Face recognition-based real-time system for surveillance

The ability to automatically recognize human faces based on dynamic facial images is important in security, surveillance and the health/independent living domains. Specific applications include access control to secure environments, identification of individuals at a particular place and intruder detection. This research proposes a real-time system for surveillance using cameras. The process is broken into two steps: (1) face detection and (2) face recognition to identify particular persons. For the first step, the system tracks and selects the faces of the detected persons. An efficient recognition algorithm is then used to recognize detected faces with a known database. The proposed approach exploits the Viola-Jones method for face detection, the Kanade-Lucas-Tomasi algorithm as a feature tracker and Principal Component Analysis (PCA) for face recognition. This system can be implemented at different restricted areas, such as at the office or house of a suspicious person or at the entrance of a sensitive installation. The system works almost perfectly under reasonable lighting conditions and image depths

Assessing Mechanical Properties of Jute, Kenaf, and Pineapple Leaf Fiber-Reinforced Polypropylene Composites: Experiment and Modelling

The application of natural fibers is increasing rapidly in the polymer-based composites. This study investigates manufacturing and characterization of polypropylene (PP) based composites reinforced with three different natural fibers: jute, kenaf, and pineapple leaf fiber (PALF). In each case, the fiber weight percentages were varied by 30 wt.%, 35 wt.%, and 40 wt.%. Mechanical properties such as tensile, flexural, and impact strengths were determined by following the relevant standards. Fourier transform infrared (FTIR) spectroscopy was employed to identify the chemical interactions between the fiber and the PP matrix material. Tensile strength and Izod impact strength of the composites significantly increased for all the composites with different fiber contents when compared to the pure PP matrix. The tensile moduli of the composites were compared to the values obtained from two theoretical models based on the modified “rule of mixtures” method. Results from the modelling agreed well with the experimental results. Tensile strength (ranging from 43 to 58 MPa), flexural strength (ranging from 53 to 67 MPa), and impact strength (ranging from 25 to 46 kJ/m2) of the composites significantly increased for all the composites with different fiber contents when compared to the pure PP matrix having tensile strength of 36 MPa, flexural strength of 53 Mpa, and impact strength of 22 kJ/m2. Furthermore, an improvement in flexural strength but not highly significant was found for majority of the composites. Overall, PALF-PP displayed better mechanical properties among the composites due to the high tensile strength of PALF. In most of the cases, T98 (degradation temperature at 98% weight loss) of the composite samples was higher (532–544 °C) than that of 100% PP (500 °C) matrix. Fractured surfaces of the composites were observed in a scanning electron microscope (SEM) and analyses were made in terms of fiber matrix interaction. This comparison will help the researcher to select any of the natural fiber for fiber-based reinforced composites according to the requirement of the final product

Staircase Detection to Guide Visually Impaired People: A Hybrid Approach

Eyes and visionary organs are an essential part of human physiology, since they are capable of receiving and processing subtle details to the brain. Some individuals are not capable to visually perceive things around the environment. They face various types of hindrances such as obstacles, potholes, staircases, pedestrians and vehicles in the daily life and are not able to navigate themselves without a guidance. This study aims to help visually impaired people to navigate around the surroundings. A hybrid approach was developed for the detection of staircase and the ground using a pre-trained model and an ultrasonic sensor. In the proposed system, staircase images are captured via an RGBD camera and compared with pre-trained template images for detection. The developed system that employs an ultrasonic sensor, an RGBD camera, a raspberry pi and a buzzer is installed on a stick. Under a variety of conditions, the proposed system was tested using different stair images and achieved an accuracy of 98.73% in average. The system works well under various conditions, such as dark and noise

Re-use of Plastic Products–Materials Perspective

Environmental pollution by plastic waste is causing a major issue around the world. In this article, focus was given on the re-use of plastics to minimize or prevent the plastic waste. First, the plastic materials are classified based on the origin, functional characteristics and recycling characteristics. A general overview on the rationale for using plastic and the pollutions caused by the plastic products are provided. Some data related to the production of plastic are presented to give an idea about the scale. Plastic waste management strategies are briefly discussed. Finally, the importance of re-using plastic and how circular economy can play a vital role in re-using plastic materials are discussed

Study on Advanced Image Processing Techniques for Remote Sensor Data Analysis

Image processing is the act of altering photographs with arithmetic computations for a variety of objectives including erasing clean lines, dividing groups of objects, noise removal from photos, detecting edges, and so on. Remote sensing is a method for observing the planet's surface or environment through satellites or from the air (via airplanes). Electromagnetic radiation reflected or radiated by the earth's surface is recorded using this approach. Numerous techniques and technologies can be used in remote sensing to monitor electromagnetic waves of different wave lengths, including visual, infrared, heat infrared, and so forth. In this study, digital cameras are utilized to acquire photos in the visible range and then used advanced image processing and filtering algorithms to the raw data. Homomorphic filtering, median filtering, Gaussian filtering, histogram-based thresholding, contrast stretching, and other techniques are used. The results of this research will be valuable for enhanced picture analysis

Effect of Nano-Clay and Jute Varieties on the Structural, Mechanical, and Thermal Properties of Polyester Composite

Two varieties of jute fiber, tossa (Corchorus olitorius) and white (Corchorus capsularis), were combined with nano-clay (montmorillonite clay) modified polyester to fabricate composites using the hand lay-up method. The goal was to examine the impact of nano-clay on structural, mechanical, and thermal properties. The two types of jute fibers were first treated with NaOH, and the polyester was then modified with montmorillonite nano-clay (1, 2, 3) (wt%). Finally, 30 cm long unidirectional fibers were used to fabricate a composite (fiber volume fraction is 19.3%). Accordingly, the structural (crystallinity), mechanical, and thermal properties of the composite were examined using the XRD, UTM, and TGA apparatus. Regarding crystallinity, treating the tossa jute fiber and its composite with nano-clay demonstrated improved crystallinity indices of 75.24% and 75.52%. The Tossa jute fiber composite treated with 1% nano-clay exhibits an increased tensile strength of 97.8805 MPa, which is 10.92% higher than the treated fiber without NC, and a flexural strength of 159.79 MPa, which is 3.12% higher than the treated fiber without NC). In comparison to white jute composite, the treated tossa jute fiber composite showed improved thermal stability; the addition of 3% nano-clay also demonstrated higher thermal stability. The use of modified polyester in composites along with nano-clay produces better outcomes in every way

Assessing Mechanical Properties of Jute, Kenaf, and Pineapple Leaf Fiber-Reinforced Polypropylene Composites: Experiment and Modelling

The application of natural fibers is increasing rapidly in the polymer-based composites. This study investigates manufacturing and characterization of polypropylene (PP) based composites reinforced with three different natural fibers: jute, kenaf, and pineapple leaf fiber (PALF). In each case, the fiber weight percentages were varied by 30 wt.%, 35 wt.%, and 40 wt.%. Mechanical properties such as tensile, flexural, and impact strengths were determined by following the relevant standards. Fourier transform infrared (FTIR) spectroscopy was employed to identify the chemical interactions between the fiber and the PP matrix material. Tensile strength and Izod impact strength of the composites significantly increased for all the composites with different fiber contents when compared to the pure PP matrix. The tensile moduli of the composites were compared to the values obtained from two theoretical models based on the modified “rule of mixtures” method. Results from the modelling agreed well with the experimental results. Tensile strength (ranging from 43 to 58 MPa), flexural strength (ranging from 53 to 67 MPa), and impact strength (ranging from 25 to 46 kJ/m2) of the composites significantly increased for all the composites with different fiber contents when compared to the pure PP matrix having tensile strength of 36 MPa, flexural strength of 53 Mpa, and impact strength of 22 kJ/m2. Furthermore, an improvement in flexural strength but not highly significant was found for majority of the composites. Overall, PALF-PP displayed better mechanical properties among the composites due to the high tensile strength of PALF. In most of the cases, T98 (degradation temperature at 98% weight loss) of the composite samples was higher (532–544 °C) than that of 100% PP (500 °C) matrix. Fractured surfaces of the composites were observed in a scanning electron microscope (SEM) and analyses were made in terms of fiber matrix interaction. This comparison will help the researcher to select any of the natural fiber for fiber-based reinforced composites according to the requirement of the final product

Global fertility in 204 countries and territories, 1950–2021, with forecasts to 2100: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BackgroundAccurate assessments of current and future fertility—including overall trends and changing population age structures across countries and regions—are essential to help plan for the profound social, economic, environmental, and geopolitical challenges that these changes will bring. Estimates and projections of fertility are necessary to inform policies involving resource and health-care needs, labour supply, education, gender equality, and family planning and support. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 produced up-to-date and comprehensive demographic assessments of key fertility indicators at global, regional, and national levels from 1950 to 2021 and forecast fertility metrics to 2100 based on a reference scenario and key policy-dependent alternative scenarios. MethodsTo estimate fertility indicators from 1950 to 2021, mixed-effects regression models and spatiotemporal Gaussian process regression were used to synthesise data from 8709 country-years of vital and sample registrations, 1455 surveys and censuses, and 150 other sources, and to generate age-specific fertility rates (ASFRs) for 5-year age groups from age 10 years to 54 years. ASFRs were summed across age groups to produce estimates of total fertility rate (TFR). Livebirths were calculated by multiplying ASFR and age-specific female population, then summing across ages 10–54 years. To forecast future fertility up to 2100, our Institute for Health Metrics and Evaluation (IHME) forecasting model was based on projections of completed cohort fertility at age 50 years (CCF50; the average number of children born over time to females from a specified birth cohort), which yields more stable and accurate measures of fertility than directly modelling TFR. CCF50 was modelled using an ensemble approach in which three sub-models (with two, three, and four covariates variously consisting of female educational attainment, contraceptive met need, population density in habitable areas, and under-5 mortality) were given equal weights, and analyses were conducted utilising the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. To capture time-series trends in CCF50 not explained by these covariates, we used a first-order autoregressive model on the residual term. CCF50 as a proportion of each 5-year ASFR was predicted using a linear mixed-effects model with fixed-effects covariates (female educational attainment and contraceptive met need) and random intercepts for geographical regions. Projected TFRs were then computed for each calendar year as the sum of single-year ASFRs across age groups. The reference forecast is our estimate of the most likely fertility future given the model, past fertility, forecasts of covariates, and historical relationships between covariates and fertility. We additionally produced forecasts for multiple alternative scenarios in each location: the UN Sustainable Development Goal (SDG) for education is achieved by 2030; the contraceptive met need SDG is achieved by 2030; pro-natal policies are enacted to create supportive environments for those who give birth; and the previous three scenarios combined. Uncertainty from past data inputs and model estimation was propagated throughout analyses by taking 1000 draws for past and present fertility estimates and 500 draws for future forecasts from the estimated distribution for each metric, with 95% uncertainty intervals (UIs) given as the 2·5 and 97·5 percentiles of the draws. To evaluate the forecasting performance of our model and others, we computed skill values—a metric assessing gain in forecasting accuracy—by comparing predicted versus observed ASFRs from the past 15 years (2007–21). A positive skill metric indicates that the model being evaluated performs better than the baseline model (here, a simplified model holding 2007 values constant in the future), and a negative metric indicates that the evaluated model performs worse than baseline. FindingsDuring the period from 1950 to 2021, global TFR more than halved, from 4·84 (95% UI 4·63–5·06) to 2·23 (2·09–2·38). Global annual livebirths peaked in 2016 at 142 million (95% UI 137–147), declining to 129 million (121–138) in 2021. Fertility rates declined in all countries and territories since 1950, with TFR remaining above 2·1—canonically considered replacement-level fertility—in 94 (46·1%) countries and territories in 2021. This included 44 of 46 countries in sub-Saharan Africa, which was the super-region with the largest share of livebirths in 2021 (29·2% [28·7–29·6]). 47 countries and territories in which lowest estimated fertility between 1950 and 2021 was below replacement experienced one or more subsequent years with higher fertility; only three of these locations rebounded above replacement levels. Future fertility rates were projected to continue to decline worldwide, reaching a global TFR of 1·83 (1·59–2·08) in 2050 and 1·59 (1·25–1·96) in 2100 under the reference scenario. The number of countries and territories with fertility rates remaining above replacement was forecast to be 49 (24·0%) in 2050 and only six (2·9%) in 2100, with three of these six countries included in the 2021 World Bank-defined low-income group, all located in the GBD super-region of sub-Saharan Africa. The proportion of livebirths occurring in sub-Saharan Africa was forecast to increase to more than half of the world's livebirths in 2100, to 41·3% (39·6–43·1) in 2050 and 54·3% (47·1–59·5) in 2100. The share of livebirths was projected to decline between 2021 and 2100 in most of the six other super-regions—decreasing, for example, in south Asia from 24·8% (23·7–25·8) in 2021 to 16·7% (14·3–19·1) in 2050 and 7·1% (4·4–10·1) in 2100—but was forecast to increase modestly in the north Africa and Middle East and high-income super-regions. Forecast estimates for the alternative combined scenario suggest that meeting SDG targets for education and contraceptive met need, as well as implementing pro-natal policies, would result in global TFRs of 1·65 (1·40–1·92) in 2050 and 1·62 (1·35–1·95) in 2100. The forecasting skill metric values for the IHME model were positive across all age groups, indicating that the model is better than the constant prediction. InterpretationFertility is declining globally, with rates in more than half of all countries and territories in 2021 below replacement level. Trends since 2000 show considerable heterogeneity in the steepness of declines, and only a small number of countries experienced even a slight fertility rebound after their lowest observed rate, with none reaching replacement level. Additionally, the distribution of livebirths across the globe is shifting, with a greater proportion occurring in the lowest-income countries. Future fertility rates will continue to decline worldwide and will remain low even under successful implementation of pro-natal policies. These changes will have far-reaching economic and societal consequences due to ageing populations and declining workforces in higher-income countries, combined with an increasing share of livebirths among the already poorest regions of the world. FundingBill & Melinda Gates Foundation

The global burden of cancer attributable to risk factors, 2010-19: a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% 47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% 32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% 27.9-42.8] and 33.3% 25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license