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• To improve data center performance, it must be efficiently cooled. • This paper undertakes a detailed examination of the most recent cooling optimization techniques. • Several methods are discussed like regular air conditioning, free cooling, and liquid cooling. • An assessment of overhead and underfloor air delivery technologies is presented. • The effects of baffles, variable airflow control, and server placement are investigated. In order to increase data centers' efficiency and performance, a proper cooling system should be applied. This article provides a comprehensive assessment which explores current cooling optimization technologies for data centers. Standard traditional technologies like air conditioning, free cooling, and liquid cooling are investigated. Their limitations as well as proposed improvements are thoroughly discussed. The article also delves into underfloor and overhead air supply technologies, presenting their advantages and drawbacks. The review introduces novel approaches at the rack level cooling, including baffles, variable airflow management, and optimal server placement. Key findings stress the efficacy of optimized airflow systems and innovative rack-level cooling, underlining their role in reducing energy consumption and enhancing overall performance. Notably, potential energy savings of up to 67.2 % compared to traditional methods are demonstrated. The adoption of advanced cooling technologies, such as direct and indirect natural cooling, liquid-cooling cold plates, submersion, heat pipe, and thermosiphon-based cooling, exhibits promising energy efficiency gains and reduced Power Usage Effectiveness (PUE) values. Research into content-specific efficiency enhancements, adaptable energy-saving tactics, and all-encompassing server power consumption models is necessary. To work toward future sustainable, energy-efficient data centers, researchers should investigate how CRACs and server fans interact, find ways to integrate thermal and power management, and how artificial intelligence can be used to cool servers in an eco-friendly way. [ABSTRACT FROM AUTHOR]

• Advantage of hot water cooling was proven by entropy and entranspy theory. • Thermodynamic model of a waste heat recovery-free cooling data center was established. • Heat transfer efficiency of waste heat recovery-free cooling data centers was proven by entranspy theory. • Performance indicators of waste heat recovery-free cooling data center was predicted. This study investigated the potential of integrating separate heat pipe and adsorption refrigeration systems as a data center efficient cooling system. Compared to water-cooled data centers, this approach demonstrates improved heat transfer efficiency and reduced energy consumption. The effectiveness of heat transfer in this approach is illustrated by applying exergy and entranspy theories. By conducting numerical simulations across five representative Chinese cities with different climates, both Power Usage Effectiveness (PUE) and key cooling system performance indicators are assessed. The analysis reveals that all cities can achieve a PUE below 1.095, realized a 65.87 % and 13.60 % reduction in cooling system energy consumption and entransy dissipation, respectively. Data centers using mechanical chiller cooling cause a high energy consumption. This paper indicates the benefits of integrating heat pipe and adsorption refrigeration systems in reducing energy consumption and optimizing overall data center performance. [ABSTRACT FROM AUTHOR]

The technological advancements of Connected and Automated Vehicles (CAVs) are outpacing the current regulatory regime, potentially resulting in a disconnect between legislators, technology, and CAV stakeholders. Although many studies explore the regulatory requirements of operations of CAVs, studies on regulatory challenges specific to the cybersecurity of CAVs are also emerging and receiving lots of attention among researchers and practitioners. However, studies providing an up-to-date synthesis and analysis on CAVs regulatory requirements specific to cyber-risk reduction or mitigation are almost non-existent in the literature. This study aims to overcome this limitation by presenting a comprehensive overview of the role of key Intelligent Transportation Systems (ITS) stakeholders in CAV's cybersecurity. These stakeholders include road operators, service providers, automakers, consumers, repairers, and the general public. The outcome of this review is an in-depth synthesis of CAV-based ITS stakeholders by visualising their scope in developing a Cybersecurity Regulatory Framework (CRF). The study demonstrated the compliance requirements for ITS communication service providers, regulatory standards for CAVs automakers, policy readiness for CAVs customers and the general public who interact with CAVs, and the role of the CAVs Network Operator Centre in regulating CAVs data flow. Moreover, the study illuminates several critical pathways necessary in future for synthesizing and forecasting the legal landscape of CAV-based transportation systems to integrate the regulatory framework for CAV stakeholders. The paper's findings and conclusions would assist policymakers in developing a comprehensive CRF. CAV Cybersecurity Regulation, Stakeholder Roles, and Conceptualization of CAVs (traffic laws) breaches. [Display omitted] • The study offers a comprehensive overview of CAV Cybersecurity's Regulatory Framework. • Considers road operators, service providers, automakers, consumers, repairers and the public. • Emphasizes automaker regulatory requisites for cyber-safe CAV operation. • Proposes CAVs Network Operation Centre to regulate data flow. • Advocates eSafety Traffic Unit for regulating CAV operations. [ABSTRACT FROM AUTHOR]

In this paper, we utilized a eutectic gallium-indium liquid metal (LM) with high thermal conductivity to immerse carbon-based phase change material (PCM) capsules to acquire a PCM-LM composite. This produced a scalable, thermally stable, mechanically reliable, high thermally conductive, and leakage-free material. This composite was then applied as a heat mitigator for CPU cooling. Results demonstrate that clear improvements could be made compared to traditional pin-fin heat-sink cooling. Here the continuous full-load operating time could be prolonged by 414.3%. Moreover, the temperature difference between the idle and full-load operating states could be significantly decreased, and prevent damage from thermal stress. Significantly, a 23% energy-saving performance was attained because of the thermal buffering effect and high responsive ability of the composite. Guidelines for an effective match between the heat absorption of the composite and the heat generation from the CPU could also be established. Here the proposed PCM-LM composite can be applied to other PCM-based energy storage industries and efficient temperature control applications, contributing to carbon neutralization and global warming mitigation. [ABSTRACT FROM AUTHOR]

Changes to which age groups are recommended for mammography may have affected screening rates for all women. This spillover effect has not previously been shown empirically in a national sample. Using data from the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System from 2002 to 2018, authors tested whether screening trends at a national level changed after the 2009 U.S. Preventive Services Task Force guideline change. The authors also tested whether state-level screening trends for women aged 40–49 years and 75+ years were associated with screening trends for women aged 50–74 years. Analyses were conducted in March–December 2022. In a model predicting state-level trends in screening for women aged 50–74 years, authors find positive, statistically significant associations with screening trends for women aged 40–49 years (p =0.033) and for women aged 75+ years (p <0.001). Deimplementation is difficult and important for controlling healthcare spending and delivering high value care. However, states most successful at reducing mammography screening rates among those aged 40–49 years and 75+ years also had greater reductions in recommended screening among women aged 50–74 years. More work is needed to understand and mitigate the unintended consequences of deimplementation. [ABSTRACT FROM AUTHOR]

The limited available data regarding the prevalence of transthyretin amyloidosis, both for wild-type (ATTRwt) and hereditary form (ATTRv), is inferred from highly selected patients and subsequent extrapolations that limit the comprehension of the clinical disease impact. The Tuscan healthcare system in 2006 developed a web-based rare disease registry, to monitor and profile patients affected by rare diseases. Clinicians belonging to regional validated healthcare data centres can register patients at the diagnosis, with a rigorous approach and distinguishing the types of amyloidosis, i.e., ATTRwt versus ATTRv. Thanks to this data collection method, available from July 2006 and extended with electronic therapy plans related to a diagnosis since May 2017, we analysed prevalence and incidence of ATTR and its subtypes. On November 30th 2022, ATTRwt prevalence in Tuscany is 90.3 per 1,000,000 persons and ATTRv prevalence is 9.5 per 1,000,000 persons, whereas the annual incidence ranges from 14.4 to 26.7 per 1,000,000 persons and from 0.8 to 2.7 per 1,000,000 persons, respectively. The male gender is predominant in both forms. All except one patient showed evidence of cardiomyopathy. This epidemiological data requires attention, not only to increase the effort for the clinical management and earlier diagnosis, but also to underline the need for the disease-specific treatments. • The exact prevalence transthyretin amyloidosis (ATTR) is still largely unknown. • Available data so far are mostly drawn from insurance claims or registries. • Prevalence of ATTRwt and ATTRv is 90.3 and 9.5 per million based on data from a Tuscany registry of rare diseases. • ATTR is increasingly being recognized and is less uncommon than previously believed. [ABSTRACT FROM AUTHOR]

• The novel two-path thermal energy storage with fin-enhanced PCM is designed and manufactured. • The thermal performance and temperature profile are experimentally investigated. • Heat transfer rate and accumulated thermal energy are fitted by test data. • Thermal environment during emergency cooling is analyzed with different inlet condition of CRAC. Emergency cooling system is necessary to guarantee high security for data center. Thermal energy storage is one of the feasible methods for emergency cooling with quick response ability to supply higher thermal safety. To simplify the traditional thermal energy storage system and improve its heat transfer efficiency, the novel two-path latent thermal energy storage unit developed for emergency cooling is experimentally studied. The effects of inlet temperature, flow rate on thermal performance of LTES, including heat transfer rate, accumulated energy, effectiveness and overall heat transfer coefficient, are investigated during both charging and discharging processes. Results from the experimental analysis show that the released energy of system could reach up to 2.9 MJ with the average heat transfer rate 1575W-2286 W. The overall heat transfer coefficient ranges from about 297–660 W/K when the flow rate of HTF varied from 12 to 16 L/min. The experimental LTES is then studied integrated with CRAC for emergency cooling. For air flow rate higher than 300 m3/h, and thermal load lower than 1800 W, the LTES meets 15 min of emergency cooling. The inlet and outlet air temperature of CRAC are successfully controlled under 40.5 °C and 23.9 °C. The newly designed LTES shows good thermal performance, and provides more flexibility and adaptability for real application in data center. [ABSTRACT FROM AUTHOR]

With the development of the information and communication technology (ICT) industry, the energy consumption of data centers is continuously increasing. High-temperature polymer electrolyte fuel cells (HT-PEFC) have a high operating temperature of 120 °C or higher; thus, the heat generated from fuel cells stack is effectively used as a heat source for a absorption refrigerator (AR) to generate cooling. The combined cooling and power (CCP) system is proposed to satisfy the energy demand of data centers that require both power and cooling. The CCP system comprises an HT-PEFC and a double-effect AR that recovers the wasted heat of the fuel cell stack. As a result of analyzing the electrical and cooling efficiency of the CCP system according to the fuel cell operating conditions, the overall efficiency increased to 95%, which was significantly higher than the existing system. Based on the simulation of the developed model, coefficient of performance (COP) and cooling capacity depending on the temperature changes of chilled water and cooling water were calculated within the stack temperature range of 150–180 °C, and the COP changed from 1.1 to 1.58 depending on the conditions. The developed CCP system model can be used to plan strategies for flexible fuel cell operation according to the power and cooling demands required in the data center. • The combined cooling and power (CCP) system to meet energy demand of data centers. • The CCP system comprises an HT-PEFC and a double-effect absorption refrigerator. • The electric and cooling efficiency of the proposed CCP system were analyzed. [ABSTRACT FROM AUTHOR]

With the development of Global Navigation Satellite System (GNSS), the detection of precipitable water vapor (PWV) using the GNSS atmospheric sounding technique becomes a research interest in GNSS meteorology. In the conversion of zenith tropospheric delay (ZTD) to PWV, the weighted mean temperature (Tm) plays a crucial role. Generally, the Tm estimated by the linear regression models based on surface temperature (Ts) cannot meet the requirement for global use, and the accuracy of Tm derived from the empirical models is limited. In this study, a new Tm model, named GGTm-Ts model, was developed using the global geodetic observing system (GGOS) atmosphere Tm data and European Centre for Medium-Range Weather Forecasts (ECMWF) data from 2011 to 2015. Resting upon a global 2.5°*2° grid of coefficients of Tm-Ts linear function, the new model can provide Tm at any site in two modes, one for the case with measured Ts provided, i.e., the accurate mode, the other for the case that Ts provided by a subroutine, i.e., the normal mode. The performance of GGTm-Ts model was assessed against the Bevis formula, GPT2w and GPT2wh model using different data sources in 2016-the GGOS atmosphere and radiosonde data. The results show that the GGTm-Ts model in accurate mode achieves best performance with an improvement of 46.9 %/15.3 %, 37.8 %/19.5 % and 34.4 %/14.2 % over other three models in the GGOS atmosphere/radiosonde comparison. For the normal mode, the GGTm-Ts model outperforms the GPT2w model and achieves equivalence results with the GPT2wh model. Moreover, the impact of Tm on GNSS-PWV was analyzed to validate the performance of the GGTm-Ts model. [ABSTRACT FROM AUTHOR]

The line graphs of some well-known graphs, such as the generalized hypercube and the crossed cube, have been adopted for the logic graphs of data center networks (DCNs). Conditional bijective connection networks (conditional BC networks) are a class of networks which have been proved to include hypercubes, crossed cubes, locally twisted cubes and Möbius cubes, etc. Hence, the researches on the line graphs of conditional BC networks can be applied to DCNs. Edge independent spanning trees (EISTs) on a graph have received extensive attention because of their applications in reliable communication, fault-tolerant broadcasting and secure message distribution. Since the line graph of an n -dimensional conditional BC network, denoted as L (X C n) , is (2 n − 2) -regular, whether there exist 2 n − 2 EISTs on L (X C n) is an open question. In this paper, we first propose a parallel algorithm to construct 2 n − 2 EISTs rooted at an arbitrary node on L (X C n) , where n ≥ 2. Then, we prove the correctness of our algorithm. Finally, we give a simulation result of our algorithm. [ABSTRACT FROM AUTHOR]

The energy consumption of data centers (DCs) is on a sharp upward trend in recent years. DCs are playing an increasingly important role in demand response (DR) programs. However, the reassignment of computing tasks among DCs leads to different energy demands of different DCs. Given that the investment cost of energy storage is high, this work proposes a shared energy storage business model for the DC cluster (DCC) to improve economic benefits and promote renewable energy accommodation. Besides, an internal energy balance mechanism is set up to make full use of the complementary energy consumption characteristics of different DCs. Considering the renewable energy uncertainty, an optimization model is proposed based on the chance-constrained goal programming (CCGP). Finally, simulation results prove that the proposed energy storage business model has a positive effect on improving the economic benefits of the DCC. It also proves that for a DCC adopting the proposed internal energy balance mechanism, its total renting power can be effectively reduced and renewable energy consumption can be greatly promoted. [ABSTRACT FROM AUTHOR]

• OS of larynx cancer patients treated at Odense DK, Manchester UK and Liverpool AUS is driven by tumour volume and PS. • Federated learning can be used to create survival models without patient-sensitive data leaving the individual institutions. • The baseline hazards of the three institutions are similar, indicating the GTV volume and PS explain the cohort differences. • Smoking during treatment has the same risk profile as a ten year older patient not smoking. Federated learning has the potential to perfrom analysis on decentralised data; however, there are some obstacles to survival analyses as there is a risk of data leakage. This study demonstrates how to perform a stratified Cox regression survival analysis specifically designed to avoid data leakage using federated learning on larynx cancer patients from centres in three different countries. Data were obtained from 1821 larynx cancer patients treated with radiotherapy in three centres. Tumour volume was available for all 786 of the included patients. Parameter selection among eleven clinical and radiotherapy parameters were performed using best subset selection and cross-validation through the federated learning system, AusCAT. After parameter selection, β regression coefficients were estimated using bootstrap. Calibration plots were generated at 2 and 5-years survival, and inner and outer risk groups' Kaplan-Meier curves were compared to the Cox model prediction. The best performing Cox model included log(GTV), performance status, age, smoking, haemoglobin and N-classification; however, the simplest model with similar statistical prediction power included log(GTV) and performance status only. The Harrell C-indices for the simplest model were for Odense, Christie and Liverpool 0.75[0.71–0.78], 0.65[0.59–0.71], and 0.69[0.59–0.77], respectively. The values are slightly higher for the full model with C-index 0.77[0.74–0.80], 0.67[0.62–0.73] and 0.71[0.61–0.80], respectively. Smoking during treatment has the same hazard as a ten-years older nonsmoking patient. Without any patient-specific data leaving the hospitals, a stratified Cox regression model based on data from centres in three countries was developed without data leakage risks. The overall survival model is primarily driven by tumour volume and performance status. [ABSTRACT FROM AUTHOR]

Aim: According to the current resuscitation guidelines, the use of mechanical chest compression devices could be considered under special circumstances like transport with ongoing resuscitation or long-term resuscitation. The aim of this study was to investigate whether survival is improved using mechanical devices under such circumstances.Methods: Out-of-hospital cardiac arrests from all high-quality data centres of the German Resuscitation Registry from 2007 to 2020 were investigated. The use of mechanical devices was compared separately for transport with ongoing resuscitation, prolonged resuscitation (>45 min), and resuscitation with fibrinolytic agents applied. Baseline characteristics, 30-day survival/discharged alive, and neurological function at discharge were analysed descriptively; and 30-day survival/discharged alive was additionally analysed using multivariate logistic regression.Results: Overall, patients who were treated with a mechanical device tended to be younger and were significantly more likely to have a witnessed cardiac arrest and a shockable initial rhythm. During the study period, 4,851 patients were transported to hospital with ongoing resuscitation (devices used in 44.2%). The 30-day survival was equal (odds ratio, OR: 1.13, 95%-CI: 0.79-1.60). In 3,920 cases, a resuscitation duration > 45 min was documented (9.5% with device). When a device was used, 30-day survival was significantly increased (OR 2.33, 95%-CI: 1.30-4.15). Fibrinolytic agents were used in 2,106 patients (22.2% with device). Here, 30-day survival was significantly worse with a device (OR: 0.52, 95%-CI: 0.30-0.91).Conclusion: Mechanical devices are not associated with better survival when used during transport, but rescuer safety could still be an important argument for their use. Devices are associated with better survival in prolonged resuscitation, but worse survival when a fibrinolytic was used. [ABSTRACT FROM AUTHOR]

As the key component of uninterruptible power supply, the anomaly detection of valve-regulated lead-acid (VRLA) batteries is the reliable guarantee for the operation of internet data centers. Although deep neural networks (DNNs) have been investigated in battery anomaly detection, the key problem of feature learning under non-ideal conditions, i.e., data scarcity and class-imbalance, is not addressed very well. In this article, a novel DNN called multi-source self-supervised domain adaptation network (MSSSDAN) is proposed for VRLA battery anomaly detection of data center under non-ideal conditions. Firstly, multi-dimensional collaborative self-supervised learning (MDCSSL) is proposed to address the class-imbalanced problem by performing auxiliary feature learning task considering the temporal dependency of different channels. Secondly, a novel transfer learning (TL) framework, multi-source adaptive transfer learning (MSATL) is developed to solve the domain shift problem by adaptively minimizing the distribution discrepancy of different domains. Finally, the outperformance of MSSSDAN is verified on the VRLA battery dataset collected from Tencent data center. The testing results demonstrate that MSSSDAN can effectively solve the problem of battery anomaly detection under non-ideal conditions. • Multi-source self-supervised domain adaptation network (MSSSDAN) is proposed for non-ideal VRLA battery anomaly detection. • Multi-dimensional collaborative self-supervised learning is proposed to address the class-imbalance problem. • Multi-source adaptive transfer learning framework is proposed for solving the problem of data scarcity and domain shift. • The experimental results prove the effectiveness of MSSSDAN for VRLA battery anomaly detection. [ABSTRACT FROM AUTHOR]

• The differences and similarities between pump- and gravity-driven modes are studied. • The mechanism of the flood limit for the pump-driven mode is illustrated. • A new correlation based on the experimental results has been proposed. With the development of big data and cloud computing, data centers have grown rapidly worldwide. A data center is a special building with high energy consumption. The energy consumption of traditional computer room air conditioning is assumed to be 45 % of the total energy consumption of the data center. Currently, the use of a loop thermosiphon as a cooling system is one of the most effective methods. Although loop thermosiphons have already been widely used, the boiling mechanism inside the evaporator needs to be studied further, particularly for pump-driven loop thermosiphons. In this study, we investigated the heat transfer and flow characteristics of a pump-driven loop thermosiphon. The inside tube diameter is 8.05 mm, the heat flux changes from 6317 to 28085 W/m2, and the mass flux changes from 437 to 1311 kg/m2s. First, the differences and similarities between pump- and gravity-driven modes are studied. Second, the matching relationship between the heat flux and mass flow rate is studied, and the mechanism of the flood limit for the pump-driven mode is illustrated. Finally, we compare different boiling correlations and propose a new correlation based on our experimental results. [ABSTRACT FROM AUTHOR]

Regional data center clusters have flourished in recent years to serve customers in a major city with low latency. The optimal coordination of data centers in a regional cluster has become a pressing issue because of their rising energy consumptions. In this paper, an online algorithm based on Lyapunov optimization framework is developed for the combined computing workload and energy coordination of data centers in a regional cluster. The proposed online algorithm is prediction-free and easy to implement. We prove that the workload queues and battery energy level will be within their physical limits, though their related time-coupling constraints are not considered explicitly in the proposed algorithm. The previous online algorithms do not have such a guarantee. A theoretical upper bound on the optimality gap between the online and offline results is derived to provide a performance guarantee for the proposed algorithm. To enable distributed implementation, an accelerated distributed coordination algorithm is developed based on the alternating direction method of multipliers (ADMM) with iteration truncation and follow-up well-designed adjustments, whereby a nearly optimal solution is attained with much enhanced computational efficiency. Case studies show that the proposed algorithm reduces the operational costs and saves computation time compared to online benchmarks. • The combined computing workload and energy coordination of data centers is studied. • A prediction-free online coordination algorithm is built with proven properties. • An accelerated ADMM algorithm is developed for distributed implementation. • The proposed coordination method greatly reduces the total cost of data centers. • The computation time is largely reduced compared to the traditional ADMM. [ABSTRACT FROM AUTHOR]

Due to the electrification and digitization of things such as public services, communication systems, and transport systems, data centers have become part of the critical infrastructure of today's society. The data centers as a resource-intensive industry, perturbates the energy and spatial systems where established. Currently, municipalities as the main entities responsible for local resource management, often lack the power and/or determination to plan spatial and energy aspects cohesively. Consequently, municipalities encounter resource management issues when planning for data center locations; high resource demand (water, electricity, land) and high resource waste (excess heat). This research aims to support urban planners, municipal energy companies, politicians, and other relevant decision-makers, in developing municipal energy and spatial systems that account for data centers' resource demand as well as using their resources produced in the form of excess heat for the community's benefit. This study compares energy and spatial aspects in relevant data center locations in Stockholm and Luleå municipalities from an urban planning perspective. The results suggest that municipalities' energy and spatial systems can increase their adaptive capacity towards data center establishment by considering certain resource planning determinants: electricity and water infrastructure availability, heat producers and users' diversity, land use diversity, and property fragmentation. • Luleå and Stockholm municipalities approach planning data center locations in diverse ways, partly due to their climate or energy availability differences, which influence local resource planning and management. • Stockholm municipality has a higher adaptive capacity than Luleå municipality, being able to adapt its energy and spatial system to integrate the data center industry and taking the opportunity to build resiliency, by using the data centers' excess heat for the local community. • An increased collaboration between diverse interest groups could allow a more cohesive spatial and energy planning. • This cohesive planning might set the critical conditions (electricity and water infrastructure availability, heat producers and users' diversity, land use diversity, property fragmentation) to facilitate a more effective integration of such resource-intensive industries within a municipality. [ABSTRACT FROM AUTHOR]

To augment situational awareness capabilities for high-frequency disturbances, the modern power system necessitates the implementation of high-sampling rates for real-time Synchro-Waveform (SW) measurements. Nevertheless, the nonlinear nature of data collected from SW measurement devices poses significant challenges to the communication and storage capacities of data centers. To effectively tackle this issue, this paper proposes a Multi-stage Hybrid Coding (MHC) method for the lossless compression of SW data. The MHC method utilizes a well-structured two-stage approach to achieve efficient compression. In the first stage, a variable frame-based method is designed to reduce redundant information and achieve initial compression using multiple periodic recursive compression. Subsequently, the dictionary-based method, specifically the Lempel–Ziv–Markov chain algorithm, is seamlessly integrated to further compress the SW data. Experimental and numerical analyses are conducted using both simulated and real-world source data, with real-time performance validation in SW measurement units. The obtained results convincingly demonstrate that the proposed method enables online compression of both event and non-event SW data, achieving an impressive reduction of over 73.4% in data space, while maintaining a data loss ratio lower than 0.4% for high-density SW data. • Multiple periodic recursive method is proposed to eliminate repetitive information. • A multi-stage hybrid coding method is implemented for the lossless compression. • A series of synchro-waveform events are collected to evaluate the performance. [ABSTRACT FROM AUTHOR]

Highly dense and integrated data centers face key challenges of realizing efficient cooling and improved energy efficiency. To overcome these challenges, this study experimentally investigated the flow and heat transfer characteristics of single-phase immersion liquid cooling (SPILC) systems. The influence of two opposite coolant flow directions on the SPILC performance was examined. Furthermore, a correlation mechanism between coolant thermal properties and SPILC performance was established, along with a control chart for regulating the working conditions of SPILC systems. The results indicate that compared to a pro-gravity flow, an anti-gravity flow scheme reduces the chip case temperature and thermal resistance by 33.8% and 55.6%, respectively, while decreasing the power usage effectiveness (PUE) by 1.4%. Using coolant with the lowest viscosity reduces the chip case temperature and thermal resistance by 9.3% and 10.5%, respectively, while decreasing the PUE by 0.4%. Moreover, the cooling water temperature has a greater impact on the performance of SPILC systems than the volume flow rate of coolants. Additionally, this paper provides control charts for the cooling water temperature and coolant flow rate to improve the PUE while ensuring the safe operation of SPILC systems, with the highest chip temperature and total electricity consumption as indicators. • Immersion cooling performance of system-level data center servers is experimentally studied. • PUE of the anti-gravity flow decreases by 1.4% compared to the pro-gravity flow. • Correlation between coolant properties and immersion cooling performance is established. • Energy-efficient control charts for cooling water and immersion coolant are provided. [ABSTRACT FROM AUTHOR]

• Evaluate the impact of different cdfs on drought magnitude. • Global-scale analysis identifies 23 parametric cdfs as best-fit for streamflow values. • Streamflow simulation accuracy and reference period length influences cdf selection. • Opting for best-fitting parametric cdf mitigates overestimating drought magnitudes. Streamflow droughts pose significant challenges to water resource management and environmental sustainability. The accurate characterization of streamflow drought magnitude is essential for effective mitigation, assessment and adaptation strategies. In this study, we explore the implications of choosing different cumulative probability distribution functions (cdfs) on the determination of streamflow drought magnitude, as assessed by the z-score values of the standardized streamflow index (SSI) and return periods. Our investigation encompasses a comprehensive global-scale analysis, with the aim of identifying the most suitable cdfs for characterizing streamflow droughts. To accomplish this objective, we incorporate global simulated streamflow values from the model WaterGAP 2.2d and observed streamflow values from 280 Global Runoff Data Centre (GRDC) stations for the period 1981 – 2010. We evaluate the performance of 118 parametric cdfs for each of the 12 calendar monthly streamflow values at each model grid cell and GRDC station to determine the best fitting parametric cdf. Moreover, we go beyond the conventional scope of such studies by further assessing the best-fitting cdfs alongside non-parametric cdfs such as the Empirical cumulative distribution function (ecdf) and the cumulative distribution function derived from the Kernel density estimation function (kcdf). Among our findings, we establish a subset of 23 parametric cdfs that exhibit the best fit for the global streamflow values concerning all 12 calendar monthly streamflow values at individual GRDC stations or grid cells within the specified period. Furthermore, we observe that the accuracy of streamflow simulation and the length of the reference period directly influence the selection of the most suitable parametric cdf. Our comparative analysis reaffirms the existing understanding that opting for the best-fitting parametric cdf, rather than a non-parametric alternative, mitigates the risk of overestimating drought magnitudes. However, we emphasize the crucial importance of considering the intended purpose, approach-specific sensitivities, and uncertainties when choosing the most suitable parametric cdf for any calendar monthly streamflow values. [ABSTRACT FROM AUTHOR]

• Calibrated and rigorously validated model of silica gel-water adsorption chiller. • Screening of more than 40 adsorbents for application under data center conditions. • Review and evaluation of adsorption heat exchanger designs. • Cooling power of adsorption chiller can be increased by 59 % if MIL-100(Fe) is used. • (Partial) energy efficiency ratio (pumps and control, no fans) of > 20 is realistic. The electricity and water consumption of data centers is growing on a global scale. A shift towards liquid cooled racks in combination with thermally driven cooling can help to reduce the electricity and water demand associated with the necessary heat rejection. To quantify the potential of adsorptive heat transformation devices in reducing the electricity and water demand, the prediction of thermal efficiency, heat flow rates and energy efficiency ratio is required. To this end, a numerical model is newly developed using basic adsorption heat exchanger theory. This model can predict the necessary performance indicators with respect to temperatures and volume flow rates, heat exchanger design and adsorbent. The full performance map of a market-available adsorption chiller (71 points) and own measurements are used for calibration and rigorous validation of the model. An average deviation (experiment vs. calculation) of 8.3 % in terms of thermal efficiency and 7.2 % in terms of heat flow rates is achieved, indicating a very good agreement for a wide range of temperatures. At a moderate liquid cooled rack outlet temperature of 50 °C, a heat rejection temperature of 26 °C and a cold aisle inlet temperature of 18 °C the cooling power of the silica gel reference chiller of 5.3 kW can be increased by 59 % to 8.5 kW at a partial energy efficiency ratio (pumps and control, no fans) of > 20 by assuming MIL-100(Fe) as adsorbent on a flat-tube lamella heat exchanger. The model can be used in subsequent annual system simulations to quantify the savings in electrical power and water consumption, which strongly depend on the ambient conditions. [ABSTRACT FROM AUTHOR]

The error of regional models gradually increases during long-term integration, and it can be reduced with the constraint of large-scale circulation by assimilating global forecast system (GFS) fields to improve the forecast performance. However, with increasing forecast time, the GFS forecast error also significantly increases and varies widely. Directly assimilating GFS fields into the regional model without processing may generate negative impacts, whereas employing postprocessing correction methods can effectively mitigate GFS forecast errors. In this study, the GFS data of European Centre for Medium-Range Weather Forecasts (ECMWF) are first corrected using a convolutional long- and short-term memory network method. The correction model can correct multiple forecasts simultaneously by training only one model, and the correction results are less erroneous than those obtained by training multiple correction models for the different forecast times and pressure layers. The corrected ECMWF forecast fields are then assimilated into Weather Research and Forecasting Model (WRF) using the nudging method. This assimilation method generally improves the forecasts for precipitation above the moderate rainfall level at forecast times of 4–7 days, and reduces the errors of surface wind speed forecasts. This study improves the accuracy of correction models and the forecasts of persistent precipitation weather event with regional model. • A correction model for global forecast system (GFS) upper-air fields is proposed. • The correction model can correct multiple GFS forecasts by training only one model. • The correction model reduces the errors of U and V components of GFS wind fields. • Correction results are assimilated into a regional model as large-scale constraint. • Adjustments to the wind fields are important for improving precipitation forecasts. [ABSTRACT FROM AUTHOR]

Cloud services have become a demand in big cloud data centers for allocating energy-efficient resources and resource utilization. Most current solutions consider a small number of resource categories, which causes an imbalanced load or the needless activation of real servers. Thus, the virtual machine placement-based bin padding algorithm was proposed and analyzed with the equilibrium state of multi-resource utilization. Additionally, the impact of imbalanced resources causes weak virtual machine task allocation that induces poor VMs' scheduling activities. Therefore, this work incorporates efficient conjectured scheduling to prioritize the task based on computational time with trust parameters calculation. Moreover, to improve the performance, the new abatement strategy was used with efficient resource management, which generates a physical host to minimize energy burning and increase the quality of service to the data center by reducing overload. Implementing the proposed algorithm efficiently utilizes the memory of up to 290 MB for processing 90 MB files with a low computational time of 23 s Further algorithms tested on ten benchmark functions show that they outperform similar algorithms. Thus, the proposed model applies to individual servers in the cloud. [ABSTRACT FROM AUTHOR]

The increasing prevalence of high-performance computing data centers necessitates the adoption of cutting-edge cooling technologies to ensure the safe and reliable operation of their powerful microprocessors. Two-phase cooling schemes are well-suited for high heat flux scenarios because of their high heat transfer coefficients and their ability to enhance chip temperature uniformity. In this study, we perform experimental characterization and deep learning driven optimization of a commercial two-phase cold plate. The initial working design of the cold plate comprises a fin height of 3 mm , fin thickness of 0.1 mm , and a channel width of 0.1 mm.A dielectric coolant, Novec /HFE 7000, was impinged into microchannel fins through impinging jets. A copper block simulated an electronic chip with a surface area of 1 " × 1 ". The experiment was conducted with three different coolant inlet temperatures of 25 ° C , 36 ° C , and 48 ° C with varying heat flux levels ranging from 7.5 to 73.5 W cm 2. The effects of coolant inlet temperatures and flow rate on the thermo-hydraulic performance of the cold plate were explored. In two-phase flow, increasing coolant inlet temperature results in more nucleation sites and improved thermal performance consequently. Thermal resistance drops with flow rate in single-phase flow while it is not affected by flow rate in nucleate boiling region. An improvement in the design of the cold plate was carried out, with the goal of increasing the number of bubble sites and flow velocity at the root fins, by cutting the original fins and creating channels perpendicular to the original channels. Three design parameters, fin height, width of machined channels, and height of short fins preserved through machined channels, were defined. It was observed that widening the machined channels and cutting fins to some point can improve the thermal performance of the cold plate. However, removing fins excessively adversely affects the thermal performance of the cold plate because of loss of heat transfer surface area. Moreover, preserving the short fins through the machined channels decreases thermal resistance as they increase heat transfer surface area and nucleation sites. Furthermore, a deep learning-based compact model is demonstrated for the two-phase cold plate design in the specific range of geometry and flow conditions. The developed compact model is utilized to drive the single and multi-objective optimization to arrive at global optimal results. [ABSTRACT FROM AUTHOR]

With the advent of the information age, the scale of data centers has developed unprecedentedly, in which the cooling system consumes a lot of energy. Therefore, it is crucial to adjust the operating parameters in real-time to maximize energy savings. In this article, the mathematical models of each component are established, and the functions of system power consumption and chip temperatures are obtained for the liquid-cooled data center based on cold plates. The method proposed in this article has good accuracy through experimental verification and can be used for the optimization of operating parameters. The server chips in data centers need to be maintained within a safe range, so we take the minimum system power consumption as the optimization goal and the chip temperature as the constraint condition to calculate the optimal cooling tower wind volume, primary side flow rate, and secondary side flow rate under different environmental temperatures and heat loads, and fit all three for real-time optimization and regulation. The results show that the intelligent control proposed in this article can save 42.7% energy and reduce PUE to 1.16 under variable heat load, and save 30.6% energy and improve PUE by 4.3% under variable environmental temperature. The intelligent control method described in this article provides guidance for real-time optimization in data centers. • The models of each equipment in the liquid-cooled data center based on cold plates were established. • The effect of three operating parameters on system power consumption and chip temperature are analyzed. • Operating parameters of the system for different ambient temperatures and thermal loads are optimized in real time. • The energy consumption of the cooling system is reduced and PUE of data center is improved. [ABSTRACT FROM AUTHOR]

• An improved empirical wavelet transform (IEWT) is proposed. • A new wave forecast method called IEWT-LSTM is introduced. • The forecast results by the LSTM, EWT-LSTM, and IWET-LSTM are analyzed. • IEWT-LSTM has significantly advantage and it is more superior than EWT-LSTM and LSTM. Due to strong non-linearity, ocean surface gravity waves are difficult to directly and accurately predict, despite their importance for a wide range of coastal, nearshore, and offshore activities. To minimize forecast errors, a hybrid combined improved empirical wavelet transform decomposition (IEWT) and long-short term memory network (LSTM) model has been proposed. Data from National Data Buoy Center buoys deployed in the North Pacific Ocean are taken as an example to verify the models. Wave forecasts using the LSTM, EWT-LSTM, and IWET-LSTM models are compared with the observations at 6, 12, 18, 24 and 48 h forecast windows. Consequently, IEWT-LSTM is superior to EWT-LSTM or LSTM models, especially for larger waves at longer long forecast windows. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel clustering approach that eliminates the need for predefined cluster centres and cluster counts, addressing common limitations in traditional clustering algorithms. We achieve this by allowing each data point within the problem space to serve as a potential cluster centre, determined by the presence of nearby data points. Three distinct algorithms are introduced: i) Identifying valid data points for clustering by assessing their presence within the specified vicinity of each potential cluster centre, allowing data points to belong to multiple clusters, ii) Calculating the fuzzy membership of shared data points among the clusters and iii) Retaining shared data points in the cluster with the highest membership and removing them from others. A comprehensive evaluation of this method using synthetic data and benchmark datasets from well-known problems demonstrates promising performance. Our approach offers increased flexibility, ease of use, and enhances the fidelity of cluster centres to the underlying data distribution. [ABSTRACT FROM AUTHOR]

As the national digitization process accelerates, the number and energy consumption of data centers are increasing year by year. Want to reduce the PUE of the data center. It is correct to start from the perspective of optimizing the energy consumption of air conditioning system operation. In this paper, a predictive optimization control method that is different from previous real-time optimization control is proposed. Predict, optimize, control and adjust the operating parameters of the air conditioning water system in the data center. At the same time, it solves the energy saving problem of the air conditioning system and the lag problem of previous adjustments. Predictive optimization control has broad prospects in research on operation optimization of data center air conditioning water systems. • Reducing air conditioning water system energy consumption in data centers can lower PUE. • Predictive optimization can solve the lag problem of water systems. • After global prediction optimization, energy consumption was saved by 12.9%. [ABSTRACT FROM AUTHOR]

Kernel methods and neural networks (NNs) are two mainstream nonlinear data modeling methods and have been widely applied to industrial process monitoring. However, they both present imperfect properties, so the relevant applications are limited. On the one hand, kernels are not so reconstructable, scalable, and robust to hyperparameters that they suffer performance degradation for large-scale data modeling and monitoring. On the other hand, the high-dimensional parameter space of NNs that is sorted to parameter initialization presents severe anomaly detection performance inconsistency, which makes the industry cautious about using NNs. Motivated by these facts, we propose to integrate kernels and NNs, forming a new model structure that is scalable, reconstructable, and performance-consistent. Specifically, a novel autoencoder-based nonstationary pattern selection kernel (AE-NPSK) is proposed by (1) selecting from the training set the critical edges and interior data as the centers of the radial basis functions in the hidden layers and (2) adaptively adjusting the kernel width in the training procedure. Also, the new NN has strong performance consistency, which facilitates the search for optimal parameters. Finally, we test the performance of the proposed method on the challenging multimode processes. The results validate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

In recent years, the energy consumption of data centers (DCs) has shown a sharp upward trend. Given the high investment cost of energy storage, this study introduces the concept of energy sharing within a data center cluster (DCC) and proposes a novel shared energy storage (SES) business model. The model realizes the co-optimization for DCC and SES and is divided into three main layers. Firstly, considering the SES's economy and the DCC's cleanliness, the upper layer establishes a novel multi-objective optimization configuration scheme for SES with lifetime degradation. In addition, a two-stage robust optimization technique for DCC is proposed in the middle layer, considering the demand response mechanism for data loads. Subsequently, the concept of contribution degree (CD) is defined, and a novel benefit distribution mechanism based on cooperative game and CD is presented in the lower layer. The simulation results demonstrate that the proposed business model positively enhances the economic benefits of the SES and DCC. The constructed uncertainty optimization model exhibits strong robustness, and the proposed benefit allocation scheme is fairer and more reasonable. [ABSTRACT FROM AUTHOR]

With the rapid development of global informatization, data center computing tasks are increasing, and the resulting energy consumption problems are particularly prominent in the context of carbon emission reduction, and affect the safe and green operation of data centers. Aiming at the problem of high and large fluctuation energy load of data center servers, this paper presents a method of optimizing data center servers load considering spatial-temporal load distribution of computing resources. Co-optimized allocation of computing resources and load is studied with examples from the property that computing resources is adjustable in time and space dimensions. The results show that the proposed method is able to reduce the peak electric and cooling loads of the data center room by 10.7 %, reduce the total annual electric consumption by 2.66 % and the total annual cooling consumption by 2.46 %, and reduce the economic and environmental costs of energy supply for the data center in the case study, which comprehensively shows that the load optimization of data center servers has been achieved through the optimal allocation of computing resources. [ABSTRACT FROM AUTHOR]

In the present paper, we focus on the study of the strongly Menger-edge-connectivity of a server-centric DCN DCell. Let D m , n be the m -dimensional DCell with n -port switches for integers m ≥ 0 and n ≥ 2. Given integers k , m and n. We prove D m , n is ((k − 1) m + (k − 1) n − k 2 + 1) -strongly Menger-edge-connected of order k − 1 for 2 ≤ k < m and n > m ≥ 2 or 2 ≤ k ≤ n and m ≥ n ≥ 2 and D m , n is (m n − m − n + 1) -strongly Menger-edge-connected of order m − 1 for n > m ≥ 2. Also, we show that each of the above results is optimal by using examples. [ABSTRACT FROM AUTHOR]

Green Data Centers (GDCs) are more and more deployed world-wide. They integrate many renewable sources to provide clean power and decrease their operating cost. GDCs are typically deployed over multiple locations where renewable energy availability, bandwidth prices and grid electricity cost have high geographical diversity. This paper focuses on delay-bounded applications in distributed GDCs (DGDCs) and performs cost and energy-effective scheduling of multiple heterogeneous applications verifying delay bound constraints of different tasks. DGDCs' operational cost minimization problem is formulated and successfully optimized using an innovative modified Firefly Algorithm (mFA). Real-life data trace-driven experiments are conducted to evaluate the effectiveness of the proposed mFA in solving this problem. High performance task scheduling results are obtained. The operational cost of each GDC is minimized, the utilization of solar and wind renewable energy from the different geographical locations is maximized while delay bound constraints of all tasks are strictly met. Compared to Bat Algorithm, Simulated-annealing Bat Algorithm and basic firefly algorithm, mFA can produce a schedule that outperforms its peers' drastically in terms of operational cost of DGDCs. Moreover, mFA finds more rapidly both global or local optima than its peers. It succeeds to meet all equality and inequality constraints at all time slots while its peers may sometimes fail to find satisfactory solutions at some particular time slots. [ABSTRACT FROM AUTHOR]

Since the onset of the 21st century, the number of data centers has been increasing continuously, and the energy consumption of these data centers has aroused people's attention. Many experiments have been conducted to reduce the energy consumption in data center cooling, and free cooling utilization based on the mechanically driven two-phase cooling loop (MTCL) system has been proven to be an effective solution for energy saving. This paper reviews the research on and developments in the practical application of the MTCL system for data center cooling in recent decades. It presents a detailed summary of the progress of research on the MTCL system in data centers. The aim is to provide an improved understanding of the system and promote the application of this system in data center energy saving. [ABSTRACT FROM AUTHOR]

Aiming at the power fluctuation and mismatch of the combined cooling, heating, and power (CCHP) system based on proton exchange membrane fuel cells (PEMFCs) and adsorption chiller, this study proposes a multi-stack coupled power supply strategy. The PEMFC stacks are divided into types Ⅰ, Ⅱ, and Ⅲ to meet the electric load and cooling load of the data center, and the heat requirements of the system. Meanwhile, economic analysis is conducted on the single-stack energy supply strategy and the multi-stack coupled energy supply strategy. The results show that with the multi-stack coupling power supply strategy, the cooling power and electric power almost completely match the load of the data center, without power fluctuations and overshoot. By smoothing the PID control results of the current of the stacks-Ⅲ, the heating power fluctuation is significantly reduced, and the maximum overshoot does not exceed 0.5 kW. Therefore, the strategy is conducive to the stable operation of the PEMFC stack and improves the lifetime of the system. Considering investment costs, maintenance costs, hydrogen costs, and electricity benefits, the multi-stack coupled energy supply strategy can save about 6.1 × 105 $ per year. In summary, the multi-stack coupled energy supply strategy has advantages in system lifetime, operational stability, and economy. • Multi-stack coupled power supply strategy is proposed. • The PEMFC are divided into types Ⅰ, Ⅱ, and Ⅲ to meet different power requirements. • The cooling and electric power completely matches the load with the strategy. • The heating power fluctuation is reduced by smoothing the current of stacks-Ⅲ. • The multi-stack coupled energy supply strategy can save about 6.1 × 105 $ per year. [ABSTRACT FROM AUTHOR]

• A new data center cooling system integrating heat recovery with free cooling is proposed. • The system can work selectively in four different modes according to conditions and demands. • Stable performance of each mode and dynamic performance in mode switching are obtained. • Annual power consumption and heat recovery analysis are conducted in five cities. Rapid growth of data centers has caused a huge increase in energy consumption. Utilizing natural cold resources and harvesting waste heat are two promising ways to reduce the total power consumption of data centers. This paper proposes an integrated system of free cooling and heat recovery. The system has four working modes: mechanical cooling, mechanical heat recovery, pump free cooling and pump heat recovery. Experimental results show that pump free cooling mode can achieve an energy efficiency ratio of 7.99 when outdoor temperature is 2 °C, and different modes can be switched smoothly. The applicability of this system has been evaluated in five different cities in China. The results show that annual average energy efficiency ratio can reach 6.18 and free cooling time can take up as high as 72.9% during operation in Xining. The system can harvest the most heat in Harbin, 7930.96 kW∙ h per year, while the least in Guangdong, 645.31 kW∙ h per year. This means that this new system has relatively great application prospects in northern cold cities of China, while is moderately limited in southern cities. [ABSTRACT FROM AUTHOR]

Spatiotemporal variations of ozone (O 3) taken from the Copernicus Atmosphere Monitoring Service (CAMS) and the second Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) were intercompared and evaluated with ground and ozone-sonde observations over China in 2018 and 2019. Intercomparison of the surface ozone from CAMS and MERRA-2 reanalysis showed significant negative bias (CAMS minus MERRA-2, same below) at Tibetan Plateau of up to 80 µg/m3, and the average R 2 was about 0.6 across China. Evaluated with the ground observations from China National Environmental Monitoring Center (CNEMC), we found that CAMS and MERRA-2 reanalysis were capable of capturing the key patterns of monthly and diurnal variations of surface ozone over China except for the western region, and MERRA-2 overestimated the observations compared to CAMS. Vertically, the CAMS profiles overestimated the ozone-sonde from the World Ozone and Ultraviolet Radiation Data Center (WOUDC) above 200 hPa with the magnitude reaching up to 150 µg/m3, while little bias was found between the reanalysis and observations below 200 hPa. Intercomparison drawn from the vertical distribution between CAMS and MERRA-2 reanalysis showed that the negative bias appeared throughout the troposphere over China, while the positive bias emerged in the upper troposphere and lower stratosphere (UTLS) with high order of magnitude exceeding 100 µg/m3, indicating large uncertainties at higher altitudes. In summary, we concluded that CAMS reanalysis showed better agreement with the observations in contrast to MERRA-2, and the large discrepancy especially at higher altitudes between these two reanalysis datasets could not be ignored. [Display omitted]. [ABSTRACT FROM AUTHOR]

Density peaks clustering (DPC) model is simple and effective in clustering data of any shape, and has attracted wide attention from scholars in recent years. However, it is difficult for DPC to determine the cutoff distance when calculating the local density of points, and to select the correct cluster centers of data with large differences of density between clusters or multi-density peaks in clusters; in addition, the point allocation method in DPC has low accuracy. To overcome these drawbacks, this paper presents a novel nearest neighbors-based adaptive DPC algorithm with an optimized allocation strategy (NADPC in short), and demonstrates its application in image clustering. First, the mutual nearest neighbor relationship between points is defined, the mutual neighborhood of point is proposed, and then a new local density of points is defined and does not need to set the cutoff distance. The candidate cluster centers and relative density are developed. According to the relative density and the high-density nearest neighbor distance of candidate cluster centers, their credibility as the cluster centers is calculated, and then the cluster centers are selected. Second, the mutual neighbor degree and similarity between two points are constructed. The neighborhoods of points are defined according to the high-density nearest neighbor, shared nearest neighbors, mutual neighbor degree and similarity, respectively. The similarity set, similarity domain, positive set, negative set, prediction set, positive value and predicted value of point are provided based on the above-mentioned neighborhoods. Then the optimized allocation strategy of points is proposed. Finally, the allocation algorithms of the non-abnormal and abnormal points are designed, respectively, and then the NADPC algorithm is designed. To evaluate the effectiveness of NADPC, it has been applied to 22 synthetic datasets and 26 actual datasets including 4 image datasets, and has great performance in terms of several evaluation metrics when compared with the other latest clustering algorithms. [ABSTRACT FROM AUTHOR]

The control of indoor temperature in greenhouses is crucial as it directly impacts the crop's thermal comfort and the performance of heating, ventilation, and air-conditioning (HVAC) systems. Conventional feedback controllers, like on/off, can sometimes make HVAC system work at full capacity when only half that capacity is needed. In contrast, the proportional-integral-derivative (PID) controller, provides precise control based on its P, I, and D parameters. However, it lacks a formal design procedure for optimizing a specified objective function. Previous studies have utilized conventional PID tuning approaches to track room setpoint temperature for residential buildings, data centers, and office buildings, with limited research in greenhouse applications. To address this gap, this study proposes a flexible PID controller that employs a deep reinforcement learning (DRL) algorithm to optimize its parameters, by tracking the setpoints and energy consumption of a greenhouse planted with tomatoes. This approach is different from the typical method of using the trained RL agent directly in HVAC controls. Through a self-made TRNSYS-Python cosimulation framework, the DRL agent interacts directly and in real time with the greenhouse and its plants. Consequently, optimized PID parameters were established and tested in the simulated environment. The resulting performance, in terms of both energy consumption and its ability to maintain the crop's comfort temperature, was compared with the simulated on/off and manually tuned PID controllers. Compared to the on/off baseline control, the proposed PID optimized parameters reduce energy use by 8.81% to 12.99% and the manually tuned PID parameters with the Ziegler-Nichols tuning method reduce energy use by 7.17 %. Additionally, the proposed method had a deviation of 2.07% to 3.13%, while the manually tuned PID controller and the on/off controller had deviations of 7.27% and 3.27%, respectively, from the minimum comfortable temperature. This study serves as a framework for improving the energy efficiency of greenhouse HVAC system operations. [ABSTRACT FROM AUTHOR]

Cold-rolled strips are a pivotal product of advanced strip rolling technology in iron and steel production with stringent process standards. Over an extended period, the existence of 'information silos' between cold strip rolling (CSR) and its upstream processes, particularly hot strip rolling (HSR), causes a persistent challenge in achieving collaborative optimization throughout the entire process, thereby constraining progress in product quality. To overcome this challenge, a digital twin framework was proposed to consolidate diverse data from HSR and CSR production lines into a centralized data centre, forming a 'data continent.' This approach establishes a comprehensive dataset for model-driven training and prediction in virtual space. An improved Light gradient boosting machine (IlightGBM) is introduced, enabling high-precision rolling force prediction in unsteady-state rolling and providing essential variables for establishing the tension optimization objective function. A control function is implemented to minimize rolling force fluctuations, and three population algorithms (TPA) are devised for its solution. This strategy successfully realizes collaborative tension optimization across multiple stands, with the rolling speed acting as a follower. The proposed approach was implemented in a large-scale steel plant equipped with a complete HSR and CSR production line, effectively mitigating flatness defects and thickness deviation in the strip's head section. Thus, the proposed framework is reliable and can be flexibly applied to strip manufacturing processes. [ABSTRACT FROM AUTHOR]

Nowadays, numerous mobile devices (MDs) provide nearly anytime and anywhere services, running on top of various computation-intensive applications. However, bearing limited battery, bandwidth, computing, and storage resources, MDs cannot completely execute all tasks of such applications in real-time. Cloud data centers (CDCs) possess enormous resources and energy, which can help execute tasks offloaded from MDs. Nonetheless, CDCs reside in remote sites, thereby leading to long transmission latency. In recent years, small base stations (SBSs) have emerged to offer close proximity, high bandwidth, and low latency services to their nearby and limited MDs. However, it becomes a new challenge to minimize the total system cost in a complex and heterogeneous architecture. To address it, this work proposes an energy-minimized partial computation offloading technique. First, a limited optimization problem of cost minimization for the system is formulated. Afterward, an improved hybrid meta-heuristic algorithm is developed, which synergistically combines a Metropolis acceptance criterion of simulated annealing and genetic operations. One uniqueness of the proposed algorithm is that it simultaneously determines task allocation among MDs, an SBS, and a CDC, the transmission power of MDs, and bandwidth allocation of wireless channels between MDs and SBS. Experiments with real-life tasks from Google data centers have shown that the proposed G enetic S imulated-annealing-based P article swarm optimization (GSP) significantly achieves lower system cost and faster convergence speed than benchmark peers. • A hybrid system architecture is designed and characterized by triple queueing models. • A constrained total cost minimization problem with multiple constraints is formulated. • The cost minimization problem is solved by a hybrid optimization algorithm named GSP. • It combines simulated annealing and genetic operations into a particle swarm optimizer. • Experiments with realistic traces show GSP achieves lower cost and faster convergence. [ABSTRACT FROM AUTHOR]

Computational Storage Devices, also known as In-Storage computing or In-Suit Processing, offer higher computing power than traditional storage devices. Innovation in computational storage devices is more important than ever because of the exponential growth of digital data produced by both individuals and organizations. With the advent of non-volatile storage devices in mainstream computing (e.g., hard disk drives and solid-state drives), the idea has gained wide appeal, with several academic and commercial prototypes becoming available. This survey aims to provide a systematic overview of the work being done in the area of computational storage and to indicate future directions. This overview considers and examines a number of research questions to comprehensively summarize, analyse and discuss various storage devices such as Hard Disk Drives (HDD), Solid-State Drives (SSD) and Computational Storage Devices (CSDs) in terms of design, programming model, acceleration and energy efficiency. Also, the Load Balancing algorithm has been reviewed from different domains such as cloud computing, data centers and the Internet of Things (IoT). This review not only focuses on the existing literature problems but also requires some effort to explore the future in this area. [ABSTRACT FROM AUTHOR]

The growing demand for cloud computing and storage necessitates the expansion of Data Centers, thereby increasing energy consumption and environmental footprint. The data-driven dynamic thermal control presents a promising solution to mitigate this issue, as it can offer improved thermal control strategies and address the intricate and stochastic nature of the thermal environment. Nonetheless, existing solutions typically exhibit deficiencies in terms of reliability and practicability. To this end, this paper proposes a feasible uncertainty-aware online learning method with reliable dynamics models and controllers. Specifically, Deep Learning models with the capacity for uncertainty quantification are employed to account for the imperfections in pre-trained models, hence providing reliable predictions. The Cross-Entropy Method and Monte Carlo trajectory sampling solve optimization problems while considering uncertainties. It is mathematically analyzed that the uncertainty quantified by the system dynamics model can be captured with sufficient trajectory samples, rendering a reliable controller. A practical framework is proposed and tested on a widely used Data Center model to mitigate the insufficient capacity of Deep Learning models to quantify uncertainties. The results demonstrate its superior performance during the initial training stage, achieving a 66% reduction in violations and 3.76% power savings compared to the default controller. Continuous active online learning enhances its adaptability, resulting in an 80% reduction in violations and a 6% energy saving. The thermal control strategies are thus improved by the proposed new solutions and insights. [ABSTRACT FROM AUTHOR]

As countries worldwide seek to reach net zero emissions within the coming decades, data center sustainability measures are becoming increasingly important as these facilities consume substantial amounts of electricity to support globally expanding digital infrastructure. Yet, local renewable energy production and waste heat utilization are still rarely used in data centers, even though recent high electricity prices have considerably increased the economic value of self-generation and energy efficiency investments. The benefits and cost-effectiveness of data center sustainability measures have also not yet been thoroughly researched in cold-climate regions, where the data center industry is currently growing rapidly. Thus, this paper assesses how solar photovoltaics (PV) and waste heat utilization can effectively be integrated into different cold climate data centers, with a case study analyzing data centers in Finland and northern Japan. The modelled results indicate that solar PV systems can cost-effectively be used to provide renewable electricity to data centers in both Finland and northern Japan, with electricity prices having the largest influence on economic performance. The use of larger external PV systems is shown to be cost-effective at the higher electricity prices seen in 2022, whereas smaller rooftop PV systems and grid electricity provide more economic benefits at 2019 price levels. Selling surplus PV electricity to the grid and using waste heat for district heating is also shown to be effective in data centers located in Finland, whereas battery storage and snow melting are better suited to utilize surplus PV electricity and data center waste heat in northern Japan. [Display omitted] • Solar photovoltaic systems can cost-effectively reduce data center emissions in cold climate countries. • Data center waste heat can provide value in district heating and snow melting applications. • The cost-effectiveness of data center sustainability measures is largely influenced by electricity prices. • The option to sell surplus electricity to the grid incentives larger solar photovoltaic investments in data centers. [ABSTRACT FROM AUTHOR]

For cloud data centers, a performant yet energy-efficient operation is critical for service quality and experience. The growing demand for cloud-based services has led to the development of large-scale hierarchical data center structures, characterized by horizontal expansion and vertical hierarchy, leading to challenges in managing Virtual Machines (VM) at a granular level. The hierarchical arrangement can increase the risk of deployment failures, often stemming from inadequate computational resources on physical hosts, even when the cluster-level resources seem sufficient. While substantial work has gone into managing VMs at the physical host level, there remains a dearth of research under hierarchical data center configurations. To fill the research gap, we address the hierarchy in cloud data centers with a novel two-stage approach named VMM-HHGT, aiming at suppressing VM deployment failures, while balancing the energy consumption and computation resource contention. VMM-HHGT comprises a Hyper-Heuristic-assisted broker (VMM-HH), which can learn the workload patterns and hardware configurations to generate cluster-selection heuristics. An offline training process is incorporated for continuous heuristic evolution with zero overhead on decision-making. Besides, a Game-Theory-assisted hypervisor (GT) is designed for inter-host live VM migration for fine-grained balancing of energy consumption and resource contention. Extensive experiments with traces from real-world VMware data centers show that VMM-HHGT achieves a higher deployment success rate compared to the state-of-the-art approaches, with a well-situated performance in energy consumption and resource contention. • Handling hierarchy in cloud data centers to suppress VM deployment failures. • A two-stage solution for initial VM placement and live migration in the long term. • A Hyper-Heuristic-assisted broker for initial cluster-level VM placement. • A Game-Theory-assisted hypervisor for host-level VM placement and long-term migration. • Effectively suppressed deployment failures with satisfying performance and energy efficiency. [ABSTRACT FROM AUTHOR]

A graph G is two-disjoint-cycle-cover [ r 1 , r 2 ] -pancyclic if for any integer ℓ satisfying r 1 ≤ ℓ ≤ r 2 , there exist two vertex-disjoint cycles C 1 and C 2 in G such that | V (C 1) | = ℓ and | V (C 2) | = | V (G) | − ℓ. The k -dimensional data center network DCell with n port switches and order t k , n , denoted by D k , n , plays an important role in big data cloud computing. In this paper, we study the structural properties of D k , n and show that D k , n is two-disjoint-cycle-cover [ 3 , ⌊ t k , n 2 ⌋ ] -pancyclic for n ≥ 7 and n + k ≥ 11 , that is, for any integer ℓ satisfying 3 ≤ ℓ ≤ ⌊ t k , n 2 ⌋ , there exist two vertex-disjoint cycles C 1 and C 2 such that | V (C 1) | = ℓ and | V (C 2) | = t k , n − ℓ in D k , n. This paper generalizes the results about the Hamiltonicity in (Wang et al., 2015 [25]) and the pancyclicity in (Hao and Tian, 2021 [11]). • It is the first study to consider two-disjoint-cycle-cover pancyclicity in data center networks. • The two-disjoint-cycle-cover pancyclicity of data center network DCell is shown. • This result extends and generalizes the Hamiltonicity and bipancyclicity of DCell. [ABSTRACT FROM AUTHOR]

• A hybrid-integrated 400 GbE ROSA using silica-based AWG and InP-based PD arrays is fabricated, which performs satisfying coupling efficiency between AWG and PD arrays, small signal O/E responses, eye diagrams and sensitivities. The fabricated ROSA has promising prospects to support higher transmission data rate by simply replacing the PD arrays and TIA arrays with higher-speed types. • In this work, a total reflection surface at the end facet of the AWG is used to realize a 90 degrees shift in the optical path, and realize a convenient hybrid integration method. This structure is designed and optimized using FDTD. • Eight-channel O-band LWDM AWG with an improved Rowland circle is designed and fabricated, which performs flat-top spectrum, satisfying uniformity and polarization insensitivity. A silica-based hybrid-integrated receiver optical subassembly (ROSA) for 400 Gbps Ethernet (400GbE) is demonstrated, which consists of an 8-channel silica-based arrayed waveguide grating (AWG), two 4-channel InP-based surface-illuminated photodetector (PD) arrays, two 4-channel transimpedance amplifier (TIA) arrays, and a high-frequency flexible printed circuit (FPC). All lanes of the ROSA exhibit responsivities of over 0.539 A/W, 3-dB bandwidths of greater than 30.86 GHz, and receiver sensitivities of less than −11.7 dBm. The ROSA has achieved a transmission rate of 8 × 50 Gbps for data center interconnect. [ABSTRACT FROM AUTHOR]

In this paper, the dynamic performance of Proton Exchange Membrane Fuel Cell-based Combined Cooling Heating and Power (PEMFC-based CCHP) system combined with a data center real-time load is studied. A novel synergistic control method is proposed to achieve optimization of energy flow between system units under fluctuating load. First, the off-design working condition models of PEMFC-based CCHP system are constructed. The delay in the changes of data center temperature is considered because of the thermal inertia under dynamic conditions. Second, the dynamic performance of PEMFC-based CCHP system to step changes of data center load is analyzed. Third, to further reveal the energy flow between units, the units output under varying load conditions are compared under the constraints of power and refrigeration supply. Finally, the dynamic performance of PEMFC-based CCHP system under the traditional control method of following electric load are revealed. A novel synergistic control method for the PEMFC-based CCHP system is proposed by refining the traditional method to optimize the energy flow between units. The result presents that the average energy efficiency reaches 78.29 %. Compared with the following electric load method, the energy efficiency is increased by 6.02 %. The response time of synergistic control method is 27.0 ms. Improved energy efficiency and fast response have been achieved. • The off-design working condition model of PEMFC-based CCHP system is constructed. • The step response characteristic of PEMFC-based CCHP system is analyzed. • Full working condition performances of PEMFC-based CCHP system are studied. • A novel synergistic control method for the PEMFC-based CCHP system is proposed. [ABSTRACT FROM AUTHOR]