Distributed Approach Research Articles

Hybrid Optimization Approach for Handoff Strategy–Based Spectrum Allocation in Cognitive Radio Network

ABSTRACTDevice‐to‐device (D2D) transmission is essential for enhancing the functionality of fifth‐generation (5G) networks. This paper addresses the need for effective power allocation and resource management for D2D users, who operate as secondary users (SUs) alongside primary users (PUs). Ensuring that D2D operations do not disrupt PU interactions is crucial. Traditional bandwidth distribution approaches rely on complete channel state information (CSI) from the base station (BS), leading to uncertainty fin resource allocation. To get rid of these challenges, this paper proposes a novel handoff strategy based on spectrum allocation in cognitive radio networks (CRNs). The data priority–based channel allocation is carried out using proposed hybrid optimization cuttlefish updated dwarf mongoose optimization (CUDMO). It is the combination of both cuttle fish algorithm (CFA) and dwarf mongoose optimization (DMO) algorithms. This optimization considers constraints such as coverage, signal strength, distance, bandwidth and improved strategy like signal‐to‐noise ratio–channel usability (SNR‐CU). Furthermore, an improved fuzzy logic–based proactive handoff mechanism, the fuzzy‐induced modified rules for channel selection (FIMRCS), is introduced. This scheme optimally selects channels, minimizing service interruption during handoff. In the dynamic multichannel selection (DMCS) scheme, parameters like channel rank, channel transmission, and channel usability are considered as the constraints while selecting the channel. They are evaluated against a set of 27 defined rules, ensuring efficient data transmission through the chosen channels. Finally, the performance of proposed CUDMO algorithm is contrasted over state‐of‐the‐art models in terms of various constraints. The CUDMO for Device 450 generated a bandwidth of 1.175 bps, surpassing the lower bandwidth achieved by conventional strategies.

A distributed algorithm for the multi-robot minimum time task allocation problem

Purpose The purpose of this study is to address the challenge of task allocation in multi-robot systems by getting the minimum overall task completion time and task allocation scheme while also minimizing robot energy consumption. This study aims to move away from traditional centralized methods and validate a more scalable distributed approach. Design/methodology/approach This paper proposes a distributed algorithm for the multi-robot task allocation problem, aimed at getting the minimum task completion time along with the task allocation scheme. The algorithm operates based on local interaction information rather than global information. By using the Consensus-Based Auction Algorithm (CBAA), it seeks to effectively minimize energy consumption without affecting the minimum completion time required for overall task allocation. Findings The proposed distributed algorithm successfully reduces robot energy consumption while effectively obtaining the shortest overall task completion time and corresponding task allocation scheme. Numerical simulations conducted using MATLAB software demonstrated its superior performance, and empirical testing on the Turtlebot3-Burger robot platform further substantiated these findings. Originality/value The original contribution of this study lies in the development of an enhanced distributed task allocation strategy using CBAA to improve efficiency in multi-robot environments. Its value extends to applications that require rapid and resource-aware coordination, such as automated logistics or search-and-rescue operations.

Adaptive connectivity control in networked multi-agent systems: A distributed approach.

Effective communication is crucial for the performance and collaboration within cooperative networked multi-agent systems. However, existing literature lacks comprehensive solutions for dynamically monitoring and adjusting communication topologies to balance connectivity and energy efficiency. This study addresses this gap by proposing a distributed approach for estimating and controlling system connectivity over time. We introduce a modified consensus protocol where agents exchange local assessments of communication link quality, enabling the estimation of a global weighted adjacency matrix without requiring centralized information. The system's connectivity is measured using the second smallest eigenvalue of the communication graph Laplacian, commonly referred to as algebraic connectivity. Additionally, we enhance the consensus protocol with an adaptive mechanism to expedite convergence, irrespective of system size or structure. Furthermore, we present an analytical method for connectivity control based on the Fiedler vector approximation, facilitating the addition or removal of communication links. This method adjusts control parameters to accommodate minor variations in link quality while reconfiguring the network in response to significant changes. Notably, it identifies and eliminates energy-consuming yet non-contributory links, improving long-term connectivity efficiency. Simulation experiments across diverse scenarios and the number of agents validate the efficacy of our proposed algebraic connectivity estimation and tracking strategy. Results demonstrate robust connectivity maintenance against external disturbances and agent failures, underscoring the practical utility of our approach for real-world multi-agent systems.

An Artificial Neural Network Prediction Model of GFRP Residual Tensile Strength

This study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks employing reactive error distribution technology assign weights to each variable influencing the residual tensile strength of GFRP. Temperature exerted the most significant influence at 100%, while sample dimensions had a minimal impact at 17.9%. In addition, the mathematical model closest to the proposed was the Bazli model, because the latter depends on two variables (thickness and temperature). The ANN accurately predicted the residual tensile strength of GFRP at elevated temperatures, achieving a correlation coefficient of 97.3% and a determination coefficient of 94.3%.

On the Impact of Oil Prices on Sectoral Inflation: Evidence from the World’s Top Oil Exporters and Importers

This paper investigates the impact of oil price variations on sectoral inflation for a sample of 10 top oil importing and exporting countries. Specifically, we analyze the effects of oil prices on the consumer price index using monthly data spanning the July 2009 to February 2021 period. Two nonlinear techniques are used to this end: The nonlinear autoregressive distributed lag approach (NARDL), and the Hansen’s model (2000). Our econometric results first indicate that the effect of oil price on inflation tends to change across sectors and countries. Second, the inflationary effects of variations in oil prices are likely to affect the energy sector, such as transport and equipment, which are the most dependent on oil. Third, the effect of oil price exists for all countries, but it is stronger in oil-importing than in oil-exporting ones. Additionally, the country most sensitive to the oil price level is China.

Federated Risk-Based Access Control Model for P2P Lending Platforms: A Multi-Agent Systems (MAS) Approach

This study addresses the inherent risk management challenges in decentralized finance, particularly for peer-to-peer (P2P) lending platforms. We propose a novel framework that leverages a Multi-Agent System (MAS) to establish a collaborative network encompassing loan originators, investors, regulators, and service providers. This distributed approach facilitates federated risk management, where risk assessment and mitigation responsibilities are shared across these entities. The MAS employs a comprehensive nine-factor assessment (detailed in Table 5) to evaluate industry risk profiles, considering industry environment, competition, and internal capabilities. This data is further visualized using a color matrix (Tables 5 & 6) and utilized alongside state diagrams (Figure 2) to depict the workflow and manage tasks within the P2P lending process. Additionally, the MAS informs a novel Federated Risk-Based Access Control (FRkBAC) system that tailors access permissions (lending origination, disbursement, etc.) based on dynamic risk assessments of industry trends and individual borrower profiles. This data-driven approach fosters trust within the P2P ecosystem and represents a significant advancement in decentralized finance risk management compared to traditional methods. Doi: 10.28991/ESJ-2024-08-06-05 Full Text: PDF

Fabrication and testing of PVDF-Fish scales based sustainable piezoelectric impact sensor

Structural health monitoring is essential to modern infrastructure management, ensuring safety, optimizing costs, and enhancing structures' overall performance and reliability. Leftover fish scales (FS) in varying quantities (5, 10, 15, and 20wt.%) are reinforced into a polyvinylidene fluoride (PVDF) matrix to develop highly flexible piezoelectric composites, which are then assessed as impact sensors. The role of FS loading on the mechanical and dielectric behavior of PVDF is also investigated. PVDF with 10% FS loading showed maximum Ultimate tensile strength (UTS) and Elastic modulus (E) of 37MPa and 1192MPa, respectively. A 56% and 67% enhancement in UTS and E values over pure PVDF matrix is recorded. Piezoelectric nanogenerators (PENGs) are then fabricated to check the piezo response of composites under various loading conditions using a human hand (finger tapping, palm tapping, film twisting, and wrist pressing). The device with a maximum piezo response is further assessed for potential impact sensing under different impact loading conditions. The recorded results are analyzed statistically using the Weibull distribution approach to cater to the o/p voltage variations for each test load. The potential for sustainability and circular economies is significantly expanded with a cost-effective, highly sensitive piezoelectric impact sensor crafted from FS.

Advancing Healthcare: Intelligent Speech Technology for Transcription, Disease Diagnosis, and Interactive Control of Medical Equipment in Smart Hospitals

Intelligent Speech Technology (IST) is revolutionizing healthcare by enhancing transcription accuracy, disease diagnosis, and medical equipment control in smart hospital environments. This study introduces an innovative approach employing federated learning with Multi-Layer Perceptron (MLP) and Gated Recurrent Unit (GRU) neural networks to improve IST performance. Leveraging the “Medical Speech, Transcription, and Intent” dataset from Kaggle, comprising a variety of speech recordings and corresponding medical symptom labels, noise reduction was applied using a Wiener filter to improve audio quality. Feature extraction through MLP and sequence classification with GRU highlighted the model’s robustness and capacity for detailed medical understanding. The federated learning framework enabled collaborative model training across multiple hospital sites, preserving patient privacy by avoiding raw data exchange. This distributed approach allowed the model to learn from diverse, real-world data while ensuring compliance with strict data protection standards. Through rigorous five-fold cross-validation, the proposed Fed MLP-GRU model demonstrated an accuracy of 98.6%, with consistently high sensitivity and specificity, highlighting its reliable generalization across multiple test conditions. In real-time applications, the model effectively performed medical transcription, provided symptom-based diagnostic insights, and facilitated hands-free control of healthcare equipment, reducing contamination risks and enhancing workflow efficiency. These findings indicate that IST, powered by federated neural networks, can significantly improve healthcare delivery, accuracy in patient diagnosis, and operational efficiency in clinical settings. This research underscores the transformative potential of federated learning and advanced neural networks for addressing pressing challenges in modern healthcare and setting the stage for future innovations in intelligent medical technology.

Assessing the impact of tourism on sustainable economic development in Indonesia: A comprehensive analysis using the Fourier bootstrap autoregressive distributed lag approach

This research analyzes the relationship between political stability, renewable energy utilization, economic progress, and tourism in Indonesia from 1990 to 2020. We employ advanced econometric techniques, including the Fourier Bootstrap Autoregressive Distributed Lag (ARDL) approach and Fourier Toda-Yamamoto causality testing, to ensure the robustness of our results while accounting for smooth structural changes in the data. The analysis uncovers a long-term equilibrium relationship between tourism and its fundamental determinants. Our research reveals significant positive impacts of political stability and renewable energy consumption on tourism in Indonesia. A stable political environment creates a favorable climate for tourism development, instilling confidence in both domestic and international tourists. Promoting renewable energy usage aligns with sustainable tourism practices, attracting environmentally conscious travelers. Furthermore, our findings demonstrate a bi-directional causal relationship between these variables over time. Changes in political stability, renewable energy consumption, and economic growth profoundly influence the tourism sector, while the growth of tourism itself can also stimulate economic development and foster political stability. Our findings underscore the need for environmentally sustainable and politically stable tourism policies. Indonesia’s tourism sector can grow sustainably with renewable energy and stability. Policymakers can develop strategies with tourism, political stability, renewable energy, and economic prosperity in mind.

Review of “Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications” by Mikis D. Stasinopoulos, Thomas Kneib, Nadja Klein, Andreas Mayr & Gillian Z. Heller

Review of “Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications” by Mikis D. Stasinopoulos, Thomas Kneib, Nadja Klein, Andreas Mayr & Gillian Z. Heller

Food, energy, and water security sustainability; an asymmetric approach for achieving sustainable development goals

AbstractThe world has arguably identified both output and population growth over time. Thus, the demand for agricultural commodities such as biofuels and the prominent challenges associated with sustainable land use, water, and food development all play a significant role in the development process, as all countries strive to achieve the use of different approaches, such as convergence in output per capita, energy use, water availability, and food production, more safely and ecologically. Therefore, this study is led by Access and Neo-Malthusian theories to investigate the effects of food, energy, and water security sustainability for achieving sustainable development goals using annual data covering 1980 to 2020 to estimate the asymmetric autoregressive distributive lag approach. Accordingly, the results revealed that the effects of positive changes in food, energy, and water security on sustainable development differ significantly from those of negative changes. A 1% change in food production, water supply development, or industrial value addition has a significant positive impact on sustainable development; however, a 1% change in energy use and carbon emission, population growth, or depletion of natural resources has substantial adverse effects on sustainable economic development in both the short run and the long run in Nigeria. The findings also revealed that restoring equilibrium convergence to achieve 2030 SDGs requires 16.2% each year. The study recommends adopting innovative systems that control and help manage available human and natural resources in productive sectors and increase value-added and renewable energy production in Nigeria.

Extent of acceptance of Government Projects for Food Security in Kabokweni Ehlanzeni District, South Africa

Food security is a major concern in different countries across the world, including South Africa. The study aimed to contextually analyse government projects in Kabokweni, Ehlanzeni district, South Africa to enhance their potential, and strengthen food security. A simple random sampling process was used to choose the 294 participants from a group of 1121 small-scale farmers. Data were gathered using a structured questionnaire, which was administered to respondents by the lead researcher with the aid of trained enumerators. After giving consent, the respondents were asked the questions in one-on-one interviews while the lead researcher and enumerators noted down their responses on the questionnaires. Descriptive statistics were employed to analyse the data using SPSS version 28 software. Descriptive statistics including frequency, percentage, mean, standard deviation, and tables were used for analysis in the study. The type of inferential statistics used is the linear regression model. The results showed that most of the participants were female (64.29%). Youth participation was found to be 25.17% (age group of 29 to 39 years), while elderly farmers were found to be 13.95% (62 years and older). The majority (55.44%) of the small-scale farmers in the study area were schooled up to secondary level, while 12.24% had no formal education. At least 35.7% of the participants owned farmland ranging from 6 to 9 acres, with an annual income of R23 000 earned by 55.44% of the participants. Educational level (P-value=0.001), employment status (P-value=0.181), farm size (P-value=0.003), type of farm enterprise (P-value=0.001), and the level of awareness about government projects (P-value=0.001) were significant, and positively associated with the decision to accept government projects aimed at improving food security levels at the study area. However, the type of crops cultivated (P-value=0.005), and the type of livestock (P-value=0.001) were also statistically significant and influenced the acceptance of government projects for food security. In conclusion, to increase participation in government projects for food security, the monitoring and distribution approach must be improved. Moreover, the study recommends that small-scale farmers should be well informed about the benefits of participating in government projects for food security in the study area. Key words: Extent, acceptance, food security, government, projects, participation, smallholder farmers

Economic Justice in the Design of a Sugarcane-Derived Biofuel Supply Chain: A Fair Profit Distribution Approach

Background: In agricultural supply chains, unequal bargaining power often leads to economic inequality, particularly for farmers. The fair profit distribution (FPD) approach offers a solution by optimizing supply chain flows (materials, information, and money) to promote economic equity among members. However, our literature review highlights a gap in applying the FPD approach to the facility location-allocation problem in supply chain network design (SCND), particularly in sugarcane-derived biofuel supply chains. Methods: Consequently, we propose a multi-period optimization model based on FPD to design a sugarcane biofuel supply chain. The methodology involves four steps: constructing a conceptual model, developing a mathematical model, designing a solution strategy, and generating insights. This model considers both investment (crop development, biorefinery construction) and operational phases over a long-term planning horizon, focusing on farm location and crop allocation. Results: By comparing the FPD model to a traditional centralized planning supply chain (CSC) approach, we examine the impact of the planning horizon, number of farms, and sugarcane prices paid by biorefineries on financial performance. While the FPD model results in lower overall system profits, it fosters a fairer economic scenario for farmers. Conclusions: This study contributes to economic justice in supply chains and offers insights to promote fair trade among stakeholders.

Islam, State, and Local Wisdom: An Examination of Widowhood Inheritance Law in Indonesia

This article highlights the urgency of applying the common property distribution approach prior to the distribution of inheritance in the Indonesian context, which is influenced by local wisdom practices. This research aims to emphasize the rights of wives as widows over joint property through the application of the Compilation of Islamic Law (KHI), so that widows can obtain a fairer share before the distribution of their inheritance rights in accordance with the principles of fiqh. The method used in this research is a normative-juridical approach, with document analysis of legal regulations and their application in the context of local wisdom in Indonesia. The findings show that the distribution of joint property, before the distribution of inheritance, is very important to protect the rights of women who are often marginalized in a society that prioritizes the tradition of male lineage. The affirmation of giving half of the joint property to the widow at the beginning of the distribution before the widow receives her inheritance rights can be equated with shirkah that in the context of marriage the wife also contributes to the collection of property. The discussion in this study emphasizes that the integration of fiqh principles and KHI provisions in the distribution of inheritance, by first recognizing the widow's right to joint property, then the widow receives her inheritance rights after the joint property is distributed, this form of model not only ensures that the widow receives a fair share, but also strengthens the position of women in inheritance issues for the welfare of her family.

'For Youth by Youth': Distributive Leadership in Action With a Youth Codesign Team.

Integrated youth services are an emerging delivery model in Canada that addresses siloed and fragmented youth mental health and other services. Youth engagement is viable for developing integrated youth services when purposefully built. However, it is not always clear how youth are involved in service transformation as decision-makers, and it requires an exploration of how to work with youth authentically and intentionally in the codesign process. This study reflects on the development of HOMEBASE, a network of integrated youth service delivery in Saskatchewan, Canada, and documents the process of actively and authentically engaging with youth through distributive leadership in the codesign process. Youth are actively and eagerly willing to participate in the codesign process of developing integrated services when there is a shared responsibility, and they are authentically involved and informed within the decision-making process. This requires time to form trust, build relationships and provide youth with low-pressure environments to foster healthy debates. By utilizing a distributive leadership approach, the Youth Codesign Team has been engaged in various levels of decision-making. By following these guiding principles, policymakers, youth development workers and researchers can engage youth in meaningful ways to improve the design and development of integrated care. Five youths from the HOMEBASE Provincial Youth Co-Design Team collaborated in writing this article based on their experiences of being engaged at varying levels of decision-making in a distributive leadership approach to building integrated youth services.

Optimized robust learning framework based on big data for forecasting cardiovascular crises

Numerous Deep Learning (DL) scenarios have been developed for evolving new healthcare systems that leverage large datasets, distributed computing, and the Internet of Things (IoT). However, the data used in these scenarios tend to be noisy, necessitating the incorporation of robust pre-processing techniques, including data cleaning, preparation, normalization, and addressing imbalances. These steps are crucial for generating a robust dataset for training. Designing frameworks capable of handling such data without compromising efficiency is essential to ensuring robustness. This research aims to propose a novel healthcare framework that selects the best features and enhances performance. This robust deep learning framework, called (R-DLH2O), is designed for forecasting cardiovascular crises. Unlike existing methods, R-DLH2O integrates five distinct phases: robust pre-processing, feature selection, feed-forward neural network, prediction, and performance evaluation. This multi-phase approach ensures superior accuracy and efficiency in crisis prediction, offering a significant advancement in healthcare analytics. H2O is utilized in the R-DLH2O framework for processing big data. The main improvement of this paper lies in the unique form of the Whale Optimization Algorithm (WOA), specifically the Modified WOA (MWOA). The Gaussian distribution approach for random walks was employed with the diffusion strategy to choose the optimal MWOA solution during the growth phase. To validate the R-DLH2O framework, six performance tests were conducted. Surprisingly, the MWOA-2 outperformed other heuristic algorithms in speed, despite exhibiting lower accuracy and scalability. The suggested MWOA was further analyzed using benchmark functions from CEC2005, demonstrating its advantages in accuracy and robustness over WOA. These findings highlight that the framework’s processing time is 436 s, mean per-class error is 0.150125, accuracy 95.93%, precision 92.57%, and recall 93.6% across all datasets. These findings highlight the framework’s potential to produce significant and robust results, outperforming previous frameworks concerning time and accuracy.

Short-Latency Trigeminocervical Reflex Obtained Without Muscle Activation: Topographic Distribution and Methodological Approach

Purpose: Electrical stimulation of trigeminal nerve branches elicits early and late reflex responses in the cervical muscles, known as the trigeminocervical reflex (TCR). This study aimed to evaluate the neurophysiological aspects, stimulation patterns, and topographic distribution of short-latency TCR components in humans in the absence of voluntary muscle activation. Methods: This prospective observational study included 30 participants. Trigeminocervical reflex responses were simultaneously recorded from the bilateral sternocleidomastoid, trapezius, and splenius capitis muscles (without voluntary muscle activation), after electrical stimulation of the supraorbital and infraorbital nerves. Two different stimulation protocols were evaluated: a 3 Hz stimulation protocol (using averaging) and a single-pulse stimulation protocol. Results: Using a 3 Hz stimulation protocol, short-latency TCR responses were recorded in the sternocleidomastoid, trapezius, and splenius capitis muscles, ipsilateral and/or contralateral, but with variable recordability depending on the recording site. The most reliable response was obtained in ipsilateral sternocleidomastoid muscle. To the best of our knowledge, this is the first study to demonstrate the elicitation of short-latency TCR components in the sternocleidomastoid muscle in the absence of voluntary muscle activation in humans without craniocervical junction disorders or lower brainstem abnormalities. Conclusions: The choice of an appropriate stimulation protocol is particularly relevant for recording short-latency TCR components, considering that the visualization of early or late responses seems to be facilitated by different stimulation methodologies.

Combining federated learning and control: A survey

AbstractThis survey provides an overview of combining federated learning (FL) and control to enhance adaptability, scalability, generalization, and privacy in (nonlinear) control applications. Traditional control methods rely on controller design models, but real‐world scenarios often require online model retuning or learning. FL offers a distributed approach to model training, enabling collaborative learning across distributed devices while preserving data privacy. By keeping data localized, FL mitigates concerns regarding privacy and security while reducing network bandwidth requirements for communication. This survey summarizes the state‐of‐the‐art concepts and ideas of combining FL and control. The methodical benefits are further discussed, culminating in a detailed overview of expected applications, from dynamical system modelling over controller design, focusing on adaptive control, to knowledge transfer in multi‐agent decision‐making systems.

Flexible Control Limits for Interval-Valued Data Using Mixture Probability Distributions

Availability of data in different forms is a boon or bane as it may require different approaches to deal with. In fact, while the construction of control charts is straightforward if the data are real-valued, in many practical situations data are essentially interval-valued with observations often represented by minimum and maximum values. While there are only fewer attempts to develop control charts for interval-valued data, no simple-to-use approaches have been proposed to deal with such type of data. In this paper, considering the idea of the development of control limits for multi-stream processes, we propose to make use of the mixture probability distributions approach to determine control limits for the interval-valued data. The resulting control limits are flexible in nature as they are linked to the control limits of the overall distribution that is based on the average of the means of two upper and lower populations along with their respective standard deviations. We have studied and numerically evaluated the average run length (ARL) properties of the proposed control chart to demonstrate the ways of determining the control chart coefficients. The proposed control chart is then illustrated using a dataset and conclusions are drawn based on the results.

Cybermycelium: a reference architecture for domain-driven distributed big data systems.

The ubiquity of digital devices, the infrastructure of today, and the ever-increasing proliferation of digital products have dawned a new era, the era of big data (BD). This era began when the volume, variety, and velocity of data overwhelmed traditional systems that used to analyze and store that data. This precipitated a new class of software systems, namely, BD systems. Whereas BD systems provide a competitive advantage to businesses, many have failed to harness the power of them. It has been estimated that only 20% of companies have successfully implemented a BD project. This study aims to facilitate BD system development by introducing Cybermycelium, a domain-driven decentralized BD reference architecture (RA). The artifact was developed following the guidelines of empirically grounded RAs and evaluated through implementation in a real-world scenario using the Architecture Tradeoff Analysis Method (ATAM). The evaluation revealed that Cybermycelium successfully addressed key architectural qualities: performance (achieving <1,000 ms response times), availability (through event brokers and circuit breaking), and modifiability (enabling rapid service deployment and configuration). The prototype demonstrated effective handling of data processing, scalability challenges, and domain-specific requirements in a large-scale international company setting. The results highlight important architectural trade-offs between event backbone implementation and service mesh design. While the domain-driven distributed approach improved scalability and maintainability compared to traditional monolithic architectures, it requires significant technical expertise for implementation. This contribution advances the field by providing a validated reference architecture that addresses the challenges of adopting BD in modern enterprises.