Transient pain and discomfort when wearing high-heeled shoes.

In the dynamic world of fashion, high-heeled footwear is revered as a symbol of style, luxury and sophistication. Yet, beneath the facade of elegance of classy footwear lies the harsh reality of discomfort and pain. Thus, this study aims to investigate the influence of wearing high-heeled shoes on the sensation of pain across different body regions over a period of 6 h. It involved fifty female participants, all habitual wearers of high-heeled shoes, aged between 20 and 30 years. Each participant kept a record of their perceptions of pain and discomfort every hour for a total of 6 h using a 0-10 pain scale with 0 indicating no pain and 10 indicating severe pain. The findings reveal a progressive rise in pain throughout wear, with the most intense pain reported in the back, calcaneus, and metatarsals. The analysis shows that after approximately 3.5 h, participants experience significant increases in pain levels. However, the relationship between heel height and pain is not linear. It appears that a heel height of 7.5 cm is the threshold where overall body pain becomes significant. The study suggests that a duration of 3.5 h of wear and a heel height of 7.5 cm serve as critical points to decrease overall body pain. Moreover, beyond this heel height, knee pain diminishes compared to other body areas possibly due to the shift towards a more neutral posture. The study findings, coupled with the recommendations, can assist footwear designers in crafting not only stylish but also comfortable shoes.

Severity of vehicle-to-vehicle accidents in the UAE: An exploratory analysis using machine learning algorithms.

The World Health Organization (WHO) identifies road traffic injuries as a global health problem. The Eastern-Mediterranean region is particularly suffering from low traffic safety levels, recording the third highest death per capita ratio in the world. It is critical to evaluate and understand the causes of crashes and their severity levels as a first step to devising policies that aim to reduce these causes. Previous studies examining the frequency or severity of crashes present important limitations that motivate the need for the current work. While these studies have investigated the relation of contributing factors to severity of crashes, not until recently the importance of these factors are bring investigated. Even then, less research have explored various Machine Learning models and none in the middle-eastern region. This is critical because the WHO report concludes that the chances of dying in a traffic crash in this region are second only to Africa per 100000 population. This is a first study analyzing the severity of vehicle-to-vehicle crashes among drivers in the United Arab Emirates. Traffic Crash Data was obtained from the Abu Dhabi Police, which consisted of 11,400 observations during the period 2014-2017. Machine learning algorithms, including gradient boosting (GB), support vector machines (SVM), and random forest (RF), were trained and tested to predict crash severity and extract (using feature analysis) its determinants. The models were evaluated using two performance metrics: prediction accuracy and F1-scores. The RF model outperformed both GB and SVM, with the confusion matrix of RF reporting a better prediction for all four crash severity classes. The feature importance analysis indicates that the age of car, age of the injured, and the age of the initiator have the highest effect on severity, which is an important finding as the listed factors were rarely considered in previous studies. Vehicle and road characteristics such as vehicle class, crash type, and lighting are slightly associated with the severity. Consistent with other studies, gender was the least essential predictor of severity. Recommendations are finally provided to the Abu Dhabi Department of Municipalities and Transport (AD-DMT) authority to guide the development of road safety policies and countermeasures to mitigate the occurrence and severity of crashes.

Blockchain and COVID-19 pandemic: applications and challenges.

The year 2020 has witnessed the emergence of coronavirus (COVID-19) that has rapidly spread and adversely affected the global economy, health, and human lives. The COVID-19 pandemic has exposed the limitations of existing healthcare systems regarding their inadequacy to timely and efficiently handle public health emergencies. A large portion of today's healthcare systems are centralized and fall short in providing necessary information security and privacy, data immutability, transparency, and traceability features to detect fraud related to COVID-19 vaccination certification, and anti-body testing. Blockchain technology can assist in combating the COVID-19 pandemic by ensuring safe and reliable medical supplies, accurate identification of virus hot spots, and establishing data provenance to verify the genuineness of personal protective equipment. This paper discusses the potential blockchain applications for the COVID-19 pandemic. It presents the high-level design of three blockchain-based systems to enable governments and medical professionals to efficiently handle health emergencies caused by COVID-19. It discusses the important ongoing blockchain-based research projects, use cases, and case studies to demonstrate the adoption of blockchain technology for COVID-19. Finally, it identifies and discusses future research challenges, along with their key causes and guidelines.

Blockchain for deep learning: review and open challenges.

Deep learning has gained huge traction in recent years because of its potential to make informed decisions. A large portion of today's deep learning systems are based on centralized servers and fall short in providing operational transparency, traceability, reliability, security, and trusted data provenance features. Also, training deep learning models by utilizing centralized data is vulnerable to the single point of failure problem. In this paper, we explore the importance of integrating blockchain technology with deep learning. We review the existing literature focused on the integration of blockchain with deep learning. We classify and categorize the literature by devising a thematic taxonomy based on seven parameters; namely, blockchain type, deep learning models, deep learning specific consensus protocols, application area, services, data types, and deployment goals. We provide insightful discussions on the state-of-the-art blockchain-based deep learning frameworks by highlighting their strengths and weaknesses. Furthermore, we compare the existing blockchain-based deep learning frameworks based on four parameters such as blockchain type, consensus protocol, deep learning method, and dataset. Finally, we present important research challenges which need to be addressed to develop highly trustworthy deep learning frameworks.

Blockchain-based solution for COVID-19 vaccine waste reduction.

Coronavirus 2019 (COVID-19) vaccines have been produced on a large scale since 2020. However, large-scale vaccine production has led to two forms of waste; namely, overproduction and underutilization. Most of today's systems and technologies used to manage waste data related to COVID-19 vaccines fall short of providing transparency, traceability, accountability, trust, and security features. In this paper, we address the problem of COVID-19 vaccines waste due to their overproduction and underutilization. We propose a blockchain-based solution that is composed of five phases: registration, commitment; production and delivery; consumption; and waste assessment. These phases make up the complete life cycle of a COVID-19 vaccine, and they are governed by several smart contracts to ensure accountability of all the actions taken by the involved entities and reduce any excessive waste caused by overproduction, overordering, or underconsumption. We ensure security, traceability, and data provenance by recording all actions through smart contracts in the form of events on an immutable ledger. We utilize decentralized storage such as the InterPlanetary File System (IPFS) to reduce the costs posed by large-sized file storage when stored on-chain. We present algorithms that describe the logic behind our developed smart contracts. We test and validate the functionalities of our proposed solution. We conduct security, cost, and scalability analyses to show that our solution is affordable, scalable, and secure. We compare our solution with the existing blockchain-based solutions to show its novelty and superiority. The smart contract code is made publicly available on GitHub.

Blockchain-based Supply Chain Traceability for COVID-19 personal protective equipment.

The COVID-19 pandemic has severely impacted many industries, in particular the healthcare sector exposing systemic vulnerabilities in emergency preparedness, risk mitigation, and supply chain management. A major challenge during the pandemic was related to the increased demand for Personal Protective Equipment (PPE), resulting in critical shortages for healthcare and frontline workers. This is due to the lack of information visibility combined with the inability to precisely track product movement within the supply chain, requiring a robust traceability solution. Blockchain technology is a distributed ledger that ensures a transparent, safe, and secure exchange of data among supply chain stakeholders. The advantages of adopting blockchain technology to manage and track PPE products in the supply chain include decentralized control, security, traceability, and auditable time-stamped transactions. In this paper, we present a blockchain-based approach using smart contracts to transform PPE supply chain operations. We propose a generic framework using Ethereum smart contracts and decentralized storage systems to automate the processes and information exchange and present detailed algorithms that capture the interactions among supply chain stakeholders. The smart contract code was developed and tested in Remix environment, and the code is made publicly available on Github. We present detailed cost and security analysis incurred by the stakeholders in the supply chain. Adopting a blockchain-based solution for PPE supply chains is economically viable and provides a streamlined, secure, trusted, and transparent mode of communication among various stakeholders.

Blockchain-Based Forward Supply Chain and Waste Management for COVID-19 Medical Equipment and Supplies.

The year 2020 has witnessed unprecedented levels of demand for COVID-19 medical equipment and supplies. However, most of today's systems, methods, and technologies leveraged for handling the forward supply chain of COVID-19 medical equipment and the waste that results from them after usage are inefficient. They fall short in providing traceability, reliability, operational transparency, security, and trust features. Also, they are centralized that can cause a single point of failure problem. In this paper, we propose a decentralized blockchain-based solution to automate forward supply chain processes for the COVID-19 medical equipment and enable information exchange among all the stakeholders involved in their waste management in a manner that is fully secure, transparent, traceable, and trustworthy. We integrate the Ethereum blockchain with decentralized storage of interplanetary file systems (IPFS) to securely fetch, store, and share the data related to the forward supply chain of COVID-19 medical equipment and their waste management. We develop algorithms to define interaction rules regarding COVID-19 waste handling and penalties to be imposed on the stakeholders in case of violations. We present system design along with its full implementation details. We evaluate the performance of the proposed solution using cost analysis to show its affordability. We present the security analysis to verify the reliability of the smart contracts, and discuss our solution from the generalization and applicability point of view. Furthermore, we outline the limitations of our solution in form of open challenges that can act as future research directions. We make our smart contracts code publicly available on GitHub.

COVID-19 Contact Tracing Using Blockchain.

Contact tracing has widely been adopted to control the spread of Coronavirus-2019 (COVID-19). It enables to identify, assess, and manage people who have been exposed to COVID-19, thereby preventing from its further transmission. Today's most of the contact tracing approaches, tools, and solutions fall short in providing decentralized, transparent, traceable, immutable, auditable, secure, and trustworthy features. In this paper, we propose a decentralized blockchain-based COVID-19 contact tracing solution. Contact tracing can greatly suffice the need for a speedy response to a pandemic. We leverage the immutable and tamper-proof features of blockchain to enforce trust, accountability, and transparency. Trusted and registered oracles are used to bridge the gap between on-chain and off-chain data. With no third parties involved or centralized servers, the users' medical information is not prone to invasion, hacking, or abuse. Each user is registered using their digital medical passports. To respect the privacy of the users, their locations are updated with a time delay of 20 minutes. Using Ethereum smart contracts, transactions are executed on-chain with emitted events and immutable logs. We present details of the implemented algorithms and their testing analysis. We evaluate the proposed approach using security, cost, and privacy parameters to show its effectiveness. The smart contracts code is publicly made available on GitHub.

Blockchain-Based Solution for Distribution and Delivery of COVID-19 Vaccines.

Distribution and delivery of Coronavirus 2019 (COVID-19) vaccines have become challenging after their emergence. Today's platforms and systems leveraged for managing data related to COVID-19 vaccines' distribution and delivery fall short in providing transparency, trackability and traceability, immutability, audit, and trust features. Also, they are vulnerable to the single point of failure problem due to centralization. Such limitations hindering the safe, secure, transparent, trustworthy, and reliable distribution and delivery process of COVID-19 vaccines. In this paper, we propose an Ethereum blockchain-based solution for managing data related to COVID-19 vaccines' distribution and delivery. We develop smart contracts to automate the traceability of COVID-19 vaccines while ensuring data provenance, transparency, security, and accountability. We integrate the Ethereum blockchain with off-chain storage to manage non-critical and large-sized data. We present algorithms and discuss their full implementation, testing, and validation details. We evaluate the proposed solution by performing cost and security analysis as well as comparing it with the existing non-blockchain and blockchain-based solutions. Performance evaluation results reveal that the proposed solution is low-cost, and our smart contracts are secure enough against possible attacks and vulnerabilities. The smart contracts code along with testing scripts is made publicly available.

Blockchain-Based Decentralized Digital Manufacturing and Supply for COVID-19 Medical Devices and Supplies.

Coronavirus 2019 (COVID-19) has disclosed the deficiencies and limitations of the existing manufacturing and supply chain systems used for medical devices and supplies. It enforces the necessity to accelerate the shift towards decentralized digital manufacturing and supply chain networks. This paper proposes a blockchain-based solution for decentralized digital manufacturing of medical devices and their supply. We develop Ethereum smart contracts to govern and track transactions in a decentralized, transparent, traceable, auditable, trustworthy, and secure manner. This allows overcoming certain issues hindering the transition towards decentralized digital manufacturing and supply, including trusted traceability, attestations, certifications, and secured intellectual property (IP) rights. We incorporate the decentralized storage of the InterPlanetary file system (IPFS) into the Ethereum blockchain to store and fetch Internet of things (IoT)-based devices records and additional manufacturing and supply details. We present the system architecture and algorithms along with their full implementation and testing details. Furthermore, we present cost and security analyses to show that the proposed solution is cost-efficient and resilient against well-known vulnerabilities and security attacks. We make our smart contracts code publicly available on GitHub.

Blockchain for drug traceability: Architectures and open challenges.

Pharmaceutical supply chain (PSC) consists of multiple stakeholders including raw material suppliers, manufacturers, distributors, regulatory authorities, pharmacies, hospitals, and patients. The complexity of product and transaction flows in PSC requires an effective traceability system to determine the current and all previous product ownerships. In addition, digitizing track and trace process provides significant benefit for regulatory oversight and ensures product safety. Blockchain-based drug traceability offers a potential solution to create a distributed shared data platform for an immutable, trustworthy, accountable and transparent system in the PSC. In this paper, we present an overview of product traceability issues in the PSC and envisage how blockchain technology can provide effective provenance, track and trace solution to mitigate counterfeit medications. We propose two potential blockchain based decentralized architectures, Hyperledger Fabric and Besu to meet critical requirements for drug traceability such as privacy, trust, transparency, security, authorization and authentication, and scalability. We propose, discuss, and compare two potential blockchain architectures for drug traceability. We identify and discuss several open research challenges related to the application of blockchain technology for drug traceability. The proposed blockchain architectures provide a valuable roadmap for Health Informatics researchers to build and deploy an end-to-end solution for the pharmaceutical industry.

The role of blockchain technology in telehealth and telemedicine.

OBJECTIVE: Telehealth and telemedicine systems aim to deliver remote healthcare services to mitigate the spread of COVID-9. Also, they can help to manage scarce healthcare resources to control the massive burden of COVID-19 patients in hospitals. However, a large portion of today's telehealth and telemedicine systems are centralized and fall short of providing necessary information security and privacy, operational transparency, health records immutability, and traceability to detect frauds related to patients' insurance claims and physician credentials. METHODS: The current study has explored the potential opportunities and adaptability challenges for blockchain technology in telehealth and telemedicine sector. It has explored the key role that blockchain technology can play to provide necessary information security and privacy, operational transparency, health records immutability, and traceability to detect frauds related to patients' insurance claims and physician credentials. RESULTS: Blockchain technology can improve telehealth and telemedicine services by offering remote healthcare services in a manner that is decentralized, tamper-proof, transparent, traceable, reliable, trustful, and secure. It enables health professionals to accurately identify frauds related to physician educational credentials and medical testing kits commonly used for home-based diagnosis. CONCLUSIONS: Wide deployment of blockchain in telehealth and telemedicine technology is still in its infancy. Several challenges and research problems need to be resolved to enable the widespread adoption of blockchain technology in telehealth and telemedicine systems.

Surface functionalized highly porous date seed derived activated carbon and MoS2 nanocomposites for hydrogenation of CO2 into formic acid.

In recent years, substantial progress has been made towards developing effective catalysts for the hydrogenation of CO2 into fuels. However, the quest for a robust catalyst with high activity and stability still remains challenging. In this study, we present a cost-effective catalyst composed of MoS2 nanosheets and functionalized porous date seed-derived activated carbon (f-DSAC) for hydrogenation of CO2 into formic acid (FA). As-fabricated MoS2/f-DSAC catalysts were characterized by FE-SEM, XRD, Raman, FT-IR, BET, and CO2-TPD analyses. At first, bicarbonate (HCO3-) was successfully converted into FA with a high yield of 88% at 200 °C for 180 min under 10 bar H2 atmosphere. A possible reaction pathway for the conversion of HCO3- into FA is postulated. The catalyst has demonstrated high activity and long-term stability over five consecutive cycles. Additionally, MoS2/f-DSAC catalyst was effectively used for the conversion of gaseous CO2 into FA at 200 °C under 20 bar (CO2/H2 = 1:1) over 15 h. The catalyst exhibited a remarkable TOF of 510 h-1 with very low activation energy of 12 kJ mol-1, thus enhancing the catalytic conversion rate of CO2 into FA. Thus, this work demonstrates the MoS2/f-DSAC nanohybrid system as an efficient non-noble catalyst for converting CO2 into fuels.

Blockchain for COVID-19: Review, Opportunities, and a Trusted Tracking System.

The sudden development of the COVID-19 pandemic has exposed the limitations in modern healthcare systems to handle public health emergencies. It is evident that adopting innovative technologies such as blockchain can help in effective planning operations and resource deployments. Blockchain technology can play an important role in the healthcare sector, such as improved clinical trial data management by reducing delays in regulatory approvals, and streamline the communication between diverse stakeholders of the supply chain, etc. Moreover, the spread of misinformation has intensely increased during the outbreak, and existing platforms lack the ability to validate the authenticity of data, leading to public panic and irrational behavior. Thus, developing a blockchain-based tracking system is important to ensure that the information received by the public and government agencies is reliable and trustworthy. In this paper, we review various blockchain applications and opportunities in combating the COVID-19 pandemic and develop a tracking system for the COVID-19 data collected from various external sources. We propose, implement, and evaluate a blockchain-based system using Ethereum smart contracts and oracles to track reported data related to the number of new cases, deaths, and recovered cases obtained from trusted sources. We present detailed algorithms that capture the interactions between stakeholders in the network. We present security analysis and the cost incurred by the stakeholders, and we highlight the challenges and future directions of our work. Our work demonstrates that the proposed solution is economically feasible and ensures data integrity, security, transparency, data traceability among stakeholders.

Ensuring protocol compliance and data transparency in clinical trials using Blockchain smart contracts.

BACKGROUND: Clinical Trials (CTs) help in testing and validating the safety and efficacy of newly discovered drugs on specific patient population cohorts. However, these trials usually experience many challenges, such as extensive time frames, high financial cost, regulatory and administrative barriers, and insufficient workforce. In addition, CTs face several data management challenges pertaining to protocol compliance, patient enrollment, transparency, traceability, data integrity, and selective reporting. Blockchain can potentially address such challenges because of its intrinsic features and properties. Although existing literature broadly discusses the applicability of blockchain-based solutions for CTs, only a few studies present their working proof-of-concept. METHODS: We propose a blockchain-based framework for CT data management, using Ethereum smart contracts, which employs IPFS as the file storage system to automate processes and information exchange among CT stakeholders. CT documents stored in the IPFS are difficult to tamper with as they are given unique cryptographic hashes. We present algorithms that capture various stages of CT data management. We develop the Ethereum smart contract using Remix IDE that is validated under different scenarios. RESULTS: The proposed framework results are advantageous to all stakeholders ensuring transparency, data integrity, and protocol compliance. Although the proposed solution is tested on the Ethereum blockchain platform, it can be deployed in private blockchain networks using their native smart contract technologies. We make our smart contract code publicly available on Github. CONCLUSIONS: We conclude that the proposed framework can be highly effective in ensuring that the trial abides by the protocol and the functions are executed only by the stakeholders who are given permission. It also assures data integrity and promotes transparency and traceability of information among stakeholders.

Evaluation of System Modelling Techniques for Waste Identification in Lean Healthcare Applications.

PURPOSE: Waste identification plays a vital role in lean healthcare applications. While the value stream map (VSM) is among the most commonly used tools for waste identification, it may be limited to visualize the behaviour of dynamic and complex healthcare systems. To address this limitation, system modelling techniques (SMTs) can be used to provide a comprehensive picture of various system-wide wastes. However, there is a lack of evidence in the current literature about the potential contribution of SMTs for waste identification in healthcare processes. METHODS: This study evaluates the usability and utility of six types of SMTs along with the VSM. For the evaluation, interview-based questionnaires were conducted with twelve stakeholders from the outpatient clinic at the Heart and Vascular Institute at Cleveland Clinic Abu Dhabi. RESULTS: VSM was found to be the most useful diagram in waste identification in general. However, some SMTs that represent the system behaviour outperformed the VSM in identifying particular waste types, e.g., communication diagram in identifying over-processing waste and flow diagram in identifying transportation waste. CONCLUSION: As behavioural SMTs and VSM have unique strengths in identifying particular waste types, the use of multiple diagrams is recommended for a comprehensive waste identification in lean. However, limited resources and time, as well as limited experience of stakeholders with SMTs, may still present obstacles for their potential contribution in lean healthcare applications.

Blockchain-Based Solution for COVID-19 Digital Medical Passports and Immunity Certificates.

COVID-19 has emerged as a highly contagious disease which has caused a devastating impact across the world with a very large number of infections and deaths. Timely and accurate testing is paramount to an effective response to this pandemic as it helps identify infections and therefore mitigate (isolate/cure) them. In this paper, we investigate this challenge and contribute by presenting a blockchain-based solution that incorporates self-sovereign identity, re-encryption proxies, and decentralized storage, such as the interplanetary file systems (IPFS). Our solution implements digital medical passports (DMP) and immunity certificates for COVID-19 test-takers. We present smart contracts based on the Ethereum blockchain written and tested successfully to maintain a digital medical identity for test-takers that help in a prompt trusted response directly by the relevant medical authorities. We reduce the response time of the medical facilities, alleviate the spread of false information by using immutable trusted blockchain, and curb the spread of the disease through DMP. We present a detailed description of the system design, development, and evaluation (cost and security analysis) for the proposed solution. Since our code leverages the use of the on-chain events, the cost of our design is almost negligible. We have made our smart contract codes publicly available on Github.

Optimal control with multiple human papillomavirus vaccines.

A two-sex, deterministic ordinary differential equations model for human papillomavirus (HPV) is constructed and analyzed for optimal control strategies in a vaccination program administering three types of vaccines in the female population: a bivalent vaccine that targets two HPV types and provides longer duration of protection and cross-protection against some non-target types, a quadrivalent vaccine which targets an additional two HPV types, and a nonavalent vaccine which targets nine HPV types (including those covered by the quadrivalent vaccine), but with lesser type-specific efficacy. Considering constant vaccination controls, the disease-free equilibrium and the effective reproduction number Rv for the autonomous model are computed in terms of the model parameters. Local-asymptotic stability of the disease-free equilibrium is established in terms of Rv. Uncertainty and Sensitivity analyses are carried out to study the influence of various important model parameters on the HPV infection prevalence. Assuming the HPV infection prevalence in the population under the constant control, optimal control theory is used to devise optimal vaccination strategies for the associated non-autonomous model when the vaccination rates are functions of time. The impact of these strategies on the number of infected individuals and the accumulated cost is assessed and compared with the constant control case. Switch times from one vaccine combination to a different combination including the nonavalent vaccine are assessed during an optimally designed HPV immunization program.