Search results for: 99.95% IoT data transmission savings

Authors: Mohammed Kafaji

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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

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Authors: Adam M. Pitz, Gillian L. Phillipson, Jayant E. Khanolkar, Andrew M. Middleton

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New emerging evidence suggests that the nose is the predominant route for entry of the SARS-CoV-2 virus into the host. A virucidal suspension test (conforming in principle to the European Standard EN14476) was conducted to determine whether a commercial liquid gel intranasal spray containing 1% of the mucoadhesive hydroxypropyl methylcellulose (HPMC) could inhibit the cellular infectivity of the SARS-CoV-2 coronavirus. Virus was added to the test product samples and to controls in a 1:8 ratio and mixed with one part bovine serum albumin as an interfering substance. The test samples were pre-equilibrated to 34 ± 2°C (representing the temperature of the nasopharynx) with the temperature maintained at 34 ± 2°C for virus contact times of 1, 5 and 10 minutes. Neutralized aliquots were inoculated onto host cells (Vero E6 cells, ATCC CRL-1586). The host cells were then incubated at 36 ± 2°C for a period of 7 days. The residual infectious virus in both test and controls was detected by viral-induced cytopathic effect. The 50% tissue culture infective dose per mL (TCID50/mL) was determined using the Spearman-Karber method with results reported as the reduction of the virus titer due to treatment with test product, expressed as log10. The controls confirmed the validity of the results with no cytotoxicity or viral interference observed in the neutralized test product samples. The HPMC formulation reduced SARS-CoV-2 titer, expressed as log10TCID50, by 2.30 ( ± 0.17), 2.60 ( ± 0.19), and 3.88 ( ± 0.19) with the respective contact times of 1, 5 and 10 minutes. The results demonstrate that this 1% HPMC gel formulation can reduce the cellular infectivity of the SARS-CoV-2 virus with an increasing viral inhibition observed with increasing exposure time. This 1% HMPC gel is well tolerated and can reside, when delivered via nasal spray, for up to one hour in the nasal cavity. We conclude that this intranasal gel spray with 1% HPMC repeat-dosed every few hours may offer an effective preventive or early intervention solution to limit the transmission and impact of the SARS-CoV-2 coronavirus.

Keywords: hydroxypropyl methylcellulose, mucoadhesive nasal spray, respiratory viruses, SARS-CoV-2

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Authors: Jonathan Sahu, Jill Aylott

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Individuals with Autism, Intellectual and Developmental disabilities (AIDD) are one of the most vulnerable groups in society, hampered not only by their own limitations to understand and interact with the wider society, but also societal limitations in perception and understanding. Communication to express their needs and wishes is fundamental to enable such individuals to live and prosper in society. This research project was designed as an organisational case study, in a large secondary health care hospital within the National Health Service (NHS), to assess the quality of care provided to people with AIDD and to review the role of advocacy to reduce health inequalities in these individuals. Methods: The research methodology adopted was as an “insider researcher”. Data collection included both quantitative and qualitative data i.e. a mixed method approach. A semi-structured interview schedule was designed and used to obtain qualitative and quantitative primary data from a wide range of interdisciplinary frontline health care workers to assess their understanding and awareness of systems, processes and evidence based practice to offer a quality service to people with AIDD. Secondary data were obtained from sources within the organisation, in keeping with “Case Study” as a primary method, and organisational performance data were then compared against national benchmarking standards. Further data sources were accessed to help evaluate the effectiveness of different types of advocacy that were present in the organisation. This was gauged by measures of user and carer experience in the form of retrospective survey analysis, incidents and complaints. Results: Secondary data demonstrate near compliance of the Organisation with the current national benchmarking standard (Monitor Compliance Framework). However, primary data demonstrate poor knowledge of the Mental Capacity Act 2005, poor knowledge of organisational systems, processes and evidence based practice applied for people with AIDD. In addition there was poor knowledge and awareness of frontline health care workers of advocacy and advocacy schemes for this group. Conclusions: A significant amount of work needs to be undertaken to improve the quality of care delivered to individuals with AIDD. An operational strategy promoting the widespread dissemination of information may not be the best approach to deliver quality care and optimal patient experience and patient advocacy. In addition, a more robust set of standards, with appropriate metrics, needs to be developed to assess organisational performance which will stand the test of professional and public scrutiny.

Keywords: advocacy, autism, health inequalities, intellectual developmental disabilities, quality of care

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Authors: Estephanie S. López Contreras, Vicente Aranda Palacios, Daniela Flores Silva, Felipe Oliveros Olivares, Romina Riquelme Escobedo, Iñaki Westerhout Usabiaga

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This research explored whether the context of online instruction has influenced the development of first-year English-teaching students' communication skills, being these speaking and listening. The theoretical basis finds its niche in the need to bridge the gap in knowledge about the Chilean online educational context and the development of English communicative skills. An interpretative paradigm and a phenomenological design were implemented in this study. Twenty- two first-year students and two teachers from an English teaching training program participated in the study. The students' ages ranged from 18 to 26 years of age, and the teachers' years of experience ranged from 5 to 13 years in the program. For data collection purposes, semi- structured interviews were applied to both students and teachers. Interview questions were based on the initial conceptualization of the central phenomenon. Observations, field notes, and focus groups with the students are also part of the data collection process. Data analysis considered two-cycle methods. The first included descriptive coding for field notes, initial coding for interviews, and creating a codebook. The second cycle included axial coding for both field notes and interviews. After data analysis, the findings show that students perceived online classes as instances in which active communication cannot always occur. In addition, changes made to the curricula as a consequence of the COVID-19 pandemic have affected students' speaking and listening skills.

Keywords: attitudes, communicative skills, EFL teaching training program, online instruction, and perceptions

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Authors: Lee Bih Ni, Emily Doreen Lee, Wee Hui Yean

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This paper was prepared to see the perceptions of online lectures regarding collaborative learning, in terms of how lecturers view online collaborative learning in the higher learning institution. The purpose of this study was conducted to determine the perceptions of online lectures about collaborative learning, especially how lecturers see online collaborative learning in the university. Adult learning education enhance collaborative learning culture with the target of involving learners in the learning process to make teaching and learning more effective and open at the university. This will finally make students learning that will assist each other. It is also to cut down the pressure of loneliness and isolation might felt among adult learners. Their ways in collaborative online was also determined. In this paper, researchers collect data using questionnaires instruments. The collected data were analyzed and interpreted. By analyzing the data, researchers report the results according the proof taken from the respondents. Results from the study, it is not only dependent on the lecturer but also a student to shape a good collaborative learning practice. Rational concepts and pattern to achieve these targets be clear right from the beginning and may be good seen by a number of proposals submitted and include how the higher learning institution has trained with ongoing lectures online. Advantages of online collaborative learning show that lecturers should be trained effectively. Studies have seen that the lecturer aware of online collaborative learning. This positive attitude will encourage the higher learning institution to continue to give the knowledge and skills required.

Keywords: collaborative online learning, lecturers’ training, learning, online

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Authors: Ghazi Bouaziz, Abderrahim Derouiche, Damien Brulin, Hélène Pigot, Eric Campo

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Automatic ADL classification is a crucial part of ambient assisted living technologies. It allows to monitor the daily life of the elderly and to detect any changes in their behavior that could be related to health problem. But detection of ADLs is a challenge, especially because each person has his/her own rhythm for performing them. Therefore, we used a correlation matrix to extract custom rules that enable to detect ADLs, including eating activity. Data collected from 3 different individuals between 35 and 105 days allows the extraction of personalized eating patterns. The comparison of the results of the process of eating activity extracted from the correlation matrices with the declarative data collected during the survey shows an accuracy of 90%.

Keywords: elderly monitoring, ADL identification, matrix correlation, meal-taking activity

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Authors: Rishi Agarwal

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Digital twins are emerging as a strong technology used to imitate and monitor physical objects digitally in real time across sectors. It is not only dealing with the digital space, but it is also actuating responses in the physical space in response to the digital space processing like storage, modeling, learning, simulation, and prediction. This paper explores the application of digital twins for enhancing physical security in retail stores. The retail sector still relies on outdated physical security practices like manual monitoring and metal detectors, which are insufficient for modern needs. There is a lack of real-time data and system integration, leading to ineffective emergency response and preventative measures. As retail automation increases, new digital frameworks must control safety without human intervention. To address this, the paper proposes implementing an intelligent digital twin framework. This collects diverse data streams from in-store sensors, surveillance, external sources, and customer devices and then Advanced analytics and simulations enable real-time monitoring, incident prediction, automated emergency procedures, and stakeholder coordination. Overall, the digital twin improves physical security through automation, adaptability, and comprehensive data sharing. The paper also analyzes the pros and cons of implementation of this technology through an Emerging Technology Analysis Canvas that analyzes different aspects of this technology through both narrow and wide lenses to help decision makers in their decision of implementing this technology. On a broader scale, this showcases the value of digital twins in transforming legacy systems across sectors and how data sharing can create a safer world for both retail store customers and owners.

Keywords: digital twin, retail store safety, digital twin in retail, digital twin for physical safety

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Authors: Eleonora Sofilda, Ria Amalia, Muhammad Zilal Hamzah

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Foreign direct investment is one of the sources of financing or capital that important for a country, especially for developing countries. This investment also provides a great contribution to development through the transfer of assets, management improving, and transfer of technology in enhancing the economy of a country. In the other side currently in ASEAN countries emerge the interesting phenomenon where some big producers are re-locate their basic production among those countries. This research is aimed to analyze the factors that affect capital inflows of foreign direct investment into the 6 ASEAN countries (Indonesia, Malaysia, Singapore, Thailand, Philippines, and Vietnam) in period 2004-2012. This study uses panel data analysis to determine the factors that affect of foreign direct investment in 6 ASEAN. The factors that affect of foreign direct investment (FDI) are the gross domestic product (GDP), global competitiveness (GCI), interest rate, exchange rate and trade openness (TO). Result of panel data analysis show that three independent variables (GCI, GDP, and TO) have a significant effect to the FDI in 6 ASEAN Countries.

Keywords: foreign direct investment, the gross domestic product, global competitiveness, interest rate, exchange rate, trade openness, panel data analysis

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Authors: Jakkireddy Sravani

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Introduction: The maintenance of hemodynamic stability is of pivotal importance in supratentorial surgeries. Anesthesia for supratentorial tumors requires an understanding of localized or generalized rising ICP, regulation, and maintenance of intracerebral perfusion, and avoidance of secondary systemic ischemic insults. We aimed to compare the effects of the combination of ketamine and propofol with propofol alone when used as an induction and maintenance anesthetic agent during supratentorial tumors. Methodology: This prospective, randomized, double-blinded controlled study was conducted at AIIMS Raipur after obtaining the institute Ethics Committee approval (1212/IEC-AIIMSRPR/2022 dated 15/10/2022), CTRI/2023/01/049298 registration and written informed consent. Fifty-two supratentorial tumor patients posted for craniotomy and excision were included in the study. The patients were randomized into two groups. One group received a combination of ketamine and propofol, and the other group received propofol for induction and maintenance of anesthesia. Intraoperative hemodynamic stability and quality of brain relaxation were studied in both groups. Statistical analysis and technique: An MS Excel spreadsheet program was used to code and record the data. Data analysis was done using IBM Corp SPSS v23. The independent sample "t" test was applied for continuously dispersed data when two groups were compared, the chi-square test for categorical data, and the Wilcoxon test for not normally distributed data. Results: The patients were comparable in terms of demographic profile, duration of the surgery, and intraoperative input-output status. The trends in BIS over time were similar between the two groups (p-value = 1.00). Intraoperative hemodynamics (SBP, DBP, MAP) were better maintained in the ketamine and propofol combination group during induction and maintenance (p-value < 0.01). The quality of brain relaxation was comparable between the two groups (p-value = 0.364). Conclusion: Ketamine and propofol combination for the induction and maintenance of anesthesia was associated with superior hemodynamic stability, required fewer vasopressors during excision of supratentorial tumors, provided adequate brain relaxation, and some degree of neuroprotection compared to propofol alone.

Keywords: supratentorial tumors, hemodynamic stability, brain relaxation, ketamine, propofol

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Authors: Rida Kanwal, Wang Yuhui, Song Weiguo

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Wildfires are one of the most prominent threats to ecosystems, human health, and economic activities, with wind acting as a critical driving factor. This study combines machine learning (ML) and remote sensing (RS) to assess the effects of wind on wildfires in Chongqing Province from August 16-23, 2022. Landsat 8 satellite images were used to estimate the difference normalized burn ratio (dNBR), representing prefire and postfire vegetation conditions. Wind data was analyzed through geographic information system (GIS) mapping. Correlation analysis between wind speed and fire radiative power (FRP) revealed a significant relationship. An autoregressive integrated moving average (ARIMA) model was developed for wind forecasting, and linear regression was applied to determine the effect of wind speed on FRP. The results identified high wind speed as a key factor contributing to the surge in FRP. Wind-rose plots showed winds blowing to the northwest (NW), aligning with the wildfire spread. This model was further validated with data from other provinces across China. This study integrated ML, RS, and GIS to analyze wildfire behavior, providing effective strategies for prediction and management.

Keywords: wildfires, machine learning, remote sensing, wind speed, GIS, wildfire behavior

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Authors: Sneha Singh, Abhimanyu Dev, Vinod Nigam

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The major issue which decides the impending use of gold nanoparticles (AuNPs) in nanobiotechnological applications is their particle size and stability. Often the AuNPs obtained biomimetically are considered useless owing to their instability in the aqueous medium and thereby limiting the widespread acceptance of this facile green synthesis procedure. So, the use of nontoxic surfactants is warranted to stabilize the biogenic nanoparticles (NPs). But does the surfactant only play a role in stabilizing by being adsorbed to the NPs surface or can it have any other significant contribution in synthesis process and controlling their size as well as shape? Keeping this idea in mind, AuNPs were synthesized by using surfactant treated (lechate) and untreated (cell lysate supernatant) Bacillus licheniformis cell extract. The cell extracts mediated reduction of chloroauric acid (HAuCl 4) in the presence of non-ionic surfactant, Tween 20 (TW20), and its effect on the AuNPs stability was studied. Interestingly, the surfactant used in the study served as potential alternative to harvest cellular enzymes involved in bioreduction process in a hassle free condition. The surfactants ability to solubilize/leach membrane proteins and simultaneously stabilizing the AuNPs could have advantage from process point of view as it will reduce the time and economics involve in the nanofabrication of biogenic NPs. The synthesis was substantiated with UV-Vis spectroscopy, Dynamic light scattering study, FTIR spectroscopy, and Transmission electron microscopy. The Zeta potential of AuNPs solutions was measured routinely to corroborate the stability observations recorded visually. Highly stable, ultra-small AuNPs of 2.6 nm size were obtained from the study. Further, the biological efficacy of the obtained AuNPs as potential antibacterial agent was evaluated against Bacilllus subtilis, Pseudomonas aeruginosa, and Escherichia coli by observing the zone of inhibition. This potential of AuNPs of size < 3 nm as antibacterial agent could pave way for development of new antimicrobials and overcoming the problems of antibiotics resistance

Keywords: antibacterial, bioreduction, nanoparticles, surfactant

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Authors: Mariana Roberta Ribeiro, Isabela de Cássia Caramello, Roberto Saverio Souza Costa

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Currently, one of the most important environmental problems is soil degradation. This wear is the result of inadequate agricultural practices, with water erosion as the main agent. As the runoff water is concentrated in certain points, it can reach a more advanced stage, which are the gullies. In view of this, the objective of this work was to evaluate which methodology is most suitable for the purpose of elaborating a project for the recovery of a gully, relating work time, data reliability, and the final cost. The work was carried out on a rural road in Monte Alto - SP, where there is 0.30 hectares of area under the influence of a gully. For the evaluation, an aerophotogrammetric survey was used with RPA, with georeferenced points, and with a GNSS L1/L2 receiver. To assess the importance of georeferenced points, there was a comparison of altimetric data using the support points with altimetric data using only the aircraft's internal GPS. Another method used was the survey by conventional topography, where coordinates were collected by total station and L1/L2 Geodetic GPS receiver. Statistical analysis was performed using analysis of variance (ANOVA) using the F test (p<0.05), and the means between treatments were compared using the Tukey test (p<0.05). The results showed that the surveys carried out by aerial photogrammetry and by conventional topography showed no significant difference for the analyzed parameters. Considering the data presented, it is possible to conclude that, when comparing the parameters of accuracy, the final volume of the gully, and cost, for the purpose of elaborating a project for the recovery of a gully, the methodologies of aerial photogrammetric survey and conventional topography do not differ significantly. However, when working time, use of labor, and project detail are compared, the aerial photogrammetric survey proves to be more viable.

Keywords: drones, erosion, soil conservation, technology in agriculture

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Authors: A. J. Regan, F. J. Lidgey, M. Betteridge, P. Georgiou, C. Toumazou, K. Hayatleh, J. R. Dibble

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Our goal is development of an algorithm capable of predicting the directional trend of the Standard and Poor’s 500 index (S&P 500). Extensive research has been published attempting to predict different financial markets using historical data testing on an in-sample and trend basis, with many authors employing excessively complex mathematical techniques. In reviewing and evaluating these in-sample methodologies, it became evident that this approach was unable to achieve sufficiently reliable prediction performance for commercial exploitation. For these reasons, we moved to an out-of-sample strategy based on linear regression analysis of an extensive set of financial data correlated with historical closing prices of the S&P 500. We are pleased to report a directional trend accuracy of greater than 55% for tomorrow (t+1) in predicting the S&P 500.

Keywords: financial algorithm, GPU, S&P 500, stock market prediction

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Authors: Salwa Aftab, Sehrish Riaz

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The objective of this study was to check the impact of grade sensitivity on learning motivation and academic performance of students and to remove the degree of difference that exists among students regarding the cause of their learning motivation and also to gain knowledge about this matter since it has not been adequately researched. Data collection was primarily done through the academic sector of Pakistan and was depended upon the responses given by students solely. A sample size of 208 university students was selected. Both paper and online surveys were used to collect data from respondents. The results of the study revealed that grade sensitivity has a positive relationship with the learning motivation of students and their academic performance. These findings were carried out through systematic correlation and regression analysis.

Keywords: academic performance, correlation, grade sensitivity, learning motivation, regression

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Authors: Yue Huang, Yiheng Feng

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Due to the global pressure on climate and energy, many countries are vigorously promoting electric vehicles and building charging (public) charging facilities. Faced with the supply-demand gap of existing electric vehicle charging stations and unreasonable space usage in China, this paper takes the central city of Nanjing as an example, establishes a site selection model through multivariate data integration, conducts multiple linear regression SPSS analysis, gives quantitative site selection results, and provides optimization models and suggestions for charging station layout planning.

Keywords: electric vehicle, charging station, allocation optimization, urban mobility, urban infrastructure, nanjing

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Authors: Pei-Jyun Hsieh, Cheng-Hsuan Li, Bor-Chen Kuo

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For hyperspectral image classification, feature reduction is an important pre-processing for avoiding the Hughes phenomena due to the difficulty for collecting training samples. Hence, lots of researches developed feature selection methods such as F-score, HSIC (Hilbert-Schmidt Independence Criterion), and etc., to improve hyperspectral image classification. However, most of them only consider the class separability in the original space, i.e., a linear class separability. In this study, we proposed a nonlinear class separability measure based on kernel trick for selecting an appropriate feature subset. The proposed nonlinear class separability was formed by a generalized RBF kernel with different bandwidths with respect to different features. Moreover, it considered the within-class separability and the between-class separability. A genetic algorithm was applied to tune these bandwidths such that the smallest with-class separability and the largest between-class separability simultaneously. This indicates the corresponding feature space is more suitable for classification. In addition, the corresponding nonlinear classification boundary can separate classes very well. These optimal bandwidths also show the importance of bands for hyperspectral image classification. The reciprocals of these bandwidths can be viewed as weights of bands. The smaller bandwidth, the larger weight of the band, and the more importance for classification. Hence, the descending order of the reciprocals of the bands gives an order for selecting the appropriate feature subsets. In the experiments, three hyperspectral image data sets, the Indian Pine Site data set, the PAVIA data set, and the Salinas A data set, were used to demonstrate the selected feature subsets by the proposed nonlinear feature selection method are more appropriate for hyperspectral image classification. Only ten percent of samples were randomly selected to form the training dataset. All non-background samples were used to form the testing dataset. The support vector machine was applied to classify these testing samples based on selected feature subsets. According to the experiments on the Indian Pine Site data set with 220 bands, the highest accuracies by applying the proposed method, F-score, and HSIC are 0.8795, 0.8795, and 0.87404, respectively. However, the proposed method selects 158 features. F-score and HSIC select 168 features and 217 features, respectively. Moreover, the classification accuracies increase dramatically only using first few features. The classification accuracies with respect to feature subsets of 10 features, 20 features, 50 features, and 110 features are 0.69587, 0.7348, 0.79217, and 0.84164, respectively. Furthermore, only using half selected features (110 features) of the proposed method, the corresponding classification accuracy (0.84168) is approximate to the highest classification accuracy, 0.8795. For other two hyperspectral image data sets, the PAVIA data set and Salinas A data set, we can obtain the similar results. These results illustrate our proposed method can efficiently find feature subsets to improve hyperspectral image classification. One can apply the proposed method to determine the suitable feature subset first according to specific purposes. Then researchers can only use the corresponding sensors to obtain the hyperspectral image and classify the samples. This can not only improve the classification performance but also reduce the cost for obtaining hyperspectral images.

Keywords: hyperspectral image classification, nonlinear feature selection, kernel trick, support vector machine

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Authors: Jung-Hong Hong, Jing-Cen Yang, Cai-Yu Ou

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The analysis of geographic inequality heavily relies on the use of location-enabled statistical data and quantitative measures to present the spatial patterns of the selected phenomena and analyze their differences. To protect the privacy of individual instance and link to administrative units, point-based datasets are spatially aggregated to area-based statistical datasets, where only the overall status for the selected levels of spatial units is used for decision making. The partition of the spatial units thus has dominant influence on the outcomes of the analyzed results, well known as the Modifiable Areal Unit Problem (MAUP). A new spatial reference framework, the Taiwan Geographical Statistical Classification (TGSC), was recently introduced in Taiwan based on the spatial partition principles of homogeneous consideration of the number of population and households. Comparing to the outcomes of the traditional township units, TGSC provides additional levels of spatial units with finer granularity for presenting spatial phenomena and enables domain experts to select appropriate dissemination level for publishing statistical data. This paper compares the results of respectively using TGSC and township unit on the mortality data and examines the spatial characteristics of their outcomes. For the mortality data between the period of January 1^st, 2008 and December 31^st, 2010 of the Taitung County, the all-cause age-standardized death rate (ASDR) ranges from 571 to 1757 per 100,000 persons, whereas the 2^nd dissemination area (TGSC) shows greater variation, ranged from 0 to 2222 per 100,000. The finer granularity of spatial units of TGSC clearly provides better outcomes for identifying and evaluating the geographic inequality and can be further analyzed with the statistical measures from other perspectives (e.g., population, area, environment.). The management and analysis of the statistical data referring to the TGSC in this research is strongly supported by the use of Geographic Information System (GIS) technology. An integrated workflow that consists of the tasks of the processing of death certificates, the geocoding of street address, the quality assurance of geocoded results, the automatic calculation of statistic measures, the standardized encoding of measures and the geo-visualization of statistical outcomes is developed. This paper also introduces a set of auxiliary measures from a geographic distribution perspective to further examine the hidden spatial characteristics of mortality data and justify the analyzed results. With the common statistical area framework like TGSC, the preliminary results demonstrate promising potential for developing a web-based statistical service that can effectively access domain statistical data and present the analyzed outcomes in meaningful ways to avoid wrong decision making.

Keywords: mortality map, spatial patterns, statistical area, variation

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Authors: Bruk Gebregziabher

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The safe and efficient operation of Autonomous Mobile Robots (AMRs) in complex environments, such as manufacturing, logistics, and agriculture, necessitates accurate multiobject tracking and predictive collision avoidance. This paper presents algorithms and techniques for addressing these challenges using Lidar sensor data, emphasizing ensemble Kalman filter. The developed predictive collision avoidance algorithm employs the data provided by lidar sensors to track multiple objects and predict their velocities and future positions, enabling the AMR to navigate safely and effectively. A modification to the dynamic windowing approach is introduced to enhance the performance of the collision avoidance system. The overall system architecture encompasses object detection, multi-object tracking, and predictive collision avoidance control. The experimental results, obtained from both simulation and real-world data, demonstrate the effectiveness of the proposed methods in various scenarios, which lays the foundation for future research on global planners, other controllers, and the integration of additional sensors. This thesis contributes to the ongoing development of safe and efficient autonomous systems in complex and dynamic environments.

Keywords: autonomous mobile robots, multi-object tracking, predictive collision avoidance, ensemble Kalman filter, lidar sensors

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Authors: Sogand Barghi

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The integration of artificial intelligence (AI) in accounting practices is reshaping the landscape of financial management. This paper explores the innovative applications of AI in the realm of accounting, emphasizing its transformative impact on efficiency, accuracy, decision-making, and financial insights. By harnessing AI's capabilities in data analysis, pattern recognition, and automation, accounting professionals can redefine their roles, elevate strategic decision-making, and unlock unparalleled value for businesses. This paper delves into AI-driven solutions such as automated data entry, fraud detection, predictive analytics, and intelligent financial reporting, highlighting their potential to revolutionize the accounting profession. Artificial intelligence has swiftly emerged as a game-changer across industries, and accounting is no exception. This paper seeks to illuminate the profound ways in which AI is reshaping accounting practices, transcending conventional boundaries, and propelling the profession toward a new era of efficiency and insight-driven decision-making. One of the most impactful applications of AI in accounting is automation. Tasks that were once labor-intensive and time-consuming, such as data entry and reconciliation, can now be streamlined through AI-driven algorithms. This not only reduces the risk of errors but also allows accountants to allocate their valuable time to more strategic and analytical tasks. AI's ability to analyze vast amounts of data in real time enables it to detect irregularities and anomalies that might go unnoticed by traditional methods. Fraud detection algorithms can continuously monitor financial transactions, flagging any suspicious patterns and thereby bolstering financial security. AI-driven predictive analytics can forecast future financial trends based on historical data and market variables. This empowers organizations to make informed decisions, optimize resource allocation, and develop proactive strategies that enhance profitability and sustainability. Traditional financial reporting often involves extensive manual effort and data manipulation. With AI, reporting becomes more intelligent and intuitive. Automated report generation not only saves time but also ensures accuracy and consistency in financial statements. While the potential benefits of AI in accounting are undeniable, there are challenges to address. Data privacy and security concerns, the need for continuous learning to keep up with evolving AI technologies, and potential biases within algorithms demand careful attention. The convergence of AI and accounting marks a pivotal juncture in the evolution of financial management. By harnessing the capabilities of AI, accounting professionals can transcend routine tasks, becoming strategic advisors and data-driven decision-makers. The applications discussed in this paper underline the transformative power of AI, setting the stage for an accounting landscape that is smarter, more efficient, and more insightful than ever before. The future of accounting is here, and it's driven by artificial intelligence.

Keywords: artificial intelligence, accounting, automation, predictive analytics, financial reporting

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Authors: H. Yousefnia, S. Zolghadri

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The measurement of organ radiation exposure dose is one of the most important steps to be taken initially, for developing a new radiopharmaceutical. In this study, the dosimetric studies of a novel agent for SPECT-imaging of the bone metastasis, 111In-1,4,7,10-tetraazacyclododecane-1,4,7,10 tetraethylene phosphonic acid (111In-DOTMP) complex, have been carried out to estimate the dose in human organs based on the data derived from rats. The radiolabeled complex was prepared with high radiochemical purity in the optimal conditions. Biodistribution studies of the complex was investigated in the male Syrian rats at selected times after injection (2, 4, 24 and 48 h). The human absorbed dose estimation of the complex was made based on data derived from the rats by the radiation absorbed dose assessment resource (RADAR) method. 111In-DOTMP complex was prepared with high radiochemical purity of >99% (ITLC). Total body effective absorbed dose for 111In-DOTMP was 0.061 mSv/MBq. This value is comparable to the other 111In clinically used complexes. The results show that the dose with respect to the critical organs is satisfactory within the acceptable range for diagnostic nuclear medicine procedures. Generally, 111In-DOTMP has interesting characteristics and can be considered as a viable agent for SPECT-imaging of the bone metastasis in the near future.

Keywords: In-111, DOTMP, Internal Dosimetry, RADAR

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Authors: Si-Jie Ji, Santhanamoorthi Nachimuthu, Jyh-Chiang Jiang

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With the rapid development of industrialization and urbanization, air pollution poses significant global environmental challenges, contributing to acid rain, global warming, and adverse health effects. Therefore, it is necessary to monitor the concentration of toxic gases in the atmospheric environment in real-time and to deploy cost-effective gas sensors capable of detecting their emissions. In this study, we systematically investigated the sensing capabilities of the two-dimensional MoSe₂ for seven key environmental gases (NO, NO₂, CO, CO₂, SO₂, SO₃, and O₂) using density functional theory (DFT) and non-equilibrium Green’s function (NEGF) calculations. We also investigated the impact of H₂O as an interfering gas. Our results indicate that the MoSe₂ monolayer is thermodynamically stable and exhibits strong gas-sensing capabilities. The calculated adsorption energies indicate that these gases can stably adsorb on MoSe₂, with SO₃ exhibiting the strongest adsorption energy (-0.63 eV). Electronic structure analysis, including projected density of states (PDOS) and Bader charge analysis, demonstrates significant changes in the electronic properties of MoSe₂ upon gas adsorption, affecting its conductivity and sensing performance. We find that oxygen (O₂) adsorption notably influenced the deformation of MoSe₂. To comprehensively understand the potential of MoSe₂ as a gas sensor, we used the NEGF method to assess the electronic transport properties of MoSe₂ under gas adsorption, evaluating current-voltage (I-V), resistance-voltage (R-V) characteristics, and transmission spectra to determine sensitivity, selectivity, and recovery time compared to pristine MoSe₂. Sensitivity, selectivity, and recovery time are analyzed at a bias voltage of 1.7V, showing excellent performance of MoSe₂ in detecting SO₃, among other gases. The pronounced changes in electronic transport behavior induced by SO₃ adsorption confirm MoSe₂’s strong potential as a high-performance gas-sensing material. Overall, this theoretical study provides new insights into the development of high-performance gas sensors, demonstrating the potential of MoSe₂ as a gas-sensing material, particularly for gases like SO₃.

Keywords: density functional theory, gas sensing, MoSe₂, non-equilibrium Green’s function, SO

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Authors: Mahmud Gungor, Ali Aydin, Erdal Akyol, Suat Tasdelen

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The understanding of geotechnical characteristics of near-surface material and the effects of the groundwater is very important problem in such as site studies. For showing the relations between seismic data and groundwater we selected about 25 km2 as the study area. It has been presented which is a detailed work of seismic data and groundwater depths of Gokpinar Damp area. Seismic waves velocity (Vp and Vs) are very important parameters showing the soil properties. The seismic records were used the method of the multichannel analysis of surface waves near area of Gokpinar Damp area. Sixty sites in this area have been investigated with survey lines about 60 m in length. MASW (Multichannel analysis of surface wave) method has been used to generate one-dimensional shear wave velocity profile at locations. These shear wave velocities are used to estimate equivalent shear wave velocity in the study area at every 2 and 5 m intervals up to a depth of 45 m. Levels of equivalent shear wave velocity of soil are used the classified of the study area. After the results of the study, it must be considered as components of urban planning and building design of Gokpinar Damp area, Denizli and the application and use of these results should be required and enforced by municipal authorities.

Keywords: seismic data, Gokpinar Damp, urban planning, Denizli

Procedia PDF Downloads 288

Authors: Jingwei Du

Abstract:

The need of optimizing air conditioning systems for existing buildings calls for control methods designed with energy-efficiency as a primary goal. The majority of current control methods boil down to two categories: empirical and model-based. To be effective, the former heavily relies on engineering expertise and the latter requires extensive historical data. Reinforcement Learning (RL), on the other hand, is a model-free approach that explores the environment to obtain an optimal control strategy often referred to as “policy”. This research adopts Proximal Policy Optimization (PPO) to improve chiller plant control, and enable the RL agent to collaborate with experienced engineers. It exploits the fact that while the industry lacks historical data, abundant operational data is available and allows the agent to learn and evolve safely under human supervision. Thanks to the development of language models, renewed interest in RL has led to modern, online, policy-based RL algorithms such as the PPO. This research took inspiration from “alignment”, a process that utilizes human feedback to finetune the pretrained model in case of unsafe content. The methodology can be summarized into three steps. First, an initial policy model is generated based on minimal prior knowledge. Next, the prepared PPO agent is deployed so feedback from both critic model and human experts can be collected for future finetuning. Finally, the agent learns and adapts itself to the specific chiller plant, updates the policy model and is ready for the next iteration. Besides the proposed approach, this study also used traditional RL methods to optimize the same simulated chiller plants for comparison, and it turns out that the proposed method is safe and effective at the same time and needs less to no historical data to start up.

Keywords: chiller plant, control methods, energy efficiency, proximal policy optimization, reinforcement learning

Procedia PDF Downloads 29

Authors: Arian Ebneyamini, Hoda Azimi, Jules Thibaults, F. Handan Tezel

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In this study, a modification of Hennepe model for pervaporation separation of butanol from aqueous solutions using Polydimethylsiloxane (PDMS) mixed matrix membranes has been introduced and validated by experimental data. The model was compared to the original Hennepe model and few other models which are applicable for membrane gas separation processes such as Maxwell, Lewis Nielson and Pal. Theoretical modifications for non-ideal interface morphology have been offered to predict the permeability in case of interface void, interface rigidification and pore-blockage. The model was in a good agreement with experimental data.

Keywords: butanol, PDMS, modeling, pervaporation, mixed matrix membranes

Procedia PDF Downloads 221

Authors: Andreas Walmsley, Shobana Partington, Rebecca Armstrong, Harold Goodwin

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In the UK, more than 1 in 5 workers earn less than a living wage. The hospitality sector is particularly affected where it has been claimed two thirds of workers earn less than the living wage. The UK Government is set to introduce (April 2016) a national living wage (NLW) which is therefore likely to have a significant impact on the hospitality sector. To date limited data exists that focus on how hotels are tackling the issue, what stakeholder perceptions are towards the change in legislation, and how the NLW may affect working patterns in the sector. This study draws on interviews with a range of key stakeholders such as hotel HR and general managers as well as industry representatives to explore these issues within the broader context of responsible tourism. Data collection is still ongoing and is scheduled to be completed by the end of June 2016.

Keywords: hospitality, living wage, responsible tourism, tourism employment

Procedia PDF Downloads 384

Authors: Luís F. da Silva, Ariadne C. Catto, Osmando F. Lopes, Khalifa Aguir, Valmor R. Mastelaro, Caue Ribeiro, Elson Longo

Abstract:

Gas detection is important for controlling industrial, and vehicle emissions, agricultural residues, and environmental control. In last decades, several semiconducting oxides have been used to detect dangerous or toxic gases. The excellent gas-sensing performance of these devices have been observed at high temperatures (~250 °C), which forbids the use for the detection of flammable and explosive gases. In this way, ultraviolet light activated gas sensors have been a simple and promising alternative to achieve room temperature sensitivity. Among the semiconductor oxides which exhibit a good performance as gas sensor, the zinc oxide (ZnO) and tin oxide (SnO2) have been highlighted. Nevertheless, their poor selectivity is the main disadvantage for application as gas sensor devices. Recently, heterostructures combining these two semiconductors (ZnO-SnO2) have been studied as an alternative way to enhance the gas sensor performance (sensitivity, selectivity, and stability). In this work, we investigated the influence of mass ratio Zn:Sn on the properties of ZnO-SnO2 nanocomposites prepared by hydrothermal treatment for 4 hours at 200 °C. The crystalline phase, surface, and morphological features were characterized by X-ray diffraction (XRD), high-resolution transmission electron (HR-TEM), and X-ray photoelectron spectroscopy (XPS) measurements. The gas sensor measurements were carried out at room-temperature under ultraviolet (UV) light irradiation using different ozone levels (0.06 to 0.61 ppm). The XRD measurements indicate the presence of ZnO and SnO2 crystalline phases, without the evidence of solid solution formation. HR-TEM analysis revealed that a good contact between the SnO2 nanoparticles and the ZnO nanorods, which are very important since interface characteristics between nanostructures are considered as challenge to development new and efficient heterostructures. Electrical measurements proved that the best ozone gas-sensing performance is obtained for ZnO:SnO2 (50:50) nanocomposite under UV light irradiation. Its sensitivity was around 6 times higher when compared to SnO2 pure, a traditional ozone gas sensor. These results demonstrate the potential of ZnO-SnO2 heterojunctions for the detection of ozone gas at room-temperature when irradiated with UV light irradiation.

Keywords: hydrothermal, zno-sno2, ozone sensor, uv-activation, room-temperature

Procedia PDF Downloads 284

Authors: Aafaf El Jazouli, Ahmed Barakat, Rida Khellouk

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Mapping of soil degradation is derived from field observations, laboratory measurements, and remote sensing data, integrated quantitative methods to map the spatial characteristics of soil properties at different spatial and temporal scales to provide up-to-date information on the field. Since soil salinity, texture and organic matter play a vital role in assessing topsoil characteristics and soil quality, remote sensing can be considered an effective method for studying these properties. The main objective of this research is to asses soil degradation by combining remote sensing data and laboratory analysis. In order to achieve this goal, the required study of soil samples was taken at 50 locations in the upper basin of Oum Er Rbia in the Middle Atlas in Morocco. These samples were dried, sieved to 2 mm and analyzed in the laboratory. Landsat 8 OLI imagery was analyzed using physical or empirical methods to derive soil properties. In addition, remote sensing can serve as a supporting data source. Deterministic potential (Spline and Inverse Distance weighting) and probabilistic interpolation methods (ordinary kriging and universal kriging) were used to produce maps of each grain size class and soil properties using GIS software. As a result, a correlation was found between soil texture and soil organic matter content. This approach developed in ongoing research will improve the prospects for the use of remote sensing data for mapping soil degradation in arid and semi-arid environments.

Keywords: Soil degradation, GIS, interpolation methods (spline, IDW, kriging), Landsat 8 OLI, Oum Er Rbia high basin

Procedia PDF Downloads 165

Authors: Nadia Chougui, Nawal Makhloufi, Farouk Rezgui, Elias Benramdane, Carmen S. R. Freire, Carla Vilela, Armando J. D. Silvestre

Abstract:

Native to Mexico, Opuntia ficus-indica was introduced in southern Spain, and thereafter, it was spread throughout the Mediterranean Basin by the Spanish conquerors in the 16th and 17th centuries. O. ficus-indica is a tropical and subtropical plant able to grow in arid and semi-arid regions, such as the Mediterranean and Central America regions. The culture of Opuntia covers about 200,000 ha in North Africa. This tree is used against soil erosion and desertification for fruit production and is encouraged to promote the livestock sector. It has recently received ever-increasing attention from researchers worldwide for the multivalent pharmaceutical and cosmetical potential of its different compartments (fruits, seeds, cladodes). The present study investigated the elaboration by casting method and characterization of new biodegradable films composed of cladodes powder (CP) of the plant raw material mentioned above, and a marine seaweed derivative, namely agar (A). The effect of glycerol concentration on the properties of the films was evaluated at four different contents (30, 40, 50 and 60 wt.%). The films present UV-blocking properties, thermal stability as well as moderate mechanical performance and water vapor transmission rate (WVTR). The results point to an increase in thickness, elongation at break, moisture content, water solubility, and WVTR with increasing glycerol content. On the contrary, Young’s modulus, tensile strength and contact angle decreased as glycerol concentration increased. The best combination is obtained for the film with 30% glycerol, based on an intermediate compromise between physical, mechanical, thermal and barrier properties. All these outcomes express the potentiality of the powder obtained from grinding the OFI cladodes as raw material to produce low-cost films for the development of sustainable packaging materials.

Keywords: Opuntia ficus-indica cladodes powder, agar, biobased films, effect of plasticizer, sustainable packaging

Procedia PDF Downloads 75

Authors: Frederic Henry-Couannier

Abstract:

Dark Gravity is a natural extension of general relativity in presence of a flat non dynamical background. Matter and radiation fields from its dark sector, as soon as their gravity dominates over our side fields gravity, produce a constant acceleration law of the scale factor. After a brief reminder of the Dark Gravity theory foundations, the confrontation with the main cosmological probes is carried out. We show that, amazingly, the sudden transition between the usual matter dominated decelerated expansion law a(t) ∝ t²/³ and this accelerated expansion law a(t) ∝ t² predicted by the theory should be able to fit the main cosmological probes (SN, BAO, CMB and age of the oldest stars data) but also direct H₀ measurements with two free parameters only: H₀ and the transition redshift.

Keywords: anti-gravity, negative energies, time reversal, field discontinuities, dark energy theory

Procedia PDF Downloads 56

Authors: D. Serero, L. Couton, J. D. Parisse, R. Leroy

Abstract:

In urban planning, an increasing number of cities require wind analysis to verify comfort of public spaces and around buildings. These studies are made using computer fluid dynamic simulation (CFD). However, this technique is often based on wind information taken from meteorological stations located at several kilometers of the spot of analysis. The approximated input data on project surroundings produces unprecise results for this type of analysis. They can only be used to get general behavior of wind in a zone but not to evaluate precise wind speed. This paper presents another approach to this problem, based on collecting wind data and generating an urban wind cartography using connected ultrasound anemometers. They are wireless devices that send immediate data on wind to a remote server. Assembled in array, these devices generate geo-localized data on wind such as speed, temperature, pressure and allow us to compare wind behavior on a specific site or building. These Netatmo-type anemometers communicate by wifi with central equipment, which shares data acquired by a wide variety of devices such as wind speed, indoor and outdoor temperature, rainfall, and sunshine. Beside its precision, this method extracts geo-localized data on any type of site that can be feedback looped in the architectural design of a building or a public place. Furthermore, this method allows a precise calibration of a virtual wind tunnel using numerical aeraulic simulations (like STAR CCM + software) and then to develop the complete volumetric model of wind behavior over a roof area or an entire city block. The paper showcases connected ultrasonic anemometers, which were implanted for an 18 months survey on four study sites in the Grand Paris region. This case study focuses on Paris as an urban environment with multiple historical layers whose diversity of typology and buildings allows considering different ways of capturing wind energy. The objective of this approach is to categorize the different types of wind in urban areas. This, particularly the identification of the minimum and maximum wind spectrum, helps define the choice and performance of wind energy capturing devices that could be implanted there. The localization on the roof of a building, the type of wind, the altimetry of the device in relation to the levels of the roofs, the potential nuisances generated. The method allows identifying the characteristics of wind turbines in order to maximize their performance in an urban site with turbulent wind.

Keywords: computer fluid dynamic simulation in urban environment, wind energy harvesting devices, net-zero energy building, urban wind behavior simulation, advanced building skin design methodology

Procedia PDF Downloads 101