Distributed Approach Research Articles

A Novel Application of In Situ Block Size Distribution and Shape Classification in Dimension Stone Quarries

Abstract The connection between rock mechanics and the world of mines and quarries is evident, as most primary resources needed by industry are concentrated in mineral deposits, often in rock masses. This can be seen at all levels, from the design phase to running the extraction process and providing protection to workers. Equally important is the optimization of the process, to enhance cost-effectiveness and reduce impact on the environment. A rock mass is characterized by blocks identified by discontinuities. It is, thus, useful to exploit the fragmented nature of the rock mass to optimize the excavation process. In the case of dimension stone quarries, the role of rock mechanics is even more evident as the precise and accurate knowledge of the features of the target rock mass is a fundamental requirement. The assessment of the block size distribution influences the feasibility of the operation and allows to identify a suitable and advantageous size for the extraction process. The orientation of the joints directly impacts the direction of excavation and the geometry of the fronts in terms of stability and productivity. The present work focuses on assessing the geometrical properties of a rock mass, namely block size and shape, aimed at optimizing the design and extraction process of a dimension stone quarry. For this purpose, a novel application of the In situ Block Size Distribution (IBSD) and shape distribution approach to dimension stones quarries is discussed. A robust statistical analysis of the rock mass geometric properties is coupled with the IBSD approach to block size and a new shape classification system. A set of synthetic data will then be employed to present a general case study, while a suitable case study is presented, describing the context of a real marble quarry (Lorgino quarry, Crevoladossola, Piemonte, Northern Italy) where the discussed approach could yield positive results.

Copper prevention values in Brazilian subtropical soils.

The Brazilian National Environmental Council (CONAMA) Resolution 420/2009 establishes soil quality guideline values for copper, as the prevention value (PV). It defines the maximum concentrations of chemical substances permitted in the soil. Thus, the aim of the present research was to evaluate the effectiveness of the copper PV adopted at the national level by conducting exposure assays with enchytraeids (Enchytraeus crypticus, Enchytraeus bigeminus and Enchytraeus dudichi) and springtails (Folsomia candida, Proisotoma minuta and Sinella curviseta) in two Brazilian subtropical soils: a Latosol and an Argisol. Results revealed that copper reduces the reproductive capacity of all organisms as its concentration increases in the evaluated soils. Argisol presented the lowest effective concentration of 20% (EC20) and 50% (EC50) in species reproduction for all organisms evaluated. The most sensitive organism was the enchytraeid E. bigeminus, with EC20 values of 58mgkg⁻1 in Latosol and 30mgkg⁻1 in Argisol, and EC50 values of 155mgkg⁻1 in Latosol and 91mgkg⁻1 in Argisol. Among the springtails, S. curviseta exhibited the highest EC20, with values of 230mgkg⁻1 in Latosol and 136mgkg⁻1 in Argisol. Conversely, the highest values of EC50 were recorded for the species P. minuta (526mgkg⁻1 in Latosol and 415mgkg⁻1 in Argisol). Based on the species sensitivity distribution (SSD) approach, the PVs calculated were 172mgkg⁻1 for Latosol and 106mgkg⁻1 for Argisol, derived from EC50 data. For EC20 data, the PV values calculated were 158mgkg⁻1 in Latosol and 100mgkg⁻1 in Argisol. By comparing the PV with the Brazilian adopted PV for copper (60mgkg-1), it was observed the efficacy of the CONAMA Resolution.

Distributed model building and recursive integration for big spatial data modeling.

Motivated by the need for computationally tractable spatial methods in neuroimaging studies, we develop a distributed and integrated framework for estimation and inference of Gaussian process model parameters with ultra-high-dimensional likelihoods. We propose a shift in viewpoint from whole to local data perspectives that is rooted in distributed model building and integrated estimation and inference. The framework's backbone is a computationally and statistically efficient integration procedure that simultaneously incorporates dependence within and between spatial resolutions in a recursively partitioned spatial domain. Statistical and computational properties of our distributed approach are investigated theoretically and in simulations. The proposed approach is used to extract new insights into autism spectrum disorder from the autism brain imaging data exchange.

Species sensitivity distribution of rare earth elements: A full overview.

Rare earth elements (REEs) encompass 15 lanthanides and play a crucial role in modern technology. Despite their essential uses, REEs are emerging environmental contaminants due to their growing presence in industrial, agricultural, and medical applications. For the first time, the species sensitivity distribution (SSD) approach was applied to REEs considering 58 papers including toxicity about Ce, Dy, Er, Gd, La, Lu, Nd, Pr, Sm, and Y. SSD curves were constructed by log-logistic model providing comprehensive comparisons of the sensitivities of different species to the relative REEs deriving the hazardous concentrations (HC) at 5% and 50%. The review reveals that arthropods, especially Daphnia magna, and echinoderms such as Sphaerechinus granularis exhibit high sensitivity especially to Ce, Er, and Lu. Conversely, vertebrates, particularly Oncorhynchus mykiss, showed greater tolerance to REEs exposure. The general hazard perception evidenced the following prioritization list according to HC5 values in increasing order: Dy (0.03μg/l)>Ce (0.15μg/l)>Sm (0.29μg/l)>La (0.64μg/l)>Y (0.81μg/l)>Gd (0.95μg/l)>Pr (1.44μg/l)>Er (1.45μg/l)>Nd (1.67μg/l)>Lu (2.19μg/l). This review highlighted gaps in REEs ecotoxicity, particularly regarding heavy REEs (HREEs), and stressed the need for more data on their long-term one-health impacts. Such knowledge is vital for developing strategies to mitigate REEs contamination, emphasizing the importance of establishing safe exposure thresholds to protect both ecosystems and human health.

SFML: A personalized, efficient, and privacy-preserving collaborative traffic classification architecture based on split learning and mutual learning

Traffic classification is essential for network management and optimization, enhancing user experience, network performance, and security. However, evolving technologies and complex network environments pose challenges. Recently, researchers have turned to machine learning for traffic classification due to its ability to automatically extract and distinguish traffic features, outperforming traditional methods in handling complex patterns and environmental changes while maintaining high accuracy. Federated learning, a distributed learning approach, enables model training without revealing original data, making it appealing for traffic classification to safeguard user privacy and data security. However, applying it to this task poses two challenges. Firstly, common client devices like routers and switches have limited computing resources, which can hinder efficient training and increase time costs. Secondly, real-world applications often demand personalized models and tasks for clients, posing further complexities. To address these issues, we propose Split Federated Mutual Learning (SFML), an innovative federated learning architecture designed for traffic classification that combines split learning and mutual learning. In SFML, each client maintains two models: a privacy model for the local task and a public model for the global task. These two models learn from each other through knowledge distillation. Furthermore, by leveraging split learning, we offload most of the computational tasks to the server, significantly reducing the computational burden on the client. Experimental results demonstrate that SFML outperforms typical training architectures in terms of convergence speed, model performance, and privacy protection. Not only does SFML improve training efficiency, but it also satisfies the personalized needs of clients and reduces their computational workload and communication overhead, providing users with a superior network experience.

A Comprehensive and Comparative Review of Global Gastric Cancer Treatment Guidelines: 2024 Update.

Differences in demographics, medical expertise, and patient healthcare resources across countries have led to significant variations in guidelines. In light of these differences, in this review, we aimed to explore and compare the most recent updates to gastric cancer treatment from five guidelines that are available in English. These English-version guidelines, which have been recently published and updated for journal publication, include those published in South Korea in 2024, Japan in 2021, China in 2023, the United States in 2024, and Europe in 2024. The South Korean and Japanese guidelines provide a higher proportion of content to endoscopic and surgical treatments, reflecting their focus on minimally invasive techniques, function-preserving surgeries, and systemic therapy. The Chinese guidelines provide recommendations addressing not only surgical approaches but also perioperative chemotherapy and palliative systemic therapy. Meanwhile, in the United States and European guidelines, a higher proportion of the content is dedicated to perioperative and palliative systemic therapy, aligning with their approaches to advanced-stage disease management. All guidelines address surgical and systemic chemotherapy treatments; however, the proportion and emphasis of content vary based on the patient distribution and treatment approaches specific to each country. With emerging research findings on gastric cancer treatment worldwide, the national guidelines are being progressively revised and updated. Understanding the commonalities and differences among national guidelines, along with the underlying evidence, can provide valuable insights into the treatment of gastric cancer.

Enhancing energy distribution through dynamic multi-hop for heterogeneous WSNs dedicated to IoT-enabled smart grids

This paper examines the impact of hetterogeneous wireless sensor networks (WSNs) on wireless communication systems, with a focus in Internet of Things (IoT) enabled smart grids. It introduces a novel approach for the fair distribution of energy and computational resources among sensor nodes (SNs), which is crucial for extending network lifespan, enhancing performance, and ensuring SG stability. The research highlights the role of initial energy and processing capacities of SNs. Although hierarchical clustering methods are effective in WSNs, finding the ideal routing protocol remains challenging due to extensive deployment. To address energy efficiency and network durability, the study proposes the integration of the heterogeneous dynamic multi-hop (HDM) approach with the low-energy adaptive clustering hierarchy (LEACH) protocol, specifically for IoT smart city applications. The HDM model combines multi-hop communication with dynamic hierarchical clustering, distinguishing between normal and advanced nodes based on energy levels to optimize cluster head selection probabilities. The methodology involves a comparative analysis between the HDM protocol and LEACH in a heterogeneous environment (H-LEACH) in terms of energy conservation, and network lifespan. Results demonstrate that the HDM protocol outperforms H-LEACH. Notably, HDM consumes half of the total energy over 4600 rounds, compared to H-LEACH’s 3000 rounds. These findings have important implications for deploying WSNs in smart grid applications, supporting sustainable and resilient urban IoT ecosystems.

Landscape of machine learning evolution: privacy-preserving federated learning frameworks and tools

Machine learning is one of the most widely used technologies in the field of Artificial Intelligence. As machine learning applications become increasingly ubiquitous, concerns about data privacy and security have also grown. The work in this paper presents a broad theoretical landscape concerning the evolution of machine learning and deep learning from centralized to distributed learning, first in relation to privacy-preserving machine learning and secondly in the area of privacy-enhancing technologies. It provides a comprehensive landscape of the synergy between distributed machine learning and privacy-enhancing technologies, with federated learning being one of the most prominent architectures. Various distributed learning approaches to privacy-aware techniques are structured in a review, followed by an in-depth description of relevant frameworks and libraries, more particularly in the context of federated learning. The paper also highlights the need for data protection and privacy addressed from different approaches, key findings in the field concerning AI applications, and advances in the development of related tools and techniques.

Accelerated quadratic penalty dynamic approaches with applications to distributed optimization.

In this paper, we explore accelerated continuous-time dynamic approaches with a vanishing damping α/t, driven by a quadratic penalty function designed for linearly constrained convex optimization problems. We replace these linear constraints with penalty terms incorporated into the objective function, where the penalty coefficient grows to +∞ as t tends to infinity. With appropriate penalty coefficients, we establish convergence rates of O(1/tmin{2α/3,2}) for the objective residual and the feasibility violation when α>0, and demonstrate the robustness of these convergence rates against external perturbation. Furthermore, we apply the proposed dynamic approach to three distributed optimization problems: a distributed constrained consensus problem, a distributed extended monotropic optimization, and a distributed optimization with separated equations, resulting in three variant distributed dynamic approaches. Numerical examples are provided to show the effectiveness of the proposed quadratic penalty dynamic approaches.

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.

An Optimal Secure Key Distribution Scheme for Internet of Things Devices in Multi-Session Network Communications

Communication network security has become increasingly vital in an era of rapidly developing technology, and protecting against unauthorized access is essential. This paper introduces a server-aided approach for secure key distribution to users participating in multiple sessions. This paper presents a system model in which each user is assigned a unique private key, enabling them to derive session keys from codewords broadcast by the server. These session keys are essential for facilitating the secure transmission of session messages within their respective sessions. The system model ensures that an eavesdropper cannot derive any session keys, despite having access to broadcast codewords, due to their lack of private keys. Our results show that our coding scheme is optimal by proving the necessary conditions for secure key distribution, indicating that secure key distribution is achievable if and only if the length of a user’s private key is at least equal to the total size of session messages across all the sessions in which they participate. This paper further illustrates the proposed secure key distribution and session message transmission mechanism through examples, emphasizing the necessity of user-specific private keys tailored to the sessions in which users are involved.

Effects of Foreign Direct Investment and Trade Openness on Tax Earnings: A Study of Selected Sub-Saharan African Economies

Every economy’s prosperity is determined by the quantity of tax income it receives. Over the years, studies have demonstrated that inflows from foreign investments and openness to international trade are important contributors to a country’s tax income. Based on this assumption, this study seeks to examine the impact of foreign direct investment (FDI) and open trade on tax income in a number of sub-Saharan African nations. The World Bank Development Indicators data on tax revenue, FDI, exports, imports, and exchange rates from 1990 to 2022 are used in the study. We also use the pooled mean group/panel autoregressive distributed lag approach to examine the data gathered for this inquiry. The results reveal that, in the long term, FDI has a significant negative impact on tax income; nevertheless, in the short run, Ghana’s tax revenue collection suffers while other nations profit from FDI. The results reveal that Nigeria’s exporting is detrimental to tax revenue collection, but South Africa’s export of goods and services is beneficial. However, imports and currency rates benefit Nigeria, Ghana, and South Africa in the near term. Thus, the research suggests improving tax rules and administration to prevent the movement of resources by foreign investors out of the host countries in order to avoid the imposition of huge tax burdens on their firms. Countries with low exports, such as Nigeria, are urged to enhance local manufacturing to meet international export standards in order to alleviate the continual negative balance of payments, which is primarily fixed by the adequate export of products and services.

Power analysis to detect misfit in SEMs with many items: Resolving unrecognized problems, relating old and new approaches, and "matching" power analysis approach to data analysis approach.

It is unappreciated that there are four different approaches to power analysis for detecting misspecification by testing overall fit of structural equation models (SEMs) and, moreover, that common approaches can yield radically diverging results for SEMs with many items (high p). Here we newly relate these four approaches. Analytical power analysis methods using theoretical null and theoretical alternative distributions (Approach 1) have a long history, are widespread, and are often contrasted with "the" Monte Carlo method-which is an oversimplification. Actually, three Monte Carlo methods can be distinguished; all use an empirical alternative distribution but differ regarding whether the null distribution is theoretical (Approach 2), empirical (Approach 3), or-as we newly propose and demonstrate the need for-adjusted empirical (Approach 4). Because these four approaches can yield radically diverging power results under high p (as demonstrated here), researchers need to "match" their a priori SEM power analysis approach to their later SEM data analysis approach for testing overall fit, once data are collected. Disturbingly, the most common power analysis approach for a global test-of-fit is mismatched with the most common data analysis approach for a global test-of-fit in SEM. Because of this mismatch, researchers' anticipated versus actual/obtained power can differ substantially. We explain how/why to "match" across power-analysis and data-analysis phases of a study and provide software to facilitate doing so. As extensions, we explain how to relate and implement all four approaches to power analysis (a) for testing overall fit using χ² versus root-mean-square error of approximation and (b) for testing overall fit versus testing a target parameter/effect. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Development and Validation of a Shortened Severity Measurement Tool for Tinnitus.

There is a need to develop and validate a brief questionnaire to measure tinnitus severity that can meet the needs of clinicians and researchers. The objective of this study is to demonstrate the validity and reliability of the 9-item Tinnitus Severity Short Form (TS-SF). A cohort study of online survey respondents recruited in October 2020 from the American Tinnitus Association electronic mailing list with a 2-week follow-up time interval. Online. Face and content validity, factor analysis, internal consistency, convergent validity, reliability, and minimal clinically important difference (MCID) were analyzed. Of 1121 participants that started the survey, 325 (29%) completed the baseline TS-SF. Through confirmatory factor analysis, the 9-item survey demonstrated a bifactor model with a substantive distress factor and a method factor. The instrument had high internal consistency with Cronbach's α of .96. Convergent validity determined the TS-SF score to be a predictor of tinnitus severity. Of 261 follow-up respondents, the test-retest reliability of r = 0.69 (95% confidence interval, 0.62-0.75) was determined. The MCID was determined using a distribution approach to be a 12-point change. The TS-SF demonstrates good internal consistency, validity, and reliability. This patient-reported outcome measure for tinnitus can be used in tinnitus treatment trials and clinical practice. The 9-item survey is less likely to result in respondent survey fatigue than longer surveys, and the 2-week reference interval allows for monitoring of tinnitus severity change in an appropriate timeframe.

Analysis Of Operational Risk Using Loss Distribution Approach : A Study Of Epc Business

This abstract describes a comprehensive analysis of operational risks in engineering, procurement, and construction (EPC) business by using calculation of loss distribution approach methods. This research involves an examination of historical operational losses data spanning from January 2019 to December 2022 to calculate the Operational Value at Risk at different percentiles. The OpVar, a key metric in this study, is determined to be Rp17,68Billion at a 95% percentile and Rp41,4Billion at a 99% percentile. These figures represent the estimated financial loss that EPC company may face due to operational risk with a higher percentile may indicate more precise. These results were declared valid through backtesting. From these points, the research emphasizes that the signifance of understanding and learning from historical data, will enable EPC companies to understand potential risks and manage risks through risk mitigation strategies in the complexity of EPC business environment.

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%.

The influence of negotiation approaches on supplier relationship management in Zimbabwe's fast-food industry

Abstract Supplier Relationship Management (SRM) has become a cornerstone of business success since the transformation of the procurement function to supply chain management. Extant literature has documented various predictors of vibrant supplier relationships. However, missing in the extant literature is the potential of negotiation approaches to cultivation of healthy supplier relationships. This study therefore sought to determine the influence of negotiation approaches on SRM. A sample of 150 dyadic transactions in the fast-food restaurant industry was surveyed. The broad dichotomous categorisation of negotiation methods into distributive and integrative approaches was used. SRM was operationalised using dimensions such as commitment, trust, communication, adaptation, and satisfaction. After conducting a Multivariate Analysis of Variance (MANOVA) the results revealed that the distributive strategy was weakly linked to supplier relationship, while a strong supplier relationship was observed in the use of integrative negotiation strategy. It was therefore recommended that procurement practitioners must employ integrative negotiation strategies and tactics in order to create sustainable supplier relationship management.

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.