Processing Resources Research Articles

An SDN-enabled fog computing framework for wban applications in the healthcare sector

For healthcare systems utilizing Wireless Body Area Networks (WBANs), maintaining the network's diverse Quality of Service (QoS) metrics necessitates effective communication among Fog Computing resources. While fog nodes efficiently handle local requests with substantial processing resources, it is crucial to acknowledge the unpredictable availability of these nodes, potentially resulting in a decline in system performance. This study explores a software-defined fog architecture supporting different healthcare applications in Internet of Things (IoT) environment to ensure consistent specialized medical care amidst evolving health issues. Even minor delays, packet losses, or network overhead could adversely affect patient health. The article establishes a mathematical foundation based on transmitted and sensed data, ensuring each fog node executes an ideal quantity of processes. This study formulates an optimization problem to maximize the utility of fog nodes, leveraging the Lagrangian approach and Karush-Kuhn-Tucker conditions to streamline and resolve the optimization problem. Performance analysis demonstrates a significant reduction in delays by approximately 38 %, 29 %, and 32 %, along with energy savings of roughly 26.89 %, 12.16 %, and 22.50 %, compared to benchmark approaches. This study holds promise in healthcare, cloud-fog simulation, and WBANs, emphasizing the critical need for swift and accurate data processing.

Integration Of Electronic Learning Resources in The Educational Process: Stages and Significance

This article describes the stages of implementation of the integration of electronic educational resources to increase the efficiency of independent education of students of higher educational institutions and the results of research on determining the practical importance of integrating electronic educational resources

Innovative Fault Current Evaluation Method for Active DC Grids

DC smart grids are a promising solution for the efficient integration of renewable energy sources and loads. Still, their widespread adoption is hindered by significant challenges related to fault response, identification, and clearance. The traditional DC fault analysis method is a useful tool for straightforwardly understanding the behaviour of fault current contributions from DC converters in LVDC networks during a fault. However, when a system with multiple converters and non-negligible fault impedance need to be considered, its accuracy is severely limited due to the assumptions included in the problem solution, thus leading to the following: (a) the dependency of the results’ reliability on fault impedance values and/or other converter fault current contributions; (b) the inaccuracy of the diode current estimation; and (c) the inaccuracy of the conductor joule integral. Thus, these results’ data may be unreliable for designing protection systems for one converter or for an entire network. In order to overcome these issues, this paper proposes an innovative, simple numerical approach to DC fault current evaluation, which can be adopted when the number of converters become significant, or the network is complex. This method arises from the primary interest in solving the circuit to extract the indicators (current peak value and time, joule integral, etc.) necessary for designing circuit protections. This approach proved to grant two main advantages over traditional methods: (a) it provides accurate results, with no need to introduce any specific assumption; (b) it can be structured to manage an arbitrary number of converters; and (c) it reduces the computational processing times and resources necessary to simulate an entire DC network in comparison to other circuit solution software.

Long-term findings on working memory neural dynamics in healthcare workers after mild COVID-19

ObjectiveUnderstanding the long-term impact of Coronavirus Disease 2019 (COVID-19) on cognitive function, even in mild cases, is critical to the well-being of individuals, especially for healthcare workers who are at increased risk of exposure to the virus. To the best of our knowledge, the electrophysiological activity underlying cognitive functioning has not yet been explored. MethodsSeventy-seven healthcare workers took part in the study (43 with mild infection about one year before the study and 34 uninfected). To assess cognitive status, event-related potentials (ERPs) and behavioural responses were recorded while participants performed a working memory task. ResultsCOVID-19 participants exhibited a distinct neural pattern with lower parieto-occipital N1 amplitudes and higher frontal P2 amplitudes as compared to non-infected healthcare workers. We found no behavioural differences (reaction times and error rates) in working memory functioning between groups. ConclusionsThis neural pattern suggests the presence of a decrement of processing resources linked to the encoding of sensory information (N1), followed by the enhanced of the P2 response which could be interpreted as the activation of compensation mechanism in COVID-19 participants. SignificanceThe current findings point out that ERPs could serve as valuable neural indices for detecting distinctive patterns in working memory functioning of COVID-19 participants, even in mild cases. However, further research is required to precisely ascertain the long-term cognitive effects of COVID-19 beyond one-year post-infection.

Novice nurses' transition shock and professional identity: The chain mediating roles of self-efficacy and resilience.

The aim of this study was to explore the association between transition shocks and professional identity and the mediating roles of self-efficacy and resilience among Chinese novice nurses. Novice nurses experience transition shock when they start their careers, which might lead to decreased professional identity and ultimately turnover. By contrast, self-efficacy and resilience are excellent psychological resources that may be associated with higher professional identity. It is unclear how transition shock affects professional identity by influencing these two internal resources. A cross-sectional survey. The STROBE guidelines were followed to report this study. Convenience sampling was used to recruit participants, and 252 novice nurses completed the Transition Shock of Novice Nurses Scale, the Professional Identity Assessment Scale, the General Self-Efficacy Scale and the Connor-Davidson Resilience Scale between April 2022 and May 2022. Influencing factors were primarily identified using independent-sample t-tests and a one-way ANOVA. Structural equation modelling was used to detect the mediating effects of self-efficacy and resilience. Differences in novice nurses' levels of professional identity were found across age groups, hospitals and departments. Transition shock was negatively related to professional identity. Self-efficacy and resilience mediated the complete chain relationship between transition shock and professional identity. To our knowledge, this study is the first to explore the mediating effect of self-efficacy and resilience on transition shock and professional identity. Higher transition shock may lead to lower professional identity by reducing self-efficacy and resilience. Nursing managers ought to emphasise the significant role of psychological resources in the work adaptation process of novice nurses. It may be more effective to improve professional identity and maintain the stability of the health care system. Nursing administrators working at seven preselected hospitals actively assisted us in the process of collecting self-report questionnaires from novice nurses, such as by booking appointments and providing access for questionnaire administration. In addition, the participants were actively involved in the data collection process.

Sentiment Analysis of Finnish Twitter Discussions on COVID-19 During the Pandemic

With the outbreak of the COVID-19 pandemic, researchers have studied how people reacted on social media during the pandemic. Sentiment analysis has been leveraged to gain insight. However, much of the research conducted on both sentiment analysis and social media analysis of COVID-19 often focuses on widespread languages, such as English and Chinese. This is partly due to the scarcity of resources for natural language processing and sentiment analysis for morphologically complex and less prevalent languages such as Finnish. This paper aims to analyze sentiments on Twitter in the Finnish language during the COVID-19 pandemic. We manually annotate with sentiments a random sample of 1943 tweets about COVID-19 in Finnish. We use it to build binomial and multinomial logistic regression models with Lasso penalty by exploiting ngrams and two existing sentiment lexicons. We also build two similar models using an existing (pre-COVID-19) Twitter dataset for comparison. The best-performing model for the Finnish language is then used to determine the trends of positive, negative, and neutral opinions on a collection of tweets in Finnish extracted between April 21 and June 18, 2020. The best sentiment polarity prediction model for the Finnish language attain 0.785 AUC, 0.710 balanced accuracy, and 0.723 macro-averaged F1 for predicting positive and negative polarity (binomial classification), and 0.667 AUC, 0.607 balanced accuracy, and 0.475 F1 when adding neutral tweets (multinomial classification). On the other hand, the pre-COVID-19 model trained on the same number of tweets exhibits higher accuracy for the multinomial model (0.687 balanced accuracy, and 0.588 F1). We hypothesize that this loss of performance is due to the COVID-19 context that makes sentiment analysis of neutral tweets more difficult for the machine learning algorithm to predict. Running the model on all the extracted Finnish tweets, we observe a decrease in negativity and an increase in positivity over the observed time as the Finnish government lifts restrictions. Our results show that applying an existing general-purpose sentiment analyzer on tweets that are domain-specific, such as COVID-19, provides lower accuracy. More effort in the future needs to be invested in using and developing sentiment analysis tools tailored to their application domain when conducting large-scale social media analysis of specific medical issues, such as a global pandemic.

Evaluation Method of Higher Education Teaching Reform Based on Deep Learning Analysis Technology

Considering the shortcomings of large evaluation errors, long time, human, and material resources in the evaluation process of the current college teaching mode to improve the accuracy of the evaluation of college teaching mode and reduce the cost of the evaluation, this study proposes an evaluation method for college teaching methods based on deep learning algorithms. Firstly, the research status of the evaluation of college teaching mode is analyzed, and the reasons for the poor evaluation results of the current college teaching mode are found; then the existing deep learning algorithm is improved, and the effectiveness and speed of the method are verified by comparing with other models. Then, the evaluation model of college teaching mode is established, and machine learning is performed on the evaluation data of college teaching mode; finally, the evaluation data of college teaching mode is collected, and the application example test of college teaching mode evaluation is performed.

Just before pre-primary education: interplay between age and resources in the transition processes of diverse pre-primary education contexts

ABSTRACT This article describes the interplay between age and children’s resources during the transition from Finnish early childhood education and care (ECEC) to pre-primary education. We describe how age operates in the structures of ECEC during the time before the actual transition. We approach the transition to pre-primary education and the relation between age and resources with the concepts of capital and informal and formal practices. The data are drawn from ethnographic fieldwork and video records at two types of ECEC centres: one that provides pre-primary education and one that does not. The findings underscore transition practices and age as the main aspects providing diverse resources for the transferring children during the transition from ECEC to pre-primary education. Investigating the interplay between resources and age helps to understand how ECEC centres with different age-related group structures and locations of pre-primary education produce resources and distinctions between the children during the transition.

On the (relation between) efficiency and secret key rate of QKD

The processes of evaluation and comparison play a vital role in the development of a scientific field. In the field of quantum cryptography (especially quantum key distribution, QKD), the so-called secret key rate is used for characterizing the performance of a protocol (scheme). However the current definition of this quantity is incomplete. It does not consider the classical communication process taking place in a QKD protocol. There exists a quantity that involves all the procedures (resources) in a communication process: it is the efficiency (total efficiency). This paper reports a definition of this parameter. Also the relation between the total efficiency and key rate is found. By means of this relation, the total secret key rate of a QKD protocol is expressed. An application of the total key rate is demonstrated: the original twin-field QKD (TF-QKD) is evaluated in terms of this rate. The paper also shows a comparison between the total key rate and the standard key rate of a TF-QKD.

Microservices and serverless functions—lifecycle, performance, and resource utilisation of edge based real-time IoT analytics

Edge Computing harnesses resources close to the data sources to reduce end-to-end latency and allow real-time process automation for verticals such as Smart City, Healthcare and Industry 4.0. Edge resources are limited when compared to traditional Cloud data centres; hence the choice of proper resource management strategies in this context becomes paramount. Microservice and Function as a Service architectures support modular and agile patterns, compared to a monolithic design, through lightweight containerisation, continuous integration/deployment and scaling. The advantages brought about by these technologies may initially seem obvious, but we argue that their usage at the Edge deserves a more in-depth evaluation. By analysing both the software development and deployment lifecycle, along with performance and resource utilisation, this paper explores microservices and two alternative types of serverless functions to build edge real-time IoT analytics. In the experiments comparing these technologies, microservices generally exhibit slightly better end-to-end processing latency and resource utilisation than serverless functions. One of the serverless functions and the microservices excel at handling larger data streams with auto-scaling. Whilst serverless functions natively offer this feature, the choice of container orchestration framework may determine its availability for microservices. The other serverless function, while supporting a simpler lifecycle, is more suitable for low-invocation scenarios and faces challenges with parallel requests and inherent overhead, making it less suitable for real-time processing in demanding IoT settings.

Optimalisasi Sumber Daya Manusia terhadap Pengolahan: Studi Kasus Industri Pengolahan Bahan Baku Rotan

Rattan is a non-wood raw material originating from Indonesia which has become the main source of income for community forests and local governments. Rattan processing has challenges in obtaining raw materials and skilled processing techniques by workers or human resources in order to produce quality output which of course can also advance the company. The use of machines and human resources in the rattan production process can increase production speed and accuracy. This research was conducted to determine the performance of human resources which influences the quality of the rattan products produced. The research method used in this research is a type of qualitative research with a descriptive approach and has an exploratory nature. The data collection technique in the research was carried out by creating an inductive scheme to make it easier for researchers to carry out research analysis and based on existing case studies. Observations were carried out by researchers regarding activities that occurred in the company during the researcher's internship period. The research object of rattan processing and human resources is information from the UD company. Alam Sultra's work. The research results show that the company UD. Karya Alam Sultra carries out empowerment by providing skills and training to its workers to improve the quality of employee performance in processing rattan so that it can become good quality rattan. This proves that there is empowerment in the processing of rattan raw materials, enabling the performance skills of human resources to increase efficiency and innovation in the processing of rattan products and produce quality rattan.

Squeezing as a resource for time series processing in quantum reservoir computing.

Squeezing is known to be a quantum resource in many applications in metrology, cryptography, and computing, being related to entanglement in multimode settings. In this work, we address the effects of squeezing in neuromorphic machine learning for time-series processing. In particular, we consider a loop-based photonic architecture for reservoir computing and address the effect of squeezing in the reservoir, considering a Hamiltonian with both active and passive coupling terms. Interestingly, squeezing can be either detrimental or beneficial for quantum reservoir computing when moving from ideal to realistic models, accounting for experimental noise. We demonstrate that multimode squeezing enhances its accessible memory, which improves the performance in several benchmark temporal tasks. The origin of this improvement is traced back to the robustness of the reservoir to readout noise, which is increased with squeezing.

Pain suffering and the self. An active allostatic inference explanation.

Distributed processing that gives rise to pain experience is anchored by a multidimensional self-model. I show how the phenomenon of pain asymbolia and other atypical pain-related conditions (Insensitivity to Pain, Chronic Pain, 'Social' Pain, Insensitivity to Pain, Chronic Pain, 'Social' Pain, empathy for pain and suffering) can be explained by this idea. It also explains the patterns of association and dissociation among neural correlates without importing strong modular assumptions. It treats pain processing as a species of allostatic active inference in which the mind co-ordinates its processing resources to optimize basic bodily functioning at different time scales. The self is inferred to be source and target of regulation in this process. The self-modelling account reconciles conflicting deaffectualization and depersonalization accounts of pain asymbolia by showing how depersonalization and pain asymbolia arise at different levels of hierarchical self modelling.

Assessment and Prediction of Future Climate Change in the Kaidu River Basin of Xinjiang under Shared Socioeconomic Pathway Scenarios

Xinjiang, located in the arid region of the northwest, is one of the areas most sensitive to global changes. The Kaidu River Basin, situated in the heart of Xinjiang, is one of the sources of China’s largest inland river—the Tarim River. The Kaidu River not only bears the responsibility for supplying water for industrial use and agricultural production and people’s daily life in the basin, but also plays a crucial role in ecological water supply to the Tarim River. Studying and analyzing the characteristics and trends of meteorological condition in the future under climate change can provide important references and a basis for a deeper understanding of changes in the hydrological process and water resources in the basin. Therefore, this paper selects seven precipitation bias correction methods and four temperature bias correction methods to adjust the precipitation and temperature output data of eight general circulation models of the Sixth Coupled Model Intercomparison Project (CMIP6) within the Kaidu River Basin. The applicability of different bias correction methods in the study area is evaluated, and based on the corrected future meteorological data and calculated extreme meteorological index, the trends of meteorological data (precipitation, temperature) in the future period (2025–2050) under four SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) in the Kaidu River Basin are analyzed. The results show that: (1) Different types of bias correction methods have different correction focus and effects; their reflections on evaluation indicators are also different. (2) In the future period (2025–2050), the annual precipitation and average temperature in the Kaidu River Basin are higher than those in the historical period (1975–2014). The average annual temperature shows an upward trend in the future, but the annual precipitation shows a downward trend in the future except for the SSP2-4.5 scenario. (3) Compared with the historical period, the extreme precipitation in the future period under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios is higher than that in the historical period, and the number of rainless days decreases. In the future, under the SSP1-2.6 and SSP5-8.5 scenarios, the probability of meteorological drought events occurring due to high temperatures in the basin may further increase, while under the SSP2-4.5 scenario, the situation of high temperatures and heavy rain in the basin may continue to increase.

Theoretical-Methodological Foundations for the Global Integration Method (Método de Integração Global-MIG) in the Treatment of Autism Spectrum Disorder.

Currently, there is no intervention model for autism spectrum disorder (ASD) that addresses all levels and factors of the International Classification of Functioning, Disability and Health (ICF, WHO). The most researched programs focus on naturalistic, developmental and behavioral approaches to socio-communication. Less attention has been paid to motor and environmental reactivity aspects (behavior/interest restriction and sensory reactivity). The evidence rationale for the Global Integration Method (MIG, "Método de Integração Global"), a model addressing sensorimotor reactivity in addition to socio-communication, is presented. MIG is an integrative, interdisciplinary, family-oriented intervention and naturalistic program that addresses all levels and moderating factors of ASD's impact. MIG's theoretical rationale is based on the predictive coding impairment and embodied cognition hypotheses. MIG incorporates both bottom-up (flexible therapeutic suit, social-motor synchronization) and top-down (schematic social information processing, narratives, imagery) strategies to promote the building and use of accurate, flexible and context-sensitive internal predictive models. MIG is based on the premises that predictive coding improves both socio-communication and environmental reactivity, and that the postural stabilization provided by the flexible therapeutic suit frees information processing resources for socio-cognitive learning. MIG builds on interdisciplinary, professionally and parentally mediated work based on behavioral principles of intensive training in a situated environment.

Slicing Enabled Flexible Functional Split and Multi-Dimensional Resource Provisioning in 5G-and-Beyond RAN

5G/B5G networks are expected to deliver huge traffic and support various use cases with diverse requirements. With the increasing demand for network capacity, a cost-effective and flexible RAN is urgently needed to provide customized services for users. On this basis, advanced flexible RAN architectures with functional splits are introduced. In this paper, we study the slice-centric fine-grained functional split and resource allocation problem in flexible RAN. We first formulate a multi-objective problem to jointly optimize the functional split selection, processing, and transmission resource allocation for slices, aiming at maximizing the functional split gain while satisfying slices’ requirements. Since a multi-objective problem may have multiple Pareto optimal solutions and is difficult to solve, we mathematically analyze and transform the problem into an equivalent parametric convex problem. Then, we propose an upper bound algorithm and a dual based resource allocation algorithm to find the solution for the optimization problem. Theoretical analysis and simulation results show that the proposed algorithms can effectively solve the functional split gain maximization problem and obtain a trade-off between processing and transmission resource gain. In addition, the proposed algorithms also outperform other benchmark approaches in terms of resource saving and flexibility.

Research on Teaching System Based on Digital Intelligence Technology

Modern digital and intelligent information technology have brought the intelligent upgrading and transformation of existing education models, promoting the transformation of teaching methods, management models, and education service supply methods. At the same time, in order to meet the teaching requirements of digital and intelligent technology, we create comprehensive, full process, and systematic undergraduate education resources from a professional perspective, and deeply integrate professional construction with the teaching, evaluation, supervision, and information technology of the "Three Comprehensives" education, forming an intelligent, integrated, and perceptual new curriculum ecosystem.

Performance on a relational integration task is impaired during hangover

Recently, researchers have proposed an updated model of executive functions that includes relational integration, the mental ability to bind information into more complex structures. Hangover is known to disrupt other core components of executive functions, but little is known about how it influences relational integration. Therefore, this study aimed to investigate how hangover influences performance on a relational integration task. Twenty-seven participants completed an online relational integration task and mood- and emotion-regulation questionnaires during a hangover and no-hangover condition in this naturalistic design study. Results indicated that relational integration was impaired in hangover (p < 0.001, ηp2 = 0.562) relative to the no-hangover condition. In addition, participants experienced greater difficulties in regulating emotions (p < 0.001, d = 0.85) and lower mood (p < 0.001, d = 0.88) during hangover. These results suggest that relational integration is impaired in hangover and add weight to the argument that cognitive impairments in hangover may be due to the hangover-related impact on domain-general processing resources.

HYBRID DISTANCE-STATISTICAL-BASED PHRASE ALIGNMENT FOR ANALYZING PARALLEL TEXTS IN STANDARD MALAY AND MALAY DIALECTS

Parallel texts corpora are essential resources in linguistics and natural language processing, especially in translation and multilingual information retrieval. The publicly available parallel text corpora are limited to certain genres, types and domains. Furthermore, the parallel dialect text is scarce, even though they are important in the analysis and study of a dialect. Collecting parallel dialect text is challenging because dialects typically appear in the form of speech and very limited dialectic texts exist. Moreover, there is no standard orthography in most dialects. The contributions of this paper are threefold. First, the paper describes a methodology in acquiring a parallel text corpus of Standard Malay and Malay dialects, particularly Kelantan Malay and Sarawak Malay. Second, we propose a hybrid of distance based and statistical-based alignment algorithm to align words and phrases the parallel text. The results show that the precision and recall values of the proposed alignment algorithm are more than 95% and better than the state-of the-art GIZA++. Third, the alignment obtained were compared to find out the lexical similarities and differences between Standard Malay and the two studied Malay dialects, contributing valuable insights into the linguistic variations within the Malay language family.

Fully Memristive Elementary Motion Detectors for a Maneuver Prediction.

Insects can efficiently perform object motion detection via a specialized neural circuit, called an elementary motion detector (EMD). In contrast, conventional machine vision systems require significant computational resources for dynamic motion processing. Here, a fully memristive EMD (M-EMD) is presented that implements the Hassenstein-Reichardt (HR) correlator, a biological model of the EMD. The M-EMD consists of a simple Wye (Y) configuration, including a static resistor, a dynamic memristor, and a Mott memristor. The resistor and dynamic memristor introduce different signal delays, enabling spatio-temporal signal integration in the subsequent Mott memristor, resulting in a direction-selective response. In addition, a neuromorphic system is developed employing the M-EMDs to predict a lane-changing maneuver by vehicles on the road. The system achieved a high accuracy (> 87%) in predicting future lane-changing maneuvers on the Next Generation Simulation (NGSIM) dataset while reducing the computational cost by 92.9% compared to the conventional neuromorphic system without the M-EMD, suggesting its strong potential for edge-level computing.