Acetabular morphometry and prevalence of hip dysplasia in the South Asian population

We carried out a cross-sectional study to measure the association of the seven acetabular parameters with pelvic morphometry and prevalence of hip dysplasia in our population. Convenience sampling was carried out and 250 consecutive patients who came to AKUH for intravenous pyelogram and had no complaints in the region of the hip joint were enrolled in the study. Post-micturition standardized plain antero-posterior pelvic radiographs of 250 asymptomatic adults (500 hip joints) was studied. There were 136 males (54.4%) and 114 females (45.6%). Mean age of our study population was 38 years (15-78 years). The average center edge angle was 35.5±6.6° standard deviation (SD), acetabular angle was 37.76±4.37°, depth to width ratio was 0.31±4.6°, roof obliquity was 10.6±6.2°, extrusion index was 0.1±5.8, lateral subluxation 8.9±2.7 mm, and peak to edge distance 17±3.98 mm. There was significant influence (p\u3c0.05) of age in all angles except depth to width ratio. A total of seven hip joints (1.4%) were dysplastic with CE angle \u3c25° while four of the seven hips were severely dysplastic with CE angle \u3c20°. In the dysplastic group there was significant correlation (p\u3c0.05) of CE angle with acetabular angle, depth to width ratio, extrusion index and peak to edge distance. Prevalence of hip dysplasia was found to be very low in our population. These results are consistent with the findings of studies carried out in other Asian countries

Morphology of the proximal femur in a Pakistani population

Purpose: To measure the morphology of the proximal femur in a Pakistani population. Methods: Standardised anteroposterior pelvic radiographs of 116 male and 20 female healthy volunteers aged 20 to 50 (mean, 33) years were taken. Morphologic dimensions of the proximal femur were measured, including canal flare index (CFI), morphological cortical index (MCI), femoral head offset, femoral head diameter, and femoral head position. Results: Based on the CFI, 67% of the subjects had normal canal shapes (CFI, 3.0-4.7), whereas 1% and 33% of the subjects had stovepipe shapes (CFI,2.7). Conclusions: Morphology of the proximal femur in our study population differed significantly from those in western populations, indicating regional variation. It could also be due to the younger age of our population

AI-Enabled Traffic Control Prioritization in Software-Defined IoT Networks for Smart Agriculture

Smart agricultural systems have received a great deal of interest in recent years because of their potential for improving the efficiency and productivity of farming practices. These systems gather and analyze environmental data such as temperature, soil moisture, humidity, etc., using sensor networks and Internet of Things (IoT) devices. This information can then be utilized to improve crop growth, identify plant illnesses, and minimize water usage. However, dealing with data complexity and dynamism can be difficult when using traditional processing methods. As a solution to this, we offer a novel framework that combines Machine Learning (ML) with a Reinforcement Learning (RL) algorithm to optimize traffic routing inside Software-Defined Networks (SDN) through traffic classifications. ML models such as Logistic Regression (LR), Random Forest (RF), k-nearest Neighbours (KNN), Support Vector Machines (SVM), Naive Bayes (NB), and Decision Trees (DT) are used to categorize data traffic into emergency, normal, and on-demand. The basic version of RL, i.e., the Q-learning (QL) algorithm, is utilized alongside the SDN paradigm to optimize routing based on traffic classes. It is worth mentioning that RF and DT outperform the other ML models in terms of accuracy. Our results illustrate the importance of the suggested technique in optimizing traffic routing in SDN environments. Integrating ML-based data classification with the QL method improves resource allocation, reduces latency, and improves the delivery of emergency traffic. The versatility of SDN facilitates the adaption of routing algorithms depending on real-time changes in network circumstances and traffic characteristics

Citrus peel extract and powder attenuate hypercholesterolemia and hyperglycemia using rodent experimental modeling

Objective: To investigate hypocholesterolemic and hypoglycemic potential of citrus peel extract and powder using rodent experimental modeling. Methods: Considering the fact, rat feeding trial was carried out for a period of 56 d to access the prophylaxis of citrus peel flavonoids by employing normal (study I), hyperglycemic (study II) and hypercholesterolemic (study III) rats. Each study was further divided into three groups to ensure the provision of selected diets, i.e., control, functional and nutraceutical diets. Each study was further divided into three groups to ensure the provision of selected diets, i.e., control, functional and nutraceutical diets. Results: Declining trend for total cholesterol was observed in all studies with maximum reduction (8.55%) in rat group fed on nutraceutical diet in study III. Likewise, levels of low density lipoproteins and triglycerides reduced 11.39% and 7.89% respectively in hypercholesterolemic rats. Moreover, nutraceutical diet alleviated the sera glucose level by 8.96% in study II. Conclusions: Conclusively, inclusion of citrus peel bioflavonoids in dietary therapies is a promising strategy to modulate lipidemic and glycemic attributes without imparting any deleterious effect on hematological parameters

Semantic Segmentation and Edge Detection—Approach to Road Detection in Very High Resolution Satellite Images

Road detection technology plays an essential role in a variety of applications, such as urban planning, map updating, traffic monitoring and automatic vehicle navigation. Recently, there has been much development in detecting roads in high-resolution (HR) satellite images based on semantic segmentation. However, the objects being segmented in such images are of small size, and not all the information in the images is equally important when making a decision. This paper proposes a novel approach to road detection based on semantic segmentation and edge detection. Our approach aims to combine these two techniques to improve road detection, and it produces sharp-pixel segmentation maps, using the segmented masks to generate road edges. In addition, some well-known architectures, such as SegNet, used multi-scale features without refinement; thus, using attention blocks in the encoder to predict fine segmentation masks resulted in finer edges. A combination of weighted cross-entropy loss and the focal Tversky loss as the loss function is also used to deal with the highly imbalanced dataset. We conducted various experiments on two datasets describing real-world datasets covering the three largest regions in Saudi Arabia and Massachusetts. The results demonstrated that the proposed method of encoding HR feature maps effectively predicts sharp segmentation masks to facilitate accurate edge detection, even against a harsh and complicated background

A Novel Secure Occupancy Monitoring Scheme Based on Multi-Chaos Mapping

Smart building control, managing queues for instant points of service, security systems, and customer support can benefit from the number of occupants information known as occupancy. Due to interrupted real-time continuous monitoring capabilities of state-of-the-art cameras, a vision-based system can be easily deployed for occupancy monitoring. However, processing of images or videos over insecure channels can raise several privacy concerns due to constant recording of an image or video footage. In this context, occupancy monitoring along with privacy protection is a challenging task. This paper presents a novel chaos-based lightweight privacy preserved occupancy monitoring scheme. Persons’ movements were detected using a Gaussian mixture model and Kalman filtering. A specific region of interest, i.e., persons’ faces and bodies, was encrypted using multi-chaos mapping. For pixel encryption, Intertwining and Chebyshev maps were employed in confusion and diffusion processes, respectively. The number of people was counted and the occupancy information was sent to the ThingSpeak cloud platform. The proposed chaos-based lightweight occupancy monitoring system is tested against numerous security metrics such as correlation, entropy, Number of Pixel Changing Rate (NPCR), Normalized Cross Correlation (NCC), Structural Content (SC), Mean Absolute Error (MAE), Mean Square Error (MSE), Peak to Signal Noise Ratio (PSNR), and Time Complexity (TC). All security metrics confirm the strength of the proposed scheme

A Novel Hybrid Secure Image Encryption Based on Julia Set of Fractals and 3D Lorenz Chaotic Map

Chaos-based encryption schemes have attracted many researchers around the world in the digital image security domain. Digital images can be secured using existing chaotic maps, multiple chaotic maps, and several other hybrid dynamic systems that enhance the non-linearity of digital images. The combined property of confusion and diffusion was introduced by Claude Shannon which can be employed for digital image security. In this paper, we proposed a novel system that is computationally less expensive and provided a higher level of security. The system is based on a shuffling process with fractals key along with three-dimensional Lorenz chaotic map. The shuffling process added the confusion property and the pixels of the standard image is shuffled. Three-dimensional Lorenz chaotic map is used for a diffusion process which distorted all pixels of the image. In the statistical security test, means square error (MSE) evaluated error value was greater than the average value of 10000 for all standard images. The value of peak signal to noise (PSNR) was 7.69(dB) for the test image. Moreover, the calculated correlation coefficient values for each direction of the encrypted images was less than zero with a number of pixel change rate (NPCR) higher than 99%. During the security test, the entropy values were more than 7.9 for each grey channel which is almost equal to the ideal value of 8 for an 8-bit system. Numerous security tests and low computational complexity tests validate the security, robustness, and real-time implementation of the presented scheme

A new color image encryption technique using DNA computing and Chaos-based substitution box

In many cases, images contain sensitive information and patterns that require secure processing to avoid risk. It can be accessed by unauthorized users who can illegally exploit them to threaten the safety of people’s life and property. Protecting the privacies of the images has quickly become one of the biggest obstacles that prevent further exploration of image data. In this paper, we propose a novel privacy-preserving scheme to protect sensitive information within images. The proposed approach combines deoxyribonucleic acid (DNA) sequencing code, Arnold transformation (AT), and a chaotic dynamical system to construct an initial S-box. Various tests have been conducted to validate the randomness of this newly constructed S-box. These tests include National Institute of Standards and Technology (NIST) analysis, histogram analysis (HA), nonlinearity analysis (NL), strict avalanche criterion (SAC), bit independence criterion (BIC), bit independence criterion strict avalanche criterion (BIC-SAC), bit independence criterion nonlinearity (BIC-NL), equiprobable input/output XOR distribution, and linear approximation probability (LP). The proposed scheme possesses higher security wit NL = 103.75, SAC ≈ 0.5 and LP = 0.1560. Other tests such as BIC-SAC and BIC-NL calculated values are 0.4960 and 112.35, respectively. The results show that the proposed scheme has a strong ability to resist many attacks. Furthermore, the achieved results are compared to existing state-of-the-art methods. The comparison results further demonstrate the effectiveness of the proposed algorithm