Treatment of a traumatic atrophic depressed scar with hyaluronic acid fillers: a case report

Background: Hyaluronic acid filler has been documented in the treatment of atrophic depressed acne scars relatively frequently in the literature but rarely in chronic depressed traumatic atrophic facial scars. Methods: This case report discusses the use of hyaluronic acid fillers in the correction of a post-traumatic facial atrophic scar on the right cheek. Results: The right cheek scar was substantially corrected with one session of two different hyaluronic acids injected in a deep and superficial plane. Conclusion: Relatively accurate, simple and effective correction of this atrophic traumatic scar may suggest that fillers are a suitable alternative to surgery for such scars

Applications of Machine Learning for Fake News Detection in Social Networks

The value of online media for getting news is questionable. People seek out and devour news from online media because it is convenient, inexpensive, and widely disseminated. In contrast, it facilitates the widespread distribution of "counterfeit news," or news of lower quality that includes fabricated data. Many people and institutions are negatively impacted by the widespread circulation of false information. As a result, detecting fake news via social media has emerged as a topic of interest for academics. Searching for and reading the news is becoming increasingly convenient as a result of the widespread availability, quick expansion, and widespread dissemination of traditional news outlets and social media. Nowadays, there is a plethora of information that can be found on social media, and it can be difficult to tell what is real and what is not. The distribution costs of releasing news via social media are inexpensive, and anyone can do it. The widespread circulation of false information could have devastating effects on both individuals and communities. Developing a reliable machine learning method for spotting fake news is the focus of this work

A scalable hybrid decision system (HDS) for Roman word recognition using ANN SVM: Study case on Malay word recognition

An off-line handwriting recognition (OFHR) system is a computerized system that is capable of intelligently converting human handwritten data extracted from scanned paper documents into an equivalent text format. This paper studies a proposed OFHR for Malaysian bank cheques written in the Malay language. The proposed system comprised of three components, namely a character recognition system (CRS), a hybrid decision system and lexical word classification system. Two types of feature extraction techniques have been used in the system, namely statistical and geometrical. Experiments show that the statistical feature is reliable, accessible and offers results that are more accurate. The CRS in this system was implemented using two individual classifiers, namely an adaptive multilayer feed-forward back-propagation neural network and support vector machine. The results of this study are very promising and could generalize to the entire Malay lexical dictionary in future work toward scaled-up applications

Experimental study of the morphine de-addiction properties of Delphinium denudatum Wall.

BACKGROUND: Our aim was to explore the de-addiction properties of Delphinium denudatum Wall. in morphine dependent rats. METHODS: Charles Foster male albino rats were made morphine dependent by injecting morphine sulphate in increasing doses twice a day for 7 days. The spontaneous withdrawal signs observed 12 h after the last dose were quantified by the 'counted' and 'checked' signs. The drug (alcoholic extract of Delphinium denudatum) was administered p.o. in different regimen: a) single dose (700 mg/kg) 10 h before the first dose of morphine, b) single dose (700 mg/kg) 10 h after the last dose of morphine, c) multiple doses (350 mg/kg) along with morphine twice a day for 7 days. RESULT: Administration of Delphinium denudatum extract caused significant reduction in the frequency of counted signs as well as the presence of checked signs of morphine withdrawal. The maximum reduction was observed in regimen 'b' followed by regimen 'c' and 'a'. CONCLUSION: Delphinium denudatum Wall. significantly reduces the aggregate scores for all parameters in morphine withdrawal syndrome by central action and thus may prove to be an alternative remedy in morphine de-addiction

Permeance Based Algorithm For Computation Of Flux Linkage Characteristics Of Non-Linear 6/4 Switched Reluctance Motor (SRM)

The concept of permeance is used in the analysis of flux linkage of 6/4 SRM. The aim of this paper is to develop an efficient algorithm exploiting the nonlinear feature of the 6/4 SRM using the aforementioned concept of permeance. The first step is to generate the relevant equations related to permeances of the 6/4 SRM under study. The 6/4 SRM’s magnetization curve is then derived from the summation of mmf drops at various blocks representing the motor. The air gap permeances are derived at various angles and 3-D leakage effects are taken into account. These permeances are used for the mmf drop computation. The algorithm is capable of efficiently computing mmf drop at every block to consequently yield a complete accurate nonlinear flux linkage feature of the 6/4 switched reluctance motor. In this way, the capability of the SRM to produce the expected four times the specific output torque due to operation in high saturation region compared to an equivalent induction motor as special the attribute of the SRM is demonstrated

Fast shot boundary detection based on separable moments and support vector machine

The large number of visual applications in multimedia sharing websites and social networks contribute to the increasing amounts of multimedia data in cyberspace. Video data is a rich source of information and considered the most demanding in terms of storage space. With the huge development of digital video production, video management becomes a challenging task. Video content analysis (VCA) aims to provide big data solutions by automating the video management. To this end, shot boundary detection (SBD) is considered an essential step in VCA. It aims to partition the video sequence into shots by detecting shot transitions. High computational cost in transition detection is considered a bottleneck for real-time applications. Thus, in this paper, a balance between detection accuracy and speed for SBD is addressed by presenting a new method for fast video processing. The proposed SBD framework is based on the concept of candidate segment selection with frame active area and separable moments. First, for each frame, the active area is selected such that only the informative content is considered. This leads to a reduction in the computational cost and disturbance factors. Second, for each active area, the moments are computed using orthogonal polynomials. Then, an adaptive threshold and inequality criteria are used to eliminate most of the non-transition frames and preserve candidate segments. For further elimination, two rounds of bisection comparisons are applied. As a result, the computational cost is reduced in the subsequent stages. Finally, machine learning statistics based on the support vector machine is implemented to detect the cut transitions. The enhancement of the proposed fast video processing method over existing methods in terms of computational complexity and accuracy is verified. The average improvements in terms of frame percentage and transition accuracy percentage are 1.63% and 2.05%, respectively. Moreover, for the proposed SBD algorithm, a comparative study is performed with state-of-the-art algorithms. The comparison results confirm the superiority of the proposed algorithm in computation time with improvement of over 38%

Burden of Ileal Perforations among Surgical Patients Admitted in Tertiary Care Hospitals of Three Asian countries: Surveillance of Enteric Fever in Asia Project (SEAP), September 2016-September 2019

Background: Typhoid fever is caused by Salmonella enterica subspecies enterica serovar Typhi (S. Typhi) and can lead to systemic illness and complications. We aimed to characterize typhoid-related ileal perforation in the context of the population-based Surveillance of Enteric Fever in Asia Project (SEAP) in Bangladesh, Nepal and Pakistan. Methods: Between September 2016 and September 2019, all cases of nontraumatic ileal perforation with a clinical diagnosis of typhoid were enrolled from 4 tertiary care hospitals in Karachi, 2 pediatric hospitals in Bangladesh, and 2 hospitals in Nepal. Sociodemographic data were collected from patients or their caregivers, and clinical and outcome data were retrieved from medical records. Tissue samples were collected for histopathology and blood cultures where available. Results: Of the 249 enrolled cases, 2 from Bangladesh, 5 from Nepal and 242 from Pakistan. In Pakistan, most of the cases were in the 0-15 (117/242; 48%) and 16-30 (89/242; 37%) age groups. In all countries, males were most affected: Pakistan 74.9% (180/242), Nepal 80% (4/5), and Bangladesh 100% (2/2). Blood culture was done on 76 cases; 8 (11%) were positive for S. Typhi, and all were extensively drug resistant (XDR) S. Typhi. Tissue cultures was done on 86 patients; 3 (3%) were positive for S. Typhi, and all were XDR S. Typhi, out of 86 samples tested for histopathology 4 (5%) revealed ileal perforation with necrosis. Culture or histopathology confirmed total 15 (11%) enteric fever cases with ileal perforation are similar to the clinically diagnosed cases. There were 16/242 (7%) deaths from Pakistan. Cases of ileal perforation who survived were more likely to have sought care before visiting the sentinel hospital (P =. 009), visited any hospital for treatment (P =. 013) compared to those who survived. Conclusions: Although surveillance differed substantially by country, one reason for the higher number of ileal perforation cases in Pakistan could be the circulation of XDR strain of S. Typhi in Karachi

Precision Agriculture Techniques and Practices: From Considerations to Applications

Internet of Things (IoT)-based automation of agricultural events can change the agriculture sector from being static and manual to dynamic and smart, leading to enhanced production with reduced human efforts. Precision Agriculture (PA) along with Wireless Sensor Network (WSN) are the main drivers of automation in the agriculture domain. PA uses specific sensors and software to ensure that the crops receive exactly what they need to optimize productivity and sustainability. PA includes retrieving real data about the conditions of soil, crops and weather from the sensors deployed in the fields. High-resolution images of crops are obtained from satellite or air-borne platforms (manned or unmanned), which are further processed to extract information used to provide future decisions. In this paper, a review of near and remote sensor networks in the agriculture domain is presented along with several considerations and challenges. This survey includes wireless communication technologies, sensors, and wireless nodes used to assess the environmental behaviour, the platforms used to obtain spectral images of crops, the common vegetation indices used to analyse spectral images and applications of WSN in agriculture. As a proof of concept, we present a case study showing how WSN-based PA system can be implemented. We propose an IoT-based smart solution for crop health monitoring, which is comprised of two modules. The first module is a wireless sensor network-based system to monitor real-time crop health status. The second module uses a low altitude remote sensing platform to obtain multi-spectral imagery, which is further processed to classify healthy and unhealthy crops. We also highlight the results obtained using a case study and list the challenges and future directions based on our work