Search Results (251)

The increasing need for materials that are both lightweight and strong in the aerospace and automotive sectors has driven the extensive use of composite sandwich structures. This study examines the impact response of honeycomb sandwich composites fabricated using the vacuum-assisted resin transfer molding (VARTM) technique. Two configurations were analyzed, namely carbon–honeycomb–carbon (CHC) and carbon–Kevlar–honeycomb–Kevlar–carbon (CKHKC), to assess the effect of Kevlar reinforcement on impact resistance. Charpy impact testing was conducted to evaluate energy absorption, revealing that CKHKC composites exhibited significantly superior impact resistance compared to CHC composites. The CKHKC composite achieved an average impact strength of 70.501 KJ/m², which is approximately 73.8% higher than the 40.570 KJ/m² recorded for CHC. This improvement is attributed to Kevlar’s superior toughness and energy dissipation capabilities. A comparative assessment of impact energy absorption further highlights the advantages of hybrid Kevlar–carbon fiber composites, making them highly suitable for applications requiring enhanced impact performance. These findings provide valuable insights into the design and optimization of high-performance honeycomb sandwich structures for impact-critical environments. Full article

This research evaluates the mechanical properties, environmental impacts, and cost-effectiveness of Hub Coal fly ash (FA)-based geopolymer concrete (FAGPC) as a sustainable alternative to ordinary Portland cement (OPC) concrete. This local FA has not been investigated previously. A total of 24 FAGPC mixes were tested under both ambient and heat curing conditions, varying the molarities of sodium hydroxide (NaOH) solution (10-M, 12-M 14-M and 16-M), sodium silicate to sodium hydroxide (Na₂SiO₃/NaOH) ratios (1.5, 2.0, and 2.5), and alkaline activator solution to fly ash (AAS/FA) ratios (0.5 and 0.6). The test results demonstrated that increasing NaOH molarity enhances the compressive strength (CS.) by 145% under ambient curing, with a peak CS. of 32.8 MPa at 16-M NaOH, and similarly, flexural strength (FS.) increases by 90% with a maximum FS. of 6.5 MPa at 14-M NaOH. Conversely, increasing the Na₂SiO₃/NaOH ratio to 2.5 reduced the CS. and FS. of ambient-cured specimens by 12.5% and 10.5%, respectively. Microstructural analysis revealed that higher NaOH molarity produced a denser, more homogeneous matrix, supported by increased Si–O–Al bond formation observed through energy-dispersive X-ray spectrometry. Environmentally, FAGPC demonstrated a 35–40% reduction in embodied CO₂ emissions compared to OPC, although the production costs of FAGPC were 30–35% higher, largely due to the expense of alkaline activators. These findings highlight the potential of FAGPC as a low-carbon alternative to OPC concrete, balancing enhanced mechanical performance with sustainability. New, green, and cheap activation solutions are sought for a new generation of more sustainable and affordable FAGPC. Full article

Depression is now one of the most common mental health concerns in the digital era, calling for powerful computational tools for its detection and its level of severity estimation. A multi-level depression severity detection framework in the Reddit social media network is proposed in this study, and posts are classified into four levels: minimum, mild, moderate, and severe. We take a dual approach using classical machine learning (ML) algorithms and recent Transformer-based architectures. For the ML track, we build ten classifiers, including Logistic Regression, SVM, Naive Bayes, Random Forest, XGBoost, Gradient Boosting, K-NN, Decision Tree, AdaBoost, and Extra Trees, with two recently proposed embedding methods, Word2Vec and GloVe embeddings, and we fine-tune them for mental health text classification. Of these, XGBoost yields the highest F1-score of 94.01 using GloVe embeddings. For the deep learning track, we fine-tune ten Transformer models, covering BERT, RoBERTa, XLM-RoBERTa, MentalBERT, BioBERT, RoBERTa-large, DistilBERT, DeBERTa, Longformer, and ALBERT. The highest performance was achieved by the MentalBERT model, with an F1-score of 97.31, followed by RoBERTa (96.27) and RoBERTa-large (96.14). Our results demonstrate that, to the best of the authors’ knowledge, domain-transferred Transformers outperform non-Transformer-based ML methods in capturing subtle linguistic cues indicative of different levels of depression, thereby highlighting their potential for fine-grained mental health monitoring in online settings. Full article

Climate change poses significant risks to food security and contributes to widespread soil degradation. Effective strategies are urgently needed to mitigate its impacts and ensure stable crop production and food quality. Biochar has shown strong potential to reduce greenhouse gas emissions, enhance carbon sequestration, and immobilize soil contaminants such as heavy metals and organic pollutants. These benefits can lead to increased crop yields, improved nutritional quality, and reduced uptake of harmful substances by plants. This review summarizes the possible mechanisms through which biochar influences the biochar–soil–plant interface, aiming to provide a comprehensive understanding of its multifaceted roles. Although positive effects of biochar on crop production are frequently reported, neutral or even negative outcomes have also been observed. Such adverse effects may be attributed to the presence of volatile organic compounds, free radicals, or heavy metals in certain biochars that inhibit plant growth. Additionally, biochar application has been found to reduce plant infections caused by pathogens, likely due to the presence of organic compounds that act as microbial inhibitors. A deeper understanding of the mechanisms by which biochar affects plant growth is essential for its effective use as a tool to combat climate change and enhance food security. Full article

The detection of abusive language in Roman Urdu is important for secure digital interaction. This work investigates machine learning (ML), deep learning (DL), and transformer-based methods for detecting offensive language in Roman Urdu comments collected from YouTube news channels. Extracted features use TF-IDF and Count Vectorizer for unigrams, bigrams, and trigrams. Of all the ML models—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Naïve Bayes (NB)—the best performance was achieved by the same SVM. DL models involved evaluating Bi-LSTM and CNN models, where the CNN model outperformed the others. Moreover, transformer variants such as LLaMA 2 and ModernBERT (MBERT) were instantiated and fine-tuned with LoRA (Low-Rank Adaptation) for better efficiency. LoRA has been tuned for large language models (LLMs), a family of advanced machine learning frameworks, based on the principle of making the process efficient with extremely low computational cost with better enhancement. According to the experimental results, LLaMA 2 with LoRA attained the highest F1-score of 96.58%, greatly exceeding the performance of other approaches. To elaborate, LoRA-optimized transformers perform well in capturing detailed subtleties of linguistic nuances, lending themselves well to Roman Urdu offensive language detection. The study compares the performance of conventional and contemporary NLP methods, highlighting the relevance of effective fine-tuning methods. Our findings pave the way for scalable and accurate automated moderation systems for online platforms supporting multiple languages. Full article

Rice (Oryza sativa) is a globally important crop that plays a central role in maintaining food security. This scientific review examines the critical role of genetic disease resistance in protecting rice yields, dissecting at the molecular level how rice plants detect and respond to pathogen attacks while evaluating modern approaches to developing improved resistant varieties. The analysis covers single-gene-mediated and multi-gene resistance systems, detailing how on one hand specific resistance proteins, defense signaling components, and clustered loci work together to provide comprehensive protection against a wide range of pathogens and yet their production is severely impacted by pathogens such as Xanthomonas oryzae (bacterial blight) and Magnaporthe oryzae (rice blast). The discussion extends to breakthrough breeding technologies currently revolutionizing rice improvement programs, including DNA marker-assisted selection for accelerating traditional breeding, gene conversion methods for introducing new resistance traits, and precision genome editing tools such as CRISPR/Cas9 for enabling targeted genetic modifications. By integrating advances in molecular biology and genomics, these approaches offer sustainable solutions to safeguard rice yields against evolving pathogens. Full article

Hate speech is a complex topic that transcends language, culture, and even social spheres. Recently, the spread of hate speech on social media sites like Facebook has added a new layer of complexity to the issue of online safety and content moderation. This study seeks to minimize this problem by developing an Arabic script-based tool for automatically detecting hate speech in Roman Urdu, an informal script used most commonly for South Asian digital communications. Roman Urdu is relatively complex as there are no standardized spellings, leading to syntactic variations, which increases the difficulty of hate speech detection. To tackle this problem, we adopt a holistic strategy using a combination of six machine learning (ML) and four Deep Learning (DL) models, a dataset from Facebook comments, which was preprocessed (tokenization, stopwords removal, etc.), and text vectorization (TF-IDF, word embeddings). The ML algorithms used in this study are LR, SVM, RF, NB, KNN, and GBM. We also use deep learning architectures like CNN, RNN, LSTM, and GRU to increase the accuracy of the classification further. It is proven by the experimental results that deep learning models outperform the traditional ML approaches by a significant margin, with CNN and LSTM achieving accuracies of 95.1% and 96.2%, respectively. As far as we are aware, this is the first work that investigates QLoRA for fine-tuning large models for the task of offensive language detection in Roman Urdu. Full article

Over the past few decades, numerous efforts have been made to increase the proportion of women in the construction industry, coupled with various calls for legislation and rules to prohibit gender discrimination. Despite these efforts, minimal progress has been noticed in the construction industry. While recruitment remains crucial, the current culture in construction reveals a knowledge gap in recruitment and retention in employment—a concept known as a ‘leaky pipeline’. Lack of awareness of career options and the challenges of working in a male-dominated, occasionally discriminatory workplace are some of the significant barriers to attracting and keeping women in the construction industry. Much of the research in South Africa shows that most construction companies employed few women but only in lower secretarial and administrative positions. Therefore, this study investigated the barriers facing women’s entry and retention in construction-related employment in South Africa using fuzzy synthetic evaluation (FSE) to understand and prioritise the barriers. Data were collected through the administration of online and paper-based questionnaires. The results of the analysis show that the barriers in the order of criticality include support and empowerment issues (SEs), educational/academic-related barriers (ABs), barriers from professional conditions and work attributes (BPs), social perception and gender stereotype barriers (SPs), professional perceptions and gender bias (PP), and individual confidence/interest/awareness/circumstance-related barriers (IBs), respectively. Based on the findings of the study, several recommendations, including on-the-job tutoring and flexible work arrangements, amongst others, were provided. Full article

The agricultural sector is evolving with the adoption of smart farming technologies, where Digital Twins (DTs) offer new possibilities for real-time monitoring, simulation, and decision-making. While previous research has explored the Internet of Things (IoT), UAVs, machine learning (ML), and remote sensing (RS) in enhancing agricultural efficiency, a systematic approach to integrating these technologies within a DTs ecosystem remains underdeveloped. This paper presents a systematic review of 167 studies published between 2018 and 2025. The objective of this study is to examine recent advancements in DTs-enabled precision agriculture and propose a comprehensive framework for designing, integrating, and optimizing DTs in smart farming. The study systematically examines the current state of DT adoption, identifies key barriers, and computational efficiency challenges, and provides a step-by-step methodology for DT implementation. The review sheds light on potential future research direction and implications for policy, with the aim to speed up the adoption of DTs-based farm management systems in their operational success and commercial viability through analysis of practical applications and future perspectives. This study presents an innovative strategy for integrating digital and physical systems into agriculture and is an important contribution to existing literature. Full article

Gender inequality remains a critical challenge in Vietnamese higher education, particularly regarding how academic roles and advancement opportunities are distributed. Despite existing policies promoting gender equality, gaps persist in leadership and career development, potentially limiting women’s representation and growth. This study examines Vietnamese educators’ perspectives on gender equality in higher education, focusing on academic rank awareness and attitudes toward gender-related issues. A quantitative research design was employed, using a structured survey distributed among faculty members across different academic ranks, including lecturers, senior lecturers, associate professors, and professors. Data were analysed through statistical measures, including frequencies and percentages, mean scores, standard deviations, the Mann–Whitney U test, the Kruskal–Wallis H-test, and post hoc analysis to assess variations in perspectives on gender equality based on academic positions. The findings reveal significant differences in gender equality awareness across academic ranks. Educators in senior positions reported greater recognition of gender disparities, especially in leadership roles and promotion processes, than those in junior roles, who exhibited less awareness of such issues. This study’s practical implications suggest that Vietnamese higher education institutions should adopt targeted interventions, such as gender awareness programs and transparent promotion processes, to foster a more inclusive environment. Additionally, mentorship programs for female academics could enhance their career advancement opportunities. This research contributes original insights into how the academic hierarchy affects gender equality perceptions within Vietnamese higher education, offering a basis for the development of policies that support equitable career pathways. Full article

Over the last 40 years, several studies have provided evidence demonstrating that viral vectors can result in effective gene targeting/insertions in a host’s genome. The traditional approaches of gene knock-down, -out, or -in involve an intensive transgenesis process that is plagued by extensive timescales. Plant viruses have the potential to target specific genes and integrate exogenous DNA molecules at the target locus. Their ability to manipulate a host’s genetic material and become a part of it makes them remarkable agents and helpful for molecular and synthetic biology. In this review, we describe how geminivirus-based vectors can be utilized to overcome traditional transgenesis. We highlight the progress that has been made so far and also discuss the hurdles that hinder the employment of geminivirus-based vectors. Furthermore, we conclude with a comparison of geminivirus-based vectors with other plant-derived vectors. Geminivirus-based vectors stand poised to revolutionize plant genome editing by making nucleic acid manipulation cheaper and easier to deploy, thus lessening the major technical constraints, including homology-directed repair (HDR)-mediated genome editing and time-inefficient tissue culture procedures. The insights given in this review illustrate a broader picture of geminiviral vectors, with an emphasis on engineering plant viruses to ease genome editing practices for crop improvements as well as boost experimental timescales from years to months. Full article

Gastrodia elata, Polygonatum sibiricum, and Poria cocos are traditional Chinese herbs commonly used as both medicinal and food ingredients, traditionally believed to improve liver and kidney functions, replenish vital energy (qi) and blood, and mitigate stress-induced damage. These herbs are combined in the Compound Gastrodia elata Formula (CGEF), a functional food formulation. Amidst growing interest in functional foods, this study explores the cognitive-enhancing effects of CGEF, focusing on cognitive function improvement. Cognitive impairment was induced in ICR mice via chronic sleep restriction. Behavioral assessments including the Y-maze test, object recognition test, Morris water maze test, and Passive avoidance test, were conducted to evaluate CGEF’s effects. Serum levels of inflammatory markers and oxidative stress were quantified while in rat hippocampus tissue expressions of inflammatory, apoptotic, and neuroprotective-related protein markers were analyzed by Western blotting. Neurotransmitter concentrations in both the hippocampus and prefrontal cortex were determined by LC-MS/MS. CGEF significantly alleviated cognitive impairments across all behavioral tests. The underlying mechanisms likely involve a reduction in oxidative stress and peripheral inflammatory factors, and suppression of the TLR2/MyD88/NF-κB signaling cascade in the hippocampus, thereby mitigating neuroinflammation and neuronal apoptosis. Furthermore, CGEF modulated the PI3K/AKT/GSK3β signaling pathway, potentially contributing to neuronal integrity and synaptic plasticity maintenance. CGEF also restored neurotransmitter balance and regulated tryptophan metabolism, further alleviating cognitive deficits associated with sleep disruption. These findings suggest CGEF’s potential as a functional food for reversing cognitive impairments caused by chronic sleep restriction, primarily through its anti-inflammatory and neuroprotective effects. Full article

This study investigates the potential of cement-less recycled aggregate concrete (C.R.A.C.) as an eco-friendly alternative to traditional ordinary Portland cement (OPC) concrete, using industrial waste (fly ash) and construction and demolition waste (recycled coarse aggregates). This research explores the effects of mixes of varying sodium hydroxide (NH) molarities and percentage substitutions of natural coarse aggregates (N.C.As.) with recycled coarse aggregates (R.C.As.) on the mechanical properties of C.R.A.C. A total of eighteen ambient-cured C.R.A.C. mixes, using Thar Coal fly ash with varying NH molarities (12 M, 14 M, and 16 M), and percentage substitutions of N.C.As. with R.C.As. (0%, 20%, 40%, 60%, 80%, and 100%), were prepared and tested under axial compression and flexure. It was observed that the compressive strength increased by about 76% with an increasing NH molarity, whereas the compressive strength decreased by about 52.9% with an increasing percentage substitution of N.C.As. with R.C.As. The flexural strength increased by about 78.3% with an increasing NH molarity, whereas the flexural strength decreased by about 50.5% with an increasing percentage substitution of N.C.As. with R.C.As. The SEM analysis of the C.R.A.C. mixes highlighted the heterogeneous morphology of fly ash particles (e.g., irregular shape, rough surface texture, and porous regions), which negatively influenced the overall performance of the concrete matrix. The environmental assessment exhibited that the C.R.A.C. mixes exhibited about 45% lower CO₂ emissions than OPC concrete; however, the cost of the C.R.A.C. mixes was about 21% higher than that of OPC concrete mixes. Full article

The phenomenal growth of digital learning platforms has brought new learner engagement and retention challenges to higher education. This study proposes a framework that integrates game mechanics—leveling systems, badges, and timely feedback—with artificial intelligence (AI)-driven personalization to meet the challenges of enhanced adaptability, motivation, and learning outcomes in online environments. Key design elements were identified through literature reviews and consultations with instructional design experts, leading to the development an adaptive learning platform prototype. The prototype underwent an eight-week pilot study with 250 Prince Sattam Bin Abdulaziz University (PSAU) students randomly assigned to a control group (non-adaptive system) or an experimental group (adaptive system). Data sources included pre- and post-tests, platform engagement analytics, and learner perception surveys. The results showed that the adaptive group outperformed the control group in the post-test scores (M = 85.2, SD = 6.4 vs. M = 78.5, SD = 7.2) and motivation levels (M = 4.2, SD = 0.7 vs. M = 3.6, SD = 0.8). Additionally, 82% of the adaptive group achieved mastery-level performance compared to 64% in the control group. These findings demonstrate the potential of integrating game mechanics and AI-driven personalization to transform digital learning, offering a roadmap for scalable, data-driven adaptive platforms. Future research will address long-term retention and diverse subject applications. Full article