The growth of Renewable Energy Communities (RECs) across Europe has increased the need for reliable tools to assess their technical performance and economic feasibility. In Italy, the implementation of the RED II Directive has made the design of RECs particularly relevant, especially under collective self-consumption schemes, where performance depends on the temporal alignment between photovoltaic generation and demand. This study proposes an integrated framework combining physics-based modeling and data-driven techniques to analyze and optimize energy sharing within RECs. Using real consumption profiles from ARERA and photovoltaic production simulated through PVGIS for a 3 kW system, multiple community configurations are evaluated to identify the structure that maximizes shared energy. Energy flows are modeled through a physically consistent formulation linking production, self-consumption, injected energy, and shared energy. After identifying the optimal REC configuration through combinatorial analysis, a machine learning approach is applied to model the relationship between energy variables and the economic value of shared energy, focusing on the best-performing configuration. The results show that the system is governed by a constrained relationship driven by the minimum between injected energy and demand. The superior performance of the linear model suggests that the problem is highly structured and largely predictable. Overall, the framework combines physical interpretability and predictive modeling, providing a practical and transparent tool for REC design and optimization.
We present an analytical series solution for steady magnetoconvection of an electrically conducting micropolar fluid in a vertical parallel plate channel under the Oberbeck-Boussinesq approximation. The fully developed model yields a coupled boundary value system for the axial velocity, microrotation, induced magnetic field, and temperature. Adomian’s decomposition method is applied directly to the coupled second order formulation, producing compact truncated series that satisfy the wall conditions and make the dependence on the governing nondimensional parameters explicit. The resulting expressions are used to examine how the Hartmann number, coupling number, micropolar parameter, and buoyancy parameter shape the profiles of V(y), W(y), and H(y). The analysis highlights departures from the Newtonian limit as micropolar coupling strengthens, while increasing magnetic effects damp the axial motion and modify magnetic induction.
Academic fraud refers to various dishonest behaviors that violate standards of integrity and transparency. Unethical activities include research misconduct, plagiarism, peer review manipulation, and data fabrication or falsification. Because academic fraud undermines the credibility of scientific research, it is crucial to investigate it. In this paper, we analyze suspicious data contained in retracted papers about the use of chloroquine for COVID-19 treatment. To accomplish this, we use Benford's law, a well-known mathematical model employed to describe the first significant digit distribution in several collections of numbers. In fact, it is considered a useful tool in data analysis to detect frauds, identify financial statement manipulation and anomalies in income reporting, assess the integrity of experimental data, and verify the accuracy of reported health statistics. A typical way to check if a statistical population adheres to Benford's law is through hypothesis testing. We report some tests to check the Benfordness of data and apply them to a real dataset.
When we aim to estimate a density function with bounded support, the naive kernel density estimation becomes strongly biased in the boundary region due to the well-known estimated density overflow problem. The reflection method appears to be an efficient way to alleviate this issue. We propose a simple domain transformation that could favor an elegant application of such a principle. A small numerical experiment and a real case study involving suicide data end the work.
We develop an ensemble machine learning framework integrating environmental, ecological, and socioeconomic variables to enable prospective hantavirus risk prediction. Trained on 689 laboratory-confirmed cases from three United States jurisdictions, the ensemble of random forest, gradient boosting, support vector machine, and logistic regression classifiers achieves area under the receiver operating characteristic curve of 0.92 on independent test data. Shapley additive explanations identify precipitation variability, land-use patterns, and rodent species richness as dominant predictors, with substantial contributions from socioeconomic determinants.
Context: From 4% in 2000 to 58% in 2019-2020, Rwanda is one of the African countries that have recorded strong growth in modem contraceptive use over the last two decades. Objective: This article examines the main changes observed in the factors associated with the use of modern contraceptives in Rwanda over the period 2005-2020. Methodology: The data used are from the four Rwanda Demographic and Health Surveys (RDHS) of 2005, 2010, 2014-2015 and 2019-2020. Binary stepwise logistic regression was used to identify the factors that determining the use of modern contraceptives, to rank them and to study their evolution from 2005 to 2020. Results: The results show that women who want fewer than 4 children, who are Catholic and who work in the modem sector have shown favorable behaviors to the use of modem contraceptives. However, the determining power of factors such as the number of live born children, the habitation, and region of residence, the woman's education, the spouse's education and the woman's sexual activity varied greatly over the course of the different surveys. Conclusion: In the light of these results, raising public awareness of the advantages of having a smaller family, providing schooling for all, creating and ensuring equal access to employment for men and women, and improving the living conditions of the population appear to be Rwanda's preferred ways of achieving the objectives of its family planning program.
The Menage problem can be viewed as finding particular Hamiltonian cycles representing a $2\times n$ array. As a generalization of this problem we introduce the $k$-level Menage problem which considers $k\times n$ arrays. Using the inclusion-exclusion principle, we give an efficient algorithm to derive solutions for the $k$-level Menage problem of order $n$ from a given solution for the $(k-1)$-level Menage problem of the same order.
This paper extends a previous conceptual proposal on the role of the Grant Office Service of the University of Foggia in open innovation ecosystems by introducing a determinant-level composite indicator for Small and Medium-sized Enterprises. The proposed OECO\_Sy model transforms three diagnostic questionnaires into twenty normalized determinants and three coefficients: the Preliminary Innovation Propensity Coefficient (PIPC), the Management Innovation Propensity Coefficient (MIPC), and the Composite Coefficient of SME Innovation Propensity (CCSIP). The methodological contribution is twofold. First, the university role of Expert is formalized as a measurable, replicable and non-discretionary diagnostic function. Secondly, the transition from the Preparation State to the Formation State is operationalized through a six-category analysis of project briefs, so that quantitative diagnosis is translated into tailored open-innovation actions. The framework is applied to a pilot sample of ten SMEs in the TCOREC case study in the Province of Foggia. The CCSIP ranges from 0.503 to 0.763; in eight firms out of ten MIPC exceeds PIPC, indicating solid organizational conditions but weaker formalization of innovation and intellectual property. A Monte Carlo sensitivity analysis on Q.2 weights supports the robustness of the equal-weight baseline. The results are exploratory, but they show that OECO\_Sy can operate as a mathematically transparent decision-support model for university-led innovation ecosystems.
In this work, we highlight two remarkable phenomena related to the action of a single family of Non-Iterative Functions (FFNI). The first result concerns the fundamental spectral distributions of Random Matrix Theory: GUE, GOE, GSE. We show that a simple Nonlinear Transformation (NLT), controlled by an exponent, establishes a direct bridge between these distributions. Each spectral distribution can be transformed into another and then retrieved by applying inverse transformation. All six possible conversions: GUE ↔ GOE, GUE ↔ GSE, GOE ↔ GSE, are obtained within the same framework. The second result concerns the convergence of numerous unimodal distributions toward a Limiting Distribution LD. By applying the same NLT, we observe that diverse distributions, bell-shaped, triangular, exponential, trapezoidal, spectral, and many synthetic types, all converge to a LD characterized by: σ/μ=1 Both phenomena, spectral distribution conversion and unimodal distribution convergence toward LD, are based on the same NIF family, revealing an unexpected functional unification between seemingly independent domains: random matrices, classical distributions, synthetic laws, and asymptotic behaviors. This forms a simple, accessible, and powerful framework for generating, transforming, and connecting numerous probability distributions. In addition, the same functional structure can analytically generate a wide variety of multimodal distributions (with multiple peaks), revealing that complex shapes can emerge without iteration, solely through parameter variation. Taken together, these results suggest the existence of a unified mathematical architecture linking: spectral distributions, the LD attractor, and a structural generator of multimodal forms.
In this manuscript, we present a numerical solution for solving singular integral equations of the second kind with Cauchy kernel. The proposed solution is based on application of differential transform method. Numerical results are shown to illustrate the efficiency and accuracy of the present solution.
An audit report is not the end of the mission carried out, but the stage of formalization of the recommendations, as far as possible shared between the auditor and the auditee since any Supreme Audit Institution (SAI) must ensure the follow-up of its recommendations. This study is the result of a literature review and a survey conducted within the Court of Accounts, the SAI of Burundi. It aims to determine when, how, and at what level of achievement the Court of Accounts contributes to the improvement of Burundi's socio-economic development by following up on its recommendations to decentralized entities. The results show that this SAI has neither an internal follow-up mechanism nor a schedule for the periodicity of follow-up on its recommendations. It allows the audited entities an average of 3 years before it follows up on its recommendations, and the entities under its audit do not produce either action plans or reports on their implementation. The same results show a low level (20.29%) of involvement in their implementation and reflect its reduced effectiveness in contributing to the improvement of public management. Binding national legislation on the follow-up of recommendations by this SAI and the production of action plans and reports on their implementation are still lacking. Finally, future studies are invited to analyze whether there is a correlation between the SAI's follow-up on its recommendations and their implementation by the audited entities.
We introduce a reversible, non-iterative transformation applied to signals with a wide variety of statistical distributions, including thirty-five general distributions as well as spectral distributions arising from random matrix theory (GUE, GOE, GSE, Wigner’s semicircle law, and the Wigner–Dyson law).In all cases studied, the transformation induces convergence toward an exponential distribution with a unit coefficient of variation, independently of the initial distribution. Applying the inverse transformation makes it possible to reconstruct the original signal with small numerical deviations.The introduction of a single control parameter then allows the coefficient of variation to be varied over a wide range while preserving the shape of the reconstructed distribution, thereby defining a geometric invariance. The results are obtained on signals containing up to points, with computation times of less than one second.
Given a nontrivial graph, a set of vertices of a graph is an \lb independent set if every pair of distinct vertices are not adjacent and it is a 2-dominating set if each vertex in its complement is adjacent to at least two vertices in the set. A set of vertices of a graph is an independent 2-dominating set if it is both an independent set and a 2-dominating set. The independent 2-domination number of a nontrivial graph is the cardinality of a minimum independent 2-dominating set. In this paper, we formulate the independent 2-domination number of some special graphs using some properties of the independent 2-dominating sets and the independent 2-domination number.
Achieving timely viral suppression among individuals undergoing antiretroviral therapy is critical for improving health outcomes and reducing HIV transmission.This study employs machine learning survival models: Support Vector Survival (SVS), Random Survival Forest (RSF), Gradient Boosted Survival (GBS) and Extreme Gradient Boosted Survival (XGBS) to estimate the time to viral suppression and determine key predictive factors among ART recipients. The SVS model adapts support vector machine principles to censored survival data, enabling the modeling of complex, nonlinear relationships in high-dimensional datasets. The RSF model, a nonparametric ensemble approach, constructs multiple survival trees to capture intricate variable interactions without relying on proportional hazard assumptions. The GBS model iteratively enhances survival predictions through gradient boosting, optimizing loss functions tailored to censored data. Model evaluation utilized performance metrics including the concordance index (C-index), integrated Brier score, and time-dependent area under the curve (AUC). Findings demonstrated that ensemble-based models, particularly RSF and GBS, outperformed SVS in predictive accuracy and robustness, effectively identifying key determinants of viral suppression such as baseline viral load, adherence levels, age, and treatment regimen. These results highlight the effectiveness of machine learning survival techniques in improving prediction of treatment outcomes and strengthening evidence-based decision-making in ART program management.
