Search

The domain of deep learning has seen significant advancements, particularly in the context of detecting macular edema from images of the retina, in recent times. This study introduces an innovative model for identifying macular edema, employing two deep learning models: Deeplabv3 + and VGG with a vision transformer. The Deeplabv3 + model is used to segment the macula region in the retinal images. The segmented macula region is then fed into the VGG for feature extraction with a vision transformer model for detection. This approach leverages the strengths of both models in detecting accurately and efficiently. The Deeplabv3 + model can accurately segment the macula region, which is crucial for accurate detection. The VGG combined with a vision transformer model proves highly efficient in detecting even subtle changes in the macular region, signifying the existence of macular edema. The results of our experiments with the dataset show that the proposed method outperforms current cutting-edge techniques. With an outstanding precision rate of 99.53%, the suggested approach firmly solidifies its superiority. The results highlight the effectiveness of the proposed technique in precisely and effectively detecting pathological fluid accumulation in retina images. This ability can have a substantial influence on the early detection and management of eye disorders.

This research presents a novel fault-tolerant predictive power control method for a Doubly-fed induction generator (DFIG) used in wind turbine control systems. Due to the proposed control mechanism, the system can continue to function effectively despite open-circuit or short-circuit faults in the insulated-gate bipolar transistors (IGBTs) of the MPC controller. Depending on the type of problem and its location, the tolerant IGBT overcomes power oscillations and limits the power converter's potential switching states. By monitoring the optimal generator speed, wind turbine control systems strive to maximize power output. For wind turbines operating in the partial-load area, a fault-tolerant model predictive control strategy is recommended in order to achieve control goals despite disturbances, uncertainties, sensor, and actuator difficulties. A high order sliding mode observer (HOSMO) is used to evaluate both the actual states and sensor-faults at the same time. A high order sliding mode (HOSM) control strategy based on the MPC controller is used to regulate the speed of wind turbines in order to harness the wind's maximum power.

Coagulase-negative staphylococci (CoNS) are primary skin commensals that have long been considered contaminants even when grown in blood cultures. This group of organisms has been recently recognized as a potential causative pathogen of septicemia. This increase has been attributed to the increased use of intravascular and prosthetic devices. Hence, we aimed to estimate the prevalence of CoNS as a true pathogen in adult and pediatric blood cultures along with their antibiotic susceptibility patterns. A total of 1076 adult and 611 pediatric blood cultures were subjected to an automated BacT/ALERT continuous monitoring system. Isolated CoNS were considered true pathogens if they met the diagnostic, clinical, and laboratory criteria. Antimicrobial susceptibility testing for pathogenic CoNS was carried out using the Kirby-Bauer disk diffusion method and interpreted as per CLSI 2021. CoNS were considered true pathogens in 23 (42.5%) of 54 adults and 12 (41.3%) of 29 pediatric CoNS isolated from blood cultures. Methicillin-resistant CoNS was detected in 66% and 70% of adult and pediatric cultures, respectively. All the CoNS isolates were sensitive to vancomycin and linezolid. Coagulase-negative staphylococci (CoNS) can either be a contaminant or a true pathogen, whose discrimination based on clinical and laboratory indices plays a pivotal role in the management of patients with sepsis.

One of the major causes of blindness across individuals of various age groups and genders is Diabetic Retinopathy (DR). It is mainly caused by Diabetes. The symptoms of DR include seeing an increased number of floaters, blurry vision, vision that changes from blurry to clear sometimes, seeing dark areas in the field of vision, poor night vision and so on. A patient who has been diagnosed with diabetes for a long time is prone to DR. This paper proposes a robust, deep learning based mobile application to address the said issue. The rationale behind the development of the mobile application is to detect DR in far-flung areas, which do not have regular access to healthcare facilities. An input image captured by an ophthalmoscope when fed to the mobile application can give result within a few seconds. The model utilizes convolution neural networks build upon the VGG model. The proposed system quantifies the “confidence level” with a high accuracy of 96 %. Another feature of the proposed system is that it can work offline without any network connectivity, thus making it more useful in remote areas. The application further has a user friendly and intuitive interface.

To measure the level of accuracy of crisp, fuzzy, intuitionistic fuzzy sets and neutrosophic set various mathematical tools are available. The generalization of these four sets is Plithogenic set. In this paper, for the first time we introduce the concept of plithogenic cubic vague set and its generalization, plithogenic fuzzy cubic vague set, plithogenic intuitionistic fuzzy cubic vague set, plithogenic neutrosophic cubic vague set. It is the combination of cubic vague set and plithogenic set. It aims to address the problems involving multiple attribute decision making. This concept is suitable and the accuracy of the result is precise as the set is described by more value of attributes. An attribute value v has a corresponding (fuzzy, intuitionistic fuzzy, neutrosophic) degree of appurtenance d(x,v) of the element x to the set P, with respect to some given criteria. Its corresponding internal and external sets are also discussed with examples. Further, P-union, P-intersection as well as R-union and R-intersection are introduced for plithogenic cubic vague sets which acts as a tool to study some of their properties. Some examples of the newly developed concepts in our everyday life is offered in this article.

Demand forecasting for new products in the fashion industry has always been challenging due to changing trends, longer lead times, seasonal shifts, and the proliferation of products. Accurate demand forecasting requires a thorough understanding of consumer preferences. This research suggests a model based on machine learning to analyse customer preferences and forecast the demand for new products. To understand customer preferences, the fitting room data are analysed, and customer profiles are created. K-means clustering, an unsupervised machine learning algorithm, is applied to form clusters by grouping similar profiles. The clusters were assigned weights related to the percentage of product in each cluster. Following the clustering process, a decision tree classification model is used to classify the new product into one of the predefined clusters to predict demand for the new product. This demand forecasting approach will enable retailers to stock products that align with customer preferences, thereby minimising excess inventory.

WSNs are now widely used for information gathering and transmission using WSN. Due to its low cost and simple communication, this type of network is widely used in many applications. Although hierarchical routing protocols may handle a variety of applications, choosing a Cluster Head (CH) and balancing network overload are difficult problems. This recommended strategy provides LEACH Protocol based on Novel Trust Management with Cryptographic RSA algorithm (NTM-LEACH-RSA) to extend the lifetime of network and to consume less energy. Here the proposed methodology includes two aspects for improving Security in WSN. Using the suggested NTM-LEACH technique, cluster formation and cluster head election are carried out in the first phase. Here, the threshold function value, the distance and density between nearby nodes, and the trust value are used to elect the cluster head. Based on the energy domain and the distance domain, the threshold function value is estimated in this case. The RSA cryptography technique is employed in the second phase to protect data transmission and guarantee data integrity. By using simulation tools, the proposed NTM-LEACH-RSA methodology's performance analysis is estimated. In comparison to other algorithms currently in use, it also offers higher performance results.

Introduction: Non-Small Cell Lung Cancer is the most prevalent type of cancer in lung cancer. Chemotherapy, radiation therapy, and other conventional cancer treatments have a low success rate. Thus, creating new medications is essential to halt the spread of lung cancer.Methods: In this study bioactive nature of lochnericine against Non-Small Cell Lung Cancer (NSCLC) was analyzed using various computational approaches such as quantum chemical calculations, molecular docking, and molecular dynamic simulation. Furthermore, the MTT assay shows the anti-proliferation activity of lochnericine.Results and Discussion: Using Frontier Molecular Orbital (FMO), the calculated band gap energy value associated with bioactive compounds and the molecule’s potential bioactivity is confirmed. The H38 hydrogen atom and O1 oxygen atom in the molecule are effectively electrophilic, and potential nucleophilic attack sites were confirmed through analysis of the Molecular electrostatic potential surface. Furthermore, the electrons within the molecule were delocalized, which confers bioactivity on the title molecule and was authorized through Mulliken atomic charge distribution analysis. A molecular docking study revealed that lochnericine inhibits non-small cell lung cancer-associated targeted protein. The lead molecule and targeted protein complex were stable during molecular dynamics simulation studies till the simulation period. Further, lochnericine demonstrated remarkable anti-proliferative and apoptotic features against A549 lung cancer cells. The current investigation powerfully suggests that lochnericine is a potential candidate for lung cancer.

Abstract Background Sexual violence is cancerous, and it malignantly affects all strata of our community regardless of age, gender, race, ability, and social status. Excellent healthcare service requires professionals who can do a sexual assault examination, collect medico-legal evidence, take photographs of patients' anatomy that was involved in the assault and document what is there and where it is in relation to the victims or perpetrators body or other parts of the crime scene, whether or not it is connected to the crime. Main text The objective of this article to orient the professions to the novel trends and approaches in forensic photography, namely e-consent, digital cameras and their accessories, dedicated forensic software, and photographic protocols and procedures. Conclusion The inadequate and improper collection, preservation and presentation of evidence can lead to crime scene errors and fewer conviction rates. Training in forensic photography is vital and a game-changer in the sexual assault examination.

Polyurethane (PU) foams are inevitable for many applications from comfort materials or energy saving to aerospace. Today, greener routes for their production have gained thoughtful attention among the scientific community by avoiding mainly the use of toxic isocyanates. An easily scalable process for the realization of self-blown isocyanate-free PU (poly (hydroxy urethane)-PHU) foams by exploiting suitable amines and cyclic carbonates is demonstrated in this work. The foaming is attributed to the release of CO2, from the reaction of cyclic carbonate of resorcinol diglycidyl ether (RDGCC) with amine terminated oligomeric phenyl hydroxy amine (AOPHA), where a parallel reaction of the hydroxyl groups and amino groups with the cyclic carbonate groups are competing during polymerization and culminate in poly (hydroxy urethane) foam. The released gas was confirmed as CO2 from GC–MS and temperature dependent FTIR. The realized flexible PHU foam is thermally stable (>250 ℃) with adhesive strength between Al-Al as 1.5 to 2.0 MPa, and with a closed cell structure possessing a density of 480 kg/m3.

In this research paper, an effort was made to investigate the dry sliding wear rate of friction stir welded AA6262/AA5456 composite performed by the Taguchi approach. Wear experiments are carried out by selecting suitable process parameters such as load (LD), sliding speed (SS), and sliding distance (SE) to recognize the individual and combination effects of parameters. Orthogonal array L27 mode is applied to make numbers of experiments to find the extreme and minimum wear amount. The experimental works are conducted on Pin on Disc (POD) apparatus and measured the weight of samples before and after test for calculating wear rate (WR) and then various analyses are performed such as ANOVA, interaction plot between parameters, and regression model with support of MINITAB software. The results revealed that maximum wear rate is found as 0.01215 mm3/Nm at LD of 50 N, SE of 600 m, and SS of 4 m/s, whereas minimum 0.00414 mm3/Nm at the grouping process parameters which are LD of 30 N, SE of 400 m, and SS of 6 m/s. From the observation of ANOVA, the most significant factor is identified as load, followed by sliding distance, sliding speed, and combination of each parameter. The load is considered as main parameter which contributes about 83.75% to achieve the maximum wear rate of developed AA6262/5456 composites. The contribution of sliding distance and speed are 3.75% and 1.85%, respectively.

In-vitro measurement has the advantage of rapid and convenient method of screening the iron bioavailability within the range of plant-based foods. It is important to do preliminary screening as it provides information which will be useful to identify promising plant sources of iron before moving to human trials. A review on in-vitro methods of bio-accessibility and bioavailability of iron in plant-based foods including fruits, vegetables, cereals and legumes is entailed here. The review will focus on in-vitro methods of iron bioavailability in plant-based foods and the effects of inhibitors and processing on the iron bioavailability. The variation of the methods and updates on a recent INFOGEST method used to measure the bioavailability of iron in plant-based foods will also be discussed.

In the present study, silver nanoparticles (Ag-NPs) were synthesized using Azadirachta indica extract and evaluated for their in vitro antioxidant activity and cytotoxicity efficacy against MCF-7 and HeLa cells. The silver nanoparticles (Ag-NPs) were formed within 40 min and after preliminary confirmation by UV-visible spectroscopy (peak observed at 375 nm), they were characterized using a transmission electron microscope (TEM) and dynamic light scattering (DLS). The TEM images showed the spherical shape of the biosynthesized Ag-NPs with particle sizes in the range of 10 to 60 nm, and compositional analysis was carried out. The cytotoxicity and antioxidant activity of various concentrations of biosynthesized silver nanoparticles, Azadirachta indica extract, and a standard ranging from 0.2 to 1.0 mg/mL were evaluated. The 2,2-Diphenyl-1-picrylhydrazyl (DPPH) activity of the biosynthesized Ag-NPs and aqueous leaf extract increased in a dose-dependent manner, with average IC50 values of the biosynthesized Ag-NPs, aqueous leaf extract, and ascorbic acid (standard) of 0.70 ± 0.07, 1.63 ± 0.09, and 0.25 ± 0.09 mg/mL, respectively. Furthermore, higher cytotoxicity was exhibited in both the MCF-7 and HeLa cell lines in a dose-dependent manner. The average IC50 values of the biosynthesized Ag-NPs, aqueous leaf extract, and cisplatin (standard) were 0.90 ± 0.07, 1.85 ± 0.01, and 0.56 ± 0.08 mg/mL, respectively, with MCF-7 cell lines and 0.85 ± 0.01, 1.76 ± 0.08, 0.45 ± 0.10 mg/mL, respectively, with HeLa cell lines. Hence, this study resulted in an efficient green reductant for producing silver nanoparticles that possess cytotoxicity and antioxidant activity against MCF-7 and HeLa cells.

We report two cases of complicated Urinary Tract Infection, one with nephrostomy tube left in-situ and other with bladder outlet obstruction, caused by Trichosporon loubieri. Both patients responded well to antifungal treatment along with change/removal of catheters. In both the cases, correct identification of T. loubieri was done by IGS1 sequencing. Prompt identification and timely management headed to good clinical outcome. Hence, clinicians should be aware of T. loubieri as an emerging fungi causing human infections. Keywords: Trichosporon loubieri, Urinary tract infection, Nephrostomy tube, Bladder outlet obstruction, IGS1 sequencing

The history of medicine shows that myocardial infarction is one of the significant causes of death in humans. The rapid evolution in autonomous technologies, the rise of computer vision, and edge computing offers intriguing possibilities in healthcare monitoring systems. The major motivation of the work is to improve the survival rate during a cardiac arrest through an automatic emergency recognition system under ambient intelligence. We present a novel approach to chest pain and fall posture-based vital sign detection using an intelligence surveillance camera to address the emergency during myocardial infarction. A real-time embedded solution persuaded from “edge AI” is implemented using the state-of-the-art convolution neural networks: single shot detector Inception V2, single shot detector MobileNet V2, and Internet of Things embedded GPU platform NVIDIA’s Jetson Nano. The deep learning algorithm is implemented for 3000 indoor color image datasets: Nanyang Technological University Red Blue Green and Depth, NTU RGB + D dataset, and private RMS dataset. The research mainly pivots on two key factors in creating and training a CNN model to detect the vital signs and evaluate its performance metrics. We propose a model, which is cost-effective and consumes low power for onboard detection of vital signs of myocardial infarction and evaluated the metrics to achieve a mean average precision of 76.4% and an average recall of 80%.

Learning is the process of generating useful information from a huge volume of data. Learning can be either supervised learning (e.g. classification) or unsupervised learning (e.g. Clustering) Clustering is the process of grouping a set of physical objects into classes of similar object. Objects in real world consist of both numerical and categorical data. Categorical data are not analyzed as numerical data because of the absence of inherit ordering. This paper describes about ten different clustering algorithms, its methodology and the factors influencing its performance. Each algorithm is evaluated using real world datasets and its pro and cons are specified. The various similarity / dissimilarity measure applied to categorical data and its performance is also discussed. The time complexity defines the amount of time taken by an algorithm to perform the elementary operation. The time complexity of various algorithms are discussed and its performance on real world data such as mushroom, zoo, soya bean, cancer, vote, car and iris are measured. In this survey Cluster Accuracy and Error rate for four different clustering algorithm (K-modes, fuzzy K-modes, ROCK and Squeezer), two different similarity measure (DISC and Overlap) and DILCA applied for hierarchy and partition algorithm are evaluated.

Aim: To evaluate the efficacy of various irrigating solutions on calcium hydroxide (Apex cal and RC cal) removal with the use of ultrasonics. Methods: The root canals of 120 single-rooted maxillary central incisors were prepared using the stepback technique. The teeth were decoronated and split longitudinally. After filling, the two halves of roots were reassembled with sticky wax and each group was further divided into four subgroups according to the irrigating solution: Smear Clear, 10% citric acid, 5% EDTA and 3% NaOCl. Evaluation for cleanliness was done under a microscope with ×12.5 magnification. Statistical analysis was done with Kruskal Wallis and Mann Whitney tests at 5% level of significance. Results: There was no statistically significant difference (p>0.05) for calcium hydroxide (Apex Cal and RC Cal) removal by different irrigants. There were more residues in the apical groove than in the coronal groove (p

We present a case of maxillary sinusitis caused by Schizophyllum commune, in a 50-year-old female. The patient presented with nasal obstruction, purulent nasal discharge from right side of the nose, cough, headache, and sneezing. Computed tomography revealed extensive opacity of the right maxillary sinus as well as erosion of the nasal wall and maxillary bone. Functional endoscopic sinus surgery was done, and fungal debris present on right side of the maxillary sinus was removed and sent to laboratory. Potassium hydroxide (KOH) examination of the nasal discharge showed hyaline, septate hyphae. Primary isolation on Sabouraud's dextrose agar (SDA) yielded a white woolly mould. Banana peel culture after 8 weeks showed macroscopically visible fan-shaped fruiting bodies. Lactophenol cotton blue (LPCB) mount of the same revealed hyaline septate hyphae, often with clamp connections. Identification was confirmed by the presence of clamp connections formed on the hyphae and by vegetative compatibility with known isolates.

This research presents a novel fault-tolerant predictive power control method for a Doubly-fed induction generator (DFIG) used in wind turbine control systems. Due to the proposed control mechanism, the system can continue to function effectively despite open-circuit or short-circuit faults in the insulated-gate bipolar transistors (IGBTs) of the MPC controller. Depending on the type of problem and its location, the tolerant IGBT overcomes power oscillations and limits the power converter’s potential switching states. By monitoring the optimal generator speed, wind turbine control systems strive to maximize power output. For wind turbines operating in the partial-load area, a fault-tolerant model predictive control strategy is recommended in order to achieve control goals despite disturbances, uncertainties, sensor, and actuator difficulties. A high order sliding mode observer (HOSMO) is used to evaluate both the actual states and sensor-faults at the same time. A high order sliding mode (HOSM) control strategy based on the MPC controller is used to regulate the speed of wind turbines in order to harness the wind’s maximum power. [ABSTRACT FROM AUTHOR]