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This article discusses the synthesis of a new P-chiral phospholene core-based ligand called LemPhos. The authors propose a method using a chiral diene to create diastereomers that are easier to separate, allowing for the production of P-chiral ligands. The ligands are synthesized and characterized, and their application as catalysts in asymmetric hydrogenation reactions is explored. The document also includes experimental procedures and spectroscopic data for the synthesized compounds. The research is dedicated to the memory of Stanisław Lem, a renowned science-fiction writer. [Extracted from the article]

The early diagnosis and treatment of foreign body aspiration (FBA) can significantly improve the overall prognosis of children. There are significant differences in the epidemiology and clinical characteristics of FBA in different regions. Therefore, we conducted a real-world study in the western region of China with over 4000 patients. The aim of this study was to improve the understanding of FBA in terms of its types, the specific months of its occurrence, and the distribution of primary caregiver characteristics in western China. We collected the clinical and epidemiological data of children who were diagnosed with FBA in our hospital over the past 20 years through a big data centre. We matched the data of healthy children who underwent routine physical examinations at the paediatric health clinic during the same period to analyse the differences in the data of actual guardians. A total of 4227 patients from five provinces were included in this study. Foreign bodies were removed by rigid bronchoscopy in 99.4% (4202/4227) of patients, with a median age of 19 months and a median surgical duration 16 min. January was the most common month of onset for 1725 patients, followed by February, with 1027 patients. The most common types of foreign objects were melon peanuts, seeds and walnuts, accounting for 47.2%, 15.3%, and 10.2%, respectively. In the FBA group, the proportion of grandparents who were primary caregivers was 70.33% (2973/4227), which was significantly greater than the 63.05% in the healthy group (2665/4227) (P < 0.01). FBA most commonly occurs in January and February. More than 60% of FBAs occur between the ages of 1 and 2 years, and the incidence of FBA may be greater in children who are cared for by grandparents. A rigid bronchoscope can be used to remove most aspirated foreign bodies in a median of 16 min. [ABSTRACT FROM AUTHOR]

A web search, an online video, a connected Nest device, and hundreds of cloud services all give us a response in a fraction of a second. But what really happens when we click search or send a request. The request travels over the public internet and into fiber network. Millions of requests or packets of data travel through miles of cable over land and under sea, converging at one of the many data centers that operate all over the world. Data Center (DC) is the core site of data operation, storage and forwarding, which is also an important part of cloud platform. A large number of commercial switches and servers are usually deployed at the DC. The DC is a complex set of facilities. Data center networks (DCN) is a network applied in the DC, because the traffic in DC presents the typical characteristics of centralized exchange data and increased traffic, which puts forward further requirements for the DCN. DCN connects a large-scale server cluster and is a bridge for data transmission and storage. With the expansion of DC and the increasing number of service types, communication within data centers becomes more frequent. On the other hand, traffic between data centers has also increased dramatically. Considering the multi-layered transmission mode and traffic characteristics of DCN, this paper proposes a software defined networking (SDN) -based multi-layered traffic scheduling scheme for DCN, which is mainly focused on hop count, criticality and cost. Moreover, based on SDN architecture and Deep Q-Network of Reinforcement Learning (RL), the intelligent multi-layered traffic scheduling scheme is proposed to obtain the current optimal global routing strategy according to the real-time traffic demand in the network. The simulation results show that the proposed scheme outperforms benchmarks in terms of average throughput, normalized total throughput, link bandwidth utilization, average round-trip time and network traffic bandwidth loss rate. [ABSTRACT FROM AUTHOR]

With the increasing enrollment of higher education in China, teaching quality evaluation is an urgent problem to be studied. Colleges and universities should also have their own set of teaching quality evaluation system with the evaluation activities, and thus pre-evaluate their teaching and ensure the teaching quality between the two teaching evaluations. At present, big data is more and more widely used, and distributing these big data to corresponding scores through content centric data center networks (CCDCNs) provides Quality of Experience (QoE) for English teaching quality assessment. Therefore, one of the challenges faced by the current network is to enhance QoE under content distribution. In this work, in order to solve this problem, we schedule the cached teaching feature vector to the corresponding score level. Three cache scheduling algorithms are constructed in CCDCNs. Firstly, an approximate dynamic algorithm is proposed, which has high complexity. Then, based on the characteristics of node centralization, we propose an improved approximate dynamic scheduling algorithm. Although the algorithm includes the scheduling of cached content and the scheduling of content transmission rate, it has low complexity in processing scheduling. In addition, we propose a cache scheduling algorithm based on deep reinforcement learning (DRL). Although the algorithm has high complexity, the scheduling accuracy is also high. Experiments show that the method proposed in this work can obtain higher QoE, and excellent performance in English teaching evaluation, about 7.8% improvement degree. [ABSTRACT FROM AUTHOR]

To face with the surge of cloud computing services, energy problem and carbon mission constraint, more and more geo-distributed hybrid powered data centers are build over the world. Geo-distributed hybrid powered data centers are located in different locations equipped with different types of green energy source. In this work, we propose a periodic requests dispatcher, including a periodic model and a spatial balance algorithm, to minimize the brown energy use. The periodic model divides the long-term period into several time slots, and the spatial balance algorithm achieves energy optimization by balancing user requests in each time slot. Besides, a research case is simulated by Cloudsim platform. We conduct several comparative analysis in aspect of energy utilization, electricity cost and carbon emission. The experiment results show that the dispatcher can effectively reduce the electricity cost, and reasonably achieve the request distribution balance in geo-distributed hybrid powered data centers. [ABSTRACT FROM AUTHOR]

Edge computing serves as a critical intermediary for secure data exchange between IoT devices and data centres in healthcare, where the protection of sensitive patient data is paramount. This study presents UdesMec, a comprehensive edge computing framework designed for efficient and secure data storage and processing in such contexts. Focusing on the challenge of implementing robust data security models in the presence of limited resources at edge nodes, UdesMec employs a unified encryption approach that is particularly suited for expert knowledge-based prediction of complex critical medical events. Using secret sharing and homomorphic encryption, it ensures the confidentiality and integrity of sensitive patient data transmitted by IoT devices, while enabling efficient computation of prediction algorithms on a cloud server. The experimental evaluation confirms the strong encryption performance and the ease of use of UdesMec, positioning it as a promising solution for the secure and reliable prediction of complex critical medical events in healthcare systems based on edge computing. [ABSTRACT FROM AUTHOR]

This paper examines the use of an Uninterruptible Power Supply (UPS) to enhance the operational efficiency of data centers. It focuses on developing an optimal energy scheduling strategy for a data center equipped with UPS, using the Markov decision process (MDP) framework. The MDP framework simulates the decision-making process involved in minimizing energy costs. Each unit's available power output in the data center is treated as a Markov state, taking into account the uncertainty associated with renewable distributed generation. This uncertainty drives the system to transition to other Markov states in subsequent decision times. A recursive optimization model is established for each Markov state at each decision time to guide state-based operations, which includes determining the unit output while considering both current and future costs. The challenge of dealing with high dimensionality, arising from a substantial number of states and actions in the model, is effectively addressed by adopting an approximate dynamic programming (ADP) method. This approach incorporates decision-state and forwards dynamic algorithms to tackle the complexity of the MDP-based model. By employing ADP, the computational burden is reduced, enabling efficient and practical solutions to be obtained. [ABSTRACT FROM AUTHOR]

This article discusses the synthesis and characterization of a thermally stable monoruthenium acetylide radical species. The researchers examined the reactivity and properties of metal acetylide complexes, specifically focusing on mononuclear ruthenium acetylide radicals. They found that the choice of metal center and ligands significantly influenced the reactivity of these complexes. The researchers were able to isolate and characterize a mononuclear ruthenium acetylide radical species, which exhibited high thermal stability. This study provides insights into the design strategy for controlling the reactivity of radicals in metal complexes. [Extracted from the article]

This article, published in the journal Synlett, discusses a study on the development of a rhodium-catalyzed direct vinylene annulation reaction. The researchers focused on using vinyl selenones as vinylene-transfer reagents and successfully demonstrated a three-component reaction to construct isoquinolone scaffolds. The article includes a table showing the optimization of reaction conditions. The study also explored the scope and limitations of the reaction system, proposing a mechanism for the coupling reaction and concluding that further optimization is needed. [Extracted from the article]

With the growth of the cloud computing market, the number and scale of cloud data centers are expanding rapidly. While cloud data centers provide a large amount of computing power, generating tremendous energy consumption has become a fundamental issue in the financial and environmental fields. Improving quality of service and reducing energy costs are fundamental challenges for next-generation cloud data centers. Task scheduling in cloud data centers grows increasingly complex due to the heterogeneity of computing resources, intricate dependencies of jobs and rising expenses resulting from high energy consumption. Efficiently utilizing computing resources is crucial, so it is necessary to develop optimal strategies for job scheduling. This paper proposes a reinforcement learning-based task scheduler (E2DSched) for online scheduling of randomly arriving directed acyclic graph jobs in cloud data centers. E2DSched divides the scheduling process into three layers: task selection layer, server selection layer and frequency control layer. It achieves joint optimization of energy consumption and quality of service through three-layer cooperation. Finally, we compare E2DSched with various other algorithms, and the results show that E2DSched can provide excellent service with less energy consumption. [ABSTRACT FROM AUTHOR]

The storage requirement for distributed tracing can be reduced significantly by sampling only the anomalous or interesting traces that occur rarely at runtime. In this paper, we introduce an unsupervised sampling pipeline for distributed tracing that ensures high sampling accuracy while reducing the storage requirement. The proposed method, SampleHST-X, extends our recent work SampleHST. It operates based on a budget which limits the percentage of traces to be sampled while adjusting the storage quota of normal and anomalous traces depending on the size of this budget. The sampling process relies on accurately defining clusters of normal and anomalous traces by leveraging the distribution of mass scores, which characterize the probability of observing different traces, obtained from a forest of Half Space Trees (HST). In our experiments, using traces from a cloud data center, SampleHST yields 2.3 × to 9.5 × better sampling performance. SampleHST-X further extends the SampleHST approach by incorporating a novel class of Half Space Trees, namely Approximate HST, that uses approximate counters to update the mass scores. These counters significantly reduces the space requirement for HST while the sampling performance remains similar. In addition to this extension, SampleHST-X includes a Family of Graph Spectral Distances (FGSD) based trace characterization component, which, in addition to point anomalies, enables it to sample traces with collective anomalies. For such traces, we observe that the SampleHST-X approach can yield 1.2 × to 19 × better sampling performance. [ABSTRACT FROM AUTHOR]

Temperature and wind observations from aircraft are regarded of major importance for aviation meteorology and numerical weather prediction (NWP). The European Meteorological Aircraft Derived Data Center (EMADDC) system processes aircraft surveillance data received from air traffic control (ATC) and converts it into observations of wind and temperature. Only so-called Mode-S Enhanced Surveillance data can be used, because this data contains the air vector and ground vector of the aircraft, from which a wind vector can be inferred. To acquire high quality observations, the data is processed in three steps: pre-processing, processing, and post-processing. The pre-processing is needed to obtain high-quality information and to calculate several correction values such as temperature corrections and heading corrections. Processing converts the aircraft data into meteorological information and finally post-processing guarantees that only high-quality information is made available. The quality of the observations produced is verified by comparing these observation to other upper air wind and temperature observations from radiosondes and comparing them with NWP data. This paper presents the EMADDC system, operational since 2019. [ABSTRACT FROM AUTHOR]

Airborne measurements are pivotal for providing detailed, spatiotemporally resolved information about atmospheric parameters, and aerosol and cloud properties, thereby enhancing our understanding of dynamic atmospheric processes. For 30 years, the U.S. Department of Energy (DOE) Office of Science supported an instrumented Gulfstream-1 (G-1) aircraft for atmospheric field campaigns. Data from the final decade of G-1 operations were archived by the Atmospheric Radiation Measurement (ARM) user facility Data Center and made publicly available at no cost to all registered users. To ensure a consistent data format and to improve the accessibility of the ARM airborne data, an integrated dataset was recently developed covering the final six years of G-1 operations (2013 to 2018). The integrated dataset includes data collected from 236 flights (766.4 hours), which covered the Arctic, the U.S. Southern Great Plains (SGP), the U.S. West Coast, the Eastern North Atlantic (ENA), the Amazon Basin in Brazil, and the Sierras de Córdoba range in Argentina. These comprehensive data streams provide much-needed insight into spatiotemporal variability of thermodynamic quantities, aerosol and cloud states and properties for addressing essential science questions in Earth system process studies. This manuscript describes the DOE ARM merged G-1 datasets, including information on the acquisition, collection, and quality control processes. It further illustrates the usage of this merged dataset to evaluate the Energy Exascale Earth System Model (E3SM) with the Earth System Model Aerosol-Cloud Diagnostics (ESMAC Diags) package. [ABSTRACT FROM AUTHOR]

This article explores the potential use of molybdenum as a catalyst in organic synthesis, specifically in cross-coupling reactions. The researchers discovered that a molybdenum species, created by reducing a molybdenum precursor with phenylmagnesium bromide, can activate carbon-halogen bonds and produce a cyclized dimerization product. The study highlights the potential of molybdenum catalysts in sustainable catalysis and their applications in cross-coupling reactions of aryl halides. The article also mentions the potential bioactivity of the resulting phthalan derivatives. [Extracted from the article]

This article discusses the synthesis of α-aryl carbonyl compounds using palladium catalysis. The authors focus on the deprotonative α-arylation of Meyers's chiral bicyclic lactams (MCBLs) as a potential method for synthesizing these compounds. They explore various bases and catalytic systems to optimize the reaction conditions and achieve high yields. The authors also investigate the reaction scope by testing different substrates and bridgehead substituents. The results suggest that steric effects play a crucial role in determining the reactivity of the substrates. The authors propose a plausible mechanism for the formation of the arylated lactam products. Overall, this study provides valuable insights into the synthesis of α-aryl carbonyl compounds and highlights the potential of MCBLs as versatile building blocks. [Extracted from the article]

This document provides information on the synthesis and characterization of various thiochromeno[2,3-c]pyrrole derivatives. The compounds were synthesized using specific reactions and their structures were confirmed using spectroscopic techniques. Crystallographic analysis was also performed on one of the compounds. The document includes detailed information on the synthesis, characterization, and analysis of each compound. [Extracted from the article]

Cloud data centers play a vital role in modern computing infrastructure, offering scalable resources for diverse applications. However, managing costs and resources efficiently in these centers has become a crucial concern due to the exponential growth of cloud computing. User applications exhibit complex behavior, leading to fluctuations in system performance and increased power usage. To tackle these obstacles, we introduce the Modified Feeding Birds Algorithm (ModAFBA) as an innovative solution for virtual machine (VM) consolidation in cloud environments. The primary objective is to enhance resource management and operational efficiency in cloud data centers. ModAFBA incorporates adaptive position update rules and strategies specifically designed to minimize VM migrations, addressing the unique challenges of VM consolidation. The experimental findings demonstrated substantial improvements in key performance metrics. Specifically, the ModAFBA method exhibited significant enhancements in energy usage, SLA compliance, and the number of VM migrations compared to benchmark algorithms such as TOPSIS, SVMP, and PVMP methods. Notably, the ModAFBA method achieved reductions in energy usage of 49.16%, 55.76%, and 65.13% compared to the TOPSIS, SVMP, and PVMP methods, respectively. Moreover, the ModAFBA method resulted in decreases of around 83.80%, 22.65%, and 89.82% in the quantity of VM migrations in contrast to the aforementioned benchmark techniques. The results demonstrate that ModAFBA outperforms these benchmarks by significantly reducing energy consumption, operational costs, and SLA violations. These findings highlight the effectiveness of ModAFBA in optimizing VM placement and consolidation, offering a robust and scalable approach to improving the performance and sustainability of cloud data centers. [ABSTRACT FROM AUTHOR]

The feedback of vegetation on cloud cover is an important link in the global water cycle. However, the relative importance of vegetation and related factors (surface properties, heat fluxes, and environmental conditions) on cloud cover in the context of greening remains unclear. Combining the Global Land Surface Satellite (GLASS) leaf area index (LAI) product and the fifth-generation reanalysis data of the European Centre for Medium-Range Weather Forecasts (ERA5), we quantified the relative contribution of vegetation and related factors to total cloud cover (TCC) in typical regions (Eastern European Plain, Western Siberian Plain, Mongolian Plateau, and Northeastern China Plain) of Eurasia over 21 years, and investigated how vegetation moderated the contribution of the other factors. Here, we show that the relative contribution of different factors to TCC was closely related to the climate and vegetation characteristics. In energy-limited (moisture-limited) areas, temperature (relative humidity) was more likely to be the factor that strongly contributed to TCC variation. Except for sparsely vegetated ecosystems, the relative contribution of LAI to TCC was stable within a range of 8–13%. The case study also shows that vegetation significantly modulated the contribution of other factors on TCC, but the degree of the regulation varied among different ecosystems. Our results highlight the important influence of vegetation on cloud cover during greening, especially the moderating role of vegetation on the contribution of other factors. [ABSTRACT FROM AUTHOR]

This article discusses the synthesis and reactivity of a new sulfonium salt that can transfer a sulfonitrene unit to C=C double bonds. The salt was synthesized on a large scale and is stable in air and water. It was used in two different transformations, expanding indenes to isoquinolines and contracting arylcyclobutenes to cyanocyclopropanes. The reagent is versatile and can tolerate various functional groups, making it a useful tool in skeletal editing. The authors acknowledge funding and provide supporting information for further details. [Extracted from the article]

Cloud data centers present a challenge to environmental sustainability because of their significant energy consumption. Additionally, performance degradation resulting from energy management solutions, such as virtual machine (VM) consolidation, impacts service level agreements (SLAs) between cloud service providers and users. Thus, to achieve a balance between efficient energy consumption and avoiding SLA violations, we propose a novel VM consolidation algorithm. Conventional algorithms result in unnecessary migrations when improperly selecting VMs. Therefore, our proposed E2SVM algorithm addresses this issue by selecting VMs with high load fluctuations and minimal resource usage from overloaded servers. These selected VMs are then placed on normally loaded servers, considering their stability index. Moreover, our approach prevents server underutilization by either applying all or no VM migrations. Simulation results demonstrate a 12.9% decrease in maximum energy consumption compared with the minimum migration time VM selection policy. In addition, a 47% reduction in SLA violations was observed when using the medium absolute deviation as the overload detection policy. Therefore, this approach holds promise for real-world data centers because it minimizes energy waste and maintains low SLA violations. [ABSTRACT FROM AUTHOR]

This article discusses the synthesis and characterization of heteroleptic coordination cages using cobalt as the metal center. The authors describe the rational combination of ligands to create three-dimensional cage structures and explore the self-sorting behavior of different ligands in the formation of these cages. They also perform calculations to understand the relative energies of different cage formations. The authors successfully synthesize a target compound and confirm its structure through various analytical techniques. The article provides detailed information on the synthesis and characterization of these cages, highlighting their potential applications in enzymatic catalysis and molecular machines. [Extracted from the article]

This article explores the synthesis and reactivity of germaaluminocene compounds, which are complexes containing germanium and aluminum atoms. The behavior of these compounds is determined by the interaction between the electrophilic aluminum atom and the nucleophilic germanium center. The article describes the synthesis of specific germaaluminocene compounds and their characterization using various analytical techniques. It also discusses the chemical reactions and structures of these compounds, as well as their reactivity with other elements and transition metal complexes. The findings provide valuable insights into the properties and behavior of germaaluminocene compounds. This research was supported by the Deutsche Forschungsgemeinschaft. [Extracted from the article]

This article discusses the metal-free synthesis of trifluoromethyl carbinol-containing imidazo[1,2-a]pyridines through dehydrative coupling. Trifluoromethyl-containing compounds are important in various fields due to their biological activity and stability. Imidazo[1,2-a]pyridines are commonly found in pharmaceuticals, and the construction of CF3-containing imidazo[1,2-a]pyridines is valuable for drug discovery. The use of 1,1,1,3,3,3-hexafluoro-2-propanol (HFIP) as a medium for organic synthesis has been explored, and it has been found to promote the hydroxy-trifluoromethylation of imidazo[1,2-a]pyridines with trifluoroacetaldehyde. The optimized reaction conditions using HFIP as a solvent resulted in good yields of the desired products. The substrate scope of this process was also investigated, demonstrating its generality and practicality. This article describes a novel approach for the synthesis of trifluoromethyl carbinol-containing imidazo[1,2-a]pyridines using imidazo[1,2-a]pyridines and trifluoroacetaldehyde via a dehydrative cross-coupling process. The reaction was promoted by HFIP and exhibited wide substrate generality, excellent functional group tolerance, and mild reaction conditions. [Extracted from the article]

In recent times, there has been a noticeable surge in the inclination towards the implementation of network automation solutions. This trend is driven by the objective of optimizing network availability within the context of hybrid data center networks, which are becoming increasingly prevalent. Reliability, performance, scalability, and minimal resource overhead are crucial solution design characteristics that significantly impact the decisionmaking process regarding adoption. Recent research has shown that 90% of network outages happen because of human factors and 69% of respondents manage their network with manual method. That high human error of up to 90%, occurs when configuring network devices using manual method. These problems had a negative impact on the functioning of the organization and were harmful to the user. In this research, we investigate solutions to reduce network outage that caused human error using network automation using the Python programming language and multi-threading method. This network automation will reduce configuration time, eliminate human error, and significantly increase efficiency. The aim of this research is to investigate the efficacy of network automation using Python and multi-threading to reduce network outages.The method used in this research is a parallel or multi-tread execution process method using GNS3 as network simulator. Based on experimental results, the multithread automation approach is significantly faster than both the serial automation method (67 seconds) and the manual method (248 seconds), this method requiring only 41 seconds to configure all Cisco router devices. If you're looking for speed, look no farther than the multithread automation approach, which is 6 times quicker than the manual method and 3.7 times quicker than the serial automation approach. The findings of this study have substantial and immediate implications for the ability of network engineers to speed up the configuration of networks and lower the rate of human error in doing so. [ABSTRACT FROM AUTHOR]

Background: The Korean Endocrine Hormone Reference Standard Data Center (KEHRS DC) has created reference standards (RSs) for endocrine hormones since 2020. This study is the first of its kind, wherein the KEHRS DC established RSs for serum Cpeptide levels in a healthy Korean population. Methods: Healthy Korean adults were recruited from May 2021 to September 2023. After excluding participants according to our criteria, serum samples were collected; each participant could then choose between fasting glucose only or fasting glucose plus an oral glucose tolerance test (OGTT). If their sample showed high glucose (=100 mg/dL) or hemoglobin A1c (HbA1c) (≥5.70%), their C-peptide levels were excluded from analyzing the RSs. Results: A total of 1,532 participants were recruited; however, only the data of 1,050 participants were analyzed after excluding those whose samples showed hyperglycemia or high HbA1c. Post-30-minute OGTT data from 342 subjects and post-120-minute OGTT data from 351 subjects were used. The means±2 standard deviations and expanded uncertainties of fasting, post-30-minute and 120-minute OGTT C-peptide levels were 1.26±0.82 and 0.34-3.18, 4.74±3.57 and 1.14-8.33, and 4.85±3.58 and 1.25-8.34 ng/mL, respectively. Serum C-peptide levels correlated with obesity, serum glucose levels, and HbA1c levels. Conclusion: The RSs for serum C-peptide levels established in this study are expected to be useful in both clinical and related fields. [ABSTRACT FROM AUTHOR]

This article proposes a novel battery management system (BMS) to ensure uninterruptible power delivery to a 48 V DC bus used for electric vehicle charging stations, data centers, telecommunication systems, and critical care units such as hospitals. The proposed BMS facilitates constant current and constant voltage charging to maintain optimal battery performance during normal operation. This BMS is designed for effective control, monitoring and protection of two lead‐acid battery units to form battery energy storage system (BESS). Furthermore, it is capable of isolating batteries in abnormal conditions and operates them independently to provide reliable supply at output terminals with full capacity. The system utilizes a 30 V DC source derived from AC mains or solar photovoltaic system. This supply is used to charge the BESS and also supply to the load. In the event of failure of 30 V supply, it seamlessly transits to BESS mode to supply power to boost converter to maintain constant 48 V DC output at load terminal. The proposed system architecture not only enhances power reliability but also improves overall system efficiency, making it well‐suited for critical applications require continuous and stable power supply. Simulation studies using Matlab/Simulink and analytical results using TINA (Tool kit for Interactive Network Analysis) are presented to show that 48 V DC supply is maintained at output terminals during failure of input 30 V DC source or failure of one battery unit. [ABSTRACT FROM AUTHOR]

Cloud data centers face various challenges, such as high energy consumption, environmental impact, and quality of service (QoS) requirements. Dynamic virtual machine (VM) consolidation is an effective approach to address these challenges, but it is a complex optimization problem that involves trade-offs between energy efficiency and QoS satisfaction. Moreover, the workload patterns in cloud data centers are often non-stationary and unpredictable, which makes it difficult to model them. In this paper, we propose a new method for dynamic VM consolidation that optimizes both energy efficiency and QoS objectives. Our approach is based on Markov chains and the artificial feeding birds (AFB) algorithm. Markov chains are used to model the resource utilization of each individual VM and PM based on the changes that happen in workload data. AFB algorithm is a metaheuristic optimization technique that mimics the behavior of birds in nature. We modify the AFB algorithm to suit the characteristics of the VM placement problem and to provide QoS-aware and energy-efficient solutions. Our approach also employs an online step detection method to capture variations in workload patterns. Furthermore, we introduce a new policy for VM selection from overloaded hosts, which considers the abrupt changes in the utilization processes of the VMs. The proposed algorithms are evaluated extensively using the CloudSim Toolkit with real workload data. The proposed system outperforms evaluation policies in multiple metrics, including energy consumption, SLA violations, and other essential metrics. [ABSTRACT FROM AUTHOR]

Nowadays, with the rapid expansion of cloud computing technology in processing Internet of Things (IoT) workloads, the demand for data centers has significantly increased, leading to a surge in CO2 emissions, power consumption, and global warming. As a result, extensive research and initiatives have been undertaken to tackle this problem. Two specific approaches focus on enhancing workload scheduling, a complex problem known as NP-Hard, and integrating scheduling into scientific workflows. In this investigation, we present a multi-objective Crow Search Algorithm (CSA) for optimizing both makespan and costs in scientific cloud workflows (CSAMOMC). We conduct a comparative analysis between our approach and the well-known HEFT and TC3pop algorithms, which are commonly used for reducing makespan and optimizing costs. Our findings demonstrate that CSAMOMC is capable of achieving an average makespan reduction of 4.42% and a cost reduction of 4.77% when compared to the aforementioned algorithms. [ABSTRACT FROM AUTHOR]

Cooling systems provide a safe thermal environment for the reliable operation of IT equipment in data centers (DCs) while generating significant energy consumption. Therefore, to achieve energy savings in cooling system control under dynamic thermal distribution in DCs, this paper proposes a multi-setpoint cooling control approach based on deep reinforcement learning (DRL). Firstly, a thermal model based on the XGBoost algorithm is constructed to precisely evaluate the thermal distribution in the rack room to guide real-time cooling control. Secondly, a multi-set point cooling control approach based on the deep Q-network algorithm (DQN-MSP) is designed to finely regulate the supply air temperature of each air conditioner by capturing the thermal fluctuations to ensure the dynamic balance of cooling supply and demand. Finally, we adopt the extended CloudSimPy simulation tool and the real workload trace of the PlanetLab system to evaluate the effectiveness and performance of the proposed approach. The simulation results show that the proposed control solution effectively reduces the cooling energy consumption by over 2.4% by raising the average air supply temperature of the air conditioner while satisfying the thermal constraints. [ABSTRACT FROM AUTHOR]

Air cooling systems are widely used in current data centers owing to their low capital costs and high reliability. To satisfy the increasing rack power density, the optimal air-cooling technology and an economic analysis should be carefully discussed. Therefore, this study discusses four airflow management technologies: Case 1: raised floor and cold aisle containment supply/computer room air conditioning (CRAC) direct return; Case 2: CRAC direct supply/hot aisle containment (HAC) return; Case 3: overhead duct supply/CRAC direct return; and Case 4: overhead duct supply/HAC return. Using a validated model, the thermal and economic performances of each case were compared. Results showed that Case 4 exhibited the best thermal performance, followed by Cases 3, 2, and 1. Case 1 cannot satisfy the heat dissipation requirement when the rack power density is larger than 12.5 kW; whereas only Case 4 can be used when the power density is larger than 15 kW. Regarding location within China, owing to the high ambient temperature, Shenzhen showed the highest annual cost value and power usage effectiveness, followed by Shanghai, Xi'an, Beijing, and Harbin. Finally, Cases 3 and 4 are recommended for application when the rack power density is greater than 10 kW. [ABSTRACT FROM AUTHOR]

Virtualization technologies are extensively utilized in data centers, particularly cloud computing. This facilitates data center management and diminishes the number of physical machines (servers) and, subsequently, their cooling requirements, leading to cost, space, and power consumption reductions. When applications in data centers are executing independent parallel transactions, but with similar performance requirements, the appropriate level of virtual machine consolidation on a server poses a fundamental challenge for capacity planning. This article introduces a method to evaluate the performance speedup achieved through virtualization on any server and the effects of virtualization and consolidation overheads on physical or virtual machine scalability. This research formalizes the speedup and overheads, using classical computer architecture statements. but at the same time proposes a new method to analyze these overhead amounts and types, showing the scalability and efficiency of different consolidations in the same server and its comparison against no consolidation. This work also proposes a new way to determine the optimal number of physical servers and the optimal number of consolidated virtual machines for a given transaction workload. The real experimentation was performed with different workload sizes, types of virtualizations and different servers. The method presented also facilitates the representation of linear scalability against the real degree of parallelism of either physical machines or consolidated virtual machines for a given transaction workload, as well as striking the right balance between speedup and energy in virtual server consolidation. [ABSTRACT FROM AUTHOR]

While cloud computing is crucial in processing data from devices with low computational power, the latency introduced by the Internet backhaul limits real-time applications. By situating computing resources at the network's edge, edge computing offers low-latency services by offloading computations from high-performance computing (HPC) data centers to the edge servers, reducing wide Area network (WAN) strain. As a result, edge computing has unlocked opportunities for innovative applications that were previously unfeasible, such as connected vehicles or medical robotics. Nonetheless, deploying the infrastructure required to support edge computing services raises sustainability and energy consumption concerns. Consequently, the development of tools enabling researchers to explore innovative approaches to reducing the energy impact of edge computing is crucial. In this work, we present MintEDGE, a network simulator focused on the energy consumption of edge computing. Our simulator allows testing energy-saving approaches and task placement algorithms in realistic large-scale scenarios encompassing entire regions. [ABSTRACT FROM AUTHOR]

Venn diagrams serve as invaluable tools for visualizing set relationships due to their ease of interpretation. Widely applied across diverse disciplines such as metabolomics, genomics, transcriptomics, and proteomics, their utility is undeniable. However, the operational complexity has been compounded by the absence of standardized data formats and the need to switch between various platforms for generating different Venn diagrams. To address these challenges, we introduce the EVenn platform, a versatile tool offering a unified interface for efficient data exploration and visualization of diverse Venn diagrams. EVenn (http://www.ehbio.com/test/venn) streamlines the data upload process with a standardized format, enhancing the capabilities for multimodule analysis. This comprehensive protocol outlines various applications of EVenn, featuring representative results of multiple Venn diagrams, data uploads in the centralized data center, and step‐by‐step case demonstrations. Through these functionalities, EVenn emerges as a valuable and user‐friendly tool for the in‐depth exploration of multiomics data. Highlights: Comprehensive Venn functionality: EVenn introduces a unified platform with diverse Venn diagram tools, from interactive diagrams to network representations, catering to the intricate needs of multiomics data analysis across metabolomics, genomics, transcriptomics, and proteomics.Efficient data exploration: EVenn's data center streamlines exploration by supporting a standardized data format, allowing researchers to effortlessly upload and analyze data. The manuscript demonstrates its practical utility through representative results and detailed case demonstrations.User‐friendly interface: With a user‐friendly interface, EVenn simplifies the generation of various Venn diagrams, Euler diagrams, UpSet plots, and more. This protocol establishes EVenn as a valuable resource for researchers seeking a cohesive and accessible tool for multiomics data interpretation. [ABSTRACT FROM AUTHOR]

Geographically distributed cloud data centers (DCs) consume enormous amounts of energy to meet the ever-increasing processing and storage demands of users. The brown energy generated using fossil fuels is expensive and significantly contributes to global warming. Considering the environmental impact caused by the high carbon emissions and relatively high energy cost of brown energy, we propose the integration of renewable energy sources (RES), especially solar and wind energy, with brown energy to power cloud data centers. In our earlier study, we addressed the intermittency of renewable energy sources, where we replaced the random initialization of artificial neural network (ANN) edge weights with the harmony search algorithm (HSA)-optimized assignment of weights. This study incorporated reliably forecast solar and wind energy into the input parameters of our proposed green energy manager (GEM), for cost minimization, carbon emission minimization, and better energy management of cloud DCs, to make our current study more reliable and trustworthy. Four power sources, on-site solar energy and wind energy, off-site solar energy and wind energy, energy stored in energy storage devices, and brown energy, were considered in this study and simulations were carried out for three different cases. The simulation results showed that case 1 (all brown) was 58% more expensive and caused 71% higher carbon emissions than case 2.1 (cost minimization). Case 1 (all brown) was 39% more expensive and had 80% higher carbon emissions than case 2.2 (carbon emission minimization). The simulation results justify the necessity and importance of the GEM, and finally the results proved that our proposed GEM is less expensive and more environmentally friendly. [ABSTRACT FROM AUTHOR]

This article presents a case study on the decomposition reactions of N-heterocyclic carbenes (NHCs) with saturated backbones in the coordination sphere of alanes. The study challenges the common assumption that NHCs are inert spectators and explores their reactivity with different Lewis acids and carbene types. The text discusses the synthesis of various compounds using different routes and analyzes the reactions and products using spectroscopy techniques. The article also discusses the formation of ringer-expanded compounds and the role of different NHC ligands in the reactions. The study provides insights into the reactivity of these compounds and their potential applications. The crystallographic data for the compounds discussed in the study are available for further reference. The research was supported by the Julius-Maximilians-Universität Würzburg and the Deutsche Forschungsgemeinschaft. [Extracted from the article]

The article discusses the synthesis and characterization of a new compound, Na2C3O2, which contains an unusual Y-shaped −C≡C−COO− anion. The compound was obtained through a reaction using sodium electride or NaC2H and the sodium salt of propargyl acid in liquid ammonia. The structure of Na2C3O2 was determined using synchrotron powder diffraction data and confirmed through spectroscopic analysis. The compound exhibits coordination of the anion to the sodium cation through both its carboxylate group and its negatively charged acetylide unit. The article provides detailed information on the synthesis, structure, and spectroscopic characterization of Na2C3O2. The authors acknowledge the assistance of various individuals and organizations in conducting the research. This report, authored by Sebastian, Renée Siegel, Jürgen Senker, Jan Hempelmann, Richard Dronskowski, and Uwe Ruschewitz, provides valuable insights into a specific topic and is a useful resource for library patrons conducting research on this subject. [Extracted from the article]

The article describes the bypassing of ammonia in the synthesis of nitrogen-containing heterocycles. Diboradiazen compounds, derived from boron-mediated activation of dinitrogen (N2), were used. New heterocyclic compounds, which are BN-isosteres of thiophene and 1,3-oxazole, were formed through reactions with sulfur and acetic anhydride. Two new heterocyclic compounds, 1-Dur and 1-Mes, were synthesized and complete the synthesis routes. The structures of the compounds exhibit certain features of aromaticity. The study emphasizes the potential benefits of main group element-mediated N2 reductions. The project was funded by various organizations and there are no conflicts of interest. [Extracted from the article]

The article describes the synthesis of diborane(6)-dianions decorated with weakly coordinating B(C6F5)3- and SiEt3+ groups. Two compounds were isolated, which demonstrate the synthetic potential and chemical versatility of the dianions. The functionalization of the dianions is of interest for stabilizing reactive dications and modulating the properties of salts with reactive cations. The thermal stability, bond lengths, and electronic properties of the compounds were investigated and confirmed by spectroscopic and electrochemical measurements. The results show that the functionalization of the diborane(6)-dianions leads to new properties. [Extracted from the article]

The "Yangtze River Economic Belt Development Strategy" is one of China's three major national development strategies. Enhancing the protection and quality of arable land in the Yangtze River Economic Belt (YEB) is pivotal for fostering regional growth. In this study, land use data spanning the years 1980 to 2020 in the YEB were extracted from the national land use database maintained by the Resource and Environment Data Center of the Chinese Academy of Sciences. Employing Geographic Information System (GIS) spatial analysis techniques and arable land change metrics, the study delineated the spatiotemporal characteristics of arable land alterations across the YEB for the period. Additionally, using grain output data at the prefecture level from 2011 to 2020, the paper calculated provincial grain output to analyze the impact of arable land changes over the last four decades on grain output. The findings revealed that: (1) From 1980 to 2020, the total arable land area in the YEB decreased by approximately 41,775 square kilometers, with the most significant decrease occurring in the downstream region. (2) From 1980 to 1990, the primary factor contributing to the decrease in arable land area was the expansion of water bodies, while from 1990 to 2020, the principal reason for the reduction in arable land area was the expansion of construction land. (3) From 1980 to 2020, the decrease in arable land area resulted in a net reduction of approximately 25.12 million tons in total grain output, with the largest decline observed in the downstream regions and the smallest decline in the upstream regions. (4) Consistent with the trends in arable land area reduction, the main reason for the decline in grain output from 1980 to 1990 was the expansion of water bodies encroaching upon arable land, whereas from 2000 to 2010, the primary cause of arable land reduction was the expansion of construction land areas. In conclusion, the research suggested that over the past four decades, the primary driver behind the reduction in arable land within the YEB has been the expansion of construction land areas. Particularly noteworthy was the period from 2000 to 2010, during which the impact of arable land reduction on grain output was most pronounced. This period coincided with the rapid economic development and accelerated urbanization process within the YEB. [ABSTRACT FROM AUTHOR]

Wir berichten über die Synthese und Charakterisierung einer Reihe von weitgehend luftstabilen Diorganyl‐bis(pyridylimino)isoindolid(BPI)−Aluminiumkomplexen und deren Reaktivität durch elektronische Anregung mittels sichtbarem Licht. Der redox‐aktive, "nicht‐unschuldige" BPI‐Pinzettenligand ermöglicht eine effiziente Charge‐Transfer‐Homolyse der Al−C‐Bindung. Dadurch dienen die vorgestellten Verbindungen als Ausgangsmaterialien für die Generierung von kohlenstoffzentrierten Radikalen. Die photo‐induzierte homolytische Spaltung der Al−C‐Bindung wurde mittels stationärer und transienter UV/Vis‐Spektroskopie, Abfangexperimenten der entsprechenden Radikale, sowie EPR‐ und NMR‐Spektroskopie analysiert und zudem mithilfe von quantenchemischen Rechnungen untersucht. Studien zur Reaktivität der entsprechenden Aluminiumkomplexe ermöglichten deren Nutzung als Reagenzien in Giese‐artigen Reaktionen und der Alkylierung von organischen Carbonylverbindungen, wobei Hinweise auf einen Wechsel von radikalischen zu polaren Mechanismen (radical‐polar‐crossover) gefunden wurden. Durch diese Arbeit wurde ein vertieftes Verständnis der physikalischen Hintergründe und der ablaufenden photochemischen Prozesse generiert, wodurch eine neue Strategie der Reaktionskontrolle von luftstabilen Organometallverbindungen des Aluminiums etabliert wurde. [ABSTRACT FROM AUTHOR]

Salzmetathesereaktionen zwischen Dikaliumgermacyclopentadiendiiden und Aluminium(III)dichloriden ermöglichen die Synthese von Alumol‐Halbsandwichkomplexen des zweiwertigen Germaniums oder von Aluminylen‐Germol‐Komplexen. Deren Molekülstrukturen und ihre delokalisierte Bindungssituation, wie sie durch Dichtefunktionalrechnungen (DFT) belegt wird, erlauben gleichermaßen ihre Beschreibung als isomere Aluminagerma[5]pyramidane mit entweder dem Germanium‐ oder dem Aluminiumatom in der apikalen Position der pentagonalen Pyramide. Die Produktbildung und die Selektivität der Reaktion hängen von dem dritten Substituenten des Aluminiumdichlorids ab. Arylgruppen begünstigen die Bildung von Alumolkomplexen und der Cp*‐Substituent die der isomeren Germolkomplexe. Mit Aminosubstituenten am Aluminiumatom werden Mischungen beider Isomere erhalten, wobei der Austausch der Positionen der beiden Heteroatome im Pyramidan durch NMR Spektroskopie nachgewiesen wurde. Zudem können, ausgehend von Alumolkomplexen des zweiwertigen Germaniums, durch reduktive Eliminierung des Germaniums die entsprechenden Alumole erhalten werden. [ABSTRACT FROM AUTHOR]

Eine allgemeine, intramolekulare Zykloisomerisierung nichtaktivierter Olefine mit Nukleophilen wird beschrieben. Die Reaktion erfolgt unter milden Bedingungen und toleriert Ether, Ester, geschützte Amine, Acetale, Pyrazole, Carbamate und Arene. Sie ist zugänglich für N‐, O‐ und C‐Nukleophile, wobei eine Reihe verschiedener Heterozyklen gebildet werden kann, darunter Pyrrolidine, Piperidine, Oxazolidinone und Lactone. Durch die Kombination eines Benzothiazinoquinoxalins als Organophotokatalysator und eines Cobaltsalen‐Katalysators sind keine stöchiometrischen Oxidations‐ oder Reduktionsmittel erforderlich. Wir zeigen den Nutzen des Protokolls für die Diversifizierung von Arzneimitteln sowie die Synthese mehrerer kleiner Naturstoffe. [ABSTRACT FROM AUTHOR]

Helikale Nanographene (NG) mit hohen Quantenausbeuten und starken chiroptischen Antworten sind entscheidend für die Entwicklung von lumineszenten Materialien mit zirkularer Polarisation (engl. circular polarized luminescence, CPL). Hier berichten wir von der erfolgreichen Synthese neuartiger π‐erweiterter Doppel‐[7]Helicene (ED7Hs), bei denen sich zwei Helicen‐Einheiten meta bzw. para am zentralen Benzenring befinden. Sie sind damit strukturelle Isomere des bekannten ortho‐ED7H. Die strukturelle Integrität und Geometrie der ED7Hs wurde eindeutig durch Röntgen‐Einkristalldiffraktometrie aufgeklärt. Die ED7Hs besitzen eine ausgeprägte Fluoreszenz mit Quantenausbeuten über 40 %. Durch Variation der relativen Positionen der Doppel‐[7]Helicene am zentralen Benzenring (ortho, meta, para) kann eine außergewöhnliche Verstärkung der chiroptischen Eigenschaften registriert werden. Diese Verbesserung zeigt sich in einem etwa fünffachen Anstieg der Absorbanz und Lumineszenz‐Dissymmetriefaktoren (gabs und glum) sowie einer erhöhten CPL‐Helligkeit von bis zu 176 M−1 cm−1, was die Leistung der meisten helicenbasierten chiralen NGs übertrifft. Des weiteren zeigen DFT‐Simulationen, dass die relative geometrische Lage der [7]Helicene eine präzise Anpassung der elektrischen und magnetischen Übergangsdipolmomente zur Folge hat, welche die beobachteten Verstärkungen der chiroptischen Eigenschaften erklären. Diese Studie liefert eine effektive Strategie zur Verstärkung der CPL‐Eigenschaften chiraler Nanographene, welche ihre Anwendbarkeit als CPL‐Emitter verbessert. [ABSTRACT FROM AUTHOR]

Hier wird die erfolgreiche kristallisationsinduzierte dynamische Racematspaltung (CIDR) eines α‐lithiierten Phosphinoborans mithilfe des leicht zugänglichen und kostengünstigen chiralen Liganden (R,R)‐TMCDA beschrieben. Ausgehend von dem wichtigen P‐prochiralen Baustein Dimethylphenylphosphinoboran konnten Phosphinoborane in Ausbeuten von bis zu 80 % und Enantiomerenverhältnissen von bis zu 98 : 2 durch Kristallisation des lithiierten Zwischenprodukts erhalten werden. NMR‐basierte Deuterium‐Markierungsexperimente zeigen, dass die Epimerisierung in Lösung auf dem intermolekularen Protonentransfer zwischen dem nicht lithiierten Phosphinoboran und dem entsprechenden lithiierten Zwischenprodukt beruht, sodass die Anwesenheit überschüssigen Eduktes in einem optimierten Lösungsmittelgemisch der Hauptfaktor für eine erfolgreiche enantioselektive Synthese ist. Quantenchemische Berechnungen mit verschiedenen auf Festkörperstrukturen basierenden Modellsystemen bestätigen diese experimentellen Ergebnisse. Durch den Einblick in den Epimerisierungsmechanismus wurden wesentliche Prinzipien für die CIDR von lithiierten Phosphinoboranen aufgeklärt, die auf andere wichtige P‐stereogene Verbindungen und einfache chirale Amine erweitert werden könnten. [ABSTRACT FROM AUTHOR]

Der Einsatz kleiner fluoreszierender Moleküle spielt in der Medizin und Arzneimittelentwicklung eine immer größere Rolle. Mit diesem Beitrag ergänzen wir das Portfolio leistungsstarker Fluorophore durch die zufällige Entdeckung und Entwicklung einer neuen Klasse mit einem Imidazo[1,2‐a]pyridiniumtriflat‐Kern, die wir „PyrAt‐Verbindungen" nennen. Diese Fluorophore werden in einem einzigen Schritt aus leicht verfügbaren Materialien synthetisiert (>60 Beispiele) und zeigen Stokes‐Verschiebungen bis zu 240 nm, während sie gleichzeitig NIR−I‐Emissionen bei λmax bis zu 720 nm erreichen. Computergestützte Studien ermöglichen die Entwicklung einer Plattform für die Vorhersage von λmax und λEm. Darüber hinaus demonstrieren wir die Kompatibilität dieser neuartigen Fluorophore mit der Lebendzellbildgebung in HEK293‐Zellen, was darauf hindeutet, dass sie potente intrazelluläre Marker sind. [ABSTRACT FROM AUTHOR]

Healthy soils are essential for sustainable food production, achieving climate neutrality and halting the loss of biodiversity. The European Commission turned the spotlights on these vital aspects of soils with the launch of the EU Soil Observatory (EUSO) in 2021 to support the European Green Deal. Also, the EU Soil Strategy for 2030 and the proposed Soil Monitoring Law marked a major milestone for soil protection. This article provides an overview of the functioning of the EUSO within this policy context. Through its activities, the EUSO supports an EU‐wide soil monitoring system and provides policy support to a wide range of policy areas. Moreover, the EUSO monitors the state of soil health in the EU through the EUSO Soil Health Dashboard. This comprehensive and easy understandable tool shows, for the first time, where current scientific evidence converges to indicate areas in the EU likely to be affected by soil degradation. Furthermore, the EUSO supports soil research and innovation, enhances the capacity and functionality of the European Soil Data Centre and supports citizen engagements regarding soil matters. Overall, since 2021, the EUSO has successfully taken up its role to be the principal knowledge hub for soil information and data to underpin EU policy development and implementation. Also in the next years, EUSO will continue to provide data and knowledge to monitor, safeguard and restore soils in the EU. [ABSTRACT FROM AUTHOR]

A common approach to improving resource utilization in data centers is to adaptively provision resources based on the actual workload. One fundamental challenge of doing this in microservice management frameworks, however, is that different components of a service can exhibit significant differences in their impact on end-to-end performance. To make resource management more challenging, a single microservice can be shared by multiple online services that have diverse workload patterns and SLA requirements. We present an efficient resource management system, namely Erms, for guaranteeing SLAs with high probability in shared microservice environments. Erms profiles microservice latency as a piece-wise linear function of the workload, resource usage, and interference. Based on this profiling, Erms builds resource scaling models to optimally determine latency targets for microservices with complex dependencies. Erms also designs new scheduling policies at shared microservices to further enhance resource efficiency. Experiments across microservice benchmarks as well as trace-driven simulations demonstrate that Erms can reduce SLA violation probability by 5× and more importantly, lead to a reduction in resource usage by 1.6×, compared to state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Scheduling workload is a demand response strategy for data centers to reshape electricity usage, which provides an opportunity for them to utilize renewable energy. Enhancing the flexibility of workload scheduling would promote the data center to integrate renewable energy. Considering that the improvement of flexibility in workload scheduling is tightly related to the Quality of Service (QoS) required by IT consumers (ITCs), it becomes desirable for the data center to collaborate with them to further facilitate renewable energy integration in demand response programs. This paper proposes a QoS‐based bi‐level demand response model for the data center, where game theory is adopted to resolve the conflict of interest in the collaboration between the data center and ITCs. At the upper level, the data center is a leader who provides differential monetary incentives to ITCs while dispatching workload to integrate renewable energy. At the lower level, ITCs act as followers who adjust the workload's QoS to enhance the flexibility of workload scheduling. Simulation results show that the proposed model can decrease the operating cost of the data center while increasing the utilization of renewable energy. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In response to the global megatrends of digitalization and transportation automation, Turku University of Applied Sciences has developed a test platform to advance autonomous maritime operations. This platform includes the unmanned surface vessel eM/S Salama and a remote operations center, both of which are detailed in this article. The article highlights the importance of collecting and annotating multi-modal sensor data from the vessel. These data are vital for developing deep learning algorithms that enhance situational awareness and guide autonomous navigation. By securing relevant data from maritime environments, we aim to enhance the autonomous features of unmanned surface vessels using deep learning techniques. The annotated sensor data will be made available for further research through open access. An image dataset, which includes synthetically generated weather conditions, is published alongside this article. While existing maritime datasets predominantly rely on RGB cameras, our work underscores the need for multi-modal data to advance autonomous capabilities in maritime applications. [ABSTRACT FROM AUTHOR]

This article reviews and summarizes research conducted during and related to ASHRAE RP-1675, Guidance for Data Center CFD. The ultimate objective is to provide users and vendors of computational fluid dynamics (CFD) tools with best-practice modeling advice to support greater adoption of the technology to design and operate reliable and sustainable facilities. RP-1675 identified and developed good CFD modeling practices by benchmarking CFD simulations against experimental measurements for a "laboratory data center," considering past and concurrent research, and performing additional sensitivity studies in CFD. This article provides a detailed review of the experimental and CFD work conducted during RP-1675, which focused on the modeling of computer room air handler units (CRAHs) with vertical-axis blowers located in the raised-floor plenum, and modeling perforated floor tiles, and raised-floor stanchions. This article ultimately summarizes current guidance for data-center CFD. [ABSTRACT FROM AUTHOR]