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Abstract Vehicle identification systems are vital components that enable many aspects of contemporary life, such as safety, trade, transit, and law enforcement. They improve community and individual well-being by increasing vehicle management, security, and transparency. These tasks entail locating and extracting license plates from images or video frames using computer vision and machine learning techniques, followed by recognizing the letters or digits on the plates. This paper proposes a new license plate detection and recognition method based on the deep learning YOLO v8 method, image processing techniques, and the OCR technique for text recognition. For this, the first step was the dataset creation, when gathering 270 images from the internet. Afterward, CVAT (Computer Vision Annotation Tool) was used to annotate the dataset, which is an open-source software platform made to make computer vision tasks easier to annotate and label images and videos. Subsequently, the newly released Yolo version, the Yolo v8, has been employed to detect the number plate area in the input image. Subsequently, after extracting the plate the k-means clustering algorithm, the thresholding techniques, and the opening morphological operation were used to enhance the image and make the characters in the license plate clearer before using OCR. The next step in this process is using the OCR technique to extract the characters. Eventually, a text file containing only the character reflecting the vehicle's country is generated. To ameliorate the efficiency of the proposed approach, several metrics were employed, namely precision, recall, F1-Score, and CLA. In addition, a comparison of the proposed method with existing techniques in the literature has been given. The suggested method obtained convincing results in both detection as well as recognition by obtaining an accuracy of 99% in detection and 98% in character recognition.

Cities’ constructed environments position urban areas at the center of importance. The concept of “urban place” has mainly evolved into a marketable and branded good as a result of the advent of numerous design-led place-making policies and practices. In this effort of place-making, aesthetics play a significant role. Despite apparent conceptual ambiguities and conflicts, the interpretation of “commodified aesthetics of place” stresses specific phenomenological and qualitative place-attributes, such as authenticity. To provide clarity on this complex issue, a reexamination of core place-concepts is necessary. This article offers an understanding of urban settings and the accompanying aesthetic elements from another perspective. Such a strategy highlights the clear distinction between an object-based and a contextual interpretation of place, as well as offering a framework for reexamining the aesthetics of place and its vital role in forming the lived world. Moreover, I correlate the relation between individuals’ existential phenomena in relation to the city’s place-making theory.

Abstract In Proton Exchange Membrane Fuel Cells (PEMFCs), the presence of residual water within the Gas Diffusion Layer (GDL) poses challenges during cold starts and accelerates degradation. A computational model based on the Lattice Boltzmann Method (LBM) was developed to consider the capillary pressure inside the PEMFC and to analyze the exact geometries of the GDLs, which were obtained using the Computed Tomography scan. The novelty of this study is to suggest a methodology to compare the quantitative water removal performance of the GDLs without long-term experimental testing. Two different samples of GDLs were considered, pristine and aged. The results of quantitative measurements revealed the amount of water columns (breakthroughs) inside each sample. Considering the volume of 12,250,000 µm3 for each sample, the pristine and the aged samples are prone to have 774,200 µm3 (6.32%) and 1,239,700 µm3 (10.12%) as water columns in their porous domain. Micro-structural properties such as connectivity, mean diameter, effective diffusivity, etc. were also compared to observe the impacts of aging on the properties of the GDL.

Introduction. This work presents a methodology for detecting inter-turn short circuit (ITSC) and broken rotor bars (BRB) fault in variable speed induction machine controlled by field oriented control. If any of these faults are not detected at an early stage, it may cause an unexpected shutdown of the industrial processes and significant financial losses. Purpose. For these reasons, it is important to develop a new diagnostic system to detect in a precautionary way the ITSC and BRB at various load condition. We propose the application of discrete wavelet transform to overcome the limitation of traditional technique for no-stationary signals. The novelty of the work consists in developing a diagnosis system that combines the advantages of both the discrete wavelet transform (DWT) and artificial neural network (ANN) to identify and diagnose defects, related to both ITSC and BRB faults. Methods. The suggested method involves analyzing the electromagnetic torque signal using DWT to calculate the stored energy at each level of decomposition. Then, this energy is applied to train neural network classifier. The accuracy of ANN based on DWT, was improved by testing different orthogonal wavelet functions on simulated signal. The selection process identified 5 pertinent wavelet energies, concluding that, Daubechies44 (db44) is the best suitable mother wavelet function for effectively detecting and classifying failures in machines. Results. We applied numerical simulations by MATLAB/Simulink software to demonstrate the validity of the suggested techniques in a closed loop induction motor drive. The obtained results prove that this method can identify and classify these types of faults under various loads of the machine. References 31, table 1, figures 9.

Forest resource conservation necessitates a deeper understanding of forest ecosystem processes and how future management decisions and climate change may affect these processes. Argania spinosa (L.) Skeels is one of the most popular species in Morocco. Despite its ability to survive under harsh drought, it is endangered due to soil land removal and a lack of natural regeneration. Remote sensing offers a powerful resource for mapping, assessing, and monitoring the forest tree species at high spatio-temporal resolution. Multi-spectral Sentinel-2 and Synthetic Aperture Radar (SAR) time series combined with Digital Elevation Model (DEM) over the Argan forest in Essaouira province, Morocco, were subjected to pixel-based machine learning classification and analysis. We investigated the influence of different SAR data parameters and DEM layers on the performance of machine learning algorithms. In addition, we evaluated the synergistic effects of integrating remote sensing data, including optical, SAR, and DEM data, for identifying argan trees in the Smimou area. We collected data from Sentinel-2, Sentinel-1, SRTM DEM, and ground truth sources to achieve our goal. Testing different SAR parameters and integrating DEM layers of different resolutions with other remote sensing data showed that the Lee Sigma filter with a size of 11×11 and a DEM layer of 30 m resolution gave the best results using the Support Vector Machine algorithm. Significant improvements in overall accuracy (OA) and kappa index (K) were observed in the following phase. After applying a smoothing technique, the combined use of two Sentinel constellation products improved map accuracy and quality. For the best scenario (VV+NDVI), the OA was 88.32% (K = 0.85), while for scenarios NDVI+DEM and VH+NDVI+DEM, the OAs were 93.25% (K = 0.91) and 93.01% (K = 0.91), respectively. Integrating a DEM layer with SAR and optical data has significantly improved the accuracy in the classification of vegetation types, especially in our study area which is characterized by high environmental heterogeneity.

The valorization of biogenic waste by hydrothermal carbonization is widely discussed in research. However, to our knowledge, no study has combined almond shells and olive pomace to synthesize a solid carbon material. The purpose of this study is to enhance the hydrochar process from AS and OP using RSM methodology and machine learning models: ANN, SVM and XG-Boost. Subsequently, a study was carried out on the removal of organic pollutants by the synthesized material. The optimum Co-HTC operating conditions obtained at 180 C, 90 min with acid catalyst corresponding to 71.51 % and 87.13 % for mass yield and carbon retention rate respectively according to RSM-CCD. The comparison between RSM-CCD and ML in terms of prediction concludes that RSM remains more efficient in terms of planning and optimization. However, ANN is more suitable for modeling and predicting mass yields and carbon retention rates. Hydrochar’s physicochemical properties were evaluated by the use of spectroscopic methods like FTIR, SEM, XRD, and CHNO. To conclude, we studied the performance of HCop in methylene blue adsorption, varying the following parameters: pH, contact time, initial dye concentration, adsorbent dose and temperature. In addition, kinetic and isothermal models were studied to describe the dominant mechanisms in the MB adsorption process. The MB maximal adsorption using HCop obtained at pH 9, with an initial dye concentration of 100 mg. L−1, 40-min contact time, 0.1 g/L adsorbent dose, and a temperature between 25 and 30 °C. In conclusion, these results provide important information on the use of Co-HTC to convert biogenic wastes into high-performance carbon materials for the appropriate removal of organic pollutants. More studies are needed to use the material in other fields of application.

The development of a bimetallic Fenton-type catalyst with desirable activity and reusability remains a major challenge for the practical degradation of organic pollutants. Herein, we focused on modifying almond shell hydrochar with a Fe/Cu bimetal (Fe/Cu-HC) to develop a catalyst capable of activating H2O2 for degrading MO. The bimetallic Fe/Cu-HC catalyst was synthesized through hydrothermal carbonization and pyrolysis and characterized using SEM, FTIR, BET analysis, and XRD to confirm the presence and uniform dispersion of Cu and Fe co-doped species on HC. The impact of various factors, such as the solution pH (X1), organic pollutant concentration (X2) and catalyst mass (X3), on dye degradation efficiency via heterogeneous Fenton oxidation, was examined using the BBD model coupled with Surface response methodology (RSM). The Fe/Cu-HC catalyst showed superior performance in degrading MO dye compared to single-metal catalysts (Cu-HC, Fe-HC), due to the synergistic interaction between Fe and Cu species. To demonstrate the heterogeneous Fenton catalytic performance of the synthesized FeCu/HC, the results showed that 98.97 % of methyl orange was eliminated under optimal conditions: 100 mg. l-1 of methyl orange, a duration of 1 h, a catalyst mass of 1.65 g. l-1, a pH of 6, and a concentration of 4 mM H2O2. In addition, the Fe/Cu-HC catalyst showed excellent stability over multiple cycles, with minimal metal leaching. These results indicate that Fe/Cu-HC is a promising catalyst for the degradation of methyl orange and pave the way for the development of other cost-effective and efficient bimetallic catalysts for environmental remediation.

Bearing faults can cause heavy disruptions in machinery operation, which is why their reliable diagnosis is crucial. While current research into bearing fault analysis focuses on analyzing vibration data under constant working conditions, it is important to consider the challenges that arise when machinery runs at variable speeds, which is usually the case. This article proposes a multistage classifier for diagnosing bearings under time-variable conditions. We validate our method using vibration signals from five bearing health states, including a combined fault case. Our approach involves decomposing the signals using Empirical Wavelet Transform and computing temporal and frequency domain attributes. We use the Expectation-Maximization Gaussian mixture model for optimization concerns to identify relevant parameters and train the Random Forest classifier with the selected features. Our method, evaluated using the Polygon Area Metric, has demonstrated high effectiveness in diagnosing bearings under time-variable conditions. Our approach offers a promising solution that efficiently addresses speed variability and combined fault recognition issues.

Grâce aux progrès réalisés dans le domaine de la biologie cellulaire et moléculaire, les scientifiques ont développé un ensemble de techniques, qui ont permis de manipuler et de réorganiser les gènes de divers organismes vivants, ce qui a abouti à la mise au point de tout un éventail de nouveaux médicaments. La biotechnologie moderne s’inscrit dans cette stratégie et regroupe le segment des produits issus de l’ADN recombinant (insuline humaine, somatotropine…) et celui des acides nucléiques qui concerne entre autres la thérapie génique. Le nombre d’essais de thérapie génique approuvés dans le monde a progressivement augmenté ces dernières années, atteignant 116 protocoles par an en 1999 et un total de 2210 protocoles approuvés en 2015[1]. Cet article retrace l’intérêt de la thérapie génique et son bénéfice clinique obtenu au cours des dix-sept dernières années.

La recherche en cancérologie doit répondre à un double défi : le diagnostic précoce des tumeurs pour permettre une thérapie efficace et le développement de nouvelles thérapies ciblées. Dans la lutte contre le cancer, les recherches actuelles s’orientent vers les nanotechnologies (nanomédicaments) qui présentent de nouvelles approches de diagnostic et de traitement : théranostique. Cet article traite de l’apport des nanotechnologies pour la conception de nouveaux médicaments destinés à la thérapeutique et au diagnostic.

Abstract In this work, we sought to validate the use of Euphorbia calyptrata (L.), a Saharan and Mediterranean medicinal plant, in traditional pharmacopeia. GC‐MS/MS identified volatile compounds of potential therapeutic interest. Antioxidant tests were performed using β‐carotene decolorization, DPPH radical scavenging, FRAP, beta‐carotene bleaching, and TAC. The antimicrobial activity was evaluated on solid and liquid media for bacterial and fungal strains to determine the zone of inhibition and the minimum growth concentration (MIC) of the microbes tested. The hemolytic activity of these essential oils was assessed on red blood cells isolated from rat blood. Phytochemical characterization of the terpenic compounds by GC‐MS/MS revealed 31 compounds, with alpha‐Pinene dominating (35.96 %). The antioxidant power of the essential oils tested revealed an IC50 of 67.28 μg/mL (DPPH), EC50 of 80.25.08±1.42 μg/mL (FRAP), 94.83±2.11 μg/mL (beta carotene) and 985.07±0.70 μg/mL (TAC). Evaluating solid media‘s antibacterial and antifungal properties revealed a zone of inhibition between 10.28 mm and 25.80 mm and 31.48 and 34.21 mm, respectively. On liquid media, the MIC ranged from 10.27 μg/mL to 24.91 μg/mL for bacterial strains and from 9.32 μg/mL to 19.08 μg/mL for fungal strains. In molecular docking analysis, the compounds naphthalene, shogunal, and manol oxide showed the greatest activity against NADPH oxidase, with Glide G scores of −5.294, −5.218 and −5.161 kcal/mol, respectively. For antibacterial activity against E. coli beta‐ketoacyl‐[acyl carrier protein] synthase, the most potent molecules were cis‐Calamenene, alpha.‐Muurolene and Terpineol, with Glide G‐scores of −6.804, −6.424 and −6.313 kcal/mol, respectively. Hemolytic activity revealed a final inhibition of 9.42±0.33 % for a 100 μg/mL concentration. The essential oils tested have good antioxidant, antimicrobial, and hemolytic properties thanks to their rich phytochemical composition, and molecular docking analysis confirmed their biological potency.

Extramedullary plasmacytoma is a rare localized plasma cell neoplasm typically found in soft tissues outside the bone marrow. Predominantly occurring in the head and neck region, particularly in the sinonasal and nasopharyngeal areas, it presents a diagnostic challenge due to its uncommon nature. Herein, we report a 38-year-old female patient with Down’s syndrome with a 2-year complaint of intermittent dysphonia, hoarseness, and progressive respiratory distress, including dyspnea, fatigue, and biphasic stridor. Examination via flexible laryngoscopy revealed a white lesion, prompting direct microscopic laryngeal surgery to excise a 1x1 cm mass. Histological findings confirmed the diagnosis as solitary extramedullary plasmacytoma. Notably, this represented the first documented case of laryngeal solitary extramedullary plasmacytoma in a patient with Down’s syndrome. This case underscores the importance of considering tumor development in the larynx among individuals with Down’s syndrome, highlighting the necessity for tailored management strategies to address such occurrences effectively. Increasing awareness of this association can aid in early detection and appropriate treatment of tumors in this population.

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.

A late HIV diagnosis is associated with increased mortality and morbidity, increased healthcare costs and increased onward viral transmission. In this regard, we retrospectively analysed the characteristics of patients who presented for care at our centre from January 2018 to December 2022 to assess the proportion of patients and factors associated with late HIV presentation. We collected data from the Liège University Hospital database, and we used binary logistic regression models to analyse the impact of individuals’ characteristics on late presentation. Among 167 participants, 38.3% were late presenters (LPs) (presenting for care with a CD4+ T-cell count < 350 cells/mm3 or after an AIDS-defining event), and 21.6% were late presenters with advanced disease (LPs-AD) (presenting for care with a CD4+ T-cell count < 200 cells/mm3 or after an AIDS-defining event). The risk of being an LPs-AD was increased in older individuals (OR on log-transformed age: 7.5) and individuals of sub-Saharan African origin compared to individuals of Belgian or other origin (ORs of 0.30 and 0.25, respectively). The results of this study suggest that broadening the focus beyond the previously common risk groups is essential to prevent late diagnosis.

Abstract Background Anxiety is one of the most common but often overlooked mood-related nonmotor symptoms in people with Parkinson’s disease (PD). To improve the well-being of people with PD, it is important to understand the impact of anxiety in PD, especially its association with depressive and motor symptoms and its impact on health-related quality of life (HRQoL). Methods 91 people with PD were assessed between June 2017 and June 2018. Anxiety was measured using the Geriatric Anxiety Scale (GAS) and its cognitive, somatic, and affective subscales. HRQoL was assessed using the Parkinson’s Disease Questionnaire 39 (PDQ-39). Moreover, sociodemographic information, depressive symptoms, cognition, motor and nonmotor symptoms were assessed. Descriptive statistics, regression analyses, and path analyses were performed to understand predictors of anxiety and its influence on HRQoL. Results Of the 91 people with PD, 35 (38.5%) experienced anxiety. Anxiety symptoms in these individuals primarily manifest as somatic sensations. Anxiety, motor, and depressive symptoms are interlinked but contribute individually to HRQoL. Beyond motor symptoms, cognitive and affective aspects of anxiety impact HRQoL. While anxiety and depression overlap, the somatic and cognitive aspects of anxiety play a significant role in determining HRQoL in addition to depressive symptoms. Conclusion Our study used the GAS and its three subscales to shed light on the connections between anxiety, depression, and motor impairment in people with PD. Although anxiety is linked to depression and motor symptoms, it independently affects the HRQoL of people with PD. Thus, it is crucial to adopt a comprehensive diagnostic approach that detects and considers the impact of anxiety on HRQoL in PD.

Lungworm infections are known to be frequent and responsible for substantial economic losses in ruminants. They are caused by Dictyocaulus filaria and various species belonging to the Protostrongylidae family of nematodes. This present study was conducted at the Tiaret municipal slaughterhouse and the parasitology laboratory of the veterinary institute for six months, from November 2016 to April 2017. The study aimed to evaluate the occurrence of lungworm infections and the determination of the circulating species affecting sheep in the region with a pathological study of infected pulmonary tissue sections. The overall incidence of the pathology in sheep was 26 % (240/921). The identified species were Muellerius capillaris 43 %, Neostrongylus linearis 8 %, Cystocaulus ocreatus 4 %, Dictyocaulus filaria 3 %, Protostrongylus rufescens 1 %, and mixed infestations 42 %. The histological examination of corresponding lesions has revealed pneumonia-type, and bronchopneumonia, inflammatory lesions, with a predominance of mononuclear cells, necrosis of bronchial epithelium, and pulmonary parenchyma as well as hyperplasia of the bronchiolar epithelium. Prevention and control of these parasites is therefore essential for releasing the potential of sheep production.