Global sustainability initiatives increasingly rely on innovative technologies to safeguard biodiversity and mitigate environmental impacts. In this paper, we present EcoWatch, a novel framework that leverages Wireless Multimedia Sensor Networks (WMSNs) using LoRaWAN technology for efficient data transmission to enable real-time bird species detection and counting in their natural habitat. EcoWatch combines YOLOv8 You Only Look Once for object detection and Learning to Count Everything (LTCE) for precise object counting at the base station. To address the inherent limitations of WSNs in terms of energy and bandwidth, EcoWatch incorporates a multi-level ROI-based video compression technique. Extensive evaluation demonstrates that EcoWatch significantly reduces energy consumption (up to 58.7%) and data transmission load (by 69.8%) compared to other methods while maintaining acceptable image quality, detection and counting accuracy. Notably, EcoWatch exhibits robust performance across seasons and adapts well to varying environmental conditions, making it a promising solution for real-world ecological monitoring applications.

This paper presents a novel metaheuristic binary crow search algorithm (CSA) designed for positive-unlabeled (PU) learning, a paradigm where only positive and unlabeled data are available, with applications in many diversified fields, such as medical diagnosis and fraud detection. The algorithm represent a useful adaptation of CSA, itself inspired by the food-hiding behavior of crows. The proposed BiCSA-PUL (binary crow search algorithm for positive-unlabeled learning) selects reliable negative samples from unlabeled data using binary vectors, and updates positions employing Hamming distance, guided by a modified F1-score, as fitness function. The algorithm was tested on 30 samples from 10 diverse datasets, outperforming seven state-of-the-art PU learning methods. The results reveal that BiCSA-PUL provides a robust and efficient approach for PU learning, significantly improving fitness and reliability. This work opens new avenues for applying metaheuristic optimization methods to challenging classification problems with limited labeled data. The main limitations are the potentially time-intensive process of parameters tuning and reliance on initial sampling.

The global wind energy industry achieved a significant milestone by reaching a total capacity of one terawatt (TW) by the end of 2023, underscoring the increasing importance of wind energy as a sustainable energy source (Global Wind Energy Outlook, 2022). This study focuses on the simulation and dynamic analysis of an H-Darrieus wind turbine rotor using 3D Finite Element Analysis (FEA). Key structural parameters, including natural frequencies, associated vibration modes, and mass participation rates, were determined to optimize the rotor performance. A novel blade design is proposed in this work, offering a lighter and more robust alternative to traditional rotor blades manufactured from composites, like fiberglass-polyester, fiberglass-epoxy, or combinations with wood and carbon. The lighter design enhances the startup performance at low wind speeds, while the improved strength and fixing mechanisms ensure resilience against the increasingly severe sandstorms reported in recent years. The vibration dynamics of the rotor under critical wind loads were analyzed using the SolidWorks Simulation software, yielding highly satisfactory results. The stability and reliability of the rotor were validated, as the dynamic performance indices, and the quality criteria meet the requirements for optimal operation.

Let (X,d,μ) be a space of homogeneous type and L be a nonnegative self-adjoint operator on L2(X) whose heat kernels satisfy Gaussian upper bounds. In this article, we introduce the weighted variable Besov space associated with the operator L and demonstrate that Peetre maximal functions can be used to characterize this space. Furthermore, we provide a detailed study of its atomic decompositions.

The article focuses on the generalization of Fuglede's theorem and the solvability of operator equations. Topics include extending Fuglede's theorem to non-normal operators, deriving criteria for solving operator equations such as AX − XB = C, and using inner inverses to establish necessary and sufficient conditions for operator equation solutions.

A Vertical Axis Wind Turbine (VAWT) comprises multiple parts constructed from different materials. This complexity presents challenges in designing the blade structure. In this study, we investigated a structural optimization of a small-scale blade for a VAWT, with Finite Element Analysis (FEA) model. The purpose is to minimize the blade mass while adhering to a suite of critical wind conditions according to the IEC 61400-2 Standard. The structure made from Aluminum material simulates structure’s global behavior to determine maximum stress and deflection levels. The same structure is modeled using Glass/Epoxy composite for optimizing its design. Twenty combinations of Glass/Epoxy layers, varying in ply thickness and orientation, are simulated to find the most suitable combination. Results demonstrated that the optimization case [45°/90°/0°/−45°] obtained the minimum values of stress and deflection, is 59% lighter than Aluminum blade (initial design). The designed Glass/Epoxy composite blade is acceptable and recommended for structural safety.

Reservoir dams in Algeria face reduced lifespans and diminished water resources due to sedimentation, which often leads to out-of-service states. To address this issue, topo-bathymetry has been identified as the preferred technique for predicting silting in dam basins. Consequently, the seek for optimal interpolation methods to conduct topo-bathymetric surveys has become increasingly important. This study compares two primary interpolation methods, deterministic and geostatistical, to determine the most effective approach for these surveys. Three specific techniques were examined in this research: inverse distance weighting, radial basis function (deterministic), and ordinary kriging (geostatistical). The study focused on five reservoir dams in Algeria, using cross-validation to assess the performance of each interpolation method. The results revealed that the geostatistical approach outperformed deterministic estimations across all five sites. The superiority of the geostatistical method was further supported by the performance metrics used in the study. Based on these findings, ordinary kriging emerged as the most suitable method for interpolating topo-bathymetric surveys for all sites, regardless of variations in morphology and spatial sampling density. This research contributes valuable insights for enhancing reservoir dam management in Algeria in order to optimize water resource allocation.

Water resources are facing significant challenges in result of rapidly growing demand, deteriorating quality, and the effects of climate change. Today, water quantity and quality issues have become prevalent in various regions across the globe, affecting both northern and southern territories. Among the sectors reliant on this resource, irrigation stands out as the largest consumer of water. When surface water becomes inaccessible due to insufficient precipitation or other factors, the use of groundwater becomes the only viable alternative for irrigation. The Remila Plain (Khenchela) is located in an endorean watershed in northeastern Algeria and extends over 250 km2 in a synclinal basin filled with water from the Mio-Plio Quaternary - the main aquifer of the region, widely used for irrigation. The aim of this work is to study the hydrochemistry of these waters, as well as the evolution of mineralisation, the identification of the origin of the chemistry, and the suitability of these waters for irrigation. Initial results indicate an evolution of mineralisation in the direction of groundwater flow, with electrical conductivity values varying between 1000μS/cm in the recharge zones, and 2700μS/cm at the outlet. This mineralisation is mainly due to the dissolution of evaporitic minerals and the alteration of silicates. In addition, the various water quality indices used indicate that the water can be used for irrigation without major risk to plants and soils.

Inspired by a real-work case, this paper introduces a profit maximization model for dynamic lot sizing considering substitution and multiple usage of returns for refurbishing at different levels of quality or for disassembly to extract key parts that will be used in the manufacturing process. This model allows studying the interactions between different types of returns and decisions. To analyze the impact of uncertainty on these interactions, we develop a robust two-stage stochastic program with uncertainty on demands and returns. The resulting problems are mixed-integer linear programs that we solve using an efficient relax-and-fix and fix-and-optimize heuristic. Extensive numerical experiments are conducted to study the different trade-offs when integrating multi-usage of returns with substitution and accordingly derive managerial insights. The experiments have revealed, for example, that: (i) the profit margin of the refurbished items is the main determinant of the total profit and when such margin in high, the total profit becomes more sensitive to different cost variations; (ii) collection efficiency becomes very sensitive to collection cost and much less sensitive to refurbishing cost especially with large profit margins; (iii) when demand and returns are uncertain parameters, substitution becomes the best option as uncertainty and prices increase; (iv) when the gap between prices of different quality levels is high, lost sales occur mainly on the lowest quality product and downgrading decreases together with substitution; and (v) Sharing the production line between refurbishing and manufacturing for low-quality products is highly motivated by small upgrading costs and their substitution level increases with increasing upgrading costs.