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Accurate crop yield forecasting is essential in the food industry’s decision-making process, where vegetation condition index (VCI) and thermal condition index (TCI) coupled with machine learning (ML) algorithms play crucial roles. The drawback, however, is that a one-fits-all prediction model is often employed over an entire region without considering subregional VCI and TCI’s spatial variability resulting from environmental and climatic factors. Furthermore, when using nonlinear ML, redundant VCI/TCI data present additional challenges that adversely affect the models’ output. This study proposes a framework that (i) employs higher-order spatial independent component analysis (sICA), and (ii), exploits a combination of the principal component analysis (PCA) and ML (i.e., PCA-ML combination) to deal with the two challenges in order to enhance crop yield prediction accuracy. The proposed framework consolidates common VCI/TCI spatial variability into their respective subregions, using Vietnam as an example. Compared to the one-fits-all approach, subregional rice yield forecasting models over Vietnam improved by an average level of 20% up to 60%. PCA-ML combination outperformed ML-only by an average of 18.5% up to 45%. The framework generates rice yield predictions 1 to 2 months ahead of the harvest with an average of 5% error, displaying its reliability.

For many years, modulators of the renin angiotensin system (RAS) have been trusted by clinicians for the control of essential hypertension. It was recently demonstrated that these modulators have other pleiotropic properties independent of their hypotensive effects, such as enhancement of cognition. Within the brain, different components of the RAS have been extensively studied in the context of neuroprotection and cognition. Interestingly, a crosstalk between the RAS and other systems such as cholinergic, dopaminergic and adrenergic systems have been demonstrated. In this review, the preclinical and clinical evidence for the impact of RAS modulators on cognitive impairment of multiple etiologies will be discussed. In addition, the expression and function of different receptor subtypes within the RAS such as: Angiotensin II type I receptor (AT1R), Angiotensin II type II receptor (AT2R), Angiotensin IV receptor (AT4R), Mas receptor (MasR), and Mas-related-G protein-coupled receptor (MrgD), on different cell types within the brain will be presented. We aim to direct the attention of the scientific community to the plethora of evidence on the importance of the RAS on cognition and to the different disease conditions in which these agents can be beneficial.

Corrosion is a crucial worldwide problem that strongly affects metals. Out of the several ways to prevent corrosion, volatile corrosion inhibitors (VCIs) are predominantly used as a method of temporary protection. These compounds have the ability to vaporize and condense on the surface of the ferrous or nonferrous material and make the substrate less susceptible to corrosion but work only in a confined space. VCI compounds form a monomolecular layer over the metal surface, thereby preventing the electrochemical reactions on the metal surface which lead to corrosion. This review article outlines the use of volatile corrosion inhibitors (VCIs) as a temporary corrosion protection technique, their working mechanisms and the various compounds used as VCI. It also anchors on the latest research works which have been carried out on VCI coatings along with a glimpse of the works that were carried out in the past. The various factors affecting the volatile corrosion inhibitors along with the merits of VCI coatings are discussed in this paper. Formulations for both strippable and permanent VCI coatings are also reported with the various testing methods. Lastly, an overview of the recent developments of VCI coatings along with the various green VCI is given.

Identification of drought is essential for many environmental and agricultural applications. To further understand drought, this study presented spatial and temporal variations of drought based on satellite derived Vegetation Condition Index (VCI) on annual (Jan–Dec), seasonal monsoon (Jun–Nov) and pre-monsoon (Mar–May) scales from 1982–2015 in Nepal. The Vegetation Condition Index (VCI) obtained from NOAA, AVHRR (National Oceanic and Atmospheric Administration, Advanced Very High Resolution Radiometer) and climate data from meteorological stations were used. VCI was used to grade the drought, and the Mann–Kendall test and linear trend analysis were conducted to examine drought trends and the Pearson correlation between VCI and climatic factors (i.e., temperature and precipitation) was also acquired. The results identified that severe drought was identified in 1982, 1984, 1985 and 2000 on all time scales. However, VCI has increased at the rate of 1.14 yr−1 (p = 0.04), 1.31 yr−1 (p = 0.03) and 0.77 yr−1 (p = 0.77) on the annual, seasonal monsoon and pre-monsoon scales, respectively. These increased VCIs indicated decreases in drought. However, spatially, increased trends of drought were also found in some regions in Nepal. For instance, northern areas mainly in the Trans-Himalayan regions identified severe drought. The foothills and the lowlands of Terai (southern Nepal) experienced normal VCI, i.e., no drought. Similarly, the Anomaly Vegetation Condition Index (AVCI) was mostly negative before 2000 which indicated deficient soil moisture. The exceedance probability analysis results on the annual time scale showed that there was a 20% chance of occurring severe drought (VCI ≤ 35%) and a 35% chance of occurring normal drought (35% ≤ VCI ≤ 50%) in Nepal. Drought was also linked with climates in which temperature on the annual and seasonal monsoon scales was significant and positively correlated with VCI. Drought occurrence and trends in Nepal need to be further studied for comprehensive information and understanding.

The substantial reliance of South Asia (SA) to rain-based agriculture makes the region susceptible to food scarcity due to droughts. Previously, most research on SA has emphasized the meteorological aspects with little consideration of agrarian drought impressions. The insufficient amount of in situ precipitation data across SA has also hindered thorough investigation in the agriculture sector. In recent times, models, satellite remote sensing, and reanalysis products have increased the amount of data. Hence, soil moisture, precipitation, terrestrial water storage (TWS), and vegetation condition index (VCI) products have been employed to illustrate SA droughts from 1982 to 2019 using a standardized index/anomaly approach. Besides, the relationships of these products towards crop production are evaluated using the annual national production of barley, maize, rice, and wheat by computing the yield anomaly index (YAI). Our findings indicate that MERRA-2, CPC, FLDAS (soil moisture), GPCC, and CHIRPS (precipitation) are alike and constant over the entire four regions of South Asia (northwest, southwest, northeast, and southeast). On the other hand, GLDAS and ERA5 remain poor when compared to other soil moisture products and identified drought conditions in regions one (northwest) and three (northeast). Likewise, TWS products such as MERRA-2 TWS and GRACE TWS (2002–2014) followed the patterns of ERA5 and GLDAS and presented divergent and inconsistent drought patterns. Furthermore, the vegetation condition index (VCI) remained less responsive in regions three (northeast) and four (southeast) only. Based on annual crop production data, MERRA-2, CPC, FLDAS, GPCC, and CHIRPS performed fairly well and indicated stronger and more significant associations (0.80 to 0.96) when compared to others. Thus, the current outcomes are imperative for gauging the deficient amount of data in the SA region, as they provide substitutes for agricultural drought monitoring.

Drought has severe impacts on human society and ecosystems. In this study, we used data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) sensors to examine the drought effects on vegetation in Afghanistan from 2001 to 2018. The MODIS data included the 16-day 250-m composites of the Normalized Difference Vegetation Index (NDVI) and the Vegetation Condition Index (VCI) with Land Surface Temperature (LST) images with 1 km resolution. The TRMM data were monthly rainfalls with 0.1-degree resolution. The relationship between drought and index-defined vegetation variation was examined by using time series, regression analysis, and anomaly calculation. The results showed that the vegetation coverage for the whole country, reaching the lowest levels of 6.2% and 5.5% were observed in drought years 2001 and 2008, respectively. However, there is a huge inter-regional variation in vegetation coverage in the study period with a significant rising trend in Helmand Watershed with R = 0.66 (p value = 0.05). Based on VCI for the same two years (2001 and 2008), 84% and 72% of the country were subject to drought conditions, respectively. Coherently, TRMM data confirm that 2001 and 2008 were the least rainfall years of 108 and 251 mm, respectively. On the other hand, years 2009 and 2010 were registered with the largest vegetation coverage of 16.3% mainly due to lower annual LST than average LST of 14 degrees and partially due to their slightly higher annual rainfalls of 378 and 425 mm, respectively, than the historical average of 327 mm. Based on the derived VCI, 28% and 21% of the study area experienced drought conditions in 2009 and 2010, respectively. It is also found that correlations are relatively high between NDVI and VCI (r = 0.77, p = 0.0002), but slightly lower between NDVI and precipitation (r = 0.51, p = 0.03). In addition, LST played a key role in influencing the value of NDVI. However, both LST and precipitation must be considered together in order to properly capture the correlation between drought and NDVI.

Machine learning (ML) has been widely used worldwide to develop crop yield forecasting models. However, it is still challenging to identify the most critical features from a dataset. Although either feature selection (FS) or feature extraction (FX) techniques have been employed, no research compares their performances and, more importantly, the benefits of combining both methods. Therefore, this paper proposes a framework that uses non-feature reduction (All-F) as a baseline to investigate the performance of FS, FX, and a combination of both (FSX). The case study employs the vegetation condition index (VCI)/temperature condition index (TCI) to develop 21 rice yield forecasting models for eight sub-regions in Vietnam based on ML methods, namely linear, support vector machine (SVM), decision tree (Tree), artificial neural network (ANN), and Ensemble. The results reveal that FSX takes full advantage of the FS and FX, leading FSX-based models to perform the best in 18 out of 21 models, while 2 (1) for FS-based (FX-based) models. These FXS-, FS-, and FX-based models improve All-F-based models at an average level of 21% and up to 60% in terms of RMSE. Furthermore, 21 of the best models are developed based on Ensemble (13 models), Tree (6 models), linear (1 model), and ANN (1 model). These findings highlight the significant role of FS, FX, and specially FSX coupled with a wide range of ML algorithms (especially Ensemble) for enhancing the accuracy of predicting crop yield.

Droughts are amongst the most destructive natural disasters in the world. In large regions of Africa, where water is a limiting factor and people strongly rely on rain-fed agriculture, droughts have frequently led to crop failure, food shortages and even humanitarian crises. In eastern and southern Africa, major drought episodes have been linked to El Niño-Southern Oscillation (ENSO) events. In this context and with limited in-situ data available, remote sensing provides valuable opportunities for continent-wide assessment of droughts with high spatial and temporal resolutions. This study aimed to monitor agriculturally relevant droughts over Africa between 2000–2016 with a specific focus on growing seasons using remote sensing-based drought indices. Special attention was paid to the observation of drought dynamics during major ENSO episodes to illuminate the connection between ENSO and droughts in eastern and southern Africa. We utilized Tropical Rainfall Measuring Mission (TRMM)-based Standardized Precipitation Index (SPI) with 0 . 25 ∘ resolution and Moderate-resolution Imaging Spectroradiometer (MODIS)-derived Vegetation Condition Index (VCI) with 500 m resolution as indices for analysing the spatio-temporal patterns of droughts. We combined the drought indices with information on the timing of site-specific growing seasons derived from MODIS-based multi-annual average of Normalized Difference Vegetation Index (NDVI). We proved the applicability of SPI-3 and VCI as indices for a comprehensive continental-scale monitoring of agriculturally relevant droughts. The years 2009 and 2011 could be revealed as major drought years in eastern Africa, whereas southern Africa was affected by severe droughts in 2003 and 2015/2016. Drought episodes occurred over large parts of southern Africa during strong El Niño events. We observed a mixed drought pattern in eastern Africa, where areas with two growing seasons were frequently affected by droughts during La Niña and zones of unimodal rainfall regimes showed droughts during the onset of El Niño. During La Niña 2010/2011, large parts of cropland areas in Somalia (88%), Sudan (64%) and South Sudan (51%) were affected by severe to extreme droughts during the growing seasons. However, no universal El Niño- or La Niña-related response pattern of droughts could be deduced for the observation period of 16 years. In this regard, we discussed multi-year atmospheric fluctuations and characteristics of ENSO variants as further influences on the interconnection between ENSO and droughts. By utilizing remote sensing-based drought indices focussed on agricultural zones and periods, this study attempts to contribute to a better understanding of spatio-temporal patterns of droughts affecting agriculture in Africa, which can be essential for implementing strategies of drought hazard mitigation.

Drought is a complex hazard, and it has an impact on agricultural, ecological, and socio-economic systems. The vegetation condition index (VCI), which is derived from remote-sensing data, has been widely used for drought monitoring. However, VCI based on the normalized difference vegetation index (NDVI) does not perform well in certain circumstances. In this study, we examined the utility of the vegetation index based on the universal pattern decomposition method (VIUPD) based VCI for drought monitoring in various climate divisions across the continental United States (CONUS). We compared the VIUPD-derived VCI with the NDVI-derived VCI in various climate divisions and during different sub-periods of the growing season. It was also compared with other remote-sensing-based drought indices, such as the temperature condition index (TCI), precipitation condition index (PCI) and the soil moisture condition index (SMCI). The VIUPD-derived VCI had stronger correlations with long-term in situ drought indices, such as the Palmer Drought Severity Index (PDSI) and the standardized precipitation index (SPI-3, SPI-6, SPI-9, and SPI-12) than did the NDVI-derived VCI, and other indices, such as TCI, PCI and SMCI. The VIUPD has considerable potential for drought monitoring. As VIUPD can make use of the information from all the observation bands, the VIUPD-derived VCI can be regarded as an enhanced VCI.

Deficiency in vitamin D (VitD), a lipid-soluble vitamin and steroid hormone, affects approximately 24% to 40% of the population of the Western world. In addition to its well-documented effects on the musculoskeletal system, VitD also contributes importantly to the promotion and preservation of cardiovascular health via modulating the immune and inflammatory functions and regulating cell proliferation and migration, endothelial function, renin expression, and extracellular matrix homeostasis. This brief overview focuses on the cardiovascular and cerebrovascular effects of VitD and the cellular, molecular, and functional changes that occur in the circulatory system in VitD deficiency (VDD). It explores the links among VDD and adverse vascular remodeling, endothelial dysfunction, vascular inflammation, and increased risk for cardiovascular and cerebrovascular diseases. Improved understanding of the complex role of VDD in the pathogenesis of atherosclerotic cardiovascular diseases, stroke, and vascular cognitive impairment is crucial for all cardiologists, dietitians, and geriatricians, as VDD presents an easy target for intervention.