Browse Articles
Discover research articles across all indexed journals
Influence of cellulose nanocrystal formulations on the properties of pregelatinized cornstarch and cornmint essential oil films
Abstract More people are switching to biodegradable packaging, which is better for the environment and safer for food products. Meanwhile, edible films and coatings might be an option, they are easily degradable, safe for consumption, and environmentally friendly. This study aimed to evaluate the influence of various CNC formulations as Pickering emulsions with pregelatinized cornstarch incorporating essential oil of cornmint on the characterization of edible coating solutions and film. The development of edible films was using pregelatinized cornstarch (PCS), including cornmint essential oil (CEO) and cellulose nanocrystals (CNC). To examine the impact of the CNC content on the physicochemical, mechanical, and optical characteristics of these films, different concentrations (0.05, 0.07, 0.10, 0.25, 0.30, and 0.50% w/w) of CNC were added. The coating solution’s pH and viscosity, as well as the thickness and roughness of the film, usually rise with an increase in CNC concentration. Conversely, this led to a reduction in the weight loss during heating, elongation, and tensile strength, as well as Young’s modulus. The different amount of CNC (%) did not have a significant effect on the water solubility, hydrophobicity, color, water vapor permeability, or surface microstructure, as observed by atomic force microscopy. The addition of CNC to the film matrix reduced its mechanical strength but basically has low water vapor permeability and high surface hydrophobicity. Consequently, the films produced in this study can be effectively used as edible films in situations that demand strong resistance to water but do not require significant mechanical strength.
Barium nanoparticles enhance efficacy of external beam radiation therapy in a preclinical basal-like mammary cancer mouse model
Abstract External beam radiation therapy (RT) using high energy x-rays is a commonly applied cancer treatment. A major advantage of RT is its unspecific nature which allows using RT in many cancer entities. RT however causes the well-known side effects on healthy tissue in the irradiated areas. Therefore, enhancing the efficacy of RT at the tumor site while simultaneously lowering the overall radiation dose has been a long term goal. Heavy metal-based contrast agents such as gold, hafnium, gadolinium and iodine have already been proposed as radio-enhancers and are partially in clinical trials. Here we present barium sulphate (BaSO4) nanoparticles as novel radio-enhancer for RT validated in a syngenic mouse breast cancer model. We demonstrate that these particles in combination with low energy RT significantly reduced tumor growth when compared to untreated controls and tumors that received RT only. Despite the fact that the absorption probability decreases with increasing photon energy, we see a stronger anti-tumoral effect at energies around 90 keV which would allow a translation of this approach into a clinical RT setting. Due to the strong contrast of barium in computed tomography such (BaSO4) nanoparticles could be used for both, better tumor delineation as well as for enhancing RT response.
Design of improved internal mode controller for hydro-turbine governing system based on generalized inverse solver method
Neuronal hyperexcitability in dystrophin-deficient mdx hippocampal neurons: the importance of interleukin-6 and GABAergic regulation
Uncertainty evaluation and model optimization in multi-source reservoir modeling
A novel smart baby cradle system utilizing IoT sensors and machine learning for optimized parental care
Abstract The IoT Smart Cradle for Baby Monitoring System & Infant Care is introduced as an innovative solution to address critical gaps in contemporary infant care. This system integrates Internet of Things (IoT) technology, machine learning, and smart automation to offer a safer, more responsive, and comfortable environment for babies. A significant challenge in current infant care is the limitations of traditional monitoring systems. These systems often fail to provide comprehensive, real-time monitoring of essential environmental parameters and lack automated responses to an infant’s immediate needs, potentially increasing parental anxiety and compromising infant safety and well-being. This smart cradle is designed to overcome these limitations by employing a comprehensive network of sensors—including temperature, humidity, gas, noise sensors, and a cry-detection microphone—to monitor the baby’s needs and environmental conditions in real time. Microcontrollers like Raspberry Pi and NodeMCU use intelligent machine-learning algorithms to process the collected data and trigger adaptive responses. These responses include regulating temperature and humidity, filtering harmful gases, and activating a motorized rocking mechanism to soothe the infant. A dedicated mobile application offers parents secure, real-time monitoring and control over the cradle’s functions. The system demonstrates high accuracy in sensor readings, with temperature and humidity measurements reaching approximately 99.6% accuracy, and cry detection achieving approximately 93.2% accuracy. User feedback indicates that 95% of parents found the interface easy to use, and 87% reported a positive impact on their parenting experience. In contrast to traditional solutions that often require manual intervention or provide limited automation, this smart cradle uses predictive analytics to proactively address potential discomforts and hazards, thus presenting a more reliable, intelligent, and user-friendly solution for modern parenting.
Extreme frequency selectivity by synchronized time-modulated metasurfaces
Abstract Two coupled, time-modulated metasurfaces are governed by Floquet dynamics and exhibit multiharmonic response once they are electromagnetically illuminated. If they get properly synchronized, some of the developed modes may overlap, a feature that changes dramatically the response of the structure with respect to the operating wavelength. This extreme frequency selectivity is theoretically demonstrated for several alternative setups and the reported findings can revolutionize the design of photonic filters, switches or sensors, based on time-varying media.
Intelligent deep learning model for targeted cancer drug delivery
Abstract Nanotechnology and information communication technology (ICT) are being combined to develop innovative drug delivery systems for targeted sites, such as tumor cells. The particulate targeted drug delivery (PTDD) system involves drugs containing nanoparticles embedded in nanoscale devices (referred to as bio-nanomachines) that can cross vascular barriers, resulting in an increased concentration of the drug in the targeted cell or tumor. An artificial intelligence bio-cyber interface (AIBCI) operates in both forward and reverse directions, enabling the transfer or control of a desired drug dose without affecting healthy cells, facilitated by the Internet of Biological Nano Things (IoBNT). This paper proposes a multi-compartmental model with an AI bio-cyber interface based on molecular communication technology. The proposed model is formulated as a set of multi-differential equations designed to identify molecular communication-based bio-nanomachines, enabling the quantification of drug concentration at the targeted cell. Unlike conventional compartmental models, the present model is designed to connect both the exterior and interior of the human body. The results suggest that the model has the potential to improve the capacity of target cells to respond to therapeutic drugs while reducing adverse effects on healthy cells. The intra-body nanonetwork proposed in the present study proved superior performance in magnetifying the drug concentrations in diseased cells.
The impact of childhood trauma on short video addiction: psychological and morphological correlates
Evaluating heat and drought resilience in ancient Indian Dwarf wheat Triticum sphaerococcum Percival using stress tolerance indices
Non-linear association between extracellular water/total body water ratio and all-cause mortality: a population-based cohort study
Unleashing the power of AI in predicting the technological and phenolic maturity of pomegranates cultivated in Lebanon
Abstract The harvesting time of pomegranates is crucial for maximizing their health benefits and market value. However, traditional methods often fail to consider the intricate interactions between environmental conditions and fruit maturity. This study is the first of its kind in Lebanon to address this limitation by applying advanced machine learning techniques to predict key food quality indicators, which can aid in forecasting or determining the optimal harvesting date. The focus is on technological and phenolic maturity. Over three months, 548 pomegranates were meticulously harvested from three distinct geographic regions in Lebanon: Hasbaya, El Jahliye, and Rachiine. By integrating environmental, physical, and geographical data, we developed predictive models, including Linear Regression (LR) and Multi-Layer Perceptron (MLP) Regressor, to estimate key food quality indicators such as Total Soluble Solids (TSS), Titratable Acidity (TA), Maturity Index (MI), phenolic content, and Color Intensity (CI). Our results demonstrated that the MLP regressor achieved high predictive accuracy, with an R-squared value of 0.84 for TA, making it a reliable tool for predicting acidity levels. The model also showed strong performance in predicting phenolic content and color intensity, with R-squared values of 0.70 and 0.65 respectively, and an average classification accuracy of 71% for categorizing polyphenol levels. Principal Component Analysis (PCA) revealed significant geographic variation in phenolic content. In El Jahliye, phenolic levels ranged from low (<185 mg Gallic Acid Equivalent (GAE) per yield of juice) to moderate (185-400 mg GAE/yield of juice). In Rachiine, levels ranged from moderate to high (>400 mg GAE/yield of juice), while Hasbaya displayed all three phenolic content levels. These findings underscore the importance of region-specific harvesting strategies. As the first study in Lebanon to utilize machine learning for predicting food quality indicators in pomegranates, it provides a novel, data-driven approach to linking these indicators with optimal harvest timing. By accurately forecasting maturity-related metrics using simple physical, geographical, and environmental features, this study offers significant implications for refining agricultural practices in Lebanon and other similar agro-ecological regions, enhancing product quality and market value.
Ocean drones enabling long-term earthquake monitoring in target zones
Abstract Seismic station coverage in the oceans is limited due to high costs and logistical challenges, leading to insufficient earthquake data from oceanic regions. Ocean drones, with quiet operation, buoyancy-driven mechanics, and autonomous underwater profiling, provide a promising alternative for near-real-time data acquisition. We evaluated an oceanic seismological platform using 6 years (2015–2021) of passive acoustic monitoring data from ocean drones in the Santos Basin, southeastern Brazil, originally not designed for earthquake monitoring. Our analysis identified 12 potential earthquake signals, characterized by low-frequency seismic energy and emergent patterns. These findings demonstrate that ocean gliders are highly effective for earthquake monitoring, offering significant advantages for long-term, targeted seismic observations in coastal and marginal areas where conventional methods often face operational limitations.
Clinical characteristics and treatment preferences of obstructive sleep apnea
Physical activity level and health-related quality of life in adults with multiple osteochondromas: a Dutch cross-sectional study
Abstract Multiple Osteochondromas (MO) can significantly impact physical functioning, yet evidence on how MO affects physical activity levels (PAL) and health-related quality of life (HRQOL) remains limited. This study aimed to: (1) characterize the PAL and physical and mental HRQOL of adult patients with MO and compare them with healthy subjects (2) explore whether illness-related symptoms, sociodemographic and psychological factors are associated with patients’ PAL and HRQOL. This cross-sectional study used a survey consisting of sociodemographic data and validated questionnaires on the PAL (Baecke Physical Activity Questionnaire) and HRQOL (SF-36). The PAL, physical and mental HRQOL were compared with reference scores of healthy subjects using a one-sample t-test. An a-priori defined theoretical framework (ICF-model) was used to select explanatory variables, including several psychological factors, for the multiple linear regression models of the dependent variables PAL and HRQOL. 342 patients (42.6% males) with a mean age of 41.8 ± 16.3 completed the survey. Mean PAL scores were 7.2 ± 1.7, physical HRQOL 41.7 ± 11.1 and mental HRQOL 49.1 ± 10.5. Except for mental HRQOL, these scores were lower than healthy subjects (p < 0.001). The final regression model for the PAL contained six factors (R2 = 0.221, p < 0.001) showing the strongest association with having a job and malignant degeneration. Fourteen variables, including pain-related disability and number of surgical procedures, explained physical HRQOL (R2 = 0.731, p < 0.001). For mental HRQOL, eight factors remained in the model (R2 = 0.618, p < 0.001) with anxiety explaining the most unique variance (9.4%). MO patients reported significantly lower PAL and physical HRQOL than healthy controls. This study provides insight in several factors associated with the PAL and HRQOL in MO which could be used to optimize patients’ treatment.
Statistical inference for the generalized exponential distribution using ordered lower k-record ranked set sampling with random sample sizes
Abstract This article presents an innovative sampling strategy, ordered moving extremes lower k-record ranked set sampling, designed to enhance parameter estimation and prediction for the generalized exponential distribution. By incorporating k-record values with random sample sizes, we develop maximum likelihood estimation, classical Bayes estimation, and empirical Bayes estimators, leveraging informative priors under balanced loss functions, including balanced squared error and balanced linear exponential. Additionally, we utilize the pivotal prediction method to construct prediction intervals for future observations under double type-II censoring. Extensive simulation studies demonstrate that our approach significantly improves estimation accuracy by achieving lower mean squared errors and reduced bias compared to conventional methods. The efficacy of the proposed sampling method is further validated through its application to real-world medical datasets, showcasing its practical utility in enhancing statistical inferences for lifetime data analysis. The key findings highlight that ordered moving extremes lower k-record ranked set sampling effectively balances efficiency and accuracy, making it particularly well-suited for reliability studies and survival analysis.
Effects of combined jaw and cervicoscapular exercises on mouth opening and muscle properties in cervical extension type
Abstract Prolonged smartphone use can lead to cervical posture deformities, with cervical extension type being a common condition characterized by increased cervical lordosis, forward head posture, and thoracic kyphosis. These changes may contribute to neck pain, restricted cervical range of motion (ROM), and increased muscle tone. Additionally, cervical extension type is linked to temporomandibular joint (TMJ) dysfunction, affecting mandibular movement and muscle activity. Given the biomechanical connection between the cervical spine and TMJ, addressing cervical dysfunction may benefit TMJ related conditions. This study compared the effects of jaw exercises combined with cervicoscapular exercises versus cervicoscapular exercises alone on mouth opening ROM, mastication muscle properties, and pressure pain threshold (PPT) in individuals with cervical extension type. Thirty-four subjects were randomly assigned to two groups: the experimental group (seventeen subjects) performed jaw exercises combined with cervicoscapular exercises, while the control group (seventeen subjects) performed only cervicoscapular exercises. After 4 weeks, significant improvements were observed in both groups in the mouth opening ROM, muscle properties, and PPT (p < 0.05). The experimental group showed significantly greater improvements in protrusive excursion, the masseter muscle tone, and the stiffness of the masseter and temporalis anterior muscles compared to the control group (p < 0.025). Both groups demonstrated significant increases in the PPT (p < 0.05). These findings suggest that incorporating jaw exercises into cervicoscapular training may provide additional benefits for individuals with cervical extension type, particularly those experiencing temporomandibular joint (TMJ) dysfunction. Further studies are needed to validate these results in a larger and more diverse population.