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Fog computing contains resource-constrained devices closer to user proximity, which are insufficient for compute-intensive and large workloads. Cloud computing can help to address this limitation by transparently integrating fog and cloud data centers. However, the existing state-of-the-art does not integrate cloud and fog resources for maintaining application performance by dynamically offloading the tasks to the cloud while minimizing the cost. This paper introduces a new workload/job placement method that performs two-way offloading between cloud and fog to efficiently utilize the fog resources while minimizing the cost of using cloud resources to serve the workload. Our proposed solution is evaluated on various setups, including small-scale and large-scale data centers having homogeneous and heterogeneous physical machines (PMs) with different job batches, and compared with the existing state-of-the-art baseline method. Overall, the proposed solution outperforms the baseline method by utilizing 40.87 % more fog resources while reducing × 1.68 times cloud cost. [ABSTRACT FROM AUTHOR]

The Global Runoff Data Centre (GRDC) provides time series of observed discharges and information on hydrometric stations that are valuable for calibrating and validating the results of hydrological models. We address a common issue in large-scale hydrology that has not been satisfactorily solved, though investigated several times. To compare simulated and observed discharge, grid-based hydrological models must fit reported station locations to the resolution-dependent gridded river network. We introduce an intersection-over-union ratio approach to selected station locations on a coarser grid scale, reducing the errors in assigning stations to the correct upstream basin. We update the 10-year-old database of watershed boundaries with additional stations based on a high-resolution (3 arcsec) river network and provide source codes and high- and low-resolution watershed boundaries to easily select stations for calibration/validation of hydrological models. The dataset is stored on Zenodo with the associated DOI: 10.5281/zenodo.6906577 (Burek and Smilovic, 2022). [ABSTRACT FROM AUTHOR]

Droughts are among the most complex and devastating natural hazards globally. High-resolution datasets of drought metrics are essential for monitoring and quantifying the severity, duration, frequency, and spatial extent of droughts at regional and particularly local scales. However, current global drought indices are available only at a coarser spatial resolution (>50 km). To fill this gap, we developed four high-resolution (5 km) gridded drought records based on the standardized precipitation evaporation index (SPEI) covering the period 1981–2022. These multi-scale (1–48 months) SPEI indices are computed based on monthly precipitation (P) from the Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS, version 2) and Multi-Source Weighted-Ensemble Precipitation (MSWEP, version 2.8), and potential evapotranspiration (PET) from the Global Land Evaporation Amsterdam Model (GLEAM, version 3.7a) and hourly Potential Evapotranspiration (hPET). We generated four SPEI records based on all possible combinations of P and PET datasets: CHIRPS_GLEAM, CHIRPS_hPET, MSWEP_GLEAM, and MSWEP_hPET. These drought records were evaluated globally and exhibited excellent agreement with observation-based estimates of SPEI, root zone soil moisture, and vegetation health indices. The newly developed high-resolution datasets provide more detailed local information and can be used to assess drought severity for particular periods and regions and to determine global, regional, and local trends, thereby supporting the development of site-specific adaptation measures. These datasets are publicly available at the Centre for Environmental Data Analysis (CEDA; 10.5285/ac43da11867243a1bb414e1637802dec) (Gebrechorkos et al., 2023). [ABSTRACT FROM AUTHOR]

In order to effectively model the potential impacts of future climate change, there is a requirement for climate data inputs which (a) are of high spatial and temporal resolution, (b) explore a range of future climate change scenarios, (c) are consistent with historical observations in the historical period, and (d) provide an exploration of climate model uncertainty. This paper presents a suite of climate projections for the United Kingdom that conform to these requirements: CHESS-SCAPE. CHESS-SCAPE is a 1 km resolution dataset containing 11 near-surface meteorological variables that can be used to as input to many different impact models. The variables are available at several time resolutions, from daily to decadal means, for the years 1980–2080. It was derived from the state-of-the art regional climate projections in the United Kingdom Climate Projections 2018 (UKCP18) regional climate model (RCM) 12 km ensemble, downscaled to 1 km using a combination of physical and empirical methods to account for local topographic effects. CHESS-SCAPE has four ensemble members, which were chosen to span the range of temperature and precipitation change in the UKCP18 ensemble, representing the ensemble climate model uncertainty. CHESS-SCAPE consists of projections for four emissions scenarios, given by the Representative Concentration Pathways 2.6, 4.5, 6.0 and 8.5, which were derived from the UKCP18 RCM RCP8.5 scenarios using time shifting and pattern scaling. These correspond to UK annual warming projections of between 0.9–1.9 K for RCP2.6 up to 2.8–4.3 K for RCP8.5 between 1980–2000 and 2060–2080. Little change in annual precipitation is projected, but larger changes in seasonal precipitation are seen with some scenarios projecting large increases in precipitation in the winter (up to 22 %) and large decreases in the summer (up to - 39 %). All four RCP scenarios and ensemble members are also provided with bias correction, using the CHESS-met historical gridded dataset as a baseline. With high spatial and temporal resolution, an extensive range of warming scenarios and multiple ensemble members, CHESS-SCAPE provides a comprehensive data resource for modellers of climate change impacts in the UK. The CHESS-SCAPE data are available for download from the NERC EDS Centre for Environmental Data Analysis: 10.5285/8194b416cbee482b89e0dfbe17c5786c. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) creates a large number of datasets, and these are handled in cloud data centers. IoT services are more delayed when data is sent over longer distances to the cloud. Node deployment is used to improve the performance of the multi-tier IoT-Fog environment by finding minimum distance with low Latency. Several methods have been discussed previously to improve the node deployment strategies but they do not provide good results. To overcome these issues, an Efficient and Multi-Tier Node Deployment Strategy using Variable Tangent Search Optimization Algorithm (VTSOA) is proposed in an IoT-Fog Environment. This Multi-Tier Node Deployment Strategy consists of several layers: IoT device layer, Fog layer, and cloud layer. The IoT device layer collects the data from external devices and is transmitted to the Fog layer. The fog layer contains several nodes. Hence, it increases the Latency of sending the data to the cloud. Therefore, VTSOA-based node deployment is done in the fog layer which finds the minimum distance nodes for effective communication. In this, the proposed approach is implemented in MATLAB. After that, the performance of this method is linked to various optimization algorithms. [ABSTRACT FROM AUTHOR]

The world is currently facing a wave of data centre construction. Fuelled by an explosion of data production and the emergence of edge computing, our cities are witnessing the materialisation of new architectural typologies that increasingly convolute notions of digital and bodily distinction. Whilst the last 2 decades have seen the proliferation of separate human and post-human urban environments, here we consider the agency and performativity of human communities within increasingly tangled contexts. As edge computing continues to bring the material reality of data processing closer to our physical bodies, perhaps its time to reassess who, or what, our cities now serve. In an age of unprecedented digital transformation, when our identities seem to be ever more intangible, how we might perform within environments that are increasingly catering to our digital rather than bodily needs? Today, the Anthropocene expels nature to the periphery. Tomorrow, humans become marginalised—"weeds", thriving in an epoch of data. [ABSTRACT FROM AUTHOR]

Summary: What does an academic statistical consulting center look like? What missions does it fulfill? Who are the clients and what kind of help are they getting? We collected 8 years of data of all consultation meetings in a consulting center at a land‐grant university. In this article, we demonstrate how the collected data could be used to learn about the daily operation in a consulting center, how various scientific fields use statistics, and changes over the years. We offer some practical guidance for academic consulting centers on how to evaluate their effectiveness and impact using appropriate metrics and data. [ABSTRACT FROM AUTHOR]

Chrome plating will be banned in all new cars starting in 2024 due to health-related issues, including the risk of lung cancer. The ban will take place in Europe and has also been proposed in California. Car companies will need to find alternative materials for exterior protection. In other news, researchers have developed a heat-resistant ceramic ink that can be used to mark metal components in the automotive industry. This ink allows for the detection of production glitches and can improve the efficiency of process chains. Additionally, materials science could help reduce energy consumption in data centers, and a new laser-based method has been developed to discover puncture-resistant materials. Finally, scientists have observed metals healing themselves, which could have significant implications for engineering. [Extracted from the article]

National holidays are associated with high mortality in some diseases, but little is known about patients undergoing peritoneal dialysis (PD). The research aimed to investigate the impact of national holidays on the health outcomes of PD patients. Over ten years, all episodes of unplanned hospitalization, death, and peritonitis in PD patients were collected in our center. Seven national holidays in China were chosen, and non-holiday days were selected as the control period. The effect of national holidays was observed by comparing the hospitalization, death, and peritonitis rates between holiday and non-holiday groups. There were 297 events in all holiday periods and 1247 in non-holiday periods. There is no significant difference in hospitalization rate between holiday and non-holiday groups (32.4% ± 6.4% vs. 29.2% ± 3.4%, p = 0.175). So is the death rate [6.3% (4.8–12.3%) vs.5.0% (4.2–8.9%), p = 0.324] and peritonitis rate [0.19 (0.13–0.53) vs. 0.22 (0.18–0.27), p = 0.445] between the two groups. Significant differences were observed in the distribution of peritonitis causes between the two groups (p = 0.017). The rate of secondary to other infections in the holiday group was significantly higher than in the non-holiday group (25.0 vs. 10.3%, p = 0.015). Our study suggested no national holiday effect on health outcomes of PD patients based on ten-year data in our center. [ABSTRACT FROM AUTHOR]

Connecting to the internet will increase our computing challenges as it becomes an integral part of our daily lives. Therefore, it is necessary to advance the service qualities of Internet of Things (IoT) applications because the data produced by all these devices will need to be processed quickly and sustainably. Previously, cloud data centers with large capacity interface IoT devices with support servers. While IoT devices proliferate and generate massive amounts of data, communicating between devices and the Cloud is becoming more complex and harder, resulting in high costs and inefficiencies. Fog computing emerges as an approach to address the growing demand for IoT solutions. In this article, an IoT-fog-cloud application's general framework is developed, followed by an algorithm for Energy efficiency through an integrated approach computation model. Fog-Enabled Smart Cities (FESC) are proposed to minimize service delay and response time by using a fog offloading policy for the fog-enabled IoTs. Also, we developed an analytical model evaluating the proposed framework's effectiveness in reducing the delay of IoT services. Comparing the proposed model and the Alternating Direction Method of Multipliers (ADMM-VS) algorithm, the proposed model performs significantly better. Thus, by optimizing response and processing times, fog-enabled smart grids determine whether computation will be performed autonomously or semi-autonomously on fog nodes or in the Cloud. [ABSTRACT FROM AUTHOR]

Data centers are facing huge bottlenecks due to the emergence of high-speed data applications. To meet speed requirements, it is proposed to shift the current technologies toward optical communication-enabled data center (DC) devices. This paper discusses the recently proposed optical switch designs and proposes a novel switch design for optical DCs. The architectures proposed in the literature are compared in terms of notable parameters. Finally, proposed switch performance is measured in the network using Monte Carlo simulation and results are obtained in terms of packet loss probability, under a random traffic model while considering both nonperiodized and prioritized packets. [ABSTRACT FROM AUTHOR]

Large data centre provides the important facilities like storage, computation, communication, etc. Therefore, we must be very careful while designing the infrastructure and networking of the data centre as these factors decides the expenses on implementation and maintenance of the infrastructure. The data servers are connected with Top of Rack (ToR) switches, which are further connected to aggregated/core switches. This paper discusses the blocking performance of the switches used at various levels of optical data networks. However it must be noted that in data centres traffic is not blocked and but it is temporarily stored but if it can't stored a negative acknowledgement is sent back and packet re-transmission is done. Blocking performance is done to judge the re-transmission of packets. Blocking performance is obtained using mathematical model as well as using Monte Carlo Simulations. [ABSTRACT FROM AUTHOR]

As the time progresses, the demand for bandwidth increases due to the evolution of new technologies supporting video on demand, music, etc., the traditional data centers heavily rely on electronics and thus dissipated a large amount of heat. To reduce both cabling and heat dissipated, fiber optic communication was supposed to be the next technology to replace current electronic systems, but unfortunately, fiber optic technology is not matured enough to be implemented. Therefore, as an alternative optical wireless communication (OWC) can be used, where advantages of optical components are availed and transmission media is free space, thus reducing cabling. This paper aims to present the modeling of wireless optical data center networks and evaluate bit error rate (BER) performance at the physical layer. To the best of our knowledge, this analysis is presented first time in case of OWC-based data centers. [ABSTRACT FROM AUTHOR]

Nowadays, the data centers have become a significant physical infrastructure for the purpose of supporting various Internet applications like cloud services, entertainment, web search and social networking. The traffic among the data centers is growing rapidly accompanied by the services that are obtained. The data centers are connected via fiber channels to support long-haul networks and high data rate transmissions by utilizing various modulation techniques. However, it has some drawbacks such as increased delay, computational complexities, high wavelength consumption, link failures, etc. Recently, researchers are focusing on improving the survivability and wavelength usage efficiency in optical data center networks. In this work, a novel framework depending on the concept of content connectivity is proposed for optical data center networks. Here, a mixed integer linear programming (MILP) is utilized for transmitting the data through optical data centers. The main intention of this research is to improve the performance and wavelength efficiency in optical data center networks. The performance of the MILP approach is evaluated and compared with the existing integer linear programming (ILP) technique and found that this new approach provides better performance with higher wavelength efficiency and reduced wavelength consumption. [ABSTRACT FROM AUTHOR]

Due to the emergence of high-speed applications, demand for more bandwidth has increased exponentially in last a few years. To serve each user for his demand very fast processing and propagation of data is desired. These two requirements of high-speed communication can be fulfilled using optical fiber technology. In the same view in past many optical switch designs have emerged with their respective advantages and disadvantages. This paper proposes a wavelength division multiplexing (WDM)-based switch incorporating arrayed waveguide grating (AWG) as wavelength router and fiber delay lines (FDL) to create optical buffer. Proposed switch design is compared with recent switch design and it is found that both in terms of BER and packet loss probability the performance of proposed design is much better in comparison to earlier designs. [ABSTRACT FROM AUTHOR]

The process of using robotic technology to examine underwater systems is still a difficult undertaking because the majority of automated activities lack network connectivity. Therefore, the suggested approach finds the main hole in undersea systems and fills it using robotic automation. In the predicted model, an analytical framework is created to operate the robot within predetermined areas while maximizing communication ranges. Additionally, a clustering algorithm with a fuzzy membership function is implemented, allowing the robots to advance in accordance with predefined clusters and arrive at their starting place within a predetermined amount of time. A cluster node is connected in each clustered region and provides the central control center with the necessary data. The weights are evenly distributed, and the designed robotic system is installed to prevent an uncontrolled operational state. Five different scenarios are used to test and validate the created model, and in each case, the proposed method is found to be superior to the current methodology in terms of range, energy, density, time periods, and total metrics of operation. [ABSTRACT FROM AUTHOR]

This paper illustrates the benefit of the Covid‐19 pandemic on global temperature projections. Global temperature projections are obtained by forecasting Earth's temperature anomalies time series with spectral analysis. Time series of Earth's Temperature Anomalies of the Globe are collected by NASA/GISS and in the Northern and Southern Hemispheres by the Climatic Research Unit of the University of East Anglia and Hadley Centre. Spectral analysis aims at unveiling temperature anomalies time series by transforming them into simplified time series after decomposition, extrapolating information nested in these simplified series and rebuilding the forecasted time series. Earth's temperature anomalies are forecasted until 2100. Two sets of forecasts are generated, one with historical data including the Covid‐19 pandemic (from 1880 up to 2021 with NASA/GISS data or from 1850 up to 2022 with East Anglia and Hadley Centre data) and one excluding the Covid‐19 pandemic up to 2019. With the sets of data not including the pandemic, forecasts show temperature anomalies skyrocketing and bringing Earth's global warming at alarming levels in Northern and Southern Hemispheres. With the set of data including the pandemic, forecasts are reverting showing lower alarming levels by the end of the century, in the Northern and Southern Hemispheres and globally. [ABSTRACT FROM AUTHOR]

The energy consumption of data centers (DCs) is rising year by year, and cooling station accounts for more than 40% of the total DCs' energy consumption, which has a huge energy-saving potential. Building models for whole DCs' cooling station can help predict total power to improve the energy efficiency of the system, before this, establishing a single model for each component is a basic work. This paper mainly studies the chiller and cooling tower models and compares the predictive performance of the empirical model, hybrid model, and neural network model of chillers and cooling towers. Giving the model selection scheme of the chiller and cooling tower for the establishment of the whole system of the refrigeration station. For the chiller model, the Yoshida function model in empirical models has the highest accuracy with a mean square error of 0.0592, followed by the neural network model with a mean square error of 0.2, and the hybrid model has a lower accuracy than the former two models. For the cooling tower model, the empirical models and the neural network model have similar accuracy, and both are higher than the hybrid model. [ABSTRACT FROM AUTHOR]

The world has witnessed a global surge in energy consumption and carbon footprint since the industrial revolution. Data centers are claimed to be the second most significant contributor of the havoc greenhouse gasses. This paper deals with modeling carbon footprint of green data centers. Initially, we use a panel dataset of a green data center that mostly relies on green energy resources for power. Our study reveals that in spite of massive renewable energy usage, the carbon footprint trend of this data center is quite significant. Alongside, due to massive nuclear energy usage in this data center, a hefty amount of nuclear waste is generated causing a global threat to sustainability. This is a novel paper that pinpoints that though green data centers claim they are zero-carbon data centers but the reality is different. We prove that green data centers also emit significant amount of greenhouse gasses and cause danger to sustainability. Alongside, we provide a nuclear footprint estimator that effectively calculates the nuclear emission and carbon footprint from the data center each hour. We also provide a remedy to this entire situation and provide a carbon footprint model in this paper that optimizes the total carbon emission from this green data center. [ABSTRACT FROM AUTHOR]

Currently, there are still no definitive consensus in the treatment of intrahepatic cholangiocarcinoma (iCCA). This study aimed to build a clinical decision support tool based on machine learning using the Surveillance, Epidemiology, and End Results (SEER) database and the data from the Fifth Medical Center of the PLA General Hospital in China. 4,398 eligible patients from the SEER database and 504 eligible patients from the hospital data, who presented with histologically proven iCCA, were enrolled for modeling by cross-validation based on machine learning. All the models were trained using the open-source Python library scikit-survival version 0.16.0. Shapley additive explanations method was used to help clinicians better understand the obtained results. Permutation importance was calculated using library ELI5. All involved treatment modalities could contribute to a better prognosis. Three models were derived and tested using different data sources, with concordance indices of 0.67, 0.69, and 0.73, respectively. The prediction results were consistent with those under actual situations involving randomly selected patients. Model 2, trained using the hospital data, was selected to develop an online tool, due to its advantage in predicting short-term prognosis. The prediction model and tool established in this study can be applied to predict the prognosis of iCCA after treatment by inputting the patient's clinical parameters or TNM stages and treatment options, thus contributing to optimal clinical decisions. A prognostic model related to disease staging and treatment mode was conducted using the method of machine learning, based on the big data of multi centers. The online calculator can predict the short-term survival prognosis of intrahepatic cholangiocarcinoma, thus, help to make the best clinical decision. The online calculator built to calculate the mortality risk and overall survival can be easily obtained and applied. [ABSTRACT FROM AUTHOR]

Group satisfaction is a trending issue in large-scale group decision-making (LSGDM) but most existing studies maximize the group satisfaction of LSGDM from the perspective of consensus. However, the clustering algorithm in LSGDM also has an impact on group satisfaction. Hence, this paper proposes a density-based spatial clustering of applications with noise (DBSCAN)-based LSGDM approach in an intuitionistic fuzzy set (IFS) environment. The DBSCAN algorithm is used to identify experts with outlier ratings that can reduce the time consumption and iterations of the LSGDM process and maximize the satisfaction of the group decision. An easy-to-use function is then provided to estimate group satisfaction. Finally, a numerical example of data centre supplier evaluation and comparative analysis is constructed to validate the rationality and feasibility of the proposed DBSCAN-based LSGDM approach in an IFS environment. The results demonstrate that the proposed method can effectively identify outliers in expert ratings and improve group satisfaction in the LSGDM process. [ABSTRACT FROM AUTHOR]

The Korea Oceanographic Data Center (KODC), overseen by the National Institute of Fisheries Science (NIFS), is a pivotal hub for collecting, processing, and disseminating marine science data. By digitizing and subjecting observational data to rigorous quality control, the KODC ensures accurate information in line with international standards. The center actively engages in global partnerships and fosters marine data exchange. A wide array of marine information is provided through the KODC website, including observational metadata, coastal oceanographic data, real-time buoy records, and fishery environmental data. Coastal oceanographic observational data from 207 stations across various sea regions have been collected biannually since 1961. This dataset covers 14 standard water depths; includes essential parameters, such as temperature, salinity, nutrients, and pH; serves as the foundation for news, reports, and analyses by the NIFS; and is widely employed to study seasonal and regional marine variations, with researchers supplementing the limited data for comprehensive insights. The dataset offers information for each water depth at a 1 m interval over 1980–2022, facilitating research across disciplines. Data processing, including interpolation and quality control, is based on MATLAB. These data are classified by region and accessible online; hence, researchers can easily explore spatiotemporal trends in marine environments. [ABSTRACT FROM AUTHOR]

In recent years, web services run by big corporations and different application-specific data centers have all been embraced by several companies worldwide. Web services provide several benefits when compared to other communication technologies. However, it still suffers from congestion and bottlenecks as well as a significant delay due to the tremendous load caused by a large number of web service requests from end users. Clustering and then aggregating similar web services as one compressed message can potentially achieve network traffic reduction. This paper proposes a dynamic Hilbert clustering as a new model for clustering web services based on convex set similarity. Mathematically, the suggested models compute the degree of similarity between simple object access protocol (SOAP) messages and then cluster them into groups with high similarity. Next, each cluster is aggregated as a compact message that is finally encoded by fixed-length or Huffman. The experiment results have shown the suggested model performs better than the conventional clustering techniques in terms of compression ratio. The suggested model has produced the best results, reaching up to 15 with fixed-length and up to 20 with Huffman. [ABSTRACT FROM AUTHOR]

The purpose of the study is to analyze the effectivity of Diabetic self management program oriented to the family at effort to increase self efficacy, self management, glicaemic control and obedience to the Diabetic therapeutic program. The design of the study in quasy experiment with pre test and post test design with control group. Based on data release by Center of Health Distric Pasar Minggu South Jakarta, which took as the respondent of intervention and control group, using interview and questionnaire method. Analysis of data statistic has shown that there is a significant different on health condition between the intervention and control group. On the control group has score 1, 937 (4,002 - 2,065) meanwhile the score of intervention group 4,312(-15.056 to -10.744) with p-value 0,000 <0,05. The result of test different mean from diabetic self management has score of intervention group 7,62 ( 5.15- 2.77) with p-value 0.000 <0,05. Meanwhile in the control group the score are 7.56 (5.18 - 12.74). Score of influence of self management upon stage of obedience in control group 1,324 (-3,062 to -1,738) meanwhile on the intervention group 1,852(-7,226 to -5374). The score of influence of self efficacy upon the obedience in control group 0,906 (-1,120 to -0,214) meanwhile in intervention group 2.889 (-8,611 to - 5,722) with p-value 0.000. on the influence of knowledge upon the obedience the score in control group is 0,928 (1.564 - 0,636) with p-value 0,000 < 0,05. Meanwhile in the intervention group the score is 2,043 (7,055 - 5,012). [ABSTRACT FROM AUTHOR]

This research aimed to investigate the effect of geological and geomorphological factors on the water quality of Gotvand Dam in Iran. Given the role of geomorphological parameters in water quality, the water quality indices were analyzed in the Excel and Chemistry applications. Water quality was evaluated by analyzing water samples obtained from the Statistical Data Center of Water and Power Organization of Khuzestan, and the total dissolved solids (TDS), electrical conductivity (EC), and pH of water were measured using the Mann-Kendall test. Then the effect of geomorphological factors on the physicochemical properties of the Karun River was assessed. The qualitative variables were pH, EC, and TDS in the statistical period of 2006–2021. The results of the Mann-Kendall test showed an insignificant decreasing trend in the TDS series in the Gotvand station. Meanwhile, August had a significant increasing trend in TDS, and considering the calculated Sen's slope, this parameter increased by 35% in the last 15 years (2006–2021). Like TDS, EC had both increasing and decreasing trends of changes. On the monthly scale, pH had a significant decreasing trend, meaning that the water is acidified, which severely affects the downstream fields and reservoirs. In the Gotvand station, the trend of changes in TDS and EC increased in 11 and 12 months of the year, respectively. [ABSTRACT FROM AUTHOR]

The concept of smart cities, which aim to enhance the quality of urban life through innovative technologies and policies, has gained significant momentum in recent years. As we approach the era of next-generation smart cities, it becomes crucial to explore the key enabling technologies that will shape their development. This work reviews the leading technologies driving the future of smart cities. The work begins by introducing the main requirements of different smart city applications; then, the enabling technologies are presented. This work highlights the transformative potential of the Internet of things (IoT) to facilitate data collection and analysis to improve urban infrastructure and services. As a complementary technology, distributed edge computing brings computational power closer to devices, reducing the reliance on centralized data centers. Another key technology is virtualization, which optimizes resource utilization, enabling multiple virtual environments to run efficiently on shared hardware. Software-defined networking (SDN) emerges as a pivotal technology that brings flexibility and scalability to smart city networks, allowing for dynamic network management and resource allocation. Artificial intelligence (AI) is another approach for managing smart cities by enabling predictive analytics, automation, and smart decision making based on vast amounts of data. Lastly, the blockchain is introduced as a promising approach for smart cities to achieve the required security. The review concludes by identifying potential research directions to address the challenges and complexities brought about by integrating these key enabling technologies. [ABSTRACT FROM AUTHOR]

Approximately 55 percent of the world's 7.3 billion people access the Internet, creating a significant demand in information technology (IT) services for both organizations and consumers. The number of data centers continue to increase to meet this demand, consuming 2 percent of the energy produced worldwide. Many organizations moving towards cloud computing due to its ability to meet users' needs on demand. Greening cloud computing technology has become an important aspect of an organization's design and use of cloud computing. One aspect of greening the cloud is through efficiently using cloud-based resources. In this study, we focus on the resource allocation applications of cloud computing technologies to green an organizations cloud. We design a pricing and allocation mechanism for a private cloud computing service that allows the firm to effectively load balance their cloud computing resources. Our optimal pricing mechanism is a dynamic pricing model, which maximizes the net value of users in a private cloud computing service. Additionally, we create a job allocation algorithm based on our dynamic pricing model to load balance the cloud. We show that balancing the job allocation such that the number of jobs at individual resource servers as close to equal as possible is optimal. Furthermore, simulations were run on the allocation mechanism to examine the effects on the cloud resources while gaining insight to effectively distribution resources to public clouds. Our model can help organizations to efficiently distribute their cloud-based resources, which allows for a greener cloud computing system. [ABSTRACT FROM AUTHOR]

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The article focuses on the potential national security risks posed by TikTok, particularly in relation to its data privacy practices and the possibility of Chinese government access. Topics include the implications of Chinese laws requiring data access, concerns about hidden malicious code within TikTok, and the broader risks associated with the app's extensive permissions on user devices.

This article discusses the importance of understanding the relationship between data and energy consumption in order to achieve the goal of net zero emissions by 2050. The energy cost of 1GB of data is a complex issue with various interpretations and a lack of transparency in the data industry. The article traces the evolution of the energy consumption of 1GB and highlights the need for a standardized approach to calculating and reporting data energy consumption. Future research and increased transparency are necessary to develop a clearer understanding of the environmental impact of data and to support policy development. [Extracted from the article]