Journal for ReAttach Therapy and Developmental Diversities (JRTDD)
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Long Term Effects Of COVID-19 Induced Home Confinement On Lifestyle And Physical Activity Levels Of Adolescents
No full textThe global COVID-19 pandemic and the resulting lockdown measures have had a profound impact on the lives of adolescents. In this article, we aim to explore the effects of the lockdown on the lifestyle and physical activity levels of teenagers. The lockdown period led to significant changes in the lifestyle of adolescents. With schools and recreational activities closed, adolescents spent significantly more time on electronic devices. This increase in screen time resulted in reduced physical activity, disrupted sleep patterns, and potential exposure to harmful online content. Lockdown drastically reduced opportunities for exercise. Closing schools meant that students wouldn\u27t have access to organised physical education programs, which are essential for teenage health. Limitations on social events and outdoor activities decreased the amount of time people could spend playing, sports, and relaxing. Teenagers embraced more sedentary lifestyles due to rising screen time and constrained physical spaces, which resulted in a decline in muscle strength, endurance, and general fitness
A Review On Artificial Intelligence-Based MPPT And Total Harmonic Distortion Reduction In PV Grids: Insights For Cognitive Adaptation In Reattach Therapy
No full textThe increasing use of photovoltaic (PV) systems for sustainable energy faces two major challenges: tracking the global maximum power point (GMPP) during partial shading conditions (PSC) and mitigating Total Harmonic Distortion (THD) caused by fluctuating solar irradiance. These challenges impact the efficiency and performance of PV systems integrated into grid networks. Artificial Intelligence (AI) has proven effective in optimizing Maximum Power Point Tracking (MPPT), addressing both GMPP tracking and THD reduction. Interestingly, AI\u27s adaptive strategies in PV systems parallel cognitive adaptation techniques in ReAttach Therapy, which fosters neuroplasticity and cognitive flexibility in individuals with developmental diversities. Both frameworks rely on timely, dynamic adjustments to optimize outcomes, whether in energy management or cognitive rehabilitation. This review examines AI-based MPPT algorithms and their role in THD reduction, drawing parallels with ReAttach Therapy’s adaptive processes. Just as adaptive filters (AF) reduce noise and disturbances in PV systems, ReAttach techniques help individuals adapt to sensory stimuli and improve emotional regulation. The paper highlights how AI-based solutions can inspire advancements in therapeutic approaches by integrating adaptive strategies to enhance performance in both fields. By providing a multidisciplinary perspective, this review offers new insights into selecting optimal AI-based MPPT methods while promoting adaptive strategies applicable in both technological and therapeutic contexts
Homoeopathic Approach In The Management Of Essential Hypertension
No full textHypertension (HTN) or elevated blood pressure is a serious medical condition that significantly increases the risk of heart, brain, kidney, and other diseases. HTN in an adult is said to be present when clinical systolic and diastolic blood pressure exceed 140 mm and 90 mm of hg, respectively. An estimated 1.28 billion people aged 30–79 years have HTN worldwide, most (two-thirds) living in low- and middle-income countries (World Health Organization). Homoeopathy can be useful in managing HTN and its complications, for which a proper knowledge of treatment modalities available in homoeopathy is essential
A Comprehensive Study Of Incorporation Of Information Science Into Financial Management
No full textThe incorporation of information science into financial management signifies a notable advancement in the financial sector, propelled by the growing intricacy and magnitude of financial data. This review paper analyses the most recent advancements, patterns, and methodologies in this integration, emphasising the revolutionary influence of cutting-edge technologies such as big data analytics, artificial intelligence, blockchain, robotic process automation, cloud computing, and natural language processing. These advancements have completely transformed the way financial decisions are made, risks are managed, and operations are streamlined. They have empowered institutions to handle massive amounts of data, automate intricate activities, and provide customised services. Although there are difficulties in guaranteeing data security, preserving data quality, merging new technologies with existing systems, and resolving talent deficiencies, the potential for development and enhancement is significant. This article thoroughly examines the aforementioned problems and investigates the possible advantages, such as better accuracy in decision-making, cost reduction through automation, the development of innovative financial products, greater consumer experiences, and more efficient compliance with regulatory requirements. The results emphasise the crucial significance of information science in influencing the future of financial management, facilitating the development of a more adaptable, streamlined, and protected financial ecosystem. As financial institutions evolve to keep up with technological breakthroughs, the incorporation of information science will be crucial in effectively navigating the intricacies of the contemporary financial environment and attaining long-term prosperity
Impact Of Time And Temperature On The Dimensional Stability Of Alginate Impressions: A Comparative In Vitro Analysis
No full textAbstract
Objective: This in vitro study aimed to evaluate the dimensional stability and shrinkage behavior of three commercially available alginate dental impression materials (Velplast, Marieflex, and Zelgan) under varying pouring times and storage temperatures.
Materials and Methods: A total of 90 impressions were made using Velplast, Marieflex, and Zelgan. Impressions were divided into three groups based on pouring times: immediate, 20 minutes, and 40 minutes. Additionally, impressions were stored at three different temperatures: 25°C, 30°C, and 40°C. Dimensional changes and shrinkage were measured using a traveling microscope, and statistical analysis was performed using p-values to assess significance.
Results: The study found that increased delay in pouring and elevated storage temperatures led to significant dimensional changes in all materials. Velplast exhibited minimal shrinkage at 25°C (0.10%) but showed the highest shrinkage at 40°C (-0.30%). Marieflex and Zelgan similarly demonstrated lower shrinkage at 25°C, but their shrinkage increased to -0.25% and -0.28%, respectively, at 40°C. Immediate pouring resulted in the highest dimensional stability, with accuracy rates of 99.5%, 99.7%, and 99.3% for Velplast, Marieflex, and Zelgan, respectively.
Delayed pouring led to greater dimensional distortion, particularly at 40 minutes.
Conclusion: The dimensional stability of alginate impression materials is significantly influenced by both pouring time and storage temperature. Immediate pouring is recommended to maintain accuracy, and elevated temperatures should be avoided to minimize shrinkage. Marieflex showed the highest stability across all conditions, making it a suitable option for clinical cases requiring delayed pouring
Revolutionizing Patient Outcomes with AI-Powered Generative Models: A New Paradigm in Specialty Pharmacy and Automated Distribution Systems
Get PDFArtificial intelligence (AI) large language models (LLMs) have been advanced with rapid advancements in science and machine learning techniques, increasingly leading to a wide range of high-level intelligent applications. Due to improved “understanding” of the context of writing prompts, AI LLMs for natural languages have been able to generate diverse and sophisticated organic text with remarkable fluency. LLMs are able to provide exceptional dialogue, vaccine material design, and academic text generation. The implementation of AI LLMs for pharmaceutical applications, however, such as prompt-driven language generation tasks in the pharmacy domain, have scarcely been attended to.
Recently, pharmaceutical patient specialists co-developed a new system, DrugSymphony, which was a language model based system of AI LLMs to enhance prompt-driven writing in the area of pharmacy. Two key complementary strategies around the specialized knowledge graphs and patient healthcare narratives can boost DrugSymphony, leading to inputs that mimic human knowledge comprehension. Through several evaluations utilizing patient sets in the real world, DrugSymphony is found to enhance well-defined tasks of writing, specifically medication program generation. It is hoped that this study will contribute to further research into machine-generated writing in the pharmacy field. At the time, however, the research sheds light on the effectiveness of LLMs in addressing real-world, professional-language, prompt-driven writing tasks, so pharmacy and pharmacotherapy specialists can maximally benefit from their potential functionality.
Recently, the implementation of advanced AI techniques for creating text has significantly improved the scope of potential applications in a broad range of sectors. The LLMs automatically generate organic text in response to a prompt of incoming text, with unprecedented fluidity and naturalness. The use of GPT has a great deal of flexibility; no specific instructions are often needed other than natural language text prompts. On this account, the beneficiaries include not only industry-insider professionals but also non-experts who may easily engage in building system prototypes for a wide range of applications. This includes technical reports, medical and science-related publications, company memoranda, marketing products, and so forth. GPT applications, on the other hand, come with the number of limitations: proprietary program formulation and large model costs
An Investigation Into The Role Of Artificial Intelligence In The Healthcare Industry
Get PDFHealthcare systems are intricate and present challenges for all involved parties. However, artificial intelligence (AI) is making significant strides across various sectors, including healthcare, where it holds promise for enhancing patient care and overall quality of life. The rapid advancements in AI have the potential to change the landscape of healthcare by actively incorporating it into clinical practices. With its growing capability to turn complex and uncertain data into actionable—albeit imperfect—clinical decisions or recommendations, AI can significantly influence healthcare operations. In the dynamic interaction between humans and AI, trust emerges as a crucial element that affects how clinicians adopt and utilize these technologies. This paper investigates the role of clinicians as the primary users of AI systems in healthcare and discusses the factors that influence the trust between clinicians and AI
Next-Generation Technologies for Early Disease Detection and Treatment: Harnessing Intelligent Systems and Genetic Innovations for Improved Patient Outcomes
Get PDFThere are significant challenges to developing improved technologies for the early detection and treatment of emerging diseases. Early detection requires reducing the time it takes to identify and test potential reasons for emerging human diseases. New technological advancements in next-generation sequencing and the annotations of human genomes provide an opportunity to leverage such information to design new ways of testing individuals for the presence and progression of diseases. Deep-sequencing approaches toward the construction of human DNA, RNA, and protein annotations present new opportunities for understanding the pathophysiology underlying differences in individual responses to pathogens and other environmental factors.This paper discusses a vision of next-generation technologies and how the challenge of early disease detection motivates research aimed at developing a set of new sensor platforms. Such technologies may revolutionize early detection diagnostic tests and dramatically improve response times. Our goal is to support the development of advanced sensor technologies required to inform treatment decisions, to manage maladies ranging from day-to-day ailments to the horrors of newly emerging infectious diseases. Our approach—developing advanced sensors and associating rapidly with annotations of genomes to inform treatment decisions—will use a virus as a case study because it is a challenge for biomarker discovery that can be used in drug design and diagnostics development. As such, our approach provides proof of concept validation and advanced next-generation sequencing methods that will enable diagnostics development for other new diseases
Leveraging Artificial Intelligence to Improve Quality of Service in Next-Generation Broadband Networks
Get PDFIn recent years, Operators are confronted with new challenges regarding the Quality of Service (QoS) they provide to their users. Changes on the traffic load after the emergence of smartphones and other mobile devices with capacity for using real-time applications have produced an overloading excess of many mobile networks. New paradigms such as the Internet of Things (IoT) and Smart Cities are expected to generate a massive traffic growth that current fixed and mobile networks may be unable to support. In addition to this, the quality of service perceived by the users is becoming the most important selection criterion in the choice of the service provider. Providers need an exhaustive management of the Internet Protocol (IP)-based services that they offer, and specifically, of the related QoS parameters. A joint approach is proposed to leverage Artificial Intelligence (AI) techniques to achieve an accurate monitoring of QoS parameters that impact on user experience. Data imported from the provider, application and content server domains enables to train Random Forest, Support Vector Machine and Decision Tree models, which are used to predict target QoS parameters values. A congestion classifies conditions of the multimedia content targeting. An extensible framework for dynamic managing of the QoS of the services offered is provided. Monitoring tools. QoS Monitoring Framework is devised for “on-the-fly” monitoring QoS controlling of usage by classifiers and models. Classifications of the IP-based services offered are needed and relevance of the parameters is estimated to identify targets for applying those policies. A clear approach should be defined on the policies available in the context of controlling the service provisioning (“active”), specification of the QoS to achieve (“passive”). A framework for evolving the adequate policy actions according to the usage of the services is required. Ensuring that customers experience a certain level of QoS has become an important issue in the design considerations of networks. To achieve this goal, it is important to identify those parameters that need to be controlled and those that impact on the customer perceived service QoE
Smart Semiconductor Testing Systems: Fusion of Embedded AI And Scalable Data Pipelines
Get PDFSemiconductor companies are challenged by increasingly complex testing requirements coming from their customers and technology, as the design of modern System-on-Chips (SoCs) evolves into multi-chiplets. New AI-driven paradigms are needed to facilitate a massively parallel high-throughput test methodology while still allowing tight test channel characterization that affects both yield and performance of the complex designs. With the increase in chip complexity and design autonomy of third-party chiplets, a whole new add-on market for in-die and package-level testing is created, and access to the test systems is kept tightly.
A new architecture for the system under test (SUT) is presented based on decisions at test time and embedded intelligence combined with a distributed AI-based device that abstracts the test flow towards a Domain-Specific Language (DSL) API. This new approach is complemented by a novel design-to-test procedure and scalable machine learning pipelines on chiplet level. With this approach, a Semantic Web-based ecosystem of tools and libraries is created that links simulators and correlators and allows engineers to compose powerful packages of tasks, lab experiments, and production data mining.
Traditional semiconductor integrated circuit (IC) test systems are fast reaching their limits with respect to both test data throughput in the order of petabytes and complexity of platform and device under test which need to be test parallelized in order to ensure operational use. For SoCs and their Subsystems, an architecture and implementation of a non-standard test methodology is proposed that is distributed, massively parallel, and AI driven. The ambition is to merge the fabrication test domain with various application domains in order to perform heterogeneous tests