Scheme For Networks Research Articles

Physics-informed surrogate modeling for a damaged rotating shaft

In the online structural health monitoring framework, surrogate modeling aims to approximate the predictions of the underlying high-fidelity model to facilitate fast simulations. Generally, conventional machine learning techniques based on purely data-driven approaches often behave as black boxes, producing predictions that may lack physical consistency. In an effort to overcome these limitations, we proposed a neural network mapping constrained by the known physical relationships of an engineering model, which can be considered as a physics-informed inductive bias. This approach aims to enhance computational efficiency while reducing the required number of training examples. The physics-informed term guides the training process to yield predictions that exhibit greater physical consistency while maintaining high performance. We here propose a surrogate modeling approach relying on a Physics-Informed Neural Network (PINN) scheme, which is designed to enable robust and real-time monitoring of ballistic impact damage on a rotor shaft. At first, a high fidelity finite element model is developed to simulate the response of the rotor shaft under various operational conditions. As a first reduction step, substructuring is used to decrease the dimensionality of the system's matrices, thereby reducing the computational cost. For comparison and validation, an alternative model based on the Timoshenko beam theory is utilized. Both models examine the effects of impact-induced vibration loads due to ballistic occurrences. The PINN model receives the external load as input and predicts the system's response. The system matrices extracted from both models constitute the physics-informed term. This term is incorporated into the PINN architecture in order to steer the learning process towards more physically consistent and reliable predictions regarding the state and the response of the structure. The algorithm's performance is evaluated using response data derived from both models.

Optimal depth and a novel approach to variational unitary quantum process tomography

In this work, we present two new methods for variational quantum circuit (VQC) process tomography (PT) onto n qubits systems: unitary PT based on VQCs (PT_VQC) and unitary evolution-based variational quantum singular value decomposition (U-VQSVD). Compared to the state of the art, PT_VQC halves in each run the required amount of qubits for unitary PT and decreases the required state initializations from 4 n to just 2 n , all while ensuring high-fidelity reconstruction of the targeted unitary channel U. It is worth noting that, for a fixed reconstruction accuracy, PT_VQC achieves faster convergence per iteration step compared to quantum deep neural network and tensor network schemes. The novel U-VQSVD algorithm utilizes variational singular value decomposition to extract eigenvectors (up to a global phase) and their associated eigenvalues from an unknown unitary representing a universal channel. We assess the performance of U-VQSVD by executing an attack on a non-unitary channel quantum physical unclonable function. By using U-VQSVD we outperform an uninformed impersonation attack (using randomly generated input states) by a factor of 2 to 5, depending on the qubit dimension. For the two presented methods, we propose a new approach to calculate the complexity of the displayed VQC, based on what we denote as optimal depth.

Social network strategies to distribute HIV self-testing kits: a global systematic review and network meta-analysis.

Social network strategies, in which social networks are utilized to influence individuals or communities, are increasingly being used to deliver human immunodeficiency virus (HIV) interventions to key populations. We summarized and critically assessed existing research on the effectiveness of social network strategies in promoting HIV self-testing (HIVST). Using search terms related to social network interventions and HIVST, we searched five databases for trials published between 1st January 2010 and 30th June 2023. Outcomes included uptake of HIV testing, HIV prevalence and linkage to antiretroviral therapy (ART) or HIV care. We used network meta-analysis to assess the uptake of HIV testing through social network strategies compared with control methods. A pairwise meta-analysis of studies with a comparison arm that reported outcomes was performed to assess relative risks (RR) and their corresponding 95% confidence intervals (CI). Among the 4496 manuscripts identified, 39 studies fulfilled the inclusion criteria, including one quasi-experimental study, 22 randomized controlled trials and 16 observational studies. Networks HIVST testing was organized by peers (distributed to known peers, 15 studies), partners (distributed to their sexual partners, 16 studies) and peer educators (distributed to unknown peers, 8 studies). Among social networks, simulating the possibilities of ranking position, peer distribution had the highest uptake of HIV testing (84% probability), followed by partner distribution (80% probability) and peer educator distribution (74% probability). Pairwise meta-analysis showed that peer distribution (RR 2.29, 95% CI 1.54-3.39, 5 studies) and partner distribution (RR 1.76, 95% CI 1.50-2.07, 10 studies) also increased the probability of detecting HIV reactivity during testing within the key population when compared to the control. All of the three social network distribution strategies enhanced the uptake of HIV testing compared to standard facility-based testing. Linkage to ART or HIV care remained comparable to facility-based testing across the three HIVST distribution strategies. Network-based HIVST distribution is considered effective in augmenting HIV testing rates and reaching marginalized populations compared to facility-based testing. These strategies can be integrated with the existing HIV care services, to fill the testing gap among key populations globally. CRD42022361782.

북부 규슈 마한 교류 네트워크의 변천 및 구조 연구

Records implying the interaction between the Korean Peninsula and the Japanese archipelago are found in works such as the 『漢書地理志』 and the 『魏志倭人伝』. In addition, considering that the Nakrang (樂浪)·Daebang (帶方) route was inevitably through the Mahan region, it can be seen that Mahan was also located within the Korea-Japan interaction network. This is evidenced by the large number of Mahan pottery found in northern Kyushu, where various Korean Peninsula-type pottery are concentrated. This article attempts to estimate the location and operational strategy of the Mahan interaction network from a comparative perspective, paying attention to the specificity of the logistics environment in northern Kyushu. The temporal and spatial aspects of Mahan type pottery excavated from northern Kyushu are re-examined and compared with those of the Nakrang and Byeon·jinhan(弁·辰韓) type pottery that were introduced at the same time. Through this, it can be admitted that the Mahan interaction network has been organized since the second half of the late Yayoi period, but it is judged that they have taken advantage of and participated in the network established around Nakrang in the past. Mahan's new initiative is recognized in the process of dismantling and reorganizing existing networks from the beginning to the first half of the Early Kofun Period. Furthermore, by looking at the background of the establishment and dismantling of the Nishijinmachi[西新町] ruins, which are the inflection points, from a relative perspective and operational strategy, the possibility that they had a trade structure vulnerable to risk management was pointed out. Through the above results, it was possible to estimate the specific status and role of the Mahan interaction network identified in northern Kyushu. It was possible to evaluate Mahan's external capabilities in a new position during the Proto-Three Kingdom Period, which had previously received relatively little attention as part of the Samhan or as part of the Baekje. Through this, it is expected to be a study that can contribute to the reconstruction of the multifaceted and comprehensive aspects of external relations formed by Mahan while evoking research on Korea-Japan interactions during the Proto-Three Kingdom Period.

A novel dynamic batching energy management strategy for multi-microgrid networks with enhanced privacy

This article presents a novel energy management approach termed Dynamic Batching EMS (DBEMS) designed specifically for day-ahead scheduling within multi-microgrid (MMG) networks. The prime aim of this strategy is to curtail operational costs within the MMG network, fortify the privacy of individual microgrids, and optimize the utilization of available resources, surpassing existing methodologies like centralized, decentralized, and nested management strategies. The emphasis is chiefly on orchestrating cost-effective operations while bolstering data privacy for each microgrid within the complex MMG framework. The efficacy of the proposed strategy is evaluated across both grid-connected and islanded modes, focusing on a network comprising four microgrids. The model formulation is executed utilizing the Mixed Integer Linear Programming method. In the islanded mode, optimization is conducted for scenarios both inclusive and exclusive of Battery Energy Storage Systems (BESS). Comparative assessments are made against centralized and nested strategies, considering parameters including operational costs, privacy enhancements, load-shedding, computation time, and computational burden.

Fostering Collaboration and Team Effectiveness in Educational Leadership: Strategies for Building High-Performing Teams and Networks

This study examines transformative leadership within Philippine educational settings to discern its impact on staff development, and institutional effectiveness. Utilizing a descriptive correlational design, the analysis involved diverse stakeholders and revealed that transformative leadership diverges significantly from traditional management, emphasizing innovation, empathy, and adaptability. It fosters a culture where staff confidence and autonomy lead to professional growth and improved educational outcomes. Additionally, the findings suggest that such leadership is essential for educational institutions navigating the complexities of modern education, including technological advancements and societal changes. Effective communication and systematic evaluation were identified as key for transformative leaders to measure and enhance student success despite facing resistance to change. The study concludes that transformative leadership will become increasingly central in education, driving institutions towards inclusivity and a forward-thinking ethos. It is recommended that Philippine educational institutions adopt transformative leadership to bridge disparities, enhance quality, and prepare for future educational demands. This approach is particularly beneficial for public schools, higher education, TVET institutions, private educational providers, policymakers, and community learning centers, promising to elevate the quality of education in a rapidly evolving global landscape

Bidirectional Braille-speech Communication System for Deafblind Students

Deafblindness is a type of dual disability wherein both visual and auditory capabilities are significantly impaired. Special communication methods have been developed for the deafblind community, yet these methods require that both people involved have prior knowledge and training to successfully communicate; limiting deafblind people’s social interactions, particularly in academic settings. This paper describes the development of a device that enables two-way communication between a severe to profoundly deafblind user and a hearing person with no prior knowledge of Braille and no additional intermediaries. A Convolutional Neural Network (CNN) scheme for speech recognition was designed and implemented along with the development of an algorithm capable of developing both text-to-speech and Finger-Braille-to-text conversion. Lastly, a system integration via 3D modelling and additive manufacturing was carried out to deliver a functional prototype. The resulting device aims to allow deafblind students to send and receive information entirely in finger Braille, using buttons and vibrotactile feedback, while the hearing tutor receives auditory messages and speaks to reply, making the educational experience as familiar as possible for both parties. Users testing the device achieved an average typing accuracy of over 95% and demonstrated understanding of commands transmitted through the device’s components.

LIVER CANCER IMAGE PREPROCESSING AND FEATURE SELECTION USING A HYBRID DEEP LEARNING NETWORK

A liver's primary duties include producing bile, which is necessary for the breakdown of fats, filtering and changing potentially harmful compounds in the blood, and storing vitamins and nutrients. The diagnosis of malignant liver lesions can be made using a variety of techniques, including magnetic resonance imaging (MRI) and/or CT scanning with multiphase contrast agent injection. focuses on the methods for creating tumor and liver segmentation with the IVGG19-UNeT hybrid deep learning network. The suggested model's deep learning network scheme structure is made up of preprocessing, feature extraction, classification, and segmentation. With over 98% accuracy in tumor categorization, the suggested method accurately identifies the greatest number of tumor regions.

Robust dynamic train regulation integrated with stop-skipping strategy in urban rail networks: An outer approximation based solution method

In dense urban rail networks with high passenger demands, uncertain disturbances occur frequently, and the resulting train delays will likely spread over the whole network rapidly, hence degrading the service quality offered to passengers. To cope with the uncertainties of frequent disturbances in urban rail networks, this paper proposes a robust train regulation strategy based on the information gap decision theory, which allows the operators to adjust the conservativeness of adjustment schemes flexibly by varying system performances but without the need for prior knowledge of uncertain disturbances. Specifically, considering the coupling relationship between train dynamic flows and passenger dynamic flows, a mixed integer quadratically constrained programming (MIQCP) model is constructed for the robust train regulation problem to generate solutions with immunity against disturbance uncertainties, in which the envelope-bound model is used to characterizing the uncertain sets of disturbances. To meet the real-time requirements of train operation adjustment, a tailored outer approximation algorithm incorporating a two-phase heuristics method is devised to effectively solve the developed robust train regulation model, thereby quickly generating high-quality solutions. Moreover, the warm start technique and domain reduction technique are carefully developed to accelerate the solving procedure. Numerical experiments based on the Beijing metro network illustrate the robustness of the proposed train regulation strategies and the effectiveness of the designed solution approach.

Distance-based dynamic caching and replacement strategy in NDN-IoT networks

The amount of data on the Internet has risen noticeably due to the exponential increase in the number of Internet of Things (IoT) devices. Recently, Information-Centric Networking (ICN) is acknowledged as a suitable framework for creating IoT network protocols. One notable feature of ICN is in-network caching, which stores the data packets on intermediate routers/nodes that can be accessed by the subscribers when required. This work proposes a novel Distance-based dynamic caching and replacement strategy (DBDCRS) which involves a light weight calculation that utilizes the request aggregation to find the request weight of the requests stored in Pending interest table (PIT) and node weight of a node based on the distance from the subscriber and IoT publisher to accomplish the caching decision. To eliminate the data redundancy, the scheme caches a particular content on a single node along the specific interest-data path. Additionally, a custom cache replacement policy is introduced which retains the essential contents in cache. Simulation results show that our proposed DBDCRS scheme outperform other in-network cache placement schemes in terms of cache hit ratio, server hit reduction ratio, average response latency, and energy consumption.

Attribute-based linear homomorphic signature scheme based on key policy for mobile crowdsensing

Compared with traditional wireless sensor networks, mobile crowdsensing networks have advantages of low cost, easy maintenance, and high scalability, which will play a role in city-level data sensing scenarios in the future. So far, linear homomorphic signatures based on Public Key Instruction,identity, as well as certificateless, have been proposed in wireless sensor networks to resist the data contamination. However, these signature schemes cannot perform finer-grained signature verification, and these signature schemes do not realize the separation of users’ sensitive information from their data. To solve the above problems, we design an attribute-based linear homomorphic signature scheme for large-scale wireless network built with mobile smart devices. First, we give the definition of the attribute-based linear homomorphic signature scheme based on key policy (KP-ABLHS). Second, we construct KP-ABLHS by incorporating attribute-based signature and linear homomorphic coding signature scheme. Finally, we prove our protocol is secure in random oracle model (ROM) and use Python pairing-based cryptography library (pypbc) to implement the scheme. The experimental results show that our scheme is as efficient as Li et al.’s scheme and has the advantage of signing the set of attributes, while the efficiency of our scheme is significantly better than that of scheme Boneh et al.’s scheme.

Strategi Kampanye Partai Amanat Nasional Pada Pemilu Legislatif 2024: Pendekatan Teori Marketing-Mix

This article examines to find out the successful campaign strategy of the National Mandate Party (Partai Amanat Nasional, PAN) in increasing its vote in the 2024 Legislative Election. Methodologically, this article uses qualitative method by applying case study approach. In data gathering, it employs library research by mining data from online news channels. In the data analysis, it utilizes the modern platform of NVivo 14 mainly word frequency query. To measure the campaign strategy of PAN, this article applies four indicators of marketing-mix theory: product, price, place, promotion (4P). The finding reveals that in terms of product, PAN promotes a platform of “helping people” with its past record as the reformist party and supported by the legislative candidates’ background of entrepreneurs and artists-celebrities. Regarding the price, the PAN’s campaign budget spent at least 223 billion IDR with its successful campaigns which conducted by each legislative candidate. PAN prefers to rely on the popularity of President of Joko Widodo and legislative candidates. In the context of place, the strategy of local network and canvassing are based on the creativity of each legislative candidates in attracting voters’ sympathy. The leader tour is conducted by visiting influential public figures. Concerning promotion, the campaign publication of PAN relies on printed mass media, television, and social media while event publication is carried out in various regions across the country. Among those four indicators, the price has the most significant impact which causes to the successful campaign of PAN in the 2024 Simultaneous Election.

Voltage control strategy of a high-permeability photovoltaic distribution network based on cluster division

The use of distributed photovoltaics (PVs) on a large scale often causes voltage over-limit problems in distribution networks. This paper proposes a distributed photovoltaic cluster collaborative optimization voltage control strategy based on an improved community algorithm to address the issue of centralized control being unable to respond quickly to the randomness of distributed photovoltaics and the difficulty of achieving overall coordination with local control. First, by improving the community algorithm, the division of reactive and active clusters, considering the power balance and node coupling degree, is realized. Then, the cluster-coordinated voltage control strategy is proposed by making full use of the power control ability of a photovoltaic inverter. Finally, a voltage regulation ability evaluation index is proposed to assess the node regulation ability within the cluster and select key nodes. This effectively reduces the number of control nodes. The simulation analysis of the improved IEEE 69 distribution network shows that the proposed voltage control strategy can mitigate the issue of voltage over-limit in high-permeability distributed photovoltaic access distribution and enhance the photovoltaic consumption capacity.

Target and non-target analysis with molecular network strategies for identifying potential index compounds from Momordica charantia L. for alleviating non-alcoholic fatty liver

The cucurbitane-type triterpenoids of Momordica charantia Linn. have blood lipid lowering effect and thereby improving non-alcoholic fatty liver. In this study, microwave-assisted extraction efficiently obtained these compounds which were further confirmed by the use of UPLC-MS/MS for target analysis and LC-HRMS for non-target analysis, combining GNPS techniques. The results indicated that the content of cucurbitane-type triterpenoids in bitter melon Hualien No. 5 was the highest among the others. Except for the immature whole fruit, the other plant parts such as fruits, seeds, vines and the whole plant of Hualien No. 5 with different maturity levels of immature, mature and overripe can significantly inhibit lipid accumulation in hepatocytes induced by oleic acid. The vines were proven as the most significant among the others where the active components for improving non-alcoholic fatty liver were rapidly screened and identified as 3β,7β,25-trihydroxycucurbita-5,23 (E)-dien-19-al and Momordicine II through non-target analysis, multivariate analysis and molecular network.

Survey on Resource Allocation for Future 6G Network Architectures: Cell-Free and Radio Stripe Technologies

Future beyond fifth-generation (B5G) and sixth-generation (6G) communication systems require a higher quality of service (QoS) along with meeting multiple objectives and traffic demands. Consequently, new multi-antenna technologies and massive multiple-input-multiple-output (mMIMO) architectures have been proposed in recent years. This paper delves into the foundational concepts that form the basis for the design of two potential future mMIMO network topologies: cell-free (CF) network and its successor, the radio stripe (RS) system. Key aspects of the mMIMO and CF network concepts are addressed, along with a practical sequential implementation based on RSs. This exploration encompasses intricate details of the channel estimation (CE) phase, as well as the uplink (UL) and downlink (DL) transmission and reception phases. We then focus on analyzing optimization concepts that underpin resource allocation (RA) algorithms, specifically those applied in UL power allocation and access point selection (APS) schemes in both CF and RS networks. This comprehensive understanding serves as a robust foundation for addressing the challenges inherent in achieving the conflicting B5G and 6G major key performance indicators (KPIs), such as enhancements on spectral efficiency (SE), power efficiency (PE), and computational complexity or load balance (LB).

An ensemble and cost-sensitive learning-based root cause diagnosis scheme for wireless networks with spatially imbalanced user data distribution

An ensemble and cost-sensitive learning-based root cause diagnosis scheme for wireless networks with spatially imbalanced user data distribution

Neural Network based Route Guidance Strategy in intelligent transportation systems

Traffic congestion in urban areas presents a significant challenge to efficient transportation and sustainable urban development. This paper introduces a Neural Network based Route Guidance Strategy to address the non-linear complexities of urban traffic flow. The strategy demonstrates its capability to optimize traffic flow in real-time by predicting the travel times of candidate routes. A comprehensive simulation study compares the performance of the Neural Network Strategy with traditional traffic management strategies, focusing on traffic flow efficiency, vehicle count stability, and route choice optimization. The results indicate that the Neural Network Strategy significantly enhances traffic management by stabilizing vehicle counts, reducing fluctuations in traffic flux, and achieving a more uniform distribution of vehicles. The paper concludes with an analysis of the experimental results, highlighting the integration of neural networks into traffic management systems as a promising approach to mitigating urban traffic congestion. Future research directions are discussed, emphasizing the potential for real-world implementation and the exploration of advanced neural network models.

Investigating Co-Innovation strategies to prevent food loss in the fruits and vegetables sector

The transition towards sustainable food systems implies the exploration of drivers and challenges for the adoption of innovative solutions by the operators on the value chains. The development of innovative approaches is critical to improve the collaboration of different stakeholders on sustainability topics. The Q-methodology combines qualitative and quantitative analysis to navigate subjective stakeholder perspectives, aiming to find shared strategies and enhance collaborative approaches on complex topics. This study employs Q-methodology to investigate stakeholders' viewpoints on reducing food losses in the Italian fruit and vegetable sector, focusing on the upstream phases of the supply chain. The aim is to explore the perspectives of producers, experts, and institutional representatives towards sustainable innovations in food losses prevention and management practices. The integration of technological innovation is described as a key factor, but the adoption process is hampered by structural supply chain burdens. The results enforced the adoption of sustainable technologies by operators, reporting that collaboration can enhance efficiency, quality management, and sustainability during fruit and vegetable production and distribution operations. Innovative solutions to mitigate food losses risk are driven by resource-efficiency network strategies that should imply a positive environmental and social performances. The application of the Q-methodology allows for a deeper understanding of the emerging challenges during the adoption of food loss reduction strategies. The findings reported the significance of collaboration, technological innovation integration, and sustainable practices across the supply chain. This research contributes to the development of effective, stakeholder-supported strategies and informs policymakers in the fruit and vegetable sector about different solutions to mitigate food losses.

Application of molecular networking to improve the compound annotation in liquid chromatography-mass spectrometry-based metabolomics analysis: A case study of Bupleuri radix.

Compound annotation is always a challenging step in metabolomics studies. The molecular networking strategy has been developed recently to organize the relationship between compounds as a network based on their tandem mass (MS2) spectra similarity, which can be used to improve compound annotation in metabolomics analysis. This study used Bupleuri Radix from different geographic areas to evaluate the performance of molecular networking strategy for compound annotation in liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. The Bupleuri Radix extract was analyzed by LC-quadrupole time-of-flight MS under MSe acquisition mode. After raw data preprocessing, the resulting dataset was used for statistical analysis, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The chemical makers related to the sample growth place were selected using variable importance in projection (VIP) > 2, fold change (FC) > 2, and p < 0.05. The molecular networking analysis was applied to conduct the compound annotation. The score plots of PCA showed that the samples were classified into two clusters depending on their growth place. Then, the PLS-DA model was constructed to explore the chemical changes of the samples further. Sixteen compounds were selected as chemical makers and tentatively annotated by the feature-based molecular networking (FBMN) analysis. The results showed that the molecular networking method fully exploits the MS information and is a promising tool for facilitating compound annotation in metabolomics studies. However, the software used for feature extraction influenced the results of library searching and molecular network construction, which need to be taken into account in future studies.

IHKM: an improved hierarchical key management scheme for wireless sensor network

IHKM: an improved hierarchical key management scheme for wireless sensor network