Distributed Approach Research Articles

A new distributed charge approach for the electric vehicle parking lots considering coordination between DSO and EV operators

A new distributed charge approach for the electric vehicle parking lots considering coordination between DSO and EV operators

ADAMT: Adaptive distributed multi-task learning for efficient image recognition in Mobile Ad-hoc Networks.

ADAMT: Adaptive distributed multi-task learning for efficient image recognition in Mobile Ad-hoc Networks.

Insights on common fungicides: A national survey on farmland soils from Mainland China.

Insights on common fungicides: A national survey on farmland soils from Mainland China.

A novel approach for rapid quantification and length distribution of microfibers released during domestic laundry.

A novel approach for rapid quantification and length distribution of microfibers released during domestic laundry.

A fast calculation approach for thermal distribution of aircraft piston pumps based on finite difference method

A fast calculation approach for thermal distribution of aircraft piston pumps based on finite difference method

An ADMM-based tripartite distributed planning approach in integrated electricity and natural gas system

An ADMM-based tripartite distributed planning approach in integrated electricity and natural gas system

Distributed multi-timescale algorithm for nonconvex optimization problem: A control perspective.

Distributed multi-timescale algorithm for nonconvex optimization problem: A control perspective.

Optimizing computation time in 3D air quality models by using aerosol superbins within a sectional size distribution approach: Application to the CHIMERE model

Optimizing computation time in 3D air quality models by using aerosol superbins within a sectional size distribution approach: Application to the CHIMERE model

Producing Proofs of Unsatisfiability with Distributed Clause-Sharing SAT Solvers

Distributed clause-sharing SAT solvers can solve challenging problems hundreds of times faster than sequential SAT solvers by sharing derived information among multiple sequential solvers. Unlike sequential solvers, however, distributed solvers have not been able to produce proofs of unsatisfiability in a scalable manner, which limits their use in critical applications. In this work, we present a method to produce unsatisfiability proofs for distributed SAT solvers by combining the partial proofs produced by each sequential solver into a single, linear proof. We first describe a simple sequential algorithm and then present a fully distributed algorithm for proof composition, which is substantially more scalable and general than prior works. Our empirical evaluation with over 1500 solver threads shows that our distributed approach allows proof composition and checking within around 3× its own (highly competitive) solving time.

Dealing with Censored Earnings in Register Data

Abstract Earnings are often top-coded (right-censored) in administrative registers. The censoring threshold in the case of Germany is the limit value for social security contributions, leading to a substantial fraction of censoring: For example, about 12 % of male workers in West Germany are affected, rising to above 30 % for highly educated prime-aged workers. This missing right tail of the earnings distribution constitutes a major problem for researchers studying earnings inequality and top incomes. We overcome this challenge by taking a distributional approach and semi-parametrically modelling the right tail as being Pareto-like. Non-censored earnings survey data matched to administrative records, derived from the SOEP-RV project, let us operate in a laboratory-like setting in which the targets are known. Our approach outperforms alternative imputation methods based on Tobit regressions.

Analisis Feedback Pengguna Aplikasi Shopee Menggunakan Distribusi Proporsi dan Teknologi Informasi

This research aims to analyze Shopee application user feedback using Google Colab-based information technology and a proportion distribution approach. The method used includes a Multinomial Naïve Bayes-based classification technique to categorize sentiment into positive and negative. The research results show that the model accuracy level is 90.84% ​​with an average precision for the positive and negative categories of 0.93 and 0.78 respectively. The statistical test produces a T-statistic value of 2759.50 with a p-value of 0.0, which shows significance in the difference in the distribution of positive and negative reviews.

Log Normal Regression and its Application

Log Normal Regression and its Application Ni Luh Sri Diantini1, Calvin Riswandi1 1Statistics Department Matana University Email: 1niluh.sridiantini@matanauniversity.ac.id, 1calvin.riswandi@student.matanauniversity.ac.id Received: 19 December 2024, revised: 17 March 2025, accepted: 24 March 2025 Abstract The log-normal distribution represents a type of continuous probability distribution that is characterized by a positive skew, which signifies a long tail on the right. A log-normal distribution describes a statistical distribution of values that have been logarithmically transformed from a related normal distribution. In situations where predictor variables affect positive outcomes, log-normal regression becomes significant. This research will construct a regression model that utilizes a continuous response variable following a log-normal distribution, known as log-normal regression (LNR). The purpose of developing the LNR model is to overcome the assumptions in classical regression that are often unmet, such as normality, since not all data can full fill these assumptions. Therefore, need an alternative method that does not require the normality assumption. One method that can be employed is a regression with a specific distribution approach, such as the log-normal distribution, known as log-normal regression. The LNR model will be developed through the maximum likelihood estimation (MLE) approach to achieve parameter estimation using a numerical method based on the Newton-Raphson iteration. Following this, hypothesis testing will be performed using the maximum likelihood ratio test (MLRT) and a partial test that employs the Wald test. The ultimate objective of this research is to illustrate how to apply the proposed LNR model to real data. LNR model will be applied to analysis the number of poor people in Indonesia, examining the factors that contribute to this issue. The results obtained in this study that variables human development index, unemployment rate, percentage of gross regional domestic product, and minimum wage in provinces influencing significance the number of poor people in Indonesia.

Parallel trusted node approach for satellite quantum key distribution

Quantum key distribution (QKD) via satellite links is widely regarded as a viable near-term solution to create quantum-backed secure communication at a global scale. To achieve intercontinental coverage with available technology one must adopt a “flying trusted node” paradigm, in which users fully trust the satellite platform. Here, inspired by the concept of distributed secret sharing and the imminent projected launch of several QKD-equipped satellites, we propose a parallel trusted node approach, in which key distribution is mediated by several satellites in parallel. This has the effect of distributing the trust, removing single points of failure and reducing the necessary assumptions. In addition, we discuss the versatility that an optical ground station should provide to execute such a protocol and, in general, to be fully integrated into a multi-party global quantum network.

Boosting Communication Efficiency in Federated Learning for Multiagent-Based Multimicrogrid Energy Management.

Privacy of user is becoming increasingly significant in constructing efficient multiagent energy management systems for multimicrogrid (MMG). As an emerging privacy-protection method, federated learning (FL) has been used to prevent data breaches in the MMG-related field. However, with the ever-growing participants, the underlying communication burden existing in FL is evident. Besides, since the neural network layers collectively determine an agent's performance, the possible difference in layer convergence speeds would cause the inconsistency problem, that is, the FL may degrade the convergence rate of those fast-convergent layers, which weakens the overall performance of the agent. To address these issues, a communication-efficient FL (CEFL) algorithm is proposed in this study. Considering the cooperative relationship among layers, a layer evaluation (LE) mechanism is developed in CEFL to evaluate layer contribution through the Shapley value (SV), a profit distribution approach for coalitions. In this way, only partial layers with the highest contributions are selected to be uploaded to the server. In addition, instead of average parameters aggregation, a communication-efficient parameter aggregation method is proposed in CEFL to update the parameters of the global model (GM), in which an aggregation model (AM) is developed to receive parameters for aggregation. The performance of the proposed CEFL is verified by the numerical analysis of MMGs with 3-8 MGs participating. Furthermore, experiments investigate the influence of the hyperparameter in the CEFL and also demonstrate performance improvements, compared with the other four state-of-the-art algorithms.

A high-order hesitancy fuzzy time series model based on improved cumulative probability distribution approach and weighted fuzzy logic relationship

A high-order hesitancy fuzzy time series model based on improved cumulative probability distribution approach and weighted fuzzy logic relationship

Euclid

We present the Flagship galaxy mock, a simulated catalogue of billions of galaxies designed to support the scientific exploitation of the Euclid mission. Euclid is a medium-class mission of the European Space Agency optimised to determine the properties of dark matter and dark energy on the largest scales of the Universe. It probes structure formation over more than 10 billion years primarily from the combination of weak gravitational lensing and galaxy clustering data. The breadth of Euclid’s data will also foster a wide variety of scientific analyses. The Flagship simulation was developed to provide a realistic approximation to the galaxies that will be observed by Euclid and used in its scientific exploitation. We ran a state-of-the-art N-body simulation with four trillion particles, producing a lightcone on the fly. From the dark matter particles, we produced a catalogue of 16 billion haloes in one octant of the sky in the lightcone up to redshift z = 3. We then populated these haloes with mock galaxies using a halo occupation distribution and abundance-matching approach, calibrating the free parameters of the galaxy mock against observed correlations and other basic galaxy properties. Modelled galaxy properties include luminosity and flux in several bands, redshifts, positions and velocities, spectral energy distributions, shapes and sizes, stellar masses, star formation rates, metallicities, emission line fluxes, and lensing properties. We selected a final sample of 3.4 billion galaxies with a magnitude cut of HE < 26, where we are complete. We have performed a comprehensive set of validation tests to check the similarity to observational data and theoretical models. In particular, our catalogue is able to closely reproduce the main characteristics of the weak lensing and galaxy clustering samples to be used in the mission main cosmological analysis. Moreover, given its depth and completeness, this new galaxy mock also provides the community with a powerful tool for developing a wide range of scientific analyses beyond the Euclid mission.

The Impact of Remittances on Climate Change: Empirical Evidence from Nepal

The existing literature has studied the impact of remittance inflow on climate change for several developing countries. However, the existing literature has not adequately addressed how different levels of remittances asymmetrically affect climate change. In this study, we address this research gap using the multiple threshold non-linear autoregressive distributed lag approach. Our study demonstrates how minor, moderate and major changes in remittances influence climate change differently. Our analysis, based on time-series data for Nepal, reveals that minor, moderate and major changes in remittances have an asymmetric impact on climate change. In the long run, a minor rise in the inflow of remittances has a significant negative effect on climate change, a moderate rise has a large positive effect and a major rise has a positive effect, although smaller than that of a moderate rise. Thus, we observe an inverted ‘U’-shaped relationship between remittances and climate change. Additionally, we present a new conceptual framework to discuss this phenomenon. Prior studies have employed the non-linear autoregressive distributed lag (NARDL) model to examine this topic, but such analysis is absent in the Nepalese context. Therefore, we also apply the NARDL model to assess how positive and negative shocks in remittances influence climate change. Our findings confirm the asymmetric effect of remittances on climate change under the NARDL framework as well. JEL Codes: C32, F24, Q54, Q56

Level of Student Knowledge About Pulmonary Tuberculosis at Medistra Health Institute Lubuk Pakam

Pulmonary tuberculosis (TB) remains a significant health problem in Indonesia due to its contagious nature. Having an adequate understanding of TB is very important, especially for students in health education institutions, considering their potential role as pioneers in efforts to prevent and control this disease. This study was conducted to examine the level of knowledge of students regarding pulmonary tuberculosis at the Medistra Health Institute, Lubuk Pakam. This study used a quantitative descriptive method with a cross-sectional approach. A total of 100 students were used as research samples selected through purposive sampling techniques. Data collection was carried out using a closed questionnaire that had undergone validity and reliability tests. The data were then analyzed descriptively with a frequency distribution approach. The results showed that the majority of students, namely 65%, had a good level of knowledge about pulmonary TB. Meanwhile, 25% of respondents had knowledge in the sufficient category, and 10% were classified as lacking. The knowledge that was most widely mastered included the symptoms and transmission of TB, while aspects of prevention and treatment still need to be improved. The conclusion of this study shows that students at the Medistra Health Institute, Lubuk Pakam, generally have good knowledge regarding pulmonary TB. However, increased education on certain aspects is still needed. The results of this study are expected to be a reference for institutions in designing more comprehensive health promotion and education programs related to tuberculosis.

Three Ways of Looking at Black–White Mortality Differences in the United States

Everyone agrees that US Black deaths happen earlier than white deaths on average, but it is surprisingly challenging to find the best ways to summarize, quantify, and compare this gap. This review argues that there are three main strategies for doing so, falling on a spectrum of more conventional (though with many novel variants) to unexpected. Distributional approaches quantify population death rates or lifespans, and there is a proliferation of new, creative distributional measures. Comparative approaches benchmark Black excess deaths against other deaths; some versions can be normatively powerful but rest on normative assumptions that are open to challenge. Meaning-based approaches attempt to measure losses—to decedents, their survivors, and the world—that follow from excess Black deaths. These losses range from lost votes and cultural production to traumatic proximity to death to lost chances for reconciliation. Each approach offers new empirical and theoretical opportunities to better understand unequal lifespans.

DINÁMICA DE LA DISTRIBUCIÓN POTENCIAL DE Coffea arabica y C. canephora ANTE EL CAMBIO CLIMÁTICO EN TRES REGIONES DE MÉXICO

<p><strong>Background:</strong> The coffee sector is fundamental in the economic, social, and environmental aspects of coffee-producing regions in Mexico. However, climate change is altering the optimal agroclimatic conditions for the cultivation of <em>Coffea arabica</em> and <em>C. canephora</em>, threatening future production. Given the regional variability of these impacts, it is necessary to identify how climate change will affect the suitability for production in specific regions. Studies focused on species distribution provide key tools to gauge these impacts and issue recommendations aimed at adapting to climate change. <strong>Objective:</strong> To assess the potential impact of climate change on the production of Coffea arabica and C. canephora, using a species distribution approach in the northeastern region of Puebla, the mountains of Veracruz, and the Mazatec region of Oaxaca. <strong>Materials and Methods:</strong> This study was conducted using a species distribution approach, with modeling carried out through MaxEnt software to project the potential distribution of the species. Future climate scenarios SSP126 and SSP585 for the period 2041–2060 were evaluated. <strong>Results and Discussion:</strong> The results suggest significant changes in the distribution of both species. For <em>C. arabica,</em> there is a general trend of reduction in optimal areas, with notable regional variations. <em>C. canephora,</em> on the other hand, shows a different response, with some areas showing an increase in the distribution of this species. The most influential variables in the distribution of both species were precipitation, elevation, and wind speed. <strong>Implications:</strong> If future agroclimatic conditions favor the expansion of C. canephora, as projections suggest for some regions, a partial transition to this species could be considered as an adaptation strategy. <strong>Conclusions:</strong> The study concluded that climate change will significantly reduce the distribution of C. arabica and moderately favor C. canephora, with differentiated regional impacts. It is crucial to adjust management strategies according to each region to ensure the sustainability of coffee production.</p>