Scheme For Networks Research Articles

Live Survival Strategy to Grow Entrepreneurial Interest in the Perspective of Maqashid Asy-Syari'ah (On Deaf-Mute Disabilities UPT RSBRW - Pasuruan)

Difficulties for people with hearing and speech impairments in accessing formal employment because companies only open and look for workers in the physically & mentally healthy category, to increase productivity & effectiveness of employee performance. While getting a job is very important & fundamental for sustainability & continuity. One of the problems faced by people with hearing impairments is how to develop skills & knowledge according to their talents and interests, so that they are able to move independently in fulfilling and prospering their needs & desires without depending on the informal sector.This research is descriptive research with a qualitative approach. The researcher will describe survival strategies to foster interest in entrepreneurship for people with hearing and speech impairments according to field conditions with the aim of finding a comprehensive survival strategy formulation. From the results of data analysis in the field, it is clear that the concept of success in the presence of limitations / impairments is that there are 3 types of survival strategies used by people with hearing and speech impairments, namely: active strategy (having, independence, confidence, enthusiasm, optimism, perseverance), passive strategy (making savings in meeting needs: dharuriyat, hajiyat, tahsiniyat, and implementing rational consumption patterns/ mustahlik al-aqlani), network strategy (using available accessibility & building relationships with related agencies).

The Infrastructural Power of the Cayman Islands and the US State Power: A Financial Networks Centrality Approach

ABSTRACT This paper explores the symbiotic relationship between the Cayman Islands and the centrality of the United States (US) in global financial networks, using their connection as a test case for a broader theory of how infrastructural power of states is achieved through transnational and networked strategies. The legal and financial infrastructure of the Cayman Islands is extensively used by US financial institutions. This infrastructure supports the development of a significantly US-centric fund industry, facilitating substantial investments into US capital markets. Additionally, it serves as a global conduit, channelling funds from regions such as Asia and Latin America into US markets, streamlining the process by which foreign investors acquire US securities, and supporting the development of complex USD-denominated financial products. This dynamic enhances the depth, liquidity, and complexity of US capital markets, thereby reinforcing US centrality in global financial networks and bolstering its geopolitical power through financial diplomacy, economic sanctions, regulatory influence, and control over critical financial infrastructure. The relationship underscores the infrastructural power of the Cayman Islands, whose financial and legal framework is essential for sustaining and amplifying US centrality. Consequently, this paper aims to integrate the transnational perspective on infrastructural power within the International Political Economy (IPE) and Geopolitics literature, demonstrating how the Cayman Islands function as a multifaceted networked site that strengthens, projects, and sustains US state power on a global scale.

Merging Worklife Organizational Innovation and Educational Programs: Promoting Continuous Adaptations to the Global Economy

AbstractThis essay honors the accomplishments of Morten Levin both by analyzing some of the key innovative educational programs he created and by extending the elements of that analysis to two other cases, that of Aker Solutions and the Mondragon Cooperatives. We acknowledge that the relationship between higher education institutions and manufacturing-service-public sector organizations is multiplex and has undergone multiple transformations over the decades. It is also affected by the national political economy and the scope and diversity of the higher education sector itself. Creating sustained and yet dynamic relations among these actors in service of both higher education and the efficacy and innovativeness of worklife organizations is a significant challenge which all the cases we present have addressed with some success. What the efforts have in common is a democratizing action research built on an ethos and worldview of the benefits of collaboration and co-creation, and organizational capabilities built on Actor Network strategies to promote collaboration and innovation as a way of adapting to the rapidly changing world conditions effectively. We argue that there are many more such examples in existence and that a key role for action research is to document them, show how more collaborative and adaptive organizational processes are possible and show how neoliberal New Public Management is an authoritarian “wrong turn” with dire consequences. Ultimately, this essay is an argument against the “iron cage” view of bureaucracy as always coercive and stultifying and show that it is not bureaucracy but the authoritarian approaches to creating coercive bureaucracies that destroy organizational collaborations and innovation.

Coal-gangue sound recognition using hybrid multi-branch CNN based on attention mechanism fusion in noisy environments

The coal-gangue recognition technology plays an important role in the intelligent realization of fully mechanized caving face and the improvement of coal quality. Although great progress has been made for the coal-gangue recognition in recent years, most of them have not taken into account the impact of the complex environment of top coal caving on recognition performance. Herein, a hybrid multi-branch convolutional neural network (HMBCNN) is proposed for coal-gangue recognition, which based on improved Mel Frequency Cepstral Coefficient (MFCC) as well as Mel spectrogram, and attention mechanism. Firstly, the MFCC and its smooth feature matrix are input into each branch of one-dimensional multi-branch convolutional neural network, and the spliced features are extracted adaptively through multi-head attention mechanism. Secondly, the Mel spectrogram and its first-order derivative are input into each branch of the two-dimensional multi-branch convolutional neural network respectively, and the effective time-frequency information is paid attention to through the soft attention mechanism. Finally, at the decision-making level, the two networks are fused to establish a model for feature fusion and classification, obtaining optimal fusion strategies for different features and networks. A database of sound pressure signals under different signal-to-noise ratios and equipment operations is constructed based on a large amount of data collected in the laboratory and on-site. Comparative experiments and discussions are conducted on this database with advanced algorithms and different neural network structures. The results show that the proposed method achieves higher recognition accuracy and better robustness in noisy environments.

A Certificate-Less Distributed Key Management Scheme for Space Networks

The specificity and complexity of space networks render the traditional key management mechanism no longer applicable. The certificate-less-based distributed spatial network key management scheme proposed in this paper combines the characteristics of space networks, solving the problems regarding the difficulty of implementing centralized key management in space networks and the excessive overhead required for maintaining public key certificates by constructing a distributed key generation center and establishing strategies such as private key updates, master key component updates, and session key negotiation. This method also avoids the key escrow problem inherent in existing identity-based key management schemes. This scheme provides the DPKG construction method for space networks; designs the update strategy for the DPKG node’s master key sharing, providing a specific update algorithm; introduces the batch private key update mechanism; and uses the mapping function to evenly distribute the node’s update requests throughout the update time period, avoiding the problem of overly concentrated update requests. After analysis and simulation verification, it was proven that the scheme can meet the necessary security requirements, offering good stability and scalability.

Towards full electrification of local public transport: A tool to guide strategies for implementing the electric charging network

This paper describes a methodology and a Decision Support Tool (DST) for the assessment of the impacts of future scenarios of public electric mobility on the electric Distribution Network (DN) of a city, aimed to support the implementation strategies of the electric charging network for the Local Public Transport (LPT).The objective of the proposed tool is supporting the decision makers in estimating the effect of the future development of electromobility on the urban electric grid considering the evolution of both electric buses and their charging infrastructure.Some scenarios for the development of electric charging infrastructure for LPT were identified together with the local public transport operator and the energy provider. The results, which are of interest to both the local Public Transport Operator (PTO) and the local Distribution System Operator (DSO), have been quantified for the city of Turin (Italy), but the methodology can be adopted in any urban context: DSOs and PTOs of each city can use the proposed tool as a support to define their public transport electrification strategies.Some results from the case study examined in this paper (Turin, Italy) show that the energy demand related to the electrification of LPT on a typical weekday is about 45 MWh in 2030 (corresponding to 40 % e-bus), and about 90 MWh when the entire bus fleet is electric. It is also shown how more distributed recharging during the day (with intermediate recharging at the terminus) can dampen the energy/power peaks required at the depot compared to overnight recharging alone: a reduction of up to 27 % of energy demand at the depot is observed due to opportunity charging.

Inverse system based decoupling design and control strategy for crude oil heat exchanger networks

This work focuses on a decoupling control approach for the crude oil heat exchanger network to improve the energy efficiency and achieve continuous energy saving. The desired control variables are identified by the signed directed graph (SDG) model based on the complex network theory. Then the principal component analysis (PCA) method and the evaluation index are employed to construct the initial controller as well as the control variables pairing. The presented system is a multi-input multi-output (MIMO) control system where the coupling problems are considered, and a decoupling control strategy based on the inverse system theory of neural network is proposed. The existence of the inverse system is proved by the reversibility derivation, and the original system is decoupled into two second-order subsystems. Consequently, the proposed decoupling control system is tested on the example of heat exchanger network (HEN) belonging to Crude Distillation Unit. The fluctuating value of temperature is reduced averagely by 57.1% compared with the non-decoupling control strategy and the mean absolute percentage error is within 0.02 under different operating conditions. The results show that the proposed decoupling control method has excellent decoupling performance and can improve the control performance of the system.

Зависимость потенциала на рельсах от сопротивления заземляющего устройства тяговой подстанции и параметров рельсовой линии

Purpose: to analyse the dependence of the potential on rails on the track coordinate at different values of conductivity of the grounding device of the traction substation (g0 = 0 S, 10 S, 25 S, 50 S). Methods: the analysis was carried out on the developed substitution diagram of the traction rail network section using the method of nodal potentials. The calculation was implemented in Mathcad 15 mathematical package. Selection of initial data was carried out taking into account the values established in the current normative documentation. Results: an equivalent substitution scheme of the traction rail network on a double-track section has been developed, in which each homogeneous section of the rail line is replaced by an equivalent U-shaped quadrupole. This allows to transform an electric circuit with distributed parameters into a circuit with concentrated parameters. If it is necessary to simulate transverse or longitudinal asymmetry of traction current in a section, such a section is replaced by a three-wire substitution diagram in which the earth conductor is taken into account. To improve the accuracy of the modelling results, the inter-track jumpers are taken into account, and the number of nodal potentials is increased to 7. It is established that the highest modulus value of voltage on rails is observed near the traction substation. It has been determined that the voltage value on rails near the traction substation decreases when the conductivity of the earthing device increases. Practical significance: values of rail-ground potential at different values of conductivity of grounding device of traction substation for tracks of both directions of traffic — even and odd tracks — are determined. The value of conductivity of the traction substation grounding device at which it is safe for the personnel to stay on the rails near the traction substation has been determined. The obtained results can be recommended for practical use in railway transport.

Aspects of inclined magnetohydrodynamics and heat transfer in a non‐Newtonian tri‐hybrid bio‐nanofluid flow past a wedge‐shaped artery utilizing artificial neural network scheme

Aspects of inclined magnetohydrodynamics and heat transfer in a non‐Newtonian tri‐hybrid bio‐nanofluid flow past a wedge‐shaped artery utilizing artificial neural network scheme

An integrated strategy for deciphering quality markers of Terminaliae Belliricae Fructus based on a three-dimensional characteristic model

Terminalia bellirica (Gaertn.) Roxb. is an ethnomedicinal plant that has been utilized in Tibetan and traditional Chinese medicine (TCM). Nevertheless, its quality standard officially listed in the Chinese Pharmacopoeia does not include any content determination of the indicator components of Terminaliae Belliricae Fructus, which constrains the effective quality evaluation of medicinal material and related products. In this paper, a three-dimensional “content-pharmacokinetics-pharmacology” network strategy was developed to identify the quality markers (Q-markers) of Terminaliae Belliricae Fructus. in terms of “measurability”, “traceability” and “effectiveness”. Chromatographic fingerprint analysis was performed to outline its chemical contour, and identify the differential components of 17 batches of Terminaliae Belliricae Fructus. combined with multivariate statistics analysis and UPLC-QTOF-MS analysis. Serum pharmacochemistry analysis was implemented on rats, and 25 prototype components absorbed into the blood were identified. By network pharmacology analysis, a component-disease-target-pathway network was constructed, thus validating the effectiveness of the chemical components of Terminaliae Belliricae Fructus. Afterwards, the above screened candidate components were put into construction of three-dimensional "radar chart". According to the calculated regression area (RA) and coefficient of variation (CV) values, the potential Q-markers was determined, followed by “specificity” evaluation. Ultimately, ellagic acid (EA), chebulagic acid (CHA), gallic acid (GA), chebulinic acid (CA), corilagin (CO) and chebulanin (CH) were specified as the Q-markers of Terminaliae Belliricae Fructus. Owing to high content, good pharmacokinetic property, high pharmacological activities and specificity. The screened Q-markers could offer a scientific foundation for the quality control of Terminaliae Belliricae Fructus, and the proposed strategy is demonstrated to be reliable and feasible for deciphering Q-markers of TCM.

NAS-BNN: Neural Architecture Search for Binary Neural Networks

Binary Neural Networks (BNNs) have gained extensive attention for their superior inferencing efficiency and compression ratio compared to traditional full-precision networks. However, due to the unique characteristics of BNNs, designing a powerful binary architecture is challenging and often requires significant manpower. A promising solution is to utilize Neural Architecture Search (NAS) to assist in designing BNNs, but current NAS methods for BNNs are relatively straightforward and leave a performance gap between the searched models and manually designed ones. To address this gap, we propose a novel neural architecture search scheme for binary neural networks, named NAS-BNN. We first carefully design a search space based on the unique characteristics of BNNs. Then, we present three training strategies, which significantly enhance the training of supernet and boost the performance of all subnets. Our discovered binary model family outperforms previous BNNs for a wide range of operations (OPs) from 20M to 200M. For instance, we achieve 68.20% top-1 accuracy on ImageNet with only 57M OPs. In addition, we validate the transferability of these searched BNNs on the object detection task, and our binary detectors with the searched BNNs achieve a novel state-of-the-art result, e.g., 31.6% mAP with 370M OPs, on MS COCO dataset. The source code and models will be released at https://github.com/VDIGPKU/NAS-BNN.

Optimization Analysis of Water Supply Mode for Fire Protection System of Super High Rise Building

Abstract In order to improve the applicability and reliability of the fire water supply capacity of super high-rise buildings, the fluid analysis software Flomaster was used for simulation. Through the existing pumps, valves, pipelines, pressure reducing valves and newly developed fire hydrants and other components, the fire water supply system of super high-rise buildings is modeled. The pressure distribution of the system pipe network under a given fire water supply mode is studied. The research shows that the simulation can analyze whether there is excessive pressure in the fire hydrant system under different flow conditions in the actual fire extinguishing process. This paper simulates the actual fire extinguishing process of the fire hydrant system through a specific engineering case. The pressure distribution and flow distribution under different flow conditions are obtained. The excessive pressure position of the fire pipe network system under the given fire pump model is obtained. It provides a method for guiding the selection of fire pump and the design of fire hydrant system pipe network. It has certain guiding significance for the subsequent optimization of the design scheme of the fire water supply network and the prediction of the performance of the R&D equipment.

Computation Offloading and Quantization Schemes for Federated Satellite-Ground Graph Networks

Computation Offloading and Quantization Schemes for Federated Satellite-Ground Graph Networks

P4-Based Proactive Monitoring Scheme in Software-Defined Networks

P4-Based Proactive Monitoring Scheme in Software-Defined Networks

Long Term 5G Base Station Traffic Prediction Method Based on Spatial-Temporal Correlations

In the domain of 5G network management, accurately predicting traffic volumes at base stations remains a critical yet challenging endeavor, primarily due to the complexities inherent in the spatial and temporal data interactions. Current methods often fall short in effectively harnessing long-term trends and spatial interconnections among base stations. To bridge these gaps, this paper introduces the GCformer model, a novel approach that capitalizes on both spatial relationships and temporal patterns for multi-base station traffic prediction. Spatially, the proposed model employs graph convolutional networks to integrate diverse spatial information and construct insightful adjacency matrices that includes Euclidean distances and non-Euclidean distances (area types of base station locations and similarities in traffic flow among various stations), thereby enhancing the predictability of traffic dynamics. Temporally, the application of the Transformer's attention mechanism enables better capture of long-term relational dependencies in the temporal domain of 5G base station traffic data. Additionally, a time-variant optimization module is designed to establish diurnal cycle data for each base station's traffic, replacing the traditional positional encoding with a more nuanced model that improves the learning of historical data correlations. Empirical results from exhaustive case studies confirm the superiority of the GCformer model in forecasting traffic volumes. The GCformer exhibits a 4.01% improvement in mean squared error and a 3.37% enhancement in mean absolute error compared to the best-performing baseline model, showcasing its potential to significantly improve operational strategies in 5G networks.

Hybrid vehicular access protocol and message prioritization for real-time safety messaging

Real-time safety services rely on the exchange of messages to enhance the operations of connected and automated vehicles (CAVs). These safety messages convey vital information about traffic conditions, enabling drivers to take necessary measures to prevent accidents. The timely and reliable delivery of these messages is essential, necessitating efficient channel access. Vehicular Deterministic Access (VDA) is employed as a channel access scheme with distinct priorities and stringent timing guidelines, particularly for urgent safety warnings. In this paper, we propose a hybrid approach that combines VDA and Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocols, along with a message prioritization algorithm, to ensure efficient and reliable communication of safety messages in vehicular networks. Our approach leverages the strengths of both VDA and CSMA/CA to avoid message collisions; VDA is more efficient under high traffic loads, while CSMA/CA is better suited for low traffic loads. Additionally, the incorporation of the message prioritization algorithm ensures strict deadline guarantees for high-priority messages, such as Decentralized Environmental Notification Messages (DENMs). We evaluate our proposed solution using the Artery simulation framework. Our results show over a 93% delivery rate for DENM exchanges while maintaining low collision probability across various traffic loads. This research provides practical guidance for the development of efficient and reliable communication systems for CAVs. It also offers a detailed analysis of the trade-offs among different access protocols and message prioritization strategies in vehicular networks.

Routing cost-integrated intelligent handover strategy for multi-layer LEO mega-constellation networks

Low Earth Orbit (LEO) mega-constellation networks, exemplified by Starlink, are poised to play a pivotal role in future mobile communication networks, due to their low latency and high capacity. With the massively deployed satellites, ground users now can be covered by multiple visible satellites, but also face complex handover issues with such massive high-mobility satellites in multi-layer. The end-to-end routing is also affected by the handover behavior. In this paper, we propose an intelligent handover strategy dedicated to multi-layer LEO mega-constellation networks. Firstly, an analytic model is utilized to rapidly estimate the end-to-end propagation latency as a key handover factor to construct a multi-objective optimization model. Subsequently, an intelligent handover strategy is proposed by employing the Dueling Double Deep Q Network (D3QN)-based deep reinforcement learning algorithm for single-layer constellations. Moreover, an optimal cross-layer handover scheme is proposed by predicting the latency-jitter and minimizing the cross-layer overhead. Simulation results demonstrate the superior performance of the proposed method in the multi-layer LEO mega-constellation, showcasing reductions of up to 8.2% and 59.5% in end-to-end latency and jitter respectively, when compared to the existing handover strategies.

Non-targeted Screening and Toxicity Study of Safety Risk Substances in Facial Skincare Products: Molecular Networking and Computational Toxicology Strategy

Non-targeted Screening and Toxicity Study of Safety Risk Substances in Facial Skincare Products: Molecular Networking and Computational Toxicology Strategy

Social and Religious Networks with Respect to the Health Behaviors of African American Women: A Systematic Review.

The aim of this systematic mixed studies review was to examine how social networks influence physical activity and dietary patterns among African American women. We searched PUBMED, CINAHL, JSTOR, SocioIndex, Medline, Cochrane Library, sportDISCUS, and PsycINFO to review articles published between Jan 1, 2007, and July 1, 2021. Only 23 studies met the inclusion criteria. All studies were conducted in the US. Social support received from family members, friends, and churches was found to positively influence physical activity and/or dietary patterns among African American women. An inconsistent relationship was found between social network size, health behavior characteristics of social networks, and physical activity and/or dietary patterns among African American women. Future research should incorporate structural and functional social network strategies to promote physical activity and healthy eating behaviors effectively and sustainably among African American women.

ACGAN: adaptive conditional generative adversarial network architecture predicting skin lesion using collaboration of transfer learning models

Skin cancer has become a serious disease which has the potential to scale up if it is not identified earlier. It is imperative to detect and give treatment to skin cancer promptly. Diagnosing skin cancer manually takes a lot of time and it is costly, and the probability of false diagnosis has increased due to the outstanding resemblances among various skin lesions. Enhancing the classification of multi-class lesions of skin needs the development of investigative systems which should be automated. Data augmentation with GANs and Adaptive Conditional Generative Adversarial Network strategies improves performance. The performance is tested using balanced and unbalanced datasets. Using a proper process of augmentation of data, the suggested system attains a 94% accuracy for the VGG16, 93% for the ResNet50 and 94.25% for ResNet101. The process of collaboration of all such methods improves accuracy further to 95%. In summary, the novelty of the work lies in its holistic approach to automated skin lesion classification, incorporating advanced deep learning models, novel data augmentation techniques and comprehensive performance evaluation on real-world datasets. These contributions collectively advance the field of computer-aided diagnosis for the detection of skin cancer and treatment.