Search results for: artificial agency

Authors: Ahmed Radwan, Ahmed Abdel Ghaney

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This paper delves into the transformative potential of artificial intelligence (AI) in fostering creativity within the domain of architecture education, especially with a specific emphasis on its implications within the Design Studios; the convergence of AI and architectural pedagogy has introduced avenues for redefining the boundaries of creative expression and problem-solving. By harnessing AI-driven tools, students and educators can collaboratively explore a spectrum of design possibilities, stimulate innovative ideation, and engage in multidimensional design processes. This paper investigates the ways in which AI contributes to architectural creativity by facilitating generative design, pattern recognition, virtual reality experiences, and sustainable design optimization. Furthermore, the study examines the balance between AI-enhanced creativity and the preservation of core principles of architectural design/education, ensuring that technology is harnessed to augment rather than replace foundational design skills. Through an exploration of Egypt's architectural heritage and contemporary challenges, this research underscores how AI can synergize with cultural context and historical insights to inspire cutting-edge architectural solutions. By analyzing AI's impact on nurturing creativity among Egyptian architecture students, this paper seeks to contribute to the ongoing discourse on the integration of technology within global architectural education paradigms. It is hoped that this research will guide the thoughtful incorporation of AI in fostering creativity while preserving the authenticity and richness of architectural design education in Egypt and beyond.

Keywords: architecture, artificial intelligence, architecture education, Egypt

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Authors: Jéssica Bernegossi, Lívia Nordi Dovigo, Marlus Chorilli

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Mucoadhesive liquid crystalline systems are emerging how delivery systems for oral cavity. These systems are interesting since they facilitate the targeting of medicines and change the release enabling a reduction in the number of applications made by the patient. The buccal mucosa is permeable besides present a great blood supply and absence of first pass metabolism, it is a good route of administration. It was developed two systems liquid crystals utilizing as surfactant the ethyl alcohol ethoxylated and propoxylated (30%) as oil phase the oleic acid (60%), and the aqueous phase (10%) dispersion of polymer polyethylenimine (0.5%) or dispersion of polymer poloxamer 407 (16%), with the intention of applying the buccal mucosa. Initially, was performed for characterization of systems the conference by polarized light microscopy and rheological analysis. For the preparation of the systems the components described was added above in glass vials and shaken. Then, 30 and 100% artificial saliva were added to each prepared formulation so as to simulate the environment of the oral cavity. For the verification of the system structure, aliquots of the formulations were observed in glass slide and covered with a coverslip, examined in polarized light microscope (PLM) Axioskop - Zeizz® in 40x magnifier. The formulations were also evaluated for their rheological profile Rheometer TA Instruments®, which were obtained rheograms the selected systems employing fluency mode (flow) in temperature of 37ºC (98.6ºF). In PLM, it was observed that in formulations containing polyethylenimine and poloxamer 407 without the addition of artificial saliva was observed dark-field being indicative of microemulsion, this was also observed with the formulation that was increased with 30% of the artificial saliva. In the formulation that was increased with 100% simulated saliva was shown to be a system structure since it presented anisotropy with the presence of striae being indicative of hexagonal liquid crystalline mesophase system. Upon observation of rheograms, both systems without the addition of artificial saliva showed a Newtonian profile, after addition of 30% artificial saliva have been given a non-Newtonian behavior of the pseudoplastic-thixotropic type and after adding 100% of the saliva artificial proved plastic-thixotropic. Furthermore, it is clearly seen that the formulations containing poloxamer 407 have significantly larger (15-800 Pa) shear stress compared to those containing polyethyleneimine (5-50 Pa), indicating a greater plasticity of these. Thus, it is possible to observe that the addition of saliva was of interest to the system structure, starting from a microemulsion for a liquid crystal system, thereby also changing thereby its rheological behavior. The systems have promising characteristics as controlled release systems to the oral cavity, as it features good fluidity during its possible application and greater structuring of the system when it comes into contact with environmental saliva.

Keywords: liquid crystal system, poloxamer 407, polyethylenimine, rheology

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Authors: Kate Benedicta Amenador, Dianjian Wang, Bright Nkrumah

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This extensive bibliometric study explores the dynamic influence of artificial intelligence in the field of English as a Foreign Language (EFL) between 2012 and 2024. The study, which examined 4,500 articles from Google Scholar, Modern Language Association Linguistics Abstracts, Web of Science, Scopus, Researchgate, and library genesis databases, indicates that AI integration in EFL is on the rise. This notable increase is ascribed to a variety of transformative events, including increased academic funding for higher education and the COVID-19 epidemic. The results of the study identify leading contributors, prominent authors, publishers and sources, with the United States, China and the United Kingdom emerging as key contributors. The co-occurrence analysis of key terms reveals five clusters highlighting patterns in AI-enhanced language instruction and learning, including evaluation strategies, educational technology, learning motivation, EFL teaching aspects, and learner feedback. The study also discusses the impact of various AIs in enhancing EFL writing skills with software such as Grammarly, Quilbot, and Chatgpt. The current study recognizes limitations in database selection and linguistic constraints. Nevertheless, the results provide useful insights for educators, researchers and policymakers, inspiring and guiding a cross-disciplinary collaboration and creative pedagogical techniques and approaches to teaching and learning in the future.

Keywords: artificial intelligence, bibliometrics study, VOSviewer visualization, English as a foreign language

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Authors: Tahammal Hussain, Khuram Zia, Mumammad Jalal Arif, Megha Parajulee, Abdul Hakeem

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The Bioassay on neonate, 2nd and 3rd instar larvae of Helicoverpa armigera (Hubner) were conducted against Bacillus thuringiensis proteins Cry1Ac. Cry1Ac was incorporated into an artificial diet and was serially diluted with distilled water and then mixed with diet at an appropriate temperature of diet. Toxins incorporated prepared diet was poured into Petri-dishes. For controls, distilled water was mixed with the diet. Five toxin doses 0.25, 0.5, 1, 2, and 4 ug / ml and one control were used for each instars of H. armigera 20 larvae were used in each replication and each treatment is replicated four times. LC50 of Cry1Ac against neonate, 2nd and 3rd instar larvae of H. armigera were 0.34, 0.81 and 1.46 ug / ml. So Cry1Ac is more effective against neonate larvae of H .armigera as compared to 2nd and 3rd instar larvae under laboratory conditions.

Keywords: B. thuringiensis, Cry1Ac, H. armigera, toxicity

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Authors: Mogeeb A. El-Sheikh

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The study has developed the previous design of an artificial anthropomorphic humanoid hand and accustomed it as a prosthetic hand. The main specifications of this design are determined. The development of our previous design involves the main artificial hand’s parts and subassemblies, palm, fingers, and thumb. In addition, the study presents an adaptable socket design for a transradial amputee. This hand has 3 fingers and thumb. It is more reliable, cosmetics, modularity, and ease of assembly. Its size and weight are almost as a natural hand. The socket cavity has the capability for different sizes of a transradial amputee. The study implements the developed design by using rapid prototype and specifies its main specifications by using a data glove and finite element method.

Keywords: adaptable socket, prosthetic hand, transradial amputee, data glove

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Authors: Kareem Adedokun

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Official secrecy is one of the colonial vestiges which upholds non – disclosure of essential information for public consumption. Information, though an indispensable tool in tax administration, is not to be divulged by any person in an official duty of the revenue agency. As a matter o fact, the Federal Inland Revenue Service (Establishment) Act, 2007 emphasizes secrecy and confidentiality in dealing with tax payer’s document, information, returns and assessment in a manner reminiscent of protecting tax payer’s privacy in all situations. It is so serious that any violation attracts criminal sanction. However, Nigeria, being a democratic and egalitarian state recently enacted Freedom of Information Act which heralded in openness in governance and takes away the confidentialities associated with official secrets Laws. Official secrecy no doubts contradicts the philosophy of freedom of information but maintaining a proper balance between protected rights of tax payers and public interest which revenue agency upholds is an uphill task. Adopting the Doctrinal method, therefore, the author of this paper probes into the real nature of the relationship between taxpayers and Revenue Agencies. It also interfaces official secrecy with the doctrine of Freedom of Information and consequently queries the retention of non – disclosure clause under Federal Inland Revenue Service (Establishment) Act (FIRSEA) 2007. The paper finds among others that non – disclosure provision in tax statutes particularly as provided for in FIRSEA is not absolute; so also is the constitutional rights and freedom of information and unless the non – disclosure clause finds justification under any recognized exemption provided under the Freedom of Information Act, its retention is antithesis to democratic ethos and beliefs as it may hinder public interest and public order.

Keywords: confidentiality, information, official secrecy, tax administration

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Authors: Goodness Onwuka, Khaled Abou-El-Hossein

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Borosilicate-crown (BK7) glass has found broad application in the optic and automotive industries and the growing demands for nanometric surface finishes is becoming a necessity in such applications. Thus, it has become paramount to optimize the parameters influencing the surface roughness of this precision lens. The research was carried out on a 4-axes Nanoform 250 precision lathe machine with an ultra-high precision grinding spindle. The experiment varied the machining parameters of feed rate, wheel speed and depth of cut at three levels for different combinations using Box Behnken design of experiment and the resulting surface roughness values were measured using a Taylor Hobson Dimension XL optical profiler. Acoustic emission monitoring technique was applied at a high sampling rate to monitor the machining process while further signal processing and feature extraction methods were implemented to generate the input to a neural network algorithm. This paper highlights the training and development of a back propagation neural network prediction algorithm through careful selection of parameters and the result show a better classification accuracy when compared to a previously developed response surface model with very similar machining parameters. Hence artificial neural network algorithms provide better surface roughness prediction accuracy in the ultra-high precision grinding of BK7 glass.

Keywords: acoustic emission technique, artificial neural network, surface roughness, ultra-high precision grinding

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Authors: Jana Ismail, Layla Fallatah, Maha Alshmaisi

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ChatGPT, a cutting-edge language model built on the GPT-3.5 architecture, has garnered attention for its profound natural language processing capabilities, holding promise for transformative applications in customer service and content creation. This study delves into ChatGPT's architecture, aiming to comprehensively understand its strengths and potential limitations. Through systematic experiments across diverse domains, such as general knowledge and creative writing, we evaluated the model's coherence, context retention, and task-specific accuracy. While ChatGPT excels in generating human-like responses and demonstrates adaptability, occasional inaccuracies and sensitivity to input phrasing were observed. The study emphasizes the impact of prompt design on output quality, providing valuable insights for the nuanced deployment of ChatGPT in conversational AI and contributing to the ongoing discourse on the evolving landscape of natural language processing in artificial intelligence.

Keywords: artificial Inelegance, chatGPT, open AI, NLP

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Authors: Hasan G. Elmazoghi, Aisha I. Alzayani, Lubna S. Bentaher

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Rainfall is a highly non-linear phenomena, which requires application of powerful supervised data mining techniques for its accurate prediction. In this study the Artificial Neural Network (ANN) technique is used to predict the mean monthly historical rainfall data collected from BENINA station in Benghazi for 31 years, the period of “1977-2006” and the results are compared against the observed values. The specific objective to achieve this goal was to determine the best combination of weather variables to be used as inputs for the ANN model. Several statistical parameters were calculated and an uncertainty analysis for the results is also presented. The best ANN model is then applied to the data of one year (2007) as a case study in order to evaluate the performance of the model. Simulation results reveal that application of ANN technique is promising and can provide reliable estimates of rainfall.

Keywords: neural networks, rainfall, prediction, climatic variables

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Authors: Haider Javed Uppal, Javed Yunas Uppal

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In today's globalized world, it is necessary to develop a form of music that is able to evoke equal emotional responses among people from diverse cultural backgrounds. Indigenous cultures throughout history have developed their own music cognition, specifically in terms of the connections between music and mood. With the advancements in artificial intelligence technologies, it has become possible to analyze and categorize music features such as timbre, harmony, melody, and rhythm and relate them to the resulting mood effects experienced by listeners. This paper presents a model that utilizes a screenshot translator to convert music from different origins into waveforms, which are then analyzed using machine learning and information retrieval techniques. By connecting these waveforms with Thayer's matrix of moods, a mood classifier has been developed using fuzzy logic algorithms to determine the emotional impact of different types of music on listeners from various cultures.

Keywords: cognition, world music, artificial intelligence, Thayer’s matrix

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Authors: Weber-Lewerenz Bianca

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Digitization is developing fast and has become a powerful tool for digital planning, construction, and operations. Its transformation bears high potentials for companies, is critical for success, and thus, requires responsible handling. This study provides an assessment of calls made in the sustainable development goals by the United Nations (SDGs), White Papers on AI by international institutions, EU-Commission and German Government requesting for the consideration and protection of values and fundamental rights, the careful demarcation between machine (artificial) and human intelligence and the careful use of such technologies. The study discusses digitization and the impacts of artificial intelligence (AI) in construction engineering from an ethical perspective by generating data via conducting case studies and interviewing experts as part of the qualitative method. This research evaluates critically opportunities and risks revolving around corporate digital responsibility (CDR) in the construction industry. To the author's knowledge, no study has set out to investigate how CDR in construction could be conceptualized, especially in relation to the digitization and AI, to mitigate digital transformation both in large, medium-sized, and small companies. No study addressed the key research question: Where can CDR be allocated, how shall its adequate ethical framework be designed to support digital innovations in order to make full use of the potentials of digitization and AI? Now is the right timing for constructive approaches and apply ethics-by-design in order to develop and implement a safe and efficient AI. This represents the first study in construction engineering applying a holistic, interdisciplinary, inclusive approach to provide guidelines for orientation, examine benefits of AI and define ethical principles as the key driver for success, resources-cost-time efficiency, and sustainability using digital technologies and AI in construction engineering to enhance digital transformation. Innovative corporate organizations starting new business models are more likely to succeed than those dominated by conservative, traditional attitudes.

Keywords: construction engineering, digitization, digital transformation, artificial intelligence, ethics, corporate digital responsibility, digital innovation

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Authors: Roberto Feltrero, Sara Osuna-Acedo

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Responsible Research and Innovation have to accomplish one fundamental aim: everybody has to participate in the benefits of innovation, but also innovation has to be democratic; that is to say, everybody may have the possibility to participate in the decisions in the innovation process. Particularly, a democratic and inclusive model of social participation and innovation includes persons with disabilities and people at risk of discrimination. Innovations on Artificial Intelligence for social development have to accomplish the same dual goal: improving equality for accessing fields of public interest like education, training and public services, as well as improving civic and democratic participation in the process of developing such innovations for all. This research aims to develop innovations, policies and policy recommendations to apply and disseminate such artificial intelligence and social model for making educational and administrative processes more accessible. First, designing a citizen participation process to engage citizens in the designing and use of artificial intelligence tools for public services. This will result in improving trust in democratic institutions contributing to enhancing the transparency, effectiveness, accountability and legitimacy of public policy-making and allowing people to participate in the development of ethical standards for the use of such technologies. Second, improving educational tools for lifelong learning with AI models to improve accountability and educational data management. Dissemination, education and social participation will be integrated, measured and evaluated in innovative educational processes to make accessible all the educational technologies and content developed on AI about responsible and social innovation. A particular case will be presented regarding access for all to educational tools and public services. This accessibility requires cognitive adaptability because, many times, legal or administrative language is very complex. Not only for people with cognitive disabilities but also for old people or citizens at risk of educational or social discrimination. Artificial Intelligence natural language processing technologies can provide tools to translate legal, administrative, or educational texts to a more simple language that can be accessible to everybody. Despite technological advances in language processing and machine learning, this becomes a huge project if we really want to respect ethical and legal consequences because that kinds of consequences can only be achieved with civil and democratic engagement in two realms: 1) to democratically select texts that need and can be translated and 2) to involved citizens, experts and nonexperts, to produce and validate real examples of legal texts with cognitive adaptations to feed artificial intelligence algorithms for learning how to translate those texts to a more simple and accessible language, adapted to any kind of population.

Keywords: responsible research and innovation, AI social innovations, cognitive accessibility, public participation

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Authors: Jaballah Jamil

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KLD (Peter Kinder, Steve Lydenberg and Amy Domini) research & analytics is an independent intermediary of social performance information that adopts an investor-pay model. KLD rating agency does not have an explicit monitoring on the rated firm which suggests that KLD ratings may not include private informations. Moreover, the incapacity of KLD to predict accurately the extra-financial rating of Enron casts doubt on the reliability of KLD ratings. Therefore, we first investigate whether KLD ratings affect investors' perception by studying the effect of KLD rating changes on firms' financial performances. Second, we study the impact of the Enron scandal on investors' perception of KLD rating changes by comparing the effect of KLD rating changes on firms' financial performances before and after the failure of Enron. We propose an empirical study that relates a number of equally-weighted portfolios returns, excess stock returns and book-to-market ratio to different dimensions of KLD social responsibility ratings. We first find that over the last two decades KLD rating changes influence significantly and negatively stock returns and book-to-market ratio of rated firms. This finding suggests that a raise in corporate social responsibility rating lowers the firm's risk. Second, to assess the Enron scandal's effect on the perception of KLD ratings, we compare the effect of KLD rating changes before and after the Enron scandal. We find that after the Enron scandal this significant effect disappears. This finding supports the view that the Enron scandal annihilates the KLD's effect on Socially Responsible Investors. Therefore, our findings may question results of recent studies that use KLD ratings as a proxy for Corporate Social Responsibility behavior.

Keywords: KLD social rating agency, investors' perception, investment decision, financial performance

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Authors: Shohreh Ghasemi

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Introduction: Melanoma is a complex disease with various clinical and histopathological features that impact prognosis and treatment decisions. Traditional methods of melanoma prognosis involve manual examination and interpretation of clinical and histopathological data by dermatologists and pathologists. However, the subjective nature of these assessments can lead to inter-observer variability and suboptimal prognostic accuracy. AI, with its ability to analyze vast amounts of data and identify patterns, has emerged as a promising tool for improving melanoma prognosis. Methods: A comprehensive literature search was conducted to identify studies that employed AI techniques for melanoma prognosis. The search included databases such as PubMed and Google Scholar, using keywords such as "artificial intelligence," "melanoma," and "prognosis." Studies published between 2010 and 2022 were considered. The selected articles were critically reviewed, and relevant information was extracted. Results: The review identified various AI methodologies utilized in melanoma prognosis, including machine learning algorithms, deep learning techniques, and computer vision. These techniques have been applied to diverse data sources, such as clinical images, dermoscopy images, histopathological slides, and genetic data. Studies have demonstrated the potential of AI in accurately predicting melanoma prognosis, including survival outcomes, recurrence risk, and response to therapy. AI-based prognostic models have shown comparable or even superior performance compared to traditional methods.

Keywords: artificial intelligence, melanoma, accuracy, prognosis prediction, image analysis, personalized medicine

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Authors: Susmita Panda, Arnab Banerjee, Ajinkya Baxy, Bappaditya Manna

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The design of short to medium-span high-speed bridges in critical locations is an essential aspect of vehicle-bridge interaction. Due to dynamic interaction between moving load and bridge, mathematical models or finite element modeling computations become time-consuming. Thus, to reduce the computational effort, a universal approximator using an artificial neural network (ANN) has been used to evaluate the dynamic response of the bridge. The data set generation and training of surrogate models have been conducted over the results obtained from mathematical modeling. Further, the robustness of the surrogate model has been investigated, which showed an error percentage of less than 10% with conventional methods. Additionally, the dependency of the dynamic response of the bridge on various load and bridge parameters has been highlighted through a parametric study.

Keywords: artificial neural network, mode superposition method, moving load analysis, surrogate models

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Authors: C. S. Subhashini, H. L. Premaratne

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Landslides are the most recurrent and prominent disaster in Sri Lanka. Sri Lanka has been subjected to a number of extreme landslide disasters that resulted in a significant loss of life, material damage, and distress. It is required to explore a solution towards preparedness and mitigation to reduce recurrent losses associated with landslides. Artificial Neural Networks (ANNs) and Hidden Markov Model (HMMs) are now widely used in many computer applications spanning multiple domains. This research examines the effectiveness of using Artificial Neural Networks and Hidden Markov Model in landslides predictions and the possibility of applying the modern technology to predict landslides in a prominent geographical area in Sri Lanka. A thorough survey was conducted with the participation of resource persons from several national universities in Sri Lanka to identify and rank the influencing factors for landslides. A landslide database was created using existing topographic; soil, drainage, land cover maps and historical data. The landslide related factors which include external factors (Rainfall and Number of Previous Occurrences) and internal factors (Soil Material, Geology, Land Use, Curvature, Soil Texture, Slope, Aspect, Soil Drainage, and Soil Effective Thickness) are extracted from the landslide database. These factors are used to recognize the possibility to occur landslides by using an ANN and HMM. The model acquires the relationship between the factors of landslide and its hazard index during the training session. These models with landslide related factors as the inputs will be trained to predict three classes namely, ‘landslide occurs’, ‘landslide does not occur’ and ‘landslide likely to occur’. Once trained, the models will be able to predict the most likely class for the prevailing data. Finally compared two models with regards to prediction accuracy, False Acceptance Rates and False Rejection rates and This research indicates that the Artificial Neural Network could be used as a strong decision support system to predict landslides efficiently and effectively than Hidden Markov Model.

Keywords: landslides, influencing factors, neural network model, hidden markov model

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Authors: P. Sawatnatee, S. Udomsuk, K-N. Areerak, K-L. Areerak, A. Srikaew

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The paper presents how to design the indirect vector control of three-phase induction motor drive systems using the artificial intelligence technique called the adaptive tabu search. The results from the simulation and the experiment show that the drive system with the controller designed from the proposed method can provide the best output speed response compared with those of the conventional method. The controller design using the proposed technique can be used to create the software package for engineers to achieve the optimal controller design of the induction motor speed control based on the indirect vector concept.

Keywords: indirect vector control, induction motor, adaptive tabu search, control design, artificial intelligence

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Authors: Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević, Lidija R. Jevrić, Stela Jokić

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The subject of this paper is to correlate antibacterial behavior of benzimidazole derivatives with their molecular characteristics using chemometric QSAR (Quantitative Structure–Activity Relationships) approach. QSAR analysis has been carried out on the inhibitory activity of benzimidazole derivatives against Staphylococcus aureus. The data were processed by linear least squares (LLS) and artificial neural network (ANN) procedures. The LLS mathematical models have been developed as a calibration models for prediction of the inhibitory activity. The quality of the models was validated by leave one out (LOO) technique and by using external data set. High agreement between experimental and predicted inhibitory acivities indicated the good quality of the derived models. These results are part of the CMST COST Action No. CM1306 "Understanding Movement and Mechanism in Molecular Machines".

Keywords: Antibacterial, benzimidazoles, chemometric, QSAR.

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Authors: Lily Ye

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The contemporary fashion industry has witnessed the global rise of Chinese fashion designers. China plays more and more important role in this sector globally. One of the key debates in contemporary time is the conception of Chinese fashion. A close look at previous discussions on Chinese fashion reveals that most of them are explored through the lens of cultural knowledge and assumptions, using the dichotomous models of East and West. The results of these studies generate an essentialist and orientalist notion of Chinoiserie and Chinese fashion, which sees individual designers from China as undifferential collective members marked by a unique and fixed set of cultural scripts. This study challenges this essentialist conceptualization and brings fresh insights to the discussion of Chinese fashion identity against the backdrop of globalisation. Different from a culturalist approach to researching Chinese fashion, this paper presents an alternative position to address the research agenda through the mobilisation of Giddens’ (1991) theory of reflexive identity formation, privileging individuals’ agency and reflexivity. This approach to the discussion of identity formation not only challenges the traditional view seeing identity as the distinctive and essential characteristics belonging to any given individual or shared by all members of a particular social category or group but highlights fashion designers’ strategic agency and their role as fashion activist. This study draws evidence from a textual analysis of published stories of a group of established Chinese designers such as Guo Pei, Huishan Zhang, Masha Ma, Uma Wang, and Ma Ke. In line with Giddens’ concept of 'reflexive project of the self', this study uses a narrative methodology. Narratives are verbal accounts or stories relating to experiences of Chinese fashion designers. This approach offers the fashion designers a chance to 'speak' for themselves and show the depths and complexities of their experiences. It also emphasises the nuances of identity formation in fashion designers, whose experiences cannot be captured in neat typologies. Thematic analysis (Braun and Clarke, 2006) is adopted to identify and investigate common themes across the whole dataset. At the centre of the analysis is individuals’ self-articulation of their perceptions, experiences and themselves in relation to culture, fashion and identity. The finding indicates that identity is constructed around anchors such as agency, cultural hybridity, reflexivity and sustainability rather than traditional collective categories such as culture and ethnicity. Thus, the old East-West dichotomy is broken down, and essentialised social categories are challenged by the multiplicity and fragmentation of self and cultural hybridity created within designers’ 'small narratives'.

Keywords: Chinoiserie, fashion identity, fashion activism, narrative inquiry

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Authors: Chen Xi, Kuan Sinman, LI Haofeng, Huang Hongming, Zeng Chengyu, Tong Zhiyuan

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Stray animals are on the rise in mainland China's cities. There are legal reasons for this, namely the lack of protection for domestic pets in mainland China, where only wildlife protection laws exist. At a social level, the ease with which families adopt pets and the lack of a social view of animal nature has led to the frequent abandonment and loss of stray animals. If left unmanaged, conflicts between humans and stray animals can also increase. This project provides an inexpensive and widely applicable management tool for urban management by collecting videos and pictures of stray animals captured by surveillance or transmitted by humans and using artificial intelligence technology (mainly using YOLOv5 recognition technology) and recording and managing them in a database.

Keywords: urban planning, urban governance, artificial intelligence, convolutional neural network

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Authors: Lourdita Llanto

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This study described and assessed the implementation of artificial insemination (AI) for cattle and carabaos in the Bicol Region, Philippines: Albay, Sorsogon and Camarines Sur. Three hundred respondents were interviewed. Results were analyzed using frequency counts, means, percentages and chi-square test. Semen samples from different stations were analyzed for motility, viability and morphology. T-test was used in semen quality evaluation. Provincial AI coordinators (PAIC) were male, averaging 59 years old, married, had college education, served in government service for 34 years, but as PAIC for 5.7 years. All had other designations. Mean AI operation was 11.33 years with annual support from the local government unit of Php76,666.67. AI technicians were males, married, with college education, and trained on AI. Problems were on mobility; inadequate knowledge of farmers in animal raising and AI; and lack of liquid nitrogen and frozen semen supply. There was 2.95 municipalities and breedable cattle/carabaos of 3,091.25 per AI technician. Mean number of artificially inseminated animals per AI technician for 2011 was 28.57 heads for carabaos and 8.64 heads for cattle. There was very low participation rate among farmers. Carabaos were 6.52 years with parity 1.53. Cattle were 5.61 years, with parity of 1.51. Semen quality significantly (p ≤ 0.05) deteriorated in normal and live sperm with storage and handling at the provincial and field stations. Breed, AI technicians practices and AI operation significantly affected conception rate. Mean conception rate was 57.62%.

Keywords: artificial insemination, carabao, parity, mother tanks, frozen semen

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Authors: Martha Matashu

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This study examined corporate governance and economic growth trends in Sub Saharan African (SSA) countries. The need for corporate governance arise from the fact that the day to day running of the business is done by management who in accordance with the neoclassical theory and agency theory have inborn tendencies to use the resources of the company to their advantage. This prevails against a background where the endogenous economic growth theory hold the assumption that economic growth is an outcome of the overall performance of all companies within an economy. This suggest that corporate governance at firm level determine economic growth through its impact on the overall performance. Nevertheless, insight into literature suggest that efforts to promote corporate governance in countries across SSA since the 1980s to date have not yet yielded desired outcomes. The board responsibilities, shareholder rights, disclosure and transparency, protection of minority shareholder, and liability of directors were thus used as proxies of corporate governance because these are believed to be mechanisms that are believed to enhance company performance their effect on enhancing accountability and transparency. Using panel data techniques, corporate governance and economic growth data for 29 SSA countries from the period of 2008 to 2019 was analysed. The findings revealed declining economic growth trend despite an increase in corporate governance aspects such as director liability, shareholders’ rights, and protection of minority shareholder in SSA countries. These findings are in contradiction to the popularly held theoretical principles of economic growth and corporate governance. The study reached the conclusion thata nonlinearrelationship exists between corporate governance and economic growth within the selectedSSA countries during the period under investigation. This study thus recommends that measures should be taken to create conditions for corporate governance that would bolster significant positive contributions to economic growth in the region.

Keywords: corporate governance, economic growth, sub saharan Africa, agency theory, endogenous theory

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Authors: Sung Hyun Yoo, In Hwan Choi, Jun Ho Jung, Choon Ki Ahn, Myo Taeg Lim

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Home energy management system (HEMS) has been issued to reduce the power consumption. The HEMS performs electric power control for the indoor electric device. However, HEMS commonly treats the smart devices. In this paper, we suggest the output estimation of home legacy device using the artificial neural fuzzy inference system (ANFIS). This paper discusses the overview and the architecture of the system. In addition, accurate performance of the output estimation using the ANFIS inference system is shown via a numerical example.

Keywords: artificial neural fuzzy inference system (ANFIS), home energy management system (HEMS), smart device, legacy device

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Authors: D. G. Badagha, C. D. Modhera, S. A. Vasanwala

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There is a great challenge for civil engineering field to contribute in environment prevention by finding out alternatives of cement and natural aggregates. There is a problem of global warming due to cement utilization in concrete, so it is necessary to give sustainable solution to produce concrete containing waste. It is very difficult to produce designated grade of concrete containing different ingredient and water cement ratio including waste to achieve desired fresh and harden properties of concrete as per requirement and specifications. To achieve the desired grade of concrete, a number of trials have to be taken, and then after evaluating the different parameters at long time performance, the concrete can be finalized to use for different purposes. This research work is carried out to solve the problem of time, cost and serviceability in the field of construction. In this research work, artificial neural network introduced to fix proportion of concrete ingredient with 50% waste replacement for M20, M25, M30, M35, M40, M45, M50, M55 and M60 grades of concrete. By using the neural network, mix design of high performance concrete was finalized, and the main basic mechanical properties were predicted at 3 days, 7 days and 28 days. The predicted strength was compared with the actual experimental mix design and concrete cube strength after 3 days, 7 days and 28 days. This experimentally and neural network based mix design can be used practically in field to give cost effective, time saving, feasible and sustainable high performance concrete for different types of structures.

Keywords: artificial neural network, high performance concrete, rebound hammer, strength prediction

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Authors: Aya Idriss, Esraa Othman, Lujain Malak

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Artificial intelligence is the emulation of human intelligence processes by machines, particularly computer systems that act logically. Some of the specific applications of AI include natural language processing, speech recognition, and machine vision. Cortana is a virtual assistant and she’s an example of an AI Application. Microsoft made it possible for this app to be accessed not only on laptops and PCs but can be downloaded on mobile phones and used as a virtual assistant which was a huge success. Cortana can offer a lot apart from the basic orders such as setting alarms and marking the calendar. Its capabilities spread past that, for example, it provides us with listening to music and podcasts on the go, managing my to-do list and emails, connecting with my contacts hands-free by simply just telling the virtual assistant to call somebody, gives me instant answers and so on. A questionnaire was sent online to numerous friends and family members to perform the study, which is critical in evaluating Cortana's recognition capacity and the majority of the answers were in favor of Cortana’s capabilities. The results of the questionnaire assisted us in determining the level of Cortana's skills.

Keywords: artificial intelligence, Cortana, AI, abstract

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Authors: Nir Wittenberg, Moshe Farhi

Abstract:

This mixed-methods study examines the integration of artificial intelligence (AI) tools in a first-year social work course to assess their potential for enhancing professional knowledge and skills. The incorporation of digital technologies, such as AI, in social work interventions, training, and research has increased, with the expectation that AI will become as commonplace as email and mobile phones. However, policies and ethical guidelines regarding AI, as well as empirical evaluations of its usefulness, are lacking. As AI is gradually being adopted in the field, it is prudent to explore AI thoughtfully in alignment with pedagogical goals. The outcomes assessed include professional identity, course satisfaction, and motivation. AI offers unique reflective learning opportunities through personalized simulations, feedback, and queries to complement face-to-face lessons. For instance, AI simulations provide low-risk practices for situations such as client interactions, enabling students to build skills with less stress. However, it is essential to recognize that AI alone cannot ensure real-world competence or cultural sensitivity. Outcomes related to student learning, experience, and perceptions will help to elucidate the best practices for AI integration, guiding faculty, and advancing pedagogical innovation. This strategic integration of selected AI technologies is expected to diversify course methodology, improve learning outcomes, and generate new evidence on AI’s educational utility. The findings will inform faculty seeking to thoughtfully incorporate AI into teaching and learning.

Keywords: artificial intelligence (AI), social work education, students, developing a professional identity, ethical considerations

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Authors: Samir Hessas

Abstract:

Modern medicine is witnessing a profound transformation as advanced technology reshapes surgical environments. Hybrid operating rooms, where state-of-the-art medical equipment, advanced imaging solutions, and Artificial Intelligence (AI) converge, are at the forefront of this revolution. In this comprehensive exploration, we scrutinize the multifaceted facets of AI and delve into an array of groundbreaking technologies. We also discuss visionary concepts that hold the potential to revolutionize hybrid operating rooms, making them more efficient and patient-centered. These innovations encompass real-time imaging, surgical simulation, IoT and remote monitoring, 3D printing, telemedicine, quantum computing, and nanotechnology. The outcome of this fusion of technology and imagination is a promising future of surgical precision, individualized patient care, and unprecedented medical advances in hybrid operating rooms.

Keywords: artificial intelligence, hybrid operating rooms, telemedicine, monitoring

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Authors: Yazan Al-Kofahi, Jamal Alqawasmi.

Abstract:

In this study, a comprehensive literature review (SLR) was conducted, with the main goal of assessing the existing literature about how artificial intelligence (AI), machine learning (ML), deep learning (DL) models are used in sustainable architecture applications and issues including thermal comfort satisfaction, energy efficiency, cost prediction and many others issues. For this reason, the search strategy was initiated by using different databases, including Scopus, Springer and Google Scholar. The inclusion criteria were used by two research strings related to DL, ML and sustainable architecture. Moreover, the timeframe for the inclusion of the papers was open, even though most of the papers were conducted in the previous four years. As a paper filtration strategy, conferences and books were excluded from database search results. Using these inclusion and exclusion criteria, the search was conducted, and a sample of 59 papers was selected as the final included papers in the analysis. The data extraction phase was basically to extract the needed data from these papers, which were analyzed and correlated. The results of this SLR showed that there are many applications of ML and DL in Sustainable buildings, and that this topic is currently trendy. It was found that most of the papers focused their discussions on addressing Environmental Sustainability issues and factors using machine learning predictive models, with a particular emphasis on the use of Decision Tree algorithms. Moreover, it was found that the Random Forest repressor demonstrates strong performance across all feature selection groups in terms of cost prediction of the building as a machine-learning predictive model.

Keywords: machine learning, deep learning, artificial intelligence, sustainable building

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Authors: Junbeom Lee, Jaehyuck Cho, Wookyeong Jeong, Jonghan Won, Jungmin Hwang, Youngseok Song, Taikyeong Jeong

Abstract:

This paper investigates and analyzes meta-learning technologies that use multiple-cameras to monitor and check abnormal behavior in people in real-time in the area of healthcare fields. Advances in artificial intelligence and computer vision technologies have confirmed that cameras can be useful for individual health monitoring and abnormal behavior detection. Through this, it is possible to establish a system that can respond early by automatically detecting abnormal behavior of the elderly, such as patients and the elderly. In this paper, we use a technique called meta-learning to analyze image data collected from cameras and develop a commercial product to determine abnormal behavior. Meta-learning applies machine learning algorithms to help systems learn and adapt quickly to new real data. Through this, the accuracy and reliability of the abnormal behavior discrimination system can be improved. In addition, this study proposes a meta-learning-based abnormal behavior detection system that includes steps such as data collection and preprocessing, feature extraction and selection, and classification model development. Various healthcare scenarios and experiments analyze the performance of the proposed system and demonstrate excellence compared to other existing methods. Through this study, we present the possibility that camera-based meta-learning technology can be useful for monitoring and testing abnormal behavior in the healthcare area.

Keywords: artificial intelligence, abnormal behavior, early detection, health monitoring

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Authors: Fadzil Ahmad, Noor Ashidi Mat Isa

Abstract:

This study introduces an improved genetic algorithm procedure that focuses search around near optimal solution corresponded to a group of elite chromosome. This is achieved through a novel crossover technique known as Segmented Multi Chromosome Crossover. It preserves the highly important information contained in a gene segment of elite chromosome and allows an offspring to carry information from gene segment of multiple chromosomes. In this way the algorithm has better possibility to effectively explore the solution space. The improved GA is applied for the automatic and simultaneous parameter optimization and feature selection of artificial neural network in pattern recognition of medical problem, the cancer and diabetes disease. The experimental result shows that the average classification accuracy of the cancer and diabetes dataset has improved by 0.1% and 0.3% respectively using the new algorithm.

Keywords: genetic algorithm, artificial neural network, pattern clasification, classification accuracy

Procedia PDF Downloads 474