Service Requirements Research Articles

Automatic 3D reconstruction of vertebrae from orthogonal bi-planar radiographs

When conducting spine-related diagnosis and surgery, the three-dimensional (3D) upright posture of the spine under natural weight bearing is of significant clinical value for physicians to analyze the force on the spine. However, existing medical imaging technologies cannot meet current requirements of medical service. On the one hand, the mainstream 3D volumetric imaging modalities (e.g. CT and MRI) require patients to lie down during the imaging process. On the other hand, the imaging modalities conducted in an upright posture (e.g. radiograph) can only realize 2D projections, which lose the valid information of spinal anatomy and curvature. Developments of deep learning-based 3D reconstruction methods bring potential to overcome the limitations of the existing medical imaging technologies. To deal with the limitations of current medical imaging technologies as is described above, in this paper, we propose a novel deep learning framework, ReVerteR, which can realize automatic 3D Reconstruction of Vertebrae from orthogonal bi-planar Radiographs. With the utilization of self-attention mechanism and specially designed loss function combining Dice, Hausdorff, Focal, and MSE, ReVerteR can alleviate the sample-imbalance problem during the reconstruction process and realize the fusion of the centroid annotation and the focused vertebra. Furthermore, aiming at automatic and customized 3D spinal reconstruction in real-world scenarios, we extend ReVerteR to a clinical deployment-oriented framework, and develop an interactive interface with all functions in the framework integrated so as to enhance human–computer interaction during clinical decision-making. Extensive experiments and visualization conducted on our constructed datasets based on two benchmark datasets of spinal CT, VerSe 2019 and VerSe 2020, demonstrate the effectiveness of our proposed ReVerteR. In this paper, we propose an automatic 3D reconstruction method of vertebrae based on orthogonal bi-planar radiographs. With the 3D upright posture of the spine under natural weight bearing effectively constructed, our proposed method is expected to better support doctors make clinical decision during spine-related diagnosis and surgery.

Cost-aware workflow offloading in edge-cloud computing using a genetic algorithm

The edge-cloud computing continuum effectively uses fog and cloud servers to meet the quality of service (QoS) requirements of tasks when edge devices cannot meet those requirements. This paper focuses on the workflow offloading problem in edge-cloud computing and formulates this problem as a nonlinear mathematical programming model. The objective function is to minimize the monetary cost of executing a workflow while satisfying constraints related to data dependency among tasks and QoS requirements, including security and deadlines. Additionally, it presents a genetic algorithm for the workflow offloading problem to find near-optimal solutions with the cost minimization objective. The performance of the proposed mathematical model and genetic algorithm is evaluated on several real-world workflows. Experimental results demonstrate that the proposed genetic algorithm can find admissible solutions comparable to the mathematical model and outperforms particle swarm optimization, bee life algorithm, and a hybrid heuristic-genetic algorithm in terms of workflow execution costs.

MERA: Meta-Learning Based Runtime Adaptation for Industrial Wireless Sensor-Actuator Networks

IEEE 802.15.4-based industrial wireless sensor-actuator networks (WSANs) have been widely deployed to connect sensors, actuators, and controllers in industrial facilities. Configuring an industrial WSAN to meet the application-specified quality of service (QoS) requirements is a complex process, which involves theoretical computation, simulation, and field testing, among other tasks. Since industrial wireless networks become increasingly hierarchical, heterogeneous, and complex, many research efforts have been made to apply wireless simulations and advanced machine learning techniques for network configuration. Unfortunately, our study shows that the network configuration model generated by the state-of-the-art method decays quickly over time. To address this issue, we develop a ME ta-learning based R untime A daptation (MERA) method that efficiently adapts network configuration models for industrial WSANs at runtime. Under MERA, the parameters of the network configuration model are explicitly trained such that a small number of optimization steps with only a few new measurements will produce good generalization performance after the network condition changes. We also develop a data sampling method to reduce the measurements required by MERA at runtime without sacrificing its performance. Experimental results show that MERA achieves higher prediction accuracy with less physical measurements, less computation time, and longer adaptation intervals compared to a state-of-the-art baseline.

Mechanical Characterization of GFRP Tiled Laminates for Structural Engineering Applications: Stiffness, Strength and Failure Mechanisms

This study investigates the mechanical properties of tiled laminates, frequently used in FRP bridges, and a completely new class of composites for which currently no experimental literature is available. In this paper, first a microscopic examination of laminates extracted from bridge deck flanges is performed, revealing complex multi-ply structures and tiled laminates in the transverse direction of the bridge deck. The subsequent fabrication of tiled laminates in the transverse (i.e., weak) and longitudinal (i.e., strong) span direction explores stiffness and strength characteristics depending on the stacking angle. It is observed that the stiffness in both directions is only slightly reduced with increasing stacking angles, reaching a maximum decrease of 10%, while the failure strength is significantly reduced, particularly with longitudinal tiling, dropping by approximately 70% for a 2° stacking angle. Transverse tiling demonstrates a more moderate 45% strength reduction due to the presence of some 90° plies. Given the small reduction in the stiffness and the fact that in many applications the design is mainly governed by serviceability (i.e., stiffness) requirements than strength, this strength reduction may be acceptable, considering other advantages of the concept. Additionally, this research sheds light on failure mechanisms, emphasizing the role of ply assembly in stress distribution and highlighting the importance of gradual ply ends in reducing strain concentrations. These findings provide valuable insights for optimizing tiled laminates in structural applications, ensuring their effective and reliable use.

Preparation and characterization of high load‐bearing needled composite preform based on novel ordered short fiber network needling method

AbstractNeedled preform and its composite using felt and base cloth stacked needled have small fiber volume fraction and relatively weak mechanical properties, which are difficult to meet the service requirements of structural and functional integrated components of high‐speed aircraft. Responding to this issue, this paper proposed a new method for preparing high load‐bearing needled composite preform based on ordered short fiber network needling. The high load‐bearing needled preforms were prepared by designing different structural parameters of ordered short fiber network and forming process parameters, and then preform structure was characterized and mechanical properties were tested. The results showed that using ordered short fiber network as carrier of the short fibers, the formed needled fiber bundles were longer and denser, volume content of needled fiber bundles and mechanical properties of needled preform were greatly improved, and needled fiber bundles volume content of ordered short fiber network reinforced needled preform was 1.5 times and 46.3 times that of half‐cut cloth and felt respectively. Compared with traditional felt needled preform and half‐cut cloth needled preform, the interlaminar peeling strength of the ordered short fiber network reinforced needled preform increased by up to 246.4% and 49.2%, respectively. The tensile strength of ordered short fiber network reinforced needled preform was 12.9% higher than that of half‐cut cloth in X‐axis direction. In addition, the effects of ordered short fiber network structure, short fiber length, areal density and needling density on the needled preform structure and properties were also revealed.Highlights High load‐bearing needled composite preform was prepared based on novel ordered short fiber network needling method. The needled fiber bundles formed by using ordered short fiber network were longer and denser, and their volume content was 46.3 times that of felt needled preform. The interlaminar peeling strength of the ordered short fiber network reinforced needled preform increased by up to 246.4%. The tensile strength of ordered short fiber network reinforced needled preform was 12.9% greater than half‐cut cloth in the X‐axis direction.

Towards a smart hospital: Smart infrastructure integration

In today’s business world, digital technologies have become vital for implementing processes in any company, including the healthcare industry. By utilizing the latest technologies to collect, process, analyze, and transmit data through smart devices and IT infrastructure, it is possible to implement trends in medicine as a value approach, personalization, predictability, and participation. Considering these trends, it is necessary to develop a methodology for creating a management system for medical organizations that can comprehensively and interconnectively develop various elements of the system. This includes business process systems, information systems and application architectures, hardware, and "smart" medical equipment that can fully participate in hospital information exchange. These elements should be developed within a single model. An architectural approach can be used to create a model of a medical organization. This paper examines the integration of smart infrastructure in medical organizations, develops requirements for smart infrastructure services, and creates architectural models based on the findings and requirements. Architecture models were developed for the following elements of the Smart Infrastructure: smart wards, wearable devices and telemedicine stands as the most necessary elements in everyday healthcare. The key discovery of the study is the architecture models of the mentioned elements and their integration with the medical organization IT architecture, described in the form of visual models and data exchange tables. The proposed models can be used both for further research or for practical implementation as the reference models to check up and adopt to each individual case. The solutions proposed in the paper give an example of open innovation philosophy, as they are developed on the basis of open information about the innovation activities of various medical organizations, and, at the same, time the results themselves are openly published and open for adaptation by the scientific and practicing medical community.

Accelerator: an intent-based intelligent resource-slicing scheme for SFC-based 6G application execution over SDN- and NFV-empowered zero-touch network

Zero-touch networks (ZTNs) can provide autonomous network solutions by integrating software-based solutions for various emerging 5G and 6G applications. The current literature does not provide any suitable end-to-end network management and resource-slicing solutions for service function chaining (SFC) and user intent–based (time and cost preference) 6G/non-6G application execution over ZTNs enabled by mobile edge computing, network function virtualization, and software-defined networking. To tackle these challenges, this work initiates an end-to-end network management and user intent–aware intelligent network resource–slicing scheme for SFC-based 6G/non-6G application execution over ZTNs, taking into account various virtual and physical resources, task workloads, service requirements, and task numbers. The results depicted that at least 25.27% average task implementation delay gain, 6.15% energy gain, and 11.52% service monetary gain are realized in the proposed scheme over the compared schemes.

Vertical Switching Algorithm for Unmanned Aerial Vehicle in Power Grid Heterogeneous Communication Networks

The rapid development of wireless network technology has led to the coexistence of various heterogeneous wireless networks (HWNs). To ensure that users enjoy normal and diversified services, research on vertical switching technology has become an inevitable trend. However, most current vertical switching algorithms only consider static situations or single services, which are not suitable for power grid scenarios. This paper studies the vertical switching problem of wireless heterogeneous networks for unmanned aerial vehicles (UAVs) performing inspection tasks in power grid scenarios. In this model, a UAV for power grid inspection needs to plan its flight trajectory, avoid obstacles, and find the optimal trajectory to reach each inspection point. Throughout the UAV inspection process, we must ensure the quality of communication services for the UAV. The UAV dynamically selects different networks for access at different locations, presenting a dynamic network selection and vertical switching problem. This paper proposes a method that combines trajectory planning and network selection, which first utilizes the A-star algorithm to obtain suitable trajectories, and then evaluates and judges networks based on the Fuzzy Analytic Hierarchy Process (FAHP) to determine the most appropriate network. It is worth noting that this paper considers three service requirements and seven network attributes under three types of heterogeneous wireless networks. Numerical results show that this method can better meet the requirements of UAV inspection tasks and reduce the number of switches, thus addressing the issue of terminal vertical switches in power grid scenarios.

دمج انترنت الاشياء والحوسبة السحابية مع ضمان جودة الخدمة

Now a days the cloud computing and Internet of Things (IoT) become the most importance technologies that are both used in our life. As we know the Internet of Things (IoT) is generally characterized by real world small things,. It allows billions of devices to be connected and communicate with each other to share information that improves the quality of our daily lives. in the other ways, Cloud Computing has virtually unlimited capabilities in terms of storage and processing power, is a much more mature technology, and has most of the IoT issues at least partially solved. That is why their adoption and use are expected to be more extensive and more importance, thus, a novel IT paradigm in which Cloud and IOT are two complementary technologies merged together is expected to offer the best quality of services, and this new paradigm called Cloud Iot paradigm. Therefore, the integration of cloud and Internet of Things to providing the best service to users and have the highest level of customer satisfaction should always provide quality of service can be guaranteed. The purpose and the aim of this paper is to evaluate the integrity requirements of cloud services and Internet of Things, and to achieve this aim we used descriptive methodology to describe and to recognize about the cloud computing and the internet of things; so that quality of service in these services must be guaranteed. Keywords: Cloud Computing, Internet of Things, Cloud based IoT, Integration and quality of services.

Wireless Edge Caching and Content Popularity Prediction Using Machine Learning

The ever pervasive growth in information services and technology has resulted in the outbreak of demand for data in the wireless networks. This has made the network operators to ponder over the imminent difficulties such as computing capabilities and fronthaul-backhaul link capacities. Hence, to bridge the gap between the cloud capacity and the requirement of mobile services by the network edges, edge computing and caching techniques have been gaining more and more attention from researchers across the world. Further, motivated by the successful applications of ML in solving complex and dynamic problems, in this article, it has been used to advance edge caching capabilities. The proposed ML based algorithms have been evaluated and proved to have better performance compared with the existing conventional algorithms. The MSE, for the proposed DL algorithm, is 20-times less than the existing algorithms and while comparing with the simple neural network, the gain in MSE for the proposed DL algorithm is observed around 27%. Similarly for the FL based caching algorithm, average cache hit gain is of the order of 10^4, hence demonstrating the benefit of the proposed algorithms. Additionally, opportunities for a promising upcoming future of ML in edge computing prediction have been discussed.

Search and Harvesting across NFDI Consortia – Gaps and Challenges

Search and harvesting use cases on harmonised metadata play an important role in several activities on National Research Data Infrastructures (NFDI). The working group Search and Harvesting of the NFDI section (meta)data, terminologies and provenance works on a common understanding of user needs (for search) and service requirements (for harvesting), analysis of the data sources landscape, and recommendations concerning common and specific needs, e.g., for spatial or sensitive data. Here, we present search and harvesting gaps and challenges across NFDI consortia and beyond, which were identified and structured in the Search and Harvesting Working Group, and the recommendations for the NFDI we derive from them. Our goal is to foster a common vision for search and harvesting in the NFDI.

Intelligent decision modeling for optimizing railway cold chain service networks under uncertainty

Railway cold chain service network design (RCC-SND) aims to optimize the utilization of stations and lines as well as train allocations in a manner that minimizes costs while satisfying the service requirements of shippers. Furthermore, the uncertainties associated with freight demand, transportation costs, quality loss, station handling capacity, and arc capacity make the RCC-SND a complex decision-making problem. To tackle this challenge, we first formulate a Mixed-Integer Nonlinear Programming (MINLP) model to determine hub locations, freight wagon flows, and service frequency. To cope with uncertain parameters with varying degrees of uncertainty incorporated in the model, we extend the problem using fuzzy programming and further convert it to its crisp counterpart. A real-world cases study in Southwest China is performed to validate the proposed model, whose results provide different strategies for decision-makers with varying preferences. There are some main findings: As the number of hubs increases from 5 to 6, a maximum total cost savings of 1.99% can be achieved. Railway operators may opt for different decision preferences, for decisions prioritizing economic efficiency, the cost can decrease by 2.69% compared to deterministic optimization.

Serviceability limit state of incrementally launched steel bridge I-girders

Bridge girders are subjected to repeated patch loading during incremental launching (i.e. travelling over supports). Plastic deformations are likely to occur in the girder already at the first patch loading. They influence the girder’s structural response and bring about a decrease in the ultimate resistance at subsequent patch loadings. This problem is analysed in the present paper through the serviceability limit state (SLS) of incrementally launched bridge I-girders (ILBGs). The SLS of ILBGs is a current problem since only a few studies, with contrasting conclusions and certain limitations, have been recognised in the literature. Research is limited on plate girders without longitudinal stiffeners made of structural steel. Flat slide-type launching shoes are considered in this paper.The serviceability requirement (repeatable behaviour of ILBGs) and criterion (that effective membrane strains should remain in the elastic range) are adopted from the literature. So, the serviceability resistance is adopted as a load at the start of the effective membrane plastic strains in the girder. In order to propose a practical and simple SLS design procedure, the serviceability resistance is normalised against the ultimate resistance, so a serviceability correction factor (ksls) is defined. The paper presents a parametric numerical analysis of the factor (ksls). A finite element model previously developed and validated by the author is employed to simulate a nonlinear response of patch-loaded girders. The parametric study includes 599 girders. ksls is inversely proportional to the web thickness and directly proportional to the flange thickness and the yield strength of the steel material. The load length, web depth and flange width do not influence ksls. The influence of the spacing between transverse stiffeners on ksls is negligible. Finally, by regression analysis of the results of parametric study, an original and reliable serviceability correction factor is proposed as the main objective of the research. The range when the SLS of ILBGs should be checked is also defined.

Hierarchical Indexing and Compression Method with AI-Enhanced Restoration for Scientific Data Service

In the process of data services, compressing and indexing data can reduce storage costs, improve query efficiency, and thus enhance the quality of data services. However, different service requirements have diverse demands for data precision. Traditional lossy compression techniques fail to meet the precision requirements of different data due to their fixed compression parameters and schemes. Additionally, error-bounded lossy compression techniques, due to their tightly coupled design, cannot achieve high compression ratios under high precision requirements. To address these issues, this paper proposes a lossy compression technique based on error control. Instead of imposing precision constraints during compression, this method first uses the JPEG compression algorithm for multi-level compression and then manages data through a tree-based index structure to achieve error control. This approach satisfies error control requirements while effectively avoiding tight coupling. Additionally, this paper enhances data restoration effects using a deep learning network and provides a range query processing algorithm for the tree-based index to improve query efficiency. We evaluated our solution using ocean data. Experimental results show that, while maintaining data precision requirements (PSNR of at least 39 dB), our compression ratio can reach 64, which is twice that of the SZ compression algorithm.

A game-theoretic approach to mitigate charging anxiety for electric vehicle users through multi-parameter dynamic pricing and real-time traffic flow

While price-based demand response is universally recognized as crucial for managing electric vehicle charging load, the academic literature has explored diverse mechanisms for its implementation. Prior research has revealed that applying load management schemes based on price-based demand response programs results in higher scheduling costs for low or constant-energy consumers. In addition, the inherent uncertainties involved in arrival, service requirements, market pricing and user behaviours have posed serious challenges in managing real-time charging operations. To handle these challenges, this work has addressed the charging problem at the scheduling level by analysing arrival, waiting, service and departure requests. This work proposed a mathematical model to coordinate charging to minimize the total charging cost and time for a given number of vehicles. We have developed an advanced charging algorithm and introduced a multi-leader multi-follower Stackelberg game. This strategic model ensures a Nash equilibrium, aligning the interests of consumers (vehicles) and providers (charging stations), ensuring a fair and balanced charging system. Where, charging load demand, real-time pricing, charging location, and vehicle types are used as input parameters. In the first stage, the real-time pricing obtained from the Spanish Electricity network is used to schedule the charging load, whereas the second stage is dedicated to using the proposed multi-parameter pricing for customer satisfaction and reduced cost. The results demonstrate the superior performance of our proposed scheme compared to existing approaches. Finally, we present a real-life case study to illustrate the practical effectiveness of the proposed techniques. The study underscores the potential of our approach in real-world scenarios, offering a promising avenue for enhancing efficiency and user experience in charging systems.

Planning, Analysis and Design of Hotel Building at Karadi, Thamarassery

The project deals with the planning, analysis and design of a hotel building. The proposed building site is Karadi, Thamarassery. The building is aimed at providing multi-dimensional and comprehensive usage of space near a crowded city highway. The building is a reinforced concrete framed structure consisting of (G+4) floors with adequate facilities and amenities. The functional planning is done using KMBR and IS codes. The structure shall be designed to resist and bear all loads liable to act on it at some point in its life. It also satisfies serviceability requirements, including limitations on deflections and cracking. Drawing is done using AUTOCAD software, and the building is analyzed using STAAD Pro software. It focuses on the multifaceted considerations required to develop a hotel that meets both functional and aesthetic standards while adhering to regulatory and sustainability guidelines. Critical elements discussed include site selection, space allocation, architectural aesthetics, structural integrity, and environmental impact.

The Prospect of the GCC Monetary Union and its Expansion to Other MENA Countries

This study investigated the possibility of expanding the GCC monetary union to other MENA countries using cluster analysis for the period 2000-2018. It also examined whether the economic characteristics of the countries provide evidence for the readiness of countries for the proposed union. The findings show that there was heterogeneity in the GCC and MENA countries suggesting that the countries were not yet economically ready for the launch of the monetary union. We found evidence in support of the expansion of the GCC monetary union to other MENA countries with two categories of countries, the core and the periphery. We also revealed key variables, including inflation differential, debt service requirement, and current account balance, which clearly demarcated the GCC and other MENA countries. These findings underscore the need for policy-makers in MENA countries to strengthen intraregional trade creating policy, policy coordination, and harmonisation for a successful and sustainable monetary union.

Gap analysis between skills requirements for research support services and LIS/IM curricula

The primary goal of this study was to identify gaps in curriculum coverage and required skills for research support services (RSS) in university libraries in the Punjab province of Pakistan. A convergent parallel mixed methods design was used to meet its objective. To collect data for the quantitative strand, a cross-sectional survey with a closed-ended questionnaire was conducted. The population for the cross-sectional survey was reference/research librarians working in university libraries in the Punjab province. SPSS was used to analyze quantitative data. In the qualitative strand, the curriculum documents of seven LIS/IM schools in the Punjab province were examined by using the content analysis technique. Results of both strands were compared for gap analysis. The findings revealed a gap between the skills required for RSS and the offerings of LIS/IM schools. The findings have practical implications for LIS/IM schools that will help to realign their curricula to meet market needs.

Empowering Light Fidelity-Enabled Internet of Things with Hybrid Fuzzy-Particle Swarm Optimization Power Allocation for Enhanced Energy Efficiency and Quality of Service

This paper proposes a hybrid fuzzy-Particle Swarm Optimization (PSO) approach for power allocation in bidirectional Light Fidelity (LiFi)-enabled Internet of Things (IoT) communication systems. The approach integrates fuzzy logic and PSO to achieve efficient and robust power allocation, considering energy efficiency and Quality of Service (QoS) requirements. The hybrid approach leverages flexibility of fuzzy logic to handle uncertainties and variations in system parameters, such as channel conditions and QoS requirements. Fuzzy sets and membership functions are defined to represent linguistic variables, and fuzzy rules are formulated based on expert knowledge and system-specific considerations. This allows the approach to adaptively adjust power allocation based on varying channel conditions and QoS requirements, enhancing the robustness of the power allocation strategy. The PSO algorithm is employed for iterative optimization to explore for ideal power allocation solution. The particles in swarm signify possible power allocation configuration, and its position in the search space corresponds to a specific power allocation. The particles update their positions based on local best-known positions and globally best-known positions, allowing the swarm to converge toward the optimal solution. Through extensive simulations and analysis, the proposed hybrid fuzzy-PSO approach is evaluated with regard to energy efficiency and QoS satisfaction. The results demonstrate its effectiveness in achieving energy-efficient power allocation while meeting diverse QoS requirements. The proposed approach outperforms existing methods such as channel- and Orthogonal Multiple Access (OMA)-based power allocation schemes.

Experimental and analytical evaluation of semi-rigid timber connection with screwed-in threaded rods and steel coupling part

A major challenge for mid and high-rise timber buildings is fulfilling serviceability requirements. Moment-resisting timber frames with semi-rigid moment-resisting connections as the primary lateral load-resisting system can be an alternative to buildings with wind bracing or shear walls, allowing for more free space, enhanced building performance, and architectural flexibility. Moreover, introducing semi-rigid timber connections can improve the performance of floors against human-induced vibrations and potentially decrease the material used. The performance of moment-resisting frames depends mainly on the properties of their beam-to-column connections. The paper presents an experimental investigation of semi-rigid moment-resisting connections with long screwed-in threaded rods and a steel coupling part. Long threaded rods (i.e., screwed-in threaded rods with wood screw threads) feature high axial stiffness and resistance and allow for immediate load transfer without initial slip. Due to these properties, threaded rods are preferred over conventional dowel-type fasteners such as bolts and dowels. Five full-scale moment-resisting timber connections were tested under cyclic and monotonic loading to investigate moment resistance, rotational stiffness, and energy dissipation. The stiffness obtained by cyclic and monotonic tests was compared with numerical and analytical methods. The beam-to-column moment-resisting connections demonstrated a high moment resistance of 92.2 kNm and rotational stiffness of 8049 kNm/rad without initial slip. The experimental results and analytical calculations of rotational stiffness differed only by 5 %, whereas the finite element model exhibited a discrepancy of approximately 35 %. The equivalent viscous damping was determined based on cyclic load tests at service load levels and was found to be in the range of 5.5 %–10.0 %. Finally, a parametric study is presented to discuss the effect of semi-rigid connections on the performance of the timber floors and material savings. The increase in rotational stiffness of the beam-to-column connections could lead to up to 13 % material savings.