Search results for: ai artificial intelligence

Authors: Yasser Ahmed Mahmoud Ali Helal

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The use of CAD (Computer Aided Design) technology is ubiquitous in the architecture, engineering and construction (AEC) industry. This has led to its inclusion in the curriculum of architecture schools in Nigeria as an important part of the training module. This article examines the ethical issues involved in implementing CAD (Computer Aided Design) content into the architectural education curriculum. Using existing literature, this study begins with the benefits of integrating CAD into architectural education and the responsibilities of different stakeholders in the implementation process. It also examines issues related to the negative use of information technology and the perceived negative impact of CAD use on design creativity. Using a survey method, data from the architecture department of University was collected to serve as a case study on how the issues raised were being addressed. The article draws conclusions on what ensures successful ethical implementation. Millions of people around the world suffer from hepatitis C, one of the world's deadliest diseases. Interferon (IFN) is treatment options for patients with hepatitis C, but these treatments have their side effects. Our research focused on developing an oral small molecule drug that targets hepatitis C virus (HCV) proteins and has fewer side effects. Our current study aims to develop a drug based on a small molecule antiviral drug specific for the hepatitis C virus (HCV). Drug development using laboratory experiments is not only expensive, but also time-consuming to conduct these experiments. Instead, in this in silicon study, we used computational techniques to propose a specific antiviral drug for the protein domains of found in the hepatitis C virus. This study used homology modeling and abs initio modeling to generate the 3D structure of the proteins, then identifying pockets in the proteins. Acceptable lagans for pocket drugs have been developed using the de novo drug design method. Pocket geometry is taken into account when designing ligands. Among the various lagans generated, a new specific for each of the HCV protein domains has been proposed.

Keywords: drug design, anti-viral drug, in-silicon drug design, hepatitis C virus (HCV) CAD (Computer Aided Design), CAD education, education improvement, small-size contractor automatic pharmacy, PLC, control system, management system, communication

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Authors: Milica Karadžić, Lidija Jevrić, Sanja Podunavac-Kuzmanović, Strahinja Kovačević, Anamarija Mandić, Aleksandar Oklješa, Andrea Nikolić, Marija Sakač, Katarina Penov Gaši

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Using chemometric approach, the relationships between the chromatographic lipophilicity and in silico molecular descriptors for twenty-nine selected steroid derivatives were studied. The chromatographic lipophilicity was predicted using artificial neural networks (ANNs) method. The most important in silico molecular descriptors were selected applying stepwise selection (SS) paired with partial least squares (PLS) method. Molecular descriptors with satisfactory variable importance in projection (VIP) values were selected for ANN modeling. The usefulness of generated models was confirmed by detailed statistical validation. High agreement between experimental and predicted values indicated that obtained models have good quality and high predictive ability. Global sensitivity analysis (GSA) confirmed the importance of each molecular descriptor used as an input variable. High-quality networks indicate a strong non-linear relationship between chromatographic lipophilicity and used in silico molecular descriptors. Applying selected molecular descriptors and generated ANNs the good prediction of chromatographic lipophilicity of the studied steroid derivatives can be obtained. This article is based upon work from COST Actions (CM1306 and CA15222), supported by COST (European Cooperation and Science and Technology).

Keywords: artificial neural networks, chemometrics, global sensitivity analysis, liquid chromatography, steroids

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Authors: R. Lekaviciene, D. Antiniene

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Attempts to split the construct of emotional intelligence (EI) into separate components – ability to understand own and others’ emotions and ability to control own and others’ emotions may be meaningful more theoretically than practically. In real life, a personality encounters various emotional situations that require exhibition of complex EI to solve them. Emotional situation solution tests enable measurement of such undivided EI. The object of the present study is to determine sociodemographic and other factors that are important for emotional situation solutions. The study involved 1,430 participants from various regions of Lithuania. The age of participants varied from 17 years to 27 years. Emotional social and interpersonal situation scale EI-DARL-V2 was used. Each situation had two mandatory answering formats: The first format contained assignments associated with hypothetical theoretical knowledge of how the situation should be solved, while the second format included the question of how the participant would personally resolve the given situation in reality. A questionnaire that contained various sociodemographic data of subjects was also presented. Factors, statistically significant for emotional situation solution, have been determined: gender, family structure, the subject’s relation with his or her mother, mother’s occupation, subjectively assessed financial situation of the family, level of education of the subjects and his or her parents, academic achievement, etc. The best solvers of emotional situations are women with high academic achievements. According to their chosen study profile/acquired profession, they are related to the fields in social sciences and humanities. The worst solvers of emotional situations are men raised in foster homes. They are/were bad students and mostly choose blue-collar professions.

Keywords: emotional intelligence, emotional situations, solution of situation, young people

Procedia PDF Downloads 180

Authors: Jessica Kate Simonds

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The last successful hijacking perpetrated by Somali pirates in 2012 represented a critical turning point for the identity and brand of Indian Ocean (IO) insecurity, coined in this paper as the era of the post-piratical. This paper explores the broadening of the PMSC business model to account and contribute to the design of a new IO security environment that prioritises foreign and insurgency drone activity and Houthi rebel operations as the main threat to merchant shipping in the post-2012 era. This study is situated within a longer history of analysing maritime insecurity and also contributes a bespoke conceptual framework that understands the sea as a space that is produced and reproduced relative to existing and emerging threats to merchant shipping based on bespoke models of information sharing and intelligence acquisition. This paper also makes a prominent empirical contribution by drawing on a post-positivist methodology, data drawn from original semi-structured interviews with senior maritime insurers and active merchant seafarers that is triangulated with industry-produced guidance such as the BMP series as primary data sources. Each set is analysed through qualitative discourse and content analysis and supported by the quantitative data sets provided by the IMB Piracy Reporting center and intelligence networks. This analysis reveals that mechanisms such as the IGP&I Maritime Security Committee and intelligence divisions of PMSC’s have driven the exchanges of knowledge between land and sea and thus the reproduction of the maritime security environment through new regulations and guidance to account dones, rebels and bombs as the key challenges in the IO, beyond piracy. A contribution of this paper is the argument that experts who may not be in the highest-profile jobs are the architects of maritime insecurity based on their detailed knowledge and connections to vessels in transit. This paper shares the original insights of those who have served in critical decision making spaces to demonstrate that the development and refinement of industry produced deterrence guidance that has been accredited to the mitigation of piracy, have shaped new editions such as BMP 5 that now serve to frame a new security environment that prioritises the mitigation of risks from drones and WBEID’s from both state and insurgency risk groups. By highlighting the experiences and perspectives of key players on both land and at sea, the key finding of this paper is outlining that as pirates experienced a financial boom by profiteering from their bespoke business model during the peak of successful hijackings, the private security market encountered a similar level of financial success and guaranteed risk environment in which to prospect business. Thus, the reproduction of the Indian Ocean as a maritime security environment reflects a new found purpose for PMSC’s as part of the broader conglomerate of maritime insurers, regulators, shipowners and managers who continue to redirect the security consciousness and IO brand of insecurity.

Keywords: maritime security, private security, risk intelligence, political geography, international relations, political economy, maritime law, security studies

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Electricity prices have sophisticated features such as high volatility, nonlinearity and high frequency that make forecasting quite difficult. Electricity price has a volatile and non-random character so that, it is possible to identify the patterns based on the historical data. Intelligent decision-making requires accurate price forecasting for market traders, retailers, and generation companies. So far, many shallow-ANN (artificial neural networks) models have been published in the literature and showed adequate forecasting results. During the last years, neural networks with many hidden layers, which are referred to as DNN (deep neural networks) have been using in the machine learning community. The goal of this study is to investigate electricity price forecasting performance of the shallow-ANN and DNN models for the Turkish day-ahead electricity market. The forecasting accuracy of the models has been evaluated with publicly available data from the Turkish day-ahead electricity market. Both shallow-ANN and DNN approach would give successful result in forecasting problems. Historical load, price and weather temperature data are used as the input variables for the models. The data set includes power consumption measurements gathered between January 2016 and December 2017 with one-hour resolution. In this regard, forecasting studies have been carried out comparatively with shallow-ANN and DNN models for Turkish electricity markets in the related time period. The main contribution of this study is the investigation of different shallow-ANN and DNN models in the field of electricity price forecast. All models are compared regarding their MAE (Mean Absolute Error) and MSE (Mean Square) results. DNN models give better forecasting performance compare to shallow-ANN. Best five MAE results for DNN models are 0.346, 0.372, 0.392, 0,402 and 0.409.

Keywords: deep learning, artificial neural networks, energy price forecasting, turkey

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Authors: Kirolos Gerges Yakoub Gerges

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Self-sustaining agricultural machines act in stochastic surroundings and therefore, should be capable of perceive the surroundings in real time. This notion can be done using image sensors blended with superior device learning, mainly Deep mastering. Deep convolutional neural networks excel in labeling and perceiving colour pix and since the fee of RGB-cameras is low, the hardware cost of accurate notion relies upon heavily on memory and computation power. This paper investigates the opportunity of designing lightweight convolutional neural networks for semantic segmentation (pixel clever class) with reduced hardware requirements, to allow for embedded usage in self-reliant agricultural machines. The usage of compression techniques, a lightweight convolutional neural community is designed to carry out actual-time semantic segmentation on an embedded platform. The community is skilled on two big datasets, ImageNet and Pascal Context, to apprehend as much as four hundred man or woman instructions. The 400 training are remapped into agricultural superclasses (e.g. human, animal, sky, road, area, shelterbelt and impediment) and the capacity to provide correct actual-time perception of agricultural environment is studied. The network is carried out to the case of self-sufficient grass mowing the usage of the NVIDIA Tegra X1 embedded platform. Feeding case-unique pics to the community consequences in a fully segmented map of the superclasses within the picture. As the network remains being designed and optimized, handiest a qualitative analysis of the technique is entire on the abstract submission deadline. intending this cut-off date, the finalized layout is quantitatively evaluated on 20 annotated grass mowing pictures. Light-weight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show aggressive performance on the subject of accuracy and speed. It’s miles viable to offer value-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.

Keywords: centrifuge pump, hydraulic energy, agricultural applications, irrigationaxial flux machines, axial flux applications, coreless machines, PM machinesautonomous agricultural machines, deep learning, safety, visual perception

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Authors: Swati Bajpai, S. P. K Jena

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Background: There is dramatic increase in the prevalence and severity of overweight in adolescents, raising concerns about their psychosocial and cognitive consequences, thereby indicating the immediate need to understand the effects of increased weight on scholastic performance. Although the body of research is currently limited, available results have identified an inverse relationship between obesity and cognition in adolescents. Aim: to examine the association between increased Body Mass Index in adolescents and their neurocognitive functioning. Methods: A case –control study of 28 subjects in the age group of 11-17 years (14 Males and 14 females) was taken on the basis of main inclusion criteria (Body Mass Index). All of them were randomized to (experimental group: overweight) and (control group: normal weighted). A complete neurocognitive assessment was carried out using validated psychological scales namely, Color Progressive Matrices (to assess intelligence); Bender Visual Motor Gestalt Test (Perceptual motor functioning); PGI-Memory Scale for Children (memory functioning) and Malin’s Intelligence Scale Indian Children (verbal and performance ability). Results: statistical analysis of the results depicted that 57% of the experimental group lack in cognitive abilities, especially in general knowledge (99.1±12.0 vs. 102.8±6.7), working memory (91.5±8.4 vs. 93.1±8.7), concrete ability (82.3±11.5 vs. 92.6±1.7) and perceptual motor functioning (1.5±1.0 vs. 0.3±0.9) as compared to control group. Conclusion: Our investigations suggest that weight gain results, at least in part, from a neurological predisposition characterized by reduced executive function, and in turn obesity itself has a compounding negative impact on the brain. Though, larger sample is needed to make more affirmative claims.

Keywords: adolescents, body mass index, neurocognition, obesity

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Authors: Bruno Cossu, Elio Carlo

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The invention (patent pending) relates to the field of devices aimed to harness wave energy (WEC) especially for artificial upwelling, forced downwelling, production of compressed air. In its basic form, the pump consists of a hydro-pneumatic machine, driven by wave energy, characterised by the fact that it has no moving mechanical parts, and is made up of only two structural components: an hollow body, which is open at the bottom to the sea and partially immersed in sea water, and a tube, both joined together to form a single body. The shape of the hollow body is like a mushroom whose cap and stem are hollow; the stem is open at both ends and the lower part of its surface is crossed by holes; the tube is external and coaxial to the stem and is joined to it so as to form a single body. This shape of the hollow body and the type of connection to the tube allows the pump to operate simultaneously as an air compressor (OWC) on the cap side, and as an airlift on the stem side. The pump can be implemented in four versions, each of which provides different variants and methods of implementation: 1) firstly, for the artificial upwelling of cold, deep ocean water; 2) secondly, for the lifting and transfer of these waters to the place of use (above all, fish farming plants), even if kilometres away; 3) thirdly, for the forced downwelling of surface sea water; 4) fourthly, for the forced downwelling of surface water, its oxygenation, and the simultaneous production of compressed air. The transfer of the deep water or the downwelling of the raised surface water (as for pump versions indicated in points 2 and 3 above), is obtained by making the water raised by the airlift flow into the upper inlet of another pipe, internal or adjoined to the airlift; the downwelling of raised surface water, oxygenation, and the simultaneous production of compressed air (as for the pump version indicated in point 4), is obtained by installing a venturi tube on the upper end of the pipe, whose restricted section is connected to the external atmosphere, so that it also operates like a hydraulic air compressor (trompe). Furthermore, by combining one or more pumps for the upwelling of cold, deep water, with one or more pumps for the downwelling of the warm surface water, the system can be used in an Ocean Thermal Energy Conversion plant to supply the cold and the warm water required for the operation of the same, thus allowing to use, without increased costs, in addition to the mechanical energy of the waves, for the purposes indicated in points 1 to 4, the thermal one of the marine water treated in the process.

Keywords: air lifted upwelling, fish farming plant, hydraulic air compressor, wave energy converter

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Authors: Rebekah Granger Ellis, Richard B. Speaker, Pat Austin

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This study used two psychological scales to examine the level of social and emotional intelligence and moral judgment of over 500 gifted and talented high school students in various academic and creative arts programs in a large metropolitan area in the southeastern United States. For decades, numerous models and programs purporting to encourage socio-affective characteristics of adolescent development have been explored in curriculum theory and design. Socio-affective merges social, emotional, and moral domains. It encompasses interpersonal relations and social behaviors; development and regulation of emotions; personal and gender identity construction; empathy development; moral development, thinking, and judgment. Examining development in these socio-affective domains can provide insight into why some gifted and talented adolescents are not successful in adulthood despite advanced IQ scores. Particularly whether nonintellectual characteristics of gifted and talented individuals, such as emotional, social and moral capabilities, are as advanced as their intellectual abilities and how these are related to each other. Unique characteristics distinguish gifted and talented individuals; these may appear as strengths, but there is the potential for problems to accompany them. Although many thrive in their school environments, some gifted students struggle rather than flourish. In the socio-affective domain, these adolescents face special intrapersonal, interpersonal, and environmental problems. Gifted individuals’ cognitive, psychological, and emotional development occurs asynchronously, in multidimensional layers at different rates and unevenly across ability levels. Therefore, it is important to examine the long-term effects of participation in various gifted and talented programs on the socio-affective development of gifted and talented adolescents. This quasi-experimental longitudinal study examined students in several gifted and talented education programs (creative arts school, urban charter schools, and suburban public schools) for (1) socio-affective development level and (2) whether a particular gifted and talented program encourages developmental growth. The following research questions guided the study: (1) How do academically and artistically talented gifted 10th and 11th grade students perform on psychometric scales of social and emotional intelligence and moral judgment? Do they differ from their age or grade normative sample? Are their gender differences among gifted students? (2) Does school environment impact 10th and 11th grade gifted and talented students’ socio-affective development? Do gifted adolescents who participate in a particular school gifted program differ in their developmental profiles of social and emotional intelligence and moral judgment? Students’ performances on psychometric instruments were compared over time and by type of program. Participants took pre-, mid-, and post-tests over the course of an academic school year with Defining Issues Test (DIT-2) assessing moral judgment and BarOn EQ-I: YV assessing social and emotional intelligence. Based on these assessments, quantitative differences in growth on psychological scales (individual and school) were examined. Change scores between schools were also compared. If a school showed change, artifacts (culture, curricula, instructional methodology) provided insight as to environmental qualities that produced this difference.

Keywords: gifted and talented education, moral development, socio-affective development, socio-affective education

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Authors: Tamar Trop, Ido Izhaki

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Small natural freshwater bodies (SNFWBs), which are vital for many waterbird species, are considered endangered habitats due to their progressive loss and extensive degradation. While SNFWBs are becoming extinct, studies have indicated that many waterbird species may greatly benefit from various types of small artificial waterbodies (SAWBs), such as floodwater and treated water reservoirs. If designed and managed with care, SAWBs hold significant potential to serve as alternative or complementary habitats for birds, and thus mitigate the adverse effects of SNFWBs loss. Currently, most reservoirs are built as infrastructural facilities and designed according to engineering best practices and site-specific considerations, which do not include catering for waterbirds' needs. Furthermore, as things stand, there is still a lack of clear and comprehensive knowledge regarding the additional factors that should be considered in tackling the challenge of attracting waterbirds' to reservoirs, without compromising on the reservoirs' original functions. This study attempts to narrow this knowledge gap by performing a systematic review of the various factors (e.g., bird attributes; physical, structural, spatial, climatic, chemical, and biological characteristics of the waterbody; and anthropogenic activities) affecting the occurrence, abundance, richness, and diversity of waterbirds in SNFWBs. The methodical review provides a concise and relatively unbiased synthesis of the knowledge in the field, which can inform decision-making and practice regarding the planning, design, and management of reservoirs with birds in mind. Such knowledge is especially beneficial for arid and semiarid areas, where natural water sources are deteriorating and becoming extinct even faster due to climate change.

Keywords: artificial waterbodies, reservoirs, small waterbodies, waterbirds

Procedia PDF Downloads 72

Authors: Maria Antonieta Erna Castillo Holly

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During the last decade, data analysis, strategic decision making, and the use of artificial intelligence (AI) tools in Latin American agriculture have been a challenge. In some countries, the availability, quality, and reliability of historical data, in addition to the current data recording methodology in the field, makes it difficult to use information systems, complete data analysis, and their support for making the right strategic decisions. This is something essential in Agriculture 4.0. where the increase in the global demand for fresh agricultural products of tropical origin, during all the seasons of the year requires a change in the production model and greater agility in the responses to the consumer market demands of quality, quantity, traceability, and sustainability –that means extensive data-. Having quality information available and updated in real-time on what, how much, how, when, where, at what cost, and the compliance with production quality standards represents the greatest challenge for sustainable and profitable agriculture in the region. The objective of this work is to present a methodological proposal for the collection of georeferenced data from the protected agriculture sector, specifically in production units (UP) with tall structures (Greenhouses), initially for Venezuela, taking the state of Mérida as the geographical framework, and horticultural products as target crops. The document presents some background information and explains the methodology and tools used in the 3 phases of the work: diagnosis, data collection, and analysis. As a result, an evaluation of the process is carried out, relevant data and dashboards are displayed, and the first satellite maps integrated with layers of information in a geographic information system are presented. Finally, some improvement proposals and tentatively recommended applications are added to the process, understanding that their objective is to provide better qualified and traceable georeferenced data for subsequent analysis of the information and more agile and accurate strategic decision making. One of the main points of this study is the lack of quality data treatment in the Latin America area and especially in the Caribbean basin, being one of the most important points how to manage the lack of complete official data. The methodology has been tested with horticultural products, but it can be extended to other tropical crops.

Keywords: greenhouses, protected agriculture, data analysis, geographic information systems, Venezuela

Procedia PDF Downloads 131

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

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

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

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Authors: Renata De Toledo Cintra, Bruno Ramos, Douglas Gouvêa

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There is a huge global concern due to the emission of greenhouse gases, consequent environmental problems, and the increase in the average temperature of the planet, caused mainly by fossil fuels, petroleum derivatives represent a big part. One of the main greenhouse gases, in terms of volume, is CO₂. Recovering a part of this product through chemical reactions that use sunlight as an energy source and even producing renewable fuel (such as ethane, methane, ethanol, among others) is a great opportunity. The process of artificial photosynthesis, through the conversion of CO₂ and H₂O into organic products and oxygen using a metallic oxide catalyst, and incidence of sunlight, is one of the promising solutions. Therefore, this research is of great relevance. To this reaction take place efficiently, an optimized reactor was developed through simulation and prior analysis so that the geometry of the internal channel is an efficient route and allows the reaction to happen, in a controlled and optimized way, in flow continuously and offering the least possible resistance. The design of this reactor prototype can be made in different materials, such as polymers, ceramics and metals, and made through different processes, such as additive manufacturing (3D printer), CNC, among others. To carry out the photocatalysis in the reactors, different types of catalysts will be used, such as ZnO deposited by spray pyrolysis in the lighting window, probably modified ZnO, TiO₂ and modified TiO₂, among others, aiming to increase the production of organic molecules, with the lowest possible energy.

Keywords: artificial photosynthesis, CO₂ reduction, photocatalysis, photoreactor design, 3D printed reactors, solar fuels

Procedia PDF Downloads 86

Authors: Jyotsna Gupta, S. Ghosh, S. Aravindan

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The requirement of artificial prostheses (such as hip and knee joints) has increased with time. Many researchers are working to develop new implants with improved properties such as excellent biocompatibility with no tissue reactions, corrosion resistance in body fluid, high yield strength and low elastic modulus. Further, the morphological properties of the artificial implants should also match with that of the human bone so that cell adhesion, proliferation and transportation of the minerals and nutrition through body fluid can be obtained. Present study attempts to make porous Ti6Al15Mo alloys through powder metallurgy route using space holder technique. The alloy consists of 6wt% of Al which was taken as α phase stabilizer and 15wt% Mo was taken as β phase stabilizer with theoretical density 4.708. Ammonium hydrogen carbonate is used as a space holder in order to generate the porosity. The porosity of these fabricated porous alloys was controlled by adding the 0, 50, 70 vol.% of the space holder content. Three phases were found in the microstructure: α, α_2 and β phase of titanium. Kirkendall pores are observed to be decreased with increase of holding time during sintering and parallelly compressive strength and elastic modulus value increased slightly. Compressive strength and elastic modulus of porous Ti-6Al-15Mo alloy (1.17 g/cm3 density) is found to be suitable for cancellous bone. Released ions from Ti-6Al-15Mo alloy are far below from the permissible limits in human body.

Keywords: bone implant, powder metallurgy, sintering time, Ti-6Al-15Mo

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Authors: Jillian M. Hagel, Joseph E. Tucker, Peter J. Facchini

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With the aim of establishing a holistic approach for the treatment of central nervous system (CNS) disorders, we are pursuing a drug development program rapidly progressing through discovery and characterization phases. The drug candidates identified in this program are referred to as neuroplastogens owing to their ability to mediate neuroplasticity, which can be beneficial to patients suffering from anxiety, depression, or posttraumatic stress disorder. These and other related neuropsychiatric conditions are associated with the onset of neuronal atrophy, which is defined as a reduction in the number and/or productivity of neurons. The stimulation of neuroplasticity results in an increase in the connectivity between neurons and promotes the restoration of healthy brain function. We have synthesized a substantial catalogue of proprietary indolethylamine derivatives based on the general structures of serotonin (5-hydroxytryptamine) and psychedelic molecules such as N,N-dimethyltryptamine (DMT) and psilocin (4-hydroxy-DMT) that function as neuroplastogens. A primary objective in our screening protocol is the identification of derivatives associated with a significant reduction in hallucination, which will allow administration of the drug at a dose that induces neuroplasticity and triggers other efficacious outcomes in the treatment of targeted CNS disorders but which does not cause a psychedelic response in the patient. Both neuroplasticity and hallucination are associated with engagement of the 5HT2A receptor, requiring drug candidates differentially coupled to these two outcomes at a molecular level. We use novel and proprietary artificial intelligence algorithms to predict the mode of binding to the 5HT2A receptor, which has been shown to correlate with the hallucinogenic response. Hallucination is tested using the mouse head-twitch response model, whereas mouse marble-burying and sucrose preference assays are used to evaluate anxiolytic and anti-depressive potential. Neuroplasticity is assays using dendritic outgrowth assays and cell-based ELISA analysis. Pharmacokinetics and additional receptor-binding analyses also contribute the selection of lead candidates. A summary of the program is presented.

Keywords: neuroplastogen, non-hallucinogenic, drug development, anxiety, depression, PTSD, indolethylamine derivatives, psychedelic-inspired, 5-HT2A receptor, computational chemistry, head-twitch response behavioural model, neurite outgrowth assay

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Authors: Supannika Klangphukhiew, Roongnapa Srichana, Rina Patramanon

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Cortisol has been used as a well-known commercial stress biomarker. A homeostasis response to psychological stress is indicated by an increased level of cortisol produced in hypothalamus-pituitary-adrenal (HPA) axis. Chronic psychological stress contributing to the high level of cortisol relates to several health problems. In this study, the cortisol biosensor was fabricated that mimicked the natural receptors. The artificial antibodies were prepared using molecular imprinted polymer technique that can imitate the performance of natural anti-cortisol antibody with high stability. Cortisol-molecular imprinted polymer (cortisol-MIP) was obtained using the multi-step swelling and polymerization protocol with cortisol as a target molecule combining methacrylic acid:acrylamide (2:1) with bisacryloyl-1,2-dihydroxy-1,2-ethylenediamine and ethylenedioxy-N-methylamphetamine as cross-linkers. Cortisol-MIP was integrated to the sensor. It was coated on the disposable screen-printed carbon electrode (SPCE) for portable electrochemical analysis. The physical properties of Cortisol-MIP were characterized by means of electron microscope techniques. The binding characteristics were evaluated via covalent patterns changing in FTIR spectra which were related to voltammetry response. The performance of cortisol-MIP modified SPCE was investigated in terms of detection range, high selectivity with a detection limit of 1.28 ng/ml. The disposable cortisol biosensor represented an application of MIP technique to recognize steroids according to their structures with feasibility and cost-effectiveness that can be developed to use in point-of-care.

Keywords: stress biomarker, cortisol, molecular imprinted polymer, screen-printed carbon electrode

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Authors: Florin G. Borcan, Ramona C. Albulescu, Adela Chirita-Emandi

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pH-responsive drug delivery systems are gaining more importance because these systems deliver the drug at a specific time in regards to pathophysiological necessity, resulting in improved patient therapeutic efficacy and compliance. Polyurethane materials are well-known for industrial applications (elastomers and foams used in different insulations and automotive), but they are versatile biocompatible materials with many applications in medicine, as artificial skin for the premature neonate, membrane in the hybrid artificial pancreas, prosthetic heart valves, etc. This study aimed to obtain the physico-chemical characterization of a drug delivery system based on polyurethane microparticles. The synthesis is based on a polyaddition reaction between an aqueous phase (mixture of polyethylene-glycol M=200, 1,4-butanediol and Tween® 20) and an organic phase (lysin-diisocyanate in acetone) combined with simultaneous emulsification. Different active agents (omeprazole, amoxicillin, metoclopramide) were used to verify the release profile of the macromolecular particles in different pH mediums. Zetasizer measurements were performed using an instrument based on two modules: a Vasco size analyzer and a Wallis Zeta potential analyzer (Cordouan Technol., France) in samples that were kept in various solutions with different pH and the maximum absorbance in UV-Vis spectra were collected on a UVi Line 9,400 Spectrophotometer (SI Analytics, Germany). The results of this investigation have revealed that these particles are proper for a prolonged release in gastric medium where they can assure an almost constant concentration of the active agents for 1-2 weeks, while they can be disassembled faster in a medium with neutral pHs, such as the intestinal fluid.

Keywords: lysin-diisocyanate, nanostructures, polyurethane, Zetasizer

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Authors: M. Gil, R. Montalvo

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Business Intelligence (BI) has become a major source of competitive advantages for firms around the world. BI has been defined as the process of data visualization and reporting for understanding what happened and what is happening. Moreover, BI has been studied for its predictive capabilities in the context of trade and financial transactions. The current literature has identified that BI permits managers to identify market trends, understand customer relations, and predict demand for their products and services. This last capability of BI has been of special concern to academics. Specifically, due to its power to build predictive models adaptable to specific time horizons and geographical regions. However, the current literature of BI focuses on predicting specific markets and industries because the impact of such predictive models was relevant to specific industries or organizations. Currently, the existing literature has not developed a predictive model of BI that takes into consideration the whole economy of a geographical area. This paper seeks to create a predictive model of BI that would show the bigger picture of a geographical area. This paper uses a data set from the Secretary of Economic Development of the state of Jalisco, Mexico. Such data set includes data from all the commercial transactions that occurred in the state in the last years. By analyzing such data set, it will be possible to generate a BI model that predicts supply and demand from specific industries around the state of Jalisco. This research has at least three contributions. Firstly, a methodological contribution to the BI literature by generating the predictive supply and demand model. Secondly, a theoretical contribution to BI current understanding. The model presented in this paper incorporates the whole picture of the economic field instead of focusing on a specific industry. Lastly, a practical contribution might be relevant to local governments that seek to improve their economic performance by implementing BI in their policy planning.

Keywords: business intelligence, predictive model, supply and demand, Mexico

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Authors: Jacob Livingston Slosser, Naja Holten Moller, Thomas Troels Hildebrandt, Henrik Palmer Olsen

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In this paper, we propose a model for human-AI interaction in public administration that involves legal decision-making. Inspired by Alan Turing’s test for machine intelligence, we propose a way of institutionalizing a continuous working relationship between man and machine that aims at ensuring both good legal quality and higher efficiency in decision-making processes in public administration. We also suggest that our model enhances the legitimacy of using AI in public legal decision-making. We suggest that case loads in public administration could be divided between a manual and an automated decision track. The automated decision track will be an algorithmic recommender system trained on former cases. To avoid unwanted feedback loops and biases, part of the case load will be dealt with by both a human case worker and the automated recommender system. In those cases an experienced human case worker will have the role of an evaluator, choosing between the two decisions. This model will ensure that the algorithmic recommender system is not compromising the quality of the legal decision making in the institution. It also enhances the legitimacy of using algorithmic decision support because it provides justification for its use by being seen as superior to human decisions when the algorithmic recommendations are preferred by experienced case workers. The paper outlines in some detail the process through which such a model could be implemented. It also addresses the important issue that legal decision making is subject to legislative and judicial changes and that legal interpretation is context sensitive. Both of these issues requires continuous supervision and adjustments to algorithmic recommender systems when used for legal decision making purposes.

Keywords: administrative law, algorithmic decision-making, decision support, public law

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Authors: Gordon James Knowles

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Historically the Chinatown district in Sandwich Islands has been plagued with the traditional vice crimes of illegal drugs, gambling, and prostitution since the early 1800s. However, a new form of psychologically addictive arcade style table gambling machines has become the dominant form of illegal revenue made in Honolulu, Hawaii. This study attempts to document the drive, desire, or will to play and wager with arcade style video gaming and understand the role of illegal game rooms in facilitating pathological gambling addiction. Indicators of police corruption by Chinese organized crime syndicates related to protection rackets, bribery, and pay-offs were revealed. Information fusion from a police science and sociological intelligence perspective indicates insurgent warfare is being waged on the streets of Honolulu by the People’s Republic of China. This state-sponsored communist terrorism in the Hawaiian Islands used “contactless” irregular warfare entailing: (1) the deployment of psychologically addictive gambling machines, (2) the distribution of the physically addictive fentanyl drug as a lethal chemical weapon, and (3) psychological warfare by circulating pro-China anti-American propaganda newspapers targeted at the small island populace.

Keywords: Chinese and Polynesian organized crime, china daily newspaper, electronic arcade style table games, gaming technology addiction, illegal compulsive gambling, and police intelligence

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Authors: Barakat Adebisi Raji

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In the workplace, Artificial Intelligence has, in recent years, emerged as a transformative technology that revolutionizes how organizations operate and perform tasks. It is a technology that has a significant impact on transportation, manufacturing, education, cyber security, robotics, agriculture, healthcare, and so many other organizations. By harnessing AI technology, workplaces can enhance productivity, streamline processes, and make more informed decisions. Given the potential of AI to change the way we work and its impact on the labor market in years to come, employers understand that it entails legal challenges and risks despite the advantages inherent in it. Therefore, as AI continues to integrate into various aspects of the workplace, understanding the legal and ethical implications becomes paramount. Also central to this study is the question of who is held liable where AI makes any defaults; the person (company) who created the AI, the person who programmed the AI algorithm or the person who uses the AI? Thus, the aim of this paper is to provide a detailed overview of how AI-related liabilities are addressed under each legal tradition and shed light on potential areas of accord and divergence between the two legal cultures. The objectives of this paper are to (i) examine the ability of Common law and Islamic law to accommodate the issues and damage caused by AI in the workplace and the legality of compensation for such injury sustained; (ii) to discuss the extent to which AI can be described as a legal personality to bear responsibility: (iii) examine the similarities and disparities between Common Law and Islamic Jurisprudence on the liability of AI in the workplace. The methodology adopted in this work was qualitative, and the method was purely a doctrinal research method where information is gathered from the primary and secondary sources of law, such as comprehensive materials found in journal articles, expert-authored books and online news sources. Comparative legal method was also used to juxtapose the approach of Islam and Common Law. The paper concludes that since AI, in its current legal state, is not recognized as a legal entity, operators or manufacturers of AI should be held liable for any damage that arises, and the determination of who bears the responsibility should be dependent on the circumstances surrounding each scenario. The study recommends the granting of legal personality to AI systems, the establishment of legal rights and liabilities for AI, the establishment of a holistic Islamic virtue-based AI ethics framework, and the consideration of Islamic ethics.

Keywords: AI, health- care, agriculture, cyber security, common law, Shari'ah

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Authors: Rui Fonseca-Pinto, Emanuel Silva, Rui Rijo, Ricardo Martinho, Bruno Carreira

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The sedentary lifestyle is associated to the development of chronic noncommunicable diseases (obesity, hypertension, Diabetes Mellitus Type 2) and the World Health Organization, given the evidence that physical activity is determinant for individual and collective health, defined the Physical Activity Level (PAL) as a vital signal. Strategies for increasing the practice of physical activity in all age groups have emerged from the various social organizations (municipalities, universities, health organizations, companies, social groups) by increasingly developing innovative strategies to promote motivation strategies and conditions to the practice of physical activity. The adaptation of cities to the new paradigms of sustainable mobility has provided the adaptation of urban training circles and mobilized citizens to combat sedentarism. This adaptation has accompanied the technological evolution and makes possible the use of mobile technology to monitor outdoor training programs and also, through the network connection (IoT), use the training data to make personalized recommendations. This work presents a physical activity counseling platform to be used in the physical maintenance circuits of urban centers, the MOVIDA.polis. The platform consists of a back office for the management of circuits and training stations, and for a mobile application for monitoring the user performance during workouts. Using a QRcode, each training station is recognized by the App and based on the individual performance records (effort perception, heart rate variation) artificial intelligence algorithms are used to make a new personalized recommendation. The results presented in this work were obtained during the proof of concept phase, which was carried out in the PolisLeiria training circuit in the city of Leiria (Portugal). It was possible to verify the increase in adherence to the practice of physical activity, as well as to decrease the interval between training days. Moreover, the AI-based recommendation acts as a partner in the training and an additional challenging factor. The platform is ready to be used by other municipalities in order to reduce the levels of sedentarism and approach the weekly goal of 150 minutes of moderate physical activity. Acknowledgments: This work was supported by Fundação para a Ciência e Tecnologia FCT- Portugal and CENTRO2020 under the scope of MOVIDA project: 02/SAICT/2016 – 23878.

Keywords: physical activity, mHealth, urban training circuits, health promotion

Procedia PDF Downloads 172

Authors: M. Moreno, M. Alique, D. Otero, C. Delgado, P. Lacharmoise, L. Gracia, L. Pires, A. Moya

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Marine environments are increasingly threatened by heavy metal contamination, including mercury (Hg), lead (Pb), and cadmium (Cd), posing significant risks to ecosystems and human health. Traditional monitoring techniques often fail to provide the spatial and temporal resolution needed for real-time detection of these contaminants, especially in remote or harsh environments. SEAWIZARD addresses these challenges by leveraging the flexibility, adaptability, and cost-effectiveness of printed electronics, with the integration of microfluidics to develop a compact, portable, and reusable sensor platform designed specifically for real-time monitoring of heavy metal ions in seawater. The SEAWIZARD sensor is a multiparametric Lab-on-Chip (LoC) device, a miniaturized system that integrates several laboratory functions into a single chip, drastically reducing sample volumes and improving adaptability. This platform integrates three printed graphene electrodes for the simultaneous detection of Hg, Cd and Pb via square wave voltammetry. These electrodes share the reference and the counter electrodes to improve space efficiency. Additionally, it integrates printed pH and temperature sensors to correct environmental interferences that may impact the accuracy of metal detection. The pH sensor is based on a carbon electrode with iridium oxide electrodeposited while the temperature sensor is graphene based. A protective dielectric layer is printed on top of the sensor to safeguard it in harsh marine conditions. The use of flexible polyethylene terephthalate (PET) as the substrate enables the sensor to conform to various surfaces and operate in challenging environments. One of the key innovations of SEAWIZARD is its integrated microfluidic layer, fabricated from cyclic olefin copolymer (COC). This microfluidic component allows a controlled flow of seawater over the sensing area, allowing for significant improved detection limits compared to direct water sampling. The system’s dual-channel design separates the detection of heavy metals from the measurement of pH and temperature, ensuring that each parameter is measured under optimal conditions. In addition, the temperature sensor is finely tuned with a serpentine-shaped microfluidic channel to ensure precise thermal measurements. SEAWIZARD also incorporates custom electronics that allow for wireless data transmission via Bluetooth, facilitating rapid data collection and user interface integration. Embedded artificial intelligence further enhances the platform by providing an automated alarm system, capable of detecting predefined metal concentration thresholds and issuing warnings when limits are exceeded. This predictive feature enables early warnings of potential environmental disasters, such as industrial spills or toxic levels of heavy metal pollutants, making SEAWIZARD not just a detection tool, but a comprehensive monitoring and early intervention system. In conclusion, SEAWIZARD represents a significant advancement in printed electronics applied to environmental sensing. By combining flexible, low-cost materials with advanced microfluidics, custom electronics, and AI-driven intelligence, SEAWIZARD offers a highly adaptable and scalable solution for real-time, high-resolution monitoring of heavy metals in marine environments. Its compact and portable design makes it an accessible, user-friendly tool with the potential to transform water quality monitoring practices and provide critical data to protect marine ecosystems from contamination-related risks.

Keywords: lab-on-chip, printed electronics, real-time monitoring, microfluidics, heavy metal contamination

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Authors: Kwok W. Mui, Ling T. Wong, Chi T. Cheung

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Cooling energy consumption in the residential sector, different from shopping mall, office or commercial buildings, is significantly subject to occupant decisions where in-depth investigations are found limited. It shows that energy consumptions could be associated with housing types. Surveys have been conducted in existing Hong Kong public housings to understand the housing characteristics, apartment electricity demands, occupant’s thermal expectations, and air–conditioning usage patterns for further cooling energy-saving assessments. The aim of this study is to develop a hybrid cooling energy prediction model, which integrated by EnergyPlus (EP) and artificial neural network (ANN) to estimate cooling energy consumption in public residential sector. Sensitivity tests are conducted to find out the energy impacts with changing building parameters regarding to external wall and window material selection, window size reduction, shading extension, building orientation and apartment size control respectively. Assessments are performed to investigate the relationships between cooling demands and occupant behavior on thermal environment criteria and air-conditioning operation patterns. The results are summarized into a cooling energy calculator for layman use to enhance the cooling energy saving awareness in their own living environment. The findings can be used as a directory framework for future cooling energy evaluation in residential buildings, especially focus on the occupant behavioral air–conditioning operation and criteria of energy-saving incentives.

Keywords: artificial neural network, cooling energy, occupant behavior, residential buildings, thermal environment

Procedia PDF Downloads 168

Authors: Chairussyuhur Arman, Totti Tjiptosumirat, Muhammad Gunawan, Mastur, Joko Priyono, Baiq Tri Ratna Erawati

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The present study was undertaken to synchronize oestrus of bali cattle and artificially inseminated with frozen-thawed sexed-semen. The experiment was carried out at District AI Station. Four pluriparous cows and four nulliparous heifers were used in this study and they were housed in free stall barns. The heifers fed with corn silage supplemented with UMMB, while the cows fed with green fodder. All animals were given 500 mg cloprostenolum i.m. injections PGF2α twice, 11 days apart, to synchronize the occurrence of estrus. Estrus was detected by visual observation twice a day and determined if all cattle accepted mount from other females. All animals were inseminated twice with Bali sexed-semen at 72 and 76 h after observed oestrus. Results suggested that the percentage of calving rate either for pluriparous cows or nulliparous heifers were recorded to be 75 percent. One cow and one heifer did not produce calves because of embryonic lost. Regardless the sex of calves, the mean of birth weight of calves in cows was higher than that of heifers (18.50 ± 2.60 kg vs 13.83 ± 5.20 kg). One female calf from heifer with lower birth weight (8.0 kg) was dead one day after born. In pluriparous group, two cows delivered male calves and the other delivered female calf. Conversely in nulliparous group, two heifers delivered female calves and the other male calf. It is concluded that under the conditions of this preliminary trials, the sex ratio between pluriparous and nulliparous groups was found to be 50:50 (male:female).

Keywords: artificial insemination, bali cattle, calves, sexed sperm

Procedia PDF Downloads 311

Authors: Jinming Du

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With the COVID-19 pandemic beginning in 2020, many higher education institutions and education systems are turning to hybrid or fully distance online courses to maintain social distance and provide a safe virtual space for learning and teaching. However, the majority of faculty members were not well prepared for the shift to blended or distance learning. Communication frustrations are prevalent in both hybrid and full-distance courses. A systematic literature review was conducted by a comprehensive analysis of 1688 publications that focused on the application of the adoption of chatbots in education. This study aimed to explore instructors' experiences with chatbots in online and blended undergraduate English courses. Language learners are overwhelmed by the variety of information offered by many online sites. The recently emerged chatbots (e.g.: ChatGPT) are slightly superior in performance as compared to those traditional through previous technologies such as tapes, video recorders, and websites. The field of chatbots has been intensively researched, and new methods have been developed to demonstrate how students can best learn and practice a new language in the target language. However, it is believed that among the many areas where chatbots are applied, while chatbots have been used as effective tools for communicating with business customers, in consulting and targeting areas, and in the medical field, chatbots have not yet been fully explored and implemented in the field of language education. This issue is challenging enough for language teachers; they need to study and conduct research carefully to clarify it. Pedagogical chatbots may alleviate the perception of a lack of communication and feedback from instructors by interacting naturally with students through scaffolding the understanding of those learners, much like educators do. However, educators and instructors lack the proficiency to effectively operate this emerging AI chatbot technology and require comprehensive study or structured training to attain competence. There is a gap between language teachers’ perceptions and recent advances in the application of AI chatbots to language learning. The results of the study found that although the teachers felt that the chatbots did the best job of giving feedback, the teachers needed additional training to be able to give better instructions and to help them assist in teaching. Teachers generally perceive the utilization of chatbots to offer substantial assistance to English language instruction.

Keywords: artificial intelligence in education, chatbots, education and technology, education system, pedagogical chatbot, chatbots and language education

Procedia PDF Downloads 66

Authors: Muna Khushaim

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Aluminum-based alloys play a key role in modern engineering, especially in the aerospace industry. Introduction of solute atoms such as Li and Cu is the main approach to improve the strength in age-hardenable Al alloys via the precipitation hardening phenomenon. Knowledge of the decomposition process of the microstructure during the precipitation reaction is particularly important for future technical developments. The objective of this study is to investigate the nano-scale chemical composition in the Al-Cu, Al-Li and Al-Li-Cu during the early stage of the precipitation sequence and to describe whether this compositional difference correlates with variations in the observed precipitation kinetics. Comparing the random binomial frequency distribution and the experimental frequency distribution of concentrations in atom probe tomography data was used to investigate the early stage of decomposition in the different binary and ternary alloys which were experienced different heat treatments. The results show that an Al-1.7 at.% Cu alloy requires a long ageing time of approximately 8 h at 160 °C to allow the diffusion of Cu atoms into Al matrix. For the Al-8.2 at.% Li alloy, a combination of both the natural ageing condition (48 h at room temperature) and a short artificial ageing condition (5 min at 160 °C) induces increasing on the number density of the Li clusters and hence increase number of precipitated δ' particles. Applying this combination of natural ageing and short artificial ageing conditions onto the ternary Al-4 at.% Li-1.7 at.% Cu alloy induces the formation of a Cu-rich phase. Increasing the Li content in the ternary alloy up to 8 at.% and increasing the ageing time to 30 min resulted in the precipitation processes ending with δ' particles. Thus, the results contribute to the understanding of Al-alloy design.

Keywords: aluminum alloy, atom probe tomography, early stage, decomposition

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Authors: Younis R. Elhaddad, Mohamed A. Alshaari

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Since 2700 B.C the problem of constructing magic squares attracts many researchers. Magic squares one of most difficult challenges for mathematicians. In this work, we describe how to construct and enumerate Pan- magic squares using genetic algorithm, using new chromosome encoding technique. The results were promising within reasonable time.

Keywords: genetic algorithm, magic square, pan-magic square, computational intelligence

Procedia PDF Downloads 576

Authors: Housein Deli, Loui Al-Shrouf, Hammoud Al Joumaa, Mohieddine Jelali

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When forming metallic materials, fluctuations in material properties, process conditions, and wear lead to deviations in the component geometry. Several hundred features sometimes need to be measured, especially in the case of functional and safety-relevant components. These can only be measured offline due to the large number of features and the accuracy requirements. The risk of producing components outside the tolerances is minimized but not eliminated by the statistical evaluation of process capability and control measurements. The inspection intervals are based on the acceptable risk and are at the expense of productivity but remain reactive and, in some cases, considerably delayed. Due to the considerable progress made in the field of condition monitoring and measurement technology, permanently installed sensor systems in combination with machine learning and artificial intelligence, in particular, offer the potential to independently derive forecasts for component geometry and thus eliminate the risk of defective products - actively and preventively. The reliability of forecasts depends on the quality, completeness, and timeliness of the data. Measuring all geometric characteristics is neither sensible nor technically possible. This paper, therefore, uses the example of car seat rail production to discuss the necessary first step of feature selection and reduction by correlation analysis, as otherwise, it would not be possible to forecast components in real-time and inline. Four different car seat rails with an average of 130 features were selected and measured using a coordinate measuring machine (CMM). The run of such measuring programs alone takes up to 20 minutes. In practice, this results in the risk of faulty production of at least 2000 components that have to be sorted or scrapped if the measurement results are negative. Over a period of 2 months, all measurement data (> 200 measurements/ variant) was collected and evaluated using correlation analysis. As part of this study, the number of characteristics to be measured for all 6 car seat rail variants was reduced by over 80%. Specifically, direct correlations for almost 100 characteristics were proven for an average of 125 characteristics for 4 different products. A further 10 features correlate via indirect relationships so that the number of features required for a prediction could be reduced to less than 20. A correlation factor >0.8 was assumed for all correlations.

Keywords: long-term SHM, condition monitoring, machine learning, correlation analysis, component prediction, wear prediction, regressions analysis

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Authors: Sirilak Areerachakul, Nat Ployong, Supayothin Na Songkla

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This research’s objective is to select the model with most accurate value by using Neural Network Technique as a way to filter potential students who enroll in IT course by electronic learning at Suan Suanadha Rajabhat University. It is designed to help students selecting the appropriate courses by themselves. The result showed that the most accurate model was 100 Folds Cross-validation which had 73.58% points of accuracy.

Keywords: artificial neural network, classification, students, e-learning

Procedia PDF Downloads 426