Additional Resource Consumption Research Articles

A joint communication and computation design for semantic wireless communication with probability graph

In this paper, the problem of joint communication and computation design for probability graph-based semantic communication over wireless networks is investigated. In the considered model, the base station (BS) extracts the compressed small-sized semantic data by removing redundant information based on the shared knowledge base between the transceivers. In particular, the knowledge base is represented as a probability graph, which summarizes the statistic relations of massive knowledge graphs. On the user side, the compressed information is accurately inferred on the basis of the same probability graph as the BS. Although this approach brings additional computation resource consumption for semantic information extraction, it effectively reduces communication resource consumption through the transmission of small-sized data. Both the communication and computation cost models are derived based on the inference process of the probability graph. Based on the formulated models, the problem of joint communication and computation resource allocation is proposed to minimize the total energy consumption of the network considering both latency and power limitations. To solve this problem, the closed-form solution of the transmission power is obtained with fixed semantic compression ratio. Then, an effective linear search-based algorithm is proposed to obtain the optimal solution of the considered problem with low complexity. Simulation results demonstrate the effectiveness of the proposed system compared with the conventional non-semantic schemes.

Loneliness impact on healthcare utilization in primary care: A retrospective study

BackgroundAn increased number of patients seek help for loneliness in primary care. ObjectiveTo analyze whether loneliness was associated with a higher utilization of healthcare facilities. MethodsObservational, retrospective study based on the review of routinely coded data in the digital medical record system in a random sample of patients aged 65 or older, stratified by population size of their residence area. A minimum sample size was estimated at 892 medical records. Loneliness was defined as the negative feeling that arises when there is a mismatch between the quantity and quality of a person's social relationships and those, they desire. Thirty-three primary care nurses (30 females and 3 males) were reviewing the data. ResultsA total of 932 medical records of patients were reviewed (72% belonged to female patients). Of these, 657 individuals were living alone (71.9%). DeJong Scale average scores was 8.9 points (SD 3.1, 95CI 8.6–9.1). The average annual attendance to primary care ranged from 12.2 visits per year in the case of family practice, 10.7 nurse, 0.7 social workers. The average number of home visits was 3.2, and the urgent consultations attended at health centers were 1.5 per year. Higher feelings of loneliness were associated with extreme values in the frequency of healthcare resource usage. Compared to their peers of the same age, the additional healthcare resource consumption amounted to €802.18 per patient per year. ConclusionLoneliness is linked to higher healthcare resource usage in primary care, with individuals experiencing poorer physical and mental health utilizing these resources up to twice as much as their peers of the same age.

Isoline Tracking in Particle-Based Fluids Using Level-Set Learning Representation

In this paper, we propose a learning model for tracking the isolines of fluid based on the physical properties of particles in particle-based fluid simulations. Our method involves analyzing which weights, closely related to surface tracking among the various physical properties of fluid particles, are significant. These weights are used as input values for the learning algorithm, enabling relatively accurate isoline tracking. In addition, compared to existing learning models such as linear regression, LSTM (long short-term memory), and learning representation (1-layer) models, our method obtained superior surface tracking results without accumulating errors. By using our proposed network structure to track the fluid surface, it learns and predicts values derived from existing fluid simulation algorithms, eliminating the need for computational processes for level-set values and enabling real-time surface tracking. As the scale of the simulation increases, our method significantly reduces the time and resources consumed compared to traditional methods and can track the fluid surface without additional resource consumption. Furthermore, due to our method’s simple network structure, the time consumed in the initial process of loading the model into memory is faster than models such as CNN and LSTM. Our proposed model occupies less than 30 kb of storage space, making it suitable for use in middleware. Lastly, to verify the generality of our method, we conducted tests in a total of five scenes, and in all test scenes, visually natural fluid isolines were represented.

B-ROS re-balanced learning method for PS-A-RoF FWA communication

The enhanced remote area communication (eRAC) scenario is an important growth point in the communication market. In some remote areas where optical fiber access cannot be realized or the laying cost is too high, fixed wireless access (FWA) is an appropriate supplementary solution for eRAC. Adopting analog radio over fiber (A-RoF) technology to implement FWA can overcome the bandwidth limitation of electronic devices and realize high-frequency carrier communication economically to achieve high-capacity wireless communication. Also, probabilistic shaping (PS) technology can be combined with A-RoF to further improve the flexibility of the network and coverage of service provision. However, in the PS-A-RoF network, the high RF power introduces more undesired nonlinear effects into the network, and it is often necessary to deploy supervised machine learning (ML) compensation modules in wireless receivers (WRs). But the module performances are affected by the uneven probability distribution of PS-QAM constellation points. In this paper, we employ the PS-A-RoF nonlinear model to theoretically investigate the correlation between the distribution of training symbols and the wireless A-RoF system’s performance. Our analysis reveals that reducing the variance of training symbol power contributes to a lower BER in the A-RoF network. We introduce a borderline random over-sampling (B-ROS) that matches with the PS-A-RoF nonlinear model, instead of the mainstream ROS, which is only at the data level. Based on the B-ROS scheme, only the minority examples below the borderline are over-sampled to reach a better variance performance. Introducing the B-ROS method into the supervised complex value nonlinear compensation module can further improve the decision accuracy of WRs with the restoration of phase information, without increasing additional computational resource consumption. The vector noise power, training symbol power variance, and noise factor metrics have been calculated to optimize the borderline value of our ML-based approach. We also present experimental data on the proof-of-concept A-RoF experiment for PS-64QAM. The results demonstrate a promising nonlinear compensation performance of the B-ROS WR, and the optimal borderline agrees well with the one deduced from the theoretical model under certain transmission conditions. Our proposed B-ROS scheme lessens the training size demand and can improve the receiver sensitivity by 0.51 dB compared to the common ML-based WR and by 0.7 dB compared to the conventional ROS scheme.

Delay/Disruption-Tolerant Networking-based the Integrated Deep-Space Relay Network: State-of-the-Art

Communication with deep-space elements poses significant challenges due to vast distances, orbital motions, and harsh environmental conditions that restrict point-to-point or end-to-end communication. Disruption/Delay-Tolerant Networking (DTNs) is a special kind of computer network architecture, that overcomes the shortcomings of the TCP/IP suite in meeting deep-space networking requirements with intermittent end-to-end connectivity. In DTN, data transmission follows a store-carry-forward approach, where bundles (data) are transferred as custody between relay nodes. This necessitates more complex routing strategies involving additional route computations and network resource consumption but provides greater flexibility for unstable connectivity. This study is an updated literature review in the context of DTN-based deep-space communication. It covers the recent studies that address the architecture of current and future deep-space communication systems, the environmental challenges of deep-space communication, various implementations and demonstrations of DTN's architecture, and the validation of its related routing strategies over the Deep Space Relay network. Additionally, this review explores the integration of machine learning techniques into DTN routing strategies, along with ongoing research directions and open issues in DTN-based DSN. Examining the state-of-the-art literature reveals that developing an intelligent unified n-copy-based forwarding routing scheme holds promise for effectively integrating into deep space communications and achieving optimal networking among the diverse segments of the future unified Interplanetary Internet.

Application of Circular Thermoeconomics to the Diagnosis of Energy Systems

This paper reviews the fundamentals of the thermoeconomic diagnosis theory. Thermoeconomic diagnosis is one of the main applications of the exergy cost theory used to identify the causes of additional resource consumption of a system due to inefficiencies in its components, published in the late 1990s. Thermoeconomic diagnosis has usually been applied to diagnose power plants with high consumption of fossil fuels and fixed production. However, it does not consider the final production and waste generation variation. In this paper, Circular Thermoeconomics is applied to analyze in depth the effect of malfunctions on additional waste generation and changes in the final output of the system. This new formulation can be applied to polygeneration systems, where there is a simultaneous variation of final products, and to process integration and industrial symbiosis, where a part of the waste generated by a plant could be reused in other processes or plants.

Environmental crisis overcoming as a factor for achieving economic sustainability in the context of the European green course

By means of the modeling methodology, the object of the study was the theoretical and practical aspects of overcoming environmental crises as a factor to achieve sustainability. In today's world, several crises are combined: social, political, cultural and moral, the crisis of democracy, ideology and the general crisis of the capitalist system. More specifically, the economic and environmental crises are linked to the financial crises and the existing disorientation is due, to a large extent, to the financial risks that have affected the ecological footprint of civilization. As a result of the tasks set, the concept of planetary boundaries has emerged as an effective means of measuring the state of the planet and its threats. In this sense, the concept of an economy in need of transformation has been formed, as environmental growth and technological progress are accompanied and even accelerated by economy and resource efficiency. It is concluded that, current trends on Earth are not sustainable, and traditional responses to these problems often depend on the type of economic growth that is strongly associated, simultaneously, with additional resource consumption and the policies that make it possible.

Heterogeneous Edge Caching Based on Actor-Critic Learning With Attention Mechanism Aiding

In recent years, the explosive growth of network traffic has placed significant strain on backbone networks. To alleviate the content access delay and reduce additional network resource consumption resulting from large-scale requests, edge caching has emerged as a promising technology. Despite capturing substantial attention from both academia and industry, most existing studies overlook the heterogeneity of the environment and the spatial-temporal characteristics of content popularity. As a result, the potential for edge caching remains largely unexploited. To address these challenges, we propose a neighborhood-aware caching (NAC) framework in this paper. The framework leverages the perimeter information from neighboring base stations (BSs) to model the edge caching problem in heterogeneous scenarios as a Markov Decision Process (MDP). To fully exploit the environmental information, we introduce an improved actor-critic method that integrates an attention mechanism into the neural network. The actor-network in our framework is responsible for making caching decisions based on local information, while the critic network evaluates and enhances the actor's performance. The multi-head attention layer in the critic network enables integration of environmental features into the model, reducing the limitations associated with local investigation. To facilitate comparison from an engineering perspective, we also propose a heuristic algorithm, Neighbor-Influence-Least-Frequently-Use (NILFU). Our extensive experiments demonstrate that the proposed NAC framework outperforms other baseline methods in terms of average delay, hit rate, and traffic offload ratio in heterogeneous scenarios. This highlights the effectiveness of the neighborhood-aware caching approach in enhancing the performance of edge caching systems in such scenarios.

EE498 Cost of Follow-Up After an Ischemic Cerebrovascular Event in Patients With Atherosclerotic Cardiovascular Disease in Spain: Results from the Reality Study

Ischemic cerebrovascular events stroke and transient ischemic attack (TIA) are a cause of morbidity and mortality in developed countries and entails a high resources consumption and costs for health systems. These patients have an increased risk of suffering a new event with the consequent additional resource consumption and an increased follow-up cost.

Material efficiency through wear protection – The contribution of tribology for reducing CO2 emissions

In the future, humanity will be under great ecological pressure to efficiently use resources and reduce energy and resource consumption. Tribology is not a directly visible consequence of human activities and is divided into energy consumption due to friction losses and additional resource consumption through wear and failures of machine elements and products. Based on the material streams allocated to applications with tribosystems or those depending on tribosystems, the contribution of increased longevity to reduce CO2 emissions is refined. Depending from the absolute amount of material stream considered, its portions allocated to tribology and assuming doubling the service life result in average in savings of 1.06–3.70 gigatons in mass of resources per year or of embedded 1.68–6.77 gigatons CO2eq.per year.

The importance of tribology for reducing CO2 emissions and for sustainability

The global community has set itself the goal of stabilizing greenhouse gas emissions at the 2004 level in order to not exceed the climate targets of 500 ppm CO2 in the atmosphere and a mean surface temperature increase of 2 °C by 2054. Tribology is a not directly visible consequence of human activities and is divided into energy consumption due to friction losses and additional resource consumption through wear and failures of machine elements. The broad range of solutions offered by tribology to reduce CO2 and increase sustainability is presented in detail. Consistent application of the existing technologies results in a medium/long-term reduction potential of 6–10 gigatons of CO2eq. or >15–26% of the 37.9 gigatons of direct CO2 emitted globally in 2019. The total greenhouse gas emission in 2019 were 51.8 gigatons of CO2eq..

SCIENTIFIC AND THEORETICAL FOUNDATIONS FOR THE DEVELOPMENT OF REGIONAL INFRASTRUCTURE

The aim of socio-economic development of the region is creating a better living standards for the population. This process involves a number of important issues such as increasing incomes and literacy level, improving health care, creating sufficient working conditions and also sustainable jobs. These criteria, in turn, are important indicators of sustainable development. These factors are believed through the main indicators of sustainable development too. It should be noted that creating new jobs is based on additional producing and resource consumption. In order to achieve it effectively is directly connected with well-organized infrastructure.

Reconfiguration Aware Orchestration for Network Function Virtualization With Time-Varied Workload in Virtualized Datacenters

Network function virtualization (NFV) is an emerging paradigm that has revolutionized the deployment and provisioning of telecommunication network services (NS). In NFV, a network service is implemented as a chain of network functions (NFs). NFs are virtualized and packaged into virtual network functions (VNF), so that they can be decoupled from the physical middleboxes and dynamically deployed on standard virtualization platforms in cloud or datacenters. Therefore, NFV has the potential to provide significant cost reductions in operating expenses (OPEX) and capital expenses (CAPEX) and increased agility for network service deployments. However, to build and manage a network function virtualization infrastructure (NFVI) is challenging. One of the key problems is to determine the mapping between underlying physical resources and VNFs in order to minimize energy consumption without violating service-level agreements (SLA). Many previous studies use embedding solutions to solve the problem by only considering the resource demand of NS and the resource capacity of physical network at a given time instance. But in practice, the resource demands of NS can vary over time, hence the embedding decision must be reconfigured accordingly. Many techniques can be used for reconfiguration, including resource scaling, instance migration and traffic re-direction. But each of these operation is associated with some costs, such as service interruptions, and additional resource or energy consumption. In this work, we investigate various reconfiguration strategies, formulate their costs by designing a model in the NFV placement problem, and propose Integer Linear Programming (ILP) and heuristic solutions, called REAP. In our evaluation, we observed 60% and 25% cost reduction for energy and reconfiguration costs, and we analyze the cost reduction from each of the reconfiguration methods to validate the design of our heuristic algorithm.

Economic aspects of interdisciplinary plastic surgery: change in Diagnosis-Related Groups (DRG) due to procedures of reconstructive microsurgery

Advances in reconstructive microsurgery have led to significant improvements in the surgical care of complex interdisciplinary cases. At the same time, however, this also increases the resource expenditure of the plastic surgeon involved. This study aimed to analyse the relationship between increase in revenue and resource expenditure in reconstructive microsurgery at a university hospital of maximum care with regard to the treatment of interdisciplinary patients. In 2018 and 2019, all cases of interdisciplinary cooperation were followed up at one location of a plastic surgery department of a university clinic. The interdisciplinary surgical cases were identified from the collective and evaluated prospectively with regard to inpatient treatment days, surgical resource expenditure and economic development (DRG before and after microsurgical reconstruction). In 2018 and 2019, a total of 68 free microsurgical flaps were performed in 64 interdisciplinary cases. 62 of these cases met the criteria for interdisciplinary surgical treatment. Considering the contribution of plastic surgery to the economic development and the associated resource expenditure, there is a significant increase in all parameters (p < 0.0001). Thus, the Case Mix Index (CMI) rose by 20.2 %, inpatient treatment days by 79.1 %, the number of surgical interventions by 62.4 %, cumulative incision-suture time by 131.4 % and total surgeon hours by 75.4 %. Reconstructive microsurgical procedures lead to a significant increase in revenue in interdisciplinary surgical cases. However, a significant increase in resource consumption is observed as well. Moreover, these additional costs are not always adequately reflected in the revenue of the DRG. This especially applies to DRGs with a high initial cost weight. To ensure modern, individual, patient-oriented and guideline-compliant patient care, there is, therefore, an urgent need to adapt the (G-)DRG system to the additional resource consumption. In addition, in the case of interdisciplinary surgical cases, a clear internal cost allocation must be carried out in accordance with the surgical resource expenditure.

A modified quantum-inspired evolutionary algorithm for minimising network coding operations

Network coding operations will benefit the multicast network performances in improving both the transmission throughput and the reliability. Meanwhile, the network coding operations can also bring some additional resource consumption and transmission delay into the multicast network. Thus, minimising the network coding operations is worthy of in-depth studying. To address this resource optimisation problem, an adaptive evolution mechanism-based modified quantum-inspired evolutionary algorithm is presented in this paper. Three evaluation operators were defined and added into the algorithm to improve the global optimisation ability. In the modified quantum-inspired evolutionary algorithm, the state of each population was jointly determined by these three operators. In the algorithm evolution process, the evolution parameters of the algorithm can be determined by the state of each population. To illustrate the effectiveness of the modified algorithm, it was applied to resolve the function optimisation and the network coding recourse minimisation problems respectively. The experiment results indicated that our adaptive evolution mechanism based modified quantum-inspired evolutionary algorithm has better performances both in searching global optimal solution and convergence speed.

A modified quantum-inspired evolutionary algorithm for minimising network coding operations

Network coding operations will benefit the multicast network performances in improving both the transmission throughput and the reliability. Meanwhile, the network coding operations can also bring some additional resource consumption and transmission delay into the multicast network. Thus, minimising the network coding operations is worthy of in-depth studying. To address this resource optimisation problem, an adaptive evolution mechanism-based modified quantum-inspired evolutionary algorithm is presented in this paper. Three evaluation operators were defined and added into the algorithm to improve the global optimisation ability. In the modified quantum-inspired evolutionary algorithm, the state of each population was jointly determined by these three operators. In the algorithm evolution process, the evolution parameters of the algorithm can be determined by the state of each population. To illustrate the effectiveness of the modified algorithm, it was applied to resolve the function optimisation and the network coding recourse minimisation problems respectively. The experiment results indicated that our adaptive evolution mechanism based modified quantum-inspired evolutionary algorithm has better performances both in searching global optimal solution and convergence speed.

How to Accelerate Sustainability in Information Technology Sector

In view of current economic and industrial growth, the whole world is facing various Sustainability challenges like Climate change and Global warming, energy management, food security, poverty, water scarcity and many more. To address these issues properly, it is necessary to mainstream sustainability in every sector of operations and it is the responsibility of every industry and every individual to contribute towards various sustainability issues / challenges for achieving long term sustainability goals. Globally, the Information Technology (IT) sector has grown up in multiple folds in a short span of time with substantial contribution to global economy and business. Rapid growth of IT industry is positively contributing to higher growth of infrastructure development (commercial &amp;amp; residential) and employment generation. But, along with the contribution to economical, industrial and developmental aspects, IT sector is also contributing to increased energy and natural resources consumption, generation of electronic waste (E-Waste) and increased carbon emissions (due to energy consumption and increased business travel activities.) The rapid economic growth and changes in IT sector has made it necessary for the companies to update and monitor their own technological competence. This further leads to additional resource consumption and E-waste generation. With high business growth potential and rising demand, IT sector is going through various major sustainability issues and challenges including high energy consumption, waste management, carbon emissions and human resource management. To the many fold growth of IT sector in last decade, IT sector SME is also one of the major contributors and the sector is growing continuously. But due to many constrains, IT-SME sector focus is mainly on business development and economic growth to sustain in the highly competitive market by providing different value added, cost effective services and sustainable solution to the clients. Including effective use of cloud computing, which is considered as one of the energy efficient option for the services offered. If we consider the environmental impact for an SME in view of above challenges of IT sector, it is not significant. However, considering the huge number of SMEs operating globally, the cumulative impact is significant and cannot be ignored. The detailed paper will include analysis of the current scenario of overall IT sector including SMEs of India and Global, in view of their awareness about the sustainability issues, possible measures of improving sustainability contribution and the challenges faced by them for implementation of sustainability measures. Further the paper will also discuss about how the sector can embrace sustainability to reduce environmental impact by energy conservation, carbon footprint reduction along with a goal to achieve Profit Growth and Stakeholder welfare.

An effective multi-objective discrete virus optimization algorithm for flexible job-shop scheduling problem with controllable processing times

The scheduling problems with controllable processing times (CPT) are commonly encountered in some manufacturing industries. CPT means the processing times of operations can be controlled by allocating additional resources. However the flexible job-shop scheduling problem (FJSP) with CPT is seldom explored due to its essential complexity. In addition, FJSP usually involves several conflicting objectives in the practical production. Therefore, the multi-objective FJSP with CPT (MOFJSP-CPT) is highly important in terms of theoretical research and practical application. Thus, this paper focuses on the MOFJSP-CPT. Firstly, this study formulates a mathematical model with the objectives of minimizing both the makespan and the total additional resource consumption. Then, to solve this problem, we propose a new multi-objective discrete virus optimization algorithm (MODVOA) with a three-part representation for each virus, an improved method for yielding the initial population, and an ensemble of operators for updating each virus. To further improve the exploitation, a problem-specific exploitation mechanism is implemented in the later stage of the search process. Finally, to evaluate the effectiveness of the MODVOA, the MODVOA is compared with other well-known multi-objective evolutionary algorithms including NSGA-II and SPEA2. Experimental results on randomly generated instances and a real-world case demonstrate that the proposed MODVOA can achieve a better performance than other algorithms for solving the MOFJSP-CPT.

A computing resource adjustment mechanism for communication protocol processing in centralized radio access networks

The centralized radio access cellular network infrastructure based on centralized Super Base Station(CSBS) is a promising solution to reduce the high construction cost and energy consumption of conventional cellular networks. With CSBS, the computing resource for communication protocol processing could be managed flexibly according the protocol load to improve the resource efficiency. Since the protocol load changes frequently and may exceed the capacity of processors, load balancing is needed. However, existing load balancing mechanisms used in data centers cannot satisfy the real-time requirement of the communication protocol processing. Therefore, a new computing resource adjustment scheme is proposed for communication protocol processing in the CSBS architecture. First of all, the main principles of protocol processing resource adjustment is concluded, followed by the analysis on the processing resource outage probability that the computing resource becomes inadequate for protocol processing as load changes. Following the adjustment principles, the proposed scheme is designed to reduce the processing resource outage probability based onthe optimized connected graph which is constructed by the approximate Kruskal algorithm. Simulation results show that compared with the conventional load balancing mechanisms, the proposed scheme can reduce the occurrence number of inadequate processing resource and the additional resource consumption of adjustment greatly.

A less resource-consumed security architecture on cloud platform

Traditional security framework in cloud platform usually brings self-vulnerability and considerable additional resource consumption. To solve these problems, we propose an external processes monitoring architecture for current popular cloud platform OpenStack with kernel-based virtual machine (KVM). With this architecture, we can monitor all active processes in online virtual machine (VMs) and scan them for their potential maliciousness in OpenStack with no agent, and can also detect hidden processes in offline VMs’ memory snapshots and notice the user to decide whether to kill them when VMs become active. Analysis and experimental results show that our architecture is able to reduce consumption of CPU, memory and bandwidth in cloud platform and can detect viruses and hidden processes effectively in VMs.