Detecting Treatment Interference under the K-Nearest-Neighbors Interference Model

We propose a model of treatment interference where the response of a unit depends only on its treatment status and the statuses of units within its K-neighborhood. Current methods for detecting interference include carefully designed randomized experiments and conditional randomization tests on a set of focal units. We give guidance on how to choose focal units under this model of interference. We then conduct a simulation study to evaluate the efficacy of existing methods for detecting network interference. We show that this choice of focal units leads to powerful tests of treatment interference which outperform current experimental methods

Time to question diabetes self-management support for Arabic-speaking migrants: exploring a new model of care

AIM: The objective of this study was to explore a new model for diabetes self-management support in Arabic-speaking migrants. METHODS: Two qualitative methods were used: face-to-face semi-structured individual interviews and focus groups. Interviews were audio-taped, transcribed verbatim and coded thematically. Arabic-speaking migrants with Type 2 diabetes were recruited from several primary, secondary and tertiary healthcare settings in metropolitan Melbourne, Australia. These settings were purposefully selected to obtain a diverse group of participants. Data collection continued until saturation was reached. This is the first study that involved members of Arabic-speaking communities in Australia in a formal process of consumer and public involvement to inform research design and recruitment in order to provide evidence for a new model of diabetes self-management for Arabic-speaking migrants. RESULTS: No self-management support was offered to Arabic-speaking migrants beyond the initial diagnosis period. Significant knowledge gaps and skills deficits in all self-management domains were evident. The provision of tailored self-management support was considered crucial. When asked about preferred structure and delivery modalities, a strong preference was reported for face-to-face storytelling interactions over telephone- or internet-based interventions. Gender-specific group education and self-management support sessions delivered by Arabic-speaking diabetes health professionals, lay peers or social workers trained in diabetes self-management were highly regarded. CONCLUSIONS: A patient and public involvement approach allows genuine engagement with Arabic-speaking migrants with diabetes. There is urgent need for a new model for self-management support among Arabic-speaking migrants. Findings yielded new recommendations for diabetes health professionals working with these migrant communities to support behaviour change

Barriers and enablers to healthcare access and use among Arabic-speaking and Caucasian English-speaking patients with type 2 diabetes mellitus: a qualitative comparative study

OBJECTIVE: The objective of this study was to explore the decision-making processes and associated barriers and enablers that determine access and use of healthcare services in Arabic-speaking and English-speaking Caucasian patients with diabetes in Australia. STUDY SETTING AND DESIGN: Face-to-face semistructured individual interviews and group interviews were conducted at various healthcare settings-diabetes outpatient clinics in 2 tertiary referral hospitals, 6 primary care practices and 10 community centres in Melbourne, Australia. PARTICIPANTS: A total of 100 participants with type 2 diabetes mellitus were recruited into 2 groups: 60 Arabic-speaking and 40 English-speaking Caucasian. DATA COLLECTION: Interviews were audio-taped, translated into English when necessary, transcribed and coded thematically. Sociodemographic and clinical information was gathered using a self-completed questionnaire and medical records. PRINCIPAL FINDINGS: Only Arabic-speaking migrants intentionally delayed access to healthcare services when obvious signs of diabetes were experienced, missing opportunities to detect diabetes at an early stage. Four major barriers and enablers to healthcare access and use were identified: influence of significant other(s), unique sociocultural and religious beliefs, experiences with healthcare providers and lack of knowledge about healthcare services. Compared with Arabic-speaking migrants, English-speaking participants had no reluctance to access and use medical services when signs of ill-health appeared; their treatment-seeking behaviours were straightforward. CONCLUSIONS: Arabic-speaking migrants appear to intentionally delay access to medical services even when symptomatic. Four barriers to health services access have been identified. Tailored interventions must be developed for Arabic-speaking migrants to improve access to available health services, facilitate timely diagnosis of diabetes and ultimately to improve glycaemic control

Bioinformatics: Computational Approaches for Genomics and Proteomics

Bioinformatics is a fast evolving field that combines biology, computer science, and statistics to analyze and comprehend enormous volumes of biological data. As a result of the introduction of high-throughput technologies like next-generation sequencing and mass spectrometry, genomic and proteomics research has generated enormous volumes of data, necessitating the development of computational tools to process and extract useful insights from these datasets. This presentation presents a survey of computational approaches in bioinformatics with a particular emphasis on their application to genomics and proteomics. The study of the entire genome is a topic covered in the discipline of genomics, which also includes genome annotation, assembly, and comparative genomics. Proteomics focuses on the investigation of proteins, including their identification, quantification, structural analysis, and functional characterization. Consequently, the importance of the area of bioinformatics has increased

Quantum Computing: Algorithms,Architectures, and Applications

Cryptography, optimization, simulation, and machine learning are just a few of the industries that might be completely transformed by quantum computing. This abstract gives a thorough introduction to quantum computing with an emphasis on its algorithms, architectures, and applications. In conclusion, this abstract offers an in-depth analysis of quantum computing, including its algorithms, structures, and applications. It highlights the revolutionary potential of quantum computing in tackling difficult issues that are beyond the scope of conventional computers, laying the groundwork for further research and understanding of this quickly developing topic

Computational Intelligence for Solving Complex Optimization Problems

Complex optimization issues may now be solved using computational intelligence (CI), which has shown to be a powerful and diverse discipline. Traditional optimization approaches frequently struggle to offer efficient and effective solutions because real-world situations are becoming more complicated. Evolutionary algorithms, neural networks, fuzzy systems, and swarm intelligence are just a few examples of the many methods that fall under the umbrella of computational intelligence and are inspired by both natural and artificial intelligence. This abstract examines how computational intelligence techniques are used to solve complicated optimization issues, highlighting their benefits, drawbacks, and most recent developments. In this, computational intelligence techniques provide a potent and adaptable solution for resolving challenging optimization issues. They are highly adapted for dealing with the non-linear connections, uncertainties, and multi-objective situations that arise in real-world problems. The limits of computational intelligence have recently been pushed by recent developments in hybrid techniques and metaheuristics, even if obstacles in algorithm design and parameter tuning still exist. Computational intelligence is anticipated to play an increasingly significant role in tackling complicated optimization issues and fostering innovation across a variety of disciplines as technology continues to advance

Parallel and Distributed Computing for High-Performance Applications

The study of parallel and distributed computing has become an important area in computer science because it makes it possible to create high-performance software that can effectively handle challenging computational tasks. In terms of their use in the world of high-performance applications, parallel and distributed computing techniques are given a thorough introduction in this study. The partitioning of computational processes into smaller subtasks that may be completed concurrently on numerous processors or computers is the core idea underpinning parallel and distributed computing. This strategy enables quicker execution times and enhanced performance in general. Parallel and distributed computing are essential for high-performance applications like scientific simulations, data analysis, and artificial intelligence since they frequently call for significant computational resources. High-performance apps are able to effectively handle computationally demanding tasks thanks in large part to parallel and distributed computing. This article offers a thorough review of the theories, methods, difficulties, and developments in parallel and distributed computing for high-performance applications. Researchers and practitioners may fully utilize the potential of parallel and distributed computing to open up new vistas in computational science and engineering by comprehending the underlying concepts and utilizing the most recent breakthroughs

Data Privacy and Security in Cloud Computing Environments

The globe has adopted the cloud computing environment, which organizes data and manages space for data storage, processing, and access. This technical development has brought up questions regarding data security and privacy in cloud computing environments, though. The purpose of this abstract is to offer a thorough review of the issues, solutions, and future developments related to data privacy and security in cloud computing. Keeping data private and secure while it is being processed and stored in outside data centres is the main difficulty in cloud computing systems. The abstract discusses the dangers of insider threats, data breaches, and illegal access to sensitive information. It digs further into the legal and compliance criteria that businesses must follow in order to protect user data in the cloud. In result, data privacy and security in cloud computing environments remain critical concerns for organizations and individuals alike. In the survey the overview of how to use cloud storage globally and its challenges, solution and future innovation is well explained. It underscores the importance of robust encryption, access controls, user awareness, and emerging technologies in safeguarding data in the cloud. By addressing these concerns, organizations can leverage the power of cloud computing while maintaining the confidentiality, integrity, and availability of their data

Demographic, Clinical, and Biomedical Profile of Diabetic Patients Receiving Home Healthcare in Saudi Arabia

Background: Identifying characteristics of diabetic patients receiving home healthcare will help in designing services that respond to their conditions and improve their health status and quality of life. The aim of this study was to describe the demographic, clinical, and biomedical characteristics of diabetic patients receiving HHC. Methods and Results: We used a descriptive cross-sectional design, and data were collected from 251 medical records of diabetic patients in two home healthcare centers in Saudi Arabia. The collected data included demographic, clinical, and biomedical profile variables. The average age was 74.7±11.6 years, with most patients (93.2%) aged 60 or older. The most common treatment modality was multiple daily insulin injections with or without oral medication (38.6%), followed by oral medication with sulfonylurea (19.9%). Pressure injury was the most reported complication/comorbidity, affecting 33.1% of patients. Cerebrovascular disease came next, affecting 20.7% of patients, followed by cardiovascular disease, ischemic heart disease, and nephropathy, affecting 12.3%, 10%, and 6.4% of patients, respectively. Only 4.2% of patients experienced hypoglycemia, and only 5.6% of patients were hospitalized due to DM complications. The mean HbA1c was 7.6±1.7%, with approximately 71.7% of the diabetic patients having HbA1c8% (P<0.0001). The median (range) LDL was 2.93 (1-317) mmol/L. The median (range) eGFR was 76.6 (9-389) mL/min/1.73m2. Around 48% of the population had an eGFR<60 mL/min/1.73m2. Conclusion: Our findings show satisfactory glycemic control, acceptable LDL levels, low incidence of hypoglycemia, and minimal hospital admissions