Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks

Recently, deep learning has been employed in medical image analysis for several clinical imaging methods, such as X-ray, computed tomography, magnetic resonance imaging, and pathological tissue imaging, and excellent performance has been reported. With the development of these methods, deep learning technologies have rapidly evolved in the healthcare industry related to hair loss. Hair density measurement (HDM) is a process used for detecting the severity of hair loss by counting the number of hairs present in the occipital donor region for transplantation. HDM is a typical object detection and classification problem that could benefit from deep learning. This study analyzed the accuracy of HDM by applying deep learning technology for object detection and reports the feasibility of automating HDM. The dataset for training and evaluation comprised 4492 enlarged hair scalp RGB images obtained from male hair-loss patients and the corresponding annotation data that contained the location information of the hair follicles present in the image and follicle-type information according to the number of hairs. EfficientDet, YOLOv4, and DetectoRS were used as object detection algorithms for performance comparison. The experimental results indicated that YOLOv4 had the best performance, with a mean average precision of 58.67.

Deep learning-based system development for black pine bast scale detection

The prevention of the loss of agricultural resources caused by pests is an important issue. Advances are being made in technologies, but current farm management methods and equipment have not yet met the level required for precise pest control, and most rely on manual management by professional workers. Hence, a pest detection system based on deep learning was developed for the automatic pest density measurement. In the proposed system, an image capture device for pheromone traps was developed to solve nonuniform shooting distance and the reflection of the outer vinyl of the trap while capturing the images. Since the black pine bast scale pest is small, pheromone traps are captured as several subimages and they are used for training the deep learning model. Finally, they are integrated by an image stitching algorithm to form an entire trap image. These processes are managed with the developed smartphone application. The deep learning model detects the pests in the image. The experimental results indicate that the model achieves an F1 score of 0.90 and mAP of 94.7% and suggest that a deep learning model based on object detection can be used for quick and automatic detection of pests attracted to pheromone traps.

Magnetic Flux Density Measurement Platform for an Inductive Wireless Power Transmitter Coil Design

This paper presents a platform developed for automated magnetic flux density measurement. The platform was designed to be used to measure the magnetic flux density of the transmitter/receiver coil of an inductive wireless power transfer system. The magnetic flux density of a transmitter was measured using a small, 3-axis search coil. The search coil was positioned in the 3D space above the transmitter coil using a 3D positioning mechanism and used to measure the magnetic flux density at a specific point. The data was then sent to a computer application to visualize the magnetic flux density. The measured magnetic field could be used in combination with electromagnetic field solvers to design and optimize transmitter coils for inductive wireless power transfer systems.

DESIGN, OPTIMIZATION, AND EVALUATION OF RAFT FORMING GASTRO RETENTIVE DRUG DELIVERY SYSTEM OF LAFUTIDINE USINGBOX–BEHNKEN DESIGN

Objective: The current research was aimed to formulate and evaluate raft forming gastro retentive floating drug delivery systems of Lafutidine for improving gastric residence time and sustained drug release for an extended time. Methods: Using Box–Behnken experimental design 17 formulations of lafutidine GRDDS were designed and evaluated for various parameters like physical appearance, pH, In vitro gelling study, in vitro buoyancy study, measurement of viscosity, density measurement, gel strength, drug content, acid neutralization capacity, the profile of neutralization, in vitro dissolution, release kinetic and stability studies. Results: All the evaluations were performed and observed that the values were within range, and the buoyancy lag time ranged within 14.76 to 25.84 sec and the formulations remained buoyant for more than 8h with the gelling time of 12h, the drug content was ranging from 98.96 to 99.55 %, and in vitro release was 86.86 to 99.34% by the end of 12h. The release kinetics followed zero-order with Higuchi’s model that indicating that drug release was found to be followed by the matrix diffusion process. Conclusion: Out of all formulations F3 was the optimized formulation and it was further characterized for FTIR, DSC, and stability studies, which exposed that there were no interactions amongst drug and excipients and no major change in the formulation and found to be stable.

Electrical, Magnetic Radiation and Power Density Analysis of Electronic Gadgets During Yajna

All of us are aware that the modern mechanical and electrical systems in collaboration with AI, CPS, and IoT have affected our lives drastically. At one end, they have provided ease and comfort. On the other end, the electrical and magnetic radiations emitted by them have threatened the mental and physical fitness. Surprisingly, the Asian Agnihotra process effectively reduces the electrical, magnetic radiations emitting from electronic devices. The chapter deals with the computational analysis of electrical, magnetic radiation, and power density measurement of environmental effects and gadgets after the Homa therapy. In the gap of a specific time, readings were recorded, and it was observed that drastic reduction occurred at the place where Yajna was performed. This gap was curtailed gradually as time increased. The phenomenon can undoubtedly be used to address human health threats due to electronic gadgets in the 21st century. Also, it supports the concepts of smart cities where one can quickly identify the numerous effects of the Yajna process.

Parameter Analysis of Electronic Gadgets during Homa

All of us are aware that the modern mechanical and electrical systems in joint collaboration of AI, CPS and IoT have affected our life much in drastic manner, at one end, they have provided ease and comfort, at other end, the electrical and magnetic radiations emitted by them have threatened the mental and physical fitness too. Surprisingly, the Asian Agnihotra process is much effective in reducing the electrical, magnetic radiations, emitting from electronic devices. The present manuscript deals with the computational analysis of electrical, magnetic radiations and power density measurement of environmental effect and gadgets after the Homa terapy. In gap of certain time, readings were recorded and it was observed that drastic reduction occurred at the place where Yajna was performed. This gap was curtailing gradually as time increases. The phenomenon can certainly be used to address the threats of human health due to electronic gadgets in 21st century.Also it supports the concepts of Smart cities where one can easily identify the numerous effects due to Yajna process.

KEPADATAN JENTIK Aedes aegypti SEBAGAI VEKTOR DEMAM BERDARAH DENGUE (DBD) DI DESA KALIANCAR WONOGIRI

Dengue Hemorrhagic Fever (DHF) is a disease caused by the dengue virus which is transmitted through the bite of the Aedes sp. These mosquitoes are found throughout the world and their breeding is quite fast. Density of larvae found in breeding sites in an area. This density can be measured by entomological indicators. This larval density measurement can indicate the potential for larvae in Kaliancar Village, Selogiri District, Wonogiri Regency. This research is an observational study (survey) with cross sectional. The sampling technique used is simple random sampling with single larvae method. The samples of this study were 100 houses of residents of Kaliancar Village RW 01, RW 03, RW 04 and RW 05. The results showed that the HI value was 20%, CI 7,22%, BI 20%, ABJ 80% and DF 3,66. The conclusion of this study is that the larval density in Kaliancar Village, Selogiri District, Wonogiri Regency is classified as moderate because the DF value is on a scale of 3 to 4 with an average of 3,66.