Transvaginal Ultrasonographic Findings of Infertile Females in Population of Lahore

Failure of conception in women after frequent unprotected intercourse for one year is known as infertility. Ultrasound examination can detect certain pathologies that may result in primary or secondary infertility. Objective: To describe the various transvaginal ultrasonographic findings of infertile females in population of Lahore. Methods: The study was started after the consent of ethical committee of the University of Lahore. All the patients were registered in this study after signing the informed consent form. Toshiba Xario with transvaginal transducer frequency ranging from 9-12 MHz was used. Pathologies were evaluated through transvaginal scanning and sonographic data was kept in the ultrasound machine. A consecutive sampling technique was used and data was further evaluated with the help of Statistical Package for the Social Sciences version 24. Results: Among 138 females, PCOS was seen in 40.6% of the subjects. The second most common pathology was ovarian cyst which was seen in 10.9% of subjects. And the incidence of multiple fibroids was 5.1% as third most common pathology in both primary and secondary infertility cases. Most ovarian pathologies were found to be bilateral. 12.3% subjects had no findings on ultrasound. Conclusion: PCOS was the utmost common pathology connected with primary and secondary infertility. The second and third most common pathologies were ovarian cyst and multiple fibroids respectively. Keywords: female infertility, PCOS, PID DOI: 10.7176/JBAH/10-12-05 Publication date:June 30th 202

A battery health monitoring method using machine learning: A data-driven approach

Batteries are combinations of electrochemical cells that generate electricity to power electrical devices. Batteries are continuously converting chemical energy to electrical energy, and require appropriate maintenance to provide maximum efficiency. Management systems having specialized monitoring features; such as charge controlling mechanisms and temperature regulation are used to prevent health, safety, and property hazards that complement the use of batteries. These systems utilize measures of merit to regulate battery performances. Figures such as the state-of-health (SOH) and state-of-charge (SOC) are used to estimate the performance and state of the battery. In this paper, we propose an intelligent method to investigate the aforementioned parameters using a data-driven approach. We use a machine learning algorithm that extracts significant features from the discharge curves to estimate these parameters. Extensive simulations have been carried out to evaluate the performance of the proposed method under different currents and temperatures

Heavy metals and radionuclides in Islamabad's industrial area: A comprehensive analysis of soil and water pollution, source apportionment and health effects using statistical and geospatial tools

Introduction: Heavy metal pollution in industrial areas around the world is a significant problem that is worsening day by day. Heavy and toxic metals are detrimental to human health and ecology in a region. Hence, determining their level and potential is critical to form effective pollution control strategies for reducing the risks associated with them. Study area: Islamabad is the capital city of Pakistan with a dedicated industrial zone. Purpose: This study evaluated the heavy metal pollution levels in the soil and water of Islamabad's industrial area and radionuclides activity in the soil using statistical, geospatial tools as well as their subsequent health and ecological hazards. Materials and methods: Elemental analysis in this study was performed using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). The radionuclides activity in the soil was quantified using High Purity Germanium Detector (HPGe). Results: The soil of Islamabad's industrial area is highly polluted with heavy and toxic metals. The risk posed by heavy metals to the eco-system was calculated using Ecological risk factor and was found to be above recommended level. Principal component analysis (PCA), Pearson's correlation and inverse distance weighting interpolation (IDW) revealed that the soil near the steel mills, landfill and marble processing plants is extremely polluted with heavy metals. Health hazards from heavy metal exposure through ingestion, inhalation, and dermal contact were also calculated. Cr posed a carcinogenic risk to children via the three exposure pathways with the value being 4 × 10−4. Natural radioactivity levels for Ra-226, Th-232 and K-40 were found to be 25.96±12.50, 15.84±2.59, 469.48±52.38 Bqkg−1, respectively. Elemental analysis of water samples coupled with geospatial analysis showed that water samples collected near the industrial complexes have elevated levels of Sb. The water quality for irrigation was assessed and the water from Islamabad's industrial area was found to be suitable for irrigation. Conclusion: In conclusion, the soil of Islamabad's industrial area is highly polluted with heavy metals and has lower levels of natural radioactivity. The water near the industries also has elevated levels of some heavy metals

Analysis of RSSI Fingerprinting in LoRa Networks

Localization has gained great attention in recent years, where different technologies have been utilized to achieve high positioning accuracy. Fingerprinting is a common technique for indoor positioning using short-range radio frequency (RF) technologies such as Bluetooth Low Energy (BLE). In this paper, we investigate the suitability of LoRa (Long Range) technology to implement a positioning system using received signal strength indicator (RSSI) fingerprinting. We test in real line-of-sight (LOS) and non-LOS (NLOS) environments to determine appropriate LoRa packet specifications for an accurate RSSI-to-distance mapping function. To further improve the positioning accuracy, we consider the environmental context. Extensive experiments are conducted to examine the performance of LoRa at different spreading factors. We analyze the path loss exponent and the standard deviation of shadowing in each environmen