Search Results (846)

Assessment is fundamental to programming education; however, it is a labour-intensive and complicated process, especially in extensive learning contexts where it relies significantly on human teachers. This paper presents an automated grading methodology designed to assess Python programming exercises, producing both continuous and discrete grades. The methodology incorporates GPT-4-Turbo, a robust large language model, and machine learning models selected by PyCaret’s automated process. The Extra Trees Regressor demonstrated superior performance in continuous grade prediction, with a Mean Absolute Error (MAE) of 4.43 out of 100 and an R² score of 0.83. The Random Forest Classifier attained the highest scores for discrete grade classification, achieving an accuracy of 91% and a Quadratic Weighted Kappa of 0.84, indicating substantial concordance with human-assigned categories. These findings underscore the promise of integrating LLMs and automated model selection to facilitate scalable, consistent, and equitable assessment in programming education, while substantially alleviating the workload on human evaluators. Full article

Background and Objectives: The cytokine IL-6, methyltransferase NSD2, pro-protein convertase Furin, and growth factor receptor IGF-1R are essential factors in the proliferation of cancer cells. These proteins are involved in the tumor process by generating several cell-signaling pathways. However, the interactions of these oncogenic biomarkers, Furin, IL-6, and NSD2, and their links with the inhibitor SERPINA-1 remain largely unknown. Materials and Methods: Cell proliferation is measured by colorimetric and enzymatic methods. The genetic expressions of SERPINA-1, Furin, IL-6, and NSD2 are measured by qRT-PCR, while the expression of IGF-1R on the cell surface is measured by flow cytometry. Results: The proliferation of cells overexpressing SERPINA-1 (JP7pSer+) is decreased by more than 90% compared to control cells (JP7pSer-). The kinetics of the gene expression ratios of Furin, IL-6, and NSD2 show an increase for 48 h, followed by a decrease after 72 h for the three biomarkers in JP7pSer+ cells compared to JP7pSer- cells. The expression of IGF-1R on the cell surface in both cell lines is low, with JP7pSer- cells expressing 1.33 times more IGF-1R than JP7pSer+ cells. Conclusions: These results suggest gene correlations of SERPINA-1 overexpression with decreased cell proliferation and modulation of gene expression of Furin, IL-6, and NSD2. This study should be complemented by molecular transcriptomic and proteomic experiments to better understand the interaction of SERPINA-1 with IL-6, Furin, and NSD2, and their effect on tumor progression. Full article

Acute lymphoblastic leukemia (ALL) is a heterogeneous hematologic malignancy defined by the uncontrolled proliferation of lymphoid precursors. Accurate diagnosis and effective therapeutic strategies hinge on a comprehensive understanding of the genetic and molecular landscape of ALL. This review synthesizes the latest updates in cytogenetic and molecular classifications, emphasizing the 2022 World Health Organization (WHO) and International Consensus Classification (ICC) revisions. Key chromosomal alterations such as BCR::ABL1 and ETV6::RUNX1 and emerging subtypes including Ph-like ALL, DUX4, and MEF2D rearrangements are examined for their prognostic significance. Furthermore, we assess novel diagnostic tools, notably next-generation sequencing (NGS) and optical genome mapping (OGM). While NGS excels at identifying point mutations and small indels, OGM offers high-resolution structural variant detection with 100% sensitivity in multiple validation studies. These advancements enhance our grasp of leukemogenesis and pave the way for precision medicine in both B- and T-cell ALL. Ultimately, integrating these innovations into routine diagnostics is crucial for personalized patient management and improving clinical outcomes. Full article

Male infertility is a multifactorial condition often associated with disruptions in sperm metabolism and mitochondrial function, yet traditional semen analysis provides limited insight into these molecular mechanisms. Understanding sperm bioenergetics and metabolic dysfunctions is crucial for improving the diagnosis and treatment of conditions such as asthenozoospermia and azoospermia. This systematic review synthesizes recent literature, focusing on advanced tools and techniques—including omics technologies, advanced imaging, spectroscopy, and functional assays—that enable comprehensive molecular assessment of sperm metabolism and development. The reviewed studies highlight the effectiveness of metabolomics, proteomics, and transcriptomics in identifying metabolic biomarkers linked to male infertility. Non-invasive imaging modalities such as Raman and magnetic resonance spectroscopy offer real-time metabolic profiling, while the seminal microbiome is increasingly recognized for its role in modulating sperm metabolic health. Despite these advances, challenges remain in clinical validation and implementation of these techniques in routine infertility diagnostics. Integrating molecular metabolic assessments with conventional semen analysis promises enhanced diagnostic precision and personalized therapeutic approaches, ultimately improving reproductive outcomes. Continued research is needed to standardize biomarkers and validate clinical utility. Furthermore, these metabolic tools hold significant potential to elucidate the underlying causes of previously misunderstood and unexplained infertility cases, offering new avenues for diagnosis and treatment. Full article

Barium chloride (BaCl₂), a known environmental pollutant, induces organ-specific oxidative stress through disruption of redox homeostasis. This study evaluated the protective effects and safety profile of sodium copper chlorophyllin (SCC) and ascorbic acid (ASC) against BaCl₂-induced oxidative damage in the liver and brain of mice using a two-phase experimental protocol. Animals received either SCC (40 mg/kg), ASC (160 mg/kg), or their combination for 14 days prior to BaCl₂ exposure (150 mg/L in drinking water for 7 days), allowing evaluation of both preventive and therapeutic effects. Toxicological and behavioral assessments confirmed the absence of systemic toxicity or neurobehavioral alterations following supplementation. Body weight, liver and kidney indices, and biochemical markers (Aspartate Aminotransferase (ASAT), Alanine Aminotransferase (ALAT), creatinine) remained within physiological ranges, and no anxiogenic or locomotor effects were observed. In the brain, BaCl₂ exposure significantly increased SOD (+49%), CAT (+66%), GPx (+24%), and GSH (+26%) compared to controls, reflecting a robust compensatory antioxidant response. Although lipid peroxidation (MDA) showed a non-significant increase, SCC, ASC, and their combination reduced MDA levels by 42%, 37%, and 55%, respectively. These treatments normalized antioxidant enzyme activities and GSH, indicating an effective neuroprotective effect. In contrast, the liver exhibited a different oxidative profile. BaCl₂ exposure increased MDA levels by 80% and GSH by 34%, with no activation of SOD, CAT, or GPx. Histological analysis revealed extensive hepatocellular necrosis, vacuolization, and inflammatory infiltration. SCC significantly reduced hepatic MDA by 39% and preserved tissue architecture, while ASC alone or combined with SCC exacerbated inflammation and depleted hepatic GSH by 71% and 78%, respectively, relative to BaCl₂-exposed controls. Collectively, these results highlight a differential, organ-specific response to BaCl₂-induced oxidative stress and the therapeutic potential of SCC and ASC. SCC emerged as a safer and more effective agent, particularly in hepatic protection, while both antioxidants demonstrated neuroprotective effects when used individually or in combination. Full article

In the modern era, the use of blockchain technology has been growing rapidly, where Ethereum smart contracts play an important role in securing decentralized application systems. However, these smart contracts are also susceptible to a large number of vulnerabilities, which pose significant threats to intelligent systems and IoT applications, leading to data breaches and financial losses. Traditional detection techniques, such as manual analysis and static automated tools, suffer from high false positives and undetected security vulnerabilities. To address these problems, this paper proposes an Artificial Intelligence (AI)-based security framework that integrates Generative Adversarial Network (GAN)-based feature selection and deep learning techniques to classify and detect malware attacks on smart contract execution in the blockchain decentralized network. After an exhaustive pre-processing phase yielding a dataset of 40,000 malware and benign samples, the proposed model is evaluated and compared with related studies on the basis of a number of performance metrics including training accuracy, training loss, and classification metrics (accuracy, precision, recall, and F1-score). Our combined approach achieved a remarkable accuracy of 97.6%, demonstrating its effectiveness in detecting malware and protecting blockchain systems. Full article

Morocco is facing a situation of alarming water stress, aggravated by climate change, overexploitation of resources, and unequal distribution of water, placing the country among the most vulnerable to water scarcity in the MENA region. This study aims to investigate the dynamic relationship between economic growth and water stress in Morocco while highlighting the importance of integrated water management and adaptive economic policies to enhance resilience to water scarcity. A mixed methodology, integrating both qualitative and quantitative methods, was adopted to overview the economic–environmental Moroccan context, and to empirically analyze the GDP (gross domestic product) and water stress in Morocco over the period 1975–2021 using an Autoregressive Distributed Lag (ARDL) approach. The empirical analysis is based on annual data sourced from the World Bank and FAO databases for GDP, agricultural value added, renewable internal freshwater resources, and water productivity. The results suggest that water productivity has a significant positive effect on economic growth, while the impacts of agricultural value added and renewable water resources are less significant and vary depending on the model specification. Diagnostic tests confirm the reliability of the ARDL model; however, the presence of outliers in certain years reflects the influence of exogenous shocks, such as severe droughts or policy changes, on the Moroccan economy. The key contribution of this study lies in the fact that it is the first to analyze the intrinsic link between economic growth and the environmental aspect of water in Morocco. According to our findings, it is imperative to continuously improve water productivity and adopt adaptive management, rooted in science and innovation, in order to ensure water security and support the sustainable economic development of Morocco. Full article

Sesame (Sesamum indicum L.) is widely cultivated for its valuable medicinal, aromatic, and oil-rich seeds. However, drought stress remains one of the most significant abiotic factors influencing its development, physiological function, and overall output. This study investigates the potential of foliar applications of silicon (Si), Sargassum muticum (Yendo) Fensholt extracts (SWE), and their combination to enhance drought tolerance and mitigate stress-induced damage in sesame. Plants were grown under well-watered conditions (80% field capacity, FC) versus 40% FC (drought conditions) and were treated with foliar applications of 1 mM Si, 10% SWE, or both. The results showed that the majority of the tested parameters were significantly (p < 0.05) lowered by drought stress. However, the combined application of Si and SWE significantly (p < 0.05) enhanced plant performance under drought stress, leading to improved growth, biomass accumulation, water status, and physiological traits. Gas exchange, photosynthetic pigment content, and photosystem activity (PSI and PSII) all increased significantly when SWE were given alone; PSII was more significantly affected. In contrast, Si alone had a more pronounced impact on PSI activity. These findings suggest that Si and SWE, applied individually or in combination, can effectively alleviate drought stress’s negative impact on sesame, supporting their use as promising biostimulants for enhancing drought tolerance. Full article

Although evidence suggests adiposity as a modifiable risk factor for postmenopausal breast cancer (BC), its association with premenopausal BC remains uncertain. This potential differential relationship for menopausal status has been insufficiently investigated in the Moroccan population due to limited data. This study aims to assess the relationship between various indicators of adiposity and the risk of BC among Moroccan women by menopausal status. A multicenter case-control study was conducted in Morocco between December 2019 and August 2023, including 1400 incident BC cases and 1400 matched controls. Detailed measures of adiposity and self-reported measures from different life stages were collected. Unconditional logistic regression analyses were conducted to estimate odds ratios (ORs) and 95% confidence intervals (95% CIs) for the association between body size indicators and the risk of BC, adjusting for a range of known risk factors for BC. Higher waist circumference (WC) and hip circumference (HC) were associated with an increased risk of BC in both pre- (p-trend < 0.001 for both WC and HC) and post-menopausal women (p-trend < 0.001 for WC, 0.002 for HC). Current body mass index (BMI) ≥30 kg/m² increased the risk of postmenopausal BC (p-trend = 0.012). Among postmenopausal women, higher weight at age 20 was positively associated with BC risk (p-trend < 0.001), while, weight at age 30 was significantly associated with increased BC risk in both pre- (p-trend = 0.008) and post-menopausal women (p-trend = 0.028). Interestingly, weight gain since age 20 was inversely associated with BC risk in postmenopausal women in the adjusted model (p-trend = 0.006). Young-adult BMI observed a significant increased trend with BC risk in both pre- (p-trend = 0.008) and post-menopausal women (p-trend < 0.001). In premenopausal women, larger body shape during childhood and early adulthood was positively associated with BC risk (p-trend = 0.01 and = 0.011, respectively). In postmenopausal women, larger childhood and adolescent body silhouettes were also associated with increased BC risk (p-trend = 0.045 and 0.047, respectively). These results suggest that anthropometric factors may have different associations with pre- and post-menopausal BC among Moroccan women. This underscores the importance of conducting large prospective studies to better understand these findings and explore their links to different molecular subtypes of BC. Full article

This work presents a new approach to introducing a new product in a production workshop, taking into account the products already introduced in the production lines. The proposal is based on a study of the skill requirements of the workforce and the mechanical modifications of the product, with a multi-criteria decision-making process using fuzzy logic. This approach makes it possible to select the most suitable production line, minimise the number of machine changes, and reduce the time required to adapt the workforce, while ensuring the shortest possible order processing time. Full article

The main thrust of the study is the need to cut down the time taken for mold changes in plastic injection molding which is fundamental to the productivity and efficiency of the process. The research encompasses Lean Manufacturing, DMAIC, and SMED which are improved using fuzzy logic and AI for rapid changeover optimization on the NEGRI BOSSI 650 machine. A decrease in downtime by 65% and an improvement in the Process Cycle Efficiency by 46.8% followed the identification of bottlenecks, externalizing tasks, and streamlining workflows. AI-driven analysis could make on-the-fly adjustments, which would ensure that resources are better allocated, and thus sustainable performance is maintained. The findings highlight how integrating Lean methods with advanced technologies enhances operational agility and competitiveness, offering a scalable model for continuous improvement in industrial settings. Full article

Background: Anterior cruciate ligament reconstruction (ACLr) often leads to asymmetries between limbs, with variable return-to-performance rates in athletes. The single-leg countermovement jump (SLCMJ) is commonly used to assess postoperative knee function. However, limited research has explored deficits specifically during the unweighting phase of the jump. Methods: This study assessed 53 recreational athletes (11 females, 42 males) between 6 and 9 months post-ACLr using a dual force plate system (1000 Hz). Each participant performed three maximal-effort SLCMJs per limb. Outcome measures included jump height, negative peak velocity, minimum force, and center of mass (COM) displacement. Paired t-tests and Wilcoxon tests were used to compare the ACLr limb with the contralateral limb. Results: Compared to the healthy limb, the ACLr limb showed significantly lower negative peak velocity (−0.80 ± 0.40 m/s vs. −0.94 ± 0.40 m/s, p < 0.001), higher minimum force (36.75 ± 17.88 kg vs. 32.05 ± 17.25 kg, p < 0.001), and reduced COM displacement (−17.62 ± 6.25 cm vs. −19.73 ± 5.34 cm, p = 0.014). Eccentric phase duration did not differ significantly. Conclusions: Athletes post-ACLr demonstrate altered neuromuscular control during the early SLCMJ phase. These findings highlight the importance of rehabilitation strategies targeting eccentric strength and symmetry restoration. Full article

Intermediate-band photovoltaics promise single-junction efficiencies that exceed the Shockley and Queisser limit, yet viable material platforms and device geometries remain under debate. Here, we perform comprehensive two-dimensional device-scale simulations using Silvaco Atlas TCAD to analyze p-i-n In_0.20Ga_0.80N solar cells in which the intermediate band is supplied by In_0.35Ga_0.65N quantum dots located inside the intrinsic layer. Quantum-dot diameters from 1 nm to 10 nm and areal densities up to 116 dots per period are evaluated under AM 1.5G, one-sun illumination at 300 K. The baseline pn junction achieves a simulated power-conversion efficiency of 33.9%. The incorporation of a single 1 nm quantum-dot layer dramatically increases efficiency to 48.1%, driven by a 35% enhancement in short-circuit current density while maintaining open-circuit voltage stability. Further increases in dot density continue to boost current but with diminishing benefit; the highest efficiency recorded, 49.4% at 116 dots, is only 1.4 percentage points above the 40-dot configuration. The improvements originate from two-step sub-band-gap absorption mediated by the quantum dots and from enhanced carrier collection in a widened depletion region. These results define a practical design window centred on approximately 1 nm dots and about 40 dots per period, balancing substantial efficiency gains with manageable structural complexity and providing concrete targets for epitaxial implementation. Full article

Social network platforms have a big impact on the development of companies by influencing clients’ behaviors and sentiments, which directly affect corporate reputations. Analyzing this feedback has become an essential component of business intelligence, supporting the improvement of long-term marketing strategies on a larger scale. The implementation of powerful sentiment analysis models requires a comprehensive and in-depth examination of each stage of the process. In this study, we present a new comparative approach for several feature extraction techniques, including TF-IDF, Word2Vec, FastText, and BERT embeddings. These methods are applied to three multilingual datasets collected from hotel review platforms in the tourism sector in English, French, and Arabic languages. Those datasets were preprocessed through cleaning, normalization, labeling, and balancing before being trained on various machine learning and deep learning algorithms. The effectiveness of each feature extraction method was evaluated using metrics such as accuracy, F1-score, precision, recall, ROC AUC curve, and a new metric that measures the execution time for generating word representations. Our extensive experiments demonstrate significant and excellent results, achieving accuracy rates of approximately 99% for the English dataset, 94% for the Arabic dataset, and 89% for the French dataset. These findings confirm the important impact of vectorization techniques on the performance of sentiment analysis models. They also highlight the important relationship between balanced datasets, effective feature extraction methods, and the choice of classification algorithms. So, this study aims to simplify the selection of feature extraction methods and appropriate classifiers for each language, thereby contributing to advancements in sentiment analysis. Full article

This study examines diurnal surface temperature dynamics across major Moroccan cities during the growing season and explores the interaction between urban and vegetated surfaces. We also introduce the Urban Thermal Impact Ratio (UTIR), a novel metric designed to quantify urban thermal comfort as a function of the surface urban heat island (SUHI) intensity. The analysis is based on outputs from a land surface model (LSM) for the year 2010, integrating high-resolution Landsat and MODIS data to characterize land cover and biophysical parameters across twelve land cover types. Our findings reveal moderate urban–vegetation temperature differences in coastal cities like Tangier (1.8 °C) and Rabat (1.0 °C), where winter vegetation remains active. In inland areas, urban morphology plays a more dominant role: Fes, with a 20% impervious surface area (ISA), exhibits a smaller SUHI than Meknes (5% ISA), due to higher urban heating in the latter. The Atlantic desert city of Dakhla shows a distinct pattern, with a nighttime SUHI of 2.1 °C and a daytime urban cooling of −0.7 °C, driven by irrigated parks and lawns enhancing evapotranspiration and shading. At the regional scale, summer UTIR values remain below one in Tangier-Tetouan-Al Hoceima, Rabat-Sale-Kenitra, and Casablanca-Settat, suggesting that urban conditions generally stay within thermal comfort thresholds. In contrast, higher UTIR values in Marrakech-Safi, Beni Mellal-Khénifra, and Guelmim-Oued Noun indicate elevated heat discomfort. At the city scale, the UTIR in Tangier, Rabat, and Casablanca demonstrates a clear diurnal pattern: it emerges around 11:00 a.m., peaks at 1:00 p.m., and fades by 3:00 p.m. This study highlights the critical role of vegetation in regulating urban surface temperatures and modulating urban–rural thermal contrasts. The UTIR provides a practical, scalable indicator of urban heat stress, particularly valuable in data-scarce settings. These findings carry significant implications for climate-resilient urban planning, optimized energy use, and the design of public health early warning systems in the context of climate change. Full article