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Abstract Background Tietz syndrome is a very rare clinical entity characterized by the association of profound bilateral congenital sensorineural deafness and generalized hypopigmentation of skin, eyes, and integuments (snow white appearance). It is an autosomal dominant syndrome due to a mutation in the melanocyte inducing transcription factor (MITF) gene. The association of a heart malformation has never been reported in this syndrome. Case presentation We report two cases of two cousins aged 5 years and 20 months respectively with a history of first degree consanguineous parents. Both girls presented with diffuse hypopigmentation of the skin, blond hair, blue eyes, and bilateral diffuse retinal hypopigmentation at ocular fundus exam. Bilateral profound sensorineural hearing loss was confirmed by auditory brainstem response in both cases. Echocardiography revealed a cardiac malformation such as interventricular communication in the older cousin and interatrial communication in the younger cousin. The family investigation did not reveal a similar case among ancestors. The diagnosis of Tietz syndrome was based on clinical criteria and pedigree. The older cousin underwent a total optical correction and a right unilateral cochlear implantation followed by speech therapy with a satisfactory result after a follow-up of two years. Unfortunately, the little cousin died following a head trauma. Conclusions Tietz syndrome is a rare autosomal dominant genetic disorder, characterized by generalized albinism with bilateral profound hearing loss. It results from a nontruncating mutation in the basic domain of in the MITF gene. Its management must include, in addition to hearing and ophthalmic rehabilitation, the research and treatment of cardiac malformations which may be life-threatening.

When implementing SVMs, two major problems are encountered: (a) the number of local minima of dual-SVM increases exponentially with the number of samples and (b) the computer storage memory required for a regular quadratic programming solver increases exponentially as the problem size expands. The Kernel-Adatron family of algorithms, gaining attention recently, has allowed us to handle very large classification and regression problems. However, these methods treat different types of samples (i.e., noise, border, and core) in the same manner, which makes these algorithms search in unpromising areas and increases the number of iterations as well. This paper introduces a hybrid method to overcome such shortcomings, called the Optimal Recurrent Neural Network and Density-Based Support Vector Machine (Opt-RNN-DBSVM). This method consists of four steps: (a) the characterization of different samples, (b) the elimination of samples with a low probability of being a support vector, (c) the construction of an appropriate recurrent neural network to solve the dual-DBSVM based on an original energy function, and (d) finding the solution to the system of differential equations that govern the dynamics of the RNN, using the Euler–Cauchy method involving an optimal time step. Density-based preprocessing reduces the number of local minima in the dual-SVM. The RNN’s recurring architecture avoids the need to explore recently visited areas. With the optimal time step, the search moves from the current vectors to the best neighboring support vectors. It is demonstrated that RNN-SVM converges to feasible support vectors and Opt-RNN-DBSVM has very low time complexity compared to the RNN-SVM with a constant time step and the Kernel-Adatron algorithm–SVM. Several classification performance measures are used to compare Opt-RNN-DBSVM with different classification methods and the results obtained show the good performance of the proposed method.

Our investigation focuses on the analysis of the conductive properties of high-mobility 2D-Si-MOSFETs as they approach the critical carrier density, nsc (approximately 0.72×1011 cm−2), which marks the metal insulator transition (MIT). In close proximity to the nsc, the conductivity exhibits a linear dependence on the temperature (T). By examining the extrapolated conductivity at the absolute zero temperature (T = 0), denoted as σ0, as a function of the electron density ns, we identify two distinct regimes with varying σ0(ns) patterns, indicating the existence of two different phases. The transition from one of these two regimes to another, coinciding with nsc, is abrupt and serves as the focus of our investigation. Our aim is to establish the possibility of a percolation type transition in the 2D-Si-MOSFETs’ sample. In fact, we observed that the model of percolation is applicable only for densities very close to nsc*=n2 (where n2 is the linear extrapolation of σ0), indicating the percolation type transition essentially represents a phase transition at the zero temperature.

To limit the adverse effects of diabetes, a personalized and long-term management strategy that includes appropriate medication, exercise and diet has become of paramount importance and necessity. Compartment-based mathematical control models for diabetes usually result in objective functions whose terms are conflicting, preventing the use of single-objective-based models for obtaining appropriate personalized strategies. Taking into account the conflicting aspects when controlling the diabetic population dynamics, this paper introduces a multi-objective approach consisting of four steps: (a) modeling the problem of controlling the diabetic population dynamics using a multi-objective mathematical model, (b) discretizing the model using the trapezoidal rule and the Euler–Cauchy method, (c) using swarm-intelligence-based optimizers to solve the model and (d) structuring the set of controls using soft clustering methods, known for their flexibility. In contrast to single-objective approaches, experimental results show that the multi-objective approach obtains appropriate personalized controls, where the control associated with the compartment of diabetics without complications is totally different from that associated with the compartment of diabetics with complications. Moreover, these controls enable a significant reduction in the number of diabetics with and without complications, and the multi-objective strategy saves up to 4% of the resources needed for the control of diabetes without complications and up to 18% of resources for the control of diabetes with complications.

La dégradation accélérée des forêts est un défi majeur pour les gestionnaires. Le reboisement constitue alors une solution importante pour l’amélioration des services écosystémiques voire la survie des écosystèmes. Comparée à d’autres essences comme l’Eucalyptus, les pins renferment une biodiversité plus élevée. Nous avons étudié la phytodiversité d’un périmètre de reboisement avec quatre espèces et deux variétés de pins (Pinus halepensis, P. pinaster var. maghrebiana et var iberica, P. pinea et P. canariensis) afin de proposer l’essence la plus convenable au reboisement. Sur les 38 relevés floristiques réalisés, 83 espèces ont été recensées appartenant à 34 familles. Les valeurs de l’indice de diversité étaient maximales dans les plantations de P. halepensis et P. pinea et minimales dans celles de P. canariensis. L’ordination multidimensionnel non métrique et la Classification Ascendante Hiérarchique ont montré trois groupes : groupe à P. halepensis et P. pinea, groupe à P. maghrebiana et Quercus suber et groupe à P. iberica et P. canariensis. P. halepensis et P. pinea ont montré un cortège floristique relativement riche mais avec un appauvrissement structurel comparé à la subéraie originale. Il ressort que le reboisement à base de P. maghrebiana et dans une deuxième place P. halepensis améliore la structure des peuplements et présente un potentiel important pour la gestion des reboisements au nord du Maroc.

There is a global concern about children presenting with inflammatory syndrome with variable clinical features during the ongoing COVID-19 pandemic. This paper reports the first pediatric case of bilateral serous retinal detachment and conjunctival hemorrhage as a revealing pattern of the pediatric inflammatory multisystem syndrome temporally associated with SARS-CoV-2 (PIMS-TS). Despite the severity of multisystemic involvement, the management with steroids was very successful. Complete remission was obtained within 3 months of acute onset.

The imidazolidine ring in the title molecule, C19H20N2O2, is slightly `ruffled'. In the crystal, a layer structure is generated by N—H...O and C—H...O hydrogen bonds plus C—H...π(ring) interactions.

Terahertz frequency has promising applications in communication, security scanning, medical imaging, and industry. THz absorbers are one of the required components for future THz applications. However, nowadays, obtaining a high absorption, simple structure, and ultrathin absorber is a challenge. In this work, we present a thin THz absorber that can be easily tuned through the whole THz range (0.1–10 THz) by applying a low gate voltage (2/graphene). Nanoribbons of MoS2/graphene heterostructure are laid over a SiO2 substrate with an applied vertical gate voltage. The computational model shows that we can achieve an absorptance of approximately 50% of the incident light. The absorptance frequency can be tuned through varying the structure and the substrate dimensions, where the nanoribbon width can be varied approximately from 90 nm to 300 nm, while still covering the whole THz range. The structure performance is not affected by high temperatures (500 K and above), so it is thermally stable. The proposed structure represents a low-voltage, easily tunable, low-cost, and small-size THz absorber that can be used in imaging and detection. It is an alternative to expensive THz metamaterial-based absorbers.

Soft computing models based on fuzzy or probabilistic approaches provide decision system makers with the necessary capabilities to deal with imprecise and incomplete information. Hybrid systems based on different soft computing approaches with complementary qualities and principles have also become popular. On the one hand, fuzzy logic makes its decisions on the basis of the degree of membership but gives no information on the frequency of an event; on the other hand, the probability informs us of the frequency of the event but gives no information on the degree of membership to a set. In this work, we propose a new measure that implements both fuzzy and probabilistic notions (i.e., the degree of membership and the frequency) while exploiting the ability of the convolution operator to combine functions on continuous intervals. This measure evaluates both the degree of membership and the frequency of objects/events in the design of decision support systems. We show, using concrete examples, the drawbacks of fuzzy logic and probability-based approaches taken separately, and we then show how a fuzzy probabilistic convolution measure allows the correction of these drawbacks. Based on this measure, we introduce a new clustering method named Fuzzy-Probabilistic-Convolution-C-Means (FP-Conv-CM). Fuzzy C-Means (FCM), Probabilistic K-Means (PKM), and FP-Conv-CM were tested on multiple datasets and compared on the basis of two performance measures based on the Silhouette metric and the Dunn’s Index. FP-Conv-CM was shown to improve on both metrics. In addition, FCM, PKM, and FP-Conv-CM were used for multiple image compression tasks and were compared based on three performance measures: Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural SImilarity Index (SSIM). The proposed FP-Conv-CM method shows improvements in all these three measures as well.

We report on theoretical investigations of a methylammonium lead halide perovskite system loaded with iron oxide and aluminum zinc oxide (ZnO:Al/MAPbI3/Fe2O3) as a potential photocatalyst. When excited with visible light, this heterostructure is demonstrated to achieve a high hydrogen production yield via a z-scheme photocatalysis mechanism. The Fe2O3: MAPbI3 heterojunction plays the role of an electron donor, favoring the hydrogen evolution reaction (HER), and the ZnO:Al compound acts as a shield against ions, preventing the surface degradation of MAPbI3 during the reaction, hence improving the charge transfer in the electrolyte. Moreover, our findings indicate that the ZnO:Al/MAPbI3 heterostructure effectively enhances electrons/holes separation and reduces their recombination, which drastically improves the photocatalytic activity. Based on our calculations, our heterostructure yields a high hydrogen production rate, estimated to be 265.05 μmol/g and 362.99 μmol/g, respectively, for a neutral pH and an acidic pH of 5. These theoretical yield values are very promising and provide interesting inputs for the development of stable halide perovskites known for their superlative photocatalytic properties.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Introduction : La perforation de cornée est une situation pathologique fréquente en urgence ophtalmologique.Et la greffe de membrane amniotique garde sa place dans sa prise en charge en favorisant l'épithélialisation cornéenne à l’aide de ses caractéristiques anti-fibrotiques, anti-inflammatoires, anti-angiogéniques et antimicrobiennes. Le But du travail est d’analyser les caractéristiques structurales de la cornée et de la membrane amniotique et d’évaluer les changements de la membrane après son intégration à la cornée à l’aide de l’OCT-SA. Matériel et méthode : étude prospective menée au sein du service d’ophtalmologie du centre hospitalier Mohammed VI de Marrakech, et portant sur 63 cas de perforations cornéennes, d’ulcères profonds ou pré-perforatifs. Résultats : 31 patients ont présenté un ulcère perforé, 23 ont présenté un ulcère pré-perforatif et 9 ont eu une perforation cornéenne post-traumatique.93,6% des patients ont bénéficié d’une greffe de membrane amniotique en multicouches.L’OCT-SA a été réalisée avant le geste opératoire à chaque fois que cela était possible.A J2, l’épaisseur moyenne de la membrane amniotique inlay était de 231±132µm. A J8, cette épaisseur a diminué à 112±40µm. A J25, plus de 90% des malades ont intégré la membrane dans l’épaisseur cornéenne avec une épaisseur cornéenne moyenne de 495±85µm.L’intégration de cette membrane a été associée à la fibrose partielle de cette dernière, permettant ainsi la restitution de l’épaisseur cornéenne et la régénération cornéenne. Discussion: La greffe de membrane amniotique constitue une meilleure alternative dans la prise en charge des perforations cornéennes et les ulcères pré-perforatifs. Dans le suivi de la greffe de membrane amniotique, l’examen à la LAF reste indispensable. Pourtant, le monitorage par OCT-SA permet de faire une meilleure quantification de l’épaisseur cornéenne et de sa structure. Conclusion : La greffe de membrane amniotique est la meilleure alternative dans la prise en charge des perforations cornéennes et des ulcères pré-perforatifs. Son intégration à l’épaisseur cornéenne est toujours associée à sa ré-épithélialisation. Ainsi, l’OCT-SA constitue l’examen de choix pour une meilleure surveillance de l’intégration de cette membrane.

New technologies have had an impact on many aspects of human life including business activities. The post-modern consumer is increasingly adopting technological innovations, the change started with the adoption of electronic commerce before the advent of mobile commerce. In 2018, the e-commerce web site Jumia declared during its annual study, that 73% of purchases made on the site in Morocco were ordered by a mobile device.During this study, it was proven, through an observation experience, followed by a semi-structured interview with 10 young Moroccan executives comprising two different groups (a group using a large smartphone and another group using a smartphone with a small size), that the size of the smartphone screen has a relative impact on the level of immersion of users in the mobile shopping experience, the participants in this study were led to test two smartphones having two different sizes in order to test the possible impact of the size of the smartphone screen on immersion in a mobile shopping experience.The results showed that mobile users feel more comfortable handling a smartphone with a larger screen, however this has little impact, according to the statements of the study participants, on the level of immersion in the experience. shopping.This exploratory study must be supplemented by a confirmatory study confirming or refuting this trend.

Studies on the bivalve family Sphaeriidae in North Africa are very limited at the surface water level, but even more for caves. During an expedition in 2019 to the Ait M’hamed cave (Oum Er Rabia Basin), six specimens of the genus Pisidium were collected. Morphometric and genetic analyses showed that these individuals belong to the species Pisidium subtruncatum Malm, 1855. This work is the first step towards future exploration of cave Sphaeriidae in North Africa.