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Blockchain technology has disclosed unprecedented opportunities in the healthcare sector by unlocking the true value of interoperability. Specifically, the striking features of blockchain technology, such as data provenance, transparency, decentralized transaction validation, and immutability, can help to compensate for stringent data management issues (e.g., patient recruitment, persistent monitoring, data management, and data analytics and accurate reporting) in clinical trials (CTs). Although several research studies show that blockchain solutions help to improve patient retention, data integrity, privacy and ensure CTs compliance with regulatory policies, a comprehensive survey on this topic is lacking. In this survey, we provide insights into the adoption of blockchain technology in CTs. We categorize and classify the literature by devising a meticulous taxonomy of the decentralized tasks of CT and practices based on indispensable parameters. Furthermore, we provide insights on works in progress toward deploying blockchain solutions in CTs. Finally, we identify and discuss several challenges that hinder the successful implementation of blockchain technologies in CTs. [ABSTRACT FROM AUTHOR]

In recent years, decision makers, policy analysts, and other actors, have become increasingly aware of sustainability, and begun to combine economic, social and environmental criteria in their efforts to maintain competitiveness, long-term growth and development. In multiple stakeholder settings, the presence of diverse objectives and conflicting criteria often leads to a complex multi-criteria decision problem. The multi-criteria decision analysis (MCDA) offers an integrated framework to model and study sustainability criteria and related inter-criteria relationships. In this paper, we review some of the most significant literature on environmental sustainability, and categorise it to show how and why MCDA models are widely used and becoming increasingly popular. Our systematic analysis suggests that, there has been significant growth in environmental applications of MCDA in diverse areas, ranging from energy management and policy to land use, recycling management and sustainable tourism. Among the various MCDA methods and techniques, analytical hierarchy process, TOPSIS, and goal programming are the most frequently used approaches. Many authors use a combination of different MCDA techniques to balance various factors important to achieve sustainability related goals. We expect sustainability related criteria to be an essential consideration in most future multi-criteria models. [ABSTRACT FROM AUTHOR]

• Building performance simulation is powerful but computationally expensive. • Machine Learning (ML) surrogate models can reduce simulation times significantly. • Building energy and peak loads are predicted under different operational settings. • Extreme gradient boosting outperformed other methods. • Linear regression showed the lowest training time with competitive predictive power. Building Performance Simulation (BPS) is a powerful and widely used technique to evaluate building design and operation strategies prior to construction or retrofitting. However, BPS models often have high computational costs, which is particularly limiting for applications that require a significantly large number of simulations, such as building design optimization or uncertainty analyses. To overcome this gap, researchers have turned to surrogate modeling, where a mathematical model, such as a machine learning algorithm, is trained to mimic the performance of a BPS, allowing to test numerous building design/operation configurations at low computational costs. Past studies have applied surrogate BPS modeling to predict the impact of building design parameters on energy performance. However, few have considered building operational parameters, such as occupancy, equipment and lighting usage, and thermostat setpoints, which significantly impact energy consumption and peak loads, especially in harsh climate conditions. This paper presents a unique evaluation and comparison of machine learning algorithms as surrogates to BPS predictions of building performance (energy consumption and peak loads) under different operational settings. Results indicate that Extreme Gradient Boosting outperformed all other methods in predictive accuracy, with R2 values reaching as high as 0.99 for some models. In contrast, linear regression models were the fastest to train and easiest to interpret while still achieving competitive prediction accuracies (R2 values > 0.9). This work provides direct evidence of the machine learning surrogate models' ability to accurately predict building performance under different operational settings. It also offers unique insights into the strengths and weaknesses of white-box and black-box predictive modeling approaches and the effect of dataset size on the results. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The United Nations agenda for sustainable development by the year 2030 proposes 17 sustainable development goals which include access to affordable, reliable and clean energy, sustained economic growth with full productive employment and, urgent action to mitigate environmental degradation. Planning for sustainable development requires integrating conflicting criteria on economy, energy, environment and social aspects. In this paper, we introduce a Weighted Goal Programming model involving criteria on the economic development (GDP), the electricity consumption, the greenhouse gas emissions, and the total number of employees to determine optimal labor allocation across various economic sectors. The proposed model is validated with data from the six members of the Gulf Cooperation Council (namely Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates). The results of the model aim to provide empirical evidence and insights to decision makers and policy analysts in developing optimal strategies able to simultaneously satisfy energy demand, economic growth, labor development and reduction in greenhouse gas emissions to achieve sustainability targets by the year 2030. [ABSTRACT FROM AUTHOR]

In this paper, we propose support vector-based supervised learning algorithms, called multiclass support vector data description with weighted dynamic time warping kernel function (MSVDD-WDTWK) and multiclass support vector machines with weighted dynamic time warping kernel function (MSVM-WDTWK), which provides a flexible and robust kernel function for time series classification between non-aligned time series data resulting in improved accuracy. The proposed WDTW kernel function provides an optimal match between two time series data by not only allowing a non-linear mapping between two data sequences, but also considering relative significance depending on the phase difference between points on time series data. We validate the proposed approaches using extensive numerical experiments on a number of multiclass UCR time series data mining archive, and demonstrate that our proposed methods provide lower classification error rates compared with existing techniques. [ABSTRACT FROM AUTHOR]

A repairable product under a non-renewing combined warranty policy that is subject to a displaced log-linear demand function of the product's price and pro rata period length is considered. Expressions for the manufacturer's long-run average profit per unit time under replacement, minimal and general repair options are obtained. In addition, expressions for the stationary points and second-order conditions of the profit function are presented. Numerical illustrations that demonstrate optimal product pricing, pro rata length determination, and repair option selection to maximize the manufacturers, profit are given. [ABSTRACT FROM AUTHOR]

This article analyzes the cultural roots of Hindu personal names and surnames in the context of retaining personal identity in a globalized world. It had been argued that the process of globalization has the tendencies of both homogenization and intensification of social and cultural differences. On one hand, the tendency for homogenization is mainly due to cross-cultural fertilization over an extended period of time, but in certain situations and historical periods, it is the result of a dominant culture imposing its will on subordinate cultures and societies. On the other hand, the fear and actuality of homogenization drive cultures in opposition to it mainly to preserve their cultural and social identity. In this context, one of the significant issues of interest at the grassroots level is what happens to individual identity as reflected in personal names and surnames. Social and cultural practices relating to naming customs in India are complex. To date, they have not been studied systematically, and the task is made more difficult by the diversity of cultural patterns that characterize the country. This cultural diversity is closely related to the differences in the religious, linguistic, regional, varna, caste, sect, and tribal backgrounds of the people. In other words, the diversity in the naming customs mirrors the general pluralism exhibited in the social and cultural patterns of the country. Although there is no one pattern in the naming customs, there are some broad, discernible features within each religious, regional, linguistic, and cultural group.

The article analyzes the immigration history and ethnic relations of Australia. The movement of people to live outside the borders of their own place of origin has been a part of human history from the earliest times. However, international migration in recent times has grown dramatically. An estimated 70 million persons live and work in countries other than their place of origin, and more than one million emigrate permanently every year. The transnational migration of people including refugees with diverse cultural backgrounds has shaped and continues to shape the ethnic profile of societies around the globe. One may not be able to find a single country in the world which does not have policies to regulate movement of people in and out of their country. These policies often contain rules for exclusion as well as inclusion of certain categories of people as immigrants. Most countries would also claim to have non-discriminatory immigration policies. The history of immigration to Australia should actually start by taking note of the theory advocated by some anthropologists and prehistorians, which states that the Aborigines--the original inhabitants--were the first group of immigrants to reach the continent some 40,000 years back. Whether one concurs with this theory or not, it is fairly established that the Aboriginal Australians inhabited the continent uninterrupted for many centuries before its discovery by the Europeans, particularly the British seaman Captain Cook in 1770, followed initially by the arrival of British convicts and later by British free settlers.

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. [ABSTRACT FROM AUTHOR]

The object of this paper is to suggest a theoretical approach to the study of social development in developing countries in Asia, Africa and Latin America. Social development is regarded here as a radical transformation of economy and society, and as something which must be assessed in primarily human terms. After a critical examination of some conventional theories of modernization and social change, the paper goes on to suggest that a fruitful line of research is to view development as fundamental structural change. This would involve examination of social structural features in a society which encourage or hinder social development. Finally, specific empirical research is to be directed to identify agents as well as obstacles to social development. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 R v 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 R v . 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. [ABSTRACT FROM AUTHOR]

Preventative maintenance is imperative for ensuring the longevity and reliability of diagnostic medical imaging equipment. However, conventional maintenance systems face challenges related to centralization, fragmented information, and ineffective tracking mechanisms. In response to these challenges, this research presents a blockchain-based solution that introduces a robust framework for managing preventative maintenance tasks. The proposed solution leverages blockchain technology, smart contracts, IPFS, and timer oracles to promote transparency, accountability, and traceability in maintenance operations. To ensure the integrity and availability of maintenance data while preserving the confidentiality of sensitive information, a detailed system architecture is outlined. Sequence diagrams and algorithms are developed to define the flow and logic of the solution, focusing on automatic fault detection and diagnosis. The smart contracts, which are made publicly available on GitHub, were rigorously tested to guarantee their intended functionality, thus addressing software reliability concerns. Additionally, the solution includes a maintenance incentive mechanism, actively motivating stakeholders to engage in preventative maintenance activities, and establishing accountability. Security testing confirms the absence of vulnerabilities in smart contracts, aligning with the scope of automatic fault detection and diagnosis. Furthermore, potential generalizations of the solution are discussed, and future directions emphasize the importance of continuous improvement and innovation in maintenance practices. • Blockchain solution for preventative maintenance of diagnostic medical Imaging equipment. • Reduces the risk of equipment failure, and automates maintenance schedules. • Proposed solution reduce costs, improves accountability and transparency. • We conduct detailed security analysis to identify and address potential vulnerabilities. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Today's healthcare data management systems are facing key challenges in terms of data transparency, traceability, immutability, audit, data provenance, flexible access, trust, privacy, and security. Also, a large portion of existing healthcare systems leveraged for managing data are centralized that pose potential risks of single point of failures in case of natural disasters. Blockchain is an emerging and disruptive decentralized technology that has the potential to significantly revolutionize, reshape, and transform the way data are being handled in healthcare industries. In this paper, we discuss how leveraging blockchain for healthcare data management systems can lead to stimulate innovations and bring major improvements. We present the key blockchain features and characteristics. We discuss the premier advantages of adopting blockchain technology along with opportunities for healthcare industries. We present recent on-going projects and case studies to show the practicality of blockchain technology for various healthcare applications. We identify and discuss important open research challenges hindering the successful adoption of blockchain in the healthcare sector. Finally, we outline several future research directions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The advent of blockchain technology can refine the concept of DTs by ensuring transparency, decentralized data storage, data immutability, and peer-to-peer communication in industrial sectors. A DT is an integrated multiphysics, multiscale, and probabilistic simulation, representation, and mirroring of a real-world physical component. The DTs help to visualize designs in 3D, perform tests and simulations virtually prior to creation of any physical component, and consequently play a vital role in sustaining and maintaining Industry 4.0. It is anticipated that DTs will become prevalent in the foreseeable future because they can be used for configuration, monitoring, diagnostics, and prognostics. This article envisages how blockchain can reshape and transform DTs to bring about secure manufacturing that guarantees traceability, compliance, authenticity, quality, and safety. We discuss several benefits of employing blockchain in DTs. We taxonomize the DTs literature based on key parameters (e.g., DTs levels, design phases, industrial use cases, key objectives, enabling technologies, and core applications). We provide insights into ongoing progress made towards DTs by presenting recent synergies and case studies. Finally, we discuss open challenges that serve as future research directions. [ABSTRACT FROM AUTHOR]

In recent years, substantial progress has been made towards developing effective catalysts for the hydrogenation of CO 2 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 MoS 2 nanosheets and functionalized porous date seed-derived activated carbon (f-DSAC) for hydrogenation of CO 2 into formic acid (FA). As-fabricated MoS 2 /f-DSAC catalysts were characterized by FE-SEM, XRD, Raman, FT-IR, BET, and CO 2 -TPD analyses. At first, bicarbonate (HCO 3 –) was successfully converted into FA with a high yield of 88% at 200 °C for 180 min under 10 bar H 2 atmosphere. A possible reaction pathway for the conversion of HCO 3 – into FA is postulated. The catalyst has demonstrated high activity and long-term stability over five consecutive cycles. Additionally, MoS 2 /f-DSAC catalyst was effectively used for the conversion of gaseous CO 2 into FA at 200 °C under 20 bar (CO 2 /H 2 = 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 CO 2 into FA. Thus, this work demonstrates the MoS 2 /f-DSAC nanohybrid system as an efficient non-noble catalyst for converting CO 2 into fuels. ga1 • CO 2 conversion to valuable products is a promising way for climate change control. • Novel MoS 2 /f-DSAC nanocatalyst was developed for CO 2 hydrogenation to formic acid. • HCO 3 – was successfully converted into formic acid over MoS 2 /f-DSAC catalyst. • Gaseous CO 2 was hydrogenated into formic acid with TOF of 510 h–1 and Ea of 12 kJ mol−1. • MoS 2 /f-DSAC catalyst is more assuring for CO 2 abatement and mitigation. [ABSTRACT FROM AUTHOR]

The prevailing trends in global energy consumption and the rapid depletion of fossil fuel present an urgent need for alternative fuels, particularly from renewable sources of biomass. In this study, date palm tree mixture wastes (DTM) and date seed (DS) biomass were used as starting materials in the production of bio-oil by pyrolysis. The yields of the pyrolysis oils from DTM and DS were optimized by tuning the experimental parameters. The DS provided a maximum yield of 68 wt% obtained from 30 min of pyrolysis with a biomass loading of 200 g, fluidizing gas flow rate of 10 mL min−1, and at a temperature of 500 °C. In addition, we evaluated the impact of the aging process of the obtained pyrolysis oils. The produced pyrolysis oils (freshly made) were aged for 15 and 30 days at room temperature under closed conditions. All the feedstock biomass were subjected to proximate and ultimate analysis. The TG-DTA results indicated that both biomasses were richer in cellulose and hemicellulose contents than in lignin content. The FT-IR and GC/MS analyses of the fresh and aged oil samples demonstrated the outstanding characteristics of the DS derived bio-oil for use as a bio-fuel. The variation in the chemical composition of the fresh and aged pyrolysis oils are completely described and presented elaborately. This study demonstrates the significance of and a new functionality for the date palm industry to process date palm wastes, particularly the DS as a rich biomass source for the production of bio-fuel. • Date palm trees biomass used as renewable sources for the production of bio-oil. • The bio-oil yield was maximized by tuning the experimental pyrolysis conditions. • Chemical composition changes in fresh and aged bio-oils were presented elaborately. • The obtained bio-oil possesses a higher heating value of 29.06 MJ/kg. • This bio-oil revealed the better composition of economic value for biofuel production. [ABSTRACT FROM AUTHOR]