Specification and Verification of Context-dependent Services

Current approaches for the discovery, specification, and provision of services ignore the relationship between the service contract and the conditions in which the service can guarantee its contract. Moreover, they do not use formal methods for specifying services, contracts, and compositions. Without a formal basis it is not possible to justify through formal verification the correctness conditions for service compositions and the satisfaction of contractual obligations in service provisions. We remedy this situation in this paper. We present a formal definition of services with context-dependent contracts. We define a composition theory of services with context-dependent contracts taking into consideration functional, nonfunctional, legal and contextual information. Finally, we present a formal verification approach that transforms the formal specification of service composition into extended timed automata that can be verified using the model checking tool UPPAAL.Comment: In Proceedings WWV 2011, arXiv:1108.208

Transforming architectural descriptions of component-based systems for formal analysis

Design time analysis is an important step in the process of developing software systems, with the goal of ensuring that the system design conforms to the design constraints that are stated as part of the functional and non-functional requirements. The well-known techniques for formally analyzing a design are model checking, axiom-based formal verification, and real-time schedulability analysis that takes into account resource constraints. In this thesis, model checking and real-time schedulability are the techniques used to verify that the system under development is both safe and secure. The architecture of a trustworthy system, formally described in Trustworthy Architectural Description Language (TADL), is taken as the input for the analysis stage. Instead of developing new tools to perform the analyses, the thesis has developed transformation techniques to transform TADL descriptions into behaviour protocols used by existing verification tools. The transformation rules are described independently of the transformation process, thus allowing both reuse and easy extendability. A tool based on such techniques has been designed and implemented which automatically generates two types of models from a TADL description. One is the UPPAAL model, on which the security and safety properties of the system under design are formally verified. The second output is the TIMES model, on which real-time schedulability analysis is performed. The techniques and tools are applied to The Common Component Modelling Example (CoCoME), a case study defined by the component development community, to demonstrate that TADL is expressive enough to formally describe component-based systems

Exploring the potential of artificial intelligence and machine learning to combat COVID-19 and existing opportunities for LMIC: A scoping review

Background: In the face of the current time-sensitive COVID-19 pandemic, the limited capacity of healthcare systems resulted in an emerging need to develop newer methods to control the spread of the pandemic. Artificial Intelligence (AI), and Machine Learning (ML) have a vast potential to exponentially optimize health care research. The use of AI-driven tools in LMIC can help in eradicating health inequalities and decrease the burden on health systems.Methods: The literature search for this Scoping review was conducted through the PubMed database using keywords: COVID-19, Artificial Intelligence (AI), Machine Learning (ML), and Low Middle-Income Countries (LMIC). Forty-three articles were identified and screened for eligibility and 13 were included in the final review. All the items of this Scoping review are reported using guidelines for PRISMA extension for scoping reviews (PRISMA-ScR).Results: Results were synthesized and reported under 4 themes. (a) The need of AI during this pandemic: AI can assist to increase the speed and accuracy of identification of cases and through data mining to deal with the health crisis efficiently, (b) Utility of AI in COVID-19 screening, contact tracing, and diagnosis: Efficacy for virus detection can a be increased by deploying the smart city data network using terminal tracking system along-with prediction of future outbreaks, (c) Use of AI in COVID-19 patient monitoring and drug development: A Deep learning system provides valuable information regarding protein structures associated with COVID-19 which could be utilized for vaccine formulation, and (d) AI beyond COVID-19 and opportunities for Low-Middle Income Countries (LMIC): There is a lack of financial, material, and human resources in LMIC, AI can minimize the workload on human labor and help in analyzing vast medical data, potentiating predictive and preventive healthcare.Conclusion: AI-based tools can be a game-changer for diagnosis, treatment, and management of COVID-19 patients with the potential to reshape the future of healthcare in LMIC

Tui Na (or Tuina) Massage: A Minireview of Pertinent Literature, 1970-2017

Background: Tuina massage is a traditional method used effectively in the treatment of various ailments in China since ancient time, and currently it is used around the world. Objective: This minireview aims to describe several aspects of Tuina massage an integral part of Traditional Chinese Medicine in order to fill up the knowledge gap concerning traditional practitioners in Saudi Arabia. Methods: Electronic searches of databases using Boolean operators and keywords were conducted to retrieve data published in English and Chinese literature. Thousands of articles were identified and screened by two independent reviewers using exclusion and inclusion criteria, and 56 articles were finally included in this study. Results: Tuina has a very rich history in Chinese culture since antiquity. With continuous advancements in research, training, regulation and clinical practice, Tuina massage became popular worldwide and now used either alone or in conjunction with other complementary and alternative medicine or conventional therapies in diverse diseases associated with pain and other symptoms with good outcome. Although Tuina has good safety profile with level of evidence (LOE) of I to III, well defined indications and contraindications, a variety of minor adverse effects together with some major complications including deaths have been reported in the literature. Besides continuous training of Tuina practitioners, Tuina massage practice needs regulatory measures and guidelines for avoiding complications and improving the clinical outcome of patients. Conclusion: Evidently, Chinese Tuina massage supported by theory, mechanisms, procedure and included randomized clinical trials snapshots, systematic reviews and meta-analysis with LOE of I to III is reported to be effective in several conditions. Further, rigorous randomized controlled studies with active comparators including other traditional modality or conventional medications or placebo with intensified quality control measures are required to provide further robust evidence-based data to support its efficacy in chronic diseases associated with pain and disabilities

Geometric Reinforcement Learning For Robotic Manipulation

Reinforcement learning (RL) is a popular technique that allows an agent to learn by trial and error while interacting with a dynamic environment. The traditional Reinforcement Learning (RL) approach has been successful in learning and predicting Euclidean robotic manipulation skills such as positions, velocities, and forces. However, in robotics, it is common to encounter non-Euclidean data such as orientation or stiffness, and failing to account for their geometric nature can negatively impact learning accuracy and performance. In this paper, to address this challenge, we propose a novel framework for RL that leverages Riemannian geometry, which we call Geometric Reinforcement Learning (G-RL), to enable agents to learn robotic manipulation skills with non-Euclidean data. Specifically, G-RL utilizes the tangent space in two ways: a tangent space for parameterization and a local tangent space for mapping to a nonEuclidean manifold. The policy is learned in the parameterization tangent space, which remains constant throughout the training. The policy is then transferred to the local tangent space via parallel transport and projected onto the non-Euclidean manifold. The local tangent space changes over time to remain within the neighborhood of the current manifold point, reducing the approximation error. Therefore, by introducing a geometrically grounded pre- and post-processing step into the traditional RL pipeline, our G-RL framework enables several model-free algorithms designed for Euclidean space to learn from non-Euclidean data without modifications. Experimental results, obtained both in simulation and on a real robot, support our hypothesis that G-RL is more accurate and converges to a better solution than approximating non-Euclidean data.Comment: 14 pages, 14 figures, journa

Effect of vaccination on clinical outcomes in COVID-19 positive chronic kidney disease patients on haemodialysis

Introduction: Coronavirus disease 2019 (COVID-19) resulted in high mortality worldwide, with significantly higher mortality among patients with comorbidities including chronic kidney disease (CKD). Vaccines were developed against COVID-19 and it was given emergency approval because of the associated high mortality. There are only few studies on the efficacy of vaccine in CKD patients on haemodialysis. Effect of vaccination on clinical outcomes in COVID-19 positive CKD patients on haemodialysis was studied. Materials and methods: In this retrospective study on CKD patients on haemodialysis with confirmed COVID-19 infection, comparison was done on the clinical outcomes between the vaccinated and unvaccinated population. Results: Of the 104 patients, 74 patients were vaccinated against COVID-19 and 30 were unvaccinated. The study population received either covishield (50) or covaxin (24) which were the approved vaccines in India at that time. Among the vaccinated group 15 (20%) needed invasive mechanical ventilation and 16 (53%) in the unvaccinated group (P value 0.001). There were 16 (22%) deaths in the vaccinated group and 15 (50%) in the unvaccinated group with a significantly higher mortality in the unvaccinated group (P value 0.005). 11 (21%) patients on covishiled and 4 (18%) on covaxin needed invasive mechanical ventilation and there were 12 (24%) deaths in the covishield group and 4 (18%) in covaxin group. Conclusion: Severity of disease and mortality is less in vaccinated CKD patients on haemodialysis compared to unvaccinated reiterating the importance of vaccination against COVID-19 in high risk patients

Challenges of Online Learning Environment Faced by Undergraduate Medical Students During Covid 19 Pandemic

Objective: This study aimed to define the challenges faced by medical students rotating in the orthopedics department and their suggestions regarding improvement during covid-19 pandemic. Study Design: A mixed method cross sectional study design. Place and Duration of Study: It was conducted on 4 and 5 year MBBS students at Shifa college of Medicine with clerkship rotation in the department of orthopedics from 16 March 2020 to 23 August 2021. Materials and Methods: Students were enquired about their comfort levels while using the internet and computer for online sessions. Data was collected through an online questionnaire and analyzed using Google forms. Frequencies, percentages, and standard deviations were calculated for qualitative variables. Results: Out of 147 study participants, 64(43.4%) students strongly agreed that they had no difficulty and were extremely comfortable using internet and computer during covid-19 pandemic. Eighty-five (58%) students used online available reading material shared on Google classrooms and what's app groups. While only 23(16%) agreed to concentrate during online sessions. One hundred and eighteen (80%) agreed with a lesser desire to study for online classes as compared to on campus. Major problems faced by the students during the pandemic included very limited patient centered learning, limited hands-on experience, less interactive sessions, problems with internet connections, technology handling and class timing issues due to time zone differences. Conclusion: We conclude that our students faced lot of challenges during Covid-19 pandemic including internet issues, lack of awareness of technology, distractions because of family, siblings and homely environment and lack of conducive learning environment like learning at bedside. Flexible class timings, multiple breaks, recorded lectures and online interaction of real patients can improve online clinical learning

Healthcare Professionals’ Practice of HIV Post-Exposure Prophylaxis in Clinical Settings in Karachi, Pakistan

The human immunodeficiency virus (HIV) is an important public health concern that has become more prevalent in Pakistan in recent decades. Healthcare professionals (HCPs) are frequently exposed to many HIV-infected patients; as a result, they are more vulnerable to HIV infection due to occupational exposure. Hence, the current study was executed to evaluate HCPs’ knowledge, attitude and practice in terms of post-exposure prophylaxis (PEP) for HIV. This cross-sectional study was carried out in several clinical and laboratory settings of Karachi and the HCPs involved in treating patients were surveyed using a structured questionnaire. The Shapiro–Wilk test was performed to establish the normality of the variables. Pearson correlation was employed to identify the relationship between the independent variables considering p-values < 0.05 as statistically significant. A total of 578 filled forms were incorporated in the study with a response rate of 72.2%. Physicians and medical students (OR = 1.68; 95% CI = 1.16–2.24; p = 0.001) belonging to private work settings (OR = 1.84; 95% CI = 1.33–2.35; p < 0.003) indicated better knowledge. The majority, 407 (70.4%), of the respondents reported having been exposed to risky occupational circumstances during their professional life; however, 65.7% took PEP for HIV after exposure and only 56.8% completed the entire course. A statistically significant association was observed between experience (p = 0.004, CI = 0.14–0.72), job category (p = 0.0001, CI = 0.16–0.62) and frequency of exposure (p = 0.003, CI = 0.42–11.31) and reporting of occupational exposure. More than half (53.8%) of respondents stated that their institute has a policy for the management of HIV exposures; however, their response was significantly associated with their organization (p = 0.004). The current study shows adequate knowledge revealing a positive attitude among respondents; however, there was a gap between the knowledge and its practical application. Even though many of the HCPs had experienced risky HIV exposure, a lack of reporting was noted in the study