Periodic Updates Research Articles

Mobile Platform with Dynamic Optimization of the Pattern in Education in Colleges Through the Perspective of Network Informatization

The combination of mobile learning platforms and network informatization offers numerous benefits to learners, educators, and institutions. Learners can take control of their learning journey, accessing educational materials at their convenience and engaging in collaborative learning activities with peers from diverse backgrounds. This paper aims to explore the integration of mobile learning platforms and network informatization, examining their impact on educational practices, learner engagement, and the overall learning experience. The network informatization is assessed and monitored with Dynamic Programming Optimization (DPO) to compute the feature in reverse osmosis in English education. The attributes and features in the English language are computed and estimated for the periodic information update within the system. The DPO process is implemented along with the mandhani fuzzy set for the estimation of features in English education in colleges and universities. The information processed is updated in the mobile learning platform for the computation of the features in the English language and classification is performed with the deep learning model. Simulation analysis stated that constructed model is effective for the estimation and computation of the features and patterns in English language teaching in colleges and universities.

Poisoning-Assisted Property Inference Attack Against Federated Learning

Federated learning (FL) has emerged as an ideal privacy-preserving learning technique which can train a global model in a collaborative way while preserving the private data in the local. However, recent advances have demonstrated that FL is still vulnerable to inference attacks, such as reconstruction attack and membership inference. Among these attacks, the property inference attack, aiming to infer properties of the training data that are irrelevant with the learning objective, has not received too much attention while resulting in severe privacy leakage. Existing property inference attack approaches either cannot achieve satisfactory performance when the global model has converged or under dynamic FL where participants can drop in and drop out freely. In this paper, we propose a novel poisoning-assisted property inference attack (PAPI-attack) against FL. The key insight is that there exists underlying discriminative ability in the periodic model updates, which reflects the change of the data distribution, especially the occurrence of the sensitive property. Thus, a binary attack model can be constructed by a malicious participant for inferring the unintended information. More importantly, we present a property-specific poisoning mechanism by modifying the label of training data from the adversary to distort the decision boundary of shared (global) model in FL. Consequently, benign participants are induced to disclose more information about the sensitive property. Extensive experiments on real-world datasets demonstrate that PAPI-attack outperforms the state-of-the-art property inference attacks against FL.

Weaponizing Artificial Intelligence

Abstract: The research paper delved into the concerns of chatbot users and the potential risks lurking in the shadows—security, privacy, data protection, and social quandaries. What emerged from the study was a disquietude among users, fearing the misuse or thievery of their personal information. Additionally, apprehension surfaced regarding the sinister deployment of chatbots in the realms of phishing scams and the propagation of deceitful news. To ensure a shielded experience when engaging with chatbots, users were provided with sagacious counsel. They were admonished to only divulge their valuable information to chatbots that have garnered a reputable and trustworthy stature. Moreover, a word of caution resonated, urging users to exercise vigilance in their cyber-surfing, refraining from clicking on dubious links and showing discretion when confronted with attachments or applications proposed by chatbots. Careful advice also dictated the periodic updating of devices and software, ensconcing the latest security fixes within their digital fortresses. As an added layer of defence, users were encouraged to change their passwords at regular intervals, an act of precautionary foresight guarding against potential breaches of security.

MOBILE MAPPING SOLUTIONS FOR THE UPDATE AND MANAGEMENT OF TRAFFIC SIGNS IN A ROAD CADASTRE FREE OPEN-SOURCE GIS ARCHITECTURE

Abstract. The adoption of open-source mobile mapping applications in public administration has risen over the last decade due to their interdisciplinary vocation and flexibility in adapting to existing Geographic Information System (GIS) software architectures. This facilitates complex procedures of data collection and management required for transportation and environmental models, emergency management, and maintenance operations. The Ministry of Infrastructure and Transport in Italy requires road-owning agencies to build and maintain a mapping inventory of their road networks, including georeferenced information about streets and ancillary elements, such as traffic signs. Innovative and integrated street-level approaches for the rapid mapping of road entities using open-source mobile mapping tools represent a valuable low-cost solution for the periodical update of road entities inventory. The adoption of these tools allows public administrators to easily consult the road inventory even outside the office, conducting in situ validation and quality evaluation. This work presents a case study focused on the update and management of traffic sign entities of the Road Cadastre of the Province of Piacenza (Italy) using Qfield and Open Data Kit (ODK) Collect as alternatives to the previous traditional survey method that consisted in the use of field papers. A comparison between the adoption of the two mobile apps is conducted, identifying benefits and limitations in terms of both data accuracy and usability. Validation scripts, project and form structure were developed with the perspective of making the entire workflow as reproducible and transparent as possible, sharing details in a dedicated GitHub repository.

Reinforcement Learning for Intrusion Detection: More Model Longness and Fewer Updates

Several works have used machine learning techniques for network-based intrusion detection over the past few years. While proposed schemes have been able to provide high detection accuracies, they do not adequately handle the changes in network traffic behavior as time passes. Researchers often assume that model updates can be performed periodically as needed, although this is not easily feasible in real-world scenarios. This paper proposes a new intrusion detection model based on a reinforcement learning approach that aims to support extended periods without model updates. The proposal is divided into two strategies. First, it applies machine learning scheme as a reinforcement learning task to long-term learning -maintaining high reliability and high classification accuracies over time. Second, model updates are performed using a transfer learning technique coped with a sliding window mechanism that significantly decreases the need for computational resources and human intervention. Experiments performed using a new dataset spanning 8TB of data and four years of real network traffic indicate that current approaches in the literature cannot handle the evolving behavior of network traffic. Nevertheless, the proposed technique without periodic model updates achieves similar accuracy rates to traditional detection schemes implemented with semestral updates. In the case of performing periodic updates on our proposed model, it decreases the false positives up to 8%, false negatives up to 34%, with an accuracy variation up to only 6%, while demanding only seven days of training data and almost five times fewer computational resources when compared to traditional approaches.

Digital Twin simulation models: a validation method based on machine learning and control charts

The adoption of simulation models as Digital Twins (DTs) has been standing out in recent years and represents a revolution in decision-making. In this context, we note increasingly faster and more efficient decisions by mirroring the behaviour of physical systems. On the other hand, we highlight the challenges to ensure the simulation models validity over time since traditional validation approaches have limitations when we consider the periodic update of the model. Thus, the present work proposes an approach based on the constant assessment of these models through Machine Learning and control charts. To this end, we suggest a monitoring tool using the K-Nearest Neighbors (K-NN) classifier, combined with a p-control chart, to periodically assess the validity of DT simulation models. The proposed approach was tested in several theoretical cases and also implemented in a real case study. The findings suggest that the proposed tool can monitor the DT functioning and identify possible special causes that could compromise its results. Finally, we highlight the wide applicability of the proposed tool, which can be used in different DT models, including near/real-time models with different characteristics regarding connection, integration, and complexity.

Data for paper "Influence of periodic forecast updates on a rolling horizon MRP planned production system"

Data for paper "Influence of periodic forecast updates on a rolling horizon MRP planned production system"

Formation of subject competence students using competence-oriented tasks in biology

Introduction. Periodic updating of the content of education in accordance with the achievements in the study area and with the specifics of the discipline taught is one of the important conditions for improving the quality of education in secondary school. The integrated use of the competency-based approach is considered an important didactic condition for improving both the content and methods of teaching biology in secondary schools, which requires an analysis of the level of subject competencies and ways of their formation in the learning process. Materials and methods. The study involved 420 students of educational institutions of the Kyrgyz Republic (220 in the experimental group and 200 in the control group). Research methods: questioning, analysis of literary sources on the subject of the study. Methods of mathematical statistics: Pearson's chi-squared test. Research results. The levels of formation of subject competencies of students through the solution of competency-oriented biological tasks are revealed and experimentally tested. It has been established that students more effectively perform various options for laboratory and practical work in biology, can independently use conventional signs, diagrams and models of biological processes and objects, and are active in performing independent research work of varying degrees of complexity. On the basis of a comparative analysis of empirical data, statistical analysis, positive dynamics and a significant increase in the values of level indicators of subject competencies (χ2 = 15.825; p<0.01) were established. Conclusions. The introduction of competence-oriented tasks in the educational process in the subject of biology will help increase students' cognitive activity, interest in obtaining biological knowledge and applying the formed subject competencies in practical activities. Keywords: updating, content of biological education, activity approach, competence-based approach, subject standard in biology, subject activity, learning activity, learning task

Survival outcomes used to generate version 9 American Joint Committee on Cancer staging system for anal cancer.

The American Joint Committee on Cancer (AJCC) staging system for all cancer sites, including anal cancer, is the standard for cancer staging in the United States. The AJCC staging criteria are dynamic, and periodic updates are conducted to optimize AJCC staging definitions through a panel of experts charged with evaluating new evidence to implement changes. With greater availability of large data sets, the AJCC has since restructured and updated its processes, incorporating prospectively collected data to validate stage group revisions in the version 9 AJCC staging system, including anal cancer. Survival analysis using AJCC eighth edition staging guidelines revealed a lack of hierarchical order in which stage IIIA anal cancer was associated with a better prognosis than stage IIB disease, suggesting that, for anal cancer, tumor (T) category has a greater effect on survival than lymph node (N) category. Accordingly, version 9 stage groups have been appropriately adjusted to reflect contemporary long-term outcomes. This article highlights the changes to the now published AJCC staging system for anal cancer, which: (1) redefined stage IIB as T1-T2N1M0 disease, (2) redefined stage IIIA as T3N0-N1M0 disease, and (3) eliminated stage 0 disease from its guidelines altogether.

Multimodal Intersection Signal Timing Considerations Framework, Performance Measures, and Case Study

Signal retiming is a cost-effective practice to improve intersection operations through periodic updating of the timing plans. Traditionally, signal retiming has focused on vehicular-based performance (e.g., reducing vehicle delay or number of stops), while often unintentionally ignoring the needs of other users, such as pedestrians, cyclists, and transit vehicle passengers. This unintended omission is further exacerbated by the signal timing tools used in the industry, which tend to primarily report vehicular metrics. To address these challenges, this research developed a multimodal signal timing framework along with easy-to-use performance measures. These performance measures include (1) vehicle delay, (2) crosswalk delay, (3) crossing stress, and (4) bus delay. To assess the approach effectiveness, the proposed framework and the developed tools were applied to 12 intersections along the North Glebe Corridor, in Arlington, VA, for the weekday a.m. peak. Analysis results showed that the proposed framework and tools can incorporate most multimodal considerations and address the limitations of the traditional vehicle-based approaches by quantifying multimodal tradeoffs at signals. With the proposed tools, the alternative timing plan developed for the a.m. peak yielded a 4.5% improvement in performance along the corridor, versus a degradation of 3.7% if only vehicle delay was considered in the analysis. Additional sensitivity analysis conducted along the corridor using various timing plan scenarios showed that the calculated values of the measures are accurately reflecting the anticipated effects of signal timing changes that aim to improve crossing stress for non-motorized users and/or reduce their delay at the intersection crosswalks.

CoVe-Tracker: An Interactive SARS-CoV-2 Pan Proteome Evolution Tracker.

SARS-CoV-2 has significantly mutated its genome during the past 3 years, leading to the periodic emergence of several variants. Some of the variants possess enhanced fitness advantage, transmissibility, and pathogenicity and can also reduce vaccine efficacy. Thus, it is important to track the viral evolution to prevent and protect the mankind from SARS-CoV-2 infection. To this end, an interactive web-GUI platform, namely, CoVe-tracker (SARS-CoV-2 evolution tracker), is developed to track its pan proteome evolutionary dynamics (https://project.iith.ac.in/cove-tracker/). CoVe-tracker provides an opportunity for the user to fetch the country-wise and protein-wise amino acid mutations (currently, 44139) of SARS-CoV-2 and their month-wise distribution. It also provides position-wise evolution observed in the SARS-CoV-2 proteome. Importantly, CoVe-tracker provides month- and country-wise distributions of 2065 phylogenetic assignment of named global outbreak (PANGO) lineages and their 177564 variants. It further provides periodic updates on SARS-CoV-2 variant(s) evolution. CoVe-tracker provides the results in a user-friendly interactive fashion by projecting the results onto the world map (for country-wise distribution) and protein 3D structure (for protein-wise mutation). The application of CoVe-tracker in tracking the closest cousin(s) of a variant is demonstrated by considering BA.4 and BA.5 PANGO lineages as test cases. Thus, CoVe-tracker would be useful in the quick surveillance of newly emerging mutations/variants/lineages to facilitate the understanding of viral evolution, transmission, and disease epidemiology.

Viral nervous necrosis resistance in gilthead sea bream (Sparus aurata) at the larval stage: heritability and accuracy of genomic prediction with different training and testing settings

BackgroundThe gilthead sea bream (Sparus aurata) has long been considered resistant to viral nervous necrosis (VNN), until recently, when significant mortalities caused by a reassortant nervous necrosis virus (NNV) strain were reported. Selective breeding to enhance resistance against NNV might be a preventive action. In this study, 972 sea bream larvae were subjected to a NNV challenge test and the symptomatology was recorded. All the experimental fish and their parents were genotyped using a genome-wide single nucleotide polymorphism (SNP) array consisting of over 26,000 markers.ResultsEstimates of pedigree-based and genomic heritabilities of VNN symptomatology were consistent with each other (0.21, highest posterior density interval at 95% (HPD95%): 0.1–0.4; 0.19, HPD95%: 0.1–0.3, respectively). The genome-wide association study suggested one genomic region, i.e., in linkage group (LG) 23 that might be involved in sea bream VNN resistance, although it was far from the genome-wide significance threshold. The accuracies (r) of the predicted estimated breeding values (EBV) provided by three Bayesian genomic regression models (Bayes B, Bayes C, and Ridge Regression) were consistent and on average were equal to 0.90 when assessed in a set of cross-validation (CV) procedures. When genomic relationships between training and testing sets were minimized, accuracy decreased greatly (r = 0.53 for a validation based on genomic clustering, r = 0.12 for a validation based on a leave-one-family-out approach focused on the parents of the challenged fish). Classification of the phenotype using the genomic predictions of the phenotype or using the genomic predictions of the pedigree-based, all data included, EBV as classifiers was moderately accurate (area under the ROC curve 0.60 and 0.66, respectively).ConclusionsThe estimate of the heritability for VNN symptomatology indicates that it is feasible to implement selective breeding programs for increased resistance to VNN of sea bream larvae/juveniles. Exploiting genomic information offers the opportunity of developing prediction tools for VNN resistance, and genomic models can be trained on EBV using all data or phenotypes, with minimal differences in classification performance of the trait phenotype. In a long-term view, the weakening of the genomic ties between animals in the training and test sets leads to decreased genomic prediction accuracies, thus periodical update of the reference population with new data is mandatory.

Indicator-based approach for fluvial flood risk assessment at municipal level in Slovakia

The article focuses on the mapping and assessment of fluvial flood risk at municipal level of Slovakia. The fluvial floods risk index (FFRI), composed of a hazard component and a vulnerability component, was computed for 2927 municipalities using spatial multicriteria analysis and geographic information systems (GIS). The fluvial flood hazard index (FFHI) was computed based on eight physical-geographical indicators and land cover representing the riverine flood potential and also the frequency of flood events in individual municipalities. The fluvial flood vulnerability index (FFVI) was calculated using seven indicators representing the economic and social vulnerability of municipalities. All of the indicators were normalized and weighted using the rank sum method. By aggregating the weighted indicators, we obtained the FFHI and FFVI in each municipality. The final FFRI is a result of a synthesis of the FFHI and FFVI. The results of this study can be used mainly in the framework of flood risk management at national spatial scale, but also for local governments and periodic update of the Preliminary Flood Risk Assessment document, which is carried out at the national level under the EU Floods Directive.

Knowledge of Pakistani Nurses about Theory Practice Gap in Nursing

Aims: To evaluate the knowledge of the participants about theory practice gap and to determine the association of knowledge with demographic variable like age and educational level of the participants. Methodology: A cross sectional study design was used to conduct this study. This study was conducted at Government and private Universalities or Hospitals Lahore. Approximately 09 months after the approval of synopsis from Research Ethical Committee (REC). Sample size of 192 cases is calculated with 95% confidence interval, 7% margin of error and convenient sampling was used. Target population of the present study was nursing instructors and clinical nurses. SPSS version 21.0 was used for data analysis. Quantitative variables mean and standard deviation was be used. For categorical variables frequency and percentage was used.Chi-square test was be used. P-Value ≤ 0.05 was being considered statistically significant. The study was conducted in private and public sector in Lahore. Results: In out of 192 respondent about 65.63% having score less than 13.8 and 34.38% scored between 13.8-18.4. However non of the participents have score greater than > 18.4. Its mean that most of the study particepents fall in the poor knoweledge score. It further illustrated that regarding to assess the knowledge level of the participents about theory practice gap; show that there were no awearness regarding the thory practice gap in nursing. The finding of this study as helpful for the future researcher and educational institute how to reduce this theroy prectice gap. Conclusion: It is not enough to have good knowledge about the existence of the gap; efforts must be made to minimize it in order to close the theory-practice gap. The theory-practice gap can be reduced or minimized through effective collaboration, improved knowledge acquisition, continuous education, periodic curriculum updates, and the incorporation of evidence-based practices into the curriculum of learning. Keywords: Theory-Practice Gap, Knowledge, Pakistani Nurse, Educators, Private and Government Institute

Геосервисное решение проблемы актуализации пространственных данных по результатам государственного топографического мониторинга

The authors reveal the informational and functional contents of the geoservice, using the example of the proposed mechanism for updating spatial data using state topographic monitoring materials. The algorithm of a geoservice functioning based on the data obtained in the process of survey, detailed topographic monitoring and its further use as geoinformation services for solving the tasks of socio-economic, environmental, defense and national security of the Russian Federation is shown. The analysis of domestic experience in this field (the national spatial data system, the geoportal of the Federal Spatial Data Fund, the Public Cadastral Map, the Roscosmos Group of Companies, the “People’s Map”, i.e. Open Street Map on the Yandex Maps geoportal, etc.) enables determining the composition of geoinformation resources requiring periodic and operational updating. Creating and actualization of digital (electronic) topographic maps and other types of digital cartographic products in real time, using continuous topographic monitoring data, and presenting them to users through the geoservice “State topographic monitoring” is a promising scientific and practical direction for development of geoinformation mapping and infrastructure designed to spread relevant spatial data.

An RRAM retention prediction framework using a convolutional neural network based on relaxation behavior

The long-time retention issue of resistive random access memory (RRAM) brings a great challenge in the performance maintenance of large-scale RRAM-based computation-in-memory (CIM) systems. The periodic update is a feasible method to compensate for the accuracy loss caused by retention degradation, especially in demanding high-accuracy applications. In this paper, we propose a selective refresh strategy to reduce the updating cost by predicting the devices’ retention behavior. A convolutional neural network-based retention prediction framework is developed. The framework can determine whether an RRAM device has poor retention that needs to be updated according to its short-time relaxation behavior. By reprogramming these few selected devices, the method can recover the accuracy of the RRAM-based CIM system effectively. This work provides a valuable retention coping strategy with low time and energy costs and new insights for analyzing the physical connection between the relaxation and retention behavior of the RRAM device.

The use of information technology applications by faculty members at the college of basic education in the public authority for applied education and training in the state of Kuwait

This study sought to determine the importance of the use of information technology by faculty members in the educational departments of the College of Basic Education in the Public Authority for Applied Education and Training in the States of Kuwait. A descriptive survey method is used to achieve these goals. A questionnaire is utilized as the main tool for the study on a stratified random sample of 42 faculty members. One of the most prominent results is that a large number of faculty members do not deal efficiently with information technology, and are not aware of all the available technological tools. The study also concluded with a proposed vision to increase the efficiency of information technology performance, including training, periodic updating, and permanent empowerment to use it as an integral part of the quality of the educational process in the Public Authority for Applied Education and Training.

Enhancing Resilience in Mobile Edge Computing Under Processing Uncertainty

Task offloading is a powerful tool in Mobile Edge Computing (MEC). However, in many practical scenarios, the number of required processing cycles of a task is unknown beforehand and only known until its completion. This poses a serious challenge in making offloading decisions as the number of processing cycles is a key parameter to determine whether a task’s deadline can be met. To cope with such processing uncertainty, we formulate a Chance-Constrained Program (CCP) that offers probabilistic guarantees to task deadlines. The goal is to minimize energy consumption for the users while meeting the probabilistic task deadlines. We assume that only the means and variances of the random processing cycles are available, without any knowledge of distribution functions. We employ a powerful tool called Exact Conic Reformulation (ECR) that reformulates probabilistic deadline constraints into deterministic ones. Subsequently, we design an online solution called EPD (Energy-minimized solution with Probabilistic Deadline guarantee) for periodic scheduling and schedule updates during run-time. We show that EPD can address the processing uncertainty with probabilistic deadline guarantees while minimizing the users’ energy consumption.

From the Editor

I hope this issue finds you all well.First, a few updates.The American Society of Appraisers has posted on its website updates to Valuing a Business, 6th edition, the current edition of Shannon Pratt's original primer on business valuation and one of the world's most respected valuation references. The updates posted include the following:The ASA announced it will be providing periodic updates to the resources and appendix to VAB6; the appendix and future updates are available at https://www.appraisers.org/shop/publications-and-subscriptions/books/valuing-a-business/appendix.Also, data on the Implied Private Company Pricing Line (IPCPL) are no longer being updated on the BVResources.com web site. The Business Valuation Review released the first article by Bob Dohmeyer and Peter Butler on the Implied Private Company Pricing Line in 2012, and subsequent articles were published on the topic in the Business Valuation Review and Business Valuation Update. Business Valuation Resources provided data for business valuation professionals to employ the IPCPL, which until recently was available at BVResources.com and was accessible through their cost-of-capital center. This information is no long being updated, but the old resources are still accessible through BVResources.com.In this issue, Joseph Thompsom and Paul Skluzak explore control premiums using decisions from the Delaware Court of Chancery. Also, Barry Cunningham provides some insights as to a real estate appraiser's consideration of property rights as well as the impact of this on the valuation of intangible assets. And, last but not least, Erin Hollis provides a current business valuation case law update. Thanks to all of our authors for sharing their insights.As always, we invite our readers to submit articles on new or interesting content, such as case law updates such as that provided by Erin, practical tips like those provided by Barry, or general observations. Feel free to e-mail me (kgarcia@finresearch.com) any thoughts for articles or letters you may have, and I will be happy to discuss those with you.Please enjoy this issue.

Evaluación de riesgos y mejora de la seguridad biológica y radiológica en la toma de radiografía torácica a pacientes con COVID-19

IntroductionHealth workers are at high risk of becoming infected with COVID-19. The objective of the study was to evaluate the risks and improve the biological and radiological safety measures for taking chest X-rays in patients with COVID-19 in a Social Security hospital in Utcubamba (Peru). Material and methodsQuasi-experimental intervention study type before and after without a control group, carried out between May and September 2020. A process map and an analysis of failure modes and effects (FMEA) of radiological care were prepared. The gravity (G), occurrence (O), and detectability (D) values ??were found and the risk priority number (RPN) was calculated for each failure mode (FM). FM with RPN ≥ 100 and G ≥ 7 were prioritized. Improvement actions were implemented based on the recommendations of recognized institutions and the O and D values ??were re-evaluated. ResultsThe process map consisted of 6 threads and 30 steps. 54 FM were identified, 37 of whom had RPN ≥ 100 and 48 had G ≥ 7. Most of the errors occurred during the examination 50% (27). After entering the recommendations, 23 FM had RPN ≥ 100. ConclusionsAlthough none of the measures applied through the FMEA made the failure mode impossible, they made it more detectable and less frequent and reduced the RPN for each failure mode; however, a periodic update of the process is necessary.