3,510 research outputs found
Maternal nutrition, maternal body composition during pregnancy and neonatal bone mass
No full textAims: to determine the maternal lifestyle and anthropometric factors before and during pregnancy that influence in utero and childhood bone accrual. In addition, to characterize the environmental predictors of changes in maternal bone mass, as measured by quantitative ultrasound of the calcaneus (QUS), during pregnancy. Methods: A cohort of healthy women was assessed before and during pregnancy and their offspring underwent anthropometric assessment, including whole body DXA, in the neonatal period. A second, older, birth cohort, now aged nine years, with records of their mother’s lifestyle and anthropometry during pregnancy, had anthropometric assessment including whole body and lumbar spine DXA. Results: Maternal fat stores, smoking in late pregnancy and parental height independently predicted neonatal whole body bone mass. Of these factors, maternal fat stores and height had persisting effects on childhood bone mass. In addition, there was a significant decline in maternal calcaneal QUS during pregnancy; greater loss was predicted by reduced triceps skin fold thickness, nulliparity, low milk intake in the pre-pregnancy period and being pregnant over the winter months. After adjustment for maternal size, greater SOS decline was associated with greater neonatal bone area and mineral content. Of the predictors of childhood anthropometry, birth weight and size predicted bone and lean mass at age nine years but not fat mass. Maternal height and cord blood calcium were independent determinants of bone mineral content at age nine years. Conclusion: We have demonstrated that maternal body build and lifestyle influence bone mineral accrual in the developing foetus and have persistent effects on post-natal growth, supporting the programming of skeletal growth by the maternal environment. The mechanism may involve maternal effects on foetal calcium homeostasis.</p
Expression through Hands_PSL
No full textCite following:Citation | Sameena Javaid, Safdar Rizvi, Muhammad Talha Ubaid, Abdou Darboe, Shakir.Mahmood. Mayo “Interpretation of Expressions through Hand Signs Using Deep LearningTechniques,” International Journal of Innovations in Science and Technology, vol. 4, no. 2, pp. 596–611, 2022.The collected dataset used in this research with a mobile device OPPO A76 having dual Cameras: 13MP, f/2.2 and LED flash. The selection of the gestures is based on the basic seven expressions of sad, happy, neutral, disgust, scared, angry, and surprise. Here to elaborate on the seven basic expressions through hand we have selected seven adjectives from Pakistan Sign Language, which are expressed as follows: disgust feeling is expressed by a bad adjective, the neutral feeling is expressed by the best adjective, the happy feeling is elaborated by glad as an adjective, the sad feeling is associated to the sad adjective, just like scared expression is associated with the scared adjective, the further the stiff adjective expresses the further angry feeling surprise expression has the same surprise adjective in PSL to portray action
Enhancing Smart City Functions through the Mitigation of Electricity Theft in Smart Grids: A Stacked Ensemble Method
Get PDFSmart grid is the primary stakeholder in smart cities integrated with modern technologies as the Internet of Things (IoT), smart healthcare systems, industrial IoT, renewable energy, energy communities, and the 6G network. Smart grids provide bidirectional power and information flow by integrating many IoT devices and software. These advanced IOTs and cyber layers introduced new types of vulnerabilities and could compromise the stability of smart grids. Some anomalous consumers leverage these vulnerabilities, launch theft attacks on the power system, and steal electricity to lower their electricity bills. The recent developments in numerous detection methods have been supported by cutting-edge machine learning (ML) approaches. Even so, these recent developments are practically not robust enough because of the limitations of single ML approaches employed. This research introduced a stacked ensemble method for electricity theft detection (ETD) in a smart grid. The framework detects anomalous consumers in two stages; in the first stage, four powerful classifiers are stacked and detect suspicious activity, and the output of these consumers is fed to a single classifier for the second-stage classification to get better results. Furthermore, we incorporate kernel principal component analysis (KPCA) and localized random affine shadow sampling (LoRAS) for feature engineering and data augmentation. We also perform comparative analysis using adaptive synthesis (ADASYN) and independent component analysis (ICA). The simulation findings reveal that the proposed model outperforms with 97% accuracy, 97% AUC score, and 98% precision
Stacked machine learning models for non-technical loss detection in smart grid: A comparative analysis
Get PDFThe growing prominence and emphasis of renewable energy to decrease carbonization in the power system and reduce the dependability of fossil fuel for energy needs play an important role in the development of smart grids. Many technological advancements are integrated into smart grid to optimize the power system and renewable energy sources. Smart grid leverages electricity and energy consumption data exchange to establish a significantly advanced, automated, and decentralized electricity network. However, this brings numerous vulnerabilities to the power system, including cyber-attacks, grid blackouts, and electricity theft. While the most significant concern is energy theft, where some culprit's consumers manipulate their energy meters to reduce their readings. This destabilizes the country's electricity utility and economic development and causes a high tariff on energy for consumers who pay the bill. Therefore, developing an advanced framework for electricity theft detection is necessary. To address this problem, we propose a machine learning-based stacked framework to detect malicious activity in the smart grid. The proposed data-based stacked ensemble model detects honest and anomalous consumers in two stages. In the first stage, the model employs four individual classifiers at the base level to analyze data and a single classifier at the meta-level to classify the results of the base learners for the second stage classification. Furthermore, the Borderline SMOTE and Principle Component Analysis techniques are employed to address the class imbalance and curse of dimensionality issues respectively. Through experimental analysis, we proved the effectiveness of the proposed framework in detecting suspicious activity in four different experiments, including preprocessed data, feature extracted data, balanced data, and lastly, both feature engineering and data balancing. The simulation outcomes demonstrate that our proposed framework enhanced energy security and overcomes the impact of theft attacks on the smart grid
Prenatal and childhood influences on osteoporosis
No full textOsteoporosis is a major cause of morbidity and mortality through its association with age-related fractures. Although most effort in fracture prevention has been directed at retarding the rate of age-related bone loss, and reducing the frequency and severity of trauma among elderly people, evidence is growing that peak bone mass is an important contributor to bone strength during later life. The normal patterns of skeletal growth have been well characterized in cross-sectional and longitudinal studies. It has been confirmed that boys have higher bone mineral content, but not volumetric bone density, than girls. Furthermore, in both genders there is a dissociation between the peak velocities for height gain and bone mineral accrual. Puberty is the period during which volumetric density appears to increase in both axial and appendicular sites. Many factors influence the accumulation of bone mineral during childhood and adolescence, including heredity, gender, diet, physical activity, endocrine status and sporadic risk factors such as cigarette smoking. Measures for maximizing bone mineral acquisition, particularly through encouraging physical activity and adequate dietary calcium intake, are likely to affect the risk of fracture in later generations. In addition to these modifiable factors during childhood, evidence has also accrued that the risk of fracture might be programmed during intrauterine life. Epidemiological studies have demonstrated a relationship between birthweight, weight in infancy and adult bone mass. This appears to be mediated through modulation of the set-point for basal activity of pituitary-dependent endocrine systems such as the hypothalamic - pitutiary - adrenal (HPA) and growth hormone/insulin-like growth factor I (GH/IGF-I) axes. Maternal smoking, diet and physical activity levels appear to modulate bone mineral acquisition during intrauterine life; furthermore, both low birth size and poor childhood growth are directly linked to the later risk of hip fracture. The optimization of maternal nutrition and intrauterine growth should also be included within preventive strategies against osteoporotic fracture, albeit for future generations
Awareness Regarding HIV AIDS Among Non Medical u niversity Students (Mehroash Irshad,Muhammad Javaid,Maryum Irshad,Nudrat Jahan,Sehar Javaid,Arisha Qawal,Aqsa Iqbal,Nadia Khalid)
No full textObjectives: To assess the awareness regarding HIV-AIDS among non medical students.Materials and Methods: This descriptive cross sectional Institute based study was conducted by the fourth yearmedical students of Bahria University Medical and Dental College, Karachi as their assigned project in the subjectof Community Health Sciences. The study was carried out among non-medical students of NUST, NED and BahriaUniversity, Karachi from Jan 2013 – June 2013. A five question, knowledge based questionnaire developed fromCarey and Schroder was used to assess the awareness of the students regarding HIV-AIDS. Convenient samplingtechnique was used for selecting the participants. After verbal informed consent 105students participated in thestudy. Five questionnaire forms were excluded due to incomplete filling.Results: A total of 133 students were approached and 105 (79%) responded that they were aware of the term HIVAIDS.100 students completely filled out the proforma and out of these only39% responded that they knew therelationship of HIV positive and having AIDS. 90% responded in favor of sexual contact as the main mode oftransmission. Homosexuals were regarded to be the highest risk group (71%) for having HIV-AIDS by the students.Regarding preventive measures highest response (50%) came in favor of commercial sex control.Conclusions: Assessment of awareness regarding HIV-AIDS among non medical students was found to be deficientin context to relationship of HIV positive and having AIDS, mode of transmission, high risk group, and preventiveand control measures
How to prevent fractures in the individual with osteoporosis
No full textThe high rate of osteoporotic fracture in Western populations has resulted in a significant burden in terms of morbidity, mortality and health care costs. The use of DXA has made the diagnosis of osteoporosis easier and identified a subgroup of individuals who are at a higher risk of fracture. It is a useful tool in determining therapy in those at greatest risk of fracture. However, widespread use of such treatments is low and greater uptake remains an elusive goal. There are now many different treatments that reduce fracture rate, and can accompany lifestyle measures such as smoking cessation, diet and exercise. Dietary supplementation with calcium has been shown to reduce the risk of vertebral fracture, and the combination of calcium with vitamin D has been shown to reduce fracture at non-vertebral sites, including the hip. Although ERT, SERMs and tibolone all retard bone loss, prospective fracture prevention has only been shown for SERMs and then only at the spine. Bisphosphonates represent a class of potent anti-resorptive agents, which have been shown to reduce fracture rate at vertebral and non-vertebral sites. Other agents such as calcitonin, PTH and fluoride are of less certain benefit in preventing fracture
Intellectual Capital Performance and Profitability of Banks: Evidence from Pakistan
No full textThe study contributes to the existing literature on intellectual capital (IC) performance and profitability by extending evidence from Pakistan. The study examines the impact of IC performance on the profitability of Pakistani financial institutions. It further examines how corporate governance, bank specific, industry specific, and country specific indicators effect Pakistani banks’ profitability. The result reports both the linear and non-linear impact of IC performance on profitability, which affirms an inverted U–shaped relationship. Among the three value added intellectual coefficient (VAIC) components, capital employed efficiency (CEE), and human capital efficiency (HCE) are found to have a significantly positive and structural capital efficiency (SCE) is found to have a significantly negative impact on bank profitability. The study notes a positive impact on profitability of factors like board independence, directors’ compensation, and higher capitalization. It reports a negative impact on profitability of factors like board size, board meetings, credit risk, industry concentration and economic growth. The results also indicate low profitability of banks during the period of government transition. The study provides insights into the important profitability drives and suggests that the impact of investment in IC on profitability is limited to an extent. The findings of this study are likely to be useful for policy makers, management, and academics
Workshop: Distributed AI, machine learning, and emerging technologies for next-generation wireless networks
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