intelligence

The aging global population requires a new social model to meet the growing social, economic, and physical needs of seniors. Western social models need to be reconsidered in light of examples that support communal ways of living, which are sustainable through smart city design for more supportive geriatric care systems. To address the complex problems of geriatric care in this growing aging population with specific needs related to increased lifespan and limited financial resources, the use of emerging technologies, such as artificial intelligence (AI) and the Internet of Things (IoT), should be considered. As retirement ages rise and funds for retirement continue to decrease automated and sustainable solutions need to be sought. The ethical need to consider citizens not as customers but as decision-makers and to validate the ethical nature of medical decisions made for and by individuals should also be prioritized. This study provides recommendations for a smart city design and highlights the need for reflection on the ethics, modernization, and management of geriatric care. It suggests that technological devices can benefit health system reform by facilitating problem-solving. Overall, this new model integrates communal living and non-Western values with emerging technologies to address the growing need for geriatric care and the well-being of seniors.

Artificial Intelligence (AI) combined with Synthetic Biology has the potential to change the way we approach medicine, agriculture, and manufacturing. AI automates tasks, optimizes experimental designs, and predicts biological behaviours, resulting in more efficient design and engineering of biological systems. However, there are challenges such as data limitations, interpretability issues, and ethical considerations like biosafety and biosecurity concerns that need to be addressed. AI can be used to analyze vast amounts of data and identify patterns. This has led to successful applications of AI in high-throughput screening and biomanufacturing, which can drive innovation and address critical challenges. AI-powered closed-loop systems for real-time monitoring and control of biological processes also show promise in providing real-time feedback and optimizing systems on the fly. Despite these advancements, it's important to consider ethical implications to ensure the responsible development and application of AI in synthetic biology. Proper consideration of challenges and ethical considerations can help leverage the power of AI to drive innovation and tackle pressing societal challenges. Overall, the potential of AI in synthetic biology is significant. By addressing challenges and ethical considerations, we can use them effectively to solve pressing problems.

Background: To assess the association between neurocognitive functioning, adaptive functioning, and health-related quality of life (HRQoL), in Children and Young Adults with Severe Chronic Kidney Disease (CKD).Methods: We included patients with severe CKD (stages 4 and 5), aged 8-30 years, on different therapy modalities (pre-dialysis, dialysis, and transplanted) and healthy controls matched on age, sex, and parental education. All patients and healthy controls performed tasks to assess neurocognitive functioning (WISC/WAIS and a comprehensive neuropsychological test battery), and completed questionnaires to assess adaptive functioning (WFIRS or WHODAS) and HRQoL (PedsQL). Group differences were explored using MANCOVA. Mediation analyses were done to explore whether the relation between neurocognitive functioning and HRQoL was mediated by adaptive functioning. Results: 28 patients with severe CKD and 21 healthy matched controls were included. CKD patients had worse HRQoL (p < .001) than healthy controls. Adaptive functioning problems increased with age in the CKD patient group but not in the healthy control group (significant interaction effect: p = .024). Significant mediation effects were found, where impaired adaptive functioning mediated the relation between both low estimated Full Scale Intelligence Quotient (eFSIQ) and worse Processing Speed & Working Memory, and impaired HRQoL (eFSIQ: 95% confidence interval = .01-.58; Processing Speed & Working Memory: 95% confidence interval = 2.31-16.36).Conclusion: We found that impaired neurocognitive functioning is associated with worse HRQoL, which is conditional to impaired adaptive functioning. Especially towards young adulthood problems in adaptive functioning are more likely to be reported than when patients are younger.

Pushing forward with leveraging water resources, promoting international cooperation, and ensuring transparency in deep-sea mining operations. There is also a concern about where to discharge the toxins of unpurified water. Artificial Intelligence has created a dominance in the applications field with a totally automated system.Out of a thorough analysis completed with indicators, satellite radar, and A Journals, there are 30 years of research and 8 years of monitored testing to achieve accuracy and relevance. The patents in place are, Oceanic Mining System (4446636), Flexible Solar Skin in Combination with an Airplane (4768738), Cargo Torpedo (4421050), Oceanic seaplow system (4398362), and previous articles like “Is Extracting Lithium and deep-sea mining more sustainable?”, “AI as a Means of Water Purification Protection”, “Global Water Distribution”, and “Can Deep Sea Water be Processed into Potable Water and Distributed into the Middle East’?To report some ballpark numbers on the proposal and how we would navigate these projects, there will be an exact location of where the plant will be built as well as a water/salinity report of the water being treated. There are still issues with analyzing the cost of desalination compared to other alternatives. Deep Sea Water proves to be a higher quality water and the investment is well worth it. The surveys lean toward clients preferring the International Standards Operating Procedure. All recipients agree there should be a sense of urgency on water shortages. Currently, AI has proven to be a vital asset in eliminating biases and expenses.

Microbiome-gut-brain axis represents a complex, bidirectional communication network connecting the gastrointestinal tract and its microbial populations with the central nervous system (CNS). This complex system is important for maintaining physiological homeostasis and has significant implications for mental health. The human gut has trillions of microorganisms, collectively termed gut microbiota, which play important roles in digestion, immune function, and production of various metabolites. Some current research shows that these microorganisms strongly influence the brain function and behaviour of individuals, forming the basis of the microbiome-gut-brain axis. The communication between gut microbiota and the brain occurs via multiple pathways: neural pathway (e.g., vagus nerve), endocrine pathway (e.g., hormone production), immune pathway (e.g., inflammation modulation), and metabolic pathway (e.g., production of short-chain fatty acids). Dysbiosis, or imbalance of gut microbiota, has been linked to mental health disorders such as anxiety, depression, multiple sclerosis, autism spectrum disorders, etc, offering new perspectives on their etiology and potential therapeutic interventions. Artificial Intelligence (AI) has emerged as a powerful tool in interpreting the complexities of the microbiome-gut-brain axis. AI techniques, such as machine learning and deep learning, enable the integration and analysis of large, multifaceted datasets, uncovering patterns and correlations that can be avoided by traditional methods. These techniques enable predictive modeling, biomarker discovery, and understanding of underlying biological mechanisms, enhancing research efficiency and covering ways for personalized therapeutic approaches. The application of AI in microbiome research has provided valuable insights into mental health conditions. AI models have identified specific gut bacteria linked to disease, offered predictive models, and discovered distinct microbiome signatures associated with specific diseases. Integrating AI with microbiome research holds promise for revolutionizing mental health care, offering new diagnostic tools and targeted therapies. Challenges remain, but the potential benefits of AI-driven insights into microbiome-gut-brain interactions are immense and offer hope for innovative treatments and preventative measures to improve mental health outcomes.

This paper reviews the transformative impact of Artificial Intelligence (AI) on civil engineering. It explores AI's fundamental concepts and its applications across structural analysis, construction management, transportation, geotechnical engineering, and sustainability. The review highlights AI's role in automating tasks, predicting outcomes, and optimizing designs throughout project lifecycles. Recent advancements in AI-driven technologies for structural health monitoring, predictive maintenance, and risk assessment are discussed, along with challenges like data quality and model interpretability. Future trends such as autonomous construction and digital twins are examined, emphasizing the need for continued research and interdisciplinary collaboration. In conclusion, this paper offers insights for leveraging AI to address evolving challenges and opportunities in civil engineering, fostering innovation, sustainability, and resilience in infrastructure development.

Herbarium records provide a valuable historical database for assessing plant phenology shifts in the context of global climate change. The herbarium specimens, collected from diverse locations and periods, offer comprehensive data illustrating how many plants are altering their blooming times in response to global climate change. The appropriate use and analysis of long-term herbarium records offer an additional dimension for the study of plant phenology through the application of advanced experimental methodologies such as bioinformatics and satellite imagery, statistics, and Artificial Intelligence (AI) which, coupled with field observations, will improve ecosystems evaluation. These efforts can significantly contribute to conservation strategies and climate change mitigation and further support the synchronization of scientific inputs for evaluating the impacts of climate change and its ecological implications.

The study is based on the characterization of different AI models applied in the public furniture design analyzing the conditions of risk, materiality, and integration of variables in two AI generative modeling algorithms. As risky since they contain flood-prone areas, low vegetation coverage, and underdevelopment of infrastructure; therefore, these characterizations are tested through artificial simulation. The experimental method is applied through laboratory tests of various material components and their structuring in 3D simulators to check their resistance and risk scenarios. The case study of one of the most risky and populated areas of the informal settlement area of the Northwest of Guayaquil, such as the Coop, is analyzed. Sergio Toral is the focal point for on-site testing. It is concluded that the generation of a planned scheme of ecological furniture with different materials responds more effectively to the territory and that through artificial simulation an advantage can be obtained in terms of execution time and results, thus demonstrating that artificial intelligence is an ideal tool. To generate furniture design proposals that are more diverse, innovative, and functional with the environment, but it generates a minimum level of error for specific designs in the experimental model_01 of 0.1% to 3% and a high level in the experimental model_02 with an increasing error from 20% to 70%. As a future line of research, it is proposed to generate a simulated system of all the new informal settlements in Guayaquil and establish focal points for the implementation of new ecological furniture.

Background: Clinical research is a vital component of medical advancements, contributing to the discovery of new treatments, procedures, and health interventions. This paper discusses the importance of clinical trials, the structure and phases of trials, ethical considerations in research, and the role of modern technologies in reshaping clinical trials.Objective: This article aims to provide a comprehensive overview of the clinical trial process, ethical compliance, and the integration of technological advancements, with real-world examples and recent studies to support the discussion.Methods: The article provides a descriptive analysis of the different types of clinical research, the various phases of clinical trials, and ethical considerations based on established guidelines such as the Declaration of Helsinki and the Belmont Report. It also examines how recent technological innovations, including AI, wearable devices, and Electronic Health Records (EHRs), have revolutionized the field.Results: The integration of technology into clinical research has resulted in more efficient, data-driven, and patient-centric trials. Ethical compliance, guided by international regulations, remains a critical factor in ensuring patient safety and maintaining public trust in clinical research.Conclusion: The future of clinical research relies heavily on technological innovation and strict adherence to ethical guidelines. As new treatments and therapies emerge, the structure of trials and the responsible use of technology will play an essential role in shaping the future of healthcare.

The article presents data from recent publications and own data on screening studies with interpretation of chest radiographs using artificial intelligence CAD (Computer-Assisted Diagnosis), which, according to WHO recommendations, provides more accurate clinical thresholds for deciding who needs to take a sputum test. Another aspect of the WHO recommendations is the cost-effectiveness of CAD as a tool for triaging patients with tuberculosis symptoms in low-income countries with a high incidence of tuberculosis. Compared with smear microscopy and GeneXpert, without preliminary sorting, the use of mobile digital X-ray machines equipped with a CAD tool reduces costs, allowing sorting of individuals suspected of having tuberculosis for testing on GeneXpert, while reducing the time to start tuberculosis treatment.Thus, conducting a study using portable X-ray machines using a CAD program is a low-cost and easy-to-implement method, does not require large funds, does not require separate rooms, is highly effective, has good image quality, allows you to quickly clarify individuals suspected of having tuberculosis, differentiating it from other pathological changes in the lungs.Our experience shows that machine analysis of chest computed tomography data, due to the higher resolution capabilities of the method and the absence of fundamental disadvantages of radiography, including the effect of shadow summation, the presence of “blind” zones, etc., is finding increasing application in both diagnostics and screening of respiratory diseases. Our use of this tool allowed us to identify additional new cases of phthisio-onco-pulmonary diseases in field conditions.

This review underscores the transformative impact of Computer-Aided Engineering (CAE) on modern engineering, emphasizing its role in advancing sustainable and efficient technological solutions. The CAE industry is further analyzed, focusing on market trends and future directions. The CAE market is projected to grow significantly in the next five years in industries like automotive, aerospace, and energy, especially with the rapid advancements in High-Performance Computing (HPC), Artificial Intelligence (AI), Internet of Things (IoT), and Digital Twin technologies that enhance real-time optimization and predictive capabilities, thus fostering innovation in sustainable product design and performance. Lastly, this review presents KYAMOS Software and its state-of-the-art CAE solutions for tackling high-demand engineering problems, mainly concerning green technologies.

This paper shows the developments and directions in feature processing. We begin by revisiting conventional feature processing methods, then focus on deep feature extraction techniques and the application of feature processing. The article also analyzes the current research challenges and outlines future development directions, providing valuable insights in related fields.

Background: Human trafficking is a significant global issue that affects millions of individuals, where victim identification remains a major challenge. Traditional methods such as DNA or fingerprint analysis are not always viable, necessitating alternative forensic approaches.Methods: This article reviews the role of dental identification  in human trafficking cases through an extensive analysis of existing literature. The study incorporates forensic odontology techniques, including dental charting, radiographic analysis, bite mark analysis, age estimation, and emerging technologies like Artificial Intelligence (AI).Results: Findings indicate that dental identification methods are essential for victim identification, especially when conventional methods prove ineffective. AI integration enhances the accuracy and efficiency of dental forensic investigations, addressing challenges such as record access and cross-border complexities.Conclusion: Dental identification, augmented by AI advancements, is an indispensable tool in forensic investigations related to human trafficking. The study underscores the necessity of international collaboration and technological innovation to enhance forensic practices.

The integration of artificial intelligence (AI) technology into various fields, particularly healthcare, has demonstrated considerable potential in improving efficiency and accuracy. However, the potential risks associated with unprofessional or inappropriate use of AI cannot be overlooked. The current landscape of healthcare demonstrates a growing reliance on AI tools, which is expected to expand in the future. The existing literature highlights the effectiveness  of various AI applications, including chatbots, in specific medical domains. This study aims to review relevant literature in the pharmaceutical and galenic fields while evaluating a prominent AI chatbot provider. Based on the findings, this article presents critical considerations for researchers and practitioners. A thorough assessment of the benefits and risks associated with AI technologies is essential as these tools become increasingly prevalent in pharmaceutical practices.