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Wireless networks have become increasingly important in the human resources industry, with cloud data centres playing a critical role in commercial information infrastructure and human resource procedures. Effective human resource management is essential for modern enterprises, so proper human resource planning is crucial for smooth and rapid growth. To achieve this, enterprises must predict the supply and demand of future human resources based on development needs and external environmental factors and formulate a human resource plan that meets their development needs. However, the success or failure of human resource planning depends on accurately forecasting human resource demand. The wireless human resource cloud data centres provide numerous advantages over traditional data centres, including flexibility, efficiency, scalability, security, and data analysis capabilities. Forecasting human resource demand through these centres remains difficult because traditional forecasting techniques can be less effective, leading to inaccurate forecasts. To address this issue, this study proposes a model that combines fuzzy categorization theory with sophisticated forecasting methods to forecast human resource demand. To enhance forecasting reliability and accuracy by efficiently utilizing the data resources of wireless human resource cloud data centres and taking into account a wide range of influencing factors. Industries that use wireless HR cloud data centres are more likely to stay ahead of the competition. It uses fuzzy classification theory and applies fuzzy classification methods to later-stage forecasting to overcome the limitations of traditional forecasting methods, which sample changes may disrupt. The study also uses the Markov chain method, which is suitable for predicting system objects with large random fluctuations. The effectiveness and feasibility of the model are demonstrated by analyzing and verifying the research object's data. This study provides a reliable and effective forecasting method for human resource demand using wireless cloud data centres, which can assist organizations in making data-driven decisions and enhancing their competitive advantage in the market. [ABSTRACT FROM AUTHOR]

Due to the extraordinary advances in tech companies and development in cloud computing, it has turned out to be ever more familiar for companies to integrate virtualization in data centers to entirely exploit their hardware sources. Accordingly, virtualization and security have gone under varied changes recently. Virtualization and its exceptional design have numerous advantages and characteristics over conventional non-virtualized technologies. Nevertheless, these novel characters form novel vulnerabilities and probable attacks in virtualized systems. Many recent studies have developed to detect those attacks, but they still have challenges like, need to invest more, if it must be done fast, data security could be at danger and scalability could be difficult. As a result, the primary goal of this study is to detect attack for getting secured virtualization in cloud. Therefore, the purpose of this research is to suggest a new, two-stage, protected virtualization paradigm for the cloud. At first, the "higher-order statistical features, flow-based features, mutual information (MI) based features and improved correlation features" are derived. Deep convolutional neural network (DCNN) 1 and 2 are then used to classify the derived features. To detect network assaults, LSTM 1 and 2 are applied to the outputs from DCNN 1 and 2. Here, the Self Customized Aquila Optimizer is used to adjust the DCNN weights to their optimum level (SC-AO). Additionally, analysis is performed using a range of metrics. Particularly, the proposed HC + SC-AO scheme attained improved accuracy of 0.938108 and 0.958249 for both the datasets respectively. [ABSTRACT FROM AUTHOR]

Background: The Victorian Government convened the second Pancreas Cancer Summit in 2021 to identify unwarranted variation in care 2016–2019, and to assess trends compared with the first Summit 2017 (reporting 2011–2015). State‐wide administrative data were assessed at population level in alignment with optimal care pathways across all stages of the cancer care continuum. Methods: Data linkage performed by Centre for Victorian Data Linkage combined data from Victorian Cancer Registry with other administrative data sets including Victorian Admitted Episodes Dataset, Victorian Radiotherapy Minimum Data Set, Victorian Emergency Minimum Dataset and Victorian Death Index. A Cancer Service Performance Indicator audit was carried out providing an in‐depth analysis of identified areas of interest. Results: Of 3138 Victorians diagnosed with pancreas ductal adenocarcinoma 2016–2019, 63% were metastatic at diagnosis. One‐year survival increased between time periods, from 29.7% overall 2011–2015 (59.1% for non‐metastatic, and 15.1% metastatic) to 32.5% overall 2016–2019 (P < 0.001), 61.2% non‐metastatic (P = 0.008), 15.7% metastatic (P = NS). A higher proportion of non‐metastatic patients progressed to surgery (35% vs. 31%, P = 0.020), and more received neoadjuvant therapy (16% vs. 4%, P < 0.001). Postoperative mortality following pancreatectomy at 30 and 90 days remained low at 2%. Utilization of 5FU‐based chemotherapy regimens increased between 2016 and 2020. Multidisciplinary Meeting (MDM) presentation was still below the 85% target (74%) as was supportive care screening (39%, target 80%). Conclusions: Surgical outcomes remain world‐class and there has been an appropriate shift in chemotherapy administration towards neoadjuvant timing with increasing use of 5FU‐based regimens. MDM presentation rates, supportive care and overall care coordination remain areas of deficiency. [ABSTRACT FROM AUTHOR]

Nowadays, several companies prefer storing their data on multiple data centers with replication for many reasons. The data that spans various data centers ensures the fastest possible response time for customers and workforces who are geographically separated. It also provides protecting the information from the loss in case a single data center experiences a disaster. However, the amount of data is increasing at a rapid pace, which leads to challenges in storage, analysis, and various processing tasks. In this paper, we propose and design a geographically distributed data management framework to manage the massive data stored and distributed among geo-distributed data centers. The goal of the proposed framework is to enable efficient use of the distributed data blocks for various data analysis tasks. The architecture of the proposed framework is composed of a grid of geo-distributed data centers connected to a data controller (DCtrl). The DCtrl is responsible for organizing and managing the block replicas across the geo-distributed data centers. We use the BDMS system as the installed system on the distributed data centers. BDMS stores the big data file as a set of random sample data blocks, each being a random sample of the whole data file. Then, DCtrl distributes these data blocks into multiple data centers with replication. In analyzing a big data file distributed based on the proposed framework, we randomly select a sample of data blocks replicated from other data centers on any data center. We use simulation results to demonstrate the performance of the proposed framework in big data analysis across geo-distributed data centers. [ABSTRACT FROM AUTHOR]

The present study evaluates the performance of high‐resolution global climate models derived from Coupled Model Intercomparison Project Phase 6 (CMIP6 HighResMIP), in simulating rainfall characteristics over Madagascar on an annual and seasonal scales for the period 1981–2014. The models and their ensemble mean are assessed based on two observational datasets sourced from Climate Hazards Group Infrared Precipitation with Station data version 2 (CHIRPS v2.0) data and the European Centre for Medium‐Range Weather Forecasts (ECMWF) reanalysis fifth generation‐Land dataset (ERA5) as the references throughout the diverse analyses. A Taylor diagram, accompanied by the Taylor skill score (TSS), is used for the annual and seasonal model‐rankings and the overall performance of the models. The best‐performing models are EC‐Earth3P‐HR, ECMWF‐IFS‐HR, ECMWF‐IFS‐LR and HadGEM3‐GC31‐MM. The least‐recommended models with remarkable biases are BCC‐CSM2‐HR, CAMS‐CSM1‐0, FGOALS‐f3‐H, MPI‐ESM1‐2‐HR and MPI‐ESM1‐2‐XR. It is worth mentioning that FGOALS‐f3‐H tends to overestimate rainfall in most analyses, while MPI‐ESM1‐2‐HR and MPI‐ESM1‐2‐XR underestimate it. The findings of this study are of great importance to climatologists and present an opportunity for further investigation of underlying processes responsible for the observed wet/dry biases in order to improve the forecast skills in the models over the study area. [ABSTRACT FROM AUTHOR]

Based on the daily average temperature data of 2374 stations in China from 1993 to 2022 and the monthly average temperature data of European Centre for Medium‐Range Weather Forecasts (ECMWF) System5 in winter, this article analyses the intraseasonal variations of winter surface air temperature (SAT) over China by using seasonal empirical orthogonal function decomposition (S‐EOF) and compare the prediction skill differences of the first three dominant modes in ECMWF System5. The first mode (S‐EOF1) is characterized by out‐of‐phase changes in SAT anomalies (SATA) between December and January. Both the Siberian high (SH) and the central Pacific El Niño‐Southern Oscillation (CP ENSO) can impact S‐EOF1. The second mode (S‐EOF2) is characterized by consistency in warm anomalies over the Northeast and Qinghai‐Tibet Plateau's variation, while the other areas are characterized by the out‐of‐phase change in SATA. S‐EOF2 is closely related to Arctic oscillation (AO). The third mode is characterized by alternating changes over 3 months in winter, which is related to the Pacific meridional mode (PMM) and the North Atlantic SST tripole (NAT). Comparing the ECMWF System5 prediction skill for the first three leading modes for the intraseasonal variations in winter SAT, the prediction skill is not high. The model did not predict the extent of warm anomalies and the intraseasonal fluctuation in S‐EOF1, which may be due to the poor prediction skills for the range and intensity of the CP ENSO and the SH. The second mode has the highest prediction skill among the three modes. For the prediction skill of AO is good. The model can capture the intraseasonal reverse SATA between December and January but did not capture the reverse SATAs between January and February. Because the model does not predict obvious PMM and NAT well. [ABSTRACT FROM AUTHOR]

The highlighted energy consumption of Internet data center (IDC) in China has become a pressing issue with the implementation of the Chinese dual carbon strategic goal. This paper provides a comprehensive review of cooling technologies for IDC, including air cooling, free cooling, liquid cooling, thermal energy storage cooling and building envelope. Firstly, the environmental requirements for the computer room and the main energy consumption items for IDC are analyzed. The evaluation indicators and government policies for promoting green IDC are also summarized. Next, the traditional cooling technology is compared to four new cooling technologies to find effective methods to maximize energy efficiency in IDC. The results show that traditional cooling consumes a significant amount of energy and has low energy efficiency. The application of free cooling can greatly improve the energy efficiency of IDC, but its actual implementation is highly dependent on geographical and climatic conditions. Liquid cooling, on the other hand, has higher energy efficiency and lower PUE compared to other cooling technologies, especially for high heat density servers. However, it is not yet mature and its engineering application is not widespread. In addition, thermal energy storage (TES) based cooling offers higher energy efficiency but must be coupled with other cooling technologies. Energy savings can also be achieved through building envelope improvements. Considering the investment and recovery period for IDC, it is essential to seek efficient cooling solutions that are suitable for IDC and take into account factors such as IDC scale, climate conditions, maintenance requirements, etc. This paper serves as a reference for the construction and development of green IDC in China. [ABSTRACT FROM AUTHOR]

Keywords: chiral allene; medium-sized ring; cycloaddition; Cope rearrangement; asymmetric catalysis EN chiral allene medium-sized ring cycloaddition Cope rearrangement asymmetric catalysis 1845 1851 7 09/15/23 20231004 NES 231004 Graph 1 Introduction Allene is a unique functional group with a three-carbon structure consisting of two I sp i SP 2 sp -hybridized terminal carbons and one I sp i -hybridized inner carbon (Scheme 1a). Since the correct structure and axial chirality of allenes was first predicted by the pioneer van't Hoff in 1875, [2] allenes have attracted continuous research interest from synthetic chemists for over a century, driven by the curiosity of their unique structure. [24] Another method to synthesize the 1,3-endocyclic allenes is the cyclization of skillfully designed acyclic allenes; but this is not applicable to the preparation of cyclic allenes with smaller rings. [Extracted from the article]

With the rapid developments of data restores and big data technologies in recent years, it aims at applying new technologies in the new stage of smart hospital development and improving scientific and proper management level of hospitals. Based on the constructions of hospital data centers and applications of Internet of things with big data technologies, hospitals can achieve intelligent operations and managements in daily work. With the aids of constructions of the data centers and mass data brought by smart hospital service models, smart management decision-making supports are effectively provided for hospital managers. Through continuous progresses of smart hospital constructions, the operations and managements of hospitals are increasingly dependent based on the supports of the data from the data center in hospital, which form the key in the smart operation and management of hospital in future. [ABSTRACT FROM AUTHOR]

Summary: The ability to design smarter, more predictive healthcare solutions for use in the community (at work and at home) and in healthcare facilities has been greatly enhanced by recent developments in the Internet of Health Things (IoHT) and cyber physical systems (CPS). These data collected by such medical sensors will be sent in a large quantity to the fog gateway of IoHT networks. These data should be forwarded to far cloud for further analysis and processing. Therefore, sending all of these data over the IoHT network to the data center will impose a significant burden on the IoHT network. This paper proposed a new lossless electroencephalogram (EEG) compression technique (NeLECoT) for fog computing‐based IoHT networks. It encodes the data of the patient at the fog gateway prior to sending it to the data center, thereby reducing the data's size. In the fog node, the NeLECoT combines three efficient techniques: clustering based on density‐based spatial clustering of applications with noise (DBSCAN), RLE (run length encoding), and Huffman encoding (HE). The clustering based on DBSCAN separates a massive volume of captured data into small groups of closely related (or similar) data. RLE encodes clustered EEG data, and the resulting file is encoded with HE. The fog gateway then transmits the encoded file to the cloud. Numerous simulation experiments were carried out, and the findings demonstrated that the suggested NeLECoT achieved better results than the competing techniques in terms of transmitted data size, compression ratio, compression power, compression time, decompression time, and average compression power. [ABSTRACT FROM AUTHOR]

Hard disk is the main storage device for cloud service, and there always contain massive disks deployed in a data center. Disk failure occur frequently in data center, which may lead to data loss and other disasters, so there have urgent needs for a failure prediction method of hard disk so as to ensure service reliability. This paper proposes a temporal prediction model based on LSTM. Firstly, the SMART data of the disk is analyzed, and the Pearson correlation coefficient is used to analyze the correlation between the monitoring time series data of the faulty disk and the normal disk, and the monitoring index with the lowest correlation is selected as the fault feature; secondly, for the problem of serious imbalance of positive and negative samples in the SMART dataset, the SMOTEENN algorithm is introduced for oversampling to obtain a balanced dataset of positive and negative samples. The proposed method improves accuracy by 8.268% and F1-score by 8.657% compared to the conventional method. [ABSTRACT FROM AUTHOR]

Multi-Access Edge Computing (MEC) is known as a promising communication paradigm that enables IoT and 5G scenarios by using edge servers located in the proximity of end users. As an integral part of MEC, edge servers provide virtualized resources and host different MEC applications. Therefore, user equipment and IoT devices can offload tasks to edge servers instead of remote cloud data centers. A proper edge server placement strategy can significantly improve the performance of mobile applications. The optimum number of edge servers and their placement is a problem called edge server placement. This optimization problem is NP-hard. In recent years, the research community has made great efforts to solve this problem. To achieve Quality of Service (QoS), various metrics are considered in different research papers. However, a comprehensive overview of the different aspects of the Edge Server Placement Problem (ESPP) is still missing. This paper first highlights the edge server's importance and gives its applications. Then, it provides a comprehensive summary and taxonomies based on the objectives, applications, datasets, frameworks, and strategies of the different research on the placement of edge servers. Also, this paper summarizes the ESPP and other optimization problems raised as joint optimization problems. Finally, considering the capabilities and features of the MEC environment, some open issues are presented. [ABSTRACT FROM AUTHOR]

A cloud data center provides various facilities such as storage, data accessibility, and running many specific applications on cloud resources. The unpredictable demand for service requests in cloud workloads affects the availability of resources during scheduling. It raises the issues of inaccurate workload prediction, lack of fulfillment in resource demands, load unbalancing, high power consumption due to heavy loads, and problems of under and overutilization of resources. Therefore, an efficient scheduling technique and an accurate forecasting model are needed to overcome these issues. Also, to deal with these challenges and provide optimal solutions, researchers must have a robust knowledge of cloud workloads, their types, issues, existing technologies, their advantages and disadvantages. However, previous research indicates limited systematic review studies exist for cloud workload applications with prediction-based scheduling techniques. Therefore, a survey is required that provides information related to cloud workload. To fulfill this requirement, the current study collects the related articles published in the past years. This paper is a systematic review study of prediction-based scheduling techniques that extract and evaluate data based on five criteria. It includes the datasets of different workload applications, resources, current prediction and scheduling techniques, and their related parameters. The survey is quite useful for academicians who want to select the problem and develop new techniques for issues related to cloud workload applications. It also gives an idea of existing approaches that are already implemented and employed. [ABSTRACT FROM AUTHOR]

To store, analyse and process the large volume of data generated by IoT traditional cloud computing, is used everywhere. However, the traditional cloud data centres have their limitations to handle high latency issues in time-critical applications of IoT and cloud. Their applications are computer gaming, e-healthcare, telemedicine and robot surgery. The high latency in IoTs and cloud includes high computational, communication latency (service) and network latencies. The vital requirement of IoT is to have minimum network, service and computation latencies for real-time applications. Network latency causes a delay in transmitting a message or communication from one location to another. Services that require data in real-time are almost impossible to access the data via the cloud. Traditional cloud computing approaches are unable to fulfil the quality-of-service (QoS) requirements in IoT devices. Researches related to latency reduction techniques are still in infancy. Some new approaches to minimize the latency for transmitting time-sensitive data in real-time are discussed in this paper for cloud and IoT devices. This research will help the researchers and industries to identify the techniques and technologies to minimize the latencies in IoT and cloud. The paper also discusses the research trends and the technical differences between the various technologies and techniques. With the increasing interest in the literature on latency minimization and its requirements for time-sensitive applications; it is important to systematically review and synthesize the approaches, tools, challenges and techniques to minimize latencies in IoT and cloud. This paper aims at systematically reviewing the state of the art of latency minimization to classify approaches, and techniques. The paper uses a PRISMA technique for a systematic review. The paper further identifies challenges and gaps in this regard for future research. We have identified 23 approaches and 32 technologies associated with latencies in the cloud and IoT. A total of 112 papers on latency reduction have been examined under this study. The existing research gaps and works for latency reduction in IoTs are discussed in detail. There are several challenges and gaps, which requires future research work for improving the latency minimization techniques and technologies. Finally, we present some open issues which will determine the future research direction. [ABSTRACT FROM AUTHOR]

With analysis occurring during a heightened concern with the Black Lives Matter movement and knife crime in the U.K., this paper aims to delineate the characteristics of a street code, constituting a specific dominant and often hegemonic form of 'street masculinity' found on an inner-city housing estate in London called Maxwell. The fieldwork ran over nine months in 2019, involving 48 Black, Asian, and minority ethnic men aged 18–22. Using an ethnographic methodology, the principal methods of data generation were observations, interviews and informal conversations. The main theories this study draws on to understand 'street masculinity' were Connell's and Messerschmidt's dominant, hegemonic, subordinate and complicit masculinity forms. Findings centre on data from two young men who exemplify different patterns of masculinity performing the street code. Findings are presented under a series of characteristics that make up the game of the 'on-road' street masculinity and include (1) authenticity, 'swagger' and not being 'pussy'; (2) a preparedness for violence; (3) knife-carrying; (4) a presence on the digital street. Although this way of living drove a desire for respect and group status, there was also an underlying and pervasive sense of vulnerability derived from risk-taking and anticipation of danger. [ABSTRACT FROM AUTHOR]

Does foreign aid improve the image of donors in recipient countries? Studies have found weak overall effects, but also positive correlations that are highly conditional on donor and recipient factors, like program characteristics and regime type. Our statistical and case study analysis of US foreign aid leads to a more skeptical perspective. We assess the impact of foreign aid on public opinion of the United States in twenty recipient countries during the Obama and Trump presidencies, using Pew Research Center survey data. A multi-level longitudinal analysis of individual and country-level variables finds that correlations between aid disbursements and foreign public opinion disappear when controlling for attitudes about the US president. These results are corroborated in crucial case studies of Kenya and Turkey. The findings are important for governments that use aid to influence foreign opinion and for scholarship that considers aid to be a source of soft power. [ABSTRACT FROM AUTHOR]

The systematic design, building and implementation of IT services for on-premise, cloud-based or hybrid data centers are a hard engineering and managerial process. Heavyweight IT service design workflows—from ITIL v2011, CMMI-SVC v1.3 and the ISO/IEC 20000 standard—have been used in the last decade. However, the current dynamic business requirements demand to accelerate IT service design, building and implementation. Consequently, agile workflows—from ITIL v4 and VeriSM—or lightweight ones—from ISO/IEC 29110-4-3 standard and FitSM—have been proposed. However, despite these IT service design workflows provide high valuable content, they are still reported in a coarse-grain level, and thus academics and practitioners interested in using to them must add ad-hoc roles, activities-tasks, and artifacts-templates. In this research, we address this relevant knowledge gap, and using a Design Science Research Methodology (DSRM), we elaborate and evaluate exploratory an agile IT service design workflow—called AVS_FD_MVITS—that provides fine-grain content regarding roles, activities-tasks and artifacts-templates. AVS_FD_MVITS was designed using design components taken from Scrum-XP and ITIL v4 IT service design workflows. To evaluate exploratory AVS_FD_MVITS, we applied it in a realistic case in an academic small data center. The exploratory qualitative-based evaluation suggests that AVS_FD_MVITS can be considered a valid agile IT service design workflow useful for academics and practitioners. However, confirmatory quantitative-based evaluations are asked for advancing this research stream. [ABSTRACT FROM AUTHOR]

In order to manage the load on dispersed data centers and cut down on energy established on time usage, agent-based resource allocation is given attention. Using a targeted load balancer (TLB), we suggest an energy-aware agent-based resource allocation in this research to enhance quality of service in a cloud setting. This agent is first set up to keep track of the resource load resulting from the request that has been assigned a job. Cloud watch also keeps an eye on energy levels to determine the typical payload size of resource execution. The TLB establishes new instance state to assign the resource based on the payload weight. To shorten the execution time, the dynamic hyper switching model develops a balancing mechanism. The suggested system achieves high performance in resource management by creating load balancer that is efficiently targeted to cut down on computation time and cost depending on energy levels. In comparison to existing techniques, the suggested parallelized homogeneous job in the cloud environment produces greater performance up to 95.5% while maintaining the time execution utilizing switching state of execution. This maintains the reduced CPU consumption, which dependent on the lowering of temporal complexity. [ABSTRACT FROM AUTHOR]

Silicon photonics is becoming a competitive technology to settle the issues of power and speed in data centers and computing systems. However, the absence of an on‐chip light source restricts the wide and deep application of silicon photonics. Essentially, efficient edge coupling from a Si‐based III‐V laser to silicon photonic components remains the major obstacle for on‐chip integration. Here, two compact edge couplers with ultra‐low coupling loss and eased fabrication for the light transmission from a Si‐based III‐V laser to Si3N4 waveguide are realized. An ultra‐low laser‐to‐chip coupling loss of only 1.175 dB for the butt coupling of a Si‐based InAs QD laser is experimentally demonstrated with high alignment tolerance. The strategies presented here provide a competitive solution for realizing ultra‐efficient integration of Si‐based light sources and silicon photonic components. [ABSTRACT FROM AUTHOR]

Northward tropical cyclones over the Bay of Bengal (BoB TCs) often interact with atmospheric circulation, transporting large amounts of water vapor to the Tibetan Plateau (TP), causing extreme precipitation. The BoB surrounded by land on three sides and the complex topography of the TP bring challenges to implementing numerical simulation in these regions. However, the scarcity of data in the two areas makes it necessary to find a technological process to perform practicable numerical simulations on the BoB TC and its induced extreme precipitation to carry out further research. In this study, the WRF 3.9.1 is used to perform many simulation experiments on a northward BoB TC Rashmi (2008) from 24 to 27 October 2008 associated with a record-breaking extreme precipitation on the TP, indicating that the selection of the simulation region, the source of initial-boundary conditions, and the cumulus convection schemes are three important factors influencing the results. We examined and compared the simulation of Rashmi with 10 experiments that were generated by combining The Final Operational Global Analysis (FNL) reanalysis data and the European Centre for Medium-Range Weather Forecasting 5(th) generation reanalysis (ERA5) data as initial-boundary conditions with five cumulus convection schemes. Most of the experiments can predict Rashmi and precipitation in the TP to a certain degree, but present different characteristics. Compared with FNL, the ERA5 performs well regarding Rashmi's intensity and thermal structure but overestimates Rashmi's moving speed. For the extreme precipitation in the TP, experiments suffice to reproduce the heavy rainfall (>25 mm/day) in the TP, with TS and ETS scores above 0.3 and most HSS scores greater than 0.4. The optimal experiments of three stations with extreme precipitation deviated from the actual precipitation by less than 15%. The ERA5 TDK scheme is recommended as the optimal solution for balancing the simulation of Rashmi and its extreme precipitation in the TP. [ABSTRACT FROM AUTHOR]

Consider general minimum variance distortionless response (MVDR) robust adaptive beamforming problems based on the optimal estimation for both the desired signal steering vector and the interference‐plus‐noise covariance (INC) matrix. The optimal robust adaptive beamformer design problem is an array output power maximization problem, subject to three constraints on the steering vector, namely, a (convex or nonconvex) quadratic constraint ensuring that the direction‐of‐arrival (DOA) of the desired signal is separated from the DOA region of all linear combinations of the interference steering vectors, a double‐sided norm constraint, and a similarity constraint; as well as a ball constraint on the INC matrix, which is centered at a given data sample covariance matrix. To tackle the nonconvex problem, a new tightened semidefinite relaxation (SDR) approach is proposed to output a globally optimal solution; otherwise, a sequential convex approximation (SCA) method is established to return a locally optimal solution. The simulation results show that the MVDR robust adaptive beamformers based on the optimal estimation for the steering vector and the INC matrix have better performance (in terms of, e.g., the array output signal‐to‐interference‐plus‐noise ratio) than the existing MVDR robust adaptive beamformers by the steering vector estimation only. [ABSTRACT FROM AUTHOR]

The present study was conducted to investigate the potential of radiomics to develop an explainable AI-based system to be applied to ultra-widefield fundus retinographies (UWF-FRTs) with the objective of predicting the presence of the early signs of Age-related Macular Degeneration (AMD) and stratifying subjects with low- versus high-risk of AMD. The ultimate aim was to provide clinicians with an automatic classifier and a signature of objective quantitative image biomarkers of AMD. The use of Machine Learning (ML) and radiomics was based on intensity and texture analysis in the macular region, detected by a Deep Learning (DL)-based macular detector. Two-hundred and twenty six UWF-FRTs were retrospectively collected from two centres and manually annotated to train and test the algorithms. Notably, the combination of the ML-based radiomics model and the DL-based macular detector reported 93% sensitivity and 74% specificity when applied to the data of the centre used for external testing, capturing explainable features associated with drusen or pigmentary abnormalities. In comparison to the human operator's annotations, the system yielded a 0.79 Cohen κ, demonstrating substantial concordance. To our knowledge, these results are the first provided by a radiomic approach for AMD supporting the suitability of an explainable feature extraction method combined with ML for UWF-FRT. [ABSTRACT FROM AUTHOR]

Container-based Hadoop distributed file system (HDFS) storage has been widely used in cloud data center networks, while traditional HDFS has single point problem resulting in overall unavailability. In this paper, we mainly study the storage reliability of the Docker container-based HDFS cluster with single point of failure. Firstly, we investigate a data volume-based persistence solution of Hadoop with the single point failure and single backup strategy of HDFS cluster. Secondly, we propose an HDFS-based replica placement algorithm for data storage with considering the performance of the host and container nodes. Thirdly, we design the KADC-KNN data segmentation algorithm to effectively store the persistent data of the Docker container. Extensive experimental results show that this method can effectively ensure the stable storage and fast migration of cluster data. Compared with the most advanced algorithm, the proposed data volume persistence algorithm DVPS can improve the data reliability by 19.8%. The data partitioning algorithm KADC-KNN improves the partitioning accuracy by 20.2% and has lower time overhead. [ABSTRACT FROM AUTHOR]

Data centres, though a necessary part of modern society, are being stigmatised for consuming vast amounts of electricity for their operational and cooling needs. Due to Ireland's reliance on fossil fuels to meet the increased energy demand of data centres, the data centres are contributing significantly to Ireland's total carbon emissions. As much of this energy is expelled from data centres as waste heat energy, the potential for recycling some of this wasted heat energy was explored using environmentally friendly systems from recent publications. The recovered waste heat energy was applied in a vertical farming system, and the benefits of this waste heat to the vertical farm were analysed and quantified in two scenarios. Using conservative estimates, it was predicted that each vertical farm could be between 5–23% the size of the data centre and produce enough food to feed between 14–61 adults their daily calorie needs, and between 13–58 people their daily fresh produce requirements, depending on the scenario applied. For a more accurate prediction, each vertical farm would have to be assessed on a case-by-case basis, as there is no current research in this area. However, there was not enough data available on Irish data centres to perform these calculations. [ABSTRACT FROM AUTHOR]

In order to reduce energy consumption in data centers and help achieve the goal of "double carbon", a modular unit for data centers is designed to meet both evaporative cooling and waste heat recovery functions according to the climate conditions and good air quality in Tibet. Based on the three refrigeration operating conditions with 100% utilization of natural cooling source, extreme meteorological conditions in Tibet and the temperature distribution inside the data center are simulated and tested on a 600 k W enthalpy difference test bench. The results show that the air volume, external static pressure, evaporative cooling module air outlet temperature and heat recovery capacity of the waste heat recovery module all meet the design requirements under the corresponding operating conditions. Comparing the air outlet temperature and waste heat recovery capacity of the unit under rated airflow operation and maximum airflow operation, the performance of the unit under rated airflow operation is better than that under maximum airflow operation. At this time, the air temperature can reach 22.82℃, the amount of waste heat recovery is 295.78 k W, and the COP is 15.91. [ABSTRACT FROM AUTHOR]

Based on the cooling load of the data center in the Pearl River Delta region and the time-of-use tariff policy, a water storage cooling system was selected as the energy storage solution. The water cold storage system and operation control plan were optimized, and the operation and energy consumption of the water cold storage system were analyzed for typical days, months, and whole year. Through economic analysis of its operation, it was found that the annual cooling operation cost was reduced by 25. 78%, further verifying the feasibility of the water storage plan. Overall, it is shown that the water cold storage system can operate efficiently while ensuring the demand for indoor end load, and has a good effect of “peak-load shifting effect” and reducing cooling electricity costs. [ABSTRACT FROM AUTHOR]

Data center is one of the indispensable infrastructures in the modern information society, however, its high energy consumption and carbon emission have become an urgent problem. In order to reduce the large energy consumption of data centers in hot and humid areas due to mechanical cooling and air-conditioning systems, the operation of an indirect evaporative cooling (IEC) air-conditioning system was simulated in a hot and humid area data center based on Matlab and the Energy Plus platform, its annual energy consumption was evaluted. The impact of solar assisted dehumidification (LDD) system on the three operation modes and the performance of IEC air-conditioning system were also investigated. The results show that the IEC air-conditioning system can achieve 16.5% annual energy saving intensity compared to a conventional mechanical cooling system, and that the addition of an LDD unit can increase the wet mode operating time of the IEC air-conditioning system by 35.9% and reduce the system energy consumption by 20.1%. Therefore, IEC air-conditioning systems have significant energy-saving advantages in hot and humid data centers and can significantly improve the operational efficiency and sustainability of data center air-conditioning systems. [ABSTRACT FROM AUTHOR]

Aiming at the difficulty of accurately reflecting the actual effect of the engineering application of the data center’s energy consumption evaluation results. In this paper, on the basis of the existing data center energy efficiency evaluation index, from the data center, data center air conditioning system, indirect evaporative cooling air conditioning equipment in three dimensions, respectively, put forward the data center indirect evaporative cooling air conditioning PUE coefficient, data center air conditioning energy utilization efficiency, indirect evaporative cooling air conditioning equipment refrigeration density and other evaluation indicators. Using the new evaluation indexes as well as the assignment analysis method, case studies were conducted on data centers with different cooling systems. The results show that the indirect evaporative cooling air-conditioning system has obvious advantages of power and water saving compared with the traditional chilled water system, but its refrigeration density index still has much room for improvement. The proposal of the new index improves the energy efficiency evaluation system of data centers and can lead to further improvement of the performance quality of related air conditioning products. [ABSTRACT FROM AUTHOR]

In order to study the airflow organization in the data center and quickly predict the outlet air temperature of the rack, a rapid prediction method that combines the proper orthogonal decomposition (POD) and the surrogate model was proposed. Taking a data center as an example, the temperature field data under a large number of operating conditions were calculated via using computational fluid dynamics (CFD) software. The effects of air conditioning on the temperature field of the micro-module was analyzed. Then, the reduced order model of the cabinet outlet air temperature was established via using the POD method. The calculated temperature field data was divided into training set and test set, and the prediction accuracy of regression tree, boosted tree, support vector regression, neural network, Gaussian process regression, cubic spline interpolation, and piecewise cubic Hermite interpolation as surrogate model was compared. The consuming time of calculating the temperature field via using surrogate model and CFD software was compared. It is found that the computational speed of surrogate model composed of POD, machine learning method, and interpolation method was more than 100 times that of CFD software. [ABSTRACT FROM AUTHOR]

The booster-driven hybrid cooling unit using ambient energy for free cooling can work for a whole year as one unit and reduce the annual cooling energy consumption effectively in data centers. Two different type booster and its hybrid cooling units were developed and tested. The suction/discharge pressure and mass flow rate of two different boosters were studied under the annual temperature difference range. The performance and differences of two hybrid cooling units were analyzed. The results indicate that the suction/discharge pressure ratio of the large torque booster (No.1) is less than that of the small torque booster (No.2). In the test temperature difference range, the suction/discharge pressure ratio of No.1 booster decreases by 53.56%,which of No.2 booster decreases by 32.89%. The cooling capacity and energy efficiency ratio of No.1 booster-driven hybrid cooling unit (14.8 kW) is higher than that of No.2 booster unit（9.2 kW） at the same temperature difference（30℃） [ABSTRACT FROM AUTHOR]

Aimed at the problem that valve regulated lead-acid (VRLA) battery aging threatening power supply reliability in cloud data centers, a method of an online battery state prediction method based on machine learning algorithms was proposed. Predicting battery status through classic machine learning algorithm models, comparative experiments were conducted on the prediction accuracy between models without extracted feature values and models with extracted feature values. Compare the prediction accuracy of the model using unrefined feature values with the model extracting feature values. The prediction accuracy of the model without extracted feature values ranges from 71. 72% to 81. 82%, and after extracting feature values, the prediction accuracy has improved by 10. 10% to 20. 20%, extracting feature values can improve prediction accuracy. The prediction model based on linear SVM method for extracting feature values is superior to other algorithms, with an accuracy of 96. 46%. Experiment results show that machine learning algorithm based prediction models can be used for online VRLA battery state prediction in cloud data centers. [ABSTRACT FROM AUTHOR]

To reduce energy consumption in data center cooling systems and ensure the healthy and rapid development of data centers, this study focuses on the cooling system of a data center in Guangzhou. Addressing the efficiency variations of chillers at different Partial Load Rates (PLR), a novel model predictive control method is proposed. The study employs Gaussian process regression to establish a chillers’ operational efficiency model and utilizes Long ShortTerm Memory (LSTM) neural networks to create an outdoor wet bulb temperature time series prediction model. Based on the data center cooling load and the prediction of outdoor wet bulb temperature, the cooling system’s chilled water storage and discharge modes, storage and discharge capacity, the number of chillers, and the PLR are adjusted. This adjustment aims to partially decouple the demand and supply sides, resulting in the chillers being operated in high efficiency or turned off, thereby reducing the operational energy consumption of the cooling system. To evaluate the performance of this strategy, annual energy consumption simulations were conducted, compared to the traditional rule-based control strategy, the model predictive control strategy reduced the overall energy consumption by 3.13% and operating costs by 3.33%. [ABSTRACT FROM AUTHOR]

In response to the issue of suboptimal thermal performance in traditional data centers, four distinct airflow management structures, namely, composite, linear, convex, and concave diversion plates, were proposed. Utilizing Airpak 3.0, numerical simulations were conducted on five different data centers, including the original configuration of the computer room air conditioner (CRAC). The objective was to assess the impact of various airflow management structures on the thermal performance of data centers, with experimental validation of the model. The findings demonstrate that these airflow management structures significantly enhance the thermal performance of data centers. Among them, the composite diversion plate yielded moderate improvements, the convex diversion plate effectively optimized velocity distribution, although with limitations in temperature optimization, and the concave diversion plate exhibited non-uniform temperature distribution. Overall, considering all factors, the linear diversion plate proved to be the most effective. [ABSTRACT FROM AUTHOR]

Current-day data centers and high-volume cloud services employ a broad set of heterogeneous servers. In such settings, client requests typically arrive at multiple entry points, and dispatching them to servers is an urgent distributed systems problem. This paper presents an efficient solution to the load balancing problem in such systems that improves on and overcomes problems of previous solutions. The load balancing problem is formulated as a stochastic optimization problem, and an efficient algorithmic solution is obtained based on a subtle mathematical analysis of the problem. Finally, extensive evaluation of the solution on simulated data shows that it outperforms previous solutions. Moreover, the resulting dispatching policy can be computed very efficiently, making the solution practically viable. [ABSTRACT FROM AUTHOR]

The growing smart cities in urban areas are becoming more intelligent day by day. Massive storage and high computational resources are required to provide smart services in urban areas. It can be provided through intelligence cloud computing. The establishment of large-scale cloud data centres is rapidly increasing to provide utility-based services in urban areas. Enormous energy consumption of data centres has a destructive effect on the environment. Due to the enormous energy consumption of data centres, a massive amount of greenhouse gases (GHG) are emitted into the environment. Virtual Machine (VM) consolidation can enable energy efficiency to reduce energy consumption of cloud data centres. The reduce energy consumption can increase the Service Level Agreement (SLA) violation. Therefore, in this research, an energy-efficient dynamic VM consolidation model has been proposed to reduce the energy consumption of cloud data centres and curb SLA violations. Novel algorithms have been proposed to accomplish the VM consolidation. A new status of any host called an almost overload host has been introduce, and determined by a novel algorithm based on the Naive Bayes Classifier Machine Learning (ML) model. A new algorithm based on the exponential binary search is proposed to perform the VM selection. Finally, a new Modified Power-Aware Best Fit Decreasing (MPABFD) VM allocation policy is proposed to allocate all VMs. The proposed model has been compared with certain well-known baseline algorithms. The comparison exhibits that the proposed model improves the energy consumption by 25% and SLA violation by 87%. [ABSTRACT FROM AUTHOR]

The growth of the electronic government and the increase in the number of internet service providers has led to the development of data centers. A major piece of equipment used in these centers is the cooling system, which is responsible for the continuous exhaust of heat generated by the equipment in the data center. Given the high energy consumption of these systems, their optimization is of great importance. Due to the significance of identifying the sources of energy inefficiency in system optimization, in the present study, first, a sample in-row data center cooling system (was manufactured by authors) is placed inside the deigned setup test room, and its performance is investigated. Subsequently, it undergoes thermodynamic and exergy analyses using a model created in EES software. The effects of changes in the evaporator and condenser temperatures, the superheat (∆Tsuperheat) and sub-cooled (∆Tsub-cooled) temperature differences, and the ambient temperature on the energetic and exergetic performance of the cycle are evaluated. Under the temperature conditions specified in this research, the COP and exergy efficiency of the system were obtained 3.47 and 19.5%, respectively. Moreover, it was determined that a rise in the evaporator temperature increases the COP and decreases the exergy efficiency of the system, while a decrease in the condenser temperature improves both. A 30° reduction in the operator's temperature in all different condenser temperatures led to a 10% increase in the system's exergy efficiency. Additionally, lowering the condenser temperature from 323 to 313 K resulted in approximately a 10% increase in the system's exergy efficiency. [ABSTRACT FROM AUTHOR]

Notably, substrates B 1s b and B 1t b bearing a heteroaryl group and a naphthyl group, respectively, also served as suitable reaction partners, participating in the formal (2+1) cycloaddition to provide the benzofuranone-based spirocyclopropanes B 3sa b and B 3ta b , respectively (entries 19 and 20). [15][16][24][32][33] As shown in Scheme 1a, benzofuran-derived oxadienes are widely used as two-atom building blocks in highly diastereoselective (2+3) cycloadditions. Moreover, I ortho i -, I meta i - and I para i -substituted phenyl groups were found to be suitable R SP 1 sp groups for the benzofuran-derived oxadienes B 1 b , affording the target products B 3fa b - B ra b in overall high yields and moderate to good diastereoselectivities (entries 6-18). Keywords: (2+1) cycloaddition; diastereoselectivity; benzofurans; oxadienes; bromomalonates; spiro compounds EN (2+1) cycloaddition diastereoselectivity benzofurans oxadienes bromomalonates spiro compounds 1704 1708 5 08/18/23 20230901 NES 230901 Graph Benzofuran is an important class of oxygen-containing heterocycles that frequently occur as substructures in natural products and synthetic compounds. [Extracted from the article]

Sea ice motion (SIM) has significant implications for sea–air interactions, thermohaline circulation, and the development of the Arctic passage. This research proposes an improved SIM retrieval method from Fengyun-3D's (FY-3D) microwave radiometer imager's (MWRI) brightness temperature (Tb) data based on the modified classical maximum cross-correlation (MCC) method and the multisource data merging method. This study utilized buoy data to establish the search area range, applied distinct thresholds across various Arctic regions, and merged the buoy data, reanalysis wind data, and SIM retrieved from FY-3D/MWRI Tb data. In 2019, for the final Arctic SIM results retrieved from the MWRI 89 GHz and 36.5 GHz Tb data, the root-mean-square error (RMSE) and the mean average error (MAE) in the east–west direction were 2.07 cm/s and 1.38 cm/s and those in the north–south direction were 1.96 cm/s and 1.15 cm/s, compared to the ice-tethered profiler (ITP) data. Compared with the daily average data of the National Snow and Ice Data Center (NSIDC), the RMSE and MAE of the SIM results obtained in this study were 0.74 cm/s and 0.93 cm/s in the east–west direction, and 0.56 cm/s and 0.72 cm/s in the north–south direction, respectively. The monthly average of the SIM retrieved from the MWRI Tb data in this research also showed a good agreement with the monthly average of the NSIDC SIM product. The comparison showed that the MWRI Tb data could be used to retrieve the Arctic SIM, and the Arctic SIM retrieval method presented in this paper was accurate and general. [ABSTRACT FROM AUTHOR]

Assessment of spring snow cover fraction (SCF) can be valuable for understanding the efficacy of certain Earth system models (ESMs) in simulating the energy exchange between land and atmosphere system, global hydrological cycle and future climate impacts. Here, we provide a comprehensive evaluation of simulated spring SCF from 23 and 20 ESMs participating CMIP5 and CMIP6, against satellite data from National Oceanic and Atmospheric Administration and National Climatic Data Center (NOAA/NCDC) and a long‐term Northern Hemisphere daily 5‐km snow cover extent product (JASMES) over the Northern Hemisphere (NH) and its 13 subregions during the period of 1982–2005 and 2072–2095. Our results show that (a) no model performs consistently better in simulating spring SCF from all aspects (mean annual, long‐term trend and climatological monthly of spring SCF); (b) the mean annual of spring SCF that was simulated by CMIP5 and CMIP6 model simulations was underestimated in most of the NH and overestimated over the Tibetan Plateau and eastern Asia; (c) most of the model simulations showed a stronger reduction trend for mean annual of spring SCF; (d) the climatological monthly variation of spring SCF is reasonably captured by all model simulations, except for overestimation over the TIB and EAS regions, and there is underestimation in other regions; (e) compared to CMIP5, most of the model simulations in CMIP6 exhibit an improved ability to simulate spring SCF across all aspects; (f) we confirm that the multi‐model ensemble mean (MME) is a better way to represent the three aspects of spring SCF than most individual model simulations. Finally, the spring SCF values predicted by models with better simulation abilities over the NH and its 13 subregions under different scenarios show decreasing trends. Specifically, the highest decreasing trend of spring SCF was found under high emission scenarios (RCP8.5 and SSP5‐8.5). [ABSTRACT FROM AUTHOR]

This study explores reconciliation tourism initiatives in Israel and the West Bank as a discursive arena in which ideas about reconciliation are constructed for tourism purposes by eight organizations that offer travel experiences that include reconciliation as a major theme. By critically analyzing the manner in which ideas about reconciliation are constructed for tourism purposes, the research provides insights into issues of reconciliation in destinations with active conflict. The presentation of fieldwork data centers on three themes: the recognition of multiple narratives, the inclusiveness of victimhood, and the commitment to transformative ontology. Each theme is supported by evidence and discussed in relation to its theoretical frameworks. Implications are drawn for practitioners tasked with marketing reconciliation tourism initiatives in conflict-ridden destinations. [ABSTRACT FROM AUTHOR]

One of the more popular usages of predictive modeling is in the forecasting of weather. We use machine learning techniques to spatially extend provided forecasts to sites across the continental United States. The forecasts and observed weather for 113 sites across the United States (2014–2017) were used, along with supplementary data on observed weather from the National Oceanic and Atmospheric Administration National Climatic Data Center. Based on the spatially extended forecasts, visual displays are created to analyze the prediction accuracy of the forecasts. Our results allow for an in-depth exploration into the accuracy of our new weather forecasts across the nation. [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]

In the last few years, fog computing has been recognized as a promising approach to support modern IoT applications based on microservices. The main characteristic of this application involve the presence of geographically distributed sensors or mobile end users acting as sources of data. Relying on a cloud computing approach may not represent the most suitable solution in these scenario due to the non-negligible latency between data sources and distant cloud data centers, which may represent an issue in cases involving real-time and latency-sensitive IoT applications. Placing certain tasks, such as preprocessing or data aggregation, in a layer of fog nodes close to sensors or end users may help to decrease the response time of IoT applications as well as the traffic towards the cloud data centers. However, the fog scenario is characterized by a much more complex and heterogeneous infrastructure compared to a cloud data center, where the computing nodes and the inter-node connecting are more homogeneous. As a consequence, the the problem of efficiently placing microservices over distributed fog nodes requires novel and efficient solutions. In this paper, we address this issue by proposing and comparing different heuristics for placing the application microservices over the nodes of a fog infrastructure. We test the performance of the proposed heuristics and their ability to minimize application response times and satisfy the Service Level Agreement across a wide set of operating conditions in order to understand which approach is performs the best depending on the IoT application scenario. [ABSTRACT FROM AUTHOR]

This study examines the characteristics of school shootings and investigates the relationship between perpetrators' race and how shootings are reported by the media. Findings, utilizing data from the Center for Homeland Defense and Security, demonstrate differences by race in the characteristics of school shootings and media reporting of school shootings. Inaccurate and incomplete portrayals of school gun violence in data repositories and the media may lead to public perceptions and policy responses that do not adequately address root causes of violence. In addition, these inaccurate depictions shroud the reality of gun violence in K-12 schools. Accurate information regarding the circumstances of gun violence in schools is a necessary component of preventing future violence. [ABSTRACT FROM AUTHOR]

The French coast of the eastern English Channel (ECC) is classified as a potential eutrophication zone by the Oslo and Paris (OSPAR) Convention and as moderate to poor according to the phytoplankton quality element of the Water Framework Directive (WFD). It is regularly affected by Phaeocystis globosa bloom events, which have detrimental effects on the marine ecosystem, economy, and public health. In this context and to improve our observation strategy, MAREL Carnot, a multi-sensor oceanographic station, was installed in the eastern English Channel in 2004 at the Carnot wall in Boulogne-sur-Mer. The aim of this station was to collect high-frequency measurements of several water quality parameters to complement conventional low-resolution monitoring programs. The purpose of this paper is to describe the MAREL Carnot dataset and show how it can be used for several research objectives. MAREL Carnot collects high-frequency, multi-parameter observations from surface water as well as meteorological measurements and sends the data in near real-time to an onshore data center. In this paper, we present several physical, chemical, and biological parameters measured by this station. We also demonstrate that the MAREL Carnot dataset can be used to assess environmental or ecological statuses and conduct research in the field of marine phytoplankton ecology and oceanography. In addition, we show that this dataset may indirectly aid in improving European environmental management strategies. The MAREL Carnot dataset is publicly accessible via 10.17882/39754 (MAREL Carnot, 2023). [ABSTRACT FROM AUTHOR]

Documenting temporal and spatial occurrence trends of Non-Indigenous Species (NIS) is essential to understand vectors and pathways of introduction, and for horizon scanning for future introductions. This study provides an overview of marine NIS found in the Republic of Ireland up to 2020. Taxonomic groups, species origin, and location of first reporting (counties) were compiled and analysed focusing on the last three decades. While the unambiguous characterisation of introduction events is challenging, analysis of 110 species corroborated the global weight of evidence that shipping activities to/from ports and marinas are the most likely vectors and pathways in Ireland. A comparable review study for the Netherlands revealed that most NIS were first introduced to mainland Europe and subsequently would take on average >15 years to reach Ireland. In the last two decades there has been an increase in NIS-focused surveys in Ireland. Incorporating data from these surveys in centralized national repositories such as the National Biodiversity Data Centre, will strongly aid the evaluation of potential NIS management responses. Furthermore, the availability of robust baseline data as well as predictions of future invaders and their associated vectors and pathways will facilitate the effective application of emerging monitoring technologies such as DNA-based approaches. [ABSTRACT FROM AUTHOR]