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Access to sufficient clean water is important for reducing the risks from COVID-19. It is unclear, however, what influence COVID-19 has had on water insecurities. The objective of this study was to assess the associations between COVID-19 control measures and household water insecurities. A survey of 1559 individuals living in vulnerable communities in five countries (Cambodia, Laos, Myanmar, Thailand, Vietnam) showed that increased needs for clean water to wash hands or facemasks made it more likely a person was water insecure along those dimensions. Water insecurities with respect to handwashing and drinking, in turn, made adoption of the corresponding good practices less likely, whereas in the case of washing facemasks there was no association. Water system infrastructure, environmental conditions such as floods and droughts, as well as gender norms and knowledge, were also important for water insecurities and the adoption of good practices. As domestic water insecurities and COVID-19 control measures are associated with each other, efforts should therefore be directed at identifying and assisting the water insecure at high risk when COVID-19 reaches their communities.

The online version contains supplementary material available at 10.1007/s10668-022-02182-0.

The online version contains supplementary material available at 10.1007/s10668-022-02182-0.In effectiveness literature, voices are rising to embrace learning contents beyond mathematics, science, and language. Meanwhile, international policy documents such as the United Nations 2019 Climate Action Summit Report point at the importance of action for sustainable development for establishing acceptable life conditions for current and future generations. Therefore, a candidate learning outcome for broadening effectiveness research's scope is action competence in sustainable development (ACiSD), which consists of the relevant knowledge, willingness, capacity expectations, and outcome expectancy regarding actions for sustainable development. In order to initiate adding ACiSD as a learning outcome to effectiveness research, the current study contributed to establishing that formal education plays a part in changes in students' ACiSD. Firstly, we studied how much variance in ACiSD can be attributed to what happens in classrooms. Secondly, we looked into how class groups' and early adolescent students' ACiSD changed after one school year. Following recommendations for rigour in effectiveness research, we performed multilevel analyses on survey data (question one n = 1398; question two n = 633). Our evidence showed that 11% of variance in ACiSD was attributable to what happens in classrooms with explained variance in the subconstructs ranging between 7.2 and 14.2%. Furthermore, individual students as well as class groups showed higher ACiSD scores when comparing measurements at beginning and end of one school year. We conclude that the classroom level matters to changes in ACiSD within early adolescents. Further research can now look into how and to which extent teachers' educational approaches affect these changes.Concerns regarding high rates of teacher stress and burnout are present globally. Yet there is limited current data regarding the severity of stress, or the role of intrapersonal and environmental factors in relation to teacher stress and burnout within the Australian context. The present study, conducted over an 18-month period, prior to the COVID pandemic, surveyed 749 Australian teachers to explore their experience of work-related stress and burnout; differences in stress and burnout across different demographic groups within the profession; as well as the contributing role of intrapersonal and environmental factors, particularly, emotion regulation, subjective well-being, and workload. Results showed over half of the sample reported being very or extremely stressed and were considering leaving the profession, with early career teachers, primary teachers, and teachers working in rural and remote areas reporting the highest stress and burnout levels. Conditional process analyses highlighted the importance of emotion regulation, workload and subjective well-being in the development of teacher stress and some forms of burnout. Implications for educational practice are discussed.Diabetic Retinopathy (DR) is defined as the Diabetes Mellitus difficulty that harms the blood vessels in the retina. It is also known as a silent disease and cause mild vision issues or no symptoms. In order to enhance the chances of effective treatment, yearly eye tests are vital for premature discovery. Hence, it uses fundus cameras for capturing retinal images, but due to its size and cost, it is a troublesome for extensive screening. Therefore, the smartphones are utilized for scheming low-power, small-sized, and reasonable retinal imaging schemes to activate automated DR detection and DR screening. In this article, the new DIY (do it yourself) smartphone enabled camera is used for smartphone based DR detection. Initially, the preprocessing like green channel transformation and CLAHE (Contrast Limited Adaptive Histogram Equalization) are performed. Further, the segmentation process starts with optic disc segmentation by WT (watershed transform) and abnormality segmentation (Exudates, microaneurysms, haemoches.Breast cancer is one of the primary causes of death that is occurred in females around the world. So, the recognition and categorization of initial phase breast cancer are necessary to help the patients to have suitable action. However, mammography images provide very low sensitivity and efficiency while detecting breast cancer. Moreover, Magnetic Resonance Imaging (MRI) provides high sensitivity than mammography for predicting breast cancer. In this research, a novel Back Propagation Boosting Recurrent Wienmed model (BPBRW) with Hybrid Krill Herd African Buffalo Optimization (HKH-ABO) mechanism is developed for detecting breast cancer in an earlier stage using breast MRI images. Initially, the MRI breast images are trained to the system, and an innovative Wienmed filter is established for preprocessing the MRI noisy image content. Moreover, the projected BPBRW with HKH-ABO mechanism categorizes the breast cancer tumor as benign and malignant. Additionally, this model is simulated using Python, and the performance of the current research work is evaluated with prevailing works. Hence, the comparative graph shows that the current research model produces improved accuracy of 99.6% with a 0.12% lower error rate.As everyone knows that in today's time Artificial Intelligence, Machine Learning and Deep Learning are being used extensively and generally researchers are thinking of using them everywhere. At the same time, we are also seeing that the second wave of corona has wreaked havoc in India. More than 4 lakh cases are coming in 24 h. In the meantime, news came that a new deadly fungus has come, which doctors have named Mucormycosis (Black fungus). This fungus also spread rapidly in many states, due to which states have declared this disease as an epidemic. It has become very important to find a cure for this life-threatening fungus by taking the help of our today's devices and technology such as artificial intelligence, data learning. It was found that the CT-Scan has much more adequate information and delivers greater evaluation validity than the chest X-Ray. Deutivacaftor clinical trial After that the steps of Image processing such as pre-processing, segmentation, all these were surveyed in which it was found that accuracy score for the deep features retrieved from the ResNet50 model and SVM classifier using the Linear kernel function was 94.7%, which was the highest of all the findings. Also studied about Deep Belief Network (DBN) that how easy it can be to diagnose a life-threatening infection like fungus. Then a survey explained how computer vision helped in the corona era, in the same way it would help in epidemics like Mucormycosis.In current times, after the rapid expansion and spread of the COVID-19 outbreak globally, people have experienced severe disruption to their daily lives. One idea to manage the outbreak is to enforce people wear a face mask in public places. Therefore, automated and efficient face detection methods are essential for such enforcement. In this paper, a face mask detection model for static and real time videos has been presented which classifies the images as "with mask" and "without mask". The model is trained and evaluated using the Kaggle data-set. The gathered data-set comprises approximately about 4,000 pictures and attained a performance accuracy rate of 98%. The proposed model is computationally efficient and precise as compared to DenseNet-121, MobileNet-V2, VGG-19, and Inception-V3. This work can be utilized as a digitized scanning tool in schools, hospitals, banks, and airports, and many other public or commercial locations.Smoking cessation efforts can be greatly influenced by providing just-in-time intervention to individuals who are trying to quit smoking. Detecting smoking activity accurately among the confounding activities of daily living (ADLs) being monitored by the wearable device is a challenging and intriguing research problem. This study aims to develop a machine learning based modeling framework to identify the smoking activity among the confounding ADLs in real-time using the streaming data from the wrist-wearable IMU (6-axis inertial measurement unit) sensor. A low-cost wrist-wearable device has been designed and developed to collect raw sensor data from subjects for the activities. A sliding window mechanism has been used to process the streaming raw sensor data and extract several time-domain, frequency-domain, and descriptive features. Hyperparameter tuning and feature selection have been done to identify best hyperparameters and features respectively. Subsequently, multi-class classification models are developed and validated using in-sample and out-of-sample testing. The developed models obtained predictive accuracy (area under receiver operating curve) up to 98.7% for predicting the smoking activity. The findings of this study will lead to a novel application of wearable devices to accurately detect smoking activity in real-time. It will further help the healthcare professionals in monitoring their patients who are smokers by providing just-in-time intervention to help them quit smoking. The application of this framework can be extended to more preventive healthcare use-cases and detection of other activities of interest.

The online version contains supplementary material available at 10.1007/s11042-022-12349-6.

The online version contains supplementary material available at 10.1007/s11042-022-12349-6.Digital medical images contain important information regarding patient's health and very useful for diagnosis. Even a small change in medical images (especially in the region of interest (ROI)) can mislead the doctors/practitioners for deciding further treatment. Therefore, the protection of the images against intentional/unintentional tampering, forgery, filtering, compression and other common signal processing attacks are mandatory. This manuscript presents a multipurpose medical image watermarking scheme to offer copyright/ownership protection, tamper detection/localization (for ROI (region of interest) and different segments of RONI (region of non-interest)), and self-recovery of the ROI with 100% reversibility. Initially, the recovery information of the host image's ROI is compressed using LZW (Lempel-Ziv-Welch) algorithm. Afterwards, the robust watermark is embedded into the host image using a transform domain based embedding mechanism. Further, the 256-bit hash keys are generated using SHA-256 algorithm for the ROI and eight RONI regions (i.