Recent publications
Understanding plant adaptive strategies to aridity is crucial for ecological research, particularly in the current context of climate change and increasing drought. This study focuses on the intraspecific phenotypic variation of the wild olive (Olea europaea subsp. europaea var. sylvestris), one of the most emblematic species of the Mediterranean Basin, widely distributed in Morocco. The research is based on measuring nine leaf and plant-size related traits in 130 trees across 13 populations under varying climate conditions and vegetation covers. The study explores the adaptive strategies of wild olive trees in response to increasing aridity and aridification. The results indicate that the nine traits exhibit significant covariation trends along environmental gradients, reflecting plant strategies related to resource acquisition, resource investment, and water use. Wild olive trees demonstrate substantial intraspecific variation both among and within populations in response to these environmental gradients. Climate, altitude, and vegetation cover together explain 93.8% of the trait covariations. The study elucidates the mechanisms underlying the adaptive strategies of wild olive trees to cope with stressful conditions. The findings suggest that wild olive trees adapt to stressful environments by adopting a conservative strategy, characterized by lower resource investment and higher water-use efficiency. This research underscores the importance of considering intraspecific variation in plant responses to environmental stressors and demonstrates the utility of trait-based approaches in understanding plant strategies under such conditions.
Pest management plays a pivotal role in ensuring sustainable crop productivity. Integrated Pest Management Systems (IPMS) gaining importance due to the need for more sustainable agricultural methods. However, implementing standard pest management practices on a large scale is challenging due to their time-consuming and labor-intensive nature. In order to enhance pest management efficiency in date palm cultivation, this research paper proposes an Artificial Intelligence-driven Pest Prediction System, explicitly targeting the Parlatoria Date Scale (PDS), a significant pest affecting date palms. The approach leverages climatological factors to predict the occurrence of PDS nymphs and adult females. To address the challenges of data scarcity and imbalance, the Synthetic Minority Over-Sampling for Regression using Gaussian Noise (SMOGN) method is employed, which significantly enhances the dataset's utility for training machine learning models. The interpretability of these models is further enhanced using SHAP values, providing insights into the driving factors behind the predictions and facilitating trust and understanding in the AI-driven decision-making process. The Stacking-based Weighted Ensemble model stands out among the evaluated ensemble-based methods, achieving R2 scores of 77%, 88%, and 90% for predicting PDS nymphs, adult females, and total population incidence, respectively. This research highlights the potential of AI in revolutionizing Date Palm Integrated Pest Management Systems (IPMS). It emphasizes the importance of model interpretability in adopting AI solutions to enhance the precision and effectiveness of pest management practices, thereby promoting a more sustainable agricultural system.
The investigation deals the potential of hydro-ethanolic extracts from green tea waste as C38 inhibitors in a hydrochloride environment. This research focuses on both cold and hot maceration methods and explores the influence of extract concentration on inhibitory efficiency. The results reveal a substantial positive correlation between extract concentration and corrosion inhibition. Inhibitory efficiency values, as measured by inhibition efficiency (IEEIS and IEPDP) exhibit remarkable effectiveness, with values reaching 93.89% and 96.52% for THC and THF extracts and 96.61% and 98.39% for the respective Tafel curves. These findings suggest that higher extract concentrations enhance corrosion inhibition through the adsorption of extracts adsorption, conforming to the model of Langmuir. This process leads to a progressive rise in Rct over time. Notably, the peak inhibitory efficiency is attained after 8 h, indicating a time-dependent corrosion inhibition process. The gravimetric test was also performed in the absence and presence of inhibitors after immersion for 2 h. The results obtained confirmed the inhibitory capacity of the extracts tested. Scanning electron microscopy analysis substantiates the adsorption mechanism. In sum, the study unequivocally demonstrates the inhibitory capabilities of the treated green tea waste extracts against C38 steel corrosion in a hydrochloride medium. The inhibitor, HCl and water interaction with iron surface was analyzed using theoretical methods, such as DFT as well as MC simulation. Catechin adsorbs on the Fe (1 1 0) and Fe2O3 (110) surfaces in a paralleled model to reach the largest coverage area. Quantum chemical calculations (DFT) also thoroughly investigate the active adsorption sites of the Catechin molecule. In general, our results indicate that DFT calculations correlate well with MC simulations and experimental measurements, and they demonstrate a strong correlation between the inhibitory efficacy of the examined catechin and its molecular structure.
Graphical Abstract
In the wake of escalating urban vehicle proliferation, major cities grapple with intensified traffic congestion, elevated air pollution, and acute scarcity of parking spaces. Addressing this issue, the research paper proposes an advanced Intelligent Parking System (IPS) using artificial intelligence, transforming the traditional regression problem of parking space prediction into a binary classification task through a novel algorithm. This algorithm inserts a binary variable into existing databases to model each parking space’s availability, thus adapting tabular parking history data (“data architecture” most widely adopted by intelligent parking solutions already in use) to a classification problem. Once data has been prepared and processed, parking spaces are classified according to their availability employing logistic regression with regularization techniques, achieving superior performance in precision, recall, and F-score metrics. Specifically, the proposed model demonstrates remarkable accuracy (up to 92.52% for Log-log with L1 &L2 regularization), high sensitivity (Sn: 96.90% for Probit with L1 &L2), and specificity (Sp: 94.42% for Log-log with L1 &L2), outperforming contemporary approaches. The comparative analysis also reveals the model’s efficiency in terms of accuracy and specificity against recent works. Additionally, the evaluation of time and energy complexity underscores the practical applicability in real-world urban environments. Thus, the research contributes significantly to smart city development and sustainable urban transportation infrastructure, laying groundwork for future advancements.
Urban transport systems represent a major infrastructure asset in contemporary cities, enabling many millions of people to commute and travel every day. Transport systems are increasingly complex because of rapid urbanization and rising vehicle ownership. Effectively predicting parking availability across a city means more efficient parking management, better urban planning, smoother traffic flow, lower fuel wastage and, ultimately, less environmental pollution. To this end, several research studies have proposed predictive approaches to parking space availability based on supervised machine learning, mainly regression algorithms. But if real-time information on parking space availability is lacking, these approaches become useless, since their driving force is historical data. What’s more, many city zones simply don’t require exact information on parking space occupancy; all that’s needed is a global view of whether there are any spaces available or not. That’s why, in this paper, we outline an approach to predicting parking availability that combines both clustering and classification. Our aim is to model parking availability through a number of typical days. A typical day is defined by a profile characterizing parking space occupancy. We start by determining precisely how many typical days per spatial cluster are required to map parking availability, and then cluster data to form groups typified by each typical day, using six clustering algorithms of ascending difficulty. Obtained results show that this number varies between 3 and 4, depending on which algorithm is applied. Thus, model evaluation reveals that distance between cluster elements is short and separation between clusters is high, in other words, clusters are far apart and not very dispersed.
In urban areas around the world, drivers face the daily challenge of finding a parking space. Unfortunately, these sought-after spaces, located close to their destination, are often either impossible to find, or excessively expensive, resulting in longer search times and increased congestion in city centers. The answer to this persistent problem is an intelligent parking solution. They provide drivers with real-time access to information on parking space availability, gathered through various sensing techniques such as crowdsourcing, parking meters and sensors. Some of these systems also offer opportunistic services, such as forecasts, to adapt to unforeseen dynamic situations. Drivers’ biggest concern is find ing a spot, not knowing the exact number of available spaces or availability rate. Typically, these two parameters are estimated using regression or image processing techniques. While such solutions guarantee high predictive accuracy, their large-scale deployment is hampered by computational and data collection costs. This paper therefore proposes a new approach combining clustering and classification models to predict parking availability. Our aim is to test new methods that are relatively simple and less expensive in terms of both processing costs and amount of training data. Experimental results have proved promising, with accuracy predictions exceeding 0.84.
SMS spam poses serious online security threats, including phishing and malware risks. Effective detection and prevention are vital for user protection. This study aims to improve SMS spam detection accuracy by exploring efficient text encoding and classification methods. We assess three text encoding techniques: Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), and Word2Vec. We conduct exploratory analysis, transform messages into numerical vectors, and enhance classification with additional features. Comparative analysis using various metrics helps select the best-performing algorithm for model construction. The research has practical implications, boosting spam prevention and enhancing cybersecurity in text messaging.
Let N=K(\root 3 \of {D}) be a cubic Kummer extension of the cyclotomic field containing a primitive cube root of unity with cube free integer radicand Denote by f the conductor of the abelian extension N/K, and by the relative genus field of N/K. The aim of the present work is to find out all positive integers D and conductors f such that the genus group is elementary bicyclic.
Legume plants rely upon multipartite interactions between rhizobia and bacterial endophytes within root nodules to facilitate plant growth. This study aimed to isolate and identify indigenous endophytic bacteria from root nodules of Sulla aculeolata L. in Northeast Morocco. Based on their tri-calcium phosphate (TCP) solubilization capacity, five endophytes were chosen for further evaluation of their plant growth traits. All isolates were hydrogen cyanide (HCN) and siderophore producers, while only BCH24 tested positive for ACC deaminase activity. Indole-3-acetic acid (IAA) synthesis ranged from 1.27 mgL− 1 to 2.89 mgL− 1, while soluble phosphate concentrations was between 7.99 mg L− 1 and 110.58 mg L− 1. Additionally, all the endophytes were able to produce more than two lytic enzymes. Based on the analysis of 16 S rRNA gene sequences five isolates were identified as Enterobacter sp (BCH13, BCH2), Pseudomonas sp (BCH16, BCH24), and Serratia sp (BCH10). The strains inhibited the growth of three phytopathogenic fungi, with BCH13 exhibiting the highest rate against Aspergillus ochraceus (45%), followed by BCH24 against Fusarium oxysporum (40%) and Botrytis cinerea (35%), respectively. In vivo inoculation of halotolerant strains Enterobacter hormaechei (BCH13) and Pseudomonas moraviensis (BCH16) under gnotobiotic conditions revealed that co-inoculation with Rhizobium sullae KS6 improved plant development compared to single inoculation, making it a promising eco-friendly bio-inoculant for legume Sulla flexuosa L. production.
Endoscopic ultrasound-guided fine-needle biopsy (EUS-FNB) is an excellent investigation to diagnose pancreatic lesions and has shown high accuracy for its use in pathologic diagnosis. Recently, macroscopic on-site evaluation (MOSE) performed by an endoscopist was introduced as an alternative to rapid on-site cytologic evaluation to increase the diagnostic yield of EUS-FNB. The MOSE of the biopsy can estimate the adequacy of the sample directly by the macroscopic evaluation of the core tissue obtained from EUS-FNB. Isolated pancreatic tuberculosis is extremely rare and difficult to diagnose because of its non-specific signs and symptoms. Therefore, this challenging diagnosis is based on endoscopy, imaging, and the bacteriological and histological examination of tissue biopsies. This uncommon presentation of tuberculosis can be revealed as pancreatic mass mimicking cancer. EUS-FNB can be very useful in providing a valuable histopathological diagnosis. A calcified lesion with a cheesy core in MOSE must be suggestive of tuberculosis, leading to the request of the GeneXpert, which can detect Mycobacterium tuberculosis deoxyribonucleic acid and resistance to rifampicin. A decent diagnostic strategy is crucial to prevent unnecessary surgical resection and to supply conservative management with antitubercular therapy.
Tracking the evolution of learners' learning in a MOOC supports the e-learning operation and allows teachers to easily manage the massive number of learners enrolled in a distance learning course. In this work we started with a study where we were interested in identifying the common parameters that allow us to have a vision on the evolution of learners through the use of SPSS statistical software. This operation allowed us to determine the level of the learners, classify them and group them into homogeneous groups that facilitated their orientation towards courses that meet the characteristics of their profiles. On the basis of our case study, we were able to develop a computer system approach based on K-means learning software and data pre-processing means, for data mining with the aim of analyzing and revealing the parameters that have a great positive impact on the learners' learning, the system uses the identified parameters to classify and group the learners according to their profiles. This type of system is characterized by its autonomy and the ability to process a large amount of data. On the basis of the data used in our case study, we carried out experimental tests on the proposed system which showed its performance in solving our problem.
Dermal nonneural granular cell tumor is a rare neoplasm of uncertain histogenesis that Le Boit and colleagues originally described in 1991. It arises commonly from the back, extremities and head and neck. To the best of our knowledge, only 50 cases have been reported in adults in the English literature. A 42-year-old man presented with a polypoid skin nodule of the front side of the chest wall, measuring 1,8 × 1,5 cm. The lesion was removed completely with tumor-free margins. Microscopically, the tumor was composed of a diffuse infiltrate of polygonal cells, S 100 negatives, with abundant granular cytoplasm and vesicular nuclei. The diagnosis of dermal nonneural granular cell tumor was retained. No recurrence was noted during follow up of 6 months. The prognosis is good.
In this paper, we prove the existence and the regularity of weak solutions for a class of nonlinear anisotropic parabolic equations with pi(·) growth conditions, degenerate coercivity and Lm(·) data, with m(·)>1 being small. The functional setting involves Lebesgue-Sobolev spaces with variable exponents.
Distance education (E-Learning) is experiencing significant, rapid and con-tinuous evolution all over the world, especially with the arrival of the covid-19 pandemic. MOOCs are considered as a personal learning process, which are addressed to a massive and varied number of learners. The problem of the free opening MOOCs puts us in front of a massive number of registrants, which means a large number of heterogeneous profiles, which makes the teacher's task more complicated, either in terms of follow-up or framing. As a solution to this problem, in this present work, we propose an approach that allows the classification and categorization of learner profiles via an intelli-gent and autonomous system developed on the basis of neural networks and in particular the self-organizing map (SOM). This approach which is based on the traceability of learners, allowed us to get homogenous groups in order to direct them towards MOOCs that meet their characteristics and needs.
The tests carried out have shown that our approach is efficient in terms of classification and grouping of profiles, which allows us to manage a large number of learners either at the level of the choice of relevant contents or during the evaluation process.
The novel hybrid phosphate pigment 6-amino hexanoic acid (6-AHA) intercalated in layered aluminum di-hydrogen tripolyphosphate (ATP-6-AHA) was successfully embedded in an alkyd coating at 5.0 wt% and applied on low carbon steel. Layered pigments used in coatings are becoming very attractive due to the improvement of the coatings barrier properties. The current study revealed superior corrosion protection of ATP-6-AHA coating on carbon steel with a polarization resistance of 1.3 10⁸ Ohm·cm² after three weeks immersed in a 3.5 wt% NaCl solution, using electrochemical impedance spectroscopy (EIS). The adhesion and surface roughness of coated samples were evaluated using pull of test and laser scanning microscopy. X-ray diffraction (XRD) and scanning electron microscopy (SEM) were employed to assess the corrosion protection of ATP-6-AHA alkyd coating. Results revealed a protective film on steel surface comprising iron phosphate.
This article presents different combinations of input parameters based on an intelligent technique, using neural networks to predict daily global solar radiation (GSR) for twenty-five Moroccan cities. The collected measured data are available for 365 days and 25 stations around Morocco. Different input parameters are used, such as clearness index KT, day number, the length of the day, minimal temperature T min , maximal temperature T max , average temperature T average , difference temperature Δ T , ratio temperature T-Ratio, average relative humidity RH, solar radiation at the top outside atmosphere TOA, average wind speed Ws, altitude, longitude, latitude, and solar declination. A different combination was employed to predict daily GSR for the considered locations in order to find the most adequate input parameter that can be used in the prediction procedure. Several statistical metrics are applied to evaluate the performance of the obtained results, such as coefficients of determination (R 2), mean absolute percentage error (MAPE), root mean square error (RMSE), normalized root mean square error (NRMSE), mean bias error (MBE), test statistic (TS), linear regression coefficients (the slope “a” and the constant “b”), and standard deviation (σ). It is found that the usage of input parameters gives highly accurate results in the artificial neural network (FFNN-BP) model, obtaining the lowest value of the statistical metrics. The results showed the best input of 25 locations, 12 inputs for Er-Rachidia, Marrakech, Medilt, Taza, Oujda, Nador, Tetouan, Tanger, Al-Auin, Dakhla, Settat, and Safi, seven inputs for Fes, Ifrane, Beni-Mellal, and Meknes, six inputs for Agadir and Rabat, five inputs for Sidi Ifni, Essaouira, Casablanca and Kenitra, four inputs for Ouarzazate, Lareche, and Al-Hoceima. In terms of accuracy, R 2 of the selected best inputs parameters varies between 0.9860% and 0.9920%, the range value of MBE (%) being from −0.1076% to −0.5931%, the RMSE between 0.1990 and 0.4580%, the range value of the NRMSE between 0.0355 and 0.8938, and the lowest value MAPE between 0.0019 and 0.0060%. This technique could be used to predict other parameters for locations where measurement instrumentation is unavailable or costly to obtain.
This review details the most recent advancement in solar electricity production devices, in order to offer a reference for the decision-makers in the field of solar plant installation worldwide. These technologies can be classified into three main categories, namely Photovoltaics, Thermal, and Hybrid (thermal/photovoltaic). Hence, this paper begins by laying out the methodology that is used for conducting this research. Next, solar electricity production technologies are investigated and their sub-classifications are detailed to determine their resource requirements and characteristics. Subsequently, a thorough discussion is carried out. Followed by an assessment of the environmental and financial performances of each technology. Moreover, a statistical study is undertaken to highlight the efficiency and performances of each solar technology, as well as to determine their rankings in terms of electricity production worldwide. Finally, research trends related to the development of solar electricity plants are provided.
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