Search results for: artificial animal intelligence

Authors: Hami Ashraf, Farid Kosari

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Graft-versus-host disease (GvHD) is a common complication of allogeneic hematopoietic stem cell transplantation that can lead to significant morbidity and mortality. Histopathological examination of affected tissues is an essential tool for diagnosing and grading GvHD in animal models, which are used to study disease mechanisms and evaluate new therapies. In this systematic review, we identified and analyzed original research articles in PubMed, Scopus, Web of Science, and Google Scholar that described grading systems for GvHD in animal models based on histopathological features. We found that several grading systems have been developed, which vary in the tissues and criteria they assess, the severity scoring scales they use, and the level of detail they provide. Skin, liver, and gut are the most commonly evaluated tissues, but lung and thymus are also included in some systems. Our analysis highlights the need for standardized criteria and consistent use of grading systems to enable comparisons between studies and facilitate the translation of preclinical findings to clinical practice.

Keywords: graft-versus-host disease, GvHD, animal model, histopathology, grading system

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Authors: K. D. Sharma, M. Parida, S. S. Jain, Anju Saini, V. K. Katiyar

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Air quality models form one of the most important components of an urban air quality management plan. Various statistical modeling techniques (regression, multiple regression and time series analysis) have been used to predict air pollution concentrations in the urban environment. These models calculate pollution concentrations due to observed traffic, meteorological and pollution data after an appropriate relationship has been obtained empirically between these parameters. Artificial neural network (ANN) is increasingly used as an alternative tool for modeling the pollutants from vehicular traffic particularly in urban areas. In the present paper, an attempt has been made to model traffic air pollution, specifically CO concentration using neural networks. In case of CO concentration, two scenarios were considered. First, with only classified traffic volume input and the second with both classified traffic volume and meteorological variables. The results showed that CO concentration can be predicted with good accuracy using artificial neural network (ANN).

Keywords: air quality management, artificial neural network, meteorological variables, statistical modeling

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Authors: Himanshu Payal, Sachin Maheshwari, Pushpendra S. Bharti

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Electrical discharge machining (EDM), a non-conventional machining process, finds wide applications for shaping difficult-to-cut alloys. Process modeling of EDM is required to exploit the process to the fullest. Process modeling of EDM is a challenging task owing to involvement of so many electrical and non-electrical parameters. This work is an attempt to model the EDM process using artificial neural network (ANN). Experiments were carried out on die-sinking EDM taking Inconel 825 as work material. ANN modeling has been performed using experimental data. The prediction ability of trained network has been verified experimentally. Results indicate that ANN can predict the values of performance measures of EDM satisfactorily.

Keywords: artificial neural network, EDM, metal removal rate, modeling, surface roughness

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Authors: Fatima Faiza Ahmed, Syed Farrukh Hussain

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The last few decades have seen a large proportion of our population bending towards e-learning technologies, starting from learning tools used in primary and elementary schools to competency based e-learning systems specifically designed for applications like finance and marketing. The huge diversity in this crowd brings about a large number of challenges for the designers of these e-learning systems, one of which is the adaptability of such systems. This paper focuses on adaptability in the learning material in an e-learning course and how artificial intelligence and the semantic web can be used as an effective tool for this purpose. The study proved that the semantic web, still a hot topic in the area of computer science can prove to be a powerful tool in designing and implementing adaptable e-learning systems.

Keywords: adaptable e-learning, HTMLParser, information extraction, semantic web

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Authors: Mario C. Nierras

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This study aimed to determine the extension services’ needs of small farmers in Biliran province, Philippines. It also sought to find out other issues/concerns of the small farmers. Extension services’ needs of small farmers were gathered through personal interviewing and observational analysis of randomly-selected small farmers in Biliran, Philippines. Biliran small farmers extension services’ needs include: raising fruits, raising legumes, raising vegetables, raising swine, raising cattle, and raising chicken (as priority broad skills). For the specific skills, diagnosing symptoms on fertilizer deficiencies, controlling plant pests and diseases, diagnosing signs on specific pest and disease damage, controlling animal pests and diseases, and doing artificial insemination were the priority skills. They considered an on-farm trial of new technology as most needed to be coupled with industry and quality-orientedness, as positive behaviors needed in farming success. The farmers still adhere to the so-called wait-and-see attitude, thus they are more convinced to follow a particular technology if they see a concrete result of the introduced changes. Technical needs prioritization of Biliran small farmers showed that they have a real need for crop and animal production skills to include the other issues/concerns. Extension service program planning for small farmers should be patterned after their technical needs giving due attention to some issues/concerns so that extension work could deliver the right skills for the right needs of the farmers.

Keywords: extension, extension service, extension service needs, extension service program, farmers, small farmers, marginal farmers

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Authors: Serena Gomez, Raeesa Tanseen, Netra Shaligram, Nithin Francis, Sandesh B. J.

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The human gut consists of a community of microbes which has a lot of effects on human health disease. Metabolic modeling can help to predict relative populations of stable microbes and their effect on health disease. In order to study and visualize microbes in the human gut, we developed a tool that offers the following modules: Build a tool that can be used to perform Flux Balance Analysis for microbes in the human gut using the Artificial Bee Colony optimization algorithm. Run simulations for an individual microbe in different conditions, such as aerobic and anaerobic and visualize the results of these simulations.

Keywords: microbes, metabolic modeling, flux balance analysis, artificial bee colony

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Authors: Raid R. O. Al-Nima, S. S. Dlay, W. L. Woo

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A relationship between face and signature biometrics is established in this paper. A new approach is developed to predict faces from signatures by using artificial intelligence. A multilayer perceptron (MLP) neural network is used to generate face details from features extracted from signatures, here face is the physical biometric and signatures is the behavioural biometric. The new method establishes a relationship between the two biometrics and regenerates a visible face image from the signature features. Furthermore, the performance efficiencies of our new technique are demonstrated in terms of minimum error rates compared to published work.

Keywords: behavioural biometric, face biometric, neural network, physical biometric, signature biometric

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Authors: Lucie Bartosova

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The rapid advancements in generative artificial intelligence (AI) have introduced transformative possibilities in education, particularly in assessment methodologies. This work provides an overview of the current state of the literature on AI-scoring methodologies for evaluating student-written responses. The focus is on how these innovations can be leveraged within large-scale assessments to address resource constraints such as limited assessors, time, and budget. Drawing from an initiative tied to a language test used for admitting candidates into a pre-service education program in the Faculty of Education at an Ontario university, the review explores the practical and ethical implications of integrating AI-driven tools into assessment processes. These tools are designed to automate the evaluation of learners’ written compositions, provide performance feedback, and support grading procedures. By synthesizing findings from recent research, the review highlights the effectiveness, reliability, and potential biases of AI in scoring, alongside considerations for transparency and fairness. This work emphasizes the dual role of generative AI as both a practical solution for scaling assessments and a subject of critical scrutiny to ensure its responsible implementation. The proposed integration of AI-scoring methodologies in our language test underscores the need to balance innovation with accountability, ensuring that AI tools enhance, rather than compromise, educational equity and rigor. OBJECTIVES OF YOUR RESEARCH To determine which generative AI model is most capable of evaluating written responses for university assessments based on specific criteria and to investigate potential biases within AI models to ensure fair assessments. METHODOLOGIES Evaluating generative AI models to determine their performance in assessing written responses against specific criteria. Collecting responses from previous assessments and annotating them with expert feedback to train and validate the AI models. MAIN CONTRIBUTIONS Introducing a tailored AI model to assess written responses on language tests. Offering a scalable and replicable model that informs broader applications of AI in educational assessments, contributing to policy-making and institutional best practices.

Keywords: artificial intelligence, assessment practices, student written performance, automated essay scoring, language proficiency

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Authors: Elham Jafari

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The aim of this study was to evaluate the effectiveness of emotional intelligence training on reducing marital conflict and marital adjustment in married students of the University of Tehran. This research is an applied type in terms of purpose and a semi-experimental design of pre-test-post-test type with the control group and with follow-up test in terms of the data collection method. The statistical population of the present study consisted of all married students of the University of Tehran. In this study, 30 married students of the University of Tehran were selected by convenience sampling method as a sample that 15 people in the experimental group and 15 people in the control group were randomly selected. The method of data collection in this research was field and library. The data collection tool in the field section was two questionnaires of marital conflict and marital adjustment. To analyze the collected data, first at the descriptive level, using statistical indicators, the demographic characteristics of the sample were described by SPSS software. In inferential statistics, the statistical method used was the test of analysis of covariance. The results showed that the effect of the independent variable of emotional intelligence on the reduction of marital conflicts is statistically significant. And it can be inferred that emotional intelligence training has reduced the marital conflicts of married students of the University of Tehran in the experimental group compared to the control group. Also, the effect of the independent variable of emotional intelligence on marital adjustment was statistically significant. It can be inferred that emotional intelligence training has adjusted the marital adjustment of married students of the University of Tehran in the experimental group compared to the control group.

Keywords: emotional intelligence, marital conflicts, marital compatibility, married students

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Authors: Shahab Shafayyan, Tara Naderi

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Biogas is the product of decomposition of organic materials. A variety of sources, including animal wastes, municipal solid wastes, sewage and agricultural wastes may be used to produce biogas in an anaerobic process. The main forming material of biogas is methane gas, which can be used directly in a variety of ways, such as heating and as fuel, which is very common in a number of countries, such as China and India. In this article, the cost of biogas production from animal fertilizers, and its refined form, bio methane gas has been studied and it is shown that it can be an alternative for natural gas in terms of costs, in the near future. The cost of biogas purification to biomethane is more than three times the cost of biogas production for an average unit. Biomethane production costs, calculated for a small unit, is about $9/MMBTU and for an average unit is about $5.9/MMBTU.

Keywords: biogas, biomethane, anaerobic digestion, economic evaluation

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Authors: Ali Christian Chinedu, Asogwa Vincent Chidindu, Ejiofor Toochukwu Eleazar, Okadi Ashagwu Ojang

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The increasing rate of Boko Haram insurgency, farmer-herder clashes, and kidnapping in Nigeria has resulted in food shortages and high cost of protein sources like beef and fish. This challenge could be curbed with the production of edible insects, which contain several nutritional benefits like calories, protein, fat, vitamins, and minerals, depending on their species, metamorphic stage, and diet. Unfortunately, the benefits and competencies in producing, preserving, and marketing edible insects are still unknown to the public, including prospective farmers in Nigeria. Hence, this study determined teachers’ perception of integrating edible insects into the Animal Husbandry Curriculum in Senior Secondary Schools in Nigeria to equip the future generation with the relevant competencies for alternative sustainable protein supply. The study was carried out in Enugu State, Nigeria. The participants for the study comprised 162 agricultural science teachers. A questionnaire titled: Edible Insects Integration in Animal Husbandry Curriculum Questionnaire (EIIAHCQ) was used to collect data using a descriptive survey research design. We conducted data collection with the help of six research assistants. The study identified 11 objectives, 11 contents, 10 teaching methods, and 9 evaluation methods that could be integrated into the existing curriculum of animal husbandry in Nigeria. Among others, the Ministry of Education should integrate the finding of this study into the curriculum of Animal Husbandry in Nigeria to enhance the protein supply and curb food insecurity now and in the future.

Keywords: animal husbandry curriculum, edible insects, entomophagy, integration, secondary school, Nigeria

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Authors: Rashima Mahajan, Dipali Bansal, Shweta Singh

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Real time non-invasive Brain Computer Interfaces have a significant progressive role in restoring or maintaining a quality life for medically challenged people. This manuscript provides a comprehensive review of emerging research in the field of cognitive/affective computing in context of human neural responses. The perspectives of different emotion assessment modalities like face expressions, speech, text, gestures, and human physiological responses have also been discussed. Focus has been paid to explore the ability of EEG (Electroencephalogram) signals to portray thoughts, feelings, and unspoken words. An automated workflow-based protocol to design an EEG-based real time Brain Computer Interface system for analysis and classification of human emotions elicited by external audio/visual stimuli has been proposed. The front end hardware includes a cost effective and portable Emotive EEG Neuroheadset unit, a personal computer and a set of external stimulators. Primary signal analysis and processing of real time acquired EEG shall be performed using MATLAB based advanced brain mapping toolbox EEGLab/BCILab. This shall be followed by the development of MATLAB based self-defined algorithm to capture and characterize temporal and spectral variations in EEG under emotional stimulations. The extracted hybrid feature set shall be used to classify emotional states using artificial intelligence tools like Artificial Neural Network. The final system would result in an inexpensive, portable and more intuitive Brain Computer Interface in real time scenario to control prosthetic devices by translating different brain states into operative control signals.

Keywords: brain computer interface, electroencephalogram, EEGLab, BCILab, emotive, emotions, interval features, spectral features, artificial neural network, control applications

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Authors: Maria Poulou

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The study investigated how teachers’ self-rated Emotional Intelligence (EI), competence in implementing Social and Emotional Learning (SEL) skills and teaching efficacy relate to teacher-student relationships and students’ emotional and behavioral difficulties. Participants were 98 elementary teachers from public schools in central Greece. They completed the Self-Rated Emotional Intelligence Scale (SREIS), the Teacher SEL Beliefs Scale, the Teachers’ Sense of Efficacy Scale (TSES), the Student-Teacher Relationships Scale-Short Form (STRS-SF) and the Strengths and Difficulties Questionnaire (SDQ) for 617 of their students, aged 6-11 years old. Structural equation modeling was used to examine an exploratory model of the variables. It was demonstrated that teachers’ emotional intelligence, SEL beliefs and teaching efficacy were significantly related to teacher-student relationships, but they were not related to students’ emotional and behavioral difficulties. Rather, teachers’ perceptions of teacher-students relationships were significantly related to these difficulties. These findings and their implications for research and practice are discussed.

Keywords: emotional intelligence, social and emotional learning, teacher-student relationships, teaching efficacy

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Authors: M. Hassani, Y. Hassani, N. Ajudanioskooei, N. N. Benvid

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Laser Forming process as a non-contact thermal forming process is widely used to forming and bending of metallic and non-metallic sheets. In this process, according to laser irradiation along a specific path, sheet is bent. One of the most important output parameters in laser forming is bending angle that depends on process parameters such as physical and mechanical properties of materials, laser power, laser travel speed and the number of scan passes. In this paper, Artificial Neural Network and Fuzzy Logic System were used to predict of bending angle in laser forming process. Inputs to these models were laser travel speed and laser power. The comparison between artificial neural network and fuzzy logic models with experimental results has been shown both of these models have high ability to prediction of bending angles with minimum errors.

Keywords: artificial neural network, bending angle, fuzzy logic, laser forming

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Authors: Kerols Seif Said Botros

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The relationship between development and human rights has been debated for a long time. Various principles, from the right to development to development-based human rights, are applied to understand the dynamics between these two concepts. Despite the measures calculated, the connection between enhancement and human rights remains vague. Despite, the connection between these two opinions and the need to strengthen human rights have increased in recent years. It will then be examined whether the right to sustainable development is acceptable or not. In various human rights instruments and this is a good vibe to the request cited above. The book then cites domestic and international human rights treaties, as well as jurisprudence and regulations defining human rights institutions, to support this view.

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development, environmental rights, economic development, social sustainability human rights protection, human rights violations, workers’ rights, justice, security.

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Authors: Raed Alsufyani, Hissam Tawfik, Victor Chang, Muthu Ramachandran

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This study investigates the factors that influence the decision by players in the healthcare sector to embrace Cloud Business Intelligence Technology with a focus on healthcare organizations in Saudi Arabia. To bring this matter into perspective, this study primarily considers the Technology-Organization-Environment (TOE) framework and the Human Organization-Technology (HOT) fit model. A survey was hypothetically designed based on literature review and was carried out online. Quantitative data obtained was processed from descriptive and one-way frequency statistics to inferential and regression analysis. Data were analysed to establish factors that influence the decision to adopt Cloud Business intelligence technology in the healthcare sector. The implication of the identified factors was measured, and all assumptions were tested. 66.70% of participants in healthcare organization backed the intention to adopt cloud business intelligence system. 99.4% of these participants considered security concerns and privacy risk have been the most significant factors in the adoption of cloud Business Intelligence (CBI) system. Through regression analysis hypothesis testing point that usefulness, service quality, relative advantage, IT infrastructure preparedness, organization structure; vendor support, perceived technical competence, government support, and top management support positively and significantly influence the adoption of (CBI) system. The paper presents quantitative phase that is a part of an on-going project. The project will be based on the consequences learned from this study.

Keywords: cloud computing, business intelligence, HOT-fit model, TOE, healthcare and innovation adoption

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Authors: Selda Uzal Seyfi

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Goat and sheep milk is quite significant in human nutrition. It is considered as more important day by day. This study was carried out in order to determine applied manure management system and their possibilities of improvement in goat and sheep farm in between 2012 and 2013 years. In the study, it was investigated manure management systems of 25 pieces of sheep and goat farms. It was analyzed the manure collecting, storage and treatment features of farms and whether or not they are suitable for animal breeding. As a result of the study, it was determined that the applied manure management systems in the farm were insufficient. Planning the manure management systems in goat and sheep breeding is appropriate technical criteria is useful in respect of the animal welfare, animal health, the health of workers in the barn and environmental pollution.

Keywords: goat farm, sheep farm, manure storage, manure management

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Authors: Dehini Rachid, Ferdi Brahim

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The shunt active power filter (SAPF) is not destined only to improve the power factor, but also to compensate the unwanted harmonic currents produced by nonlinear loads. This paper presents a SAPF with identification and control method based on artificial neural network (ANN). To identify harmonics, many techniques are used, among them the conventional p-q theory and the relatively recent one the artificial neural network method. It is difficult to get satisfied identification and control characteristics by using a normal (ANN) due to the nonlinearity of the system (SAPF + fast nonlinear load variations). This work is an attempt to undertake a systematic study of the problem to equip the (SAPF) with the harmonics identification and DC link voltage control method based on (ANN). The latter has been applied to the (SAPF) with fast nonlinear load variations. The results of computer simulations and experiments are given, which can confirm the feasibility of the proposed active power filter.

Keywords: artificial neural networks (ANN), p-q theory, harmonics, total harmonic distortion

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Authors: Marla Wendy Spergel

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The purpose of this qualitative study is to showcase graduated high school students’ to voice on the impact past teachers had on their motivation to learn, and if this impact has affected their post-high-school lives. Through a focus group strategy, 21 graduated high school alumni participated in three separate focus groups. Participants discussed their former teacher’s emotional intelligence skills, which influenced their motivation to learn or not. A focused review of the literature revealed that teachers are a major factor in a student’s motivation to learn. This research was guided by Bandura’s Social Cognitive Theory of Motivation and constructs related to learning and motivation from Carl Rogers’ Humanistic Views of Personality, and from Brain-Based Learning perspectives with a major focus on the area of Emotional Intelligence. Findings revealed that the majority of participants identified teachers who most motivated them to learn and demonstrated skills associated with emotional intelligence. An important and disturbing finding relates to the saliency of negative experiences. Further work is recommended to expand this line of study in Higher Education, perform a long-term study to better gain insight into long-term benefits attributable to experiencing positive teachers, study the negative impact teachers have on students’ motivation to learn, specifically focusing on student anxiety and acquired helplessness.

Keywords: emotional intelligence, learning, motivation, pedagogy

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Authors: Yu-Hsiu Lin, Wen-Chun Lin, Yen-Chang Cheng, Chia-Ju Yeh, Yu-Chuan Chen, Tai-You Li

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Demand-Side Management (DSM) has the potential to reduce electricity costs and carbon emission, which are associated with electricity used in the modern society. A home Energy Management System (EMS) commonly used by residential consumers in a down-stream sector of a smart grid to monitor, control, and optimize energy efficiency to domestic appliances is a system of computer-aided functionalities as an energy audit for residential DSM. Implementing fault detection and classification to domestic appliances monitored, controlled, and optimized is one of the most important steps to realize preventive maintenance, such as residential air conditioning and heating preventative maintenance in residential/industrial DSM. In this study, a cloud intelligence-based green EMS that comes up with an Internet of Things (IoT) technology stack for residential DSM is developed. In the EMS, Arduino MEGA Ethernet communication-based smart sockets that module a Real Time Clock chip to keep track of current time as timestamps via Network Time Protocol are designed and implemented for readings of load phenomena reflecting on voltage and current signals sensed. Also, a Network-Attached Storage providing data access to a heterogeneous group of IoT clients via Hypertext Transfer Protocol (HTTP) methods is configured to data stores of parsed sensor readings. Lastly, a desktop computer with a WAMP software bundle (the Microsoft® Windows operating system, Apache HTTP Server, MySQL relational database management system, and PHP programming language) serves as a data science analytics engine for dynamic Web APP/REpresentational State Transfer-ful web service of the residential DSM having globally-Advanced Internet of Artificial Intelligence (AI)/Computational Intelligence. Where, an abstract computing machine, Java Virtual Machine, enables the desktop computer to run Java programs, and a mash-up of Java, R language, and Python is well-suited and -configured for AI in this study. Having the ability of sending real-time push notifications to IoT clients, the desktop computer implements Google-maintained Firebase Cloud Messaging to engage IoT clients across Android/iOS devices and provide mobile notification service to residential/industrial DSM. In this study, in order to realize edge intelligence that edge devices avoiding network latency and much-needed connectivity of Internet connections for Internet of Services can support secure access to data stores and provide immediate analytical and real-time actionable insights at the edge of the network, we upgrade the designed and implemented smart sockets to be embedded AI Arduino ones (called embedded AIduino). With the realization of edge analytics by the proposed embedded AIduino for data analytics, an Arduino Ethernet shield WizNet W5100 having a micro SD card connector is conducted and used. The SD library is included for reading parsed data from and writing parsed data to an SD card. And, an Artificial Neural Network library, ArduinoANN, for Arduino MEGA is imported and used for locally-embedded AI implementation. The embedded AIduino in this study can be developed for further applications in manufacturing industry energy management and sustainable energy management, wherein in sustainable energy management rotating machinery diagnostics works to identify energy loss from gross misalignment and unbalance of rotating machines in power plants as an example.

Keywords: demand-side management, edge intelligence, energy management system, fault detection and classification

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Authors: Lina Zhong, Sunny Sun, Rob Law

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Artificial intelligence has been developing rapidly. Previous studies have evaluated hotel technology either from an employee or consumer perspective. However, impacts, which mainly include the social and economic impacts of hotel robots, are unknown as they are newly introduced. To bridge the aforementioned research gap, this study evaluates hotel robots from contextual, diagnostic, evaluative, and strategic aspects using framework analysis as a basis to assist hotel managers in real-time hotel marketing strategy management, adjustment and revenue achievement. Findings show that, from a consumer perspective, the overall acceptance of hotel robots is low. The main implication is that the cost of hotel robots should be carefully estimated, and the investment should be made based on phases.

Keywords: application, evaluation, framework analysis, hotel robot

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Authors: Jihen Bennaceur, Wissem Zouaghi, Ali Mabrouk

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The major aim of cyber threat intelligence (CTI) is to provide sophisticated knowledge about cybersecurity threats to ensure internal and external safeguards against modern cyberattacks. Inaccurate, incomplete, outdated, and invaluable threat intelligence is the main problem. Therefore, data analysis based on AI algorithms is one of the emergent solutions to overcome the threat of information-sharing issues. In this paper, we propose a supervised machine learning-based algorithm to improve threat information sharing by providing a sophisticated classification of cyber threats and data. Extensive simulations investigate the accuracy, precision, recall, f1-score, and support overall to validate the designed algorithm and to compare it with several supervised machine learning algorithms.

Keywords: threat information sharing, supervised learning, data classification, performance evaluation

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Authors: Seyedabdolali Mortazavi Kamalabadi, Faegheh Taheran

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By giving an overview of previous arguments and findings concerned with the concept of competitive advantage, first, we define the overall concept of competitive advantage and discuss nuances of understanding such an important and strategic idea. Finally, by considering the major concerns of marketing academia, including globalization, AI-based technologies, consumer well-being, and internal coopetition between a firm’s units, fruitful avenues to be explored by future studies are presented in the form of research propositions. In the end, relevant gaps mentioned by numerous studies that are worth investigating are demonstrated.

Keywords: artificial intelligence, competitive advantage, consumer well-being, coopetition, globalization, literature review, temporary competitive advantage

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

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

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

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Authors: Neeta Kumari, Gopal Pathak

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Artificial neural network has been proved to be an efficient tool for non-parametric modeling of data in various applications where output is non-linearly associated with input. It is a preferred tool for many predictive data mining applications because of its power , flexibility, and ease of use. A standard feed forward networks (FFN) is used to predict the groundwater fluoride content. The ANN model is trained using back propagated algorithm, Tansig and Logsig activation function having varying number of neurons. The models are evaluated on the basis of statistical performance criteria like Root Mean Squarred Error (RMSE) and Regression coefficient (R2), bias (mean error), Coefficient of variation (CV), Nash-Sutcliffe efficiency (NSE), and the index of agreement (IOA). The results of the study indicate that Artificial neural network (ANN) can be used for groundwater fluoride prediction in the limited data situation in the hard rock region like western parts of Jharkhand with sufficiently good accuracy.

Keywords: Artificial neural network (ANN), FFN (Feed-forward network), backpropagation algorithm, Levenberg-Marquardt algorithm, groundwater fluoride contamination

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Authors: Akhatjon Nasullaev, Raffaella Manzini, Vincent Frigant

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The prior research largely discussed open innovation practices both in large and small and medium-sized enterprises (SMEs). Open Innovation compels firms to observe and analyze the external environment in order to tap new opportunities for inbound and/or outbound flows of knowledge, ideas, work in progress innovations. As SMEs are different from their larger counterparts, they face several limitations in utilizing open innovation activities, such as resource scarcity, unstructured innovation processes and underdeveloped innovation capabilities. Technology intelligence – the process of systematic acquisition, assessment and communication of information about technological trends, opportunities and threats can mitigate this limitation by enabling SMEs to identify technological and market opportunities in timely manner and undertake sound decisions, as well as to realize a ‘first mover advantage’. Several studies highlighted firm-level barriers to successful implementation of open innovation practices in SMEs, namely challenges in partner selection, intellectual property rights and trust, absorptive capacity. This paper aims to investigate the question how technology intelligence can be useful for SMEs to overcome the barriers to effective open innovation. For this, we conduct a case study in four Estonian life-sciences SMEs. Our findings revealed that technology intelligence can support SMEs not only in inbound open innovation (taking into account inclination of most firms toward technology exploration aspects of open innovation) but also outbound open innovation. Furthermore, the results of this study state that, although SMEs conduct technology intelligence in unsystematic and uncoordinated manner, it helped them to increase their innovative performance.

Keywords: technology intelligence, open innovation, SMEs, life sciences

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Authors: Muhittin Fatih Demirhan, Sibel Alapala Demirhan

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Agriculture, is also regarded as the primary activity area in all economies. When international comparisons are made Turkey has comparative advantages in agricultural potential. However, it is diffi cult to say that Turkey's agricultural productivity and use of technology is well developed in terms of sufficieny. Turkey, in terms of agricultural production, is one of the rare self-sufficient countries, but for supplying excessive demand of its domesticproduction to foreign markets to obtain the necessary income it is rather insufficient. On the basis of wrong policies implemented during the crisis and found that bottlenecks in agriculture and animal husbandry or agriculture policies of the IMF and World Bank are imposed on countries like Turkey. The IMF and the World Bank, the reduction of support in the agricultural and livestock Turkey, is known to put pressure for the abolition. Under these circumstances, our farmers, livestock producers and breeders of, not a chance to compete in the same market with EU producers. Animal products that capture the productivity levels of developed countries, seems to be our chance to compete with the quality and hygiene criteria. Thus, the discussion of the issue must be raised as for the sector's contribution to the economy in terms of further increasing production of the existing potential in mobilization.

Keywords: agricultural development, animal production, competition, economic crisis, food supply

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Authors: William Batson, Yunsik Song, Abel Simie

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Recent trends in suburban design and planning have centered on economic efficiency in construction and completion. In doing so, developers, builders, and architects have bypassed free and reliable sustainable solutions to minimize the carbon footprint and improve the environment. Often, suburban areas are designed without landscape features, sidewalks, parks, adequate lighting, or walking space. Much of the design concern involves minimizing construction costs and streamlining streets and utilities. A new development in creating retention ponds to mitigate flooding and slow runoff is one step in the positive direction. However, "if you build them (suburbs), they (fauna) will come." The inevitable flora and fauna that soon propagate and take refuge within these artificial retention ponds create an additional dilemma. Architects, planners, and developers know the requirements and current strategies to provide residents and wildlife with a viable and sustainable environment. This includes habitat for hibernating animals and facilitating opportunities, especially for cold-blooded mammals. Many species that migrate to these artificial ponds struggle to survive, especially during flooding and when the water table drains below the artificial rim, preventing aquatic mammals from climbing on land. This flooding often results from large areas of impervious asphalt and concrete. These impervious surfaces retain and dispense large amounts of rainwater and contaminants that carry industrial pollutants, oil, plastics, animal waste, and fertilizers into storm drains and then deposited in these retention ponds. This paper will identify and show how simple and logical solutions are used to create a sustainable suburb and reduce the carbon footprint using landscape architectural strategies and cost-free design solutions. We will also demonstrate simple changes in the present suburban design model to provide a viable and sustainable suburb for the 21st century.

Keywords: sustainavilty, suburban, flora, fauna, carbon footprint

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Authors: Elabed Saud

Abstract:

This paper introduces the new ability of Artificial Neural Networks (ANNs) in estimating speed and controlling the separately excited DC motor. The neural control scheme consists of two parts. One is the neural estimator which is used to estimate the motor speed. The other is the neural controller which is used to generate a control signal for a converter. These two neutrals are training by Levenberg-Marquardt back-propagation algorithm. ANNs are the standard three layers feed-forward neural network with sigmoid activation functions in the input and hidden layers and purelin in the output layer. Simulation results are presented to demonstrate the effectiveness of this neural and advantage of the control system DC motor with ANNs in comparison with the conventional scheme without ANNs.

Keywords: Artificial Neural Network (ANNs), excited DC motor, convenional controller, speed Controller

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Authors: Annalisa Gaetani, Anna Nora Tassetti, Gianna Fabi

Abstract:

The hydroacoustic technology is an efficient tool to study the sea environment: the most recent advancement in artificial habitat mapping involves acoustic systems to investigate fish abundance, distribution and behavior in specific areas. Along with a detailed high-coverage bathymetric mapping of the seabed, the high-frequency Multibeam Echosounder (MBES) offers the potential of detecting fine-scale distribution of fish aggregation, combining its ability to detect at the same time the seafloor and the water column. Surveying fish schools distribution around artificial structures, MBES allows to evaluate how their presence modifies the biological natural habitat overtime in terms of fish attraction and abundance. In the last years, artificial habitat mapping experiences have been carried out by CNR-ISMAR in the Adriatic sea: fish assemblages aggregating at offshore gas platforms and artificial reefs have been systematically monitored employing different kinds of methodologies. This work focuses on two case studies: a gas extraction platform founded at 80 meters of depth in the central Adriatic sea, 30 miles far from the coast of Ancona, and the concrete and steel artificial reef of Senigallia, deployed by CNR-ISMAR about 1.2 miles offshore at a depth of 11.2 m . Relating the MBES data (metrical dimensions of fish assemblages, shape, depth, density etc.) with the results coming from other methodologies, such as experimental fishing surveys and underwater video camera, it has been possible to investigate the biological assemblage attracted by artificial structures hypothesizing which species populate the investigated area and their spatial dislocation from these artificial structures. Processing MBES bathymetric and water column data, 3D virtual scenes of the artificial habitats have been created, receiving an intuitive-looking depiction of their state and allowing overtime to evaluate their change in terms of dimensional characteristics and depth fish schools’ disposition. These MBES surveys play a leading part in the general multi-year programs carried out by CNR-ISMAR with the aim to assess potential biological changes linked to human activities on.

Keywords: artificial habitat mapping, fish assemblages, hydroacustic technology, multibeam echosounder

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