American University of Sharjah

This paper explores the role of Internet of Things (IoT) and its impact on supply chain management (SCM) through an extensive literature review. Important aspects of IoT in SCM are covered including IoT definition, main IoT technology enablers and various SCM processes and applications. We offer several categorisation of the extant literature, such as based on methodology, industry sector and focus on a classification based on major supply chain processes. In addition, a bibliometric analysis of the literature is also presented. We find that most studies have focused on conceptualising the impact of IoT with limited analytical models and empirical studies. In addition, most studies have focused on the delivery supply chain process and the food and manufacturing supply chains. Areas of future SCM research that can support IoT implementation are also identified.

We conducted preregistered replications of 28 classic and contemporary published findings, with protocols that were peer reviewed in advance, to examine variation in effect magnitudes across samples and settings. Each protocol was administered to approximately half of 125 samples that comprised 15,305 participants from 36 countries and territories. Using the conventional criterion of statistical significance ( p < .05), we found that 15 (54%) of the replications provided evidence of a statistically significant effect in the same direction as the original finding. With a strict significance criterion ( p < .0001), 14 (50%) of the replications still provided such evidence, a reflection of the extremely high-powered design. Seven (25%) of the replications yielded effect sizes larger than the original ones, and 21 (75%) yielded effect sizes smaller than the original ones. The median comparable Cohen’s ds were 0.60 for the original findings and 0.15 for the replications. The effect sizes were small (< 0.20) in 16 of the replications (57%), and 9 effects (32%) were in the direction opposite the direction of the original effect. Across settings, the Q statistic indicated significant heterogeneity in 11 (39%) of the replication effects, and most of those were among the findings with the largest overall effect sizes; only 1 effect that was near zero in the aggregate showed significant heterogeneity according to this measure. Only 1 effect had a tau value greater than .20, an indication of moderate heterogeneity. Eight others had tau values near or slightly above .10, an indication of slight heterogeneity. Moderation tests indicated that very little heterogeneity was attributable to the order in which the tasks were performed or whether the tasks were administered in lab versus online. Exploratory comparisons revealed little heterogeneity between Western, educated, industrialized, rich, and democratic (WEIRD) cultures and less WEIRD cultures (i.e., cultures with relatively high and low WEIRDness scores, respectively). Cumulatively, variability in the observed effect sizes was attributable more to the effect being studied than to the sample or setting in which it was studied.

In spite of the fact that petroleum-based plastics are convenient in terms of fulfilling the performance requirements of many applications, they contribute significantly to a number of ecological and environmental problems. Recently, the public awareness of the negative effects of petroleum-based plastics on the environment has increased. The present utilization of natural resources cannot be sustained forever. Furthermore, oil is often subjected to price fluctuations and will eventually be depleted. The increase in the level of carbon dioxide due to the combustion of fossil fuel is causing global warming. Concerns about preservation of natural resources and climate change are considered worldwide motivations for academic and industrial researchers to reduce the consumption and dependence on fossil fuel. Therefore, bio-based polymers are moving towards becoming the favorable option to be utilized in polymer manufacturing, food packaging, and medical applications. This paper represents an overview of the feasibility of both Poly Lactic Acid (PLA) and polyhydroxyalkanoates (PHAs) as alternative materials that can replace petroleum-based polymers in a wide range of industrial applications. Physical, thermal, rheological, and mechanical properties of both polymers as well as their permeability and migration properties have been reviewed. Moreover, PLA's recyclability, sustainability, and environmental assessment have been also discussed. Finally, applications in which both polymers can replace petroleum-based plastics have been explored and provided.

The paper presents a survey and analysis on the current status and concerns of Internet of things (IoT) security. The IoT framework aspires to connect anyone with anything at anywhere. IoT typically has a three layers architecture consisting of Perception, Network, and Application layers. A number of security principles should be enforced at each layer to achieve a secure IoT realization. The future of IoT framework can only be ensured if the security issues associated with it are addressed and resolved. Many researchers have attempted to address the security concerns specific to IoT layers and devices by implementing corresponding countermeasures. This paper presents an overview of security principles, technological and security challenges, proposed countermeasures, and the future directions for securing the IoT.

In the ever-evolving era of technological advancements, generative artificial intelligence (GAI) emerges as a transformative force, revolutionizing education. This review paper, guided by the PRISMA framework, presents a comprehensive analysis of GAI in education, synthesizing key insights from a selection of 207 research papers to identify research gaps and future directions in the field. This study begins with a content analysis that explores GAI’s transformative impact in specific educational domains, including medical education and engineering education. The versatile applications of GAI encompass assessment, personalized learning support, and intelligent tutoring systems. Ethical considerations, interdisciplinary collaboration, and responsible technology use are highlighted, emphasizing the need for transparent GAI models and addressing biases. Subsequently, a bibliometric analysis of GAI in education is conducted, examining prominent AI tools, research focus, geographic distribution, and interdisciplinary collaboration. ChatGPT emerges as a dominant GAI tool, and the analysis reveals significant and exponential growth in GAI research in 2023. Moreover, this paper identifies promising future research directions, such as GAI-enhanced curriculum design and longitudinal studies tracking its long-term impact on learning outcomes. These findings provide a comprehensive understanding of GAI’s potential in reshaping education and offer valuable insights to researchers, educators, and policymakers interested in the intersection of GAI and education.

Increasing cost and demand of energy has led many organizations to find smart ways for monitoring, controlling and saving energy. A smart Energy Management System (EMS) can contribute towards cutting the costs while still meeting energy demand. The emerging technologies of Internet of Things (IoT) and Big Data can be utilized to better manage energy consumption in residential, commercial, and industrial sectors. This paper presents an Energy Management System (EMS) for smart homes. In this system, each home device is interfaced with a data acquisition module that is an IoT object with a unique IP address resulting in a large mesh wireless network of devices. The data acquisition System on Chip (SoC) module collects energy consumption data from each device of each smart home and transmits the data to a centralized server for further processing and analysis. This information from all residential areas accumulates in the utility's server as Big Data. The proposed EMS utilizes off-the-shelf Business Intelligence (BI) and Big Data analytics software packages to better manage energy consumption and to meet consumer demand. Since air conditioning contributes to 60% of electricity consumption in Arab Gulf countries, HVAC (Heating, Ventilation and Air Conditioning) Units have been taken as a case study to validate the proposed system. A prototype was built and tested in the lab to mimic small residential area HVAC systems1.

Ultrasound has an ever-increasing role in the delivery of therapeutic agents, including genetic material, protein and chemotherapeutic agents. Cavitating gas bodies, such as microbubbles, are the mediators through which the energy of relatively non-interactive pressure waves is concentrated to produce forces that permeabilise cell membranes and disrupt the vesicles that carry drugs. Thus, the presence of microbubbles enormously enhances ultrasonic delivery of genetic material, proteins and smaller chemical agents. Numerous reports show that the most efficient delivery of genetic material occurs in the presence of cavitating microbubbles. Attaching the DNA directly to the microbubbles, or to gas-containing liposomes, enhances gene uptake even further. Ultrasonic-enhanced gene delivery has been studied in various tissues, including cardiac, vascular, skeletal muscle, tumour and even fetal tissue. Ultrasonic-assisted delivery of proteins has found most application in transdermal transport of insulin. Cavitation events reversibly disrupt the structure of the stratus corneum to allow transport of these large molecules. Other hormones and small proteins could also be delivered transdermally. Small chemotherapeutic molecules are delivered in research settings from micelles and liposomes exposed to ultrasound. Cavitation appears to play two roles: it disrupts the structure of the carrier vesicle and releases the drug; and makes cell membranes and capillaries more permeable to drugs. There remains a need to better understand the physics of cavitation of microbubbles and the impact that such cavitation has on cells and drug-carrying vesicles.

Construction delay is considered one of the most recurring problems in the construction industry. Delays have an adverse impact on project success in terms of time, cost, quality and safety. The effects of construction delays are not confined to the construction industry only, but influence the overall economy of a country like UAE, where construction plays a major role in its development and contributes 14% to the GDP. Thus, it is essential to define the most significant causes of delay in order to avoid or minimise their impact on construction projects. A detailed questionnaire was developed and used to get input from professionals associated with the UAE construction industry. The perspective of contractors and consultants has been analysed to rank the causes of delays based on their Relative Importance Index. Contractors and consultants were in agreement on the most significant causes of delays. The research revealed that 50% of the construction projects in UAE encounter delays and are not completed on time. The top 10 most significant causes of construction delays have been identified by this research. Approval of drawings, inadequate early planning and slowness of the owners' decision‐making process are the top causes of delay in the UAE construction industry.

Conventional damage detection techniques are gradually being replaced by state-of-the-art smart monitoring and decision-making solutions. Near real-time and online damage assessment in structural health monitoring (SHM) systems is a promising transition toward bridging the gaps between the past’s applicative inefficiencies and the emerging technologies of the future. In the age of the smart city, Internet of Things (IoT), and big data analytics, the complex nature of data-driven civil infrastructures monitoring frameworks has not been fully matured. Machine learning (ML) algorithms are thus providing the necessary tools to augment the capabilities of SHM systems and provide intelligent solutions for the challenges of the past. This article aims to clarify and review the ML frontiers involved in modern SHM systems. A detailed analysis of the ML pipelines is provided, and the in-demand methods and algorithms are summarized in augmentative tables and figures. Connecting the ubiquitous sensing and big data processing of critical information in infrastructures through the IoT paradigm is the future of SHM systems. In line with these digital advancements, considering the next-generation SHM and ML combinations, recent breakthroughs in (1) mobile device-assisted, (2) unmanned aerial vehicles, (3) virtual/augmented reality, and (4) digital twins are discussed at length. Finally, the current and future challenges and open research issues in SHM-ML conjunction are examined. The roadmap of utilizing emerging technologies within ML-engaged SHM is still in its infancy; thus, the article offers an outlook on the future of monitoring systems in assessing civil infrastructure integrity.

Abstract In this article we introduce Positive Psychology (PP), a relatively new subfield of psychology, and outline its development since the year 2000. We describe ways in which PP represents an exciting addition to the Second Language Acquisition (SLA) literature and the ways it is already influencing trends in education generally, thus creating promising expectations of its impact on language teaching and learning. After reviewing the progress made thus far under the rubric of PP in SLA, we offer suggestions for an agenda to move forward with theory, research, and practice.

This paper studies the advertising agency industry in Manhattan to infer networking benefits among agencies in close spatial proximity. We use economic census data that allow us to distinguish locations at a fine level of geographic detail, so as to infer the strong effect on productivity of having more near advertising agency neighbours. Paying close attention to identification issues, we show, however, that there is extremely rapid spatial decay in the benefits of more near neighbours, even in the close quarters of southern Manhattan, a finding that is new to the literature. This suggests that high density of similar commercial establishments is important in enhancing local productivity for those industries found in large cities, where information sharing plays a critical role. Our results indicate that the benefits of more near neighbours are largely capitalized into rents rather than wages, challenging an existing literature, which estimates wage equations alone to infer agglomeration benefits. Copyright 2008, Wiley-Blackwell.

Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of ground transportation. It is anticipated that ordinary vehicles will one day be replaced with smart vehicles that are able to make decisions and perform driving tasks on their own. In order to achieve this objective, self-driving vehicles are equipped with sensors that are used to sense and perceive both their surroundings and the faraway environment, using further advances in communication technologies, such as 5G. In the meantime, local perception, as with human beings, will continue to be an effective means for controlling the vehicle at short range. In the other hand, extended perception allows for anticipation of distant events and produces smarter behavior to guide the vehicle to its destination while respecting a set of criteria (safety, energy management, traffic optimization, comfort). In spite of the remarkable advancements of sensor technologies in terms of their effectiveness and applicability for AV systems in recent years, sensors can still fail because of noise, ambient conditions, or manufacturing defects, among other factors; hence, it is not advisable to rely on a single sensor for any of the autonomous driving tasks. The practical solution is to incorporate multiple competitive and complementary sensors that work synergistically to overcome their individual shortcomings. This article provides a comprehensive review of the state-of-the-art methods utilized to improve the performance of AV systems in short-range or local vehicle environments. Specifically, it focuses on recent studies that use deep learning sensor fusion algorithms for perception, localization, and mapping. The article concludes by highlighting some of the current trends and possible future research directions.

A review of some papers published in the last fifty years that focus on the semiconducting metal oxide (SMO) based sensors for the selective and sensitive detection of various environmental pollutants is presented.

This study determines whether it is possible to distinguish between conventional and Islamic banks in the Gulf Cooperation Council (GCC) region on the basis of financial characteristics alone. Islamic banks operate under different principles, such as risk sharing and the prohibition of interest, yet both types of banks face similar competitive conditions. The combination of effects makes it unclear whether financial ratios will differ significantly between the two categories of banks. We input 26 financial ratios into logit, neural network, and k-means nearest neighbor classification models to determine whether researchers or regulators could use these ratios to distinguish between the two types of banks. Although the means of several ratios are similar between the two categories of banks, non-linear classification techniques (k-means nearest neighbors and neural networks) are able to correctly distinguish Islamic from conventional banks in out-of-sample tests at about a 92% success rate.

The rising world population and its corresponding energy demands pose a considerable burden on natural energy sources. The exploitation of fossil fuels at such an alarming rate blurs the goals of sustainable development and controlling global warming as pledged during the Paris Agreement. Due to the detrimental effects of exhausts from conventional diesel fuel on the environment, biodiesel has earned significant importance during the last decade. Biodiesel is produced from different feedstocks such as neem oil, palm oil, waste frying oil, vegetable oil, animal fat, microbial oil, etc. These feedstocks react with acidic, alkaline, enzymic, homogeneous, heterogeneous, and hybrid Deep Eutectic Solvents (DES) catalysts, along with monohydric alcohol via transesterification reaction. The flexibility in its feedstock and the type of catalysts used, production cost, biodegradable and renewable nature makes it a promising alternative fuel than conventional diesel. The selection of apt feedstock and catalyst is the challenging task and governing factor of economic biodiesel production. Green solvents such as DES have high thermal stability and low volatility and can address the economic and green production issues significantly as compared to conventional alkali and acid catalysts. This review bridges the gap between the selection of feedstock and optimal catalyst for the respective feedstock. The exploration of DES fills the gap by attributing to 3Rs (i.e., recyclability, recovery, and reusability). This review highlights the contemporary trends and prospects in the selection of the feedstocks, synthesis routes, and catalysts for the transesterification reactions for biodiesel production.

BACKGROUND: For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. METHODS: The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. FINDINGS: Total numbers of global DALYs grew 6·1% (95% UI 4·0-8·1), from 2·64 billion (2·46-2·86) in 2010 to 2·80 billion (2·57-3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0-14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31-1·61) global DALYs in 2010, increasing to 1·80 billion (1·63-2·03) in 2023, alongside a concurrent 4·1% (1·9-6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90·2 million [75·2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0-107·5]), depressive disorders (26·3% [11·6-42·9]), and diabetes (14·9% [7·5-25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837-917) in 2010 to 681 million (642-736) in 2023, and a 25·8% (22·6-28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7-61·0) for diarrhoeal diseases, 42·9% (38·0-48·0) for HIV/AIDS, and 42·2% (23·6-56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6-22·0) and 24·8% (7·4-36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7-19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18-1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation-with high SBP accounting for 8·4% (6·9-10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories-behavioural, metabolic, and environmental and occupational-risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8-37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0-11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023-eg, declining by 54·4% (38·7-65·3) for unsafe sanitation, 50·5% (33·3-63·1) for unsafe water source, and 45·2% (25·6-72·0) for no access to handwashing facility, and by 44·9% (37·3-53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [-2·7 to 15·6]; non-significant). INTERPRETATION: Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors-eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG-including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic-the complex interaction of multiple health risks, social determinants, and systemic challenges-will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. FUNDING: Gates Foundation and Bloomberg Philanthropies.

ABSTRACT A stream of research over the past decade that identifies predictors of e‐learning success suggests that there are several critical success factors (CSFs) that must be managed effectively to fully realize promise for e‐learning. Grounded in constructivist learning theories, this study advances previous work on CSFs in university online education. Structural equation modeling is applied to examine the determinants of students’ satisfaction and their perceived learning outcomes in the context of university online courses. The independent variables of motivation (intrinsic and extrinsic), student self‐regulation, dialogue (instructor‐student, and student‐student), instructor, and course design are examined as potential determinants of online learning outcomes. A total of 372 responses from students who have completed at least one online course at a university in the Midwestern United States were used to examine the structural model. Findings indicate that instructor‐student dialogue, student‐student dialogue, instructor, and course design significantly affect students’ satisfaction and learning outcomes. However, both extrinsic student motivation and student self‐regulation have no significant relationship with user satisfaction and learning outcomes. Finally, intrinsic student motivation affects learning outcomes but not user satisfaction. The findings suggest that course design, instructor, and dialogue are the strongest predictors of user satisfaction and learning outcomes.

Purpose Through social media technologies, small and medium-sized enterprises (SMEs) can communicate information and respond to competitors with minimal cost. The ability to share and access information can affect SMEs’ performance, but there is little research on the link between SMEs’ social media adoption and their performance. The purpose of this paper is to present a quantitative survey to explore factors that influenced social media adoption by SMEs in the United Arab Emirates (UAE), and its impact on performance. Design/methodology/approach The study used a multi-perspective framework combining technological, organizational and environmental elements affecting SMEs. Survey questionnaires were used to collect data from a random sample of SMEs operating in the UAE. Using partial least squares and structural equation modeling techniques, 144 responses were analyzed. Findings Social media adoption had no effect on SMEs’ performance. These findings could help managers and decision makers in the SME sector to try to keep pace with research on social media innovations, and enable them to benefit from social commerce as it becomes more ubiquitous. Research limitations/implications This has implications for social media experts and anyone wishing to encourage social media use by SMEs. Originality/value The study developed a suitable multi-perspective framework covering various factors that may affect social media use. It also tested the framework empirically on a sample of SMEs from the UAE.

An online GPRS-Sensors Array for air pollution monitoring has been designed, implemented, and tested. The proposed system consists of a Mobile Data-Acquisition Unit (Mobile-DAQ) and a fixed Internet-Enabled Pollution Monitoring Server (Pollution-Server). The Mobile-DAQ unit integrates a single-chip microcontroller, air pollution sensors array, a General Packet Radio Service Modem (GPRS-Modem), and a Global Positioning System Module (GPS-Module). The Pollution-Server is a high-end personal computer application server with Internet connectivity. The Mobile-DAQ unit gathers air pollutants levels (CO, NO2, and SO2), and packs them in a frame with the GPS physical location, time, and date. The frame is subsequently uploaded to the GPRS-Modem and transmitted to the Pollution-Server via the public mobile network. A database server is attached to the Pollution-Server for storing the pollutants level for further usage by various clients such as environment protection agencies, vehicles registration authorities, and tourist and insurance companies. The Pollution-Server is interfaced to Google Maps to display real-time pollutants levels and locations in large metropolitan areas. The system was successfully tested in the city of Sharjah, UAE. The system reports real-time pollutants level and their location on a 24-h/7-day basis.

Big data is considered to be the key to unlocking the next great waves of growth in productivity. The amount of collected data in our world has been exploding due to a number of new applications and technologies that permeate our daily lives, including mobile and social networking applications, and Internet of Thing-based smart-world systems (smart grid, smart transportation, smart cities, and so on). With the exponential growth of data, how to efficiently utilize the data becomes a critical issue. This calls for the development of a big data market that enables efficient data trading. Via pushing data as a kind of commodity into a digital market, the data owners and consumers are able to connect with each other, sharing and further increasing the utility of data. Nonetheless, to enable such an effective market for data trading, several challenges need to be addressed, such as determining proper pricing for the data to be sold or purchased, designing a trading platform and schemes to enable the maximization of social welfare of trading participants with efficiency and privacy preservation, and protecting the traded data from being resold to maintain the value of the data. In this paper, we conduct a comprehensive survey on the lifecycle of data and data trading. To be specific, we first study a variety of data pricing models, categorize them into different groups, and conduct a comprehensive comparison of the pros and cons of these models. Then, we focus on the design of data trading platforms and schemes, supporting efficient, secure, and privacy-preserving data trading. Finally, we review digital copyright protection mechanisms, including digital copyright identifier, digital rights management, digital encryption, watermarking, and others, and outline challenges in data protection in the data trading lifecycle.