Search Results (15,399)

The Internet of Vehicles (IoV), a burgeoning technology, merges advancements in the internet, vehicle electronics, and wireless communications to foster intelligent vehicle interactions, thereby enhancing the efficiency and safety of transportation systems. Nonetheless, the continual and high-frequency communications among vehicles, coupled with regional limitations in system capacity, precipitate significant challenges in allocating wireless resources for vehicular networks. In addressing these challenges, this study formulates the resource allocation issue as a multi-agent deep reinforcement learning scenario and introduces a novel multi-agent actor-critic framework. This framework incorporates a prioritized experience replay mechanism focused on distributed execution, which facilitates decentralized computing by structuring the training processes and defining specific reward functions, thus optimizing resource allocation. Furthermore, the framework prioritizes empirical data during the training phase based on the temporal difference error (TD error), selectively updating the network with high-priority data at each sampling point. This strategy not only accelerates model convergence but also enhances the learning efficacy. The empirical validations confirm that our algorithm augments the total capacity of vehicle-to-infrastructure (V2I) links by 9.36% and the success rate of vehicle-to-vehicle (V2V) transmissions by 6.74% compared with a benchmark algorithm. Full article

This study analyzes the relationships between the onset, the duration, and the maximum extent of the Atlantic Cold Tongue (ACT) using ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) over the period 1979–2019. After calculating the start and end dates of the ACT each year, this study investigates potential relationships between early or late onset that may be linked to the maximum duration and extent of the ACT, which is known to influence weather patterns and precipitation in surrounding regions and the West African Monsoon System. Results show that 68% of years with a short ACT duration are associated with a late-onset ACT, while 70% of years with a long ACT duration are associated with early ACT onset years. In addition, 63% of years with a short duration of ACT have a cold tongue with a low maximum extent, while 83% of years with a long duration of ACT have a cold tongue with a greater maximum extent. Finally, 78% of early ACT onset years are associated with the coldest SST tongue in the eastern equatorial Atlantic Ocean. A comparison of the last 20 years (1999–2019) with the previous 20 years (1979–1998) shows a cooling trend in SST, with ACT occurring and ending earlier in recent years than in the past. However, as the changes in the end date are greater than those in the onset date, the duration of the ACT has been 5–12 days shorter in the last 20 years than in the previous 20 years. Knowledge of these ACT characteristics and their interrelations and drivers is crucial for understanding the West African Monsoon System and for improving climate models and seasonal forecasts. Full article

This is a comprehensive review of the significance of solubility theories, internal stability, and external compatibility within petroleum science and pavement engineering. The historical development and future trends of solubility methods in bitumen are discussed, emphasizing the importance of separating bitumen components based on solubility to establish a link with chemistry. The paper also highlights the development of solubility theories and various characterization tests for bitumen, as well as the distribution of functional groups of solvents and their parameters. Additionally, it explores the generation of solubility profiles for different types and aging states of bitumen based on solubility data and statistical correlation, and the use of stability diagrams to assess the internal stability of bitumen in different states. The potential for continued research in this field is emphasized to bridge the gap between fundamental chemistry and practical application, leading to improved formulations and enhanced performance of bitumen in various applications, ultimately resulting in more durable and stable pavement structures. Full article

This study investigates the mediating role of pro-environmental behaviours (PEBs) in the relationship between minimalism, collectivist culture, environmental concern, and green purchase intention (GPI) in emerging economies. This study aims to fill a gap in our understanding of how lifestyle choices, cultural values, and environmental consciousness influence sustainable consumption in collectivist settings. The study presents a new viewpoint on minimalism as an antecedent of pro-environmental behaviours, addressing deficiencies in the current literature regarding sustainability and consumer behaviour. The study utilises data from 385 participants across emerging economies and employs Partial Least Squares Structural Equation Modelling (PLS-SEM) to examine the links between components. Moreover, stringent validation methods, such as the heterotrait–monotrait ratio (HTMT), guarantee the trustworthiness and validity of the results. The findings indicate that minimalism, collectivist culture, and environmental concern favourably affect pro-environmental behaviours, which considerably mediate their influence on green purchase intention. Private PEBs exert a more significant impact on GPI than public PEBs, underscoring the significance of individual-level sustainable behaviours. These findings enhance the theoretical discussion on sustainability in emerging economies and provide practical insights for fostering sustainable consumer behaviours through culturally adapted techniques. Full article

Objectives: This study investigated the inflammatory responses of fibroblast-like synoviocytes (FLS) isolated from osteoarthritis (OA) patients, stimulated with lipopolysaccharide (LPS) and interleukin-6 (IL-6). Both experimental and synthetic data were utilised to investigate the variability in IL-6 and myeloperoxidase (MPO) production and its implications for OA pathogenesis. Methods: Synovial biopsies were obtained from OA patients undergoing joint replacement surgery. FLS were isolated, cultured, and stimulated with varying concentrations of LPS and IL-6. The production of IL-6 and MPO was measured using enzyme-linked immunosorbent assays (ELISA). Synthetic data generation techniques expanded the dataset to support comprehensive statistical analyses. Results: The patterns of inflammatory responses revealed distinct patient subgroups, highlighting individual variability. The integration of synthetic data with experimental observations validated their reliability and demonstrated dose-dependent differences in IL-6 and MPO production across patients. Conclusions: The results highlighted the importance of patient-specific factors in OA inflammation and demonstrated the utility of combining experimental and synthetic data to model individual variability. The results support the development of personalised treatment strategies in OA. Future research should include larger patient datasets and an exploration of molecular mechanisms underlying these responses. Full article

The study on the link concerning FDI and environmental emissions has been the interest in recent environmental economics subject. The interest of this research work is to dynamically understand the effect of FDI on environmental emissions in South Africa. The research applied the renowned Markov-switching regression to explore the association among the variables. Prior to the formal estimation, the data were subjected to a linearity test, non-linear unit root test and cusum test so to ascertain whether the variables conform to non-linearity modeling. The results demonstrated that in both regimes (lower or higher emissions), the influence of FDI is positive and statistically significant. This finding implies that foreign investment is detrimental to our environment, irrespective of regime changes. This finding supports the Pollution Haven Hypothesis (PHH). Furthermore, the results show that emissions in South Africa stay in a low or high regime for a short period between one and two years. Policy implications to the results are that economic and climate change policy makers in South Africa should start to regulate FDI to be environmentally friendly. Full article

Entrepreneurship support is undoubtedly necessary but often fails to meet expectations. To investigate the reasons behind this, a sequential exploratory methodology, with both qualitative and quantitative data, was used in this research. Within the Entrepreneurship Ecosystem (EE) paradigm, a conceptual model linking the macro environment, support system, support received, and entrepreneurship performance was developed based on qualitative data from 56 entrepreneurs’ responses and three in-depth interviews in Study 1. Then empirical data from a survey of 244 entrepreneurs was used to validate the model in Study 2. The findings identified two reasons for the ineffectiveness of entrepreneurship policies. One is the constraint imposed by the macro environment, which presents significant challenges for improvement, and the other arises from the policies themselves, which are improvable and require targeted attention. The research reminds policymakers to consider not only the quantity of support but also its quality. Our study refines the EE Microfoundation theory, particularly the causal and mediating mechanisms linking entrepreneurs to their EE. Full article

Irrigation schemes represent the backbone of Sudan’s food security and economy. The Gezira, Rahad, and El-Gunied irrigation schemes depend mainly on the Blue Nile as their primary water source. However, the construction of the Grand Ethiopian Renaissance Dam (GERD) in the Blue Nile at the Sudan border has changed water flow regulations along the Blue Nile. Therefore, the Sudanese irrigation schemes that depend on the Blue Nile are affected by the operation and management of the GERD. This study used datasets derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), specifically the enhanced vegetation index (EVI) and crop water use efficiency (CWUE), alongside climate time-series data obtained from the Climate Research Unit, to evaluate the performance of irrigation schemes in Sudan affected by climate variability and the construction and filling of the GERD. The analysis was carried out using R version 4.4.1 and spreadsheets. A dummy variable approach was employed to examine the effects of the GERD on the EVI, given the limited timeframe of the study, whilst Grey Relational Analysis was applied to investigate the influence of selected climate variables on the EVI. The results revealed that in the Gezira scheme, the impact of the GERD on the EVI was minimal, with rainfall and temperature identified as the predominant factors. In contrast, the construction of the GERD had significant negative repercussions on the EVI in the Rahad scheme, while it positively affected the El-Gunied scheme. The advantageous effects observed in the El-Gunied scheme were linked to the mitigation measures employed by the heightening of the Roseires Dam in Sudan since 2013. The Rahad and El-Gunied schemes exhibited heightened sensitivity to GERD-induced changes, primarily due to their reliance on irrigation water sourced from pumping stations dependent on Blue Nile water levels. Additionally, this study forecasts a decrease in cropping intensity attributed to the GERD, estimating reductions of 3.9% in Rahad, 1.5% in Gezira, and 0.8% in El-Gunied. Ultimately, this study highlights the detrimental impact of the GERD on Blue Nile water levels as a significant adverse factor associated with its construction and filling, which has led to a marked decline in CWUE across the irrigation schemes. The research underscores the intricate inter-relationship among environmental, political, institutional, and infrastructural elements that shapes irrigation efficiency and water management practices. This study concludes that enhancing irrigation efficiency and assessing the performance of irrigation schemes require significant consideration of institutional, economic, and political factors, especially in Sub-Saharan Africa. Full article

Developing more precise NOx emission prediction models is pivotal for effectively controlling NOx emissions from gas turbines. In this paper, a Reformer is combined with random forest (RF) feature selection and the chaos game optimization (CGO) algorithm to predict NOx in gas turbines. Firstly, RF evaluates the importance of data features and reduces the dimensionality of multidimensional data to improve the predictive performance of the model. Secondly, the Reformer model extracts the inherent pattern of different data and explores the intrinsic connection between gas turbine variables to establish a more accurate NOx emission prediction model. Thirdly, the CGO algorithm is a parameter-free meta-heuristic optimization algorithm used to find the best parameters for the prediction model. The CGO algorithm was improved using Chebyshev Chaos Mapping to improve the initial population quality of the CGO algorithm. To evaluate the efficiency of the proposed model, a dataset of gas turbines in north-western Turkey is studied, and the results obtained are compared with seven benchmark models. The final results of this paper show that RF can select appropriate input variables, and the Reformer can extract the intrinsic links of the data and build a more accurate NOx prediction model. At the same time, ICGO can optimize the parameters of the Reformer effectively. Full article

The jobs-housing mismatch phenomenon in urban China stems from the combined effects of housing commodification and the improvement of transportation infrastructure. These factors have contributed to the emergence of lengthy commutes and a range of urban challenges. This study examines the issue of jobs-housing mismatch in large cities, focusing on Hangzhou. It utilizes mobile signaling big data, geographically weighted regression, and spatial analysis to investigate the link between land mixed-use and this mismatch. The results reveal that Hangzhou faces a significant residential-employment mismatch, particularly in a ring-like pattern. Central urban areas are relatively balanced, while residential areas band around the center, and employment areas are scattered both centrally and on the outskirts. Land mixed-use impacts this mismatch spatially. In new developments, increased land use mix exacerbates the mismatch, while in ecological green spaces, it has a suppressive effect. Based on these findings, Hangzhou’s main urban area is divided into nine zones, each with tailored suggestions for balancing residential and employment spaces. This study demonstrates that mobile signaling data can precisely capture micro-level characteristics of residential and employment patterns. A multi-dimensional approach to land mixed-use offers a more comprehensive understanding than a single perspective. The zoning strategy helps establish spatial differences and balance residential-employment relations, providing valuable insights for urban renewal and land function optimization. Full article

There are numerous ways of portraying cancer complexity based on combining multiple types of data. A common approach involves developing signatures from gene expression profiles to highlight a few key reproducible features that provide insight into cancer risk, progression, or recurrence. Normally, a selection of such features is made through relevance or significance, given a reference context. In the case of highly metastatic cancers, numerous gene signatures have been published with varying levels of validation. Then, integrating the signatures could potentially lead to a more comprehensive view of the connection between cancer and its phenotypes by covering annotations not fully explored in individual studies. This broader understanding of disease phenotypes would improve the predictive accuracy of statistical models used to identify meaningful associations. We present an example of this approach by reconciling a great number of published signatures into meta-signatures relevant to Osteosarcoma (OS) metastasis. We generate a well-annotated and interpretable interactome network from integrated OS gene expression signatures and identify key nodes that regulate essential aspects of metastasis. While the connected signatures link diverse prognostic measurements for OS, the proposed approach is applicable to any type of cancer. Full article

A parallelized field-programmable gate array (FPGA) architecture is proposed to realize an ultra-fast, compact, and low-cost dual-channel ultra-wideband (UWB) pulsed-radar system. This approach resolves the main shortcoming of current FPGA-based radars, namely their low processing throughput, which leads to a significant loss of data provided by the radar receiver. The architecture is integrated with an in-house UWB pulsed radar operating at a sampling rate of 20 gigasamples per second (GSa/s). It is demonstrated that the FPGA data-processing speed matches that of the radar output, thus eliminating data loss. The radar system achieves a remarkable speed of over 9000 waveforms per second on each channel. The proposed architecture is scalable to accommodate higher sampling rates and various waveform periods. It is also multi-functional since the FPGA controls and synchronizes two transmitters and a dual-channel receiver, performs signal reconstruction on both channels simultaneously, and carries out user-defined averaging, trace windowing, and interference suppression for improving the receiver’s signal-to-noise ratio. We also investigate the throughput rate while offloading radar data onto an external device through an Ethernet link. Since the radar data rate significantly exceeds the Ethernet link capacity, we show how the FPGA-based averaging and windowing functions are leveraged to reduce the amount of offloaded data while fully utilizing the radar output. Full article

The goal of this study is to look into the dynamic relationship between stock prices, foreign portfolio investment, and financial development in the South African economy. Federal Reserve Economic Data (FRED) provided quarterly time series data from 1960 (Q1) to 2024 (Q2). This study uses a structural VAR estimation approach and dynamic conditional correlation (DCC GARCH model). The DCC GARCH approach displays time-varying correlations between stock prices, credit given to the private sector as a measure of financial growth, and foreign portfolio investments. The dynamic links between stock prices, financial development, and foreign private investment (FPI) are examined using the SVAR technique. Our findings show that a financial development shock encourages and provokes a substantial influx of foreign portfolio investment into the South African economy. This suggests that overseas portfolio investments react favorably and notably well to favorable shocks in the financial development process. We suggest that a stable financial system framework and lower credit costs would strengthen the impact of higher stock prices on private sector credit and guarantee that higher stock prices have a beneficial impact on other financial development metrics. Better financial development metrics, such as credit to the private sector, will therefore increase foreign portfolio investment. Full article

The use of electrospun carbon nanofibers (ECNs) has been the focus of considerable interest due to their potential implementation in sensing. These ECNs have unique structural and morphological features such as high surface area-to-volume ratio, cross-linked pore structure, and good conductivity, making them well suited for sensing applications. Electrospinning technology, in which polymer solutions or melts are electrostatically deposited, enables the production of high-performance nanofibers with tailored properties, including fiber diameter, porosity, and composition. This controllability enables the use of ECNs to optimize sensing applications, resulting in improved sensor performance and sensitivity. While carbon nanofiber mats have potential for sensor applications, several challenges remain to improve selectivity, sensitivity, stability and scalability. Sensor technologies play a critical role in the global sharing of environmental data, facilitating collaboration to address transboundary pollution issues and fostering international cooperation to find solutions to common environmental challenges. The use of carbon nanofibers for the detection of air pollutants offers a variety of possibilities for industrial applications in different sectors, ranging from healthcare to materials science. For example, optical, piezoelectric and resistive ECNs sensors effectively monitor particulate matter, while chemoresistive and catalytic ECNs sensors are particularly good at detecting gaseous pollutants. For heavy metals, electrochemical ECNF sensors offer accurate and reliable detection. This brief review provides in-sights into the latest developments and findings in the fabrication, properties and applications of ECNs in the field of sensing. The efficient utilization of these resources holds significant potential for meeting the evolving needs of sensing technologies in various fields, with a particular focus on air pollutant detection. Full article

Background/Objectives: Colorectal cancer (CRC) holds the third and second position among cancers affecting men and women, respectively. Frequently, the first-line treatment for metastatic CRC consists of the intravenous administration of 5-fluorouracil and leucovorin in combination with oxaliplatin or irinotecan. Physiologically-based pharmacokinetic models (PBPK) aim to mechanistically incorporate body physiology and drug physicochemical attributes, enabling the description of both systemic and organ drug exposure based on the treatment specificities. This bottom-up approach represents an opportunity to personalize treatment and minimize the therapeutic risk/benefit ratio through the understanding of drug distribution within colorectal tissue. This project has the goal of characterizing the systemic and tissue exposure of four anti-cancer drugs in humans using a PBPK platform fed with data from the literature. Methods: A literature search was performed to collect clinical data on systemic concentration versus time profiles. Physicochemical features were obtained from the literature, as well as parameters associated with distribution, metabolism, and excretion. The PBPK models were built using PK-Sim^®. Results: The data from 51 clinical studies were extracted and combined in one single dataset. The PBPK models successfully described the exposure vs. time profiles with respect to both, with both the typical tendency and dispersion shown by the data. The percentage of observations falling within the two-fold error bounds ranged between 94 and 100%. The colon/plasma AUC_inf ratios were similar for 5-FU, oxaliplatin, and leucovorin, but it was significantly higher for irinotecan. Conclusions: The PBPK models support tailored treatment approaches by linking in vitro studies to organ exposure. These models serve as the initial step towards incorporating a dedicated tumor compartment, which will further account for the variability in tumor microenvironment characteristics to improve therapeutic strategies. Full article