Application Of System Research Articles

Third-Generation Anticancer Photodynamic Therapy Systems Based on Star-like Anionic Polyacrylamide Polymer, Gold Nanoparticles, and Temoporfin Photosensitizer.

Photodynamic therapy (PDT) is a non-invasive anticancer treatment that uses special photosensitizer molecules (PS) to generate singlet oxygen and other reactive oxygen species (ROS) in a tissue under excitation with red or infrared light. Though the method has been known for decades, it has become more popular recently with the development of new efficient organic dyes and LED light sources. Here we introduce a ternary nanocomposite: water-soluble star-like polymer/gold nanoparticles (AuNP)/temoporfin PS, which can be considered as a third-generation PDT system. AuNPs were synthesized in situ inside the polymer molecules, and the latter were then loaded with PS molecules in an aqueous solution. The applied method of synthesis allows precise control of the size and architecture of polymer nanoparticles as well as the concentration of the components. Dynamic light scattering confirmed the formation of isolated particles (120 nm diameter) with AuNPs and PS molecules incorporated inside the polymer shell. Absorption and photoluminescence spectroscopies revealed optimal concentrations of the components that can simultaneously reduce the side effects of dark toxicity and enhance singlet oxygen generation to increase cancer cell mortality. Here, we report on the optical properties of the system and detailed mechanisms of the observed enhancement of the phototherapeutic effect. Combinations of organic dyes with gold nanoparticles allow significant enhancement of the effect of ROS generation due to surface plasmonic resonance in the latter, while the application of a biocompatible star-like polymer vehicle with a dextran core and anionic polyacrylamide arms allows better local integration of the components and targeted delivery of the PS molecules to cancer cells. In this study, we demonstrate, as proof of concept, a successful application of the developed PDT system for in vitro treatment of triple-negative breast cancer cells under irradiation with a low-power LED lamp (660 nm). We consider the developed nanocomposite to be a promising PDT system for application to other types of cancer.

SISTEM CASH DAN MEMBER GET MEMBER PADA PENGGUNA APLIKASI SNACK VIDEO SEBAGAI SUMBER INCOME PENDAPATAN

Snack Video is a short video social platform for all users to record and share their life, with the core mission of “Capture the World, Share Your Story”. The Member Get Member System in the Snack Video application is another way to make money through the snack video application by inviting and registering friends, then users who invite friends via the invitation code will receive money worth IDR 52,600 (can be adjusted according to promos). From snack video) in cash. This study uses a type of field research (field research). Data collected in this study through interviews, and directly to the object of research, and literature study using documentation from several literature books related to research. Primary data from this thesis were obtained through data obtained directly from the field through the results of interviews with informants related to the research. Secondary data was obtained by means of literature studies such as documentation from primary books which were considered representative of the research, and were also supported by secondary and thesis books as a complement to the existing primary books. And the data analysis used is a qualitative descriptive method using inductive thinking methods, namely studying a specific phenomenon that exists in the field to obtain its rules. Based on this research, it can be concluded that (1) Snack Video is a short video social platform where all users can record and share their lives, with missions completed and users get lots of coins collected from these missions. One of them is the Member Get Member System in the Snack Video application which is another way to make money through the snack video application by inviting and registering friends, then users who invite friends via the invitation code will receive money worth IDR 52,600 (can be adjusted according to promo from snack video) in cash. (2) The cash and Member Get Member system for snack video application users as income / income in the perspective of Sharia Economic Law uses the Ju’ālah contract because the video snack application pays users after the user has carried out the mission in the application by giving a predetermined number of points from each mission in the video snack application which in general have fulfilled the conditions and pillars and have been based on principles and principles in Sharia Economic Law, in accordance with Islamic law. Keywords: Snack Video, Cash System and Member Get Member, Akad, Ju’ālah

A low-cost spectroscopic nutrient management system for Microscale Smart Hydroponic system.

Hydroponics offers a promising approach to help alleviate pressure on food security for urban residents. It requires minimal space and uses less resources, but management can be complex. Microscale Smart Hydroponics (MSH) systems leverage IoT systems to simplify hydroponics management for home users. Previous work in nutrient management has produced systems that use expensive sensing methods or utilized lower cost methods at the expense of accuracy. This study presents a novel inexpensive nutrient management system for MSH applications that utilises a novel waterproofed, IoT spectroscopy sensor (AS7265x) in a transflective application. The sensor is submerged in a hydroponic solution to monitor the nutrients and MSH system predicts the of nutrients in the hydroponic solution and recommends an adjustment quantity in mL. A three-phase model building process was carried out resulting in significant MLR models for predicting the mL, with an R2 of 0.997. An experiment evaluated the system's performance using the trained models with a 30-day grow of lettuce in a real-world setting, comparing the results of the management system to a control group. The sensor system successfully adjusted and maintained nutrient levels, resulting in plant growth that outperformed the control group. The results of the models in actual deployment showed a strong, significant correlation of 0.77 with the traditional method of measuring the electrical conductivity of nutrients. This novel nutrient management system has the potential to transform the way nutrients are monitored in hydroponics. By simplifying nutrient management, this system can encourage the adoption of hydroponics, contributing to food security and environmental sustainability.

Anti-fouling rotating polymer-based heat exchanger for zero liquid discharge humidification-dehumidification desalination

The objective of this investigation is to assess the performance of various heat exchangers for application in a novel solar-powered zero liquid discharge humidification-dehumidification desalination system. In this study four heat exchangers (HX) having two different flow configurations namely counter flow (cf), cross flow (cr) made up of three different materials namely high-density polyethylene (HDPE), aluminum (Al), and Polypropelyne (PP) were compared in terms of their effectiveness and overall heat transfer coefficient under varying salinity levels (up to 10%) and mixing ratios (0.22–0.45). At a mixing ratio of 0.22 and 0% salinity, PP-HXcr and Al-HXcf exhibited similar effectiveness (∼85%), surpassing that of HDPE-HXcf (∼65%). Despite PP-HXcr's lower thermal conductivity in comparison to Al-HXcf, comparable effectiveness was achieved due to the superior flow distribution in PP-HXcr. Further investigations focused on the impact of salinity on heat exchanger performance. At 3.5% salinity, all heat exchangers experienced a decline in effectiveness and heat transfer coefficient (HTC), with Al-HXcf experiencing a more pronounced decrease compared to PP-HXcr. The higher thermal conductivity of Al-HXcf led to greater salt accumulation, while PP-HXcr demonstrated minimal fouling. As the experiment progressed, fouling increased for all heat exchangers, with the Al-HXcf being practically ineffective at 10% salinity with an effectiveness below 10%. To address the issue of fouling, a rotating cross-flow heat exchanger (RPP-HXcr) was introduced. While the effectiveness of the PP-HXcr drops from 85% to approximately 60% with increasing salinity from 0% to 10%, the RPP-HXcr demonstrates only a marginal decline in effectiveness with increasing salinity. For instance, at mixing ratio of 0.22 when the salinity is increased from 0% to 10%, the effectiveness of RPP-HXcr only drops from 83% to 77%. This exceptional performance was attributed to the continuous contact between the rotating tubes and the incoming feed, effectively preventing fouling and ensuring sustained efficiency. Rotating cross-flow heat exchanger (RPP-HXcr) is introduced and validated as a potentially reliable solution for mitigating fouling, as it demonstrates sustained efficiency and minimal performance degradation across different salinity conditions.

The Code Analysis of ‘Post-Apocalyptic Era’ Movies: Take Mad Max: Fury Road as an Example

In the 88th Academy Awards, directed by George Miller, a "wasteland" theme film "Mad Max 4: Fury Road" is the world's most recognized female theme film. 50% of film history is one of the films in the explosive stage. After it won the Oscar six awards, it has become a "wasteland" theme film's new benchmark. Through semiotics and the Bible, this paper deconstructs the image codes in movies. Based on the symbolic interactionism theory, this paper analyses the character behaviour, identity construction and prop application in the Christian symbol system and combs and deconstructs it, trying to explore the symbolic metaphors in movies and reflect on the artistic value and ideological connotation contained in movies.

Different data-driven prediction of global ionospheric TEC using deep learning methods

Ionospheric Total Electron Content (TEC) is a crucial parameter for monitoring the ionosphere and space weather disasters. Its accurate prediction is vital for precise applications of Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS). This study proposes a novel method for ionospheric TEC prediction that considers multiple TEC-related factors. We present the random forest method and the Autoformer deep learning with multilayer perceptron (Autoformer-MLP) to predict the global TEC by incorporating the geomagnetic and solar activity parameters. Two schemes with different ionospheric data, i.e., spherical harmonic coefficients (SHC) and vertical TEC (VTEC), are performed by Autoformer-MLP. Ionospheric products from 2009 to 2019 (Cycle 24), obtained from the Center for Orbit Determination in Europe (CODE), are collected to test and evaluate the proposed method. Experimental results demonstrate that the root mean square errors (RMSEs) of predicted global ionospheric maps (GIMs) using the SHC scheme are 3.79 and 1.39 TECU in 2015 and 2019, respectively, and those are 3.55 and 1.28 TECU for the VTEC scheme. Moreover, the RMSEs of the prediction results are 0.5 and 0.2 TECU lower than that of CODE's 1-day predicted global ionospheric map (GIM) product (C1PG) during high and low solar activity years, respectively. These analyses indicate that the proposed random forest and Autoformer-MLP deep learning methods exhibit high accuracy and stability for data-driven prediction of global ionospheric TEC in various scenarios.

MLMSign: Multi-lingual multi-modal illumination-invariant sign language recognition

Sign language (SL) serves as a visual communication tool bearing great significance for deaf people to interact with others and facilitate their daily life. Wide varieties of SLs and the lack of interpretation knowledge necessitate developing automated sign language recognition (SLR) systems to attenuate the communication gap between the deaf and hearing communities. Despite numerous advanced static SLR systems, they are not practical and favorable enough for real-life scenarios once assessed simultaneously from different critical aspects: accuracy in dealing with high intra- and slight inter-class variations, robustness, computational complexity, and generalization ability. To this end, we propose a novel multi-lingual multi-modal SLR system, namely MLMSign, by taking full strengths of hand-crafted features and deep learning models to enhance the performance and the robustness of the system against illumination changes while minimizing computational cost. The RGB sign images and 2D visualizations of their hand-crafted features, i.e., Histogram of Oriented Gradients (HOG) features and a∗ channel of L∗a∗b∗ color space, are employed as three input modalities to train a novel Convolutional Neural Network (CNN). The number of layers, filters, kernel size, learning rate, and optimization technique are carefully selected through an extensive parametric study to minimize the computational cost without compromising accuracy. The system’s performance and robustness are significantly enhanced by jointly deploying the models of these three modalities through ensemble learning. The impact of each modality is optimized based on their impact coefficient determined by grid search. In addition to the comprehensive quantitative assessment, the capabilities of our proposed model and the effectiveness of ensembling over three modalities are evaluated qualitatively using the Grad-CAM visualization model. Experimental results on the test data with additional illumination changes verify the high robustness of our system in dealing with overexposed and underexposed lighting conditions. Achieving a high accuracy (>99.33%) on six benchmark datasets (i.e., Massey, Static ASL, NUS II, TSL Fingerspelling, BdSL36v1, and PSL) demonstrates that our system notably outperforms the recent state-of-the-art approaches with a minimum number of parameters and high generalization ability over complex datasets. Its promising performance for four different sign languages makes it a feasible system for multi-lingual applications.

Using stacked pot connection of wetland microbial fuel cells to charge the battery: Potential and effecting factor

In the practical application of wetland microbial fuel cells (WMFCs), suitable designs and stacked connection systems have consistently been employed to increase and harvest power generation. Our study compares different WMFCs designs and demonstrates that the cylinder pot design outperforms the small hanging pot design in terms of electrical energy production. Moreover, power generation from the cylinder pot can be further optimized through separator modification and stacked connections. The stacked WMFCs design exhibited no voltage reversal, with an average power output ranging from 0.03 ± 0.01 mW (single pot) to 0.11 ± 0.05 mW (stacked connection of 5 pots) over a 60-day operational period. Additionally, our study identifies distinct patterns in both anodic and cathodic physiochemical factors including electrical conductivity (EC), pH, and nitrate (NO3−), highlighting the significant influence of plant involvement on altering concentrations and levels in different electrode zones. The WMFCs bioelectricity production system, employing 15 pots stacked connections achieves an impressive maximum power density of 9.02 mW/m2. The system's practical application is evidenced by its ability to successfully power a DC-DC circuit and charge a 1.2 V AAA battery over a period of 30 h, achieving an average charging rate of 0.0.2 V per hour.

A 6 Mbps 7 pJ/bit CMOS Integrated Wireless Simultaneous Lightwave Information and Power Transfer System for Biomedical Implants

A 6 Mbps 7 pJ/bit CMOS Integrated Wireless Simultaneous Lightwave Information and Power Transfer System for Biomedical Implants

Microsporidia: Pervasive natural pathogens of Caenorhabditis elegans and related nematodes.

The nematode Caenorhabditis elegans is an invaluable host model for studying infections caused by various pathogens, including microsporidia. Microsporidia represent the first natural pathogens identified in C. elegans, revealing the previously unknown Nematocida genus of microsporidia. Following this discovery, the utilization of nematodes as a model host has rapidly expanded our understanding of microsporidia biology and has provided key insights into the cell and molecular mechanisms of antimicrosporidia defenses. Here, we first review the isolation history, morphological characteristics, life cycles, tissue tropism, genetics, and host immune responses for the four most well-characterized Nematocida species that infect C. elegans. We then highlight additional examples of microsporidia that infect related terrestrial and aquatic nematodes, including parasitic nematodes. To conclude, we assess exciting potential applications of the nematode-microsporidia system while addressing the technical advances necessary to facilitate future growth in this field.

First-principles exploration of N- and Pd-doped graphene as CO and NO2 gas sensor for operation status evaluation in AIS

To evaluate the operation status of air insulated switchgears (AIS), this work purposes N- and Pd- embedded graphene (N- and Pd-graphene) as potential gas sensors upon two typical faults gases (CO and NO2) from the first-principles simulations. It is found that the N and Pd atoms can be stably trapped on the C-vacancy of the C-defected graphene with the formation energy of −12.17 and −5.12 eV, respectively. N-graphene behaves physisorption towards CO and NO2 molecules while Pd-graphene behaves chemisorption instead. The resistance-type and work function (WF)-based sensing mechanisms of N- and Pd-graphene upon such two gas species are illustrated and uncovered by analyzing their deformations of electronic property and WF in the gas adsorption systems, which reveals the potential of Pd-graphene as a resistive CO and NO2 sensor, N-graphene as a resistive NO2 sensor, as well as the N- and Pd-graphene as WF-based gas sensor for NO2 detection. This work highlights the comparison of adsorption and sensing performances between N- and Pd-graphene upon two typical gas sensors in AIS, which would be meaningful to explore novel graphene-based sensing materials facilitating their investigations and applications in the power system.

Novel Hydrogen and Biomethanol Production From Pinecone Biomass Using an Integrated Steam Gasification System

This study develops a new biomass-based integrated energy system where a steam gasification process is considered for hydrogen and biomethanol production in a viable manner using woody bioresources from pinecones. The modeling and simulation studies are performed using both The Aspen Plus and Matlab software packages in a combined manner to practically investigate the integrated system's operating and management conditions and potential performance for a strategically identified location in British Columbia where bioresources are widely available, and there is a strong need to create a value out of these. The emphasis is on three main stages: gasification, syngas post-treatment, and methanol production. This comprehensive approach ensures an in-depth understanding of the production process, from resource extraction to end-product progress. A key aspect of the study is estimating essential flow rates, notably CO2, H2, and CH4O, in various operational conditions. The analysis goes beyond product yields, providing a thorough evaluation of mass flow rates for all streams in the system. This enables a more sophisticated evaluation of performance under various operating situations and component varieties. The integrated system's performance is evaluated using rigorous thermodynamic studies. This analytical technique provides details on the efficiencies and environmental impacts of the proposed system. The simulation findings show that the existing system can produce 0.17 kg/s methanol and 0.025 kg/s hydrogen. These findings establish a foundation for further improvement and scalability of the integrated system for practical applications. The study not only helps advance the field of renewable energy era but also provides a practical and sustainable solution to the pressing issue of pine beetle infestation, demonstrating the possibility of combining technology and ecology to benefit the environment.

A novel solution methodology for electrical vehicles charging schedule in coordinated with full flexible resources in microgrid

This paper proposes a new model for the day-ahead Electric Vehicle charging schedule in Microgrids (MGs) with flexible resources such as Wind Turbine (WT) and Photovoltaic (PV) electrical power, Combined Heat and Power (CHP), Energy Storage Source (ESS), and load Demand Responses (DR). The proposed Mixed Integer Nonlinear Multi-Objective Optimization (MINMO) model considers uncertainty properties with optimal operation objects. The cost function, and power loss minimization are the proposed model's objects for considering optimal operation objects. This model is solved by a three-level decomposition method in master-sub problem presentation and Binary-Continues PSO algorithm (BC PSO) to quickly calculate the optimal solution. In the first layer, the main constraint of electric vehicle aggregators (EVAs) is satisfied by the binary PSO algorithm. According to the top-layer solution, flexible recourses are managed to satisfy the operation objects by the Bi-level Continues PSO algorithm in the second layer. Then, after converging to the optimum solution of these two layers, Electric Vehicles (EVs) charging stations are solved at the last layer by algebraic equations. Finally, the efficiency of the proposed method is illustrated via its application in IEEE 33-bus MG system. The numerical results show that the proposed solution methodology reduces the runtime of the problem up to 95 % compared to intelligence-based and full Newton–Raphson AC power flow method. Also, numerical results illustrate that considering CHP and DR in addition to the other flexible resources reduces the operation cost and power loss up to 21 % and 5 % respectively. In top of that the minimum total cost for day-ahead EV charging schedule by the proposed model and solution methodology is 578,312.7$.

Amplitude-Time Dual-View Fused EEG Temporal Feature Learning for Automatic Sleep Staging.

Electroencephalogram (EEG) plays an important role in studying brain function and human cognitive performance, and the recognition of EEG signals is vital to develop an automatic sleep staging system. However, due to the complex nonstationary characteristics and the individual difference between subjects, how to obtain the effective signal features of the EEG for practical application is still a challenging task. In this article, we investigate the EEG feature learning problem and propose a novel temporal feature learning method based on amplitude-time dual-view fusion for automatic sleep staging. First, we explore the feature extraction ability of convolutional neural networks for the EEG signal from the perspective of interpretability and construct two new representation signals for the raw EEG from the views of amplitude and time. Then, we extract the amplitude-time signal features that reflect the transformation between different sleep stages from the obtained representation signals by using conventional 1-D CNNs. Furthermore, a hybrid dilation convolution module is used to learn the long-term temporal dependency features of EEG signals, which can overcome the shortcoming that the small-scale convolution kernel can only learn the local signal variation information. Finally, we conduct attention-based feature fusion for the learned dual-view signal features to further improve sleep staging performance. To evaluate the performance of the proposed method, we test 30-s-epoch EEG signal samples for healthy subjects and subjects with mild sleep disorders. The experimental results from the most commonly used datasets show that the proposed method has better sleep staging performance and has the potential for the development and application of an EEG-based automatic sleep staging system.

A New Hyperchaotic Map Based on Discrete Memristor and Meminductor: Dynamics Analysis, Encryption Application, and DSP Implementation

At present, discrete memristor is a focus of chaotic mapping. However, the study of discrete memcapacitor and discrete meminductor has not begun. In order to study the dynamics of discrete meminductor and the dynamics of chaotic map containing discrete meminductor. In this paper, a five dimensional Hyperchaotic mapping is constructed by combining discrete memristor and discrete meminductor. In addition, the dynamical behavior of chaotic mapping is analyzed by means of the phase diagram of chaotic attractor, LEs, Bifurcation diagram and spectral entropy complexity. Theoretical analysis and numerical simulation show that the five -dimensional chaotic map not only has hyperchaotic phenomenon, but also can generate different types of chaotic attractors with the change of parameters. Especially, the special phenomenon of coexistence of attractors is discovered. This makes this five-dimensional hyperchaotic map more suitable for many chaotic based engineering applications. So, an application of discrete chaotic system in chaotic image encryption is proposed. The security and reliability of the encryption scheme are analyzed by NIST test, entropy, correlation and histogram. Finally, the results of the numerical simulation are verified by digital circuit. Due to its complex dynamical characteristics, the 5-D hyperchaotic map has great value in engineering applications.

Effect of Mn (as an acceptor) on the structural and electrical properties of non-stoichiometry Sodium Bismuth Titanate/NBT ceramics

The lead free Na0.5Bi0.5TiO3 (NBT) ceramic is proven as a potential piezoelectric candidate; however, non-stoichiometry NBT serves as an outstanding oxide-ion conductor proposed by (M. Li et al., Nature Materials 13 (2014) 31–35). According to this point here we have systematically report the structural and electrical properties of non-stoichiometry Na0.5Bi0.49TiO3/NB0.49T and B-site Mn3+ acceptor-modified NB0.49T system. Therefore, we are trying to prepare the nominal formula of Na0.5Bi0.49Ti1-xMnxO3; NB0.49T ceramic with 0.0≤x ≤ 0.05 by solid state reaction method. The bulk oxide ion conductivity gradually increases with respect to the doping concentration and the maximum value was obtained for x = 0.05 composition. This conductivity maximum is in good consistent with R. A. De Souza's oxygen vacancy diffusivity limit concept for perovskite systems (Advanced Functional Materials, 25 (2015)). On doping of 5mol% of Mn in the Ti-site/B-site NB0.49T system, it was observed that the oxygen-ion conduction inside the grain boundary increased considerably. Grain conductivity of NaBi0.49TiO3 at 500 °C is ∼3.0 ± 0.5 mS cm−1, which is approximately 122 % greater than that pure NBT compositions. Additionally, NB0.9T -exhibited a conductivity value of 4.7 mS/cm at 500 °C, which increases to 9.2 mS/cm on 5 mol% Mn-modification. The observed increase in conductivity was ascribed due to the electrostatic interactions between Mn–Ti or Mn–O bond. Therefore, our current findings on NB0.49T illustrates that present approach should be applicable to modify the system for SOFC application.

Investigating the sustainability of biogas recovery systems in wastewater treatment plants- A circular bioeconomy approach

Advanced wastewater treatment options offer a unique opportunity to recover valuable resources such as energy (biogas), nutrients, and minerals embedded in the wastewater streams. However, considerable challenges remain when designing and planning sustainable wastewater treatment systems. This study aims to evaluate the sustainability of waste-to-energy (WtE) recovery in the form of biogas in a wastewater treatment plant (WWTP). The study investigates the performance of a biogas recovery system in different scenarios and applications from 2018 to 2040, considering the city of Tbilisi in Georgia. The study results reveal a significant biogas production potential, with an average annual energy (heat and power) generation of 137 GWh when biogas is recovered from the wastewater (WW) and sewage sludge (SS). The WWTP-based WtE systems can avoid up to 38,500 tCO2eq emissions every year. The combined recovery (from WW and SS) scenario is financially feasible with a net present value of 14.88 million EUR and a levelized cost of biogas of 0.08 EUR/m³. Recovered biogas can help avoid the usage of 172.34 million m³ of natural gas worth 42.4 million EUR. Sensitivity analysis shows that the quality of wastewater, price of energy, and capital cost of the anaerobic digestion plant are the key factors in determining the economics of WtE recovery systems. This research also demonstrates the interlinkages between sustainable development goals and various benefits of resource recovery systems. This study could be helpful for decision-makers involved in planning and deploying resource recovery systems in a circular bioeconomy approach in WWTPs globally.

A review on recent progress in battery thermal management system in electric vehicle application

Global challenges of energy crisis and air pollution have presented a tremendous opportunity for the development of electric cars. Li-ion batteries are crucial for electric cars (EVs). Nevertheless, Li-ion batteries are sensitive to the operating temperature and only function optimally within a certain temperature range. Hence, EVs require a battery thermal management system (BTMS). A cooling system's advantage is that it prevents the premature deterioration of battery life. This article examines several battery temperature management techniques and classifies them as either active or passive. In addition, the hybrid BTMS with an active/passive combination is described. This article gives an overview of current advancements in the direction of BTMS with direct and indirect cooling technologies, as well as key insights into hybrid BTMS.

Development of A Palm-Vein Recognition System for Identification and Verification Systems using Enhanced Convolutional Neural Network

Biometric authentication systems have gained significant attention in access control applications due to their ability to provide enhanced security and convenience. Among various biometric modalities, palm-vein recognition has emerged as a promising approach, offering high accuracy, reliability, and resistance to forgery. However, existing palm-vein recognition systems often face challenges in implementation costs, computational efficiency, and performance limitations. This research aimed to develop an enhanced palm-vein recognition system for access control applications by optimizing a Convolutional Neural Network (CNN) architecture. A palm-vein dataset comprising 1000 images from 200 LAUTECH students was acquired, with 5 images per individual. The dataset was split into 700 training images and 300 testing images. The acquired images were pre-processed for quality enhancement and region of interest extraction. A Gravitational Search Algorithm (GSA) optimized CNN (GSA-CNN) was then employed to extract deep features from the pre-processed images, which were classified using a SoftMax layer. Experimental results revealed that the CNN technique achieved a specificity, sensitivity, False Positive Rate (FPR), accuracy of 74.60%, 79.89%, 25.40%, 77.67% at 117.52 seconds, respectively. In contrast, the proposed GSA-CNN technique demonstrated superior performance, achieving a specificity, sensitivity, FPR, accuracy of 92.06%, 92.53%, 7.94%, 92.33% at 97.14 seconds, respectively. The GSA-CNN system outperformed the conventional CNN approach in terms of accuracy, specificity, sensitivity, FPR, and processing time, demonstrating its potential for reliable and efficient palm-vein recognition in access control applications. The findings have significant implications for developing robust and secure access control systems, contributing to enhanced privacy and security across various domains.

Fabrication of a tissue engineering mat by emulsion‐based electrospinning of polycaprolactone/silk fibroin using a new solvent system

AbstractThe development of electrospun tissue engineering scaffolds using silk fibroin and polycaprolactone (PCL) has been reported. The challenge in this system is the design of a suitable solvent with high stability. In this study, a mat was electrospun with PCL‐fibroin fibers using an emulsion consisting of PCL and a blend of fibroin and polyethylene oxide (PEO). The effects of different concentrations of fibroin and PEO and ratios of formic acid: distilled water (FA:DW, solvent for fibroin and PEO) and chloroform: formic acid (Chl:FA, solvent for PCL) were studied. The FA:DW and Chl:FA ratios of 70:30 were proved to make an optimized and stable solvent system. In addition, by controlling the water absorption of the mat, we optimized its weight loss and maintained its integrity for up to 14 days. Also, the mechanical and cell attachment properties were as expected. Chorioallantoic membrane (CAM) assay exhibited the potential of the mats to induce angiogenesis. Furthermore, subcutaneous implantation in a mouse model elicited no significant inflammatory response. This study provides a simple and promising electrospinning fabrication with an optimized solvent system for tissue engineering applications.