Search Results (173)

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

Background: AI has been adopted in dentistry for diagnosis, decision making, and therapy prognosis prediction. This systematic review aimed to identify AI models in dentistry, assess their performance, identify their shortcomings, and discuss their potential for adoption and integration in dental practice in the future. Methodology: The sources of the papers were the following electronic databases: PubMed, Scopus, and Cochrane Library. A total of 20 out of 947 needed further studies, and this was encompassed in the present meta-analysis. It identified diagnostic accuracy, predictive performance, and potential biases. Results: AI models demonstrated an overall diagnostic accuracy of 82%, primarily leveraging artificial neural networks (ANNs) and convolutional neural networks (CNNs). These models have significantly improved the diagnostic precision for dental caries compared with traditional methods. Moreover, they have shown potential in detecting and managing conditions such as bone loss, malignant lesions, vertical root fractures, apical lesions, salivary gland disorders, and maxillofacial cysts, as well as in performing orthodontic assessments. However, the integration of AI systems into dentistry poses challenges, including potential data biases, cost implications, technical requirements, and ethical concerns such as patient data security and informed consent. AI models may also underperform when faced with limited or skewed datasets, thus underscoring the importance of robust training and validation procedures. Conclusions: AI has the potential to revolutionize dentistry by significantly improving diagnostic accuracy and treatment planning. However, before integrating this tool into clinical practice, a critical assessment of its advantages, disadvantages, and utility or ethical issues must be established. Future studies should aim to eradicate existing barriers and enhance the model’s ease of understanding and challenges regarding expense and data protection, to ensure the effective utilization of AI in dental healthcare. Full article

Wind is one of the most important resources in the renewable energy basket. However, there are questions regarding wind as a sustainable solution, especially concerning its upfront costs, visual impact, noise pollution, and bird collisions. These challenges arise in commercial windmills, whereas for domestic small-scale windmills, these challenges are limited. On the other hand, accurate wind speed prediction (WSP) is crucial for optimizing power management in renewable energy systems. Existing research focuses on proposing model architectures and optimizing hyperparameters to improve model performance. This approach often results in larger models, which are hosted on cloud servers. Such models face challenges, including bandwidth utilization leading to data delays, increased costs, security risks, concerns about data privacy, and the necessity of continuous internet connectivity. Such resources are not available for domestic windmills. To overcome these obstacles, this work proposes a transformer model integrated with Long Short-Term Memory (LSTM) units, optimized for memory-constrained devices (MCDs). A contribution of this research is the development of a novel cost function that balances the reduction of mean squared error with the constraints of model size. This approach enables model deployment on low-power devices, avoiding the challenges of cloud-based deployment. The model, with its tuned hyperparameters, outperforms recent methodologies in terms of mean squared error, mean absolute error, model size, and R-squared scores across three different datasets. This advancement paves the way for more dynamic and secure on-device wind speed prediction (WSP) applications, representing a step forward in renewable energy management. Full article

When extreme values or outliers occur in asymmetric datasets, conventional mean estimation methods suffer from low accuracy and reliability. This study introduces a novel class of robust Särndal-type mean estimators utilizing re-descending M-estimator coefficients. These estimators effectively combine the benefits of robust regression techniques and the integration of extreme values to improve mean estimation accuracy under simple random sampling. The proposed methodology leverages distinct re-descending coefficients from prior studies. Performance evaluation is conducted using three real-world datasets and three synthetically generated datasets containing outliers, with results indicating superior performance of the proposed estimators in terms of mean squared error (MSE) and percentage relative efficiency (PRE). Hence, the robustness, adaptability, and practical importance of these estimators are illustrated by these findings for survey sampling and more generally for data-intensive contexts. Full article

Highlighting the importance of artificial intelligence and machine learning approaches in engineering and fluid mechanics problems, especially in heat transfer applications is main goal of the presented article. With the advancement in Artificial Intelligence (AI) and Machine Learning (ML) techniques, the computational efficiency and accuracy of numerical results are enhanced. The theme of the study is to use machine learning techniques to examine the thermal analysis of MHD boundary layer flow of Eyring-Powell Hybrid Nanofluid (EPHNFs) passing a horizontal cylinder embedded in a porous medium with heat source/sink and viscous dissipation effects. The considered base fluid is water ($H_{2}O$) and hybrid nanoparticles titanium oxide (${TiO}_2$) and Copper oxide ($CuO$). The governing flow equations are nonlinear PDEs. Non-similar system of PDEs are obtained with efficient conversion variables. The dimensionless PDEs are truncated using a local non-similarity approach up to third level and numerical solution is evaluated using MATLAB built-in-function bvp4c. Artificial Neural Networks (ANNs) simulation approach is used to trained the networks to predict the solution behavior. Thermal boundary layer improves with the enhancement in the value of $Rd$. The accuracy and reliability of ANNs predicted solution is addressed with computation of correlation index and residual analysis. The RMSE is evaluated [0.04892, 0.0007597, 0.0007596, 0.01546, 0.008871, 0.01686] for various scenarios. It is observed that when concentration of hybrid nanoparticles increases then thermal characteristics of the Eyring-Powell Hybrid Nanofluid (EPHNFs) passing a horizontal cylinder. Full article

Venous thromboembolism (VTE) is a relatively common condition that is the leading cause of preventable deaths in developed nations. VTE encompasses deep vein thrombosis (DVT) and pulmonary embolism (PE) and affects both hospitalized and non-hospitalized patients. When left untreated, VTE is associated with substantial morbidity and mortality; accurate risk assessment and appropriate prophylaxis programs are therefore vital, as overlooked risk factors of these processes can potentially result in misdiagnosis and inappropriate treatment of the condition, with associated complications. In this study, we aimed to assess the impact of active monitoring on VTE management practices among healthcare providers to improve patient outcomes at Imam Abdulrahman Al Faisal Hospital (IAFH) in Riyadh, Saudi Arabia, from April 2018 to July 2023. In this study, a longitudinal retrospective study design was utilized and data from 33,237 admitted patients were analyzed using a Statistical Process Control (SPC) chart to evaluate the relationship between VTE risk assessment, active monitoring, and patient outcomes. In total, 11 cases of hospital-acquired VTE were identified, with patients aged 18–40 years representing most cases (7 out of 11 cases) and a male predominance of 54.5%. The overall VTE incidence rate during the study period was 0.31%, or one case per 11,000 admissions, including four cases of PE and seven cases of DVT. The results of this study indicate that active monitoring through continuous education and regular patient rounds significantly improves adherence to VTE risk assessment and prophylaxis at IAFH. The researchers attributed the increased identification and timely reporting of VTE cases to vigilance by healthcare providers and not to a decline in the quality of care. A comprehensive multidisciplinary strategy for VTE management and continuous quality improvement can aid in reducing VTE-related morbidity and improve patient outcomes. Lastly, we recommend addressing the risk factors associated with the occurrence of hospital-acquired VTE and performing post-discharge follow-ups of patients. Full article

Worldwide energy needs are growing, requiring new extraction techniques for crude oil from old reservoirs. However, conventional chemicals face difficulties when exposed to harsh reservoir environments such as solubility in high saline water and heat stability under harsh reservoir environments. This study investigates the potential of newly synthesized polymeric ionic liquids (PILs) as alternative options. A series of PILs was synthesized and characterized by using NMR and FTIR techniques. It was noticed that a PIL without ethoxy groups exhibits precipitation and therefore is not suitable for oilfield applications. However, the incorporation of ethoxy groups in the chemical structure of PILs leads to excellent solubility in low to high salinity brine. The solubility of the synthesized PILs in formation water, seawater, and deionized water, as well as their thermal stability using thermal gravimetric analysis (TGA), was assessed. In addition, the surface properties, including critical micelle concentration (cmc), surface tension (γ_cmc), surface excess concentration (Γ_max), minimal surface area per molecule (A_min), free adsorption energy (ΔG°_ads), and free micellization energy (ΔG°_mic), were also evaluated. The findings revealed that adding ethoxy groups in PILs led to a drop in Γ_max and an increase in A_min, suggesting reduced monolayer compactness at the air/water interface. The synthesized PILs demonstrated remarkable solubility, heat stability, and resistance to salt, rendering them well-suited for oilfield applications under challenging reservoir environments. Full article

Background/Objectives: Deep transfer learning, leveraging convolutional neural networks (CNNs), has become a pivotal tool for brain tumor detection. However, key challenges include optimizing hyperparameter selection and enhancing the generalization capabilities of models. This study introduces a novel CART-ANOVA (Cartesian-ANOVA) hyperparameter tuning framework, which differs from traditional optimization methods by systematically integrating statistical significance testing (ANOVA) with the Cartesian product of hyperparameter values. This approach ensures robust and precise parameter tuning by evaluating the interaction effects between hyperparameters, such as batch size and learning rate, rather than relying solely on grid or random search. Additionally, it implements seven distinct classification schemes for brain tumors, aimed at improving diagnostic accuracy and robustness. Methods: The proposed framework employs a ResNet18-based knowledge transfer learning (KTL) model trained on a primary dataset, with 20% allocated for testing. Hyperparameters were optimized using CART-ANOVA analysis, and statistical validation ensured robust parameter selection. The model’s generalization and robustness were evaluated on an independent second dataset. Performance metrics, including precision, accuracy, sensitivity, and F1 score, were compared against other pre-trained CNN models. Results: The framework achieved exceptional testing accuracy of 99.65% for four-class classification and 98.05% for seven-class classification on the source 1 dataset. It also maintained high generalization capabilities, achieving accuracies of 98.77% and 96.77% on the source 2 datasets for the same tasks. The incorporation of seven distinct classification schemes further enhanced variability and diagnostic capability, surpassing the performance of other pre-trained models. Conclusions: The CART-ANOVA hyperparameter tuning framework, combined with a ResNet18-based KTL approach, significantly improves brain tumor classification accuracy, robustness, and generalization. These advancements demonstrate strong potential for enhancing diagnostic precision and informing effective treatment strategies, contributing to advancements in medical imaging and AI-driven healthcare solutions. Full article

In the face of rising the threat of resistant pathogens, antimicrobial peptides (AMPs) offer a viable alternative to the current challenge due to their broad-spectrum activity. This study focuses on enhancing the efficacy of temporin-SHa derived NST-2 peptide (1), which is known for its antimicrobial and anticancer activities. We synthesized new analogs of 1 using three strategies, i.e., retro analog preparation, lysine addition/substitution, and levofloxacin conjugation. Analogs were tested in terms of their antibacterial, antifungal, and anticancer activities. Analog 2, corresponding to retro analog of NST-2, was found to be more active but also more hemolytic, reducing its selectivity index and therapeutic potential. The addition of lysine (in analog 3) and lysine substitution (in analog 7) reduced the hemolytic effect resulting in safer peptides. Conjugation with levofloxacin on the lysine side chain (in analogs 4 and 5) decreased the hemolytic effect but unfortunately also the antimicrobial and anticancer activities of the analogs. Oppositely, conjugation with levofloxacin at the N-terminus of the peptide via the β-alanine linker (in analogs 6 and 8) increased their antimicrobial and anticancer activity but also their hemolytic effect, resulting in less safe/selective analogs. In conclusion, lysine addition/substitution and levofloxacin conjugation, at least at the N-terminal position through the β-alanine linker, were found to enhance the therapeutic potential of retro analogs of NST-2 whereas other modifications decreased the activity or increased the toxicity of the peptides. Full article

Hedgehogs can act as reservoirs for the transmission of tick-borne pathogens (TBPs) to domestic livestock, wild animals, and humans. Understanding host–tick dynamics is essential to evaluate the impact of TBPs. This study was conducted in Pakistan and aimed to determine the prevalence and species of TBPs in the blood and ticks of Indian long-eared hedgehogs captured from various environments. A total of 64 hedgehogs were captured to check for tick infestation. Tick species were identified morphologically and molecularly including ITS-2 region amplification by PCR and subsequent Sanger sequencing. Moreover, TBPs were identified in both ticks and the blood of hedgehogs through conventional PCR and sequencing, targeting the regions msp1b, 18S rRNA, and cytb for Anaplasma spp., Babesia spp., and Theileria spp., respectively. Out of 64 hedgehogs, 16 (25%) were found to be infested with ticks. Morphological and molecular analysis identified all 109 collected ticks as Rhipicephalus turanicus. Only one hedgehog (6.2%) was infected with A. marginale. From the tick samples, 3.7% tested positive for Theileria lestoquardi, 2.8% for Anaplasma marginale, and another 2.8% for Babesia bigemina. This study provides critical insights into circulating TBPs in this region and what possible role hedgehogs might play in disease maintenance for Anaplasma marginale while identifying multiple pathogens that are of concern to human and animal health. Full article

Recently, there has been a remarkable move towards interprofessional collaboration in response to the COVID-19 pandemic and the care of comorbidities. In Germany, there has been a gradual increase in interprofessional learning in medical and pharmacy education, aiming to enhance patient care. To adapt the pharmacy curriculum for collaborative practice between pharmacy and medical students, we developed an immersive interprofessional collaboration course for pharmacy students using adult and pediatric high-fidelity simulators (HFS) to assess and train medication consultation skills. In a randomized controlled trial, we investigated whether interprofessional training between pharmacy and medical students results in differences in pharmacy students’ performance of medication therapy consultation compared to the case of mono-professional training of pharmacy students only. Before and after inter/mono-professional training, each pharmacy student performed an objective structured clinical examination (OSCE) and completed a self-assessment questionnaire. Additionally, an attitude survey towards interprofessional learning was completed by pharmacy and medical students at the end of the training. As expected, interprofessional as well as mono-professional training showed a statistically significant increase in medication consultation skills. Of importance, the performance in the interprofessional training group was significantly better than in the mono-professional group, particularly in drug therapy counselling and consultation behaviors. There was a significant difference between the intervention and control groups in self-assessment scores, and all study participants had positive attitudes toward interprofessional collaboration and training. Therefore, interprofessional training using HFS has been shown to appropriately train pharmacy students for collaborative practice and consultation skills. Full article

(1) Background: The SARS-CoV-2 papain-like protease (PLpro) remains an underexplored antiviral target so far. The reduced efficacy of approved treatments against novel variants highlights the importance of developing new agents. This review aims to provide a comprehensive understanding of phytochemicals as inhibitors of PLpro, identify gaps, and propose novel insights for future reference. (2) Methods: A thorough literature search was conducted using Google Scholar, ScienceDirect, and PubMed. Out of 150 articles reviewed, 57 met inclusion criteria, focusing on SARS-CoV-2 PLpro inhibitors, excluding studies on other coronaviruses or solely herbal extracts. Data were presented class-wise, and phytochemicals were grouped into virtual, weak, modest, and potential inhibitors. (3) Results: Approximately 100 phytochemicals are reported in the literature as PLpro inhibitors. We classified them as virtual inhibitors (70), weak inhibitors (13), modest inhibitors (11), and potential inhibitors (6). Flavonoids, terpenoids, and their glycosides predominated. Notably, six phytochemicals, including schaftoside, tanshinones, hypericin, and methyl 3,4-dihydroxybenzoate, emerged as potent PLpro inhibitors with favorable selectivity indices and disease-mitigation potential; (4) Conclusions: PLpro stands as a promising therapeutic target against SARS-CoV-2. The phytochemicals reported in the literature possess valuable drug potential; however, certain experimental and clinical gaps need to be filled to meet the therapeutic needs. Full article

Antimicrobial peptides (AMPs) are a promising class of therapeutic alternatives with broad-spectrum activity against resistant pathogens. Small AMPs like temporin-SHa (1) and its first-generation analog [G10a]-SHa (2) possess notable efficacy against Gram-positive and Gram-negative bacteria. In an effort to further improve this antimicrobial activity, second-generation analogs of 1 were synthesised by replacing the natural glycine residue at position-10 of the parent molecule with atypical amino acids, such as D-Phenylalanine, D-Tyrosine and (2-Naphthyl)-D-alanine, to study the effect of hydrophobicity on antimicrobial efficacy. The resultant analogs (3–6) emerged as broad-spectrum antibacterial agents. Notably, the [G10K]-SHa analog (4), having a lysine substitution, demonstrated a 4-fold increase in activity against Gram-negative (Enterobacter cloacae DSM 30054) and Gram-positive (Enterococcus faecalis DSM 2570) bacteria relative to the parent peptide (1). Among all analogs, [G10f]-SHa peptide (3), featuring a D-Phe substitution, showed the most potent anticancer activity against lung cancer (A549), skin cancer (MNT-1), prostate cancer (PC-3), pancreatic cancer (MiaPaCa-2) and breast cancer (MCF-7) cells, achieving an IC₅₀ value in the range of 3.6–6.8 µM; however, it was also found to be cytotoxic against normal cell lines as compared to [G10K]-SHa (4). Peptide 4 also possessed good anticancer activity but was found to be less cytotoxic against normal cell lines as compared to 1 and 3. These findings underscore the potential of second-generation temporin-SHa analogs, especially analog 4, as promising leads to develop new broad-spectrum antibacterial and anticancer agents. Full article