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Aziza cave, which is also known as kef Aziza or Tazouguert cave, represents an important and large karstic system that consists of more than 3.5 km of surveyed galleries, standing as the fifth most extensive cave system in Morocco and one of the ten largest in North Africa. This study unveils Aziza cave as an important spot of subterranean diversity in Africa. Here, we provide the first checklist of subterranean fauna in this cave, with 26 taxa, comprising 22 troglobiotic and 4 stygobiotic species. Of this total, eight species still require further confirmation of their status. The richest taxa include Coleoptera (5 species), Araneae (4 species), Entomobryomorpha (3 species), and Isopoda (2 species). However, it is noteworthy that only around 34.6% of the cave-restricted species found in the cave have been formally described to date. Additionally, the biodiversity of large system areas remains to be discovered as these areas need to be further explored. Furthermore, this paper highlights the broader conservation challenges faced by subterranean habitats in Morocco, particularly considering human-induced impacts on these remarkable ecosystems. We aim to draw attention to the crucial ecological role of subterranean environments and their extraordinary biological diversity. By doing so, we aim to inspire increased research and conservation initiatives, not just in this area but across Africa.

The dihydroimidazole ring in the title molecule, C20H20N2O3S, is slightly distorted and the lone pair on the tri-coordinate nitrogen atom is involved in intra-ring π bonding. The methylsulfanyl substituent lies nearly in the plane of the five-membered ring while the ester substituent is rotated well out of that plane. In the crystal, C—H...O hydrogen bonds form inversion dimers, which are connected along the a- and c-axis directions by additional C—H...O hydrogen bonds, forming layers parallel to the ac plane. The major contributors to the Hirshfeld surface are C...H/H...C, O...H/H...O and S...H/H...S contacts at 20.5%, 14.7% and 4.9%, respectively.

The title compound, C8H8ClNO2, is significantly distorted from planarity, with a twist angle between the planes through the hydroxybenzene and acetamide groups being 23.5 (2)°. This conformation is supported by intramolecular C—H...O and N—H...Cl contacts. In the crystal, N—H...O hydrogen-bonding contacts between acetamide groups and O—H...O contacts between hydroxyl groups form tapes propagating parallel to [103].

In the title molecule, C21H17N3O2, the five-membered ring is slightly ruffled and dihedral angles between the pendant six-membered rings and the central, five-membered ring vary between 50.78 (4) and 86.78 (10)°. The exocyclic nitrogen lone pair is involved in conjugated π bonding to the five-membered ring. In the crystal, a layered structure is generated by O—H...N and N—H...O hydrogen bonds plus C—H...π(ring) and weak π-stacking interactions.

Photovoltaics (PV) energy is a solution for the electrification of developing countries, especially for remote rural zones. The use of solar energy in water pumping is the most adopted solution for rural and desert regions. The modeling, simulation and analysis of the PV system of water pumping is a vital step before assembling this system on any place, which allows a better understanding of its behavior in real weather conditions on that place. In this paper, we investigate the performance of a water pumping photovoltaic system consisting of a PV generator coupled with a DC-motor which drives a centrifugal pump that draws water from a well and delivers it to a reservoir. The characteristics of the components of this system allow researchers, manufacturers, and social communities to understand better the functioning of these components. Our simulation results were meticulously cross-verified against the module manufacturer’s data sheet, revealing a maximum relative percentage error of 1.12 %. This attests to the coherence between the manufacturer’s stipulations and our simulated values. Furthermore, we ascertain commendable efficiency in the motor-pump system, particularly noteworthy for elevated irradiation levels, while maintaining acceptable performance even under minimal irradiation conditions.

The Synthetic Minority Over-Sampling Technique (SMOTE) is one of the most well-known methods to solve the unequal class distribution problem in imbalanced datasets. However, it has three shortcomings: (1) it may cause the over-generalization problem due to oversampling of noisy samples, (2) over-sampling of uninformative samples, and (3) increasing the overlaps between different classes around the class boundaries. Different approaches SMOTE based have been proposed to handle these problems, but most of them implement hyperparameters and tend to generate noise because the synthetic sample is generated, randomly, in the area delimited by current random minority data. In this research, an improved SMOTE-based method, namely Fuzzy-ADAptative-SMOTE-Based-Methods (FADA-SOMTE-Ms), which targets all three problems at the same time, is introduced. In this regard, the $\alpha $ -SMOTE is chosen in such a way that the synthetic data is as far as possible from the two closest majority data. More precisely, this method processes into six steps: (a) clustering minority class into k groups (b) selecting a safe region (c) selecting random two minority data, (d) finding the M closest majority data to these minority data using original membership functions based on Fuzzy mean and flirting results, (e) finding the $\alpha $ -SMOTE producing a synthetic data as close as possible to the minority class and as far as possible from the M majority data by solving a very simple multi-objective mathematical optimization model, and (f) using SMOTE to generate synthetic samples using optimal $\alpha $ -SMOTE. FADA-SOMTE-Ms is evaluated using 5 classifiers, 21 unbalanced datasets, and it’s compared to 8 oversampling methods using three performance measures. FADA-SOMTE-Ms consistently outperforms other popular oversampling methods.

The fuzzy C-means (FCM) clustering algorithm is a widely used unsupervised learning method known for its ability to identify natural groupings within datasets. While effective in many cases, FCM faces challenges such as sensitivity to initial cluster assignments, slow convergence, and difficulty in handling non-linear and overlapping clusters. Aimed at these limitations, this paper introduces a novel fractional fuzzy C-means (Frac-FCM) algorithm, which incorporates fractional derivatives into the FCM framework. By capturing non-local dependencies and long memory effects, fractional derivatives offer a more flexible and precise representation of data relationships, making the method more suitable for complex datasets. Additionally, a genetic algorithm (GA) is employed to optimize a new least-squares objective function that emphasizes the geometric properties of clusters, particularly focusing on the Fukuyama–Sugeno and Xie–Beni indices, thereby enhancing the balance between cluster compactness and separation. Furthermore, the Frac-FCM algorithm is evaluated on several benchmark datasets, including Iris, Seed, and Statlog, and compared against traditional methods like K-means, SOM, GMM, and FCM. The results indicate that Frac-FCM consistently outperforms these methods in terms of the Silhouette and Dunn indices. For instance, Frac-FCM achieves higher Silhouette scores of most cases, indicating more distinct and well-separated clusters. Dunn’s index further shows that Frac-FCM generates clusters that are better separated, surpassing the performance of traditional methods. These findings highlight the robustness and superior clustering performance of Frac-FCM. The Friedman test was employed to enhance and validate the effectiveness of Frac-FCM.

Diabetes is becoming more and more dangerous, and the effects continue to grow due to the population’s ignorance of the seriousness of this phenomenon. The studies that have been carried out have not been able to follow the phenomenon more precisely, which has led to the use of the fractional derivative tool, which has a very great capability to study real problems and phenomena but is somewhat limited on nonlinear models. In this work, we will develop a new fractional derivative model of a diabetic population, the Takagi–Sugeno fractional fuzzy model, which will enable us to study the phenomenon with these nonlinear terms in order to obtain greater precision in the results. We will study the existence and uniqueness of the solution using the Lipschizian theorem and then turn to the new fuzzy model, which leads us to four dynamical systems. The interpretation results show the quality of fuzzy membership in tracking the malleable phenomena of nonlinear terms existing in the system.

A growing adoption of Terahertz (THz) frequency across various applications is witnessed in the recent years. This specific frequency range is said to yield a breakthrough in high-speed electronics and communications, besides its significant role in the medical field regarding scanning and treatment. As frequency absorbers are one of the common blocks in the mentioned applications, they were the focus of many research work. In this paper, we present a theoretical model for a selective THz frequency absorber that has more than 90 % absorptivity and can reach 95 % and near unity absorptivity at some frequencies. The structure is based on monolayers of nanoribbons of graphene, MoS2, and phosphorene. The utilization of the surface plasmon oscillations of these two-dimensional (2D) materials, in addition to the impedance matching, is employed to achieve maximum absorptivity. The structure has an electrically tunable selective frequency from 1.3 THz to 10 THz that nearly covers the whole THz range, and a bandwidth ranging from 0.9 THz to 1.3 THz. Electrical tunability is done through varying the applied voltage on the MoS2/graphene heterostructure (0.2 V ∼ 5 V) based on the tunable conductivity of graphene. In addition to the voltage tunability of the design, the absorption frequency is also swept by varying the nanoribbons widths (20 nm ∼ 160 nm). The proposed design surpasses previous ones by its simple structure and high absorption through the entire THz range, besides its low cost of fabrication. The structure is ultrathin (∼10 nm) that it can fit in ultrathin electronics. It has a relatively small bandwidth compared to previous work, that represents the basis for narrowband absorbers. The structure is simulated using the finite element method (FEM) and verified using the transmission line circuit theory which demonstrated the validity of the structure for higher frequency ranges. The possibility of future synthesis is also discussed.

Cloeon vanharteni Gattolliat & Sartori, 2008 was newly discovered in the framework of our study of Ephemeroptera in the Draa basin, located in the southern region of the High Atlas in Morocco. This discovery is rather unexpected as the species was never reported outside the Arabian Peninsula and Levant; it is thus the first record for the Maghreb. The identification was based on morphological evidence and confirmed by the mitochondrial COI barcode.

In this paper, we present a novel approach to inventory management modeling, specifically tailored for growing items. We extend traditional economic growth quantity (EGQ) models by introducing the fractional economic growing quantity (FEGQ) model. This new approach improves the model’s symmetry and dynamic responsiveness, providing a more precise representation of the changing nature of inventory items. Additionally, the use of fractional derivatives allows our model to incorporate the memory effect, introducing a new dynamic concept in inventory management. This advancement enables us to select the optimal business policy to maximize profit. We adopt the fractional derivative in terms of Caputo derivative sense to model the inventory level associated with the items. To analytically solve the (FEGQ) model, we use the Laplacian transform to obtain an algebraic equation. As for the logistic function, known for its symmetrical S-shaped curve, it closely mirrors real-life growth patterns and is defined using fractional calculus. We apply an iterative approximation method, specifically the Adomian decomposition method, to solve the fractional logistic function. Through a sensitivity analysis, we delve for the first time into the discussion of the initial weights, which have a massive impact on the total profit level. The provided numerical data indicate that the firm began with a favorable policy. In the following years, several misguided practices were implemented that led to a decrease in profitability. The healing process began once again by selecting more effective strategies.

The continuous Hopfield network (CHN) is a common recurrent neural network. The CHN tool can be used to solve a number of ranking and optimization problems, where the equilibrium states of the ordinary differential equation (ODE) related to the CHN give the solution to any given problem. Because of the non-local characteristic of the “infinite memory” effect, fractional-order (FO) systems have been proved to describe more accurately the behavior of real dynamical systems, compared to the model’s ODE. In this paper, a fractional-order variant of a Hopfield neural network is introduced to solve a Quadratic Knap Sac Problem (QKSP), namely the fractional CHN (FRAC-CHN). Firstly, the system is integrated with the quadratic method for fractional-order equations whose trajectories have shown erratic paths and jumps to other basin attractions. To avoid these drawbacks, a new algorithm for obtaining an equilibrium point for a CHN is introduced in this paper, namely the optimal fractional CHN (OPT-FRAC-CHN). This is a variable time-step method that converges to a good local minima in just a few iterations. Compared with the non-variable time-stepping CHN method, the optimal time-stepping CHN method (OPT-CHN) and the FRAC-CHN method, the OPT-FRAC-CHN method, produce the best local minima for random CHN instances and for the optimal feeding problem.

Convolutional Neural Networks (CNNs) are a kind of artificial neural network designed to extract features and find out patterns for tasks such as segmentation, recognizing objects, and drawing up classification. Within a CNNs architecture, pooling operations are used until the number of parameters and the computational complexity are reduced. Numerous papers have focused on investigating the impact of pooling on the performance of Convolutional Neural Networks (CNNs), leading to the development of various pooling models. Recently, a fuzzy pooling operation based on type-1 fuzzy sets was introduced to cope with the local imprecision of the feature maps. However, in fuzzy set theory, it is not always accurate to assume that the degree of non-membership of an element in a fuzzy set is simply the complement of the degree of membership. This is due to the potential existence of a hesitation degree, which implies a certain level of uncertainty. To overcome this limitation, intuitionistic fuzzy sets (IFS) were introduced to incorporate the concept of a degree of hesitation. In this paper, we introduce a novel pooling operation based on intuitionistic fuzzy sets to incorporate the degree of hesitation heretofore neglected by a fuzzy pooling operation based on classical fuzzy sets, and we investigate its performance in the context of image classification. Intuitionistic pooling is performed in four steps: bifuzzification (by the transformation of data through the use of membership and non-membership maps), first aggregation (through the transformation of the IFS into a standard fuzzy set, second aggregation (through the transformation and use of a sum operator), and the defuzzification of feature map neighborhoods by using a max operator. IFS pooling is used for the construction of an intuitionistic pooling layer that can be applied as a drop-in replacement for the current, fuzzy (type-1) and crisp, pooling layers of CNN architectures. Various experiments involving multiple datasets demonstrate that an IFS-based pooling can enhance the classification performance of a CNN. A benchmarking study reveals that this significantly outperforms even the most recent pooling models, especially in stochastic environments.