Intelligent model for the detection and classification of encrypted network traffic in cloud infrastructure.

This article explores detecting and categorizing network traffic data using machine-learning (ML) methods, specifically focusing on the Domain Name Server (DNS) protocol. DNS has long been susceptible to various security flaws, frequently exploited over time, making DNS abuse a major concern in cybersecurity. Despite advanced attack, tactics employed by attackers to steal data in real-time, ensuring security and privacy for DNS queries and answers remains challenging. The evolving landscape of internet services has allowed attackers to launch cyber-attacks on computer networks. However, implementing Secure Socket Layer (SSL)-encrypted Hyper Text Transfer Protocol (HTTP) transmission, known as HTTPS, has significantly reduced DNS-based assaults. To further enhance security and mitigate threats like man-in-the-middle attacks, the security community has developed the concept of DNS over HTTPS (DoH). DoH aims to combat the eavesdropping and tampering of DNS data during communication. This study employs a ML-based classification approach on a dataset for traffic analysis. The AdaBoost model effectively classified Malicious and Non-DoH traffic, with accuracies of 75% and 73% for DoH traffic. The support vector classification model with a Radial Basis Function (SVC-RBF) achieved a 76% accuracy in classifying between malicious and non-DoH traffic. The quadratic discriminant analysis (QDA) model achieved 99% accuracy in classifying malicious traffic and 98% in classifying non-DoH traffic.

Safety and Efficacy of the Erbium Laser in Debonding Dental Accessories: A Narrative Review.

Objective: This article aims to review the safety and efficacy of the Er:YAG laser in debonding dental accessories. Methods: This review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Articles published between 2010 and 2022 on the removal of dental accessories using erbium laser were searched. The selected articles were then classified according to the accessories used: adhesives, brackets, restorations, or implant crowns. Enamel surface roughness, shear bond strength, adhesive remnant index, duration time (t), pulp chamber temperature (T), morphology (M), and other variables were then noted. Results: The dental accessories and adhesives used were described along with the laser parameters used, such as frequency, pulse width, irradiation time, scanning mode, water-air cooling, and other variables. Conclusions: Laser removal using Er:YAG laser of dental accessories such as brackets, crowns, and veneers is fundamentally safe, time-saving, and does not cause damage to the enamel nor the underlying dentin. However, there was no distinct advantage with laser removal seen, such as those residual adhesives of brackets on the tooth surface and temporary adhesives of restorations.

Effect of nanosecond- and microsecond-pulse Er,Cr:YSGG laser ablation on dentin shear bond strength of universal adhesives.

OBJECTIVE: The study aimed to evaluate the bond strength of universal adhesives to dentin after Er,Cr:YSGG laser irradiation with nanosecond-domain and microsecond-domain pulses. METHODS: Eighty extracted caries-free, sound human molars were divided into eight groups. The enamel was removed until the dentin occlusal flat dentin surface was exposed. Etch-and-rinse followed by adhesive was applied to group 1, and a self-etch adhesive was applied to group 2. Er,Cr:YSGG laser (3 mJ, 100 Hz, 100 ns), (3 mJ, 100 Hz, 150 µs), and (20 mJ, 100 Hz, 150 µs) were applied to groups 3-4, 5-6, and 7-8, respectively. The laser preparation was followed by self-etch adhesives or adhesives treatment. When the composite resin had been built up on the samples, the shear bond strength was tested, and the data were statistically analyzed using analysis of variance (ANOVA). RESULTS: Groups prepared with nanosecond-pulse laser showed significantly higher bond strength values than the microsecond-pulse laser groups and self-etch mode group, and the SEM photographs also showed more dentinal tubules and no damage in the ablation area. The shear bond strength of long pulse laser ablated was comparable to that of self-etching system when it was combined with a self-etch adhesive at low energy, but higher energy laser degraded shear bond strength. CONCLUSIONS: The pulse width of Er,Cr:YSGG laser affects the bond strength, nanosecond pulses of laser irradiation without water cooling can enhance bond strength, but microsecond pulses of laser cannot enhance bond strength.

A Deep Learning Approach for Liver and Tumor Segmentation in CT Images Using ResUNet.

According to the most recent estimates from global cancer statistics for 2020, liver cancer is the ninth most common cancer in women. Segmenting the liver is difficult, and segmenting the tumor from the liver adds some difficulty. After a sample of liver tissue is taken, imaging tests, such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US), are used to segment the liver and liver tumor. Due to overlapping intensity and variability in the position and shape of soft tissues, segmentation of the liver and tumor from computed abdominal tomography images based on shade gray or shapes is undesirable. This study proposed a more efficient method for segmenting liver and tumors from CT image volumes using a hybrid ResUNet model, combining the ResNet and UNet models to address this gap. The two overlapping models were primarily used in this study to segment the liver and for region of interest (ROI) assessment. Segmentation of the liver is done to examine the liver with an abdominal CT image volume. The proposed model is based on CT volume slices of patients with liver tumors and evaluated on the public 3D dataset IRCADB01. Based on the experimental analysis, the true value accuracy for liver segmentation was found to be approximately 99.55%, 97.85%, and 98.16%. The authentication rate of the dice coefficient also increased, indicating that the experiment went well and that the model is ready to use for the detection of liver tumors.

A state-of-the-art technique to perform cloud-based semantic segmentation using deep learning 3D U-Net architecture.

Glioma is the most aggressive and dangerous primary brain tumor with a survival time of less than 14 months. Segmentation of tumors is a necessary task in the image processing of the gliomas and is important for its timely diagnosis and starting a treatment. Using 3D U-net architecture to perform semantic segmentation on brain tumor dataset is at the core of deep learning. In this paper, we present a unique cloud-based 3D U-Net method to perform brain tumor segmentation using BRATS dataset. The system was effectively trained by using Adam optimization solver by utilizing multiple hyper parameters. We got an average dice score of 95% which makes our method the first cloud-based method to achieve maximum accuracy. The dice score is calculated by using Sørensen-Dice similarity coefficient. We also performed an extensive literature review of the brain tumor segmentation methods implemented in the last five years to get a state-of-the-art picture of well-known methodologies with a higher dice score. In comparison to the already implemented architectures, our method ranks on top in terms of accuracy in using a cloud-based 3D U-Net framework for glioma segmentation.

Assessing the reliability and degradation of 10-35 years field-aged PV modules.

The objective of this study was to conduct a reliability analysis on photovoltaic (PV) modules from the oldest PV installation site in Pakistan. Four sets of modules; Type A & B (30 years old), Type C (10 years old), and Type D (35 years old) were identified for this analysis. It has been observed that modules have shown degradation after working for a good number of years in the field. Comparing with nameplate data (available for Type B & C only), a drop of 28.68% and 2.99 percentage points (pp) was observed in the output power (Pmax) and efficiency (Eff.) respectively for Type B, while a drop of 22.21% and 4.05 pp was observed in Pmax and Eff. respectively for Type C. A greater drop in ISC and Pmax was observed in Type B, which is attributed to severe browning of EVA in them. While the greater drop in Pmax, in case of Type C, is attributed to the poor quality of materials used. Amongst the different defects observed, the junction box defects which include cracking and embrittlement, etc., and backsheet defects which include discoloration, delamination and cracking, etc. were found in all four types of modules. Other defects include browning of EVA, observed in Type B and D, and corrosion of frame and electrical wires, found in Type A, B, and D. This first-ever study will provide valuable information in understanding the degradation mechanism and henceforth, improving the long term reliability of PV modules in the humid-subtropical conditions of Pakistan.

Novel perovskite solar cell with Distributed Bragg Reflector.

This paper reports numerical modeling of perovskite solar cell which has been knotted with Distributed Bragg Reflector pairs to extract high energy efficiency. The geometry of the proposed cells is simulated with three different kinds of perovskite materials including CH3NH3PbI3, CH3NH3PbBr3, and CH3NH3SnI3. The toxic perovskite material based on Lead iodide and lead bromide appears to be more efficient as compared to non-toxic perovskite material. The executed simulated photovoltaic parameters with the highest efficient structure are open circuit voltage = 1.409 (V), short circuit current density = 24.09 mA/cm2, fill factor = 86.18%, and efficiency = 24.38%. Moreover, a comparison of the current study with different kinds of structures has been made and surprisingly our novel geometry holds enhanced performance parameters that are featured with back reflector pairs (Si/SiO2). The applied numerical approach and presented designing effort of geometry are beneficial to obtain results that have the potential to address problems with less efficient thin-film solar cells.

EEMDS: An Effective Emergency Message Dissemination Scheme for Urban VANETs.

In Vehicular Adhoc Networks (VANETs), disseminating Emergency Messages (EMs) to a maximum number of vehicles with low latency and low packet loss is critical for road safety. However, avoiding the broadcast storm and dealing with large-scale dissemination of EMs in urban VANETs, particularly at intersections, are the challenging tasks. The problems become even more challenging in a dense network. We propose an Effective Emergency Message Dissemination Scheme (EEMDS) for urban VANETs. The scheme is based on our mobility metrics to avoid communication overhead and to maintain a stable cluster structure. Every vehicle takes into account its direction angle and path loss factor for selecting a suitable cluster head. Moreover, we introduce estimated link stability to choose a suitable relay vehicle that reduces the number of rebroadcasts and communication congestion in the network. Simulation results show that EEMDS provides an acceptable end-to-end delay, information coverage, and packet delivery ratio compared to the eminent EM dissemination schemes.

A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal.

Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model's classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models' effectiveness.

Effects of single- and tri-frequency ultrasound on self-assembly and characterizations of bionic dynamic rat stomach digestion of pepsin-soluble collagen from chicken leg skin.

Chicken feet, aplenty by-products in the chicken industry, are rich in collagen and contain abundant amino acids so that it can be used as an important source for the collagen market. Pepsin-soluble collagen (PSC) was extracted from chicken leg skin and explored the effects of single- and tri-frequency ultrasound on the self-assembly and vitro digestion characteristics. By the diverging and tri-frequency ultrasound reactor, PSC was treated with 20 kHz/270w (C20H5m), 40 kHz/270w (C40H5m), 60 kHz/270w (C60H5m), 20/40/60 kHz/90w × 3 (CtH5m) for 5 min. Results showed that ultrasound could accelerate the process of collagen self-assembly, and 60 kHz/270w was the fastest. Microfiber diameters of C60H5m were 65-89 nm, which was significantly lower than the control without ultrasound (80-161 nm). The digestion results indicated polypeptides with relative molecular weights founded in the range 200-5000 Da were exceeded 85%. The final digested product had the highest content of oligopeptide, consistent rheological properties, and elastic behavior. The cavitation and mechanical of ultrasound have effects on the self-assembly process and collagen gel structure and digestion characteristics, which is of great significance for the development of the chicken industry and collagen market.

Optimization and Modeling of Process Parameters in Multi-Hole Simultaneous Drilling Using Taguchi Method and Fuzzy Logic Approach.

In industries such as aerospace and automotive, drilling many holes is commonly required to assemble different structures where machined holes need to comply with tight geometric tolerances. Multi-spindle drilling using a poly-drill head is an industrial hole-making approach that allows drilling several holes simultaneously. Optimizing process parameters also improves machining processes. This work focuses on the optimization of drilling parameters and two drilling processes-namely, one-shot drilling and multi-hole drilling-using the Taguchi method. Analysis of variance and regression analysis was implemented to indicate the significance of drilling parameters and their impact on the measured responses i.e., surface roughness and hole size. From the Taguchi optimization, optimal drilling parameters were found to occur at a low cutting speed and feed rate using a poly-drill head. Furthermore, a fuzzy logic approach was employed to predict the surface roughness and hole size. It was found that the fuzzy measured values were in good agreement with the experimental values; therefore, the developed models can be effectively used to predict the surface roughness and hole size in multi-hole drilling. Moreover, confirmation tests were performed to validate that the Taguchi optimized levels and fuzzy developed models effectively represent the surface roughness and hole size.

Optimization of CNN through Novel Training Strategy for Visual Classification Problems.

The convolution neural network (CNN) has achieved state-of-the-art performance in many computer vision applications e.g., classification, recognition, detection, etc. However, the global optimization of CNN training is still a problem. Fast classification and training play a key role in the development of the CNN. We hypothesize that the smoother and optimized the training of a CNN goes, the more efficient the end result becomes. Therefore, in this paper, we implement a modified resilient backpropagation (MRPROP) algorithm to improve the convergence and efficiency of CNN training. Particularly, a tolerant band is introduced to avoid network overtraining, which is incorporated with the global best concept for weight updating criteria to allow the training algorithm of the CNN to optimize its weights more swiftly and precisely. For comparison, we present and analyze four different training algorithms for CNN along with MRPROP, i.e., resilient backpropagation (RPROP), Levenberg-Marquardt (LM), conjugate gradient (CG), and gradient descent with momentum (GDM). Experimental results showcase the merit of the proposed approach on a public face and skin dataset.

A predictive nomogram improved diagnostic accuracy and interobserver agreement of perirectal lymph nodes metastases in rectal cancer.

OBJECTIVE: To develop a predictive nomogram to improve the diagnostic accuracy and interobserver agreement of pre-therapeutic lymph nodes metastases in patients with rectal cancer. MATERIALS AND METHODS: An institutional database of 411 patients with rectal cancer was used to develop a nomogram to predict perirectal lymph nodes metastases. Patients' clinicopathological and MRI-assessed imaging variables were included in the multivariate logistic regression analysis. The model was externally validated and the performance was assessed by area under curve (AUC) of the receiver operator characteristics (ROC) curves. The interobserver agreement was measured between two independent radiologists. RESULTS: The diagnostic accuracy of the conventional MRI-assessed cN stage was 68%; 14.2% of the patients were over-staged and 17.8% of the patients were under-staged. A total of 35.1% of the patients had disagreed diagnosis for the cN stage between the two radiologists, with a kappa value of 0.295. A nomogram for predicting pathological lymph nodes metastases was successfully developed, with an AUC of 0.78 on the training data and 0.71 on the validation data. The predictors included in the nomogram were MRI cT stage, CRM involvement, preoperative CEA, tumor grade and lymph node size category. This nomogram yielded improved prediction in cN stage than the conventional MRI-based assessment. CONCLUSIONS: By incorporating clinicopathological and MRI imaging features, we established a nomogram that improved the diagnostic accuracy and remarkably minimized the interobserver disagreement in predicting lymph nodes metastases in rectal cancers.

Prognostic nomograms for predicting survival and distant metastases in locally advanced rectal cancers.

AIM: To develop prognostic nomograms for predicting outcomes in patients with locally advanced rectal cancers who do not receive preoperative treatment. MATERIALS AND METHODS: A total of 883 patients with stage II-III rectal cancers were retrospectively collected from a single institution. Survival analyses were performed to assess each variable for overall survival (OS), local recurrence (LR) and distant metastases (DM). Cox models were performed to develop a predictive model for each endpoint. The performance of model prediction was validated by cross validation and on an independent group of patients. RESULTS: The 5-year LR, DM and OS rates were 22.3%, 32.7% and 63.8%, respectively. Two prognostic nomograms were successfully developed to predict 5-year OS and DM-free survival rates, with c-index of 0.70 (95% CI = [0.66, 0.73]) and 0.68 (95% CI = [0.64, 0.72]) on the original dataset, and 0.76 (95% CI = [0.67, 0.86]) and 0.73 (95% CI = [0.63, 0.83]) on the validation dataset, respectively. Factors in our models included age, gender, carcinoembryonic antigen value, tumor location, T stage, N stage, metastatic lymph nodes ratio, adjuvant chemotherapy and chemoradiotherapy. Predicted by our nomogram, substantial variability in terms of 5-year OS and DM-free survival was observed within each TNM stage category. CONCLUSIONS: The prognostic nomograms integrated demographic and clinicopathological factors to account for tumor and patient heterogeneity, and thereby provided a more individualized outcome prognostication. Our individualized prediction nomograms could help patients with preoperatively under-staged rectal cancer about their postoperative treatment strategies and follow-up protocols.

Preparation and biocompatibility of grafted functional ß-cyclodextrin copolymers from the surface of PET films.

The hydrophobic inert surface of poly(ethylene terephthalate) (PET) film has limited its practical bioapplications, in which case, better biocompatibility should be achieved by surface modification. In this work, the copolymer of functional ß-cyclodextrin derivatives and styrene grafted surfaces was prepared via surface-initiated atom transfer radical polymerization (SI-ATRP) on initiator-immobilized PET. The structures, composition, properties, and surface morphology of the modified PET films were characterized by fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), contact angle measurement, and scanning electronic microscopy (SEM). The results show that the surface of PET films was covered by a thick targeted copolymer layer, and the hydrophobic surface of PET was changed into an amphiphilic surface. The copolymer-grafted surfaces were also shown good biocompatibility on which SGC-7901 A549 and A549/DDP cells readily attached and proliferated, demonstrating that the functional copolymer-grafted PET films could be a promising alternative to biomaterials especially for tissue engineering.