Spatiotemporal variation in land use land cover in the response to local climate change using multispectral remote sensing data

Climate change is likely to have serious social, economic, and environmental impacts on farmers whose subsistence depends on nature. Land Use Land Cover (LULC) changes were examined as a significant tool for assessing changes at diverse temporal and spatial scales. Normalized Difference Vegetation Index (NDVI) has the potential ability to signify the vegetation structures of various eco-regions and provide valuable information as a remote sensing tool in studying vegetation phenology cycles. In this study, we used remote sensing and Geographical Information System (GIS) techniques with Maximum Likelihood Classification (MLC) to identify the LULC changes for 40 years in the Sahiwal District. Later, we conducted 120 questionnaires administered to local farmers which were used to correlate climate changes with NDVI. The LULC maps were prepared using MLC and training sites for the years 1981, 2001, and 2021. Regression analysis (R2) was performed to identify the relationship between temperature and vegetation cover (NDVI) in the study area. Results indicate that the build-up area was increased from 7203.76 ha (2.25%) to 31,081.3 ha (9.70%), while the vegetation area decreased by 14,427.1 ha (4.5%) from 1981 to 2021 in Sahiwal District. The mean NDVI values showed that overall NDVI values decreased from 0.24 to 0.20 from 1981 to 2021. Almost 78% of farmers stated that the climate has been changing during the last few years, 72% of farmers stated that climate change had affected agriculture, and 53% of farmers thought that rainfall intensity had also decreased. The R2 tendency showed that temperature and NDVI were negatively connected to each other. This study will integrate and apply the best and most suitable methods, tools, and approaches for equitable local adaptation and governance of agricultural systems in changing climate conditions. Therefore, this research outcome will also meaningfully help policymakers and urban planners for sustainable LULC management and strategies at the local level

An Automated Mobile Game-based Screening Tool for Patients with Alcohol Dependence

Traditional methods for screening and diagnosis of alcohol dependence are typically administered by trained clinicians in medical settings and often rely on interview responses. These self-reports can be unintentionally or deliberately false, and misleading answers can, in turn, lead to inaccurate assessment and diagnosis. In this study, we examine the use of user-game interaction patterns on mobile games to develop an automated diagnostic and screening tool for alcohol-dependent patients. Our approach relies on the capture of interaction patterns during gameplay, while potential patients engage with popular mobile games on smartphones. The captured signals include gameplay performance, touch gestures, and device motion, with the intention of identifying patients with alcohol dependence. We evaluate the classification performance of various supervised learning algorithms on data collected from 40 patients and 40 age-matched healthy adults. The results show that patients with alcohol dependence can be automatically identified accurately using the ensemble of touch, device motion, and gameplay performance features on 3-minute samples (accuracy=0.95, sensitivity=0.95, and specificity=0.95). The present findings provide strong evidence suggesting the potential use of user-game interaction metrics on existing mobile games as discriminant features for developing an implicit measure to identify alcohol dependence conditions. In addition to supporting healthcare professionals in clinical decision-making, the game-based self-screening method could be used as a novel strategy to promote alcohol dependence screening, especially outside of clinical settings

Monitoring the dynamic changes in vegetation cover using spatio-temporal remote sensing data from 1984 to 2020

Anthropogenic activities and natural climate changes are the central driving forces of global ecosystems and agriculture changes. Climate changes, such as rainfall and temperature changes, have had the greatest impact on different types of plant production around the world. In the present study, we investigated the spatiotemporal variation of major crops (cotton, rice, wheat, and sugarcane) in the District Vehari, Pakistan, from 1984 to 2020 using remote sensing (RS) technology. The crop identification was pre-processed in ArcGIS software based on Landsat images. After pre-processing, supervised classification was used, which explains the maximum likelihood classification (MLC) to identify the vegetation changes. Our results showed that in the study area cultivated areas under wheat and cotton decreased by almost 5.4% and 9.1% from 1984 to 2020, respectively. Vegetated areas have maximum values of NDVI (>0.4), and built-up areas showed fewer NDVI values (0 to 0.2) in the District Vehari. During the Rabi season, the temperature was increased from 19.93 °C to 21.17 °C. The average temperature was calculated at 34.28 °C to 35.54 °C during the Kharif season in the District Vehari. Our results showed that temperature negatively affects sugarcane, rice, and cotton crops during the Rabi season, and precipitation positively affects sugarcane, rice, and cotton crops during the Kharif season in the study area. Accurate and timely assessment of crop estimation and relation to climate change can give very useful information for decision-makers, governments, and planners in formulating policies regarding crop management and improving agriculture yields

Current challenges and potential solutions to the use of digital health technologies in evidence generation: a narrative review

Digital health is a field that aims to improve patient care through the use of technology, such as telemedicine, mobile health, electronic health records, and artificial intelligence. The aim of this review is to examine the challenges and potential solutions for the implementation and evaluation of digital health technologies. Digital tools are used across the world in different settings. In Australia, the Digital Health Translation and Implementation Program (DHTI) emphasizes the importance of involving stakeholders and addressing infrastructure and training issues for healthcare workers. The WHO's Global Task Force on Digital Health for TB aims to address tuberculosis through digital health innovations. Digital tools are also used in mental health care, but their effectiveness must be evaluated during development. Oncology supportive care uses digital tools for cancer patient intervention and surveillance, but evaluating their effectiveness can be challenging. In the COVID and post-COVID era, digital health solutions must be evaluated based on their technological maturity and size of deployment, as well as the quality of data they provide. To safely and effectively use digital healthcare technology, it is essential to prioritize evaluation using complex systems and evidence-based medical frameworks. To address the challenges of digital health implementation, it is important to prioritize ethical research addressing issues of user consent and addressing socioeconomic disparities in access and effectiveness. It is also important to consider the impact of digital health on health outcomes and the cost-effectiveness of service delivery

Remote heart rate monitoring - Assessment of the Facereader rPPg by Noldus

Remote photoplethysmography (rPPG) allows contactless monitoring of human cardiac activity through a video camera. In this study, we assessed the accuracy and precision for heart rate measurements of the only consumer product available on the market, namely the Facereader™ rPPG by Noldus, with respect to a gold standard electrocardiograph. Twenty-four healthy participants were asked to sit in front of a computer screen and alternate two periods of rest with two stress tests (i.e. Go/No-Go task), while their heart rate was simultaneously acquired for 20 minutes using the ECG criterion measure and the Facereader™ rPPG. Results show that the Facereader™ rPPG tends to overestimate lower heart rates and underestimate higher heart rates compared to the ECG. The Facereader™ rPPG revealed a mean bias of 9.8 bpm, the 95% limits of agreement (LoA) ranged from almost -30 up to +50 bpm. These results suggest that whilst the rPPG Facereader™ technology has potential for contactless heart rate monitoring, its predictions are inaccurate for higher heart rates, with unacceptable precision across the entire range, rendering its estimates unreliable for monitoring individuals

FEATURE SELECTION OF ELECTROENCEPHALOGRAPHY (EEG) BIOMARKERS FOR UNIPOLAR MAJOR DEPRESSIVE DISORDER (MDD)

Major Depressive Disorder (MDD), a leading cause of functional disability worldwide, is a mental illness and commonly known as unipolar depression. The clinical management of MDD patients has been challenging that includes an early diagnosis and antidepressant’s treatment selection. The electroencephalography (EEG)-based studies for diagnosis and treatment selection have shown less clear clinical utilities and warrant further investigations. This research advocates the use of EEG as a biomarker for early diagnosis and antidepressant’s treatment selection for unipolar MDD patients. More specifically, the study has presented an improved feature selection and classification system involving pre-treatment EEG data termed as Intelligent Treatment Management System (ITMS) for unipolar depression. The ITMS involved an integration of the most significant EEG features as input data. The study hypothesized that the MDD patients and healthy controls could be discriminated based on integrating the EEG alpha asymmetry and synchronization likelihood (the EEG measure to quantify the brain functional connectivity). The method helped during diagnosis of MDD patients and was termed asITMS for diagnosis (ITMS�diagnosis). In addition, the study hypothesized that the integration of the time and frequency information involving wavelet transform (WT) analysis and EEG signal complexity measures (composite permutation entropy index, sample entropy, and fractal dimension) could discriminate the antidepressants treatment response and non�response. The method helped during antidepressant’s treatment selection such as classifying MDD patients as either respondents or non-respondents to treatment with selective serotonin re-uptake inhibitors (SSRIs): Escitalopram (E), Fluvoxamine (F), Sertraline (S), Fluoxetine (Fl), and was termed as the ITMS for treatment selection (ITMS-treatment selection). The proposed ITMS for depression includes a general machine learning (ML) framework for EEG feature extraction, the selection of most noteworthy features that could give high-performance classification models such as the logistic regression (LR), support vector machine (SVM) and naïve bayesian (NB) classifiers

MDD Patients and Healthy Controls EEG Data (New)

'H' stands for Healthy Controls<div>'MDD' stands for Major Depressive Disorder </div><div>'EC' stands for eyes closed</div><div>'EO' stands for eyes open</div><div>'TASK' stands for P300 data</div

A deep learning framework for automatic diagnosis of unipolar depression

International audienc

Hybrid deep shallow network for assessment of depression using electroencephalogram signals

International audienc

Využití grafové teoretické analýzy EEG na automatickou diagnostiku alkoholismu

U alkoholismu (AUD) bývá pozorována abnormální funkční konektivita mozku (FC). Tato práce popisuje analýzu FC s využitím grafové teoretické analýzy založené na EEG a strojového učení (ML). Mozková FC byla kvantifikována s využitím synchronization likelihood (SL). Neorientované grafy byly vytvořeny pro každou dvojici EEG kanálů s využitím hodnot SL. Dále byly vypočteny příznaky založené na grafech, jako je minimální kostra, vzdálenosti mezi uzly a maximální tok mezi uzly. Příznaky byly použity jako vstupní data pro klasifikaci účastníků studie. Klasifikace byla ověřena daty získanými od 30 pacientů s AUD a 30 zdravých účastníků.Abnormal functional connectivity (FC) has been commonly observed during alcohol use disorder (AUD). In this work, FC analysis has been performed by incorporating EEG-based graph-theoretic analysis and a machine learning (ML) framework. Brain FC was quantified with synchronization likelihood (SL). Undirected graphs for each channel pair were constructed involving the SL measures. Furthermore, the graph-based features such as minimum spanning tree, distances between nodes, and maximum flow between the graph nodes were computed. The matrix was used as input data to the ML framework to classify the study participants. The ML framework was validated with data acquired from 30 AUD patients and an age-matched group of 30 healthy controls