Request Rate Research Articles

E-Commerce Platform for Farmers

Abstract: A husbandry sector is backbone of country’s frugality. Utmost of the population of our country is directly involved in this sector. Farmers works hard but when it comes vend their crop products, they face issue due to intermediatory persons, dealers and some big selling companies. Similar people get benefit from planter’s products. Similar companies and dealers purchase products from growers at low cost in mandi or request, shot and vend it to end stoner or significances in huge requests at veritably high cost. To get relieve of interposers, E-commerce system we've introduced. Through this system growers can have direct communication or can vend their crop products at their own price. E-commerce is easily beginning to have a major impact in the agrarian sector. The way people go about copping Agrarian products is of great concern. Utmost of the time guests have to travel far distances to get agrarian products and getting the right quality isn't assured. The website will guide the growers to pierce new husbandry ways, compare current request rate of different products, the total trade and the earned profit for the vended products. The website builds a platform for growers to insure lesser profitability through end stoner communication. The website will serve as an exclusive and protected platform for conducting agricultural marketing. The relinquishment of ecommerce platforms in the agrarian sector has the implicit to profit smallholder growers by furnishing them with access to new requests, developing their logrolling power, and adding their income.

Dynamic departure pushback control at airports: Part A—Linear penalty‐based algorithms and policies

AbstractAirport surface congestion can lead to significantly long taxi‐out times, thus resulting in increased fuel‐burn costs as well as excessive emissions of greenhouse gases. To curtail this undesirable syndrome, in this article, we propose a new penalty‐based dynamic departure pushback control (PDPC) strategy, which employs a linear penalty function dependent not only on the taxiway queue limit but also on the current queue length to ration the pushback frequency at airports, and trades taxiway queueing times with gate‐hold delays to minimize the total operational cost (fuel‐burn and gate‐hold costs). Using data from Beijing Capital International (PEK) airport, four different departure pushback control policies, namely: (i) no‐control (baseline case); (ii) traditional ‐control; (iii) PDPC with a constant taxiway limit; and (iv) PDPC with varying taxiway limits; are compared. Detailed Monte Carlo simulations, which showcase the sensitivity of the total cost function to various problem parameters are presented, and our results indicate that deploying the PDPC policy results in a 42% reduction in total operational costs and a 68% reduction in fuel‐burn (kg) as compared to the baseline case. To analytically reinforce these simulation results, an iterative Markov chain‐based optimization algorithm is also developed to estimate the optimal values of the pushback rate and taxiway queue limit that minimize the total cost function. Such an analytical framework is very useful in the absence of reliable airport data as it only requires estimates of the historical pushback request rates and service times at the taxiway, while yet retaining the capability to closely mirror the simulation results. Our Monte Carlo simulations as well as the Markov chain optimization model validate the strength and impact of the proposed PDPC policy, and demonstrate its practical efficacy in reducing airport surface congestion when applied using data from PEK airport.

CoTEV: Trustworthy and Cooperative Task Execution in Internet of Vehicles

Due to the increasing number of service requests from the vehicles, the load at the road side units (RSUs) increases, which affects the delay-sensitive vehicle services. In Internet of Vehicles (IoV), the vehicles can communicate directly with other vehicles and take help from the vehicles to cooperatively accomplish a task. However, it is very challenging to cooperatively execute a task in an IoV environment with high traffic and dynamic vehicle movements. Furthermore, it is difficult for a task vehicle to choose trustworthy and cooperative vehicles. In this paper, we propose algorithms for cooperative task execution by taking the help of trusted vehicles, when it is not possible to complete a deadline-specified task through the RSUs. We propose a hedonic coalition formation game-based approach to form distributed coalitions of cooperative vehicles. We consider the trust score of the vehicles along with their computational capabilities and journey routes. After each task execution, the service feedback is reflected in the trust score of each cooperative vehicle in the coalition. Our proposed algorithms allow the cooperative vehicles to autonomously choose the coalitions and select a vehicle task to maximize their payoffs. To satisfy the task deadlines in multiple coalitions, we design the merging of vehicle coalitions. We consider the simulation of urban mobility (SUMO) tool to generate the mobility traces of the vehicles in a real road network of Berlin city, which considers the traffic junctions and vehicle density on the roads. Through extensive simulations, we show that the proposed algorithms significantly increase the service rate of delay-sensitive task requests by at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$30.5 \%$</tex-math></inline-formula> and the trust score by at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$20.61 \%$</tex-math></inline-formula> , compared to the benchmark schemes.

Who Requests and Receives Workplace Accommodations? An Intersectional Analysis.

This study investigates who requests workplace accommodations and who is more likely to have requests granted. We investigate the role of demographic characteristics and their intersection, including disability, gender, race/ethnicity, and age. We also consider the role of other personal and job-related factors. We use the data from the Current Population Survey (CPS) 2021 Disability Supplement to estimate the odds ratio of having requested workplace accommodations and having such request granted during the COVID-19 pandemic when the survey was conducted. In supplementary analyses, we explore the relationship between remote work and flexible scheduling and workplace accommodations, as well as possible trends using CPS 2019 Disability Supplement. Our results indicate that Hispanics with disabilities are more likely than others to request workplace accommodations, but they are substantially less likely to be granted accommodations. Consistent with other studies, our paper also finds that people with disabilities, women, and older people are more likely to request accommodations than their respective counterparts. Other personal and job-related factors such as higher education, parenthood, being single, being a citizen, and working in management-related occupations are associated with higher likelihood of requesting workplace accommodations compared to their counterparts, while receiving accommodations is largely explained by occupational differences. Our findings show that there are still disparities in the rates of workplace accommodation requests and provision for multiply marginalized groups, and as such, taking into account intersectional differences in addition and in relation to disability is important.

How low can we go? A randomized controlled trial of low-quantity initial opioid prescriptions for shoulder surgery

Orthopedic surgeons are the third most frequent prescribers of opioid medications. Given the current opioid addiction crisis, it is critical to limit opioid prescriptions to the lowest effective dose. In this study, we investigated how the initial opioid prescription after shoulder surgery affects maximum possible opioid consumption. We hypothesized that fewer pills in the initial opioid prescription would lead to less opioid consumption, a lower refill request rate, and fewer post-surgery office contacts for pain. In this single-center, prospective, randomized controlled clinical trial, 74 adults who underwent shoulder arthroplasty, rotator cuff repair, or other arthroscopic shoulder procedures were enrolled from December 2020 to July 2022. Follow-up was completed by February 2023. Participants were randomly assigned to receive postoperative prescriptions of seven 5-mg oxycodone pills (n=20), 15 pills (n=29), or 23 pills (n=25). The primary outcome was maximum possible opioid consumption within 2 weeks after surgery, calculated by assuming consumption of all pills in the initial prescription, as well as any refills. Secondary outcomes were the opioid prescription refill request rates, post-surgery pain-related telephone calls or messages to the provider's office ("office contacts") within 2 weeks after surgery, and American Shoulder and Elbow Surgeons (ASES) pain scores 2 weeks after surgery. Baseline characteristics did not differ among groups except for mean age, which was younger in the 7-pill group (p=0.047). Maximum possible opioid consumption increased with the number of pills initially prescribed, with means of 78 morphine milligram equivalents (MME) for the 7-pill group, 118 MME for the 15-pill group, and 199 MME for the 23-pill group (p<0.001). None of the secondary outcome measures differed among groups. Refill request rates were 20% for the 7-pill group, 3.4% for the 15-pill group, and 12% for the 23-pill group (p=0.20). The proportions of patients with at least 1 office contact were 35% in the 7-pill group, 45% in the 15-pill group, and 28% in the 23-pill group (p=0.43). Mean ASES pain scores were 49 in the 7-pill group, 44 in the 15-pill group, and 40 in the 23-pill group (p=0.20). After shoulder surgery, an initial prescription of fewer opioid pills was associated with less maximum possible opioid consumption without an increase in the percentage of patients requesting opioid refills or contacting the provider's office for pain-related concerns. An initial postoperative prescription of fewer 5-mg oxycodone pills may be equally or more effective compared with larger quantities for most patients.

SecEG: A Secure and Efficient Strategy against DDoS Attacks in Mobile Edge Computing

Application-layer distributed denial-of-service (DDoS) attacks incapacitate systems by using up their resources, causing service interruptions, financial losses, and more. Consequently, advanced deep-learning techniques are used to detect and mitigate these attacks in cloud infrastructures. However, in mobile edge computing (MEC), it becomes economically impractical to equip each node with defensive resources, as these resources may largely remain unused in edge devices. Furthermore, current methods are mainly concentrated on improving the accuracy of DDoS attack detection and saving CPU resources, neglecting the effective allocation of computational power for benign tasks under DDoS attacks. To address these issues, this paper introduces SecEG, a secure and efficient strategy against DDoS attacks for MEC that integrates container-based task isolation with lightweight online anomaly detection on edge nodes. More specifically, a new model is proposed to analyze resource contention dynamics between DDoS attacks and benign tasks. Subsequently, by employing periodic packet sampling and real-time attack intensity predicting, an autoencoder-based method is proposed to detect DDoS attacks. We leverage an efficient scheduling method to optimize the edge resource allocation and the service quality for benign users during DDoS attacks. When executed in the real-world edge environment, our experimental findings validate the efficacy of the proposed SecEG strategy. Compared to conventional methods, the service rate of benign requests increases by 23% under intense DDoS attacks, and the CPU resource is saved up to 35%.

Reinforcement Learning Based Approaches to Adaptive Context Caching in Distributed Context Management Systems

Real-time applications increasingly rely on context information to provide relevant and dependable features. Context queries require large-scale retrieval, inferencing, aggregation, and delivery of context using only limited computing resources, especially in a distributed environment. If this is slow, inconsistent, and too expensive to access context information, the dependability and relevancy of real-time applications may fail to exist. This paper argues, transiency of context (i.e., the limited validity period), variations in the features of context query loads (e.g., the request rate, different Quality of Service (QoS), and Quality of Context (QoC) requirements), and lack of prior knowledge about context to make near real-time adaptations as fundamental challenges that need to be addressed to overcome these shortcomings. Hence, we propose a performance metric driven reinforcement learning based adaptive context caching approach aiming to maximize both cost- and performance-efficiency for middleware-based Context Management Systems (CMSs). Although context-aware caching has been thoroughly investigated in the literature, our approach is novel because existing techniques are not fully applicable to caching context due to (i) the underlying fundamental challenges and (ii) not addressing the limitations hindering dependability and consistency of context. Unlike previously tested modes of CMS operations and traditional data caching techniques, our approach can provide real-time pervasive applications with lower cost, faster, and fresher high quality context information. Compared to existing context-aware data caching algorithms, our technique is bespoken for caching context information, which is different from traditional data. We also show that our full-cycle context lifecycle-based approach can maximize both cost- and performance-efficiency while maintaining adequate QoC solely based on real-time performance metrics and our heuristic techniques without depending on any previous knowledge about the context, variations in query features, or quality demands, unlike any previous work. We demonstrate using a real world inspired scenario and a prototype middleware based CMS integrated with our adaptive context caching approach that we have implemented, how realtime applications that are 85% faster can be more relevant and dependable to users, while costing 60.22% less than using existing techniques to access context information. Our model is also at least twice as fast and more flexible to adapt compared to existing benchmarks even under uncertainty and lack of prior knowledge about context, transiency, and variable context query loads.

Controller-driven vector autoregression model for predicting content popularity in programmable named data networking devices.

Named Data Networking (NDN) has emerged as a promising network architecture for content delivery in edge infrastructures, primarily due to its name-based routing and integrated in-network caching. Despite these advantages, sub-optimal performance often results from the decentralized decision-making processes of caching devices. This article introduces a paradigm shift by implementing a Software Defined Networking (SDN) controller to optimize the placement of highly popular content in NDN nodes. The optimization process considers critical networking factors, including network congestion, security, topology modification, and flowrules alterations, which are essential for shaping content caching strategies. The article presents a novel content caching framework, Popularity-aware Caching in Popular Programmable NDN nodes (PaCPn). Employing a multi-variant vector autoregression (VAR) model driven by an SDN controller, PaCPn periodically updates content popularity based on time-series data, including 'request rates' and 'past popularity'. It also introduces a controller-driven heuristic algorithm that evaluates the proximity of caching points to consumers, considering factors such as 'distance cost,' 'delivery time,' and the specific 'status of the requested content'. PaCPn utilizes customized DATA named packets to ensure the source stores content with a valid residual freshness period while preventing intermediate nodes from caching it. The experimental results demonstrate significant improvements achieved by the proposed technique PaCPn compared to existing schemes. Specifically, the technique enhances cache hit rates by 20% across various metrics, including cache size, Zipf parameter, and exchanged traffic within edge infrastructure. Moreover, it reduces content retrieval delays by 28%, considering metrics such as cache capacity, the number of consumers, and network throughput. This research advances NDN content caching and offers potential optimizations for edge infrastructures.

Availability Aware Online Virtual Network Function Backup in Edge Environments

With the rapid advancement of edge computing and network function virtualization, it is promising to provide flexible and low-latency network services at the edge. However, due to the vulnerability of edge services and the volatility of edge computing system states, i.e., service request rates, failure rates, and resource prices, it is challenging to minimize the online service cost while providing the availability guarantee. This paper considers the problem of online virtual network function backup under availability constraints (OVBAC) for cost minimization in edge environments. We formulate the problem based on the characteristics of the volatility system states derived from real-world data and show the hardness of the formulated problem. We use an online backup deployment scheme named Drift-Plus-Penalty (DPP) with provable near-optimal performance for the AVBAC problem. In particular, DPP needs to solve an integer programming problem at the beginning of each time slot. We propose a dynamic programming-based algorithm that can optimally solve the problem in pseudo-polynomial time. Extensive real-world data-driven simulations demonstrate that DPP significantly outperforms popular baselines used in practice.

Scheduling Slice Requests in 5G Networks

Network slicing is a 5G paradigm that enables the creation of on-demand logical networks over shared physical infrastructure. In this paper, we present a framework that allows users to make advance slice reservations with the End-to-End Orchestrator (EEO). Our reservation mechanism enables the EEO to make admission decisions instantly upon request arrival, providing guarantees as to when the request can be enabled. We then proceed to address a relevant revenue maximization problem through an optimal solution, which has factorial time complexity. We also propose a low-complexity algorithm that can efficiently allocate resources for the online version of the problem. We conduct evaluations that demonstrate how the reservation mechanism can potentially improve EEO’s revenue. Additionally, we conduct a study on scenarios where the arrival rates of slice requests exhibit a positive correlation with reservation discounts provided by EEO.

AD/ADRD Data Sources: Access and Utilization to Advance Research for Improving Human Health

AbstractBackgroundAlzheimer’s Disease and Related Dementia (AD/ADRD) cases affect more than 6 million Americans and more than 55 million people globally, leading to the deaths of 1 in every 3 older adults (Alzheimer’s Association, 2023). By 2050, these numbers are projected to climb. The National Institute on Aging is leading efforts to effectively prevent and treat AD/ADRD to improve human health and advance research enabled by accessible, utilizable, and shareable AD/ADRD data sources and investigated the utilization of AD/ADRD genomic datasets for secondary research from generalist and disease‐specific repositories. Multifactorial approaches about the use of the NIA Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS) and Database of Genotypes and Phenotypes (dbGaP) were investigated to understand efficacies in secondary data useMethodA correlational study was conducted of AD/ADRD data access requests approved in NIAGADS and dbGaP (2020‐2022). NIAGADS (n = 12) and dbGaP (n = 12) datasets (Figures 1 and 2) were obtained for inclusion in the final analysis. A literature review was also conducted of studies published in PubMed, ScienceDirect, PLOS One, and gray literature (2013‐2023) to understand AD/ADRD data access and utilization key drivers (Figure 3). Hypotheses: H1: There is no difference in the approval rates of data access requests from NIAGADS and dbGaP between 2020‐2022; H2: Utilization of AD/ADRD secondary data sources, NIAGADS and dbGaP, have contributed to growth rates of AD/ADRD publications. Independent two‐sample t‐tests were performed to test the hypotheses.ResultH1: dbGaP data access requests approved (M = 0.793, SD = 0.036) compared to NIAGADS data access requests approved (M = 0.887, SD = 0.032) demonstrated no significance in approval rates between 2020‐2022, t(9) = 2.26, p = 0.160. H2: Publication rates from dbGaP (M = 18.8, SD = 20.8) compared to NIAGADS (M = 2.33, SD = 6.24) demonstrated a significance in publication growth rates, t(13) = 2.16, p = 0.002.ConclusionResearchers trust the use of dbGaP and NIAGADS, and other generalist and disease‐specific data sources (such as ADNI, AlzPathway, ALSOD, dbGaP, NCRAD, OMIM, and UPDB), for secondary data analyses. Researchers utilizing these data sources discovered hidden disease‐gene associations, AD/ADRD pathophenotypes, genetic variants affecting gene transcripts, fMRI functional connectivity changes, relationship modeling of inflammatory factors, candidate gene prioritization, and drug therapies. Researchers recognize the value of using data sources for secondary research.

Ethnoracial disparities in care on a consultation-liaison service at an academic hospital

BackgroundThere is currently an increasing recognition of and focus on structural and institutional racism and its impacts on health disparities. In psychiatry and mental health, research has focused on racial and ethnic disparities in the availability and utilization of mental health services, care in emergency departments, and inpatient psychiatric services. Little is known about disparities in care on general hospital psychiatry consultation-liaison (CL) services. MethodsIn this exploratory study, we conducted a retrospective chart review using electronic health record (EHR) data of all adults (≥ 18 years of age) admitted to inpatient medical or surgical floors at an urban academic medical center for whom a psychiatric consultation was requested during the study period. We examined differences by race and ethnicity in: rates of consultation requests; use of legal holds, constant observation, restraints; follow-up by the CL service; and ultimate disposition. ResultsThe service received 310 unique consults during the study period. Compared to hospital-wide numbers, Black-identifying patients were over-represented in our sample (11.9% vs 6.6%), while Latinx patients were underrepresented (6.1% vs 9.8%). Of the clinical and outcome variables collected, there were higher odds of being placed on a legal hold both prior to (OR 2.6) and after the consult question (OR 2.98) and in the odds of having a one-to-one observer prior to (OR 2.47) and after (OR 2.9) the initial consult visit for Black-identifying patients, when adjusting for confounders. There were no other measurable differences in care or outcomes by racial or ethnic categories. ConclusionBlack-identifying patients may be more likely to receive psychiatric consultation and be placed on legal holds because of a combination of chronic adverse social determinants of health and race-based bias. Conversely, Latinx patients may be less likely to receive psychiatric consultation because of language barriers among other factors. The lack of disparities identified in other domains may be encouraging, but larger studies are needed. Further research is also needed to identify causality and interventions that could help close the gap in care and outcomes for racial and ethnic minorities.

CPSR-HQKDN: A Hybrid Trusted Relay Quantum Key Distribution Network Routing Scheme Based on Classification of Packet Security Requirements

To ensure the security of information exchange in software-defined optical networks, quantum key distribution (QKD) based on quantum mechanics is introduced. However, the slow and valuable process of generating quantum key resources contradicts the high-speed data transmission requirements of optical networks. To address this issue, this paper proposes the CPSR-HQKDN scheme, which takes into account factors such as security requests, key demand, key residual, and key update rates for trusted and untrusted links. This approach improves resource utilization and service efficiency by optimizing the processing order of key requests. Moreover, the routing strategy dynamically adjusts based on the network resource environment, thereby increasing the success rate of key requests. Through simulation experiments comparing the performance of the CPSR-HQKDN routing scheme with existing schemes, it is observed that in high-concurrent scenarios, the CPSR-HQKDN routing scheme can improve the success rate of key requests by at least 5%.

An investigation into the optimal allocation of resources for China's universities to participate in global education governance based on queuing theory

Abstract Queuing theory is used to classify the regularity of educational resource allocation by hierarchy in this paper. The Poisson distribution is used to label the resource queue interval parameters to obtain the expected value of individual queues. The average delay of each class of packets can be reasonably predicted by concatenating individual resources using the Lagrange multiplier algorithm. The load balancing algorithm in each queueing series calculates the average arrival rate of task requests to achieve an optimal model design. The results show that, under the guidance of queuing theory, the coefficient of variance in the participation of Chinese university students in global education governance is reduced by 0.25% on average, and the average of comprehensive efficiency reaches about 1.01%, which realizes the optimal and reasonable allocation of resources.

VNF Migration in Digital Twin Network for NFV Environment

Network Function Virtualization (NFV) allows for the dynamic provisioning of Virtual Network Functions (VNFs), adapting services to the complex and dynamic network environment to enhance network performance. However, VNF migration and energy consumption pose significant challenges due to the dynamic nature of the physical network. In order to maximize the acceptance rate of Service Function Chain Requests (SFCR), and reduce VNF migration and energy consumption as much as possible, we summarize several related factors such as the node hosting state, link hosting state, energy consumption, migrated nodes, and whether the mapping is successful. We define the Markov decision process by considering the factors mentioned above. Next, we design the VNF migration algorithm utilizing actor–critic models, graph convolution networks, and LSTM networks. In order to reduce the risk of trial and error during training and prediction in deep reinforcement learning scenarios, we designed a network architecture based on a digital twin (DT). In simulation experiments, compared with the FF algorithm that greedily selects the first available node, our AC_GCN algorithm significantly improves the acceptance rate of SFC requests by 2.9 times more than the FF algorithm in small topology experiments, and 27 times more than the FF algorithm in large topology experiments. Compared with the deep reinforcement learning (DRL) algorithm, which does not consider all the above factors together, for the small topology experiment, our AC_GCN algorithm outperforms the DRL algorithm in terms of request acceptance rate by 13%, underperforms compared to the DRL algorithm in terms of energy consumption by 3.8%, and underperforms compared to the DRL algorithm in terms of the number of migrated nodes for 22%; for the large topology experiment, our AC_GCN algorithm outperforms the DRL algorithm in terms of the request acceptance rate by 7.7%, outperforms the DRL algorithm in terms of energy consumption by 0.4%, and outperforms the DRL algorithm in terms of the number of migrated nodes by 1.6%.

Reinforcement Learning for Dynamic Dimensioning of Cloud Caches: A Restless Bandit Approach

We study the dynamic cache dimensioning problem, where the objective is to decide how much storage to place in the cache to minimize the total costs with respect to the storage and content delivery latency. We formulate this problem as a Markov decision process, which turns out to be a restless multi-armed bandit problem and is provably hard to solve. For given dimensioning decisions, it is possible to develop solutions based on the celebrated Whittle index policy. However, Whittle index policy has not been studied for dynamic cache dimensioning, mainly because cache dimensioning needs to be repeatedly solved and jointly optimized with content caching. To overcome this difficulty, we propose a low-complexity fluid Whittle index policy, which jointly determines dimensioning and content caching. We show that this policy is asymptotically optimal. We further develop a lightweight reinforcement learning augmented algorithm dubbed fW-UCB when the content request and delivery rates are unavailable. fW-UCB is shown to achieve a sub-linear regret as it fully exploits the structure of the near-optimal fluid Whittle index policy and hence can be easily implemented. Extensive simulations using real traces support our theoretical results.

Utilization and Diagnostic Value of CA-15.3 Test in Breast Cancer: Insights from a Longitudinal Study Based on Turkish Ministry of Health Data

Aim: The objective of this study was to analyze the utilization of the CA-15.3 test for breast cancer monitoring and treatment and evaluate its patterns across different populations.&#x0D; Materials and Method: Data from a five-year period (2017-2021) were collected, including 21.579.044 CA-15.3 tests from 2.981.142 individuals. Descriptive statistics were used to analyze the data, including test counts, test rates per population, rates of exceeding the reference range, and cancer diagnosis rates.&#x0D; Results: The study revealed an increasing trend in the number of CA-15.3 tests requested from 2017 to 2019, followed by a significant decrease in 2020 and 2021. The test was more frequently requested for women, particularly in the age group of 18-64. The rate of cancer diagnosis in individuals undergoing the CA-15.3 test also showed an upward trend over the years. Istanbul had the highest test request rate, followed by Ankara and Izmir. University hospitals requested the most tests, followed by state hospitals, private hospitals, and training and research hospitals.&#x0D; Conclusion: The study provides valuable insights into the utilization patterns of the CA-15.3 test in breast cancer management. While the test showed an increasing trend in test requests and cancer diagnosis rates, caution should be exercised in interpreting the results due to potential limitations. Further research is needed to establish a definitive understanding of the relationship between CA-15.3 levels and cancer diagnosis. The findings emphasize the importance of personalized interpretation of test results and the need for comprehensive studies to validate these findings.

Large-Scale Service Function Chaining Management and Orchestration in Smart City

In the core networking of smart cities, mobile network operators need solutions to reflect service function chaining (SFC) orchestration policies while ensuring efficient resource utilization and preserving quality of service (QoS) in large-scale networking congestion states. To offer this solution, we observe the standardized QoS class identifiers of smart city scenarios. Then, we reflect the service criticalities via cloning virtual network function (VNF) with reserved resources for ensuring effective scheduling of request queue management. We employ graph neural networks (GNN) with a message-passing mechanism to iteratively update hidden states of VNF nodes with the objectives of enhancing allocation of resource blocks, accurate detection of availability statuses, and duplication of heavily congested instances. The deployment properties of smart city use cases are presented along with their intelligent service functions, and we aim to activate a modular architecture with multi-purpose VNFs and chaining isolation for generalizing global instances. Experimental simulation is conducted to illustrate how the proposed scheme performs under different congestion levels of SFC request rates, while capturing the key performance metrics of average delay, acceptance ratios, and completion ratios.

Analysis of requests for reservation of blood concentrates in patients referred to urgent surgeries in a teaching hospital

Introduction: The rates of request and use of blood concentrates are still poorly reported in the literature. This study aimed to analyze the rates of requests for blood concentrates and their use in patients undergoing emergency surgery in a teaching hospital. Methods: A retrospective, quantitative and descriptive study was conducted in 359 medical records of patients in urgent surgery scheduled with a request for a reserve of blood concentrate. The ratios between crossmatched and transfused units (C/T), transfusion index (TI), and probability (TP) were calculated, and the times between request and delivery at the transfusion agency (TA) and patient admission to the surgical center (SC). Results: The mean age was 58.5 ± 22.2 years, with the majority being male (53.1%). There was an average of 27.5 monthly requests (min 12, max 44). Ninety-seven units of blood concentrates were transfused into 44 patients (C/T ratio 7.59; TI 0.27; TP 12.3%). Only seven patients had their requests made after admission to the OR. The median time between the request and arrival at the TA was 1h15min, while that between the request and the patient's arrival at the SC was 5h23min. There was greater transfusion in major surgery (major 37, 14.8% vs. medium 7, 6.5%; p = 0.027) and non-orthopedic surgery (orthopedic 9, 4.0% vs. non-orthopedic 35, 26.9%; p &lt; 0.001). Conclusion: there was a significant discrepancy between the number of requests for blood reservation and its real use and an increased time between reservation requests and their arrival at the TA.

Data conversion control of virtual network devices in cloud computing: A deep reinforcement learning approach

As the response time of virtual space task requests is affected by different data conversion processes, it becomes more serious to solve the load imbalance problem between the underlying nodes and the underlying data links. The traditional least square method needs to distinguish each node for the remapping of the original virtual space. After the trajectories in different spaces and times are mapped again, it is easier to cause new resource bottlenecks. Aiming at the necessary virtual space remapping cycle of cloud service nodes, this paper selects the nodes with the highest upper and lower limits of load rate according to the dynamic critical value of each node's load rate. It migrates part of the virtual network space on the overloaded service nodes to the non-important nodes with a lower load rate, which is to avoid the gradual formation of new resource bottlenecks after remapping. The data transformation model based on reinforcement learning can further improve the acceptance rate of virtual space requests and reduce the computational overhead caused by space remapping. The experiments compare the performance evaluation of different algorithm models on the simulation platform data conversion tasks, including complex instructions test, virtual network performance test, average remapping link overhead, and root mean square error of the underlying node load rate test. The test results indicate that the calculation accuracy of the proposed method is 7.25% higher than other comparison methods, and the computational overhead is reduced by 15.22%. The overall performance is improved by 10.74%. It is verified that the technology in this paper improves the data conversion control method of virtual network devices in the cloud computing environment, and has practical significance for virtual network remapping and data services in cloud computing.