measurement) to be checked. As a consequence, there is a continuous improvement of TM monitoring systems in order to reduce the time required to respond to changes in a satellite's state of health. A fast conception of the current state of the satellite is thus very important in order to respond to occurring failures. Statistical multivariate latent techniques are one of the vital learning tools that are used to tackle the aforementioned problem coherently. Information extraction from such rich data sources using advanced statistical methodologies is a challenging task due to the massive volume of data. To solve this problem, in this paper, we present a proposed unsupervised learning algorithm based on Principle Component Analysis (PCA) technique. The algorithm is particularly applied on an actual remote sensing spacecraft. Data from the Attitude Determination and Control System (ADCS) was acquired under two operation conditions: normal and faulty states. The models were built and tested under these conditions and the results shows that the algorithm could successfully differentiate between these operations conditions. Furthermore, the algorithm provides competent information in prediction as well as adding more insight and physical interpretation to the ADCS operation.

Keywords: space telemetry monitoring, multivariate analysis, PCA algorithm, space operations

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Authors: Freddy Munzhelele

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Setting: the empirical research has shown that the life cycle theory has an impact on the firms’ financing decisions, particularly the dividend pay-outs. Accordingly, the life cycle theory posits that as a firm matures, it gets to a level and capacity where it distributes more cash as dividends. On the other hand, the young firms prioritise investment opportunities sets and their financing; thus, they pay little or no dividends. The research on firms’ financing decisions also demonstrated, among others, the adoption of trade-off and pecking order theories on the dynamics of firms capital structure. The trade-off theory talks to firms holding a favourable position regarding debt structures particularly as to the cost and benefits thereof; and pecking order is concerned with firms preferring a hierarchical order as to choosing financing sources. The case of life cycle hypothesis explaining the financial managers’ decisions as regards the firms’ capital structure dynamics appears to be an interesting link, yet this link has been neglected in corporate finance research. If this link is to be explored as an empirical research, the financial decision-making alternatives will be enhanced immensely, since no conclusive evidence has been found yet as to the dynamics of capital structure. Aim: the aim of this study is to examine the impact of life cycle theory on the capital structure dynamics trade-off and pecking order theories of firms listed in retail, industrial and mining sectors of the JSE. These sectors are among the key contributors to the GDP in the South African economy. Design and methodology: following the postpositivist research paradigm, the study is quantitative in nature and utilises secondary data obtainable from the financial statements of sampled firm for the period 2010 – 2022. The firms’ financial statements will be extracted from the IRESS database. Since the data will be in panel form, a combination of the static and dynamic panel data estimators will used to analyse data. The overall data analyses will be done using STATA program. Value add: this study directly investigates the link between the life cycle theory and the dynamics of capital structure decisions, particularly the trade-off and pecking order theories.

Keywords: life cycle theory, trade-off theory, pecking order theory, capital structure, JSE listed firms

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Authors: David Edgar

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To understand how the brain’s subconscious and conscious functions, we must conquer the physics of Unity, which leads to duality’s algorithm. Where the subconscious (bottom-up) and conscious (top-down) processes function together to produce and consume intelligence, we use terms like ‘time is relative,’ but we really do understand the meaning. In the brain, there are different processes and, therefore, different observers. These different processes experience time at different rates. A sensory system such as the eyes cycles measurement around 33 milliseconds, the conscious process of the frontal lobe cycles at 300 milliseconds, and the subconscious process of the thalamus cycle at 5 milliseconds. Three different observers experience time differently. To bridge observers, the thalamus, which is the fastest of the processes, maintains a synchronous state and entangles the different components of the brain’s physical process. The entanglements form a synchronous cohesion between the brain components allowing them to share the same state and execute in the same measurement cycle. The thalamus uses the shared state to control the firing sequence of the brain’s linear subconscious process. Sharing state also allows the brain to cheat on the amount of sensory data that must be exchanged between components. Only unpredictable motion is transferred through the synchronous state because predictable motion already exists in the shared framework. The brain’s synchronous subconscious process is entirely based on energy conservation, where prediction regulates energy usage. So, the eyes every 33 milliseconds dump their sensory data into the thalamus every day. The thalamus is going to perform a motion measurement to identify the unpredictable motion in the sensory data. Here is the trick. The thalamus conducts its measurement based on the original observation time of the sensory system (33 ms), not its own process time (5 ms). This creates a data payload of synchronous motion that preserves the original sensory observation. Basically, a frozen moment in time (Flat 4D). The single moment in time can then be processed through the single state maintained by the synchronous process. Other processes, such as consciousness (300 ms), can interface with the synchronous state to generate awareness of that moment. Now, synchronous data traveling through a separate faster synchronous process creates a theoretical time tunnel where observation time is tunneled through the synchronous process and is reproduced on the other side in the original time-relativity. The synchronous process eliminates time dilation by simply removing itself from the equation so that its own process time does not alter the experience. To the original observer, the measurement appears to be instantaneous, but in the thalamus, a linear subconscious process generating sensory perception and thought production is being executed. It is all just occurring in the time available because other observation times are slower than thalamic measurement time. For life to exist in the physical universe requires a linear measurement process, it just hides by operating at a faster time relativity. What’s interesting is time dilation is not the problem; it’s the solution. Einstein said there was no universal time.

Keywords: neural synchronization, natural intelligence, 99.95% IoT data transmission savings, artificial subconscious intelligence (ASI)

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Authors: Marianna Iaroslavska

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ChatGPT is becoming commonplace. However, only a few people think about the legal risks of using Large Language Model in their daily work. The main dilemmas concern the following areas: who owns the copyright to what somebody creates through ChatGPT; what can OpenAI do with the prompt you enter; can you accidentally infringe on another creator's rights through ChatGPT; what about the protection of the data somebody enters into the chat. This paper will present these and other legal risks of using large language models at work using dogmatic methods and case studies. The paper will present a legal analysis of AI risks against the background of European Union law and Polish law. This analysis will answer questions about how to protect data, how to make sure you do not violate copyright, and what is at stake with the AI Act, which recently came into force in the EU. If your work is related to the EU area, and you use AI in your work, this paper will be a real goldmine for you. The copyright law in force in Poland does not protect your rights to a work that is created with the help of AI. So if you start selling such a work, you may face two main problems. First, someone may steal your work, and you will not be entitled to any protection because work created with AI does not have any legal protection. Second, the AI may have created the work by infringing on another person's copyright, so they will be able to claim damages from you. In addition, the EU's current AI Act imposes a number of additional obligations related to the use of large language models. The AI Act divides artificial intelligence into four risk levels and imposes different requirements depending on the level of risk. The EU regulation is aimed primarily at those developing and marketing artificial intelligence systems in the EU market. In addition to the above obstacles, personal data protection comes into play, which is very strictly regulated in the EU. If you violate personal data by entering information into ChatGPT, you will be liable for violations. When using AI within the EU or in cooperation with entities located in the EU, you have to take into account a lot of risks. This paper will highlight such risks and explain how they can be avoided.

Keywords: EU, AI act, copyright, polish law, LLM

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Authors: Olusola Abiodun Arinde

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This research work was prompted by the very important role e-commerce plays in every organization, be it private or public. The underlying objective of this study is to have a critical appraisal of the extent to which e-commerce impacts on organizational effectiveness. This research was carried out using Zenith Bank Plc as a case study. Relevant data were collected through structured questionnaire, oral interview, journals, newspapers, and textbooks. The data collected were analyzed and hypotheses were tested. Based on the result of the hypotheses, it was observed that e-commerce is significant to every organization. Through e-commerce, fast services delivery would be guaranteed to customers, this would lead to higher productivity and profit for organizations. E-commerce should be managed in such a way that it does not alienate customers; it should also prevent enormous risks that are associated with e-commerce.

Keywords: e-commerce, fast service, productivity, profit

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Authors: Garry Gorman, Nigel McKelvey, James Connolly

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This study will evaluate a growth mindset intervention with Junior Cycle Coding and Senior Cycle Computer Science students in Ireland, where gamification will be used to incentivise growth mindset behaviour. An artificial intelligence (AI) driven personalised learning system will be developed to present computer programming learning tasks in a manner that is best suited to the individuals’ own learning preferences while incentivising and rewarding growth mindset behaviour of persistence, mastery response to challenge, and challenge seeking. This research endeavours to measure mindset with before and after surveys (conducted nationally) and by recording growth mindset behaviour whilst playing a digital game. This study will harness the capabilities of AI and aims to determine how a personalised learning (PL) experience can impact the mindset of a broad range of students. The focus of this study will be to determine how personalising the learning experience influences female and disadvantaged students' sense of belonging in the computer science classroom when tasks are presented in a manner that is best suited to the individual. Whole Brain Learning will underpin this research and will be used as a framework to guide the research in identifying key areas such as thinking and learning styles, cognitive potential, motivators and fears, and emotional intelligence. This research will be conducted in multiple school types over one academic year. Digital games will be played multiple times over this period, and the data gathered will be used to inform the AI algorithm. The three data sets are described as follows: (i) Before and after survey data to determine the grit scores and mindsets of the participants, (ii) The Growth Mind-Set data from the game, which will measure multiple growth mindset behaviours, such as persistence, response to challenge and use of strategy, (iii) The AI data to guide PL. This study will highlight the effectiveness of an AI-driven personalised learning experience. The data will position AI within the Irish educational landscape, with a specific focus on the teaching of CS. These findings will benefit coding and computer science teachers by providing a clear pedagogy for the effective delivery of personalised learning strategies for computer science education. This pedagogy will help prevent students from developing a fixed mindset while helping pupils to exhibit persistence of effort, use of strategy, and a mastery response to challenges.

Keywords: computer science education, artificial intelligence, growth mindset, pedagogy

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Authors: R. Dubey, M. Sarwan, S. Singh

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We have investigated the phase transition pressure and associated volume collapse in Sm1– X EuX S alloy (0≤x≤1) which shows transition from discontinuous to continuous as x is reduced. The calculated results from present approach are in good agreement with experimental data available for the end point members (x=0 and x=1). The results for the alloy counter parts are also in fair agreement with experimental data generated from the vegard’s law. An improved interaction potential model has been developed which includes coulomb, three body interaction, polarizability effect and overlap repulsive interaction operative up to second neighbor ions. It is found that the inclusion of polarizability effect has improved our results.

Keywords: elastic constants, high pressure, phase transition, rare earth compound

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Authors: Mina Sadat Orooje, Mohammad Mehdi Latifi, Behnam Fereydooni Eftekhari

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The integration of cutting-edge Internet of Things (IoT) technologies with advanced Artificial Intelligence (AI) systems is revolutionizing healthcare facility management. However, the current landscape of hospital building maintenance suffers from slow, repetitive, and disjointed processes, leading to significant financial, resource, and time losses. Additionally, the potential of Building Information Modeling (BIM) in facility maintenance is hindered by a lack of data within digital models of built environments, necessitating a more streamlined data collection process. This paper presents a robust framework that harmonizes AI with BIM-IoT technology to elevate healthcare Facility Maintenance Management (FMM) and address these pressing challenges. The methodology begins with a thorough literature review and requirements analysis, providing insights into existing technological landscapes and associated obstacles. Extensive data collection and analysis efforts follow to deepen understanding of hospital infrastructure and maintenance records. Critical AI algorithms are identified to address predictive maintenance, anomaly detection, and optimization needs alongside integration strategies for BIM and IoT technologies, enabling real-time data collection and analysis. The framework outlines protocols for data processing, analysis, and decision-making. A prototype implementation is executed to showcase the framework's functionality, followed by a rigorous validation process to evaluate its efficacy and gather user feedback. Refinement and optimization steps are then undertaken based on evaluation outcomes. Emphasis is placed on the scalability of the framework in real-world scenarios and its potential applications across diverse healthcare facility contexts. Finally, the findings are meticulously documented and shared within the healthcare and facility management communities. This framework aims to significantly boost maintenance efficiency, cut costs, provide decision support, enable real-time monitoring, offer data-driven insights, and ultimately enhance patient safety and satisfaction. By tackling current challenges in healthcare facility maintenance management it paves the way for the adoption of smarter and more efficient maintenance practices in healthcare facilities.

Keywords: artificial intelligence, building information modeling, healthcare facility maintenance, internet of things integration, maintenance efficiency

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Authors: Jihee Jang, Seowon Yoon, Gaeun Son, Minjung Kang, Joon Yeon Choeh, Kee-Hong Choi

Abstract:

Background: Self-report multiple choice questionnaires have been widely utilized to quantitatively measure one’s personality and psychological constructs. Despite several strengths (e.g., brevity and utility), self-report multiple-choice questionnaires have considerable limitations in nature. With the rise of machine learning (ML) and Natural language processing (NLP), researchers in the field of psychology are widely adopting NLP to assess psychological constructs to predict human behaviors. However, there is a lack of connections between the work being performed in computer science and that psychology due to small data sets and unvalidated modeling practices. Aims: The current article introduces the study method and procedure of phase II, which includes the interview questions for the five-factor model (FFM) of personality developed in phase I. This study aims to develop the interview (semi-structured) and open-ended questions for the FFM-based personality assessments, specifically designed with experts in the field of clinical and personality psychology (phase 1), and to collect the personality-related text data using the interview questions and self-report measures on personality and psychological distress (phase 2). The purpose of the study includes examining the relationship between natural language data obtained from the interview questions, measuring the FFM personality constructs, and psychological distress to demonstrate the validity of the natural language-based personality prediction. Methods: The phase I (pilot) study was conducted on fifty-nine native Korean adults to acquire the personality-related text data from the interview (semi-structured) and open-ended questions based on the FFM of personality. The interview questions were revised and finalized with the feedback from the external expert committee, consisting of personality and clinical psychologists. Based on the established interview questions, a total of 425 Korean adults were recruited using a convenience sampling method via an online survey. The text data collected from interviews were analyzed using natural language processing. The results of the online survey, including demographic data, depression, anxiety, and personality inventories, were analyzed together in the model to predict individuals’ FFM of personality and the level of psychological distress (phase 2).

Keywords: personality prediction, psychological distress prediction, natural language processing, machine learning, the five-factor model of personality

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Authors: Zahid Khalil, Saad Ul Haque, Asif Khan

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Decision making about identifying suitable sites for any project by considering different parameters is difficult. Using GIS and Multi-Criteria Analysis (MCA) can make it easy for those projects. This technology has proved to be an efficient and adequate in acquiring the desired information. In this study, GIS and MCA were employed to identify the suitable sites for small dams in Dadu Tehsil, Sindh. The GIS software is used to create all the spatial parameters for the analysis. The parameters that derived are slope, drainage density, rainfall, land use / land cover, soil groups, Curve Number (CN) and runoff index with a spatial resolution of 30m. The data used for deriving above layers include 30-meter resolution SRTM DEM, Landsat 8 imagery, and rainfall from National Centre of Environment Prediction (NCEP) and soil data from World Harmonized Soil Data (WHSD). Land use/Land cover map is derived from Landsat 8 using supervised classification. Slope, drainage network and watershed are delineated by terrain processing of DEM. The Soil Conservation Services (SCS) method is implemented to estimate the surface runoff from the rainfall. Prior to this, SCS-CN grid is developed by integrating the soil and land use/land cover raster. These layers with some technical and ecological constraints are assigned weights on the basis of suitability criteria. The pairwise comparison method, also known as Analytical Hierarchy Process (AHP) is taken into account as MCA for assigning weights on each decision element. All the parameters and group of parameters are integrated using weighted overlay in GIS environment to produce suitable sites for the Dams. The resultant layer is then classified into four classes namely, best suitable, suitable, moderate and less suitable. This study reveals a contribution to decision-making about suitable sites analysis for small dams using geospatial data with minimal amount of ground data. This suitability maps can be helpful for water resource management organizations in determination of feasible rainwater harvesting structures (RWH).

Keywords: Remote sensing, GIS, AHP, RWH

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Authors: Hong-Yi Lin, Feng-Tyan Lin

Abstract:

In a Bike-sharing system (BSS), users can easily rent bikes from any station in the city for mid-range or short-range trips. BSS can also be integrated with other types of transport system, especially Green Transportation system, such as rail transport, bus etc. Since BSS records time and place of each pickup and return, the operational data can reflect more authentic and dynamic state of user behaviors. Furthermore, land uses around docking stations are highly associated with origins and destinations for the BSS users. As urban researchers, what concerns us more is to take BSS into consideration during the urban planning process and enhance the quality of urban life. This research focuses on the simulation of travel behavior of BSS users in Kaohsiung. First, rules of users’ behavior were derived by analyzing operational data and land use patterns nearby docking stations. Then, integrating with Monte Carlo method, these rules were embedded into a travel behavior simulation model, which was implemented by NetLogo, an agent-based modeling tool. The simulation model allows us to foresee the rent-return behaviour of BSS in order to choose potential locations of the docking stations. Also, it can provide insights and recommendations about planning and policies for the future BSS.

Keywords: agent-based model, bike-sharing system, BSS operational data, simulation

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Authors: Shan-Ken Chine

Abstract:

This research aims to a one-year course run of String Music Appreciation, an international online course launched on the British open education platform. It explains how to present music teaching videos with three main features. They are music lesson explanations, instrumental playing demonstrations, and live music performances. The plan of this course is with four major themes and a total of 97 steps. In addition, the paper also uses the testing data provided by the education platform to analyze the performance of learners and to understand the operation of the course. It contains three test data in the statistics dashboard. They are course-run measures, total statistics, and statistics by week. The paper ends with a review of the course's star rating in this one-year run. The result of this course run will be adjusted when it starts again in the future.

Keywords: music online courses, MOOCs, ubiquitous learning, string music, general music education

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Authors: Mohammad Javad Zehtab, S. Alireza Mirghasemi, Ali Majlesara, Parvin Tajik, Babak Siavashi

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Objective: There are different modalities proposed for tennis elbow treatment with few randomized trials comparing them. We designed a study to compare the effectiveness of five different modalities and determine the usefulness of recently proposed extensor grip test (EGT) in predicting the response to treatment. Methods: In a randomized controlled clinical trial 92 of 98 tennis elbow patients in Sina hospital of Tehran, Iran between 2006 and 2007 fulfill trial entry criteria, among these patients 56 (60.9%) had positive EGT result. Stratified on EGT result, patients allocated randomly to 5 treatment groups: Brace (B) group, physiotherapy (P), brace + physiotherapy (BP), injection (I) and injection + physiotherapy (IP). Results: Patients who had positive result of EGT had better response to treatments: less SOC (p = 0.06), less PFFQ and patients’ satisfaction scores (p < 0.001). Among the treatment IP was the most successful, then BP, P and B, respectively; injection was the worst treatment modality. Response to treatment was comparable in all groups between EGT positive and negative patients except bracing; in which positive EGT was correlated with a dramatic response to treatment. Conclusion: In all patients IP and then BP is recommended but in EGT negatives, bracing seems to be of no use. Injection alone is not recommended in either group.

Keywords: tennis elbow, extensor grip test, physiotherapy, tennis elbow treatment

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Authors: Mohammed Kafaji

Abstract:

This paper aims to investigate the influence of quality of education and quality of research, provided by local educational institutions, on the adoption of Information and Communication Technology (ICT) in managing business operations for companies in Saudi market. A model was developed and tested using data collected from 138 CEO’s of foreign companies in diverse business sectors. The data is analysed and managed using multivariate approaches through standard statistical packages. The results showed that educational quality has little contribution to the ICT adoption while research quality seems to play a more prominent role. These results are analysed in terms of business environment and market constraints and further extended to the perceived effectiveness of applied pedagogical approaches in schools and universities.

Keywords: quality of education, quality of research, mediation, domestic competition, ICT adoption

Procedia PDF Downloads 450