Resource Contention Research Articles

Whānau Māori and Pacific peoples’ knowledge, perceptions, expectations and solutions regarding antibiotic treatment of upper respiratory tract infections: a qualitative study

IntroductionThe rate of community antibiotic use is high in Aotearoa New Zealand (NZ) when compared to other nations, and in NZ, as in most other nations, antibiotics are very commonly prescribed for self-limiting upper respiratory tract infections (URTIs). Resources that build knowledge, perceptions and understanding can potentially reduce unnecessary antibiotic consumption.MethodsTo inform the content of educational resources, we conducted an in-depth qualitative study with 47 participants via 6 focus groups of the knowledge, attitudes, and expectations of whānau Māori and Pacific peoples about antibiotics and URTIs.ResultsFocus groups with 47 participants identified four themes: Knowledge that might influence expectations to receive antibiotics for URTIs; Perceptions - the factors that influence when and why to seek medical care for URTI; Expectations - the features of successful medical care for URTI; Solutions - how to build community knowledge about URTI and their treatment and prevention. Knowledge that might reduce expectations to receive antibiotics for URTI included confidence in the use of alternative remedies, knowledge that URTI are usually caused by viruses, and concerns about antibiotic adverse effects. Participants commonly reported that they would confidently accept their doctor’s recommendation that an antibiotic was not necessary for an URTI, provided that a thorough assessment had been performed and that treatment decisions were clearly communicated.ConclusionThese findings suggest that building patients’ knowledge and skills about when antibiotics are necessary, and increasing doctors’ confidence and willingness not to prescribe an antibiotic for patients with an URTI, could significantly reduce inappropriate antibiotic prescribing in NZ.

What do patients want to know about gender-affirming surgery? Analysis of common patient concerns and online health materials

Patients considering gender-affirming surgery often utilize online health materials to obtain information about procedures. However, the distribution of patient concerns and content of online resources for gender-affirming surgery have not been examined. We aimed to quantify and comprehensively analyze the most searched questions of patients seeking gender-affirming surgery and to examine the quality and readability of associated websites providing the answers. Questions were extracted from Google using the search phrases "gender-affirming surgery," "transgender surgery," "top surgery," and "bottom surgery." Questions were categorized by topic and average search volume per month was determined. Websites linked to questions were categorized by type, and quality of the health information was evaluated utilizing the DISCERN instrument (16-80). Readability was assessed with the Flesch Reading Ease Score and Flesch-Kincaid Grade Level. Ninety questions and associated websites were analyzed. Common questions were most frequently answered by academic websites (30%). Topics included cost (27%), technical details of surgery (23%), and preoperative considerations (11%). Median (interquartile range) DISCERN score across all website categories was 42 (18). The mean readability was of a 12th-grade level, well above the grade six reading level recommended by the American Medical Association. Online gender-affirming surgery materials are difficult to comprehend and of poor quality. To enhance patient knowledge, informed consent, and shared decision-making, there is a substantial need to create understandable and high-quality online health information for those seeking gender-affirming surgery.

Is the internet a sufficient source of information on sarcoidosis?

Many patients use the internet as a source of health information. Sarcoidosis is a complex disease, and internet resources have not yet been analyzed for reliability and content on sarcoidosis. Our study aimed to investigate the content and the quality of information on sarcoidosis provided by internet resources. Google, Yahoo, and Bing were searched for the term "sarcoidosis," and the first 200 hits were saved in each case. Those websites that met the inclusion criteria (English language, no registration fees, and relevant to sarcoidosis) were then analyzed by two independent investigators for readability, quality (HON, JAMA, and DISCERN), and content (25 predefined key facts) of the provided information. The websites were most commonly scientific or governmental (n = 57, 46%), and the median time since the last update was 24 months. Quality was rated with a median JAMA score of 2 (1; 4) and a median overall DISCERN score of 2.4 (1.1; 4.1), both scores represent partially sufficient information. In total, 15% of websites had a HON certificate. Website content measured by the median key fact score was 19 (ranging from 2.5 to 25) with the lowest scores for acute vs. chronic course of the disease, screening for extrapulmonary disease, and diffuse body pain. Poor results were achieved in industry websites and blogs (p = 0.047) with significant differences regarding definition (p = 0.004) and evaluation (p = 0.021). Sarcoidosis-related content of internet resources is partially sufficient; however, several important aspects are frequently not addressed, and the quality of information is moderate. Future directions should focus on providing reliable and comprehensive information on sarcoidosis; physicians from different disciplines and patients including self-support groups should collaborate on achieving this.

A deep reinforcement learning-based optimization method for long-running applications container deployment

Unlike the short execution cycles of batch jobs, intelligent algorithmic applications typically run in long-cycle containers in the cloud(Long-Running Applications, LRA). Both need to meet strict SLO (service level objective) requirements, consider performance scaling to cope with peak load demands, and face issues such as I/O dependencies and resource contention and interference from coexisting containers. The above greatly complicates container deployment and can easily lead to performance bottlenecks. Therefore, the optimization of LRA-like container deployment is one of the key issues that cannot be avoided and needs to be addressed in the cloud computing model. This research uses deep reinforcement learning (DRL) to optimize the deployment of LRAs class containers. The proposed non-generic model is able to customize a dedicated model for each container group, providing high-quality placement and low training complexity; meanwhile, the proposed batch deployment scheme is able to optimize various scheduling objectives that are not directly supported by existing constraint-based schedulers, such as minimizing SLO violations. The experimental results show that the performance of the DRL deployment algorithm improves by 56.2% compared to the average RPS of the baseline, indicating that the manual deployment scheme can only meet the basic requirements but cannot cover the complex interactions between containers under constraints from a global perspective. This limitation severely limits the performance of the whole pod. Meanwhile, based on previous experience, the time consumption of a single deployment scheme is about 1 hour, while the time consumption of the DRL deployment algorithm may be less than 7.5 minutes.

Performance Bug Analysis and Detection for Distributed Storage and Computing Systems

This article systematically studies 99 distributed performance bugs from five widely deployed distributed storage and computing systems (Cassandra, HBase, HDFS, Hadoop MapReduce and ZooKeeper). We present the TaxPerf database, which collectively organizes the analysis results as over 400 classification labels and over 2,500 lines of bug re-description. TaxPerf is classified into six bug categories (and 18 bug subcategories) by their root causes; resource, blocking, synchronization, optimization, configuration, and logic. TaxPerf can be used as a benchmark for performance bug studies and debug tool designs. Although it is impractical to automatically detect all categories of performance bugs in TaxPerf, we find that an important category of blocking bugs can be effectively solved by analysis tools. We analyze the cascading nature of blocking bugs and design an automatic detection tool called PCatch , which (i) performs program analysis to identify code regions whose execution time can potentially increase dramatically with the workload size; (ii) adapts the traditional happens-before model to reason about software resource contention and performance dependency relationship; and (iii) uses dynamic tracking to identify whether the slowdown propagation is contained in one job. Evaluation shows that PCatch can accurately detect blocking bugs of representative distributed storage and computing systems by observing system executions under small-scale workloads.

"Fruits of our past karma": a qualitative study on knowledge and attitudes about congenital anomalies among women in Pune district, India.

Congenital anomalies are distressing events for future parents/parents when a foetal anomaly is detected during pregnancy or when the infant is born with a disability ora congenital disorder. Maternal health services in India do not provide information on these disorders as part of routine activities. The objective is to understand women's knowledge and attitude on causes, prevention, rights; attituted towards disability; and knowledge on medical care, rehabilitation, and welfare services in Pune district, India, with the goal of identifying the contents of birth defects education resources. The study used a qualitative descriptive design. Six focus group discussions were conducted with 24 women from Pune district. Qualitative content analysis was used to identify emergent themes. Three themes emerged. Firstly, women's knowledge on congenital anomalies was limited. These conditions were discussed generally with other adverse pregnancy experiences, and with reference to children with disabilities. Secondly, pregnancy termination for conditions considered untreatable was majorly advocated by most women. Directive counselling for pregnancy termination by doctors was common. Thirdly, stigmatizing attitudes were responsible for children with disabilities being considered a burden, for maternal blaming, and for the stigma and isolation of families. Knowledge on rehabilitation was limited. The study identified that participants. Three target groups and contents for birth defects education were identified. Women's resources should include knowledge on preconception and antenatal opportunities for reducing risks, available medical care, and legal rights. Parents' resources should provide information on treatment, rehabilitation, legal provisions, and rights of disabled children. Resources for the general community should additionally include disability sensitization messages to ensure the inclusion of children with congenital disabilities.

OVERVIEW AND CLASSIFICATION OF DIGITAL EDUCATIONAL RESOURCES FOR ECONOMIC AND MANAGERIAL EDUCATION OF MASTER DEGREE STUDENTS MAJORING IN INFORMATION TECHNOLOGIES

A wide range of forms and content of digital educational resources already exists and new forms appear regularly. Formal and informal organizations and societies work on the classification of resources. Countries and international organizations are establishing initiatives to support the development, sharing, and use of digital educational resources. Some of these resources are open, while others are accessible only to certain communities or geographic areas. This work intends to study the digital educational resources that enhance the development of managerial and economic knowledge and skills in the curriculum of masters majoring in IT. Methods of research appled: analysis, systematization, generalization, and classification were used to describe the proposed digital educational resources and their classification. The work provided the overview and classification of the digital educational resources for economic and managerial education of IT masters. The main varieties of the proposed digital resources are considered. Examples of the use of such digital educational resources in global institutions of higher education are given. The prospects for the future research require the analysis of the effective implementation of the latest solutions in the area of generative machine learning models. These technologies are reaching the levels of development when they become useful in the technologically-aided work of humans in routine as well as in creative jobs. Time is needed to see the first results of the usage of generative models in educational activities, as the parts of digital educational resources and their role in the digital transformation of educational institutions.

COVID-19 Education and Resource Development for Punjabi-Speaking Sikh Farmworkers in California

ABSTRACT Objective While the vast majority of farmworkers in California are Latinx, a small proportion of the farmworkers are Asian Indian who primarily speak Punjabi. To date, there are few COVID-19 resources developed that specifically target Punjabi-speaking farmworkers. This study examines the COVID-19 educational needs of Punjabi-speaking farmworkers in California and aims to inform future development of educational materials for Punjabi-speaking farmworkers. Methods During early 2021, a two-phase qualitative study was conducted. In Phase 1, five key informant interviews were conducted using a semi-structured interview guide to assess the content, visual, and cultural relevance of current COVID-19 educational resources. Based on informant feedback, new agriculture-specific COVID-19 educational resources were developed in Punjabi. In Phase 2, three focus groups were conducted (in Fresno and Yuba Counties) with five participants in each group to evaluate the newly developed COVID-19 resources. Results Informant interviews showed that Punjabi-speaking farmworkers preferred printed handouts, videos, and radio messages to receive COVID-19 related information. Participants preferred 8–1/2”x11” sized printed handouts that were colorful and had culturally relevant photographs. Participant video preferences included live action videos that were short (1–3 mins) with characters representing the Punjabi community. A substantial majority of focus group participants approved the newly developed COVID-19 educational and safety resources. Conclusion Current COVID-19 resources are not meeting the educational needs of Punjabi-speaking farmworkers. This community needs COVID-19 educational and safety materials that are culturally relevant and linguistically appropriate to be available in different formats: handouts, videos, and radio messages.

Energy-efficient cooperative resource allocation and task scheduling for Internet of Things environments

Offloading Internet of Things (IoT) tasks to the cloud for further processing might not always lead to an optimal execution time, particularly in situations such as resource contention, under-provisioning, over-provisioning, and fragmentation. In addition, dynamically optimizing the number of Virtual Machines (VMs) for resource scheduling in order to meet application requirements remains a major research challenge. Further, existing resource scheduling algorithms focus primarily on minimizing operational costs while maximizing resource sharing and utilization. Considering energy utilization as part of the resource allocation and scheduling process as an optimization objective for maintaining load balancing has often been neglected. To address these challenges and more, we propose a cooperative energy-aware resource allocation and scheduling strategy based on a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision-making method. We used the Grid Workloads Archive dataset to evaluate our proposed TOPSIS-based Resource Allocation (TOPREAL) approach. Experimental results with respect to the allocation of VM resources when considering processing a large segment of tasks indicate that TOPREAL outperforms existing algorithms in terms of energy savings, with an average improvement of 40.25%, while maintaining an average improvement of 16.21% when it comes to execution time. Results also demonstrate that our method can save an average of 78.06 processing hours and 63,215 kJ of energy when compared to existing scheduling algorithms. These results demonstrate the effectiveness of our proposed model and the viability of using multi-criteria decision-making techniques such as TOPSIS to solve the resource allocation and scheduling problem in edge environments.

Upper Limb Spasticity: The Quality of Online Patient Resources.

The aim of the study is to assess the quality, readability, content, accessibility, and structure of online resources for patients with upper limb spasticity. This was a cross sectional study examined Internet searches across three search engines related to patient resources for upper limb spasticity. Search phrases for either hand or upper limb spasticity were used. The top 20 Websites from each search were evaluated using the four readability metrics and the DISCERN scale for quality assessment. YouTube videos with exact search phrases were evaluated using DISCERN. Descriptive statistical analyses were performed using SPSS software. Thirty-six Websites and 33 videos met the inclusion criteria for this study. The average Flesh-Kincaid Grade Level of the Websites was 11.7 ± 3.1, showing low Website readability. According to this index, only two Websites were written at the suggested sixth-grade level (5.4%). With a maximum score of 75, the mean DISCERN score for both Websites and videos scored in the "good" range (53.5 ± 8 and 50.5 ± 6.6), respectively. Current online resources for upper limb spasticity are good in quality but are written above the health literacy level of American citizens. Organizations should consider reviewing their present materials and developing high-quality patient education materials that are easier to understand.

Canada's Federal Electoral Districts, 1867–2021: New Digital Boundary Files and a Comparative Investigation of District Compactness

AbstractWe introduce new data resources to enable spatial and nonspatial research on Canadian elections, electoral history and political geography. These include a comprehensive set of distinct identification codes for every federal electoral district in Canada from 1867 to the present, a complete set of digital boundary files for these electoral districts, historical census data aggregated to federal electoral districts, and tools to connect our district identification codes to federal election results. After describing the construction and content of these new resources, we provide an example of their use in a comparative-historical analysis of district compactness in Canada and the United States. We find that, in contrast to the United States, postwar institutional changes to district boundary-drawing processes had little effect on district compactness in Canada.

A rule-based modeling approach for network application availability assessment under dynamic network restoration scheme

The efficient and accurate assessment method for application availability is essential for network operators to make the resilience-cost trade-off policy. However, the backup path of application is dynamically discovered upon both other application requests and global network resource distribution under network restoration scheme, makes the current analytical network availability models difficult for assessing application availability effectively in large-scale network scenarios. This paper proposed a rule-based modeling approach to capture application resilience behavior and measure the uncertainties during dynamic restoration process. Firstly, we propose an evolution object formalism for modeling the attributes of both logical application and network infrastructure. Secondly, the network evolution condition model is proposed to describe the failure modes of individual components. Finally, the network evolution rule is proposed, which utilizes general graph algorithm and resource match function as local rules to determine application restoration resource contention outcomes during the state transition. Through simulations, the proposed approach evaluates application availability in terms of recovery time during system state transition controlled by the rules. The effectiveness of the proposed approach is verified to an actual optical network case under two typical network restoration schemes and compare its accuracy against two analytical approaches based on RBD (Reliability Block Diagram) and Markov Chain. The sensitivity results reveal that link capacity has a more significant impact on application availability than link failures.

Data Flooding against Ransomware: Concepts and Implementations

Ransomware is one of the most infamous kinds of malware, particularly the “crypto” subclass, which encrypts users’ files, asking for some monetary ransom in exchange for the decryption key. Recently, crypto-ransomware grew into a scourge for enterprises and governmental institutions. The most recent and impactful cases include an oil company in the US, an international Danish shipping company, and many hospitals and health departments in Europe. Attacks result in production lockdowns, shipping delays, and even risks to human lives.To contrast ransomware attacks (crypto, in particular), we propose a family of solutions, called Data Flooding against Ransomware, tackling the main phases of detection, mitigation, and restoration, based on a mix of honeypots, resource contention, and moving target defence. These solutions hinge on detecting and contrasting the action of ransomware by flooding specific locations (e.g., the attack location, sensible folders, etc.) of the victim’s disk with files. Besides the abstract definition of this family of solutions, we present an open-source tool that implements the mitigation and restoration phases, called Ranflood.In particular, Ranflood supports three flooding strategies, apt for different attack scenarios. At its core, Ranflood buys time for the user to counteract the attack, e.g., to access an unresponsive, attacked server and shut it down manually. We benchmark the efficacy of Ranflood by performing a thorough evaluation over 6 crypto-ransomware (e.g., WannaCry, LockBit) for a total of 78 different attack scenarios, showing that Ranflood consistently lowers the amount of files lost to encryption.

Nurse educators' suggestions for a digital educational resource suitable for undergraduate student nurses' placement studies: Qualitative empirical research.

To explore and describe nurse educators' suggestions regarding a digital educational resource addressing quality in placement studies for first-year student nurses in nursing homes. A qualitative, explorative, and descriptive research design. Focus group interviews with eight nurse educators and individual interviews with six nurse educators. The interviews were audio-recorded and transcribed verbatim; subsequently, data were analysed in lines with content analysis as described by Graneheim and Lundman. The analysis revealed three main categories: 'Suggestions for a digital educational resource to strengthen and support nurse educators' role in follow-up students', 'Suggestions for a digital educational resource to complement and support interaction between stakeholders in placement', and 'Suggestions for a digital educational resource to facilitate student nurses' learning processes'. The categories were captured by the overarching theme, 'A digital educational resource facilitating interaction between stakeholders and students' learning processes. This study revealed nurse educators' suggestions regarding design elements, content, and use of a digital educational resource addressing placement studies for first-year student nurses' in nursing homes. Implications for the profession and/or patient care: Nurse educators should be involved in designing, developing, and implementing digital educational resources aiming to support student learning in nursing education placement studies. This study explored nurse educators' suggestions for a digital educational resource. They suggested a digital educational resource to strengthen and support their role, support interaction between stakeholders, and facilitate student nurses' learning processes. Further, they suggested a digital educational resource to be used as a supplement rather than as a replacement for nurse educators' physical presence in placements. The Consolidated Criteria for Reporting Qualitative Research reporting guidelines were used. No Patient or Public Contribution.

Evaluation of the usability, content, readability and cultural appropriateness of online alcohol and other drugs resources for Aboriginal and Torres Strait Islander Peoples in New South Wales, Australia

ObjectivesThis study aimed to analyse the usability, content, readability and cultural appropriateness of alcohol and other drugs (AODs) resources for Aboriginal and Torres Strait Islander Peoples in New South Wales...

Concurrency and Operating Systems, Processors, and Programming Languages

The main content of our research is to optimize and improve the concurrency of the system from the three aspects of operating system, processor and programming language. Previous solutions have focused on hardware techniques to improve concurrency. Our goal is to solve the problem of resource occupancy and contention through algorithms, data structures, optimization interrupts, and optimization of process and coroutine scheduling. In the final ideal case, the finite-state machine switching code generated by the compiler can be used to switch the coroutines in the same process. For threads, the thread code provided by the kernel does the switching. The asynchronous system call is optimized to automatically generate the corresponding system call request code according to the execution compiler and maintain the coroutine control block data structure. This is also the most difficult part of this research.

STfusion: Fast and Flexible Multi-NN Execution Using Spatio-Temporal Block Fusion and Memory Management

To maximize the cost-effectiveness of neural network (NN) accelerators, architects are actively developing single-chip accelerators which can execute many NNs simultaneously. However, previous approaches fail to achieve full performance potential by exploiting only spatial or temporal resource sharing (SS or TS). They also do not consider memory management that can significantly affect performance. This limitation leads to the dire need for a new multi-NN accelerator taking both opportunities with careful memory management. But, it is extremely challenging to design an ideal spatio-temporal sharing accelerator because it requires (1) an algorithm that determines the degree of SS/TS in large exploration spaces, (2) a new STS-enabled accelerator devised with diverse design points, and (3) carefully-designed memory management that minimizes resource contention during numerous data transfers upon reconfiguration. To this end, we propose STfusion, a fast and flexible multi-NN execution architecture. First, STfusion partitions an accelerator into multiple smaller TS-enabled accelerators. Second, STfusion dynamically fuses small accelerators to adjust the accelerator sizes. Third, STfusion manages on-chip buffer in a page-granularity for stall-free data transfers. Lastly, STfusion provides an algorithm that determines the degree of SS/TS to achieve high throughput while satisfying QoS goals. Our evaluation shows that STfusion significantly outperforms state-of-the-art multi-NN accelerators.

A Cluster-Based UE-Scheduling Scheme for NR-V2X

The New Radio Vehicle-to-Everything (NR-V2X) technology investigated by the Third Generation Partnership Project (3GPP) explores the high reliability requirement to support message dissemination by vehicle-to-vehicle (V2V) distributed communications for safety applications via a direct communication interface. However, the natural commonality of distributed resource selection may lead to severe resource collisions in a dense high mobility environment. In this paper, we propose a UE-scheduling resource allocation scheme to reduce distributed resource contentions, where vehicles are clustered autonomously based on orthogonal resource sets detection and the cluster size is determined by the cluster-joining threshold. In addition, the algorithms of clustering maintenance are designed to improve the stability of clusters by periodically checking the status of the resource sets and the movement of vehicles. Furthermore, a detailed frame structure design is described, and theoretical performance analysis models of the proposed scheme in different traffic scenarios are investigated. Finally, simulations and numerical results demonstrate that the proposed scheme can reduce packet collisions and increase the packet delivery ratio compared to the conventional distributed scheme, and outperforms some existing schemes in cluster stability.

TEBAS: A Time-Efficient Balance-Aware Scheduling Strategy for Batch Processing Jobs

Common schedulers for long-term running services that perform task-level optimization fail to accommodate short-living batch processing (BP) jobs. Thus, many efficient job-level scheduling strategies are proposed for BP jobs. However, the existing scheduling strategies perform time-consuming objective optimization which yields non-negligible scheduling delay. Moreover, they tend to assign BP jobs in a centralized manner to reduce monetary cost and synchronization overhead, which can easily cause resource contention due to the task co-location. To address these problems, this paper proposes TEBAS, a time-efficient balance-aware scheduling strategy, which spreads all tasks of a BP job into the cluster according to the resource specifications of a single task based on the observation that computing tasks of a BP job commonly possess similar features. The experimental results show the effectiveness of TEBAS in terms of scheduling efficiency and load balancing performance.

Механизмы оптимизации и обновления содержательно-методической подготовки будущих учителей начальных классов

The article presents a structural practice-oriented model of preparing a future teacher for an active professional activity, taking into account the educational standards of primary general education, based on practical forms of educational activity, provided with a high level of theoretical knowledge and possession of the methodology and technologies of self-knowledge. The model embodies the original scientific and methodological support for the process of professional training of future teachers at Kazan University, the system priority of which was the realization of the student's personal capabilities through interactive methods of his cognitive, communicative and axiological activities. Scientific and methodological support is considered as an applied empirical mechanism for optimizing and updating the content and methodological training of future teachers who are capable of creative organization of the process of teaching younger students, of effective adjustment and development of basic educational programs in elementary school. The information-logistical approach has become the leading one in the study of the problem, which is important in conditions when the content of education is shifting from memorization to the search, selection and comprehension of information, and teacher training is carried out in an environment that provides an integrated perception of experience, allowing him to be included in the advanced design of the content of education. The article describes a system of interrelated practice-oriented activities, determined by the dynamics of content modules, the conditions for their implementation and evaluation criteria, aimed at developing the professional competencies of future teachers, improving their qualifications and professional skills in obtaining personal, meta-subject and subject results. The original scientific and methodological support for the process of professional training of future teachers offers interactive methods that represent innovative and unique tools for designing the learning process and predicting its effectiveness, allowing you to most accurately determine the advantages and problems of the real educational process of teacher training when testing new technologies and teaching methods. The implementation of the proposed scientific and methodological support expands and actualizes the resource content of the educational process through empirical research based on the study, analysis and generalization of real innovative pedagogical experience.