Search results for: motor intelligence

Authors: Edgar Santoyo, Daniel Perez-Zarate, Agustin Acevedo, Lorena Diaz-Gonzalez, Mirna Guevara

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Four new gas geothermometers have been derived from a multivariate geo chemometric analysis of a geothermal fluid chemistry database, two of which use the natural logarithm of CO₂ and H2S concentrations (mmol/mol), respectively, and the other two use the natural logarithm of the H₂S/H₂ and CO₂/H₂ ratios. As a strict compilation criterion, the database was created with gas-phase composition of fluids and bottomhole temperatures (BHTM) measured in producing wells. The calibration of the geothermometers was based on the geochemical relationship existing between the gas-phase composition of well discharges and the equilibrium temperatures measured at bottomhole conditions. Multivariate statistical analysis together with the use of artificial neural networks (ANN) was successfully applied for correlating the gas-phase compositions and the BHTM. The predicted or simulated bottomhole temperatures (BHTANN), defined as output neurons or simulation targets, were statistically compared with measured temperatures (BHTM). The coefficients of the new geothermometers were obtained from an optimized self-adjusting training algorithm applied to approximately 2,080 ANN architectures with 15,000 simulation iterations each one. The self-adjusting training algorithm used the well-known Levenberg-Marquardt model, which was used to calculate: (i) the number of neurons of the hidden layer; (ii) the training factor and the training patterns of the ANN; (iii) the linear correlation coefficient, R; (iv) the synaptic weighting coefficients; and (v) the statistical parameter, Root Mean Squared Error (RMSE) to evaluate the prediction performance between the BHTM and the simulated BHTANN. The prediction performance of the new gas geothermometers together with those predictions inferred from sixteen well-known gas geothermometers (previously developed) was statistically evaluated by using an external database for avoiding a bias problem. Statistical evaluation was performed through the analysis of the lowest RMSE values computed among the predictions of all the gas geothermometers. The new gas geothermometers developed in this work have been successfully used for predicting subsurface temperatures in high-temperature geothermal systems of Mexico (e.g., Los Azufres, Mich., Los Humeros, Pue., and Cerro Prieto, B.C.) as well as in a blind geothermal system (known as Acoculco, Puebla). The last results of the gas geothermometers (inferred from gas-phase compositions of soil-gas bubble emissions) compare well with the temperature measured in two wells of the blind geothermal system of Acoculco, Puebla (México). Details of this new development are outlined in the present research work. Acknowledgements: The authors acknowledge the funding received from CeMIE-Geo P09 project (SENER-CONACyT).

Keywords: artificial intelligence, gas geochemistry, geochemometrics, geothermal energy

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Authors: Tsega Asresa, Getahun Tigistu, Melaku Bayih

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Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively.

Keywords: aromatic medicinal plant, computer vision, convolutional neural network, deep learning, plant classification, residual neural network

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Authors: John Hardy

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The domestic security environment in Europe has changed dramatically in recent years. Since January 2015, a significant number of domestic security threats that emerged in Europe were located in Belgium, France and the United Kingdom. While some threats were detected in the planning phase, many also resulted in terrorist attacks. Authorities in all three countries instituted special or emergency measures to provide additional security to their populations. Each country combined an additional policing presence with a specific military operation to contribute to a comprehensive security response to domestic threats. This study presents a cross-case analysis of three countries’ civilian and military responses to domestic security threats in Europe. Each case study features a unique approach to combining civilian and military capabilities in similar domestic security operations during the same time period and threat environment. The research design focuses on five variables relevant to the relationship between civilian and military roles in each security response. These are the distinction between policing and military roles, the legal framework for the domestic deployment of military forces, prior experience in civil-military coordination, the institutional framework for threat assessments, and the level of public support for the domestic use of military forces. These variables examine the influence of domestic social, political, and legal factors on the design of combined civil-military operations in response to domestic security threats. Each case study focuses on a specific operation: Operation Vigilant Guard in Belgium, Operation Sentinel in France, and Operation Temperer in the United Kingdom. The results demonstrate that the level of distinction between policing and military roles and the existence of a clear and robust legal framework for the domestic use force by military personnel significantly influence the design and implementation of civilian and military roles in domestic security operations. The findings of this study indicate that Belgium, France and the United Kingdom experienced different design and implementation challenges for their domestic security operations. Belgium and France initially had less-developed legal frameworks for deploying the military in domestic security operations than the United Kingdom. This was offset by public support for enacting emergency measures and the strength of existing civil-military coordination mechanisms. The United Kingdom had a well-developed legal framework for integrating civilian and military capabilities in domestic security operations. However, its experiences in Ireland also made the government more sensitive to public perceptions regarding the domestic deployment of military forces.

Keywords: counter-terrorism, democracy, homeland security, intelligence, militarization, policing

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Authors: Martin Pellon Consunji

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Due to the rise in popularity of Bitcoin and other crypto assets as a store of wealth and speculative investment, there is an ever-growing demand for automated trading tools, such as bots, in order to gain an advantage over the market. Traditionally, trading in the stock market was done by professionals with years of training who understood patterns and exploited market opportunities in order to gain a profit. However, nowadays a larger portion of market participants are at minimum aided by market-data processing bots, which can generally generate more stable signals than the average human trader. The rise in trading bot usage can be accredited to the inherent advantages that bots have over humans in terms of processing large amounts of data, lack of emotions of fear or greed, and predicting market prices using past data and artificial intelligence, hence a growing number of approaches have been brought forward to tackle this task. However, the general limitation of these approaches can still be broken down to the fact that limited historical data doesn’t always determine the future, and that a lot of market participants are still human emotion-driven traders. Moreover, developing markets such as those of the cryptocurrency space have even less historical data to interpret than most other well-established markets. Due to this, some human traders have gone back to the tried-and-tested traditional technical analysis tools for exploiting market patterns and simplifying the broader spectrum of data that is involved in making market predictions. This paper proposes a method which uses neuro evolution techniques on both sentimental data and, the more traditionally human-consumed, technical analysis data in order to gain a more accurate forecast of future market behavior and account for the way both automated bots and human traders affect the market prices of Bitcoin and other cryptocurrencies. This study’s approach uses evolutionary algorithms to automatically develop increasingly improved populations of bots which, by using the latest inflows of market analysis and sentimental data, evolve to efficiently predict future market price movements. The effectiveness of the approach is validated by testing the system in a simulated historical trading scenario, a real Bitcoin market live trading scenario, and testing its robustness in other cryptocurrency and stock market scenarios. Experimental results during a 30-day period show that this method outperformed the buy and hold strategy by over 260% in terms of net profits, even when taking into consideration standard trading fees.

Keywords: neuro-evolution, Bitcoin, trading bots, artificial neural networks, technical analysis, evolutionary algorithms

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Authors: Anup Anand Deshmukh, Catherine Soladie, Renaud Seguier

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Emotion plays a key role in many applications like healthcare, to gather patients’ emotional behavior. Unlike typical ASR (Automated Speech Recognition) problems which focus on 'what was said', it is equally important to understand 'how it was said.' There are certain emotions which are given more importance due to their effectiveness in understanding human feelings. In this paper, we propose an approach that models human stress from audio signals. The research challenge in speech emotion detection is finding the appropriate set of acoustic features corresponding to an emotion. Another difficulty lies in defining the very meaning of emotion and being able to categorize it in a precise manner. Supervised Machine Learning models, including state of the art Deep Learning classification methods, rely on the availability of clean and labelled data. One of the problems in affective computation is the limited amount of annotated data. The existing labelled emotions datasets are highly subjective to the perception of the annotator. We address the first issue of feature selection by exploiting the use of traditional MFCC (Mel-Frequency Cepstral Coefficients) features in Convolutional Neural Network. Our proposed Emo-CNN (Emotion-CNN) architecture treats speech representations in a manner similar to how CNN’s treat images in a vision problem. Our experiments show that Emo-CNN consistently and significantly outperforms the popular existing methods over multiple datasets. It achieves 90.2% categorical accuracy on the Emo-DB dataset. We claim that Emo-CNN is robust to speaker variations and environmental distortions. The proposed approach achieves 85.5% speaker-dependant categorical accuracy for SAVEE (Surrey Audio-Visual Expressed Emotion) dataset, beating the existing CNN based approach by 10.2%. To tackle the second problem of subjectivity in stress labels, we use Lovheim’s cube, which is a 3-dimensional projection of emotions. Monoamine neurotransmitters are a type of chemical messengers in the brain that transmits signals on perceiving emotions. The cube aims at explaining the relationship between these neurotransmitters and the positions of emotions in 3D space. The learnt emotion representations from the Emo-CNN are mapped to the cube using three component PCA (Principal Component Analysis) which is then used to model human stress. This proposed approach not only circumvents the need for labelled stress data but also complies with the psychological theory of emotions given by Lovheim’s cube. We believe that this work is the first step towards creating a connection between Artificial Intelligence and the chemistry of human emotions.

Keywords: deep learning, brain chemistry, emotion perception, Lovheim's cube

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Authors: Maryam Mohammadi, Mohammadjavad Mahdavinejad, Mojtaba Ansari

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The main driving force for increasing movement towards the design of High-Performance Buildings (HPB) are building codes and rating systems that address the various components of the building and their impact on the environment and energy conservation through various methods like prescriptive methods or simulation-based approaches. The methods and tools developed to meet these needs, which are often based on building performance simulation tools (BPST), have limitations in terms of compatibility with the integrated design process (IDP) and HPB design, as well as use by architects in the early stages of design (when the most important decisions are made). To overcome these limitations in recent years, efforts have been made to develop Design Decision Support Systems, which are often based on artificial intelligence. Numerous needs and steps for designing and developing a Decision Support System (DSS), which complies with the early stages of energy-efficient architecture design -consisting of combinations of different methods in an integrated package- have been listed in the literature. While various review studies have been conducted in connection with each of these techniques (such as optimizations, sensitivity and uncertainty analysis, etc.) and their integration of them with specific targets; this article is a critical and holistic review of the researches which leads to the development of applicable systems or introduction of a comprehensive framework for developing models complies with the IDP. Information resources such as Science Direct and Google Scholar are searched using specific keywords and the results are divided into two main categories: Simulation-based DSSs and Meta-simulation-based DSSs. The strengths and limitations of different models are highlighted, two general conceptual models are introduced for each category and the degree of compliance of these models with the IDP Framework is discussed. The research shows movement towards Multi-Level of Development (MOD) models, well combined with early stages of integrated design (schematic design stage and design development stage), which are heuristic, hybrid and Meta-simulation-based, relies on Big-real Data (like Building Energy Management Systems Data or Web data). Obtaining, using and combining of these data with simulation data to create models with higher uncertainty, more dynamic and more sensitive to context and culture models, as well as models that can generate economy-energy-efficient design scenarios using local data (to be more harmonized with circular economy principles), are important research areas in this field. The results of this study are a roadmap for researchers and developers of these tools.

Keywords: integrated design process, design decision support system, meta-simulation based, early stage, big data, energy efficiency

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Authors: Henda Gnakate Biba, Ndassa Mouafon Issa

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Dictionaries modeled on the Western model [tonic accent languages] are not suitable and do not account for tonal languages phonologically, which is why the [prosodic and phonological] ethnographic dictionary was designed. It is a glossary that expresses the tones and the rhythm of words. It recreates exactly the speaking or singing of a tonal language, and allows the non-speaker of this language to pronounce the words as if they were a native. It is a dictionary adapted to tonal languages. It was built from ethnomusicological theorems and phonological processes, according to Jean. J. Rousseau 1776 hypothesis /To say and to sing were once the same thing/. Each word in the French dictionary finds its corresponding language, ekaη. And each word ekaη is written on a musical staff. This ethnographic dictionary is also an inventive, original and innovative research thesis, but it is also an inventive, original and innovative research thesis. A contribution to the theoretical, musicological, ethno musicological and linguistic conceptualization of languages, giving rise to the practice of interlocution between the social and cognitive sciences, the activities of artistic creation and the question of modeling in the human sciences: mathematics, computer science, translation automation and artificial intelligence. When you apply this theory to any text of a folksong of a world-tone language, you do not only piece together the exact melody, rhythm, and harmonies of that song as if you knew it in advance but also the exact speaking of this language. The author believes that the issue of the disappearance of tonal languages and their preservation has been structurally resolved, as well as one of the greatest cultural equations related to the composition and creation of tonal, polytonal and random music. The experimentation confirming the theorization designed a semi-digital, semi-analog application which translates the tonal languages of Africa (about 2,100 languages) into blues, jazz, world music, polyphonic music, tonal and anatonal music and deterministic and random music). To test this application, I use a music reading and writing software that allows me to collect the data extracted from my mother tongue, which is already modeled in the musical staves saved in the ethnographic (semiotic) dictionary for automatic translation ( volume 2 of the book). Translation is done (from writing to writing, from writing to speech and from writing to music). Mode of operation: you type a text on your computer, a structured song (chorus-verse), and you command the machine a melody of blues, jazz and, world music or, variety etc. The software runs, giving you the option to choose harmonies, and then you select your melody.

Keywords: music, language, entenglement, science, research

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Authors: Evgeny Pokushalov, Andrey Ponomarenko, John Smith, Michael Johnson, Claire Garcia, Inessa Pak, Evgenya Shrainer, Dmitry Kudlay, Sevda Bayramova, Richard Miller

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Background: Obesity is a complex, multifactorial chronic disease that poses significant health risks. Recent advancements in artificial intelligence (AI) offer the potential for more personalized and effective dietary supplement (DS) regimens to promote weight loss. This study aimed to evaluate the efficacy of AI-guided DS prescriptions compared to standard physician-guided DS prescriptions in obese patients. Methods: This randomized, parallel-group pilot study enrolled 60 individuals aged 40 to 60 years with a body mass index (BMI) of 25 or greater. Participants were randomized to receive either AI-guided DS prescriptions (n = 30) or physician-guided DS prescriptions (n = 30) for 180 days. The primary endpoints were the percentage change in body weight and the proportion of participants achieving a ≥5% weight reduction. Secondary endpoints included changes in BMI, fat mass, visceral fat rating, systolic and diastolic blood pressure, lipid profiles, fasting plasma glucose, hsCRP levels, and postprandial appetite ratings. Adverse events were monitored throughout the study. Results: Both groups were well balanced in terms of baseline characteristics. Significant weight loss was observed in the AI-guided group, with a mean reduction of -12.3% (95% CI: -13.1 to -11.5%) compared to -7.2% (95% CI: -8.1 to -6.3%) in the physician-guided group, resulting in a treatment difference of -5.1% (95% CI: -6.4 to -3.8%; p < 0.01). At day 180, 84.7% of the AI-guided group achieved a weight reduction of ≥5%, compared to 54.5% in the physician-guided group (Odds Ratio: 4.3; 95% CI: 3.1 to 5.9; p < 0.01). Significant improvements were also observed in BMI, fat mass, and visceral fat rating in the AI-guided group (p < 0.01 for all). Postprandial appetite suppression was greater in the AI-guided group, with significant reductions in hunger and prospective food consumption, and increases in fullness and satiety (p < 0.01 for all). Adverse events were generally mild-to-moderate, with higher incidences of gastrointestinal symptoms in the AI-guided group, but these were manageable and did not impact adherence. Conclusion: The AI-guided dietary supplement regimen was more effective in promoting weight loss, improving body composition, and suppressing appetite compared to the physician-guided regimen. These findings suggest that AI-guided, personalized supplement prescriptions could offer a more effective approach to managing obesity. Further research with larger sample sizes is warranted to confirm these results and optimize AI-based interventions for weight loss.

Keywords: obesity, AI-guided, dietary supplements, weight loss, personalized medicine, metabolic health, appetite suppression

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Authors: Majid Alenezi, Abdelbare Algamode, Amy Hayes, Gavin Lawrence, Nichola Callow

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The purpose of this study was to translate and culturally adapt the Vividness of Movement Imagery Questionnaire-2 (VMIQ-2) from English to produce a new Arabic version (VMIQ-2A), and to evaluate the reliability and validity of the translated questionnaire. The questionnaire assesses how vividly and clearly individuals are able to imagine themselves performing everyday actions. Its purpose is to measure individuals’ ability to conduct movement imagery, which can be defined as “the cognitive rehearsal of a task in the absence of overt physical movement.” Movement imagery has been introduced in physiotherapy as a promising intervention technique, especially when physical exercise is not possible (e.g. pain, immobilisation.) Considerable evidence indicates movement imagery interventions improve physical function, but to maximize efficacy it is important to know the imagery abilities of the individuals being treated. Given the increase in the global sharing of knowledge it is desirable to use standard measures of imagery ability across language and cultures, thus motivating this project. The translation procedure followed guidelines from the Translation and Cultural Adaptation group of the International Society for Pharmacoeconomics and Outcomes Research and involved the following phases: Preparation; the original VMIQ-2 was adapted slightly to provide additional information and simplified grammar. Forward translation; three native speakers resident in Saudi Arabia translated the original VMIQ-2 from English to Arabic, following instruction to preserve meaning (not literal translation), and cultural relevance. Reconciliation; the project manager (first author), the primary translator and a physiotherapist reviewed the three independent translations to produce a reconciled first Arabic draft of VMIQ-2A. Backward translation; a fourth translator (native Arabic speaker fluent in English) translated literally the reconciled first Arabic draft to English. The project manager and two study authors compared the English back translation to the original VMIQ-2 and produced the second Arabic draft. Cognitive debriefing; to assess participants’ understanding of the second Arabic draft, 7 native Arabic speakers resident in the UK completed the questionnaire, and rated the clearness of the questions, specified difficult words or passages, and wrote in their own words their understanding of key terms. Following review of this feedback, a final Arabic version was created. 142 native Arabic speakers completed the questionnaire in community meeting places or at home; a subset of 44 participants completed the questionnaire a second time 1 week later. Results showed the translated questionnaire to be valid and reliable. Correlation coefficients indicated good test-retest reliability. Cronbach’s a indicated high internal consistency. Construct validity was tested in two ways. Imagery ability scores have been found to be invariant across gender; this result was replicated within the current study, assessed by independent-samples t-test. Additionally, experienced sports participants have higher imagery ability than those less experienced; this result was also replicated within the current study, assessed by analysis of variance, supporting construct validity. Results provide preliminary evidence that the VMIQ-2A is reliable and valid to be used with a general population who are native Arabic speakers. Future research will include validation of the VMIQ-2A in a larger sample, and testing validity in specific patient populations.

Keywords: motor imagery, physiotherapy, translation and validation, imagery ability

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Authors: George Hanna Abdelmelek Henien

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Quality and safety issues are common in Ethiopia's food processing industry, which can negatively impact consumers' health and livelihoods. The country is known for its various agricultural products that are important to the economy. However, food quality and safety policies and management practices in the food processing industry have led to many health problems, foodborne illnesses and economic losses. This article aims to show the causes and consequences of food safety and quality problems in the food processing industry in Ethiopia and discuss possible solutions to solve them. One of the main reasons for food quality and safety in Ethiopia's food processing industry is the lack of adequate regulation and enforcement mechanisms. Inadequate food safety and quality policies have led to inefficiencies in food production. Additionally, the failure to monitor and enforce existing regulations has created a good opportunity for unscrupulous companies to engage in harmful practices that endanger the lives of citizens. The impact on food quality and safety is significant due to loss of life, high medical costs, and loss of consumer confidence in the food processing industry. Foodborne diseases such as diarrhoea, typhoid and cholera are common in Ethiopia, and food quality and safety play an important role in . Additionally, food recalls due to contamination or contamination often cause significant economic losses in the food processing industry. To solve these problems, the Ethiopian government began taking measures to improve food quality and safety in the food processing industry. One of the most prominent initiatives is the Ethiopian Food and Drug Administration (EFDA), which was established in 2010 to monitor and control the quality and safety of food and beverage products in the country. EFDA has implemented many measures to improve food safety, such as carrying out routine inspections, monitoring the import of food products and implementing labeling requirements. Another solution that can improve food quality and safety in the food processing industry in Ethiopia is the implementation of food safety management system (FSMS). FSMS is a set of procedures and policies designed to identify, assess and control food safety risks during food processing. Implementing a FSMS can help companies in the food processing industry identify and address potential risks before they harm consumers. Additionally, implementing an FSMS can help companies comply with current safety and security regulations. Consequently, improving food safety policy and management system in Ethiopia's food processing industry is important to protect people's health and improve the country's economy. . Addressing the root causes of food quality and safety and implementing practical solutions that can help improve the overall food safety and quality in the country, such as establishing regulatory bodies and implementing food management systems.

Keywords: food quality, food safety, policy, management system, food processing industry food traceability, industry 4.0, internet of things, block chain, best worst method, marcos

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Authors: Vlad Andrei Raducanu, Mariana Lucia Angelescu, Ion Cinca, Vasile Danut Cojocaru, Doina Raducanu

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By using smart digital tools, such as generative design (GD) and digital fabrication (DF), problems of high actuality concerning resources optimization (materials, energy, time) can be solved and applications or products of free-form type can be created. In the new digital technology materials are active, designed in response to a set of performance requirements, which impose a total rethinking of old material practices. The article presents the design procedure key steps of a free-form architectural object - a column type one with connections to get an adaptive 3D surface, by using the parametric design methodology and by exploiting the properties of conventional metallic materials. In parametric design the form of the created object or space is shaped by varying the parameters values and relationships between the forms are described by mathematical equations. Digital parametric design is based on specific procedures, as shape grammars, Lindenmayer - systems, cellular automata, genetic algorithms or swarm intelligence, each of these procedures having limitations which make them applicable only in certain cases. In the paper the design process stages and the shape grammar type algorithm are presented. The generative design process relies on two basic principles: the modeling principle and the generative principle. The generative method is based on a form finding process, by creating many 3D spatial forms, using an algorithm conceived in order to apply its generating logic onto different input geometry. Once the algorithm is realized, it can be applied repeatedly to generate the geometry for a number of different input surfaces. The generated configurations are then analyzed through a technical or aesthetic selection criterion and finally the optimal solution is selected. Endless range of generative capacity of codes and algorithms used in digital design offers various conceptual possibilities and optimal solutions for both technical and environmental increasing demands of building industry and architecture. Constructions or spaces generated by parametric design can be specifically tuned, in order to meet certain technical or aesthetical requirements. The proposed approach has direct applicability in sustainable architecture, offering important potential economic advantages, a flexible design (which can be changed until the end of the design process) and unique geometric models of high performance.

Keywords: parametric design, algorithmic procedures, free-form architectural object, sustainable architecture

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Authors: Viktorya Sargsyan, Arax Hovhannesyan, Karine Abelyan

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Introduction: During the last decade, scientific studies have proven the importance of Early Childhood Development (ECD) interventions. These interventions are shown to create strong foundations for children’s intellectual, emotional and physical well-being, as well as the impact they have on learning and economic outcomes for children as they mature into adulthood. Many children in rural Armenia fail to reach their full development potential due to lack of early brain stimulation (playing, singing, reading, etc.) from their parents, and lack of community tools and services to follow-up children’s neurocognitive development. This is exacerbated by high rates of stunting and anemia among children under 3(CU3). This research study tested the effectiveness of an integrated ECD and Maternal, Newborn and Childhood Health (MNCH) model, called “Go Baby, Go!” (GBG), against the traditional (MNCH) strategy which focuses solely on preventive health and nutrition interventions. The hypothesis of this quasi-experimental study was: Children exposed to GBG will have better neurocognitive and nutrition outcomes compared to those receiving only the MNCH intervention. The secondary objective was to assess the effect of GBG on parental child care and nutrition practices. Methodology: The 14 month long study, targeted all 1,300 children aged 0 to 23 months, living in 43 study communities the in Gavar and Vardenis regions (Gegharkunik province, Armenia). Twenty-three intervention communities, 680 children, received GBG, and 20 control communities, 630 children, received MCHN interventions only. Baseline and evaluation data on child development, nutrition status and parental child care and nutrition practices were collected (caregiver interview, direct child assessment). In the intervention sites, in addition to MNCH (maternity schools, supportive supervision for Health Care Providers (HCP), the trained GBG facilitators conducted six interactive group sessions for mothers (key messages, information, group discussions, role playing, video-watching, toys/books preparation, according to GBG curriculum), and two sessions (condensed GBG) for adult family members (husbands, grandmothers). The trained HCPs received quality supervision for ECD counseling and screening. Findings: The GBG model proved to be effective in improving ECD outcomes. Children in the intervention sites had 83% higher odd of total ECD composite score (cognitive, language, motor) compared to children in the control sites (aOR 1.83; 95 percent CI: 1.08-3.09; p=0.025). Caregivers also demonstrated better child care and nutrition practices (minimum dietary diversity in intervention site is 55 percent higher compared to control (aOR=1.55, 95 percent CI 1.10-2.19, p =0.013); support for learning and disciplining practices (aOR=2.22, 95 percent CI 1.19-4.16, p=0.012)). However, there was no evidence of stunting reduction in either study arm. he effect of the integrated model was more prominent in Vardenis, a community which is characterised by high food insecurity and limited knowledge of positive parenting skills. Conclusion: The GBG model is effective and could be applied in target areas with the greatest economic disadvantages and parenting challenges to improve ECD, care practices and developmental outcomes. Longitudinal studies are needed to view the long-term effects of GBG on learning and school readiness.

Keywords: early childhood development, integrated interventions, parental practices, quasi-experimental study

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Authors: Jirapan Khruesarn, Wimwipa Boonklin

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Background: Currently, Thailand has an aging population. In 2017, the elderly population was over 11.14 million. There will be an increase in the number of elderly people, 8.39 million, some people grumble to themselves and have conflicts with their offspring or those close to them. It is a source of stress. Mental health promotion should be given to the elderly in order to cope with these changes. Due to the family characteristics of Thai society, these family members will act as caregivers for the elderly. Therefore, a group-counseling program based on Personnel-Centered Theory for Elderly Caregivers in Mental Health Promotion for Older People in Na Kaeo Municipality, Kau Ka District, Lampang Province, has been developed to compare the elderly care behavior before and after the participation. Methods: This research was study for 20 elderly' caregiver: Those aimed to compare the before and after use of group program for caregiver to promoting for the elderly by the following methods: Step 1 Establish a framework for evaluating elderly care behaviors and develop a group counseling program for promote mental health for elderly on: 1) Body 2) Willpower 3) Social and community management and 4) Organizing learning process. Step 2 Assessing an Elderly Care Behaviors by using "The behavior assessment on caring for the elderly" and assessing the mental health power level of the elderly and follow the counseling program 9 times and compare of the elderly care behaviors before and after joined a group program, and compare of mental health level of caregiver attends a group program. Results: This study is developing a group counseling program to promoting for the resilience quotient in elderly people that the results of the study could be summarized as follows: 1) Before the elderly's caregivers join a group counseling program: Mental health promotion behaviors of the elderly were at the high level of (3.32), and after: were at the high level of (3.44). 2) Before the elderly's caregiver attends a group counseling program: the mental health level of the elderly the mean score was (47.85 percent), and the standard deviation was (0.21 percent) and after. The elderly had a higher score of (51.45 percent) In summary, after the elderly caregivers joined the group, the elderly are higher in all aspects promote mental health for elderly and the statistically significance at the 0.05, It shows that programs are fit for personal and community condition in promoting the mental health of the elderly because this theory has the idea that: Humans have the ability to use their intelligence to solve problems or make decisions effectively, And member of group counseling program have ventured and express grievances that the counselor is a facilitator who focuses on personal development by building relationships among people. In other words, the factors contributing to higher levels of elderly care behaviors is group counseling, that isn't a hypothetical process but focus on building relationships that are based on mutual trust and Unconditional acceptance.

Keywords: group counseling base on person-centered theory, elderly person, resilience quotient: RQ, caregiver

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Authors: Jan F. Talts, Amit Saxena, Kåre Engkilde

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Chemotherapy-induced peripheral neuropathy (CIPN) is one of the most frequent side effects caused by anti-neoplastic agents, with a prevalence from 19 % to 85 %. Clinically, CIPN is a mostly sensory neuropathy leading to pain and to motor and autonomic changes. Due to its high prevalence among cancer patients, CIPN constitutes a major problem for both cancer patients and survivors, especially because currently, there is no single effective method of preventing CIPN. Hearing loss is the most common form of sensory impairment in humans and can be caused by ototoxic chemical compounds such as chemotherapy (platinum-based antineoplastic agents).In rodents, single or repeated cisplatin injections induce peripheral neuropathy and hearing impairment mimicking human disorder, allowing studying the efficacy of new pharmacological candidates in chemotherapy-induced hearing loss and peripheral neuropathy. RNA sequencing data from full term amniotic fluid (TAF) mesenchymal stemcell (MSC) clones was used to identify neural-specific markers present on TAF-MSC. Several prospective neural markers were tested by flow cytometry on cultured TAF-MSC. One of these markers was used for cell-sorting using Tyto MACSQuant cell sorter, and the neural marker positive cell population was expanded for several passages to the final therapeutic product stage. Peripheral neuropathy and hearing loss was induced in mice by administration of cisplatin in three week-long cycles. The efficacy of neural-specific TAF-MSC in treating hearing loss and pain perception was evaluated by administration of three injections of 3 million cells/kg by intravenous route or three injections of 3 million cells/kg by intra-arterial route after each cisplatin cycle treatment. Auditory brainstem responses (ABR) are electric potentials recorded from scalp electrodes, and the first ABR wave represents the summed activity of the auditory nerve fibers contacting the inner hair cells. For ABR studies, mice were anesthetized, then earphones were placed in the left ear of each mouse, an active electrode was placed in the vertex of the skull, a reference electrode under the skin of the mastoid bone, and a ground electrode in the neck skin. The stimuli consisted of tone pips of five frequencies (2, 4, 6, 12, 16, and 24 kHz) at various sound levels (from 0 to 90 dB) ranging to cover the mouse auditory frequency range. The von Frey test was used to assess the onset and maintenance of mechanical allodynia over time. Mice were placed in clear plexiglass cages on an elevated mesh floor and tested after 30 min of habituation. Mechanical paw withdrawal threshold was examined using an electronic von Frey anesthesiometer. Cisplatin groups treated with three injections of 3 million cells/kg by intravenous route and three injections of 3 million cells/kg by intra-arterial route after each cisplatin cycle treatment presented, a significant increase of hearing acuity characterized by a decrease of ABR threshold and a decrease of neuropathic pain characterized by an increase of von Frey paw withdrawal threshold compared to controls only receiving cisplatin. This study shows that treatment with MSCselected for neural specificity presents significant positive efficacy on the chemotherapy-induced neuropathic pain and the chemotherapy-induced hearing loss.

Keywords: mesenchymal stem cell, peripheral neuropathy, amniotic fluid, regenerative medicine

Procedia PDF Downloads 166

Authors: Jim Weese

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The Kinesiology, Exercise and Sport Science environment remain excellent venues for leadership research. Prescribed leadership (coaching), emergent leadership (players and organizations), and executive leadership are all popular themes in the research literature. Leadership remains a popular area of inquiry in the sport management domain as well as an interesting area for practitioners who wish to heighten their leadership practices and effectiveness. The need for effective leadership in these areas given competing demands for attention and resources may be at an all-time high. The presenter has extensive research and practical experience in the area and has developed his concept based on the latest leadership literature. He refers to this as the Five ’C’s of Leadership. These components, noted below, have been empirically validated and have served as the foundation for extensive consulting with academic, sport, and business leaders. Credibility (C1) is considered the foundation of leadership. There are two components to this area, namely: (a) leaders being respected for having the relevant knowledge, insights, and experience to be seen as credible sources of information, and (b) followers perceiving the leader as being a person of character, someone who is honest, reliable, consistent, and trustworthy. Compelling Vision (C2) refers to the leader’s ability to focus the attention of followers on a desired end goal. Effective leaders understand trends and developments in their industry. They also listen attentively to the needs and desires of their stakeholders and use their own instincts and experience to shape these ideas into an inspiring vision that is effectively and continuously communicated. Charismatic Communicator (C3) refers to the leader’s ability to formally and informally communicate with members. Leaders must deploy mechanisms and communication techniques to keep their members informed and engaged. Effective leaders sprinkle in ‘proof points’ that reinforce the vision’s relevance and/or the unit’s progress towards its attainment. Contagious Enthusiasm (C4) draws on the emotional intelligence literature as it relates to exciting and inspiring followers. Effective leaders demonstrate a level of care, commitment, and passion for their people and feelings of engagement permeate the group. These leaders genuinely care about the task at hand, and for the people working to make it a reality. Culture Builder (C5) is the capstone component of the model and is critical to long-term success and survival. Organizational culture refers to the dominant beliefs, values and attitudes of members of a group or organization. Some have suggested that developing and/or imbedding a desired culture for an organization is the most important responsibility for a leader. The author outlines his Five ‘C’s’ of Leadership concept and provide direct application to executive leadership in Kinesiology, Exercise and Sport Science.

Keywords: effectiveness, leadership, management, sport

Procedia PDF Downloads 300

Authors: B. Lefranc, C. Aufauvre-Poupon, C. Martin-Krumm, M. Trousselard

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Isolated and confined environments (ICE) present several challenges which may adversely affect human’s psychology and physiology. Submariners in Sub-Surface Ballistic Nuclear (SSBN) mission exposed to these environmental constraints must be able to perform complex tasks as part of their normal duties, as well as during crisis periods when emergency actions are required or imminent. The operational and environmental constraints they face contribute to challenge human adaptability. The impact of such a constrained environment has yet to be explored. Establishing a knowledge framework is a determining factor, particularly in view of the next long space travels. Ensuring that the crews are maintained in optimal operational conditions is a real challenge because the success of the mission depends on them. This study focused on the evaluation of the impact of stress on mental health and sensory degradation of submariners during a mission on SSBN using cardiac biosignal (heart rate variability, HRV) clustering. This is a pragmatic exploratory study of a prospective cohort included 19 submariner volunteers. HRV was recorded at baseline to classify by clustering the submariners according to their stress level based on parasympathetic (Pa) activity. Impacts of high Pa (HPa) versus low Pa (LPa) level at baseline were assessed on emotional state and sensory perception (interoception and exteroception) as a cardiac biosignal during the patrol and at a recovery time one month after. Whatever the time, no significant difference was found in mental health between groups. There are significant differences in the interoceptive, exteroceptive and physiological functioning during the patrol and at recovery time. To sum up, compared to the LPa group, the HPa maintains a higher level in psychosensory functioning during the patrol and at recovery but exhibits a decrease in Pa level. The HPa group has less adaptable HRV characteristics, less unpredictability and flexibility of cardiac biosignals while the LPa group increases them during the patrol and at recovery time. This dissociation between psychosensory and physiological adaptation suggests two treatment modalities for ICE environments. To our best knowledge, our results are the first to highlight the impact of physiological differences in the HRV profile on the adaptability of submariners. Further studies are needed to evaluate the negative emotional and cognitive effects of ICEs based on the cardiac profile. Artificial intelligence offers a promising future for maintaining high level of operational conditions. These future perspectives will not only allow submariners to be better prepared, but also to design feasible countermeasures that will help support analog environments that bring us closer to a trip to Mars.

Keywords: adaptation, exteroception, HRV, ICE, interoception, SSBN

Procedia PDF Downloads 182

Authors: Ivan Maguire, Alan Barrett, Alex Forte, Sandra Kwiatkowska, Rohit Mishra, Jens Ducrèe, Fiona Regan

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Biofouling is defined as the gradual accumulation of Biomimetics refers to the use and imitation of principles copied from nature. Biomimetics has found interest across many commercial disciplines. Among many biological objects and their functions, aquatic animals deserve a special attention due to their antimicrobial capabilities resulting from chemical composition, surface topography or other behavioural defences, which can be used as an inspiration for antifouling technology. Marine biofouling has detrimental effects on seagoing vessels, both commercial and leisure, as well as on oceanographic sensors, offshore drilling rigs, and aquaculture installations. Sensor optics, membranes, housings and platforms can become fouled leading to problems with sensor performance and data integrity. While many anti-fouling solutions are currently being investigated as a cost-cutting measure, biofouling settlement may also be prevented by creating a surface that does not satisfy the settlement conditions. Brill (Scophthalmus rhombus) is a small flatfish occurring in marine waters of Mediterranean as well as Norway and Iceland. It inhabits sandy and muddy coastal waters from 5 to 80 meters. Its skin colour changes depending on environment, but generally is brownish with light and dark freckles, with creamy underside. Brill is oval in shape and its flesh is white. The aim of this study is to translate the unique micro-topography of the brill scale, to design marine inspired biomimetic surface coating and test it against a typical fouling organism. Following extensive study of scale topography of the brill fish (Scophthalmus rhombus) and the settlement behaviour of the diatom species Psammodictyon sp. via SEM, two state-of-the-art antifouling surface solutions were designed and investigated; A brill fish scale bioinspired surface pattern platform (BFD), and generic and uniformly-arrayed, circular micropillar platform (MPD), with offsets based on diatom species settlement behaviour. The BFD approach consists of different ~5 μm by ~90 μm Brill-replica patterns, grown to a 5 μm height, in a linear array pattern. The MPD approach utilises hexagonal-packed cylindrical pillars 10.6 μm in diameter, grown to a height of 5 μm, with vertical offset of 15 μm and horizontal offset of 26.6 μm. Photolithography was employed for microstructure growth, with a polydimethylsiloxane (PDMS) chip-based used as a testbed for diatom adhesion on both platforms. Settlement and adhesion tests were performed using this PDMS microfluidic chip through subjugation to centrifugal force via an in-house developed ‘spin-stand’ which features a motor, in combination with a high-resolution camera, for real-time observing diatom release from PDMS material. Diatom adhesion strength can therefore be determined based on the centrifugal force generated at varying rotational speeds. It is hoped that both the replica and bio-inspired solutions will give comparable anti-fouling results to these synthetic surfaces, whilst also assisting in determining whether anti-fouling solutions should predominantly be investigating either fully bioreplica-based, or a bioinspired, synthetically-based design.

Keywords: anti-fouling applications, bio-inspired microstructures, centrifugal microfluidics, surface modification

Procedia PDF Downloads 317

Authors: Adriana De Serio, Adrian Korek

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Aims: This experiential research work presents a Group-MusicTherapy-Theatre-Plan (MusThePlan) the authors have carried out to support the actors. The MusicTherapy gives rise to individual psychophysical feedback and influences the emotional centres of the brain and the subconsciousness. Therefore, the authors underline the effectiveness of the preventive, educational, and training goals of the MusThePlan to lead theatre students and actors to deal with anxiety and to overcome psychophysical weaknesses, shyness, emotional stress in stage performances, to increase flexibility, awareness of one's identity and resources for a positive self-development and psychophysical health, to develop and strengthen social bonds, increasing a network of subjects working for social inclusion and reduction of stigma. Materials-Methods: Thirty students from the University Theatre Faculty participated in weekly music therapy sessions for two months; each session lasted 120 minutes. MusThePlan: Each session began with a free group rhythmic-sonorous-musical-production by body-percussion, voice-canto, instruments, to stimulate communication. Then, a synchronized-structured bodily-rhythmic-sonorous-musical production also involved acting, dances, movements of hands and arms, hearing, and more sensorial perceptions and speech to balance motor skills and the muscular tone. Each student could be the director-leader of the group indicating a story to inspire the group's musical production. The third step involved the students in rhythmic speech and singing drills and in vocal exercises focusing on the musical pitch to improve the intonation and on the diction to improve the articulation and lead up it to an increased intelligibility. At the end of each musictherapy session and of the two months, the Musictherapy Assessment Document was drawn up by analysis of observation protocols and two Indices by the authors: Patient-Environment-Music-Index (time to - tn) to estimate the behavior evolution, Somatic Pattern Index to monitor subject’s eye and mouth and limb motility, perspiration, before, during and after musictherapy sessions. Results: After the first month, the students (non musicians) learned to play percussion instruments and formed a musical band that played classical/modern music on the percussion instruments with the musictherapist/pianist/conductor in a public concert. At the end of the second month, the students performed a public musical theatre show, acting, dancing, singing, and playing percussion instruments. The students highlighted the importance of the playful aspects of the group musical production in order to achieve emotional contact and harmony within the group. The students said they had improved kinetic and vocal and all the skills useful for acting activity and the nourishment of the bodily and emotional balance. Conclusions: The MusThePlan makes use of some specific MusicTherapy methodological models, techniques, and strategies useful for the actors. The MusThePlan can destroy the individual "mask" and can be useful when the verbal language is unable to undermine the defense mechanisms of the subject. The MusThePlan improves actor’s psychophysical activation, motivation, gratification, knowledge of one's own possibilities, and the quality of life. Therefore, the MusThePlan could be useful to carry out targeted interventions for the actors with characteristics of repeatability, objectivity, and predictability of results. Furthermore, it would be useful to plan a University course/master in “MusicTherapy for the Theatre”.

Keywords: musictherapy, sonorous-musical energy, quality of life, theatre

Procedia PDF Downloads 77

Authors: Maria Areias, Diogo Simões, Ana Figueiredo, Anishur Rahman, Filipa Figueiredo, João Nunes

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It is expected that close to 7 billion people will live in urban areas by 2050. In order to improve the sustainability of the territories and its transition towards circular economy, it’s necessary to understand its metabolism and promote and guide the entrepreneurship answer. The study of a macroeconomic metabolism involves the quantification of the inputs, outputs and storage of energy, water, materials and wastes for an urban region. This quantification and analysis representing one opportunity for the promotion of green entrepreneurship. There are several methods to assess the environmental impacts of an urban territory, such as human and environmental risk assessment (HERA), life cycle assessment (LCA), ecological footprint assessment (EF), material flow analysis (MFA), physical input-output table (PIOT), ecological network analysis (ENA), multicriteria decision analysis (MCDA) among others. However, no consensus exists about which of those assessment methods are best to analyze the sustainability of these complex systems. Taking into account the weaknesses and needs identified, the CiiM - Circular Innovation Inter-Municipality project aims to define an uniform and globally accepted methodology through the integration of various methodologies and dynamic approaches to increase the efficiency of macroeconomic metabolisms and promoting entrepreneurship in a circular economy. The pilot territory considered in CiiM project has a total area of 969,428 ha, comprising a total of 897,256 inhabitants (about 41% of the population of the Center Region). The main economic activities in the pilot territory, which contribute to a gross domestic product of 14.4 billion euros, are: social support activities for the elderly; construction of buildings; road transport of goods, retailing in supermarkets and hypermarkets; mass production of other garments; inpatient health facilities; and the manufacture of other components and accessories for motor vehicles. The region's business network is mostly constituted of micro and small companies (similar to the Central Region of Portugal), with a total of 53,708 companies identified in the CIM Region of Coimbra (39 large companies), 28,146 in the CIM Viseu Dão Lafões (22 large companies) and 24,953 in CIM Beiras and Serra da Estrela (13 large companies). For the construction of the database was taking into account data available at the National Institute of Statistics (INE), General Directorate of Energy and Geology (DGEG), Eurostat, Pordata, Strategy and Planning Office (GEP), Portuguese Environment Agency (APA), Commission for Coordination and Regional Development (CCDR) and Inter-municipal Community (CIM), as well as dedicated databases. In addition to the collection of statistical data, it was necessary to identify and characterize the different stakeholder groups in the pilot territory that are relevant to the different metabolism components under analysis. The CIIM project also adds the potential of a Geographic Information System (GIS) so that it is be possible to obtain geospatial results of the territorial metabolisms (rural and urban) of the pilot region. This platform will be a powerful visualization tool of flows of products/services that occur within the region and will support the stakeholders, improving their circular performance and identifying new business ideas and symbiotic partnerships.

Keywords: circular economy tools, life cycle assessment macroeconomic metabolism, multicriteria decision analysis, decision support tools, circular entrepreneurship, industrial and regional symbiosis

Procedia PDF Downloads 101

Authors: Adam Gushgari

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The SARS-CoV-2 pandemic has catalyzed the rapid adoption of wastewater-based epidemiology (WBE) methodologies both domestically and internationally. To support the rapid scale-up of pandemic-response wastewater surveillance systems, multiple federal agencies (i.e. US CDC), non-government organizations (i.e. Water Environment Federation), and private charities (i.e. Bill and Melinda Gates Foundation) have funded over $220 million USD supporting development and expanding equitable access of surveillance methods. Funds were primarily distributed directly to municipalities under the CARES Act (90.6%), followed by academic projects (7.6%), and initiatives developed by private companies (1.8%). In addition to federal funding for wastewater monitoring primarily conducted at wastewater treatment plants, state/local governments and private companies have leveraged wastewater sampling to obtain health and lifestyle data on student, prison inmate, and employee populations. We explore the viable paths for expansion of the WBE m1ethodology across a variety of analytical methods; the development of WBE-specific samplers and real-time wastewater sensors; and their application to various governments and private sector industries. Considerable investment in, and public acceptance of WBE suggests the methodology will be applied to other future notifiable diseases and health risks. Early research suggests that WBE methods can be applied to a host of additional “biological insults” including communicable diseases and pathogens, such as influenza, Cryptosporidium, Giardia, mycotoxin exposure, hepatitis, dengue, West Nile, Zika, and yellow fever. Interest in chemical insults is also likely, providing community health and lifestyle data on narcotics consumption, use of pharmaceutical and personal care products (PPCP), PFAS and hazardous chemical exposure, and microplastic exposure. Successful application of WBE to monitor analytes correlated with carcinogen exposure, community stress prevalence, and dietary indicators has also been shown. Additionally, technology developments of in situ wastewater sensors, WBE-specific wastewater samplers, and integration of artificial intelligence will drastically change the landscape of WBE through the development of “smart sewer” networks. The rapid expansion of the WBE field is creating significant business opportunities for professionals across the scientific, engineering, and technology industries ultimately focused on community health improvement.

Keywords: wastewater surveillance, wastewater-based epidemiology, smart cities, public health, pandemic management, substance abuse

Procedia PDF Downloads 108

Authors: Elaheh Talebi, Fariba Yavari, Lucy Philip, Lesley Town

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The mining industry generates vast amounts of data, necessitating robust data management systems and advanced analytics tools to achieve better decision-making processes in the development of mining production and maintaining safety. This paper highlights the advantages of Power BI, a powerful intelligence tool, over traditional Excel-based approaches for effectively managing and harnessing mining data. Power BI enables professionals to connect and integrate multiple data sources, ensuring real-time access to up-to-date information. Its interactive visualizations and dashboards offer an intuitive interface for exploring and analyzing geotechnical data. Advanced analytics is a collection of data analysis techniques to improve decision-making. Leveraging some of the most complex techniques in data science, advanced analytics is used to do everything from detecting data errors and ensuring data accuracy to directing the development of future project phases. However, while Power BI is a robust tool, specific visualizations required by geotechnical engineers may have limitations. This paper studies the capability to use Python or R programming within the Power BI dashboard to enable advanced analytics, additional functionalities, and customized visualizations. This dashboard provides comprehensive tools for analyzing and visualizing key geotechnical data metrics, including spatial representation on maps, field and lab test results, and subsurface rock and soil characteristics. Advanced visualizations like borehole logs and Stereonet were implemented using Python programming within the Power BI dashboard, enhancing the understanding and communication of geotechnical information. Moreover, the dashboard's flexibility allows for the incorporation of additional data and visualizations based on the project scope and available data, such as pit design, rock fall analyses, rock mass characterization, and drone data. This further enhances the dashboard's usefulness in future projects, including operation, development, closure, and rehabilitation phases. Additionally, this helps in minimizing the necessity of utilizing multiple software programs in projects. This geotechnical dashboard in Power BI serves as a user-friendly solution for analyzing, visualizing, and communicating both new and historical geotechnical data, aiding in informed decision-making and efficient project management throughout various project stages. Its ability to generate dynamic reports and share them with clients in a collaborative manner further enhances decision-making processes and facilitates effective communication within geotechnical projects in the mining industry.

Keywords: geotechnical data analysis, power BI, visualization, decision-making, mining industry

Procedia PDF Downloads 92

Authors: Soowon Park, Min-Ji Kim, Jun-Young Lee

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Motivation: The stigma of mental illness has been studied in many disciplines, including social psychology, counseling psychology, sociology, psychiatry, public health care, and related areas, because individuals labeled as ‘mentally ill’ are often deprived of their rights and their life opportunities. To understand the factors that deepen the stigma of mental illness, it is important to understand the influencing factors of the stigma. Problem statement: Depression is a common disorder in adults, but the incidence of help-seeking is low. Researchers have believed that this poor help-seeking behavior is related to the stigma of mental illness, which results from low mental health literacy. However, it is uncertain that increasing mental health literacy decreases mental health stigmatization. Furthermore, even though decreasing stigmatization is important, the stigma of mental illness is still a stable and long-lasting phenomenon. Thus, factors other than knowledge about mental disorders have the power to maintain the stigma. Investigating the influencing factors that facilitate the stigma of psychiatric disease could help lower the social stigmatization. Approach: Face-to-face interviews were conducted with a multi-clustering sample. A total of 700 Korean participants (38% male), ranging in age from 18 to 78 (M(SD)age= 48.5(15.7)) answered demographical questions, Korean version of Link’s Perceived Devaluation and Discrimination (PDD) scale for the assessment of social stigmatization against depression, and the Korean version of the WHO-Composite International Diagnostic Interview for the assessment of mental disorders. Multiple-regression was conducted to find the predicting factors of social stigmatization against depression. Ages, sex, years of education, income, living location, and experience of mental illness were used as the predictors. Results: Predictors accounted for 14% of the variance in the stigma of depressive disorders (F(6, 693) = 20.27, p < .001). Among those, only age, years of education, and experience of mental illness significantly predicted social stigmatization against depression. The standardized regression coefficient of age had a negative association with stigmatization (β = -.20, p < .001), but years of education (β = .20, p < .001) and experience of mental illness (β = .08, p < .05) positively predicted depression stigmatization. Conclusions: The present study clearly demonstrates the association between personal factors and depressive disorder stigmatization. Younger age, more education, and self-stigma appeared to increase the stigmatization. Young, highly educated, and mentally ill people tend to reject patients with depressive disorder as friends, teachers, or babysitters; they also tend to think that those patients have lower intelligence and abilities. These results suggest the possibility that people from a high social class, or highly educated people, who have the power to make decisions, help maintain the social stigma against mental illness patients. To increase the awareness that people from high social classes have more stigmatization against depressive disorders will help decrease the biased attitudes against mentally ill patients.

Keywords: depressive disorder stigmatization, age, education, self-stigma

Procedia PDF Downloads 405

Authors: Marcos Silva Tavares, Jamile Raquel Regazzo, Edson José de Souza Sardinha, Murilo Mesquita Baesso

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Common beans are a widely cultivated and consumed legume globally, serving as a staple food for humans, especially in developing countries, due to their nutritional characteristics. Nitrogen (N) is the most limiting nutrient for productivity, and foliar analysis is crucial to ensure balanced nitrogen fertilization. Excessive N applications can cause, either isolated or cumulatively, soil and water contamination, plant toxicity, and increase their susceptibility to diseases and pests. However, the quantification of N using conventional methods is time-consuming and costly, demanding new technologies to optimize the adequate supply of N to plants. Thus, it becomes necessary to establish constant monitoring of the foliar content of this macronutrient in plants, mainly at the V4 stage, aiming at precision management of nitrogen fertilization. In this work, the objective was to evaluate the performance of a deep learning model, Resnet-50, in the classification of foliar nitrogen in common beans using RGB images. The BRS Estilo cultivar was sown in a greenhouse in a completely randomized design with four nitrogen doses (T1 = 0 kg N ha-1, T2 = 25 kg N ha-1, T3 = 75 kg N ha-1, and T4 = 100 kg N ha-1) and 12 replications. Pots with 5L capacity were used with a substrate composed of 43% soil (Neossolo Quartzarênico), 28.5% crushed sugarcane bagasse, and 28.5% cured bovine manure. The water supply of the plants was done with 5mm of water per day. The application of urea (45% N) and the acquisition of images occurred 14 and 32 days after sowing, respectively. A code developed in Matlab© R2022b was used to cut the original images into smaller blocks, originating an image bank composed of 4 folders representing the four classes and labeled as T1, T2, T3, and T4, each containing 500 images of 224x224 pixels obtained from plants cultivated under different N doses. The Matlab© R2022b software was used for the implementation and performance analysis of the model. The evaluation of the efficiency was done by a set of metrics, including accuracy (AC), F1-score (F1), specificity (SP), area under the curve (AUC), and precision (P). The ResNet-50 showed high performance in the classification of foliar N levels in common beans, with AC values of 85.6%. The F1 for classes T1, T2, T3, and T4 was 76, 72, 74, and 77%, respectively. This study revealed that the use of RGB images combined with deep learning can be a promising alternative to slow laboratory analyses, capable of optimizing the estimation of foliar N. This can allow rapid intervention by the producer to achieve higher productivity and less fertilizer waste. Future approaches are encouraged to develop mobile devices capable of handling images using deep learning for the classification of the nutritional status of plants in situ.

Keywords: convolutional neural network, residual network 50, nutritional status, artificial intelligence

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Authors: Beatrice Arthur

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The rapid development of artificial intelligence (AI) has transformed various industries, enabling faster and more accurate decision-making processes. However, with these advancements come increased risks, including data privacy issues, systemic biases, and challenges related to transparency and accountability. As AI technologies become more integrated into business processes, there is a growing need for comprehensive auditing and assurance frameworks to manage these risks and ensure ethical use. This paper provides a literature review on AI auditing and assurance programs, highlighting the importance of adapting traditional audit methodologies to the complexities of AI-driven systems. Objective: The objective of this review is to explore current AI audit practices and their role in mitigating risks, ensuring accountability, and fostering trust in AI systems. The study aims to provide a structured framework for developing audit programs tailored to AI technologies while also investigating how AI impacts governance, risk management, and regulatory compliance in various sectors. Methodology: This research synthesizes findings from academic publications and industry reports from 2014 to 2024, focusing on the intersection of AI technologies and IT assurance practices. The study employs a qualitative review of existing audit methodologies and frameworks, particularly the COBIT 2019 framework, to understand how audit processes can be aligned with AI governance and compliance standards. The review also considers real-time auditing as an emerging necessity for influencing AI system design during early development stages. Outcomes: Preliminary findings indicate that while AI auditing is still in its infancy, it is rapidly gaining traction as both a risk management strategy and a potential driver of business innovation. Auditors are increasingly being called upon to develop controls that address the ethical and operational risks posed by AI systems. The study highlights the need for continuous monitoring and adaptable audit techniques to handle the dynamic nature of AI technologies. Future Directions: Future research will explore the development of AI-specific audit tools and real-time auditing capabilities that can keep pace with evolving technologies. There is also a need for cross-industry collaboration to establish universal standards for AI auditing, particularly in high-risk sectors like healthcare and finance. Further work will involve engaging with industry practitioners and policymakers to refine the proposed governance and audit frameworks. Funding/Support Acknowledgements: This research is supported by the Information Systems Assurance Management Program at Concordia University of Edmonton.

Keywords: AI auditing, assurance, risk management, governance, COBIT 2019, transparency, accountability, machine learning, compliance

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Authors: Waleed Omri

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With the growth of the small business sector in emerging markets, developing a better understanding of what drives 'day-to-day' entrepreneurial activities has become an important issue for academicians and practitioners. Innovation, as an entrepreneurial behavior, revolves around individuals who creatively engage in new organizational efforts. In a similar vein, the innovation behaviors and processes at the organizational member level are central to any corporate entrepreneurship strategy. Despite the broadly acknowledged importance of entrepreneurship and innovation at the individual level in the establishment of successful ventures, the literature lacks evidence on how entrepreneurs can effectively harness their skills and knowledge in the workplace. The existing literature illustrates that religion can impact the day-to-day work behavior of entrepreneurs, managers, and employees. Religious beliefs and practices could affect daily entrepreneurial activities by fostering mental abilities and traits such as creativity, intelligence, and self-efficacy. In the present study, we define religious capital as a set of personal and intangible resources, skills, and competencies that emanate from an individual’s religious values, beliefs, practices, and experiences and may be used to increase the quality of economic activities. Religious beliefs and practices give individuals a religious satisfaction, which can lead them to perform better in the workplace. In addition, religious ethics and practices have been linked to various positive employee outcomes in terms of organizational change, job satisfaction, and entrepreneurial intensity. As investigations of their consequences beyond direct task performance are still scarce, we explore if religious capital plays a role in entrepreneurs’ innovative behavior. In sum, this study explores the determinants of individual entrepreneurial behavior by investigating the relationship between religious capital and entrepreneurs’ innovative behavior in the context of small businesses. To further explain and clarify the religious capital-innovative behavior link, the present study proposes a model to examine the mediating role of entrepreneurial creativity. We use both Islamic work ethics (IWE) and Islamic religious practices (IRP) to measure Islamic religious capital. We use structural equation modeling with a robust maximum likelihood estimation to analyze data gathered from 289 Tunisian small businesses and to explore the relationships among the above-described variables. In line with the theory of planned behavior, only religious work ethics are found to increase the innovative behavior of small businesses’ owner-managers. Our findings also clearly demonstrate that the connection between religious capital-related variables and innovative behavior is better understood if the influence of entrepreneurial creativity, as a mediating variable of the aforementioned relationship, is taken into account. By incorporating both religious capital and entrepreneurial creativity into the innovative behavior analysis, this study provides several important practical implications for promoting innovation process in small businesses.

Keywords: entrepreneurial behavior, small business, religion, creativity

Procedia PDF Downloads 244

Authors: Éva Pintér, Péter Bagó, Nikolett Deutsch, Miklós Hetényi

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The process of digital transformation has a profound influence on all sectors of the worldwide economy and the business environment. The influence of blockchain technology can be observed in the digital economy and e-government, rendering it an essential element of a nation's growth strategy. The banking industry is experiencing significant expansion and development of financial technology firms. Utilizing developing technologies such as artificial intelligence (AI), machine learning (ML), and big data (BD), these entrants are offering more streamlined financial solutions, promptly addressing client demands, and presenting a challenge to incumbent institutions. The advantages of digital transformation are evident in the corporate realm, and firms that resist its adoption put their survival at risk. The advent of digital technologies has revolutionized the business environment, streamlining processes and creating opportunities for enhanced communication and collaboration. Thanks to the aid of digital technologies, businesses can now swiftly and effortlessly retrieve vast quantities of information, all the while accelerating the process of creating new and improved products and services. Big data analytics is generally recognized as a transformative force in business, considered the fourth paradigm of science, and seen as the next frontier for innovation, competition, and productivity. Big data, an emerging technology that is shaping the future of the banking sector, offers numerous advantages to banks. It enables them to effectively track consumer behavior and make informed decisions, thereby enhancing their operational efficiency. Banks may embrace big data technologies to promptly and efficiently identify fraud, as well as gain insights into client preferences, which can then be leveraged to create better-tailored products and services. Moreover, the utilization of big data technology empowers banks to develop more intelligent and streamlined models for accurately recognizing and focusing on the suitable clientele with pertinent offers. There is a scarcity of research on big data analytics in the banking industry, with the majority of existing studies only examining the advantages and prospects associated with big data. Although big data technologies are crucial, there is a dearth of empirical evidence about the role of big data analytics (BDA) capabilities in bank performance. This research addresses a gap in the existing literature by introducing a model that combines the resource-based view (RBV), the technical organization environment framework (TOE), and dynamic capability theory (DC). This study investigates the influence of Big Data Analytics (BDA) utilization on the performance of market and risk management. This is supported by a comparative examination of Hungarian mobile banking services.

Keywords: big data, digital transformation, dynamic capabilities, mobile banking

Procedia PDF Downloads 64

Authors: Pardis Moslemzadeh Tehrani

Abstract:

Due to the emergence of blockchain technology and the benefits of cryptocurrencies, intelligent or smart contracts are gaining traction. Artificial intelligence (AI) is transforming our lives, and it is being embraced by a wide range of sectors. Smart contracts, which are at the heart of blockchains, incorporate AI characteristics. Such contracts are referred to as "smart" contracts because of the underlying technology that allows contracting parties to agree on terms expressed in computer code that defines machine-readable instructions for computers to follow under specific situations. The transmission happens automatically if the conditions are met. Initially utilised for financial transactions, blockchain applications have since expanded to include the financial, insurance, and medical sectors, as well as supply networks. Raw material acquisition by suppliers, design, and fabrication by manufacturers, delivery of final products to consumers, and even post-sales logistics assistance are all part of supply chains. Many issues are linked with managing supply chains from the planning and coordination stages, which can be implemented in a smart contract in a blockchain due to their complexity. Manufacturing delays and limited third-party amounts of product components have raised concerns about the integrity and accountability of supply chains for food and pharmaceutical items. Other concerns include regulatory compliance in multiple jurisdictions and transportation circumstances (for instance, many products must be kept in temperature-controlled environments to ensure their effectiveness). Products are handled by several providers before reaching customers in modern economic systems. Information is sent between suppliers, shippers, distributors, and retailers at every stage of the production and distribution process. Information travels more effectively when individuals are eliminated from the equation. The usage of blockchain technology could be a viable solution to these coordination issues. In blockchains, smart contracts allow for the rapid transmission of production data, logistical data, inventory levels, and sales data. This research investigates the legal and technical advantages and disadvantages of AI-blockchain technology in the supply chain business. It aims to uncover the applicable legal problems and barriers to the use of AI-blockchain technology to supply chains, particularly in the food industry. It also discusses the essential legal and technological issues and impediments to supply chain implementation for stakeholders, as well as methods for overcoming them before releasing the technology to clients. Because there has been little research done on this topic, it is difficult for industrial stakeholders to grasp how blockchain technology could be used in their respective operations. As a result, the focus of this research will be on building advanced and complex contractual terms in supply chain smart contracts on blockchains to cover all unforeseen supply chain challenges.

Keywords: blockchain, supply chain, IoT, smart contract

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Authors: Sanjeev Kumar Appicharla

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This paper presents the results of the modelling and analysis of the European Railway Traffic Management (ERTMS) safety-critical incident to raise awareness of biases in the systems engineering process on the Cambrian Railway in the UK using the RAIB 17/2019 as a primary input. The RAIB, the UK independent accident investigator, published the Report- RAIB 17/2019 giving the details of their investigation of the focal event in the form of immediate cause, causal factors, and underlying factors and recommendations to prevent a repeat of the safety-critical incident on the Cambrian Line. The Systems for Investigation of Railway Interfaces (SIRI) is the methodology used to model and analyze the safety-critical incident. The SIRI methodology uses the Swiss Cheese Model to model the incident and identify latent failure conditions (potentially less than adequate conditions) by means of the management oversight and risk tree technique. The benefits of the systems for investigation of railway interfaces methodology (SIRI) are threefold: first is that it incorporates the “Heuristics and Biases” approach advanced by 2002 Nobel laureate in Economic Sciences, Prof Daniel Kahneman, in the management oversight and risk tree technique to identify systematic errors. Civil engineering and programme management railway professionals are aware of the role “optimism bias” plays in programme cost overruns and are aware of bow tie (fault and event tree) model-based safety risk modelling techniques. However, the role of systematic errors due to “Heuristics and Biases” is not appreciated as yet. This overcomes the problems of omission of human and organizational factors from accident analysis. Second, the scope of the investigation includes all levels of the socio-technical system, including government, regulatory, railway safety bodies, duty holders, signaling firms and transport planners, and front-line staff such that lessons are learned at the decision making and implementation level as well. Third, the author’s past accident case studies are supplemented with research pieces of evidence drawn from the practitioner's and academic researchers’ publications as well. This is to discuss the role of system thinking to improve the decision-making and risk management processes and practices in the IEC 15288 systems engineering standard and in the industrial context such as the GB railways and artificial intelligence (AI) contexts as well.

Keywords: accident analysis, AI algorithm internal audit, bounded rationality, Byzantine failures, heuristics and biases approach

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Authors: Jayashree And Gajjam

Abstract:

Intellectual humility (IH) is defined as a virtuous mean between intellectual arrogance and intellectual self-diffidence by the ‘Doxastic Account of IH’ studied, researched and developed by western scholars not earlier than 2015 at the University of Edinburgh. Ancient Indian philosophical texts or the Upanisads written in the Sanskrit language during the later Vedic period (circa 600-300 BCE) have long addressed the virtue of being humble in several stories and narratives. The current research paper questions and revisits these character traits in an Indian context following an experimental method. Based on the subjective reports of more than 400 Indian teenagers and adults, it argues that while a few traits of IH (such as trustworthiness, respectfulness, intelligence, politeness, etc.) are panhuman and pancultural, a few are not. Some attributes of IH (such as proper pride, open-mindedness, awareness of own strength, etc.) may be taken for arrogance by the Indian population, while other qualities of Intellectual Diffidence such as agreeableness, surrendering can be regarded as the characteristic of IH. The paper then gives the reasoning for this discrepancy that can be traced back to the ancient Indian (Upaniṣadic) teachings that are still prevalent in many Indian families and still anchor their views on IH. The name Upanisad itself means ‘sitting down near’ (to the Guru to gain the Supreme knowledge of the Self and the Universe and setting to rest ignorance) which is equivalent to the three traits among the BIG SEVEN characterized as IH by the western scholars viz. ‘being a good listener’, ‘curious to learn’, and ‘respect to other’s opinion’. The story of Satyakama Jabala (Chandogya Upanisad 4.4-8) who seeks the truth for several years even from the bull, the fire, the swan and waterfowl, suggests nothing but the ‘need for cognition’ or ‘desire for knowledge’. Nachiketa (Katha Upanisad), a boy with a pure mind and heart, follows his father’s words and offers himself to Yama (the God of Death) where after waiting for Yama for three days and nights, he seeks the knowledge of the mysteries of life and death. Although the main aim of these Upaniṣadic stories is to give the knowledge of life and death, the Supreme reality which can be identical with traits such as ‘curious to learn’, one cannot deny that they have a lot more to offer than mere information about true knowledge e.g., ‘politeness’, ‘good listener’, ‘awareness of own limitations’, etc. The possible future scope of this research includes (1) finding other socio-cultural factors that affect the ideas on IH such as age, gender, caste, type of education, highest qualification, place of residence and source of income, etc. which may be predominant in current Indian society despite our great teachings of the Upaniṣads, and (2) to devise different measures to impart IH in Indian children, teenagers, and younger adults for the harmonious future. The current experimental research can be considered as the first step towards these goals.

Keywords: ethics and virtue epistemology, Indian philosophy, intellectual humility, upaniṣadic texts in ancient India

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Authors: Iris Xiaohong Quan

Abstract:

The global digital health market reached 175 billion U.S. dollars in 2019, and is expected to grow at about 25% CAGR to over 650 billion USD by 2025. Different terms such as digital health, e-health, mHealth, telehealth have been used in the field, which can sometimes cause confusion. The term digital health was originally introduced to refer specifically to the use of interactive media, tools, platforms, applications, and solutions that are connected to the Internet to address health concerns of providers as well as consumers. While mHealth emphasizes the use of mobile phones in healthcare, telehealth means using technology to remotely deliver clinical health services to patients. According to FDA, “the broad scope of digital health includes categories such as mobile health (mHealth), health information technology (IT), wearable devices, telehealth and telemedicine, and personalized medicine.” Some researchers believe that digital health is nothing else but the cultural transformation healthcare has been going through in the 21st century because of digital health technologies that provide data to both patients and medical professionals. As digital health is burgeoning, but research in the area is still inadequate, our paper aims to clear the definition confusion and provide an overall picture of digital health companies. We further investigate how business models are designed and differentiated in the emerging digital health sector. Both quantitative and qualitative methods are adopted in the research. For the quantitative analysis, our research data came from two databases Crunchbase and CBInsights, which are well-recognized information sources for researchers, entrepreneurs, managers, and investors. We searched a few keywords in the Crunchbase database based on companies’ self-description: digital health, e-health, and telehealth. A search of “digital health” returned 941 unique results, “e-health” returned 167 companies, while “telehealth” 427. We also searched the CBInsights database for similar information. After merging and removing duplicate ones and cleaning up the database, we came up with a list of 1464 companies as digital health companies. A qualitative method will be used to complement the quantitative analysis. We will do an in-depth case analysis of three successful unicorn digital health companies to understand how business models evolve and discuss the challenges faced in this sector. Our research returned some interesting findings. For instance, we found that 86% of the digital health startups were founded in the recent decade since 2010. 75% of the digital health companies have less than 50 employees, and almost 50% with less than 10 employees. This shows that digital health companies are relatively young and small in scale. On the business model analysis, while traditional healthcare businesses emphasize the so-called “3P”—patient, physicians, and payer, digital health companies extend to “5p” by adding patents, which is the result of technology requirements (such as the development of artificial intelligence models), and platform, which is an effective value creation approach to bring the stakeholders together. Our case analysis will detail the 5p framework and contribute to the extant knowledge on business models in the healthcare industry.

Keywords: digital health, business models, entrepreneurship opportunities, healthcare

Procedia PDF Downloads 183