• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.219-230
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• 2023

Wireless Body Area Networks (WBANs) have made it easier for healthcare workers and patients to monitor patients' status continuously in real time. WBANs have complex and diverse network structures; thus, management and control can be challenging. Therefore, considering emerging Software-defined networks (SDN) with WBANs is a promising technology since SDN implements a new network management and design approach. The SDN concept is used in this study to create more adaptable and dynamic network architectures for WBANs. The study focuses on comparing the performance of two SDN controllers, POX and Ryu, using Mininet, an open-source simulation tool, to construct network topologies. The performance of the controllers is evaluated based on bandwidth, throughput, and round-trip time metrics for networks using an OpenFlow switch with sixteen nodes and a controller for each topology. The study finds that the choice of network controller can significantly impact network performance and suggests that monitoring network performance indicators is crucial for optimizing network performance. The project provides valuable insights into the performance of SDN-based WBANs using POX and Ryu controllers and highlights the importance of selecting the appropriate network controller for a given network architecture.

• Journal of Dental Anesthesia and Pain Medicine
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• v.23 no.5
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• pp.281-285
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• 2023

Background: Dentists bend needles prior to certain injections; however, there are concerns regarding needle fracture, lumen occlusion, and sharps handling. A previous study found that a 30-gauge needle fractures after four to nine 90° bends. This fatigue life study evaluated how many 90° bends a 30-gauge dental needle will sustain before fracture when bent using a needle guide. Methods: Two operators at Element Materials Technology, an independent testing, inspection, and certification company tested 48 30-gauge needles. After applying the needle guide, the operators bent the needle to a 90° angle and expressed the anesthetic from the tip. The needle was then bent back to a 0° angle, and the functionality was tested again. This process was repeated until the anesthetic failed to pass through the end of the needle due to fracture or obstruction. Each operator tested 24 needles (12 needles from each lot), and the number of sustained bends before the needle fracture was recorded. Results: The average number of sustained bends before needle failure was 40.33 (95% confidence interval = 37.41-43.26), with a minimum of 20, median of 40, and a maximum of 54. In each trial, the lumen remained patent until the needle fractured. The difference between the operators was statistically significant (P < 0.001). No significant differences in performance between needle lots were observed (P = 0.504). Conclusion: Our results suggest that using a needle guide increases the number of sustained bends before needle fracture (P < 0.000001) than those reported in previous studies. Future studies should further evaluate the use of needle guides with other needle types across a variety of operators. Furthermore, additional opportunities lie in exploring workplace safety considerations and clinical applications of anesthetic delivery using a bent needle.

• Nutrition Research and Practice
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• v.17 no.6
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• pp.1238-1254
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• 2023

BACKGROUND/OBJECTIVES: Weight loss via a mobile application (App) or a paper-based diary (Paper) may confer favorable metabolic and anthropometric changes. SUBJECTS/METHODS: A randomized parallel trial was conducted among 57 adults whose body mass indices (BMIs) were 25 kg/m² or greater. Participants randomly assigned to either the App group (n = 30) or the Paper group (n = 27) were advised to record their foods and supplements through App or Paper during the 12-week intervention period. Relative changes of anthropometries and biomarker levels were compared between the 2 intervention groups. Untargeted metabolic profiling was identified to discriminate metabolic profiles. RESULTS: Out of the 57 participants, 54 participants completed the trial. Changes in body weight and BMI were not significantly different between the 2 groups (P = 0.11). However, body fat and low-density lipoprotein (LDL)-cholesterol levels increased in the App group but decreased in the Paper group, and the difference was statistically significant (P = 0.03 for body fat and 0.02 for LDL-cholesterol). In the metabolomics analysis, decreases in methylglyoxal and (S)-malate in pyruvate metabolism and phosphatidylcholine (lecithin) in linoleic acid metabolism from pre- to post-intervention were observed in the Paper group. CONCLUSIONS: In the 12-week randomized parallel trial of weight loss through a App or a Paper, we found no significant difference in change in BMI or weight between the App and Paper groups, but improvement in body fatness and LDL-cholesterol levels only in the Paper group under the circumstances with minimal contact by dietitians or health care providers.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.3
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• pp.601-607
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• 2024

The discharge of oily wastewater into water bodies and soil poses a serious hazard to the environment and public health. Various conventional techniques have been employed to treat oil-water mixtures and emulsions; Unfortunately, these approaches are frequently expensive, time-consuming, and unsatisfactory outcomes. Porous materials and adsorbents are commonly used for purification, but their use is limited by low separation efficiencies and the risk of secondary contamination. Recent advancements in nanotechnology have driven the development of innovative materials and technologies for oil-contaminated wastewater treatment. Nanomaterials can offer enhanced oil-water separation properties due to their high surface area and tunable surface chemistry. The fabrication of nanofiber membranes with precise pore sizes and surface properties can further improve separation efficiency. Notably, novel technologies have emerged utilizing nanomaterials with special surface wetting properties, such as superhydrophobicity, to selectively separate oil from oil-water mixtures or emulsions. These special wetting surfaces are promising for high-efficiency oil separation in emulsions and allow the use of materials with relatively large pores, enhancing throughput and separation efficiency. In this study, we introduce a facile and scalable method for fabrication of superhydrophobic-superoleophilic felt fabrics for oil/water mixture and emulsion separation. AlN nanopowders are hydrolyzed to create the desired microstructures, which firmly adhere to the fabric surface without the need for a binder resin, enabling specialized wetting properties. This approach is applicable regardless of the material's size and shape, enabling efficient separation of oil and water from oil-water mixtures and emulsions. The oil-water separation materials proposed in this study exhibit low cost, high scalability, and efficiency, demonstrating their potential for broad industrial applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.46-63
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• 2024

One of the biggest dangers in the globe is water contamination. Water is a necessity for human survival. In most cities, the digging of borewells is restricted. In some cities, the borewell is allowed for only drinking water. Hence, the scarcity of drinking water is a vital issue for industries and villas. Most of the water sources in and around the cities are also polluted, and it will cause significant health issues. Real-time quality observation is necessary to guarantee a secure supply of drinking water. We offer a model of a low-cost system of monitoring real-time water quality using IoT to address this issue. The potential for supporting the real world has expanded with the introduction of IoT and other sensors. Multiple sensors make up the suggested system, which is utilized to identify the physical and chemical features of the water. Various sensors can measure the parameters such as temperature, pH, and turbidity. The core controller can process the values measured by sensors. An Arduino model is implemented in the core controller. The sensor data is forwarded to the cloud database using a WI-FI setup. The observed data will be transferred and stored in a cloud-based database for further processing. It wasn't easy to analyze the water quality every time. Hence, an Optimized Neural Network-based automation system identifies water quality from remote locations. The performance of the feed-forward neural network classifier is further enhanced with a hybrid GA- PSO algorithm. The optimized neural network outperforms water quality prediction applications and yields 91% accuracy. The accuracy of the developed model is increased by 20% because of optimizing network parameters compared to the traditional feed-forward neural network. Significant improvement in precision and recall is also evidenced in the proposed work.

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.257-268
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• 2024

Drone-mounted hyperspectral sensors (DHSs) have revolutionized remote sensing in agriculture by offering a cost-effective and flexible platform for high-resolution spectral data acquisition. Their ability to capture data at low altitudes minimizes atmospheric interference, enhancing their utility in agricultural monitoring and management. This study focused on addressing the challenges of radiometric and geometric distortions in preprocessing drone-acquired hyperspectral data. Radiometric correction, using the empirical line method (ELM) and spectral reference panels, effectively removed sensor noise and variations in solar irradiance, resulting in accurate surface reflectance values. Notably, the ELM correction improved reflectance for measured reference panels by 5-55%, resulting in a more uniform spectral profile across wavelengths, further validated by high correlations (0.97-0.99), despite minor deviations observed at specific wavelengths for some reflectors. Geometric correction, utilizing a rubber sheet transformation with ground control points, successfully rectified distortions caused by sensor orientation and flight path variations, ensuring accurate spatial representation within the image. The effectiveness of geometric correction was assessed using root mean square error(RMSE) analysis, revealing minimal errors in both east-west(0.00 to 0.081 m) and north-south directions(0.00 to 0.076 m).The overall position RMSE of 0.031 meters across 100 points demonstrates high geometric accuracy, exceeding industry standards. Additionally, image mosaicking was performed to create a comprehensive representation of the study area. These results demonstrate the effectiveness of the applied preprocessing techniques and highlight the potential of DHSs for precise crop health monitoring and management in smart agriculture. However, further research is needed to address challenges related to data dimensionality, sensor calibration, and reference data availability, as well as exploring alternative correction methods and evaluating their performance in diverse environmental conditions to enhance the robustness and applicability of hyperspectral data processing in agriculture.

• International Journal of Heart Failure
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• v.6 no.1
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• pp.11-19
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• 2024

The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.

• Journal of the Korean Chemical Society
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• v.68 no.4
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• pp.191-198
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• 2024

Endocrine disrupting chemicals (EDCs) are compounds that come from outside the body and disrupt hormone action within the body's endocrine system. Examples include parabens, benzophenones, bisphenols, and phthalates, which are currently used in a wide range of applications. However, continuous exposure to them can have negative effects on glycemic control, reproduction, metabolism, nervous system development, pregnancy, childbirth, and growth. In this study, human samples (urine) were pretreated using liquid-liquid-extraction to determine the exposure level of EDCs and then analyzed effectively and rapidly by UPLC-MS/MS. In this way, the analytical conditions were established and the reliability of the simultaneous analysis method was evaluated through method validation. The results showed that the accuracy ranged from 75.28 to 122.36% and the precision ranged from 2.16 to 22.74%. The analytical method established in this study can be used as a methodology for future studies to evaluate and monitor the exposure of EDCs in human samples.

• Journal of Web Engineering
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• v.14 no.9
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• pp.1822-1832
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• 2022

We explored the physiological effects of inhaling basil essential oil (BEO) and/or linalool and identified odor-active aroma compounds in BEO using gas chromatography/mass spectrometry (GC-MS) and GC-olfactometry (GC-O). Linalool was identified as the major volatile compound in BEO. Three groups of rats were administered BEO and linalool via inhalation, while rats in the control group were not. Inhalation of BEO for 20 min only reduced the total weight gain (190.67 ± 2.52 g) and increased the forced swimming time (47.33 ± 14.84 s) compared with the control group (219.67 ± 2.08 g, 8.33 ± 5.13 s). Inhalation of BEO for 5 min (392 ± 21 beats/min) only reduced the pulse compared with the control group (420 ± 19 beats/min). Inhalation of linalool only reduced the weight of white adipose tissue (5.75 ± 0.61 g). The levels of stress-related hormones were not significantly different among the groups. The total cholesterol and triglyceride levels decreased after inhalation of BEO for 20 min (by more than -10% and -15%, respectively). Low-density lipoprotein cholesterol levels were lowered (by more than -10%) by the inhalation of BEO and linalool, regardless of the inhalation time. In particular, BEO inhalation for 20 min was associated with the lowest level of low-density lipoprotein cholesterol (53.94 ± 2.72 mg/dL). High-density lipoprotein cholesterol levels increased after inhalation of BEO (by more than +15%). The atherogenic index and cardiac risk factors were suppressed by BEO inhalation. Animals exposed to BEO and linalool had no significant differences in hepatotoxicity. These data suggest that the inhalation of BEO and linalool may ameliorate cardiovascular and lipid dysfunctions. These effects should be explored further for clinical applications.

• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1803-1812
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• 2024

The attractive properties of gadolinium-based nanoparticles as a positive contrast agent for magnetic resonance imaging (MRI) have piqued the interest of both researchers and clinicians. Nonetheless, due to the biotoxicity of gadolinium (III) ions' free radicals, there is a need to address this issue. Therefore, this research aimed to develop a biocompatible, dispersible, stable, hydrophilic, and less toxic cellulose nanocrystals/gadolinium oxide nanocomposite as contrast agent properties for MRI purposes. This study aimed to synthesize gadolinium oxide nanoparticles coated with cellulose nanocrystals with polyethylene glycol and sodium hydroxide (CNCs-PEG/NaOH)/Gd₂O₃ using the gamma irradiation method to reduce the particle size. The results showed that using a gamma irradiation dose of 10 kGy, quasi-spherical morphology with a size of approximately 5.5 ± 0.65 nm could be produced. Furthermore, the cytocompatibility of (CNCs-PEG/NaOH)/Gd₂O₃ nanocomposite synthesized was assessed through MTT assay tests on Hep G2 cells, which demonstrated good cytocompatibility without any cytotoxic effects within a concentration range of (10 ㎍/mL - 150 ㎍/mL) and had sufficient cellular uptake. Moreover, the T₁-weighted MRI of (CNCs-PEG/NaOH)/Gd₂O₃ nanocomposite revealed promising results as a positive contrast agent. It is envisaged that the gamma irradiation method is promising in synthesizing (CNCs-PEG/NaOH)/Gd₂O₃ nanocomposite with nanoscale for different applications, especially in the radiotherapy field.