Search results for: artificial groundwater recharge

Authors: Durvi Yogesh Vagani

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This research paper explores the effectiveness of Research-informed teaching and Case-based teaching in enhancing the retention and recall of memory during discussions among university students. Additionally, it investigates the impact of using Artificial Intelligence (AI) tools on the quality of research conducted by students and its correlation with better recollection. The study hypothesizes that Case-based teaching will lead to greater recall and storage of information. The research gap in the use of AI in educational settings, particularly with actual participants, is addressed by leveraging a multi-method approach. The hypothesis is that the use of AI, such as ChatGPT and Bard, would lead to better retention and recall of information. Before commencing the study, participants' attention levels and IQ were assessed using the Digit Span Test and the Wechsler Adult Intelligence Scale, respectively, to ensure comparability among participants. Subsequently, participants were divided into four conditions, each group receiving identical information presented in different formats based on their assigned condition. Following this, participants engaged in a group discussion on the given topic. Their responses were then evaluated against a checklist. Finally, participants completed a brief test to measure their recall ability after the discussion. Preliminary findings suggest that students who utilize AI tools for learning demonstrate improved grasping of information and are more likely to integrate relevant information into discussions compared to providing extraneous details. Furthermore, Case-based teaching fosters greater attention and recall during discussions, while Research-informed teaching leads to greater knowledge for application. By addressing the research gap in AI application in education, this study contributes to a deeper understanding of effective teaching methodologies and the role of technology in student learning outcomes. The implication of the present research is to tailor teaching methods based on the subject matter. Case-based teaching facilitates application-based teaching, and research-based teaching can be beneficial for theory-heavy topics. Integrating AI in education. Combining AI with research-based teaching may optimize instructional strategies and deepen learning experiences. This research suggests tailoring teaching methods in psychology based on subject matter. Case-based teaching suits practical subjects, facilitating application, while research-based teaching aids understanding of theory-heavy topics. Integrating AI in education could enhance learning outcomes, offering detailed information tailored to students' needs.

Keywords: artificial intelligence, attention, case-based teaching, memory recall, memory retention, research-informed teaching

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Authors: Bachir Chemani, Rachid Halfaoui, Madani Maalem

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The manufacturing technology of band cotton is very delicate and depends to choice of certain parameters such as torsion of warp yarn. The fabric elasticity is achieved without the use of any elastic material, chemical expansion, artificial or synthetic and it’s capable of creating pressures useful for therapeutic treatments.Before use, the band is subjected to treatments of specific preparation for obtaining certain elasticity, however, during its treatment, there are some regression parameters. The dependence of manufacturing parameters on the quality of the chemical treatment was confirmed. The aim of this work is to improve the properties of the fabric through the development of manufacturing technology appropriately. Finally for the treatment of the strip pancake 100% cotton, a treatment method is recommended.

Keywords: elastic, cotton, processing, torsion

Procedia PDF Downloads 387

Authors: Javed Mallick

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In metropolitan areas, rapid changes in land use and land cover (LULC) have ecological and environmental consequences. Saudi Arabia's cities have experienced tremendous urban growth since the 1990s, resulting in urban heat islands, groundwater depletion, air pollution, loss of ecosystem services, and so on. From 1990 to 2020, this study examines the variance and heterogeneity in land surface temperature (LST) caused by LULC changes in Abha-Khamis Mushyet, Saudi Arabia. LULC was mapped using the support vector machine (SVM). The mono-window algorithm was used to calculate the land surface temperature (LST). To identify LST clusters, the local indicator of spatial associations (LISA) model was applied to spatiotemporal LST maps. In addition, the parallel coordinate (PCP) method was used to investigate the relationship between LST clusters and urban biophysical variables as a proxy for LULC. According to LULC maps, urban areas increased by more than 330% between 1990 and 2018. Between 1990 and 2018, built-up areas had an 83.6% transitional probability. Furthermore, between 1990 and 2020, vegetation and agricultural land were converted into built-up areas at a rate of 17.9% and 21.8%, respectively. Uneven LULC changes in built-up areas result in more LST hotspots. LST hotspots were associated with high NDBI but not NDWI or NDVI. This study could assist policymakers in developing mitigation strategies for urban heat islands

Keywords: land use land cover mapping, land surface temperature, support vector machine, LISA model, parallel coordinate plot

Procedia PDF Downloads 78

Authors: Muhammad Bilal Shaikh

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Mineral processing plants play a pivotal role in extracting valuable minerals from raw ores, contributing significantly to various industries. However, the optimization of plant throughput remains a complex challenge, necessitating innovative approaches for increased efficiency and productivity. This research paper investigates the application of Multimodal Artificial Intelligence (MAI) techniques to address this challenge, aiming to improve overall plant throughput in mineral processing operations. The integration of multimodal AI leverages a combination of diverse data sources, including sensor data, images, and textual information, to provide a holistic understanding of the complex processes involved in mineral extraction. The paper explores the synergies between various AI modalities, such as machine learning, computer vision, and natural language processing, to create a comprehensive and adaptive system for optimizing mineral processing plants. The primary focus of the research is on developing advanced predictive models that can accurately forecast various parameters affecting plant throughput. Utilizing historical process data, machine learning algorithms are trained to identify patterns, correlations, and dependencies within the intricate network of mineral processing operations. This enables real-time decision-making and process optimization, ultimately leading to enhanced plant throughput. Incorporating computer vision into the multimodal AI framework allows for the analysis of visual data from sensors and cameras positioned throughout the plant. This visual input aids in monitoring equipment conditions, identifying anomalies, and optimizing the flow of raw materials. The combination of machine learning and computer vision enables the creation of predictive maintenance strategies, reducing downtime and improving the overall reliability of mineral processing plants. Furthermore, the integration of natural language processing facilitates the extraction of valuable insights from unstructured textual data, such as maintenance logs, research papers, and operator reports. By understanding and analyzing this textual information, the multimodal AI system can identify trends, potential bottlenecks, and areas for improvement in plant operations. This comprehensive approach enables a more nuanced understanding of the factors influencing throughput and allows for targeted interventions. The research also explores the challenges associated with implementing multimodal AI in mineral processing plants, including data integration, model interpretability, and scalability. Addressing these challenges is crucial for the successful deployment of AI solutions in real-world industrial settings. To validate the effectiveness of the proposed multimodal AI framework, the research conducts case studies in collaboration with mineral processing plants. The results demonstrate tangible improvements in plant throughput, efficiency, and cost-effectiveness. The paper concludes with insights into the broader implications of implementing multimodal AI in mineral processing and its potential to revolutionize the industry by providing a robust, adaptive, and data-driven approach to optimizing plant operations. In summary, this research contributes to the evolving field of mineral processing by showcasing the transformative potential of multimodal artificial intelligence in enhancing plant throughput. The proposed framework offers a holistic solution that integrates machine learning, computer vision, and natural language processing to address the intricacies of mineral extraction processes, paving the way for a more efficient and sustainable future in the mineral processing industry.

Keywords: multimodal AI, computer vision, NLP, mineral processing, mining

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Authors: Kamran Baksh Soomro, Amin Talei, Sina Alaghmand

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Water scarcity has exacerbated in the last couple of decades; it is incumbent on agriculture to maximize the use of water of all qualities. The drip irrigation system practice has shown a vast increase in profit and research interests in the last two decades. However, the application of this system is still limited. The two years field experiment was conducted with three replications at Malir, Karachi (a semi-arid region) in Pakistan. The aim was to evaluate the effects of two qualities of irrigation water IT1 (EC 0.56 dS.m⁻¹) and IT2 (EC 2.89 dS.m⁻¹) on water use efficiency. To achieve the aim, bitter gourd was grown under the drip irrigation system in 2016-17. The uniformity co-efficient (UC) ranged from 93 to 96%. Water use efficiency, of 1.60 and 1.21 kg.m⁻³ under IT1 was recorded higher in season 1 and 2. Using t-test at 5% significance level, the crop yield was higher in both seasons under IT1 compared to IT2. Using pairwise t-test at 5% significance level, the parameters related with the quality of fruit, like length, weight, and diameter, were higher in IT1 than IT2 in all plants; and in both seasons. A correlational study was also conducted to observe the trends in the variables associated with both irrigation treatments for the two seasons. Results showed that most of the parameters exhibited a similar linear trend in both the seasons. The study concluded that bitter gourd crop could be grown successfully in sandy loam using drip irrigation system, supplying saline ground-water. The sustainable use of saline irrigation water should be utilized for vegetable cultivation to meet the food demand in the rural areas of Pakistan.

Keywords: uniformity co-efficient, water use efficiency, drip irrigation, ground-water, t-test, correlation

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Authors: Marie Oldfield

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Anthropomorphism is a technique used by humans to make sense of their surroundings. Anthropomorphism is a widely used technique used to influence consumers to purchase goods or services. These techniques can entice consumers into buying something to fulfill a gap or desire in their life, ranging from loneliness to the desire to be exclusive. By manipulating belief systems, consumer behaviour can be exploited. This paper examines a series of studies to show how anthropomorphism can be used as a basis for exploitation. The first set of studies in this paper examines how anthropomorphism is used in marketing and the effects on humans engaging with this technique. The second set of studies examines how humans can be potentially exploited by artificial agents. We then discuss the consequences of this type of activity within the context of dehumanisation. This research has found potential serious consequences for society and humanity, which indicate an urgent need for further research in this area.

Keywords: anthropomorphism, ethics, human-computer interaction, AI

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Authors: Alexander Heckman, Rohan Goswami, Zachi Attia, Paul Friedman, Peter Noseworthy, Demilade Adedinsewo, Pablo Moreno-Franco, Rickey Carter, Tathagat Narula

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Background: Postoperative atrial fibrillation (POAF) is associated with adverse health consequences, higher costs, and longer hospital stays. Utilizing existing predictive models that rely on clinical variables and circulating biomarkers, multiple societies have published recommendations on the treatment and prevention of POAF. Although reasonably practical, there is room for improvement and automation to help individualize treatment strategies and reduce associated complications. Methods and Results: In this retrospective cohort study of solid organ transplant recipients, we evaluated the diagnostic utility of a previously developed AI-based ECG prediction for silent AF on the development of POAF within 30 days of transplant. A total of 2261 non-cardiac transplant patients without a preexisting diagnosis of AF were found to have a 5.8% (133/2261) incidence of POAF. While there were no apparent sex differences in POAF incidence (5.8% males vs. 6.0% females, p=.80), there were differences by race and ethnicity (p<0.001 and 0.035, respectively). The incidence in white transplanted patients was 7.2% (117/1628), whereas the incidence in black patients was 1.4% (6/430). Lung transplant recipients had the highest incidence of postoperative AF (17.4%, 37/213), followed by liver (5.6%, 56/1002) and kidney (3.6%, 32/895) recipients. The AUROC in the sample was 0.62 (95% CI: 0.58-0.67). The relatively low discrimination may result from undiagnosed AF in the sample. In particular, 1,177 patients had at least 1 AI-ECG screen for AF pre-transplant above .10, a value slightly higher than the published threshold of 0.08. The incidence of POAF in the 1104 patients without an elevated prediction pre-transplant was lower (3.7% vs. 8.0%; p<0.001). While this supported the hypothesis that potentially undiagnosed AF may have contributed to the diagnosis of POAF, the utility of the existing AI-ECG screening algorithm remained modest. When the prediction for POAF was made using the first postoperative ECG in the sample without an elevated screen pre-transplant (n=1084 on account of n=20 missing postoperative ECG), the AUROC was 0.66 (95% CI: 0.57-0.75). While this discrimination is relatively low, at a threshold of 0.08, the AI-ECG algorithm had a 98% (95% CI: 97 – 99%) negative predictive value at a sensitivity of 66% (95% CI: 49-80%). Conclusions: This study's principal finding is that the incidence of POAF is rare, and a considerable fraction of the POAF cases may be latent and undiagnosed. The high negative predictive value of AI-ECG screening suggests utility for prioritizing monitoring and evaluation on transplant patients with a positive AI-ECG screening. Further development and refinement of a post-transplant-specific algorithm may be warranted further to enhance the diagnostic yield of the ECG-based screening.

Keywords: artificial intelligence, atrial fibrillation, cardiology, transplant, medicine, ECG, machine learning

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Authors: Ratih Fitria Putri, Josaphat Tetuko Sri Sumantyo, Hiroaki Kuze

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Jakarta Metropolitan City is located on the northwest coast of West Java province with geographical location between 106º33’ 00”-107º00’00”E longitude and 5º48’30”-6º24’00”S latitude. Jakarta urban area has been suffered from land subsidence in several land use type as trading, industry and settlement area. Land subsidence hazard is one of the consequences of urban development in Jakarta. This hazard is caused by intensive human activities in groundwater extraction and land use mismanagement. Geologically, the Jakarta urban area is mostly dominated by alluvium fan sediment. The objectives of this research are to make an analysis of Jakarta urban land use type on land subsidence zone areas. The process of producing safer land use and settlements of the land subsidence areas are very important. Spatial distributions of land subsidence detection are necessary tool for land use management planning. For this purpose, Differential Synthetic Aperture Radar Interferometry (DInSAR) method is used. The DInSAR is complementary to ground-based methods such as leveling and global positioning system (GPS) measurements, yielding information in a wide coverage area even when the area is inaccessible. The data were fine tuned by using X-Band image satellite data from 2010 to 2013 and land use mapping data. Our analysis of land use type that land subsidence movement occurred on the northern part Jakarta Metropolitan City varying from 7.5 to 17.5 cm/year as industry and settlement land use type areas.

Keywords: land use analysis, land subsidence mapping, urban area, X-band satellite image

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Authors: Julio Vega

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Expression recognition is a field of Artificial Intelligence whose main objectives are to recognize basic forms of affective expression that appear on people’s faces and contributing to behavioral studies. In this work, a ROS node has been developed that, based on Deep Learning techniques, is capable of detecting the facial expressions of the people that appear in the image. These algorithms were optimized so that they can be executed in real time on an embedded platform. The experiments were carried out in a PC with a USB camera and in a Raspberry Pi 4 with a PiCamera. The final results shows a plausible system, which is capable to work in real time even in an embedded platform.

Keywords: python, low-cost, raspberry pi, emotion detection, human-robot interaction, ROS node

Procedia PDF Downloads 129

Authors: Aliya Ashraf, Mehreen Sirshar, Fatima Akhtar, Farwa Kazmi, Jawaria Wazir

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Vision, the basis for performing navigational tasks, is absent or greatly reduced in visually impaired people due to which they face many hurdles. For increasing the navigational capabilities of visually impaired people a desktop application ODAPTA is presented in this paper. The application uses camera to capture video from surroundings, apply various image processing algorithms to get information about path and obstacles, tracks them and delivers that information to user through voice commands. Experimental results show that the application works effectively for straight paths in daylight.

Keywords: visually impaired, ODAPTA, Region of Interest (ROI), driver fatigue, face detection, expression recognition, CCD camera, artificial intelligence

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Authors: Gilberto Goracci, Fabio Curti

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This work presents a robust, light, and inexpensive algorithm to perform autonomous orbit determination using onboard magnetometer data in real-time. Magnetometers are low-cost and reliable sensors typically available on a spacecraft for attitude determination purposes, thus representing an interesting choice to perform real-time orbit determination without the need to add additional sensors to the spacecraft itself. Magnetic field measurements can be exploited by Extended/Unscented Kalman Filters (EKF/UKF) for orbit determination purposes to make up for GPS outages, yielding errors of a few kilometers and tens of meters per second in the position and velocity of a spacecraft, respectively. While this level of accuracy shows that Kalman filtering represents a solid baseline for autonomous orbit determination, it is not enough to provide a reliable state estimation in the absence of GPS signals. This work combines the solidity and reliability of the EKF with the versatility of a Recurrent Neural Network (RNN) architecture to further increase the precision of the state estimation. Deep learning models, in fact, can grasp nonlinear relations between the inputs, in this case, the magnetometer data and the EKF state estimations, and the targets, namely the true position, and velocity of the spacecraft. The model has been pre-trained on Sun-Synchronous orbits (SSO) up to 2126 kilometers of altitude with different initial conditions and levels of noise to cover a wide range of possible real-case scenarios. The orbits have been propagated considering J2-level dynamics, and the geomagnetic field has been modeled using the International Geomagnetic Reference Field (IGRF) coefficients up to the 13th order. The training of the module can be completed offline using the expected orbit of the spacecraft to heavily reduce the onboard computational burden. Once the spacecraft is launched, the model can use the GPS signal, if available, to fine-tune the parameters on the actual orbit onboard in real-time and work autonomously during GPS outages. In this way, the provided module shows versatility, as it can be applied to any mission operating in SSO, but at the same time, the training is completed and eventually fine-tuned, on the specific orbit, increasing performances and reliability. The results provided by this study show an increase of one order of magnitude in the precision of state estimate with respect to the use of the EKF alone. Tests on simulated and real data will be shown.

Keywords: artificial intelligence, extended Kalman filter, orbit determination, magnetic field

Procedia PDF Downloads 105

Authors: S. Wisutmethangoon, S. Pannaray, T. Plookphol, J. Wannasin

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2024 Aluminium alloy was squeezed cast by the Gas Induced Semi Solid (GISS) process. Effect of artificial aging on microstructure and mechanical properties of this alloy was studied in the present work. The solutionized specimens were aged hardened at temperatures of 175°C, 200°C, and 225°C under various time durations. The highest hardness of about 77.7 HRE was attained from specimen aged at the temperature of 175 °C for 36 h. Upon investigation the microstructure by using Transmission Electron Microscopy (TEM), the phase was mainly attributed to the strengthening effect in the aged alloy. The apparent activation energy for precipitation hardening of the alloy was calculated as 133,805 J/mol.

Keywords: 2024 aluminium alloy, gas induced semi solid, T6 heat treatment, aged hardening, transmission electron microscopy

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Authors: Victor Sotero, Cindy Castro, Ena Velazco, Ursula Monteiro, Dora Garcia

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The aim of this study is to carry out the evaluation of the artificial pollination of the female flowers of E. oleifera with pollen of E. guineensis, to obtain the hybrid Palma OXG, which presents two characteristics of interest, such as high resistance to the disease of spear rot and high concentration of oleic acid. The works were carried out with matrices from the experimental fields and INIA in the Province of Colonel Portillo in the Ucayali Region-Peru. From the pollination of five species of E. oleifera, fruits were obtained in two of them, called O7 and O68, with a percentage of 23.6% and 18.6% of fertile fruits. When germination was carried out in a controlled environment of temperature, air, and humidity, only the O17 species were germinated with a yield of 68.7%.

Keywords: Elaeis oleífera, Elaeis guineensis, palm OXG, pollination

Procedia PDF Downloads 141

Authors: Ali Fallah Yeznabad, Mohammad H. Baziar, Alireza Saedi Azizkandi

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Rafts may be used when a low bearing capacity exists underneath the foundation and may be combined by piles in some special circumstances; such as to reduce settlements or high groundwater to control buoyancy. From structural point of view, these piles could be both connected or disconnected from the raft and are to be classified as Piled Rafts (PR) or Disconnected Piled Rafts (DPR). Although the researches about the behavior of piled rafts subjected to vertical loading is really extensive, in the context of dynamic load and earthquake loading, the studies are very limited. In this study, to clarify these foundations’ performance under dynamic loading, series of Shaking Table tests have been performed. The square raft and four piles in connected and disconnected configurations were used in dry silica sand and the model was experimented using a shaking table under 1-g conditions. Moreover, numerical investigation using finite element software have been conducted to better understand the differences and advantages. Our observations demonstrates that in connected Piled Rafts piles have to bear greater amount of moment in their upper parts, however this moments are approximately 40% lower in disconnected piled rafts in the same conditions and loading. Considering the Rafts’ lateral movement which be of crucial importance in foundations performance evaluation, connected piled rafts show much better performance with about 30% less lateral movement. Further, it was observed on confirmed both through laboratory tests and numerical analysis, that adding the superstructure over the piled raft foundation the raft separates from the soil and it significantly increases rocking of the raft which was observed to be the main reason of increase in piles’ moments under superstructure interaction with the foundation.

Keywords: Piled Rafts (PR), Disconnected Piled Rafts (DPR), dynamic loading, shaking table, seismic performance

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Authors: Rajendra Kumar Kanojia

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Correction of the gap created by the resection of large juxtra-articular tumours of the femur would be difficult to manage, several bone substitutes, bone grafts, and artificial bone granules were tried but the results were not as good as with the distraction osteogensis, by the help of either Ilizarov ring fixator or the mono-rail fixators. We are presenting a small study of five cases of malignant tumours of the distal femur, removed, custom made mega prosthesis was applied and that failed twice in a span of five years. We had no better option left then to apply mono-rail fixator, and start the process of distraction osteogeneis, we got the union, gap was filled with new bone and patient has been made walking in few months.

Keywords: distal femur tumour, resection, defect non-union, mono-rail fixator

Procedia PDF Downloads 375

Authors: P. W. Tsai, W. L. Hong, C. W. Chen, C. Y. Chen

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In this paper, we present a neural network (NN) based approach represent a nonlinear Tagagi-Sugeno (T-S) system. A linear differential inclusion (LDI) state-space representation is utilized to deal with the NN models. Taking advantage of the LDI representation, the stability conditions and controller design are derived for a class of nonlinear structural systems. Moreover, the concept of utilizing the Parallel Particle Swarm Optimization (PPSO) algorithm to solve the common P matrix under the stability criteria is given in this paper.

Keywords: Lyapunov stability, parallel particle swarm optimization, linear differential inclusion, artificial intelligence

Procedia PDF Downloads 656

Authors: Badrane Hasnaa, Bouzahir Brahim

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This article examines the impact of fintech technologies on crowdfunding, particularly their potential to enhance financial inclusion and promote responsible finance. It explores how the adoption of blockchain, artificial intelligence, and other fintech innovations is transforming crowdfunding by making it more accessible, transparent, and ethical. By analyzing case studies and recent data, the article illustrates how these technologies help overcome traditional barriers to financing while promoting sustainable financial practices. The findings suggest that integrating fintech into crowdfunding can not only broaden access to funding for marginalized populations but also encourage more responsible management of financial resources, contributing to a fairer and more resilient economy.

Keywords: crowdfunding, fintech, inclusion financière, finance responsible, blockchain, resilience financière

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Authors: Sarah Mohammed, Huda Allagany, Ayah Barakat, Muna Elyas

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This paper delves into the history and development of ChatGPT, tracing its evolution from its inception by OpenAI to its current state, and emphasizing its design improvements and strategic partnerships. It also explores the performance and applicability of ChatGPT versions 3.5 and 4 in various contexts, examining its capabilities and limitations in producing accurate and relevant responses. Utilizing a quantitative approach, user satisfaction, speed of response, learning capabilities, and overall utility in academic performance were assessed through surveys and analysis tools. Findings indicate that while ChatGPT generally delivers high accuracy and speed in responses, the need for clarification and more specific user instructions persists. The study highlights the tool's increasing integration across different sectors, showcasing its potential in educational and professional settings.

Keywords: artificial intelligence, chat GPT, analysis, education

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Authors: Mohammed A. Zidan, Alaa Sagheer, Nasser Metwally

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Real-time learning is an important goal that most of artificial intelligence researches try to achieve it. There are a lot of problems and applications which require low cost learning such as learn a robot to be able to classify and recognize patterns in real time and real-time recall. In this contribution, we suggest a model of quantum competitive learning based on a series of quantum gates and additional operator. The proposed model enables to recognize any incomplete patterns, where we can increase the probability of recognizing the pattern at the expense of the undesired ones. Moreover, these undesired ones could be utilized as new patterns for the system. The proposed model is much better compared with classical approaches and more powerful than the current quantum competitive learning approaches.

Keywords: competitive learning, quantum gates, quantum gates, winner-take-all

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Authors: Aron Witkowski, Andrzej Wodecki

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Purpose: This study aims to explore the feasibility and effectiveness of utilizing Generative Artificial Intelligence (GenAI) agents as product managers within the manufacturing sector. It seeks to evaluate whether current GenAI capabilities can fulfill the complex requirements of product management and deliver comparable outcomes to human counterparts. Study Design/Methodology/Approach: This research involved the creation of a support application for product managers, utilizing high-quality sources on product management and generative AI technologies. The application was designed to assist in various aspects of product management tasks. To evaluate its effectiveness, a study was conducted involving 10 experienced product managers from the manufacturing sector. These professionals were tasked with using the application and providing feedback on the tool's responses to common questions and challenges they encounter in their daily work. The study employed a mixed-methods approach, combining quantitative assessments of the tool's performance with qualitative interviews to gather detailed insights into the user experience and perceived value of the application. Findings: The findings reveal that GenAI-based product management agents exhibit significant potential in handling routine tasks, data analysis, and predictive modeling. However, there are notable limitations in areas requiring nuanced decision-making, creativity, and complex stakeholder interactions. The case study demonstrates that while GenAI can augment human capabilities, it is not yet fully equipped to independently manage the holistic responsibilities of a product manager in the manufacturing sector. Originality/Value: This research provides an analysis of GenAI's role in product management within the manufacturing industry, contributing to the limited body of literature on the application of GenAI agents in this domain. It offers practical insights into the current capabilities and limitations of GenAI, helping organizations make informed decisions about integrating AI into their product management strategies. Implications for Academic and Practical Fields: For academia, the study suggests new avenues for research in AI-human collaboration and the development of advanced AI systems capable of higher-level managerial functions. Practically, it provides industry professionals with a nuanced understanding of how GenAI can be leveraged to enhance product management, guiding investments in AI technologies and training programs to bridge identified gaps.

Keywords: generative artificial intelligence, GenAI, NPD, new product development, product management, manufacturing

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Authors: Darn H. Hsiao, Zhu-Yun Zheng

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Since Taiwan is located between the Eurasian and Filipino plates and earthquakes often thus occur. The coastal plains in western Taiwan are alluvial plains, and the soils of the alluvium are mostly from the Lao-Shan belt in the central mountainous area of ​​southern Taiwan. It could come mostly from sand/shale and slate. The previous investigation found that the soils in the Kaohsiung area of ​​southern Taiwan are mainly composed of slate, shale, quartz, low-plastic clay, silt, silty sand and so on. It can also be found from the past earthquakes that the soil in Kaohsiung is highly susceptible to soil subsidence due to liquefaction. Insufficient bearing capacity of building will cause soil liquefaction disasters. In this study, the boring drilling data from nine districts among the Love River Basin in the city center, and some factors affecting liquefaction include the content of fines (FC), standard penetration test N value (SPT N), the thickness of clay layer near ground-surface, and the thickness of possible liquefied soil were further discussed for liquefaction potential as well as groundwater level. The results show that the liquefaction potential is higher in the areas near the riverside, the backfill area, and the west area of ​​the study area. This paper also uses the old paleo-geological map, soil particle distribution curve, compared with LPI map calculated from the analysis results. After all the parameters finally were studied for five sub zones in the Love River Basin by maximum-minimum method, it is found that both of standard penetration test N value and the thickness of the clay layer will be most influential.

Keywords: liquefaction, western Taiwan, liquefaction potential map, high liquefaction potential areas

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Authors: R. H. Fattepur, Sameer R. Fattepur, D. K. Sreekantha

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Dairy Farming is one of the key industries in India. India is the leading producer and also the consumer of milk, milk-based products in the world. In this paper, we have attempted to the replace the human expert system and to develop an artificial expert system prototype to increase the speed and accuracy of decision making dairy farming credit risk evaluation. Fuzzy logic is used for dealing with uncertainty, vague and acquired knowledge, fuzzy rule base method is used for representing this knowledge for building an effective expert system.

Keywords: expert system, fuzzy logic, knowledge base, dairy farming, credit risk

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Authors: Yaseein S. Hussein, Victor P. Gil Jiménez, Abdulmajeed Al-Jumaily

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Due to the rapid advancement of technology, there is an increasing demand for wireless connections that are both fast and reliable, with minimal latency. New wireless communication standards are developed every decade, and the year 2030 is expected to see the introduction of 6G. The primary objectives of 6G network and terminal designs are focused on sustainability and environmental friendliness. The International Telecommunication Union-Recommendation division (ITU-R) has established the minimum requirements for 6G, with peak and user data rates of 1 Tbps and 10-100 Gbps, respectively. In this context, Light Fidelity (Li-Fi) technology is the most promising candidate to meet these requirements. This article will explore the various advantages, features, and potential applications of Li-Fi technology, and compare it with 5G networking, to showcase its potential impact among other emerging technologies that aim to enable 6G networks.

Keywords: 6G networks, artificial intelligence (AI), Li-Fi technology, Terahertz (THz) communication, visible light communication (VLC)

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Authors: P. Krachodnok

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In this paper, the design of a multiple U-slotted microstrip patch antenna with frequency selective surface (FSS) as a superstrate for WLAN and WiMAX applications is presented. The proposed antenna is designed by using substrate FR4 having permittivity of 4.4 and air substrate. The characteristics of the antenna are designed and evaluated the performance of modelled antenna using CST Microwave studio. The proposed antenna dual resonant frequency has been achieved in the band of 2.37-2.55 GHz and 3.4-3.6 GHz. Because of the impact of FSS superstrate, it is found that the bandwidths have been improved from 6.12% to 7.35 % and 3.7% to 5.7% at resonant frequencies 2.45 GHz and 3.5 GHz, respectively. The maximum gain at the resonant frequency of 2.45 and 3.5 GHz are 9.3 and 11.33 dBi, respectively.

Keywords: multi-slotted antenna, microstrip patch antenna, frequency selective surface, artificial magnetic conduction

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Authors: Andreas Aceranti, Riccardo Dossena, Marco Colorato, Simonetta Vernocchi

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By the term “limbic resonance,” we usually refer to a state of deep connection, both emotional and physiological, between people who, when in resonance, find their limbic systems in tune with one another. Limbic resonance is not only about sharing emotions but also physiological states. In fact, people in such resonance can influence each other’s heart rate, blood pressure, and breathing. Limbic resonance is fundamental for human beings to connect and create deep bonds among a certain group. It is fundamental for our social skills. A relationship between gifted and resonant subjects is perceived as feeling safe, living the relation like an isle of serenity where it is possible to recharge, to communicate without words, to understand each others without giving explanations, to strengthen the balance of each member of the group. Within the circle, self-esteem is consolidated and makes it stronger to face what is outside, others, and reality. The idea that gifted people who are together may be unfit for the world does not correspond to the truth. The circle made up of people with high cognitive potential characterized by a limbic resonance is, in general, experienced as a solid platform from which you can safely move away and where you can return to recover strength. We studied 8 adults (between 21 and 47 years old). All of them with IQ higher than 130. We monitored their brain waves frequency (alpha, beta, theta, gamma, delta) by means of biosensing tracker along with their physiological states (heart beat frequency, blood pressure, breathing frequency, pO2, pCO2) and some blood works only (5-HT, dopamine, catecholamines, cortisol). The subjects of the study were asked to adhere to a protocol involving bonding activities (such as team building activities), role plays, meditation sessions, and group therapy. All these activities were carried out together. We observed that after about 4 months of activities, their brain waves frequencies tended to tune quicker and quicker. After 9 months, the bond among them was so important that they could “sense” each other inner states and sometimes also guess each others’ thoughts. According to our findings, it may be hypothesized that large synchronized outbursts of cortex neurons produces not only brain waves but also electromagnetic fields that may be able to influence the cortical neurons’ activity of other people’s brain by inducing action potentials in large groups of neurons and this is reasonably conceivable to be able to transmit information such as different emotions and cognition cues to the other’s brain. We also believe that upcoming research should focus on clarifying the role of brain magnetic particles in brain-to-brain communication. We also believe that further investigations should be carried out on the presence and role of cryptochromes to evaluate their potential roles in direct brain-to-brain communication.

Keywords: limbic resonance, psychotherapy, brain waves, emotion regulation, giftedness

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Authors: Emmanuel Barrantes Chaves, Ernesto Rivera Alvarado

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This paper presents a method to enable agents for the Terraria game to evaluate algorithms commonly used in general video game artificial intelligence competitions. The usage of the ‘You Only Look Once’ model in the first layer of the process obtains information from the screen, translating this information into a video game description language known as “Video Game Description Language”; the agents take that as input to make decisions. For this, the state-of-the-art algorithms were tested and compared; Monte Carlo Tree Search and Rolling Horizon Evolutionary; in this case, Rolling Horizon Evolutionary shows a better performance. This approach’s main advantage is that a VGDL beforehand is unnecessary. It will be built on the fly and opens the road for using more games as a framework for AI.

Keywords: AI, MCTS, RHEA, Terraria, VGDL, YOLOv5

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Authors: J. Jessa

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Background: Autism is a pervasive developmental disorder, the incidence of which has significantly increased in recent years. Children with autism have impairments in social skills, communication, and imagination. Children with autism has more common than healthy children feeding problems: food selectivity, problems with gastrointestinal tract: diarrhea, constipations, abdominal pain, reflux and others. Many parents of autistic children report that after implementation of gluten-, casein- and sugar free diet those symptoms disappear and even cognitive functions become better. Some children begin to understand speech and to communicate with parents, regain eye contact, become more calm, sleep better and has better concentration. Probably at the root of this phenomenon lies elimination from the diet peptides construction of which is similar to opiates. Enhanced permeability of gut causes absorption of not fully digested opioid-like peptides from food, like gluten and casein and probably others (proteins from soy and corn) which impact on brain of autistic children. Aim of the study: The aim of the study is to assess the safety of gluten-free diet in children with autism, aged 2,5-7. Methods: Participants of the study (n=70) – children aged 2,5-7 with autism are divided into 3 groups. The first group (research group) are patients whose parents want to implement a gluten-free diet. The second group are patients who have been recommended to eliminate from the diet artificial substances, such as preservatives, artificial colors and flavors, and others (control group 1). The third group (control group 2) are children whose parents did not agree for implementation of the diet. Caregivers of children on the diet are educated about the specifics of the diet and how to avoid malnutrition. At the start of the study we exclude celiac disease. Before the implementation of the diet we performe a blood test for patients (morphology, ferritin, total cholesterol, dry peripheral blood drops to detect some genetic metabolic diseases), plasma aminogram) and urine tests (excretion of ions: Mg, Na, Ca, the profile of organic acids in urine), which assess nutritional status as well as the psychological test assessing the degree of the child's psychological functioning (PEP-R). All of these tests will be repeated after one year from the implementation of the diet. Results: To the present moment we examined 42 children with autism. 12 of children are on gluten- free diet. Our preliminary results are promising. Parents of 9 of them report that, there is a big improvement in child behavior, concentration, less aggression incidents, better eye contact and better verbal skills. Conclusion: Our preliminary results suggest that dietary intervention may positively affect developmental outcome for some children diagnosed with ASD.

Keywords: gluten free diet, autism spectrum disorder, autism, blood test

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Authors: Santiago Quesada-García, María Lozano-Gómez, Pablo Valero-Flores

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The recent digital revolution, together with modern technologies, is changing the environment and the way people interact with inhabited space. However, in society, the elderly are a very broad and varied group that presents serious difficulties in understanding these modern technologies. Outpatients with cognitive disabilities, such as those suffering from Alzheimer's disease (AD), are distinguished within this cluster. This population group is in constant growth, and they have specific requirements for their inhabited space. According to architecture, which is one of the health humanities, environments are designed to promote well-being and improve the quality of life for all. Buildings, as well as the tools and technologies integrated into them, must be accessible, inclusive, and foster health. In this new digital paradigm, artificial intelligence (AI) appears as an innovative resource to help this population group improve their autonomy and quality of life. Some experiences and solutions, such as those that interact with users through chatbots and voicebots, show the potential of AI in its practical application. In the design of healthy spaces, the integration of AI in architecture will allow the living environment to become a kind of 'exo-brain' that can make up for certain cognitive deficiencies in this population. The objective of this paper is to address, from the discipline of neuroarchitecture, how modern technologies can be integrated into everyday environments and be an accessible resource for people with cognitive disabilities. For this, the methodology has a mixed structure. On the one hand, from an empirical point of view, the research carries out a review of the existing literature about the applications of AI to build space, following the critical review foundations. As a unconventional architectural research, an experimental analysis is proposed based on people with AD as a resource of data to study how the environment in which they live influences their regular activities. The results presented in this communication are part of the progress achieved in the competitive R&D&I project ALZARQ (PID2020-115790RB-I00). These outcomes are aimed at the specific needs of people with cognitive disabilities, especially those with AD, since, due to the comfort and wellness that the solutions entail, they can also be extrapolated to the whole society. As a provisional conclusion, it can be stated that, in the immediate future, AI will be an essential element in the design and construction of healthy new environments. The discipline of architecture has the compositional resources to, through this emerging technology, build an 'exo-brain' capable of becoming a personal assistant for the inhabitants, with whom to interact proactively and contribute to their general well-being. The main objective of this work is to show how this is possible.

Keywords: Alzheimer’s disease, artificial intelligence, healthy architecture, neuroarchitecture, architectural design

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Authors: Timothy Garvey, Don Driscoll

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Within Tasmania, the growling grass frog (Litoria raniformis) has experienced a rapid contraction in distribution. This decline is primarily attributed to habitat loss through landscape modification and improved land drainage. Reductions in seasonal water-sources have placed increasing importance on permanent water bodies for reproduction and foraging. Tasmanian agricultural and commercial forestry landscapes often feature small artificial ponds, utilized for watering livestock and fighting wildfires. Improved knowledge of how L. raniformis may be exploiting anthropogenic ponds is required for improved conservation management. We implemented telemetric tracking in order to evaluate the spatial ecology of L. raniformis (n = 20) within agricultural and managed forestry sites, with tracking conducted periodically over the breeding season (November/December, January/February, March/April). We investigated (1) potential differences in habitat utilization between agricultural and plantation sites, and (2) the post-breeding dispersal of individual frogs. Frogs were found to remain in close proximity to ponds throughout November/December, with individuals occupying vegetative depauperate water bodies beginning to disperse by January/February. Dispersing individuals traversed exposed plantation understory and agricultural pasture land in order to enter patches of native scrubland. By March/April all individuals captured at minimally vegetated ponds had retreated to adjacent scrub corridors. Animals found in ponds featuring dense riparian vegetation were not recorded to disperse. No difference in behavior was recorded between sexes. Rising temperatures coincided with increased movement by individuals towards native scrub refugia. The patterns of movement reported in this investigation emphasize the significant contribution of manmade water-bodies towards the conservation of L. raniformis within modified landscapes. The use of natural scrubland as cyclical retreats between breeding seasons also highlights the importance of the continued preservation of remnant vegetation corridors. Loss of artificial dams or buffering scrubland in heavily altered landscapes could see the breakdown of the greater L. raniformis meta-population further threatening their regional persistence.

Keywords: habitat loss, modified landscapes, spatial ecology, telemetry

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Authors: Won Young Choi, Kwan Kyu Park

Abstract:

Recently, the road depression happening in the urban area is different from the cause of the sink hole and the generation mechanism occurring in the limestone area. The main cause of sinkholes occurring in the city center is the loss of soil due to the damage of old underground buried materials and groundwater discharge due to large underground excavation works. The method of detecting the sinkhole in the urban area is mostly using the Ground Penetration Radar (GPR). However, it is challenging to implement compact system and detecting watery state since it is based on electromagnetic waves. Although many ultrasonic underground detection studies have been conducted, near-ground detection (several tens of cm to several meters) has been developed for bulk systems using geophones as a receiver. The goal of this work is to fabricate a miniaturized sinkhole detecting system based on low-cost ultrasonic transducers of 40 kHz resonant frequency with high transmission pressure and receiving sensitivity. Motived by biomedical ultrasonic imaging methods, we detect air layers below the ground such as asphalt through the pulse-echo method. To improve image quality using multi-channel, linear array system is implemented, and image is acquired by classical synthetic aperture imaging method. We present the successful feasibility test of multi-channel sinkhole detector based on ultrasonic transducer. In this work, we presented and analyzed image results which are imaged by single channel pulse-echo imaging, synthetic aperture imaging.

Keywords: road depression, sinkhole, synthetic aperture imaging, ultrasonic transducer

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