Search Results (112)

Background: Plantar fasciitis (PF) is a common enthesopathy in patients with ankylosing spondylitis (AS). Shear wave elastography (SWE) and the Belgrade ultrasound enthesitis score (BUSES) may detect PF, but their comparative diagnostic performance is unclear. Objective: To compare SWE with the BUSES for identifying PF in individuals with and without AS. Methods: In this cross-sectional study, 96 participants were stratified into AS and non-AS populations, each further divided based on the presence or absence of clinical PF. Demographic data, the American Orthopedic Foot and Ankle Society Score (AOFAS), and the BASDAI score were recorded. All subjects underwent grayscale ultrasonography, the BUSES scoring, and SWE assessment of the plantar fascia. Logistic regression models were constructed for each population, controlling for age, body mass index (BMI), and fascia–skin distance. ROC curve analyses were performed to evaluate diagnostic accuracy. Results: In both AS and non-AS groups, SWE and the BUSES were significant predictors of PF (p < 0.05). SWE demonstrated slightly higher diagnostic accuracy, with area under the curve (AUC) values of 0.845 (AS) and 0.837 (non-AS), compared to the BUSES with AUCs of 0.785 and 0.831, respectively. SWE also showed stronger adjusted odds ratios in regression models. The interobserver agreement was good to excellent for both modalities. Conclusions: Both SWE and the BUSES are effective for PF detection, with SWE offering marginally superior diagnostic performance, particularly in AS patients. SWE may enhance the early identification of biomechanical changes in the plantar fascia. Full article

The integration of autonomous vehicles (AVs) into urban transportation systems has significant potential to enhance traffic efficiency and reduce environmental impacts. This study evaluates the impact of different AV penetration scenarios (0%, 10%, 30%, 50%) on traffic performance and carbon emissions along Prince Mohammed bin Salman bin Abdulaziz Road in Riyadh, Saudi Arabia. Using microscopic simulation (SUMO) based on real-world datasets, we assess key performance indicators such as travel time, stop frequency, speed, and CO₂ emissions. Results indicate notable improvements with increasing AV deployment, including up to 25.5% reduced travel time and 14.6% lower emissions at 50% AV penetration. Coordinated AV behavior was approximated using adjusted simulation parameters and Python-based APIs, effectively modeling vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-network (V2N) communications. These findings highlight the benefits of harmonized AV–human vehicle interactions, providing a scalable and data-driven framework applicable to smart urban mobility planning. Full article

The detection of particulate matter, particularly pathogenic bacteria, is essential in environmental monitoring, food safety, and clinical diagnostics. Among the various sensing techniques used, cantilever-based sensors offer a promising platform for label-free, real-time detection due to their high sensitivity. Here, we present a coupled cantilever sensor incorporating interdigitated comb-shaped structures to enhance dielectrophoretic (DEP) capture of Escherichia coli in liquid samples. During operation, one cantilever is externally actuated and the other oscillates passively through fluid-mediated coupling. The sensor was experimentally evaluated across a broad concentration range from 10 to 10⁵ cells/mL and the resonant frequency shifts were recorded for both beams. The results showed a strong linear frequency shift across all tested concentrations, without saturation. This demonstrates the sensor’s ability to detect both trace and high bacterial loads without needing recalibration. High frequency shifts of 4863 Hz were recorded for 10⁵ cells/mL and 225 Hz for the lowest concentration of 10 cells/mL, giving a limit of detection of 10 cells/mL. The sensor also showed a higher signal to noise ratio of 265.7 compared to previously reported designs. These findings showed that the enhanced sensor design enables sensitive, linear, and reliable bioparticle detection across a wide range, making it suitable for diverse applications. Full article

Background: Solid tumors have long presented a significant challenge in the field of oncology due to their ability to develop resistance to multiple drugs, known as multidrug resistance (MDR). This phenomenon often leads to treatment failure and poor patient outcomes. In recent years, researchers have been exploring innovative approaches to combat MDR, including the use of hydrogels for localized drug delivery. Methods: Through the biological crosslinking of an MB-smDNA-MB agent to form a pH sensitive hydrogel matrix, we introduce the injection coating of a novel PVA-MB-smDNA-MB-Mxene (PMSDMM) carrier for Adriamycin (a potent chemotherapy drug) and miR-375 (as tumor-suppressive microRNA) delivery. Results: We aimed to enhance the effectiveness of drug delivery to solid tumors while minimizing systemic toxicity via the pH-sensitive characteristics of methylene blue at the end of smDNA as a dsDNA biological crosslinking agent, i.e., ^anti-miR-375 PMSDMM _ADR. Our hydrogel was shown to improve the release of the drug in the acid tumor environment. In the first 24 h, the cumulative release rate was higher at pH = 5.5 than at pH = 7.4. Conclusions: We show that this DNA bio-inspired PMSDMM hydrogel has potential in hydrogel injection applications for tumor suppression and tissue regeneration after the surgical resection of tumors. Full article

Strict sustainability objectives have been established for the upcoming generation of aircraft. A promising innovative airframe concept is the ultra-high-aspect-ratio Strut-Braced-Wing Aircraft (SBWA). Hydrogen-powered concepts are strong candidates for sustainable propulsion. The study investigates the influence of Liquid Hydrogen (LH2) propulsion on the optimal wing geometry of medium-range SBWA for minimum-cost and minimum-emission objectives. Multiobjective optimizations are performed in two optimization frameworks of differing fidelity for both kerosene- and LH2-propelled SBWA concepts. Furthermore, a range of Pareto-optimal designs show the changes in the optimized planform for variable weighting of the two objectives. The results show that the differences in the optimal wing geometry between the kerosene- and LH2-powered results for each respective objective function are small. For both aircraft, the minimum-emission objective optimizes for lower fuel burns and hence lower emissions, albeit at an increase in wing structural mass. The minimum-cost objective balances the reductions in structural and fuel masses, resulting in a lighter design at lower aspect ratios. Other wing-shape parameters only have minor contributions. Although the wing aspect ratios for both objectives differ by ca. 50%, the actual changes are only 2.5% in fuel and 1.5% in Direct Operating Cost (DOC). Due to a larger set of design variables used in the higher-fidelity optimizations, further parasite and wave drag reduction opportunities result in increased optimal aspect ratios. Full article

In this research, a nanoscale magnetic biosorbent was synthesized by incorporating magnetic nanoparticles (Fe₃O₄ NPs) into a natural carbon framework derived from black cumin (BC) seeds. The prepared Fe₃O₄/BC was utilized as a low-cost, eco-friendly, and reusable nanobiosorbent for the removal of organic (e.g., methylene blue (MB) dye) pollutants from synthetic solutions. The results indicated that Fe₃O₄/BC had extensive surface oxygenous functional groups with a high affinity for MB dye capture at different concentrations such as 10–60 mg L⁻¹. The optimization results suggested the removal of ~99% of methylene blue from its initial concentration (i.e., 10 mg L⁻¹) using 2.0 g L⁻¹ of Fe₃O₄/BC at pH = 7, temperature = 27 °C, and contact time = 120 min, with equilibrium adsorption capacity = 5.0 mg g⁻¹ and partition coefficient = ~57.0 L g⁻¹. The equilibrium adsorption efficacy at the highest initial concentration (i.e., 60.0 mg L⁻¹) was found to be 29.0 mg g⁻¹. The adsorption isotherm was well explained by the Freundlich model for MB. The renderability of this magnetic bioadsorbent by acid treatments showed a ~66% decline in removal efficiency (%) (~99% to ~33%; ~5.0 to ~1.7 mg g⁻¹) for MB after six repetitive cycles of adsorption and desorption. The current Fe₃O₄/BC gives a better partition coefficient than previously reported acid-washed BC seeds and other BC-seed-based nanobioadsorbents, Hence, a synthesized Fe₃O₄/BC nanobiosorbent demonstrates potential for use in treating water contaminated with organic pollutants. Full article

Blockchain technology has transformed modern digital ecosystems by enabling secure, transparent, and automated transactions through smart contracts. However, the increasing complexity of these contracts introduces significant challenges, including high computational costs, scalability limitations, and difficulties in detecting anomalous behavior. In this study, we propose an AI-based optimization framework that enhances the efficiency and security of blockchain smart contracts. The framework integrates Neural Architecture Search (NAS) to automatically design optimal Convolutional Neural Network (CNN) architectures tailored to blockchain data, enabling effective anomaly detection. To address the challenge of limited labeled data, transfer learning is employed to adapt pre-trained CNN models to smart contract patterns, improving model generalization and reducing training time. Furthermore, Model Compression techniques, including filter pruning and quantization, are applied to minimize the computational load, making the framework suitable for deployment in resource-constrained blockchain environments. Experimental results on Ethereum transaction datasets demonstrate that the proposed method achieves significant improvements in anomaly detection accuracy and computational efficiency compared to conventional approaches, offering a practical and scalable solution for smart contract monitoring and optimization. Full article

This study reports the preparation of a novel hybrid composite and its application in adsorption. For this composite preparation, zirconia (ZrO₂) was precipitated onto an integrated framework of reduced graphene oxide (rGO) and black cumin (BC) seeds. Characterization using Fourier-transform infrared spectroscopy, X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray analysis, and transmission electron microscopy confirmed the successful incorporation of ZrO₂ nanoparticles (5–20 nm) into the integrated carbon framework of rGO and seed powder. The microscopic analysis further revealed that the ZrO₂ NPs were dispersed throughout the integrated rGO-BC framework. Using the rGO-BC@ZrO₂ composite, methylene blue dye was decontaminated from water through a batch adsorption process. The rGO-BC@ZrO₂ composite achieved 96% MB adsorption at an adsorbent dose of 2.0 g/L, and nearly 100% when the adsorbent concentration was 3.0 g/L. Modeling of the experimental adsorption values was also established to verify the adsorption viability and mechanism. Thermodynamic modeling confirmed the feasibility and spontaneity of the present batch adsorption process. Isotherm modeling, which showed its compatibility with the Freundlich isotherm, suggested multilayer adsorption. rGO-BC@ZrO₂ demonstrated good persistence and reusability for methylene blue for up to five consecutive adsorption cycles. Thus, this study presents optimistic results regarding water purification. Full article

Acute myeloid leukemia (AML) is a highly heterogenous and aggressive myeloid neoplasm. To sustain growth and survival, AML cells, like other neoplasms, require energy. This process is orchestrated by mitochondria and is under the control of several genes, such as PPRC1 (PRC), a member of the PGC-1 family, which is a key player in the transcription control of mitochondrial biogenesis. We have shown here that eugenol inhibits cell growth and promotes apoptosis through the mitochondrial pathway in AML cell lines as well as in cells from AML patients but not in cells from healthy donors. Similar effects were also observed on cytarabine-resistant AML cells. Interestingly, eugenol downregulated PPRC1 at both the protein and mRNA levels and reduced mitochondrial membrane potential in AML cells. We have also shown that PPRC1 expression is higher in cancer cells from blood, breast, and other types of cancer relative to normal cells, and high PPRC1 levels correlate significantly with short overall survival (OS). In addition, PPRC1 gene mutations significantly correlate with short OS and/or disease-free survival in several cancers. PPRC1 mutations also correlated significantly with poor OS (p < 0.0001) when tested in a total of 23,456 cancer patients. These findings suggest an oncogenic role of PPRC1 in various types of cancer and the possible eugenol-targeting of this gene for the treatment of AML patients, especially those exhibiting resistance to cytarabine. Full article

Stringent sustainability goals are set for the next generation of aircraft. A promising novel airframe concept is the ultra-high aspect ratio Strut-Braced Wing (SBW) aircraft. Hydrogen-based concepts are active contenders for sustainable propulsion. The study compares a medium-range Liquid Hydrogen (LH2) to a kerosene-based SBW aircraft designed with the same top-level requirements. For both concepts, overall design, operating costs, and emissions are evaluated using the tool SUAVE. Furthermore, aerostructural optimizations are performed for the wing mass of SBW aircraft with and without wing-based fuel tanks. Results show that the main difference in the design point definition results from a higher zero-lift drag due to an extended fuselage housing the LH2 tanks, with a small reduction in the required wing loading. Structural mass increases of the LH2 aircraft due to additional tanks and fuselage structure are mostly offset by fuel mass savings. While the fuel mass accounts for nearly 25% of the kerosene design’s Maximum Take-Off Mass (MTOM), this reduces to 10% for the LH2 design. The LH2 aircraft has 16% higher operating costs with emission levels reduced to 57–82% of the kerosene aircraft, depending on the LH2 production method. For static loads, the absence of fuel acting as bending moment relief in the wing results in an increase in wing structural mass. However, the inclusion of roll rate requirements causes large wing mass increases for both concepts, significantly outweighing dry wing penalties. Full article

Objective: The study aims to assess the overall safety of cultured tilapias in Jeddah City, Saudi Arabia by assessing the impact of infection and anthropogenic pollution on farmed tilapias based on fish sex, body weight, length, and heavy metals contamination. Materials and methods: A total of 111 fish were collected from an aquaculture farm in Hada Al-Sham, Jeddah, Saudi Arabia. Physicochemical parameters of water from the culture system were evaluated. Both ecto- and endoparasites were checked. Haematological, biochemical and histopathological investigations were evaluated. In addition, heavy metals, namely, cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn) were evaluated in different fish tissues and water samples from the aquaculture system. Results: The study revealed stressed aquaculture system. Tilapias were infested by both ectoparasites including Trichodina, Icthyophthirius multifiliis, Dactylogrus, and Cichlidogyrus, and endoparasites as Icthyophonus hoferi, the nematode Capillaria and coccidian protozoa. The study showed that male tilapias had greater infestation rates than females and longer and heavier male fish tended to be more susceptible to Dactylogyrus infection. Infected fish showed altered biochemical markers with subsequent increases in inflammatory and oxidative stress markers. The post-mortem lesion in the skin, gill lamellae, intestine, spleen, and liver showed significant pathological remarks. All investigated fish tissues revealed higher rates of heavy metals bioaccumulation compared to the surrounding waters. On the other hand, infected Nile tilapia tissues showed higher rate of metals accumulation compared to non-infected ones. Metals accumulated at a higher rate in the liver followed by kidney, intestine, gills, and muscles, respectively. Conclusions: This study is recognized as the first to address the food safety of farmed tilapias in Jeddah, Saudi Arabia. The results emphasized a significant relation between parasites and heavy metal in disrupting fish defense systems and harming fish’s physiological homeostasis and the histological state of tissues. The parasitized and polluted farmed fish pose health risk to humans due to possible zoonosis from parasitic infections and its subsequent bacterial infections with long-term exposure to toxic chemicals. Addressing the need for a combination of improved aquaculture practices, and stringent regulatory oversight. Full article

While entrepreneurship continues to gain significance worldwide as a means for economic development and a tool for youth employment, coffee cultivation entrepreneurial intention becomes an essential goal to investigate and a necessary instrument. Accordingly, this research investigates the role of external factors, namely Access to Finance (ATF), Structural and Institutional Support (SIS), Physical Infrastructure Support (PIS), Social Influence (SIF) and Education and Training (ET), in stimulating Coffee Farming Entrepreneurial Intention (CFEI) among potential entrepreneurs (students). A sample of 318 participants from various universities in Saudi Arabia responded to an online questionnaire, forming the basis for analysis using Partial Least Squares-Structural Equation Modelling (PLS-SEM). The study reported different findings, such as a positive relationship between CFEI and other factors, namely PIS, SIF and ET. However, the study found no positive connection between ATF, SIS and CFEI. The study concluded by providing actionable recommendations for policymakers about stimulating coffee farming among students and contributing to the economic development process and youth employment. It also assists in the establishment of sustainable business environments for future generations. Full article

Contemporary nutraceutical and biomedical sectors are witnessing fast progress in efficient product development due to the advancements in nanoscience and encapsulation technology. Nutraceuticals are generally defined as food substances, or a section thereof, that provide us with health benefits such as disease prevention and therapy. Nutraceutical and biomedical compounds as well as food supplements are a natural approach for attaining therapeutic outcomes with negligible or ideally no adverse effects. Nonetheless, these materials are susceptible to deterioration due to exposure to heat, oxygen, moisture, light, and unfavorable pH values. Tocosomes, or bilayered lyotropic vesicles, are an ideal encapsulation protocol for the food and nutraceutical industries. Biocompatibility, high entrapment capacity, storage stability, improved bioavailability, site specific delivery, and sustained-release characteristics are among the advantages of this nanocarrier. Similar to liposomal carriers and nanoliposomes, tocosomes are able to encapsulate hydrophilic and hydrophobic compounds separately or simultaneously, offering synergistic bioactive delivery. This manuscript describes different aspects of tocosome in parallel to liposome and nanoliposome technologies pertaining to nutraceutical and nanonutraceutical applications. Different properties of these nanocarriers, such as their physicochemical characteristics, preparation approaches, targeting mechanisms, and their applications in the biomedical and nutraceutical industries, are also covered. Full article

As blockchain technology increasingly underpins digital transactions, smart contracts have emerged as a pivotal tool for automating these transactions. While smart contracts offer efficiency and security, their automation introduces significant legal challenges. Detecting and preventing fraud is a primary concern. This paper proposes a novel application of artificial intelligence (AI) to address these challenges. We will develop a machine learning model, specifically a Convolutional Neural Network (CNN), to effectively detect and mitigate fraudulent activities within smart contracts. The AI model will analyze both textual and transactional data from smart contracts to identify patterns indicative of fraud. This approach not only enhances the security of digital transactions on blockchain platforms but also informs the development of legal standards and regulatory frameworks necessary for governing these technologies. By training on a dataset of authentic and fraudulent contract examples, the proposed AI model is expected to offer high predictive accuracy, thereby supporting legal practitioners and regulators in real-time monitoring and enforcement. The ultimate goal of this project is to contribute to legal scholarship by providing a robust technological tool that aids in preventing cybercrimes associated with smart contracts, thereby laying a foundation for future legal research and development at the intersection of law, technology, and security. Full article

This work conducts a rigorous examination of the economic influence of tourism in Saudi Arabia, with a particular focus on predicting tourist spending patterns and classifying spending behaviors during the COVID-19 pandemic period and its implications for sustainable development. Utilizing authentic datasets obtained from the Saudi Tourism Authority for the years 2015 to 2021, the research employs a variety of machine learning (ML) algorithms, including Decision Trees, Random Forests, K-Neighbors Classifiers, Gaussian Naive Bayes, and Support Vector Classifiers, all meticulously fine-tuned to optimize model performance. Additionally, the ARIMA model is expertly adjusted to forecast the economic landscape of tourism from 2022 to 2030, providing a robust predictive framework for future trends. The research framework is comprehensive, encompassing diligent data collection and purification, exploratory data analysis (EDA), and extensive calibration of ML algorithms through hyperparameter tuning. This thorough process tailors the predictive models to the unique dynamics of Saudi Arabia’s tourism industry, resulting in robust forecasts and insights. The findings reveal the growth trajectory of the tourism sector, highlighted by nearly 965,073 thousand tourist visits and 7,335,538 thousand overnights, with an aggregate tourist expenditure of SAR 2,246,491 million. These figures, coupled with an average expenditure of SAR 89,443 per trip and SAR 9198 per night, form a solid statistical basis for the employed predictive models. Furthermore, this research expands on how ML and AI innovations contribute to sustainable tourism practices, addressing key aspects such as resource management, economic resilience, and environmental stewardship. By integrating predictive analytics and AI-driven operational efficiencies, the study provides strategic insights for future planning and decision-making, aiming to support stakeholders in developing resilient and sustainable strategies for the tourism sector. This approach not only enhances the capacity for navigating economic complexities in a post-pandemic context, but also reinforces Saudi Arabia’s position as a premier tourism destination, with a strong emphasis on sustainability leading into 2030 and beyond. Full article