One of the popular approaches in controlling drug delivery from the polymeric carriers is suitably achieved by the inclusion of crosslinking agents into the formulations at different concentrations. Nevertheless, addition of the chemical crosslinkers such as glutaraldehyde, formaldehyde etc, used in the drug delivery systems causes very serious cytotoxic reactions. These chemical crosslinking agents did not offer any significant advantageous effects when compared to the natural crosslinking agents for instance genipin, which is quite less toxic, biocompatible and offers very stable crosslinked products. Based on the earlier reports the safety of this particular natural crosslinker is very well established, since it has been widely used as a Chinese traditional medicine for long-time, isolated from fruits of the plant Gardenia jasminoides Ellis. This concise article largely portrayed the value of this unique natural crosslinker, utilized in controlling the drug delivery from the various formulations.
Drug delivery constitutes the formulations, technologies, and systems for the transport of pharmaceutical compounds to specific areas in the body to exert safe therapeutic effects. The main criteria for selecting the correct medium for drug delivery are the quantity of the drug being carried and the amount of time required to release the drug. Hence, this research aimed to improve the aforementioned criteria by synthesizing a medium based on calcium carbonate-nanocellulose composite and evaluating its efficiency as a medium for drug delivery. Specifically, the efficiency was assessed in terms of the rates of uptake and release of 5-fluorouracil. Through the evaluation of the morphological and chemical properties of the synthesized composite, the established 3D printing profiles of nanocellulose and CaCO3 took place following the layer-by-layer films. The 3D printed double laminated CaCO3-nanocellulose managed to release the 5-fluorouracil as an effective single composition and in a time-controlled manner.
As the technology for 3D photography has developed rapidly in recent years, an enormous amount of 3D images has been produced, one of the directions of research for which is face recognition. Improving the accuracy of a number of data is crucial in 3D face recognition problems. Traditional machine learning methods can be used to recognize 3D faces, but the face recognition rate has declined rapidly with the increasing number of 3D images. As a result, classifying large amounts of 3D image data is time-consuming, expensive, and inefficient. The deep learning methods have become the focus of attention in the 3D face recognition research. In our experiment, the end-to-end face recognition system based on 3D face texture is proposed, combining the geometric invariants, histogram of oriented gradients and the fine-tuned residual neural networks. The research shows that when the performance is evaluated by the FRGC-v2 dataset, as the fine-tuned ResNet deep neural network layers are increased, the best Top-1 accuracy is up to 98.26% and the Top-2 accuracy is 99.40%. The framework proposed costs less iterations than traditional methods. The analysis suggests that a large number of 3D face data by the proposed recognition framework could significantly improve recognition decisions in realistic 3D face scenarios.
A bottom-up approach for synthesizing silver nanoparticles (AgNPs-GA) phytomediated by Garcinia atroviridis leaf extract is described. Under optimized conditions, the AgNPs-GA were synthesized at a concentration of 0.1 M silver salt and 10% (w/v) leaf extract, 1:4 mixing ratio of reactants, pH 3, temperature 32 °C and 72 h reaction time. The AgNPs-GA were characterized by various analytical techniques and their size was determined to be 5-30 nm. FTIR spectroscopy indicates the role of phenolic functional groups in the reduction of silver ions into AgNPs-GA and in supporting their subsequent stability. The UV-Visible spectrum showed an absorption peak at 450 nm which reflects the surface plasmon resonance (SPR) of AgNPs-GA and further supports the stability of these biosynthesized nanoparticles. SEM, TEM and XRD diffractogram analyses indicate that AgNPs-GA were spherical and face-centered-cubic in shape. This study also describes the efficacy of biosynthesized AgNPs-GA as anti-proliferative agent against human breast cancer cell lines, MCF-7 and MCF-7/TAMR-1. Our findings indicate that AgNPs-GA possess significant anti-proliferative effects against both the MCF-7 and MCF-7/TAMR-1 cell lines, with inhibitory concentration at 50% (IC50 values) of 2.0 and 34.0 µg/mL, respectively, after 72 h of treatment. An induction of apoptosis was evidenced by flow cytometry using Annexin V-FITC and propidium iodide staining. Therefore, AgNPs-GA exhibited its anti-proliferative activity via apoptosis on MCF-7 and MCF-7/TAMR-1 breast cancer cells in vitro. Taken together, the leaf extract from Garcinia atroviridis was found to be highly capable of producing AgNPs-GA with favourable physicochemical and biological properties.
A minimally-sized, triple-notched band ultra-wideband (UWB) antenna, useful for many applications, is designed, analyzed, and experimentally validated in this paper. A modified maple leaf-shaped main radiating element with partial ground is used in the proposed design. An E-shaped resonator, meandered slot, and U-shaped slot are implemented in the proposed design to block the co-existing bands. The E-shaped resonator stops frequencies ranging from 1.8⁻2.3 GHz (Advanced Wireless System (AWS1⁻AWS2) band), while the meandered slot blocks frequencies from 3.2⁻3.8 GHz (WiMAX band). The co-existing band ranging from 5.6⁻6.1 GHz (IEEE 802.11/HIPERLANband) is blocked by utilizing the U-shaped section in the feeding network. The notched bands can be independently controlled over a wide range of frequencies using specific parameters. The proposed antenna is suitable for many applications because of its flat gain, good radiation characteristics at both principal planes, uniform group delay, and non-varying transfer function ( S 21 ) for the entire UWB frequency range.
The environmental concerns of global warming and energy consumption are among the most severe issues and challenges facing human beings worldwide. Due to the relatively higher predicted temperatures (150-180 °C), the latest research on pavement energy consumption and carbon dioxide (CO2) emission assessment mentioned contributing to higher environmental burdens such as air pollution and global warming. However, warm-mix asphalt (WMA) was introduced by pavement researchers and the road construction industry instead of hot-mix asphalt (HMA) to reduce these environmental problems. This study aims to provide a comparative overview of WMA and HMA from environmental and economic perspectives in order to highlight the challenges, motivations, and research gaps in using WMA technology compared to HMA. It was discovered that the lower production temperature of WMA could significantly reduce the emissions of gases and fumes and thus reduce global warming. The lower production temperature also provides a healthy work environment and reduces exposure to fumes. Replacing HMA with WMA can reduce production costs because of the 20-75% lower energy consumption in WMA production. It was also released that the reduction in energy consumption is dependent on the fuel type, energy source, material heat capacity, moisture content, and production temperature. Other benefits of using WMA are enhanced asphalt mixture workability and compaction because the additives in WMA reduce asphalt binder viscosity. It also allows for the incorporation of more waste materials, such as reclaimed asphalt pavement (RAP). However, future studies are recommended on the possibility of using renewable, environmentally friendly, and cost-effective materials such as biomaterials as an alternative to conventional WMA-additives for more sustainable and green asphalt pavements.
Different parts of a plant (seeds, fruits, flower, leaves, stem, and roots) contain numerous biologically active compounds called "phytoconstituents" that consist of phenolics, minerals, amino acids, and vitamins. The conventional techniques applied to extract these phytoconstituents have several drawbacks including poor performance, low yields, more solvent use, long processing time, and thermally degrading by-products. In contrast, modern and advanced extraction nonthermal technologies such as pulsed electric field (PEF) assist in easier and efficient identification, characterization, and analysis of bioactive ingredients. Other advantages of PEF include cost-efficacy, less time, and solvent consumption with improved yields. This review covers the applications of PEF to obtain bioactive components, essential oils, proteins, pectin, and other important materials from various parts of the plant. Numerous studies compiled in the current evaluation concluded PEF as the best solution to extract phytoconstituents used in the food and pharmaceutical industries. PEF-assisted extraction leads to a higher yield, utilizes less solvents and energy, and it saves a lot of time compared to traditional extraction methods. PEF extraction design should be safe and efficient enough to prevent the degradation of phytoconstituents and oils.
In this paper, a dielectric resonator antenna (DRA) with high gain and wide impedance bandwidth for fifth-generation (5G) wireless communication applications is proposed. The dielectric resonator antenna is designed to operate at higher-order TEδ15x mode to achieve high antenna gain, while a hollow cylinder at the center of the DRA is introduced to improve bandwidth by reducing the quality factor. The DRA is excited by a 50Ω microstrip line with a narrow aperture slot. The reflection coefficient, antenna gain, and radiation pattern of the proposed DRAs are analyzed using the commercially available full-wave electromagnetic simulation tool CST Microwave Studio (CST MWS). In order to verify the simulation results, the proposed antenna structures were fabricated and experimentally validated. Measured results of the fabricated prototypes show a 10-dB return loss impedance bandwidth of 10.7% (14.3-15.9GHz) and 16.1% (14.1-16.5 GHz) for DRA1 and DRA2, respectively, at the operating frequency of 15 GHz. The results show that the designed antenna structure can be used in the Internet of things (IoT) for device-to-device (D2D) communication in 5G systems.
A low-cost, low-power, and low data-rate solution is proposed to fulfill the requirements of information monitoring for actual large-scale agricultural farms. A small-scale farm can be easily managed. By contrast, a large farm will require automating equipment that contributes to crop production. Sensor based soil properties measurement plays an integral role in designing a fully automated agricultural farm, also provides more satisfactory results than any manual method. The existing information monitoring solutions are inefficient in terms of higher deployment cost and limited communication range to adapt the need of large-scale agriculture farms. A serial based low-power, long-range, and low-cost communication module is proposed to confront the challenges of monitoring information over long distances. In the proposed system, a tree-based communication mechanism is deployed to extend the communication range by adding intermediate nodes. Each sensor node consists of a solar panel, a rechargeable cell, a microcontroller, a moisture sensor, and a communication unit. Each node is capable to work as a sensor node and router node for network traffic. Minimized data logs from the central node are sent daily to the cloud for future analytics purpose. After conducting a detailed experiment in open sight, the communication distance measured 250 m between two points and increased to 750 m by adding two intermediate nodes. The minimum working current of each node was 2 mA, and the packet loss rate was approximately 2-5% on different packet sizes of the entire network. Results show that the proposed approach can be used as a reference model to meet the requirements for soil measurement, transmission, and storage in a large-scale agricultural farm.
Decreasing water pollution is a big problem in coastal waters. Coastal health of ecosystems can be affected by high concentrations of suspended sediment. In this work, a Modified Hopfield Neural Network Algorithm (MHNNA) was used with remote sensing imagery to classify the total suspended solids (TSS) concentrations in the waters of coastal Langkawi Island, Malaysia. The adopted remote sensing image is the Advanced Land Observation Satellite (ALOS) image acquired on 18 January 2010. Our modification allows the Hopfield neural network to convert and classify color satellite images. The samples were collected from the study area simultaneously with the acquiring of satellite imagery. The sample locations were determined using a handheld global positioning system (GPS). The TSS concentration measurements were conducted in a lab and used for validation (real data), classification, and accuracy assessments. Mapping was achieved by using the MHNNA to classify the concentrations according to their reflectance values in band 1, band 2, and band 3. The TSS map was color-coded for visual interpretation. The efficiency of the proposed algorithm was investigated by dividing the validation data into two groups. The first group was used as source samples for supervisor classification via the MHNNA. The second group was used to test the MHNNA efficiency. After mapping, the locations of the second group in the produced classes were detected. Next, the correlation coefficient (R) and root mean square error (RMSE) were calculated between the two groups, according to their corresponding locations in the classes. The MHNNA exhibited a higher R (0.977) and lower RMSE (2.887). In addition, we test the MHNNA with noise, where it proves its accuracy with noisy images over a range of noise levels. All results have been compared with a minimum distance classifier (Min-Dis). Therefore, TSS mapping of polluted water in the coastal Langkawi Island, Malaysia can be performed using the adopted MHNNA with remote sensing techniques (as based on ALOS images).
The importance of body area sensor networks (BASNs) is increasing day by day because of their increasing use in Internet of things (IoT)-enabled healthcare application services. They help humans in improving their quality of life by continuously monitoring various vital signs through biosensors strategically placed on the human body. However, BASNs face serious challenges, in terms of the short life span of their batteries and unreliable data transmission, because of the highly unstable and unpredictable channel conditions of tiny biosensors located on the human body. These factors may result in poor data gathering quality in BASNs. Therefore, a more reliable data transmission mechanism is greatly needed in order to gather quality data in BASN-based healthcare applications. Therefore, this study proposes a novel, multiobjective, lion mating optimization inspired routing protocol, called self-organizing multiobjective routing protocol (SARP), for BASN-based IoT healthcare applications. The proposed routing scheme significantly reduces local search problems and finds the best dynamic cluster-based routing solutions between the source and destination in BASNs. Thus, it significantly improves the overall packet delivery rate, residual energy, and throughput with reduced latency and packet error rates in BASNs. Extensive simulation results validate the performance of our proposed SARP scheme against the existing routing protocols in terms of the packet delivery ratio, latency, packet error rate, throughput, and energy efficiency for BASN-based health monitoring applications.
Data redundancy or fusion is one of the common issues associated with the resource-constrained networks such as Wireless Sensor Networks (WSNs) and Internet of Things (IoTs). To resolve this issue, numerous data aggregation or fusion schemes have been presented in the literature. Generally, it is used to decrease the size of the collected data and, thus, improve the performance of the underlined IoTs in terms of congestion control, data accuracy, and lifetime. However, these approaches do not consider neighborhood information of the devices (cluster head in this case) in the data refinement phase. In this paper, a smart and intelligent neighborhood-enabled data aggregation scheme is presented where every device (cluster head) is bounded to refine the collected data before sending it to the concerned server module. For this purpose, the proposed data aggregation scheme is divided into two phases: (i) identification of neighboring nodes, which is based on the MAC address and location, and (ii) data aggregation using k-mean clustering algorithm and Support Vector Machine (SVM). Furthermore, every CH is smart enough to compare data sets of neighboring nodes only; that is, data of nonneighbor is not compared at all. These algorithms were implemented in Network Simulator 2 (NS-2) and were evaluated in terms of various performance metrics, such as the ratio of data redundancy, lifetime, and energy efficiency. Simulation results have verified that the proposed scheme performance is better than the existing approaches.
Wireless Sensor Networks (WSNs) are vulnerable to Node Replication attacks or Clone attacks. Among all the existing clone detection protocols in WSNs, RAWL shows the most promising results by employing Simple Random Walk (SRW). More recently, RAND outperforms RAWL by incorporating Network Division with SRW. Both RAND and RAWL have used SRW for random selection of witness nodes which is problematic because of frequently revisiting the previously passed nodes that leads to longer delays, high expenditures of energy with lower probability that witness nodes intersect. To circumvent this problem, we propose to employ a new kind of constrained random walk, namely Single Stage Memory Random Walk and present a distributed technique called SSRWND (Single Stage Memory Random Walk with Network Division). In SSRWND, single stage memory random walk is combined with network division aiming to decrease the communication and memory costs while keeping the detection probability higher. Through intensive simulations it is verified that SSRWND guarantees higher witness node security with moderate communication and memory overheads. SSRWND is expedient for security oriented application fields of WSNs like military and medical.
A new design and analysis of a wide-band double-negative metamaterial, considering a frequency range of 0.5 to 7 GHz, is presented in this paper. Four different unit cells with varying design parameters are analyzed to evaluate the effects of the unit-cell size on the resonance frequencies of the metamaterial. Moreover, open and interconnected 2 × 2 array structures of unit cells are analyzed. The finite-difference time-domain (FDTD) method, based on the Computer Simulation Technology (CST) Microwave Studio, is utilized in the majority of this investigation. The experimental portion of the study was performed in a semi-anechoic chamber. Good agreement is observed between the simulated and measured S parameters of the developed unit cell and array. The designed unit cell exhibits negative permittivity and permeability simultaneously at S-band (2.95 GHz to 4.00 GHz) microwave frequencies. In addition, the designed unit cell can also operate as a double-negative medium throughout the C band (4.00 GHz to 4.95 GHz and 5.00 GHz to 5.57 GHz). At a number of other frequencies, it exhibits a single negative value. The two array configurations cause a slight shift in the resonance frequencies of the metamaterial and hence lead to a slight shift of the single- and double-negative frequency ranges of the metamaterial.
A novel hybrid Harris Hawk-Arithmetic Optimization Algorithm (HHAOA) for optimizing the Industrial Wireless Mesh Networks (WMNs) and real-time pressure process control was proposed in this research article. The proposed algorithm uses inspiration from Harris Hawk Optimization and the Arithmetic Optimization Algorithm to improve position relocation problems, premature convergence, and the poor accuracy the existing techniques face. The HHAOA algorithm was evaluated on various benchmark functions and compared with other optimization algorithms, namely Arithmetic Optimization Algorithm, Moth Flame Optimization, Sine Cosine Algorithm, Grey Wolf Optimization, and Harris Hawk Optimization. The proposed algorithm was also applied to a real-world industrial wireless mesh network simulation and experimentation on the real-time pressure process control system. All the results demonstrate that the HHAOA algorithm outperforms different algorithms regarding mean, standard deviation, convergence speed, accuracy, and robustness and improves client router connectivity and network congestion with a 31.7% reduction in Wireless Mesh Network routers. In the real-time pressure process, the HHAOA optimized Fractional-order Predictive PI (FOPPI) Controller produced a robust and smoother control signal leading to minimal peak overshoot and an average of a 53.244% faster settling. Based on the results, the algorithm enhanced the efficiency and reliability of industrial wireless networks and real-time pressure process control systems, which are critical for industrial automation and control applications.
This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF-FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model's performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF-FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF-FFA model can be applied as an efficient technique for the accurate prediction of vertical handover.
Silver nanoparticles are one of the most extensively studied nanomaterials due to their high stability and low chemical reactivity in comparison to other metals. They are commonly synthesized using toxic chemical reducing agents which reduce metal ions into uncharged nanoparticles. However, in the last few decades, several efforts were made to develop green synthesis methods to avoid the use of hazardous materials. The natural biomolecules found in plants such as proteins/enzymes, amino acids, polysaccharides, alkaloids, alcoholic compounds, and vitamins are responsible for the formation of silver nanoparticles. The green synthesis of silver nanoparticles is an eco-friendly approach, which should be further explored for the potential of different plants to synthesize nanoparticles. In the present review we describe the green synthesis of nanoparticles using plants, bacteria, and fungi and the role of plant metabolites in the synthesis process. Moreover, the present review also describes some applications of silver nanoparticles in different aspects such as antimicrobial, biomedicine, mosquito control, environment and wastewater treatment, agricultural, food safety, and food packaging.
Matched MeSH terms: Green Chemistry Technology/methods
One of the most efficient methods to observe the impact of geographical, environmental, and geological changes is remote sensing. Nowadays, nanosatellites are being used to observe climate change using remote sensing technology. Communication between a remote sensing nanosatellite and Earth significantly depends upon antenna systems. Body-mounted solar panels are the main source of satellite operating power unless deployable solar panels are used. Lower ultra-high frequency (UHF) nanosatellite antenna design is a crucial challenge due to the physical size constraint and the need for solar panel integration. Moreover, nanosatellite space missions are vulnerable because of antenna and solar panel deployment complexity. This paper proposes a solar panel-integrated modified planner inverted F antenna (PIFA) to mitigate these crucial limitations. The antenna consists of a slotted rectangular radiating patch with coaxial probe feeding and a rectangular ground plane. The proposed antenna has achieved a -10 dB impedance bandwidth of 6.0 MHz (447.5 MHz⁻453.5 MHz) with a small-sized (80 mm× 90 mm× 0.5 mm) radiating element. In addition, the antenna achieved a maximum realized gain of 0.6 dB and a total efficiency of 67.45% with the nanosatellite structure and a solar panel. The challenges addressed by the proposed antenna are to ensure solar panel placement between the radiating element and the ground plane, and provide approximately 55% open space to allow solar irradiance into the solar panel.
Cardiovascular diseases (CVDs) are the leading cause of death today. The current identification method of the diseases is analyzing the Electrocardiogram (ECG), which is a medical monitoring technology recording cardiac activity. Unfortunately, looking for experts to analyze a large amount of ECG data consumes too many medical resources. Therefore, the method of identifying ECG characteristics based on machine learning has gradually become prevalent. However, there are some drawbacks to these typical methods, requiring manual feature recognition, complex models, and long training time. This paper proposes a robust and efficient 12-layer deep one-dimensional convolutional neural network on classifying the five micro-classes of heartbeat types in the MIT- BIH Arrhythmia database. The five types of heartbeat features are classified, and wavelet self-adaptive threshold denoising method is used in the experiments. Compared with BP neural network, random forest, and other CNN networks, the results show that the model proposed in this paper has better performance in accuracy, sensitivity, robustness, and anti-noise capability. Its accurate classification effectively saves medical resources, which has a positive effect on clinical practice.
Environmental influence is one of the attributing factors for health status. Chronic interaction with electronic display technology and lack of outdoor activities might lead to health issues. Given the concerns about the digital impact on lifestyle and health challenges, we aimed to investigate the daily activity inclination and health complaints among the Malaysian youth. A self-administered questionnaire covering lifestyle and health challenges was completed by 220 youths aged between 16 and 25. There were a total of 22 questions. Seven questions inspected the patterns of indoor and outdoor activities. Fifteen questions focused on the visual and musculoskeletal symptoms linked to both mental and physical health. The total time spent indoors (15.0 ± 5.4 hours/day) was significantly higher than that spent outdoors (2.5 ± 2.6 hours/day) (t = 39.01, p < 0.05). Total time engrossed in sedentary activities (13.0 ± 4.5 hours/day) was significantly higher than that in nonsedentary activities (4.5 ± 3.8 hours/day) comprised of indoor sports and any outdoor engagements (t = 27.10, p < 0.05). The total time spent on electronic related activities (9.5 ± 3.7 hours/day) was were higher than time spent on printed materials (3.4 ± 1.6 hours/day) (t = 26.01, p < 0.05). The association of sedentary activities was positive in relation to tired eyes (χ2 = 17.58, p < 0.05), sensitivity to bright light (χ2 = 12.10, p < 0.05), and neck pain (χ2 = 17.27, p < 0.05) but negative in relation to lower back pain (χ2 = 8.81, p < 0.05). Our youth spent more time in building and engaged in sedentary activities, predominantly electronic usage. The health-related symptoms, both visual and musculoskeletal symptoms, displayed a positive association with a sedentary lifestyle and a negative association with in-building time.