OPTYMALIZACJA OBLICZEŃ TRÓJWYMIAROWYCH LOKALNYCH MAP KIERUNKOWYCH Z UŻYCIEM ŚRODOWISKA OBLICZENIOWEGO MATLAB

This paper presents the development and evaluation of a new approach toward the optimization of 3D local orientation map calculation in the Matlab framework. This new approach can be detailed as: optimize eigenvector calculation for PCA analysis of X-ray micro tomography images of lamellar Titanium alloys image. We use two different methods to find the eigenvector of the largest eigenvalue and compare them with the Matlab built-in function (eigs). The results show a steep decrease of the calculation time using the authors' method compared to the Matlab built-in function.W artykule przedstawiono rozwój i ocenę nowego podejścia dotyczącego optymalizacji obliczeń 3D lokalnych orientacji map w środowiska Matlab. Zastosowano dwie różne metody wyznaczania wektora własnego największej wartości własnej. Wyniki są porównywane z wynikami otrzymanymi przy pomocy wbudowanych w pakiecie Matlab funkcji wyznaczające wektory i wartości własne. Wyniki porównania pokazują redukcję czasu obliczeń przy użyciu autorskiej metody w stosunku do funkcji wbudowanej w Matlab

KLASTERYZACJA K-ŚREDNICH OBRAZÓW TEKSTUROWYCH LAMELARNYCH MIKROSTRUKTUR STOPÓW TYTANU

This paper presents an implementation of the k-means clustering method, to segment cross sections of X-ray micro tomographic images of lamellar Titanium alloys. It proposes an approach for estimating the optimal number of clusters by analyzing the histogram of the local orientation map of the image and the choice of the cluster centroids used to initialize k-means. This is compared with the classical method considering random coordinates of the clusters.W artykule przedstawiono implementację metody klasteryzacji k-średnich, do segmentacji dwuwymiarowych rentgenowskich obrazów mikro tomograficznych lamelarnych stopów tytanu. Zaproponowano metody szacowania optymalnej liczbę klastrów oraz wyboru centro idów poprzez analizę histogramu mapy lokalnych kierunków obrazu. Dokonano porównania zaproponowanych metod z losowym doborem początkowego położenia klastrów

SURVEILLANCE OF MULTIDRUG-RESISTANT UROPATHOGENIC ESCHERICHIA COLI IN HOSPITALIZED PATIENTS AND COMMUNITY SETTINGS IN THE SOUTH OF LEBANON

Urinary Tract Infection (UTI) is one of the common infectious diseases in both hospitals as well as community settings; they are recognized to be among the most serious worldwide bacterial infections impacting 150 million people globally every year. The purpose of this study was to assess the changing antibiotics resistance profile for uropathogenic Escherichia coli isolated from community and hospital setting over a period of time (2018–2019) with a special emphasis on ESBL/MDR producing Escherichia coli. A descriptive retrospective study was conducted among patients with uropathogenic Escherichia coli from both community and hospital settings in south Lebanon. Out of 863 patients with positive uropathogenic Escherichia coli, 451 (52.25 %) comes from the community while 412 (47.74 %) came from the hospital settings. Almost 60.83 % are not Extended Spectrum Beta-Lactamases (ESBL), 31.4 % ESBL, and 7.76 % Multiple drug resistance (MDR). The majority of urinary tract infections are related to the female population (78.21 %). The most vulnerable age for both gender to develop UTI belong to elderly population (\u3e64 years) which account 37.19 % of all isolates. Statistically, we observed a high resistance rate toward all antibiotics using in the treatment of urinary tract infections such as Cefixime (45.30 %), Sulfamethoxazole (44.95 %), Ciprofloxacin (38.23 %) and Augmentin (38.93 %). A statistically significant association was observed between risk factors for hospitalized patients and all age categories with (P \u3c 0.05). Susceptibility profiles are critical to be evaluated in countries such as Lebanon where excessive use of antibiotics is observed at all levels. Therefore, this finding is useful for the determination of appropriate antimicrobial treatment in UTI patients that are caused by Escherichia coli and to follow the antimicrobial stewardship program to reduce the rate of resistance toward antibiotics

Recognizing Different Foot Deformities Using FSR Sensors by Static Classification of Neural Networks

تُعَدُّ أنظمة النعال الحسّاسة للحركة تقنية واعدة للعديد من التطبيقات في الرعاية الصحية والرياضة. حيث يمكن أن توفّر هذه الأنظمة معلومات قيّمة حول توزيع الضغط على القدم وأنماط المشي لأفراد مختلفين. ومع ذلك، فإن تصميم وتنفيذ مثل هذه الأنظمة يواجه العديد من التحديات، مثل اختيار الحسّاسات والمعايرة ومعالجة البيانات والتفسير. في هذه الدراسة، نقترح نظام نعل حساس باستخدام مقاومات استشعار القوى  لقياس الضغط المطبّق من القدم على مناطق مختلفة من النعل. يقوم هذا النظام بتصنيف أربعة أنواع من تشوهات القدم: طبيعي، مسطح، انحراف القدم إلى الداخل، وزيادة انحراف القدم إلى الخارج. تستخدم مرحلة التصنيف قيم الضغط الفرقية على نقاط الضغط كمدخلات لنموذج التغذية الأمامية للشبكات العصبية. تم جمع البيانات من 60 فرداً تم تشخيصهم بالحالات المدروسة. حقق تنفيذ التغذية الأمامية للشبكات العصبية دقة بنسبة 96.6٪ باستخدام 50٪ من المجموعة البيانية كبيانات تدريبية و 92.8٪ باستخدام 30٪ من البيانات التدريبية فقط. ويوضح المقارنة مع الأعمال ذات الصلة الأثر الإيجابي لاستخدام القيم الفرق لنقاط الضغط كمدخلات للشبكات العصبية مقارنة بالبيانات الأولية.Sensing insole systems are a promising technology for various applications in healthcare and sports. They can provide valuable information about the foot pressure distribution and gait patterns of different individuals. However, designing and implementing such systems poses several challenges, such as sensor selection, calibration, data processing, and interpretation. This paper proposes a sensing insole system that uses force-sensitive resistors (FSRs) to measure the pressure exerted by the foot on different regions of the insole. This system classifies four types of foot deformities: normal, flat, over-pronation, and excessive supination. The classification stage uses the differential values of pressure points as input for a feedforward neural network (FNN) model. Data acquisition involved 60 subjects diagnosed with the studied cases. The implementation of FNN achieved an accuracy of 96.6% using 50% of the dataset as training data and 92.8% using only 30% training data. The comparison with related work shows good impact of using the differential values of pressure points as input for neural networks compared with raw data

An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles

Large datasets are now ubiquitous as technology enables higher-throughput experiments, but rarely can a research field truly benefit from the research data generated due to inconsistent formatting, undocumented storage or improper dissemination. Here we extract all the meaningful device data from peer-reviewed papers on metal-halide perovskite solar cells published so far and make them available in a database. We collect data from over 42,400 photovoltaic devices with up to 100 parameters per device. We then develop open-source and accessible procedures to analyse the data, providing examples of insights that can be gleaned from the analysis of a large dataset. The database, graphics and analysis tools are made available to the community and will continue to evolve as an open-source initiative. This approach of extensively capturing the progress of an entire field, including sorting, interactive exploration and graphical representation of the data, will be applicable to many fields in materials science, engineering and biosciences

Morphometric comparison of Simulium perflavum larvae (Diptera: Simuliidae) in relation to season and gender in Central Amazônia, Brazil

Number of larval instars, age structure and environmental effects on these parameters represent basic information in the study of insect population biology. When species have economic importance, this information is essential in order to choose the best period to apply different control methods and to determine the stages of the life cycle of the insect that are most susceptible to each treatment. The family Simuliidae has many species of medical/veterinary importance in the world, and some studies in the temperate region have suggested that the number of larval instars and the larval size can vary according to the season, gender and some environmental factors, such as temperature and diet. This study, with the zoophilic species Simulium perflavum Roubaud, is the first in the Neotropics observing some of these factors and will serve as a template for other species of medical importance in the region. S. perflavum larvae were collected in five streams in Central Amazônia (Manaus and Presidente Figueiredo counties, State of Amazonas), in Sept./Oct. 1996 (dry season) and Feb./Mar. 1997 (rainy season). These larvae were measured (lateral length of head capsule and width of cephalic apodema) to determine the number of larval instars (n=3985), to compare the larval size between seasons and genders (last and penultimate larval instars, n=200). Seven larval instars were determined for this species using frequency distributions, t-tests and Crosby´s growth rule. Significant differences were not detected (t-test, p>0.05) in larval size between seasons and genders. Our results differ from some found in temperate regions suggesting that in the Neotropical region the larval size in different seasons and different genders remains constant, although some environmental parameters, such as diet, change depending on the season