In order to achieve global climate goals, it is necessary to estimate greenhouse gases (GHG) fluxes from ecosystems. To obtain a comprehensive assessment of CO2, СН4, N2O natural fluxes for the Russian Federation we used the "bottom-up" method according to [1]. We updated estimates for forest ecosystems based on State Forest Inventory data and satellite monitoring of forest disturbances. For grassland ecosystems, it was based on the correct distribution of areas between steppe and non-steppe zones. The estimated net uptake of natural ecosystems of Russia was 1.1 ± 1.8 billion tons of CO2-eq./year. The study shows that if only CO2 is taken into account, the net absorption of terrestrial ecosystems in Russia corresponds to more than -2.5 billion tons of CO2 (35% of forests’ contribution). However, given the emissions of non-CO2 GHGs, total net absorption in Russia’s natural ecosystems is reduced to about -1 billion tons of CO2-eq (with the forests’ contribution increasing to 80%). With regard to anthropogenic fluxes, the overall balance of GHGs in Russia corresponds to net-emissions of 1 billion tons of CO2-eq/year into the atmosphere. To improve reporting under the Paris Agreement, countries should aim to include only anthropogenic ("manageable") GHG fluxes on managed land.

Combustion power plants emit carbon dioxide (CO₂), a major contributor to climate change. Direct emissions measurement is cost-prohibitive globally, while reporting varies in detail, recency, and granularity. To fill this gap and greatly increase the number of power plants worldwide with independent emissions monitoring, we developed and applied machine learning (ML) models using power plant plumes as proxy signals to estimate electric power generation and CO₂ emissions using Landsat 8, Sentinel-2, and PlanetScope imagery. Our ML models estimated power plant activity on each image snapshot, and then an aggregation model predicted plant utilization over a 30-day period. Lastly, emissions factors specific to region, fuel, and plant technology were used to convert estimated electricity generation to CO₂ emissions. Models were trained with reported hourly generation data in the US, Europe, and Australia and were validated with additional generation and emissions data from the US, Europe, Australia, Türkiye, and India. All results with sufficiently large sample sizes indicate that our models outperformed baseline approaches. In conducting external validation to compare modeled versus reported annual generation and emissions in the countries where available, we calculated the root mean square error overall for modeled countries with validation data as respectively 1.75 TWh (236 plants across 17 countries over 4 years) and 2.04 Mt CO₂ (207 plants across 17 countries over 4 years). Ultimately, we applied our ML method to plants that comprise 32% of global power plant CO₂ emissions averaged over 2015-2022. This dataset is the most comprehensive independent and free-of-cost global power plant point-source emissions monitoring system currently known to the authors and is publicly available at climatetrace.org to support global emissions reduction.

This study employed 15 CMIP6 GCMs and evaluated their ability to simulate rainfall over Uganda during 1981-2019. The models and the ensemble mean were assessed based on the ability to reproduce the annual climatologyseasonal rainfall distribution, trend, and statistical metrics, including mean bias error, root mean square error, and pattern correlation coefficient. The Taylor diagram and Taylor skill score (TSS) were used in ranking the models. The models performance varies greatly from one season to the other. The models reproduced the observed bimodal rainfall pattern of March to May (MAM) and September to November (SON) rains occurring over the region. Some models slightly overestimated, while some slightly underestimated, the MAM rainfall. However, there was a high rainfall overestimation during SON by most models. The models showed a positive spatial correlation with observed dataset, whereas a low correlation was shown interannually. Some models could not capture the rainfall patterns around local-scale features, for example, around the Lake Victoria basin and mountainous areas. The best performing models identified in the study include GFDL-ESM4, BCC-CMC-MR, IPSL-CM6A-LR, CanESM5, GDFL-CM4-gr1, and GFDL-CM4-gr2. The models CNRM-CM6-1 and CNRM-ESM2 underestimated rainfall throughout the annual cycle and mean climatology. However, these two models better reproduced the spatial trends of rainfall during both MAM and SON. The model spread in CMIP6 over the study area calls for further investigation on the attributions and possible implementation of robust approaches of Machine learning to minimize the biases.

The Earth's fresh water resources predominantly is groundwater that is pumped out due to rapid urbanization and aggravated by climate change. Groundwater modeling is a crucial tool for understanding aquifer systems, employing 1D, 2D, and 3D numerical models with distinct applications. One-dimensional models focus on vertical dynamics, examining aquifer properties and simulating vertical contaminant transport. Two-dimensional models extend to regional scales, considering horizontal variations and assessing groundwater-surface water interactions, making them valuable for watershed-scale studies. Three-dimensional models provide a comprehensive representation of hydrogeological systems, capturing intricate flow patterns and aiding site-specific assessments. Comparative analysis reveals model strengths and limitations, emphasizing the importance of calibration for reliable results. Case studies showcase practical applications, such as 1D models in flooding analysis and 2D models for simulating debris flows. Three-dimensional modeling proves essential for understanding complex river-aquifer exchange fluxes. Future directions call for a global groundwater platform to address data variability and technical challenges. Despite substantial investments required, the anticipated returns in scientific advancements, societal benefits, and economic gains are expected to outweigh the initial costs. This review aims in highlighting the significance of advancing all three-dimension groundwater modeling for sustainable water resource management and environmental protection.

Plastic contamination of small dimensions (

The Upper Hunter Valley is a major coal mining area in New South Wales (NSW), Australia. Due to the ongoing increase of mining activities, PM10 (air-borne particle with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. The early part of this study identified two air quality subregions in the valley. This paper aims to provide a holistic summarisation of the relationships between elevated PM10 pollution in two subregions and the local- and synoptic-scale meteorological conditions for spring and summer, when PM10 pollution is relatively high. A catalogue of twelve synoptic types and a set of six local meteorological patterns have been quantitatively derived and linked to each other using the self-organising map (SOM) technique. The complex meteorology-air pollution relationships have been visualised/interpretated on the SOM planes for two representative locations. It was found that the influence of local meteorological patterns differed significantly for mean PM10 levels vs the occurrence of elevated pollution events and between air quality subregions. In contrast, synoptic types showed generally similar relationships with mean vs elevated PM10 pollution in the valley. Two local meteorological patterns, the hot-dry-northwesterly wind conditions and the hot-dry-calm conditions, were found most PM10 pollution conducive in the valley when combined with a set of synoptic counterparts. These synoptic types are featured with the influence of eastward migrating continental high pressure system and westerly troughs, or a ridge extending northwest towards coastal northern NSW or southern Queensland from the Tasman Sea. The method and results can be used in air quality research for other locations of NSW, or similar regions elsewhere.

The Upper Hunter Valley is a major coal mining area containing approximately 40% of the currently identified total coal reserves in New South Wales (NSW), Australia. Due to the ongoing increase of mining activities, PM10 (air-borne particle with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. This paper summarises the spatial and temporal variability modes of PM10 pollution in the region, based on long-term multi-site monitoring data and the application of the rotated principal component analysis (RPCA) and wavelet analysis techniques. RPCA identified two distinct air quality clusters/subregions in the valley, one in the west/northwest, the other in the southeast. Wavelet analysis revealed the annual cycle to be most persistent temporal mode of PM10 variability in both subregions, with intermittent signals observed at time scales of around 120, 30~90 and under 30 days. How these variation modes are related to the effects of local PM10 emissions and the influence of meteorology at different time scales deserves further attention in future work. The findings will be used in air quality reporting and forecasting in NSW. The methodology and results can also be useful for air quality research in similar regions elsewhere.

Determining the estrous cycle stages in mice is essential for optimizing breeding strategies, synchronizing experimental time lines, and facilitating studies in behavior, drug testing, and genetics. It is critical for reducing the production of genetically unmodified offspring in the generation and investigation of genetically modified animal models. An accurate detection of the estrus cycle is particularly relevant in the context of the 3Rs—Replacement, Reduction and Refinement. The estrous cycle, encompassing the reproductive phases of mice, is key to refining experimental designs and addressing ethical issues related to the use of animals in research. This study presents results from two independent laboratories on the efficacy of the Mouse Estrus Detector (MED) from Elmi Ltd. (Latvia) for the accurate determination of the estrus phase. The female mice of five strains/stocks (CD1, FVB/N, C57Bl6/J, B6D2F1, and Swiss) were used. The results showed that the MEDProTM is a low -traumatic, simple, rapid and painless method of estrus detection that supports the principles of the 3Rs. The use of the MEDProTM for estrus detection in mice caused minimal stress, enhanced mating efficiency, facilitated an increase in the number of embryos for the in vitro fertilization and allowed the production of the desired number of foster animals.

The impact of interaction SS with MJO on rainfall along Java Island to East Nusa Tenggara has been studied using daily rainfall observation data from 2140 weather observation stations, which are also equipped with GSMaP, OLR, and ERA5 data. There were 61 SS events found, 17 during active MJO and 44 in inactive MJO. Rainfall increases more in the western study area when the active MJO and more in the eastern study area when the inactive MJO is due to SS. SS reduces rainfall in the Jakarta region, regardless of the activity or inactivity of the MJO. SS causes extreme rainfall to only occur in a small part of certain areas, so it tends to significantly reduce the possibility of extreme rainfall. In the southern part of the IMC, SS predominates over MJO in supporting increased water vapor transport. Large-scale synoptic circulations like the monsoon, SS, and MJO can interact with local land-sea wind circulation to produce spatiotemporal variability in rainfall on Java Island. Rainfall mostly increases in the afternoon and decreases in the morning when SS occurs, whether there is MJO or not. Convective instability analysis indicates that SS increases precipitation, most likely by lowering CIN and raising VIMFC.

Despite saltmarshes being considered as nature-based solutions (NbS) towards climate change mitigation, India’s saltmarsh ecosystems are least studied. This study quantifies the seasonal variation in carbon stocks mono-specific saltmarsh species (Porteresia coarctata) and its potential to play an important role in India’s climate change mitigation plans. Seasonal (pre-and post-monsoon) sampling of P. coarctata surface water, biomass and 30 cm sediment core was collected across four locations on the east coast of India to quantify sediment dry bulk density (DBD), organic matter (OM%), organic carbon (Corg%), Corg stocks of sediment and biomass, total carbon (C%) and nitrogen (N%) and stable isotopes of δ13C and δ15N. In general, the sediment DBD, OM and Corg of P. coarctata meadows was higher in post-monsoon and was influenced by salinity and pH changes. Isotope (δ¹³C) modelling of various sediment C sources suggested, particulate organic matter (POM) contribution was highest (0.04-0.79%) followed by P. coarctata (0.01–052%) and other macrophytes across our study locations. The seasonal variation of δ¹³C showed increased contribution of marine derived particulate and dissolved organic matter into P. coarctata meadows in post-monsoon season. Heavier δ¹⁵N values were observed in post-monsoon season suggesting anthropogenic input, that was utilized by P. coarctata to increase its above and below-ground biomass and shoot density. The combined ecosystem (30 cm sediment + biomass) Corg stocks of P. coarctata was 1.7-fold higher in post-monsoon (4021.20± 917 Mg C) compared to pre-monsoon (2297.36 ±647 Mg C) season among the four locations with a sediment Corg contribution >70%. Based on the International Panel for Climate Change Tier II assessment the P. coarctata meadows (443 ha) can help in avoiding the pre and post-monsoon emissions of 8431.34 and 14757.84 Mg CO₂ respectively. The combined price of the total CO₂ equivalent stored in P. coarctata meadows in pre- and post-monsoon is US$ 14.50 and US$ 25.38 million respectively. Further studies quantifying the NbS potential of P. coarctata mono-specific and mixed meadows of India’s coast is needed along with integration of saltmarsh ecosystems into India’s National Action Plan on Climate Change.

This paper aims to investigate appropriate mathematical models devoted to optimization of some cleaning processes related to pharmaceutical contaminants removal. In our previous recent works has been reported that a possibility for removal from water sources of this type of micropollutants is to rehabilitate the existing cleaning plants by introducing efficient techniques such as adsorption on granulated active carbon filters, micro-, nano- or ultrafiltration. To have such processes under a better control and to pass from small to large scale treatment stations, specific mathematical models are necessary. Starting from Navier-Stokes equations and imposing proper boundary conditions, some mathematical physics problems are obtained for which adequate solving methods via variational methods and surjectivity results are proposed. The importance of these solution characterizations consists in their continuation in a numerical method and the possibility to visualize the result by using CFD program.

Aerosol-cloud interactions play a crucial role in shaping Earth’s climate and hydrological 1 cycle. Observing these interactions with high precision and accuracy is of the utmost importance 2 for improving climate models and predicting Earth’s climate. Over the past few decades, lidar 3 techniques have emerged as powerful tools for investigating aerosol-cloud interactions due to their 4 ability to provide detailed vertical profiles of aerosol particles and clouds with high spatial and 5 temporal resolutions. This review paper provides an overview of recent advancements in the study 6 of aerosol-cloud interactions using lidar techniques. The paper begins with a description of the 7 different cloud microphysical processes that are affected by the presence of the aerosol, and with 8 an outline of lidar remote sensing application in characterizing aerosol particles and clouds. The 9 subsequent sections delve into the key findings and insights gained from lidar-based studies of 10 aerosol-cloud interactions. This includes investigations into the role of aerosol particles in cloud 11 formation, evolution, and microphysical properties. Finally, the review concludes with an outlook 12 on future research. By reporting the latest findings and methodologies, this review aims to provide 13 valuable insights for researchers engaged in climate science and atmospheric research.

Wounds are an increasing global concern, mainly due to a sedentary lifestyle, frequently associated to the occidental way of life. The current prevalence of obesity in western societies, leading to an increase in type II diabetes, and an elderly population, are also a key factor associated to the problem of wounds healing. Therefore, it stands essential to find wound dressing systems allowing reestablishing the skin integrity in the shortest possible time and with the lowest cost, avoiding further damage and promoting patients´ well-being. Wounds can be classified into acute or chronic, depending essentially on the duration of the healing process, which is associated with. It depends on the extent and depth of wound, localization, level of infection and on the patient health status. For each kind of wound and respective healing stage there is a more suitable dressing. The aim of this review was to focus on the possible wound dressing management, aiming the more adequate healing approach for each kind of wound.

Antibiotic resistance is a global health problem. The human gut microbiome is implicated in the dynamics of antibiotic resistance acquisition and transmission, with the gut microbiota thought to play a crucial role. This study aimed to determine the potential influence of human gut bacteria microbiota on the gut resistome and the relationship between gut background microbiota and Escherichia coli resistome. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines was used to systematically review studies that characterised gut microbiota and resistome using metagenomic analysis and/or those that reported gut E. coli resistome in healthy individuals. Changes in the diversity and abundance of bacterial gut microbiota and the resistome across different groups and the microbiota composition of the gut harbouring antibiotic-resistant E. coli were summarised. Findings suggest that lower bacterial microbiota diversity is likely associated with an increased abundance of the overall gut resistome. Age-related differences were observed, with younger infants exhibiting lower microbiota diversity and higher ARG abundance compared to older infants and adults. Studies that reported correlation revealed positive correlations between the compositional relative abundance of Proteobacteria and ARGs abundance, mainly driven by members within the Enterobacteriaceae family, particularly E. coli. This study also revealed that human gut microbiome studies that investigate gut resistome using metagenomic sequencing approaches in apparently healthy individuals are at least uncommon.

The COVID-19 pandemic has triggered seismic shifts in how we work, and organizations behave. It has caused many organizations to transition from an office-centric culture to more flexible ways of working. This shift is necessitated by health conditions and the need for controls. It is also driven by the assumption that remote work or working from home offer a certain assurance of safety for employees against the threat of infection in highly vulnerable office conditions. The shift to remote work has so far been mainly experimental as most businesses continue to learn about what works and what doesn’t. There is also the assumption that all that matters for the efficient operability of a remote work regime is the availability and effectiveness of computing technologies and internet connectivity. Thus, once those supplies are in place remote work is expected to be fully functional and without problems. As learning continues about what works and what doesn’t, it has also become increasingly clear that beyond the threat of covid-19 and the relative safety that individual homes may offer, there are other vulnerabilities which are ignored or underestimated in the process. In most instances, the home environment of the employee, the conditions under which employees operate from home and the health and safety such environments provide (or not) are either ignored or underestimated in discussions around remote work practices. This paper highlights remote work as an emergent practice while exploring associated, but ignored, health and safety issues in employee homes.

Protected and conserved areas are a key area-based strategy to address the biodiversity and climate change crises. Fundamental to understand their performance over time are indicators. The Digital Observatory for Protected Areas (DOPA) was born in 2013 as a set of open access web services and applications to be used to assess, monitor, and report on protected and conserved areas. For over a decade it has delivered over 50 indicators to support policy processes, national and regional governments, and practitioners. DOPA has also developed a versatile and efficient back-end approach that is widely applicable in other area-based conservation contexts. Here, we describe the methods and workflows behind DOPA´s back end and provide examples of policy relevant questions it can answer. We discuss key advantages and limitations of this systematic and replicable approach and explore the use of this back-end architecture to inform progress in area-based conservation targets for the following decades. This approach, embedded in multiple services provided by the Knowledge Centre for Biodiversity of the European Commission (KCBD), can also support the implementation and monitoring of area-based targets of the Kunming Montreal Global Biodiversity Framework at international, regional, and national levels.

Off-grid electrification planning increasingly recognizes the importance of productive use of electricity (PUE) to promote community value creation and (financial) project sustainability. To ensure a sustainable and efficient integration in the community and energy system, PUE assets must carefully be evaluated to match both the community needs and the residential electricity demand patterns. We propose a novel methodology interlinking qualitative interviews, statistical analysis, and energy system modelling to optimize decision-making for PUE integration in off-grid energy systems in rural Madagascar by aligning relevant PUE effectively with anticipated residential electricity demand patterns based on socio-economic determinants of the community. We find that a possible contribution of the PUE to reducing the electricity costs depends signifi-cantly on three factors: 1. the residential electricity consumption patterns that are influenced by the socio-economic composition of the community, 2. the degree of flexibility of i) PUE assets and ii) operational preferences of the PUE user, and 3. the capacity of community members to finance and operate PUE assets. Our study demonstrates that significant cost reductions for PUE-integrated off-grid energy systems can be achieved by applying our proposed methodology. When matching PUE and residential consumption patterns, the integration of PUE assets in residential community energy system can reduce the financial risk of operators, provided the PUE enterprise operates reliably and sustainably. We highlight that the consideration of local value chains and co-creation approaches are essential to ensure the energy system is addressing the community’s needs, creates value for the community, enhances the project´s financial sustainability, and is achieving the overall objectives of decentralized energy system planning.

To understand dynamics in climate change, informing policy decisions and prompting timely action to mitigate its impact, this study provides a comprehensive analysis of the short-term trend of year-on-year CO2 emission changes across ten countries, considering a broad range of factors including socioeconomic, CO2-related industry, and education. This study uniquely goes beyond the common country-based analysis, offering a broader understanding of the interconnected impact of CO2 emissions across countries. Our preliminary regression analysis, using the ten most significant features, could only explain 66% of variations in the target. To capture emissions trend variation, we categorized countries by the change in CO2 emission volatility (high, moderate, low with upward or downward trends), assessed using standard deviation. We employed machine learning techniques, including feature importance analysis, Partial Dependence Plots (PDPs), sensitivity analysis, and Pearson and Canonical correlation analyses, to identify influential factors driving these short-term changes. The Decision Tree Classifier was the most accurate model, with an accuracy of 96%. It revealed population size, CO2 emissions from coal, the three-year average change in CO2 emissions, GDP, CO2 emissions from oil, education level (incomplete primary), and contribution to temperature rise as the most significant predictors, in order of importance. Furthermore, this study estimates the likelihood of a country transitioning to a higher emission category. Our findings provide valuable insights into the temporal dynamics of factors influencing CO2 emissions changes, contributing to global efforts to address climate change

Polar stratospheric clouds (PSCs) form in polar regions typically between 15 and 25 km, when the local temperature is sufficiently low. PSCs play an important role in the ozone chemistry and the dehydration and denitrification of the stratosphere. Lidars with a depolarization channel may be used to detect and classify different classes of PSCs. The main PSC classes are water ice, nitric acid trihydrate (NAT) and supercooled ternary solutions (STS), the latter being liquid droplets consisting of water, nitric acid and sulphuric acid. PSCs have been observed at the lidar observatory at Concordia Station from 2014 on. The harsh environmental conditions at Concordia during winter render successful lidar operation difficult. In order to facilitate the operation of the observatory several measures have been put in place to achieve an almost complete remote control of the system. PSC occurrence is strongly correlated with local temperatures and is affected by dynamics, as the PSC coverage during the observation season shows.

This study addresses a gap in the literature by conducting a comparative analysis of statistical and artificial intelligence techniques for predicting bankruptcy in small and medium-sized enterprises (SMEs). Using a comprehensive database of 7,104 Belgian SMEs, our findings demonstrate that intelligent techniques offer superior predictive accuracy for bankruptcy. Notably, our results indicate that a firm's position in the global value chain significantly affects prediction accuracy, with lower performance for firms operating upstream in the production process. This suggests that bankruptcy risk may spread upward along the value chain. This insight is particularly valuable for practitioners, as it highlights the importance of focusing on specific performance factors based on the type of firms they manage, advise, or collaborate with.

In 2020, the Covid-19 outbreak led many countries across the globe to introduce lockdowns (LDs) that effectively caused most anthropic activities to either stop completely or be significantly reduced. In Europe, Italy played a pioneeristic role via the early introduction of a strict nationwide LD on March 9^th. This study is aimed at evaluating, using both chemical and meteorological data, the environmental response to that occurrence as observed by the Lamezia Terme (LMT) GAW/WMO station in Calabria, Southern Italy. The first 2020 lockdown has therefore been used as a “proving ground” to assess CO, CO₂, CH₄, BC, and NO_x concentrations in a rather unique context by exploiting the location of LMT in the context of the Mediterranean Basin. In fact, its location on the Tyrrhenian coast of Calabria and local wind circulation both lead to daily cycles where western-seaside winds depleted in anthropogenic pollutants can be easily differentiated from northeastern-continental winds, enriched in anthropogenic outputs. In addition to that, the first Italian LD occurred during the seasonal transition from Winter to Spring and, consequently, Summer, thus providing new insights on emission outputs correlated with seasons. Findings have clearly indicated BC and, in particular, CO as strongly correlated with average daily temperatures, and possibly domestic heating. CO₂’s reduction during the lockdown and consequent increase in the post-lockdown period, combined with wind data, has allowed to constrain local source of emissions located northeast from LMT. NO_x reductions during specific circumstances are consistent with hypotheses from previous research which linked them to rush hour traffic and other forms of transportation emissions. CH₄’s stable patterns are consistent with livestock, landfills, and other sources assumed to be nearly constant during LD periods.

Introduction. The management of anemia in chronic kidney disease (CKD-An) presents significant challenges for nephrologists due to variable responsiveness to erythropoietin-stimulating agents (ESA), hemoglobin (Hb) cycling, and multiple clinical factors affecting erythropoiesis. The Anemia Control Model (ACM) is a decision support system designed to personalize anemia treatment, which has shown improvements in achieving Hb targets, reducing ESA doses, and maintaining Hb stability. This study aimed at evaluating the association between ACM-guided anemia management with hospitalizations and survival in a large cohort of hemodialysis patients. Methods. This mul-ti-center, retrospective cohort study evaluated adult hemodialysis patients within the European Fresenius Medical Care NephroCare network from 2014 to 2019. Patients treated according to ACM recommendations were compared to those from centers without ACM. Data on demographics, comorbidities, and dialysis treatment were used to compute a propensity score estimating the like-lihood of receiving ACM-guided care. The primary endpoint was hospitalizations during follow-up; the secondary endpoint was survival. A 1:1 propensity score-matched design was used to minimize confounding bias. Results. A total of 20,209 eligible patients were considered (reference group: 17,101; ACM adherent group: 3,108). Before matching, the mean age was 65.3±14.5 years, with 59.2% men. Propensity score matching resulted in two groups of 1,950 patients each. Matched ACM adherent and non-ACM patients showed negligible differences in baseline characteristics. Hospi-talization rates were lower in the ACM group both before matching (71.3 vs. 82.6 per 100 per-son-years, p<0.001) and after matching (74.3 vs. 86.7 per 100 person-years, p<0.001). During fol-low-up, 385 patients died, showing no significant survival benefit for ACM-guided care (hazard ratio = 0.93; p=0.51). Conclusions: ACM-guided anemia management was associated with a sig-nificant reduction in hospitalization risk among hemodialysis patients. These results further support the utility of ACM as a decision support tool enhancing anemia management in clinical practice.

In this study, a basic study was performed to analyze the seasonal temperature status of a research room in the Global Environment Research Building where ceiling-embedded indoor units are installed to study the room temperature status of the building as well as to improve its thermal environment. In addition, a direction for improvement of the indoor thermal environment in the winter was proposed through a CFD (computational fluid dynamics) simulation and was proven by an additional experiment. Through the results of this study, it appeared that if the ceiling-embedded indoor unit was installed in the small indoor space without considering the thermal vulnerability of its perimeter boundary, the air temperature of the upper part was greatly different from that of the bottom part in the winter. Hence, in this study, as a means to improve it, convectors were installed to minimize the effect of the external thermal environment and angle-controllable air flowing fans were installed to mitigate the stratification distribution. With such result, it was intended to present the essential data for improvement of the thermal environment as well as conservation of heating energy in the winter for buildings by reviewing the use of the ceiling-embedded indoor unit in the future.

(1) Background: A simple approach to map irrigated landcover has been introduced by using measures derived from optical spectral range as an alternative to thermal range. It has been demonstrated that substituting surface temperature (Ts, ‘thermal approach’) with SWIR transformed reflectance (STR, ‘optical approach’) to detect surface moisture is feasible with comparable results. (2) Methods: Using an iterative thresholding procedure to minimize within-class variance, the bilevel segmentation of variables derived from Landsat-8 representing surface moisture and vegetation cover was achieved for the 2020-21 summer for a key irrigation district in Australia. (3) Results: The results of irrigated landcover by optical approach found to be comparable with those by thermal approach. The classification accuracy was assessed using water delivery records at farm level. Though the overall accuracy is high in both cases, the optical approach (97.6%) performed slightly better than the thermal approach (93.9%). (4) Conclusions: The feasibility of using STR to map irrigated landcover has been confirmed by a high-level overall accuracy assessment. This has broader implications in terms of irrigated landcover assessment, as the use of satellite imagery in these applications may not necessarily be limited to microwave or thermal sensors.

Due to its high short-term global warming potential (GWP) compared to carbon dioxide, methane (CH₄) is a considerable agent of climate change. This research is aimed at analyzing data on methane gathered at the GAW (Global Atmosphere Watch) station of Lamezia Terme (Calabria, Southern Italy) spanning seven years of continuous measurements (2016-2022) and integrating the results with key meteorological data. Compared to previous studies on detected methane mole fractions at the same station, daily-to-yearly patterns have become more prominent thanks to the analysis of a much larger data set. Overall, the yearly increase of methane at the Lamezia Terme station is in general agreement with global measurements by NOAA, though local peaks are present, and an increase linked to Covid-19 is identified. Seasonal changes and trends have proved to be fully cyclic, with the daily cycles being largely driven by local wind circulation patterns and synoptic features. Outbreak events have been statistically evaluated depending on their weekday of occurrence to test possible correlations with anthropic activities. A cross analysis between methane peaks and specific wind directions has also proved that local sources may be deemed responsible for the highest mole fractions.

Background/Objectives: The global burden of metabolic dysfunction-associated steatotic liver disease (MASLD) is increasing at an alarming rate. Cardiovascular disease (CVD) is the leading cause of mortality in MASLD patients, therefore, the therapeutic interventions for MASLD should improve coronary risk factors such as obesity, diabetes and dyslipidemia, in addition to improving liver function. The evidence on effect of sodium-glucose cotransporter 2 inhibitors (SGLT2is) which have beneficial pharmacological properties for MASLD, especially the long-term effects, on the progression of MASLD in Asian population is very limited. Methods: The retrospective longitudinal study was performed by using the medical records at the National Center for Global Health and Medicine, Kohnodai Hospital, Japan. We picked up patients with type 2 diabetes who had taken SGLT2is for at least 3 years or longer between April 1, 2014 and March 31, 2018. We collected the data on metabolic parameters, including blood tests and anthropometric parameters, and compared the data before and after the initiation of SGLT2is treatment. Results: During the observation period, 324 patients had taken SGLT2is for 3 years. The 3-year-SGLT2is treatment significantly reduced body weight, hemoglobin A1c, low-density lipoprotein-cholesterol, triglyceride and non-high-density lipoprotein-cholesterol (HDL-C) and significantly increased HDL-C. Furthermore, this treatment significantly improved liver function and the markers for hepatic steatosis and hepatic fibrosis. Conclusions: Considering that the development of CVD determines the prognosis of MASLD patients, the long-term SGLT2is treatment may be an ideal therapy for MASLD patients.

The key towards a sustainable future is the reduction of mankind’s impact on natural systems via the development of new technologies and the improvement of source apportionment. Though days, years and seasons are arbitrarily set, their mechanisms are based on natural cycles driven by Earth’s orbital periods. This is not the case for weeks, which are a pure anthropic category and are known from literature to influence emission cycles. For the first time since it started data gathering operations, CO (carbon monoxide), CO₂ (carbon dioxide), CH₄ (methane) and eBC (equivalent black carbon) values detected by the Lamezia Terme WMO/GAW station in Calabria, Southern Italy have been evaluated via a two-pronged approach accounting for weekly variations in absolute concentrations, as well as the number of hourly averages exceeding select thresholds. The analyses were performed on seven continuous years of measurements, from 2016 to 2022. Moreso, the two results have been combined into a new parameter: the hereby defined WDWO (Weighed Distribution of Weekly Outbreaks) normalizes weekly trends of CO, CO₂, CH₄ and eBC on an absolute scale as percentages, with the scope of providing regulators and researchers alike with a new tool meant to better evaluate anthropogenic pollution and mitigate its effects.

German noise action plans aim to reduce negative health outcomes from noise exposure and identify quiet areas free of noise pollution. Quiet area identification in German noise action plans is based primarily on noise mapping and spatial analysis and not empirical or qualitative data about acoustic environments, thus leaving a gap in the understanding of the quality of formally recognized quiet areas in noise action plans. This work presents a comparative empirical case study in Dortmund, Germany, with the aim to describe the diurnal dB(A) and biophonic properties of quiet areas versus noise 'hot spots'. Sound observations were collected (n=282,764) in five different natural or recreational land use patch types larger than four acres within 33 proposed quiet areas in Dortmund (n=70) and 23 noise hot spots between April 27^th, 2022 and March 2^nd, 2023. We find that quiet areas are on average more than 20 dB(A) quieter than noise hot spots almost every hour of the day. Forests, managed tree stands, cemeteries, and agriculture diel patterns are dominated by dawn dusk chorus in spring and summer, whereas sport and recreation as well as noise hot spots are dominated by traffic and human noise. A novel composite biophony mapping procedure is presented that finds distinct temporal distribution of biophony in forested and agriculture peri-urban locations positively associated with patch size, distance away from LDEN > 55, proximity to water, and the number of vegetation layers in the plant community. Anthrophony distribution dominates urban land uses in all hours of the day, but expands during day and evening and contracts at night and in dusk hours. The procedures presented here illustrate how qualitative information regarding quiet areas can be integrated into German noise action planning.

Human induced climate change led to the recognition of Community-Based Adaptation (CBA) as one of the remedies in building adaptive capacity and resilience of communities. It recognizes the resources and the skills as well as the agency of communities in shaping the optimal strategy. CBA interventions are vital in addressing community needs and contextual specificities, enable participatory and inclusive engagement and thereby facilitate more profound adaptation impacts. Agent-based modelling (ABM) harbors the potential to be the ideal tool to support developing of tailored adaptation strategies while considering capacities, resources, skills, priorities, needs and the cultural context of local communities. ABM allows to capture the complex relationships among various components within community decision making on CBA actions, the CBA actions themselves and the environment. There exist plenty of tools, however they provide for varying degrees of freedom in agent definition, sensitivity analysis, scalability, experiment design and output analysis to name a few. A set of required key criteria for CBA modelling is developed and all available ABM modelling and simulation tools are assessed for their suitability for CBA. NetLogo emerges as the most fitting tool for incorporating and handling the special features of CBA. It is closely followed by GAMA and Envision. The article provides insights and guidance to researchers and practitioners in selecting ABM tools aligned with the specific requirements of their CBA simulations and projects.

Various symptoms have been reported to persist beyond acute phase of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, which is referred as long coronavirus disease 19 (long COVID-19). Over 65 million individuals suffer from long COVID-19. However, the causes of long COVID-19 have been largely unknown. Since long COVID-19 symptoms are observed throughout the body, vascular endothelial dysfunction may be a strong candidate to induce long COVID-19. The angioten-sin-converting enzyme 2 (ACE2), the entry receptor of SARS-CoV-2, is ubiquitously expressed in endothelial cells. We previously found that the risk factors for athero-sclerotic cardiovascular disease (ASCVD) and the history of ASCVD can be the risk for severe COVID-19, suggesting a contribution of pre-existing endothelial dysfunction to severe COVID-19. Here, we show a significant association of endothelial dysfunction with development of long COVID-19 and show that biomarkers for endothelial dysfunction in patients with long COVID-19 are also crucial players in development of ASCVD. We consider the influence of long COVID-19 on development of chronic kidney disease (CKD) and ASCVD. Furthermore, we suggest the therapeutic interventions for long COVID-19 by considering endothelial dysfunction as the treatment tar-gets for long COVID-19. Such interventions may prevent pandemic of CKD and ASCVD in post COVID-19 era.

The demographic profile of patients transitioning from chronic kidney disease to renal replacement therapy is changing, with a higher prevalence of aging patients with multiple comorbidities such as diabetes mellitus and heart failure. Cardiovascular disease remains the leading cause of mortality in this population, exacerbated by the cardiovascular stress imposed by the HD procedure. The first year after transitioning to hemodialysis is associated with increased risks of hospitalization and mortality, particularly within the first 90–120 days, with greater vulnerability observed among the elderly. This review, based on data from clinics of Fresenius Medical Care Europe, Middle East, and Africa NephroCare, aims to optimize hemodialysis procedures to reduce mortality risk in stable incident and prevalent patients. It addresses critical aspects such as treatment duration, frequency, choice of dialysis membrane, dialysate composition, blood and dialysate flow rates, electrolyte composition, temperature control, target weight management, dialysis adequacy, and additional protocols, with a focus on mitigating prevalent intradialytic complications, particularly intradialytic hypotension prevention.

This article presents and discusses analytical data on the scientific publication record from 1910 to 2020 on two topics: "climate" and "climate change/global warming/climate emergency". The goal is to visualize how the publication record on these two topics has evolved over time, from different classification perspectives (year, country, source and organization). Three hypotheses are tested using data collected from Web of Science and various graphical representations of the data. It is found that research output related to the Earth’s contemporary changing climate overtook that of general climate research in 2011, and the publication ratio has been expanding in the last decade. There are significant differences in the publication countries and sources between the two topics, and conversely less significant differences in terms of organizations publishing these works. Differentiation factors that affect the level of research output and engagement on the climate challenge include: island versus landlocked nations, specialized versus general scientific journals, academic versus institutional organizations. The future of the publication records is discussed, such as the emergence of new terms to refer to the climate challenge, such as “climate emergency”.

Climate simulations in West Africa have been attributed with large uncertainties. Global climate projections are not consistent with changes in observations at the regional or local level of the Niger basin, making management of hydrological projects in the basin uncertain. This study evaluates the potential of using the quantile mapping bias correction to improve the Coupled Model Intercomparison Project (CMIP5) outputs for use in hydrology impact studies. Rainfall and temperature projections from 8 CMIP5 Global Climate Models (GCM) were bias corrected using the quantile mapping approach. Impacts of climate change was evaluated with bias corrected rainfall, temperature and potential evapotranspiration (PET). The IHACRES hydrological model was adapted to the Niger basin and used to simulate impacts of climate change on discharge under present and future conditions. Bias correction significantly improved the accuracy of rainfall and temperature simulations compared to observations. Nash coefficient (NSE) for monthly rainfall comparisons of 8 GCMs to the observed was improved by bias correction from 0.69 to 0.84. The standard deviations among the 8 GCM rainfall data were significantly reduced from 0.13 to 0.03. Increasing rainfall, temperature, PET and river discharge were projected for all GCMs used in this study under the RCP8.5 scenario. These results will help improving projections and contribute to the development of sustainable climate change adaptation strategies.

Global coverage and scale up of interventions to reduce newborn mortality remains low, though progress has been achieved in improving newborn survival in many low-income settings. An important factor in the success of newborn health interventions, and moving to scale, is appropriate design of community-based programs and strategies for local implementation. We report the results of formative research undertaken to inform the design of a newborn health intervention in Cambodia. Information was gathered on newborn care practices over a period of three months using multiple qualitative methods of data collection in the primary health facility and home setting. Analysis of the data indicated important gaps, both at home and facility level, between recommended newborn care practices and those typical in the study area. The results of this formative research have informed strategies for behavior change and improving referral of sick infants in the subsequent implementation study. Collection and dissemination of data on newborn care practices from settings such as these can contribute to efforts to advance survival, growth and development of newborns for intervention research, and for future newborn health programming.

We identified six natural "control knobs" for global weather and climate, which clearly regulate, increase and decrease global temperatures, thus performing a true temperature "control" on Earth. Identified control knobs act as Earth's orbital forcing. We present a detailed Earth orbital model, from where the analysis proceeds. Based on our orbital model, we compare meteorological data from all over the globe, in order to detect orbital forcing fingerprints. Ninety temperature graphs and bar charts demonstrate the results. Empirical meteorological data covers the Pacific ocean, the Atlantic ocean, Arctic and Antarctic sea ice extent, and presents land based meteorological measurements from all continents for the year 2020, as well as multi-decadal data from global daily temperature datasets. A supplementary file provides another 100 additional graphs of weather data and temperature charts. The global orbital "temperature control" is clearly observable in all graphs and charts. Until today, orbital forcing has not been integrated in weather models and CMIP6 climate models.It is concluded that the proposed new orbital forcing should be part of weather and climate forecasting models.

Developing countries, including Small Island Developing States (SIDS) and Least Developed Countries (LDCs), are exceptionally vulnerable to climate change due to their distinct geographical and environmental characteristics. The impacts of climate change extend across their ecosystems, water resources, and vital economic sectors, such as food systems, health, education, and energy. Escalating sea levels and heightened salinity levels imperil freshwater reserves, while warmer ocean temperatures and acidification disrupt water demand, tourism, health services, and fisheries. Furthermore, developing countries bear the brunt of water shortages, flooding, and deteriorating water quality. Alarmingly, nearly two-thirds of the global population experience severe water scarcity for at least one month annually, with the majority residing in developing countries. By 2025, half of the world's population could inhabit areas grappling with water scarcity, predominantly concentrated in developing nations. In this context, innovative adaptation strategies, notably transformative wastewater projects, are imperative for fostering sustainability and resilience in these regions. However, significant barriers impede progress, creating a stark disparity between current financing capabilities and the imperative to fund water security initiatives. Compounding these challenges, developing countries find themselves ensnared in a burgeoning debt crisis, marked by escalating interest rates and inflation, which threatens to impede developmental progress and curtail investments in adaptation and mitigation endeavors. Consequently, there arises a critical necessity to harness innovative financial mechanisms to transform these debts into assets that facilitate climate action. This paper meticulously explores the potential of debt-for-climate swaps as a catalyst for advancing transformative wastewater projects, with a focused lens on their strategic deployment to underpin critical initiatives. Through rigorous examination of case studies and empirical evidence, the paper elucidates how debt-for-climate swaps can effectively bolster sustainable wastewater management systems in developing countries. Furthermore, it delineates best practices for leveraging these mechanisms to fortify environmental sustainability and resilience. Additionally, the paper delineates the roles and responsibilities of key stakeholders, including governments, policymakers, the private sector, communities, and climate financial institutions. By amalgamating theoretical insights with tangible examples, this paper furnishes a comprehensive framework for harnessing debt-for-climate swaps to enhance water security and resilience in developing countries. It endeavors to furnish actionable strategies for policymakers, practitioners, and stakeholders to navigate the complex terrain of climate change and engender sustainable development.

The extent of access to energy of displaced populations in settlements and camps in Africa is largely 14 unknown. A multitude of contextual factors, such as the location and the characteristics of housing, 15 the legal status, the socio-cultural background, and the availability of humanitarian and public ser- 16 vices impact the living conditions and the needed energy services. Limitations in accessing energy 17 services have direct, multilayered, and far-reaching implications, including impacts on health, nu- 18 trition, education, protection, and livelihood. The objective of this article is to contribute to a more 19 comprehensive overview of the current state of energy access in displacement contexts on the Afri- 20 can continent by identifying and utilizing existing data. After a screening of the vast and various 21 available information, setting up of a database, consolidating the gathered data as well as assessing 22 the quality through a quality assessment method, the currently available information is visualized 23 and discussed. Considerable differences in the access to electricity for displaced persons across the 24 countries are found. For both electricity and clean cooking, the availability for displaced persons 25 ranges from nearly no access at all up to an access rate of 100%. More strikingly, the results also 26 show that besides South Africa and countries in the Maghreb region, the access to both clean cook- 27 ing and electricity for displaced persons is very low. At the same time, the fragmented data availa- 28 bility, the poor data quality, and the questionable expediency of available data allows neither solid 29 theoretical conclusions nor planning of effective practical implementation measures. Novel inter- 30 disciplinary research, conceptual frameworks and indicators are needed for the purpose of compa- 31 rability and consistency. Future research has the potential to more comprehensively capture the 32 current state of access to energy in displacements contexts and in a next step to examine how energy 33 is interwoven in the lives of displaced people to derive a set of more detailed context-sensitive en- 34 ergy indicators. It is essential that the concerned persons, the displaced persons themselves are in- 35 cluded in the research in a meaningful way.

Two decades after the antiretroviral therapy (ART) introduction, several challenges still prevail in maintaining people living with HIV (PLHIV), even with "Test and Treat" and/or "Rapid Start of ART" initiatives, as well as the scale-up of ART worldwide to promote access and adherence to treatment. This review examined articles on ART adherence in Africa between 2016 and 2023, published in English and indexed in PubMed. A total of 16 articles out of 2415 were eligible and included for analyses. Overall, ART adherence rates in SSA regions ranged from 43% to 84%. Rates in the centre of the SSA region ranged from 58% to 80%, in the north from 50% to 83%, in the south from 77% to 84%, in the west from 43% to 60% and in the east from 69% to 73%. Most African countries use the self-report to assess treatment adherence, although unreliable. The main factors with negative influence on ART adherence were comorbidities, lack of motivation, socioeconomic difficulties or side effects. Conclusion: Adherence to ART is a good indicator for controlling the spread of HIV. It is important to overcome the barriers that make it difficult to comply with ART and reinforce the factors that facilitate the accession of medication.

This study explores how the Household Dietary Diversity Score (HDDS) and spatial visualization can inform food governance in Chile, focusing on socio-demographic and geographical determinants affecting food consumption patterns. A national household database (n=4,407) provided by the “Family Support Program of Food Self-Sufficiency” (FSPFS) of the Ministry of Social Development and Family was analyzed. The findings revealed that Chilean vulnerable households were led mostly by women (86.6%), with an age average of 55.9 15.6 years old, versus 68.9 12.9 years in the case of men. The intake frequency analysis showed that dairy, fruits, and vegetables were below the recommended values in at least half of the households, and that fats and sugars were above recommended levels. Regarding the HDDS [0-189], the national average was of 91.4 ± 20.6 and was significantly influenced by the number of minors in the households, water access, food access issues, and residing in the Zona Sur. Finally, the spatial visualization showed that the Zona Central had higher consumption of fruits and vegetables, while the extreme zones Norte Grande and Zona Austral showed higher intakes of fats and sugars. These findings emphasize the importance of leveraging data insights like HDDS and spatial visualization to enhance food security and inform food governance strategies.

Introduction: The academic landscape is increasingly recognizing the complex interplay of personal and professional responsibilities on scholarly productivity. For women scholars, particularly those with significant parenting duties, this balance is even more precarious. This study aims to explore how family responsibilities, personal vulnerabilities, and motivational theories such as Imposter Syndrome and Self-Determination Theory (SDT) influence the motivation to publish scholarly work. Understanding these dynamics is crucial for developing supportive mechanisms that can enhance academic productivity and personal fulfilment. Methods: Utilizing a Qualitative Evidence Synthesis (QES) framework, this study will systematically review and synthesize qualitative research on women scholars' experiences in academia, focusing on the challenges and motivators related to publishing. Data sources will include academic databases, grey literature, and first-hand narratives. Thematic analysis will be employed to identify key themes and patterns across the collected data. Results/Findings: Preliminary findings suggest that the dual demands of parenting—especially in extreme cases such as assuming responsibility for seven children—and academic work significantly impact women scholars' publishing activities. Personal vulnerabilities, including imposter syndrome and low self-efficacy, further exacerbate these challenges. However, the data also highlight resilience strategies and the potential for intrinsic and extrinsic motivational factors to mitigate these effects. Discussion: The study underscores the nuanced impact of familial and personal factors on academic motivation. The preliminary findings suggest that addressing imposter syndrome and enhancing self-determination could be pivotal in supporting women scholars. The role of institutional policies and academic culture in either exacerbating or alleviating these challenges is also discussed. Significance: By elucidating the specific challenges and motivators for women scholars in publishing, this study will contribute to the development of targeted interventions and policies that support work-life integration and promote academic productivity. The insights that will be gained could inform mentorship programs, institutional support systems, and policy reforms aimed at fostering a more inclusive and supportive academic environment.

The World Meteorological Organization (WMO) recommends that the most recent 30-year period, i.e. 1991-2020, shall be used to compute climate normals of geophysical variables. A unique aspect this recent 30-year period is that the satellite-based observations of many different essential climate variables are available during this period, thus opening up new possibilities to provide a robust, global basis for the 30-year reference period in order to allow climate monitoring and climate change studies. Here, using the satellite-based climate data record of cloud and radiation properties, CLARA-A3, for the month of January between 1981 to 2020, we illustrate the difference between climate normal, as defined by guidelines from WMO on calculations of 30-yr climate normals, and climatology. It is shown that this difference is strongly dependent on the climate variable in question. We discuss the impacts of the nature and availability of satellite observations, variable definition, retrieval algorithm and programmatic configuration. It is shown that the satellite-based climate data records show enormous promise in providing climate normal for the recent 30-year period (1991-2020) globally. We finally argue that the holistic perspectives from the global satellite community should be increasingly considered while formulating the future WMO guidelines on computing climate normals.

Over the last two decades, a considerable amount of equipment has been acquired (spreaders, seeders, sprayers, among others) to respond to the challenges of Precision Agriculture (PA) concept. Most of this equipment has been purchased at a high cost. However, many of them, despite still being functional and equipped with sensors, actuators, and electronic processing units capable of adjusting to variations in speed, have become obsolete in terms of communication, and incompatible with new monitoring and control systems based on the “Isobus” protocol. This work aims to present a solution for updating the control system (“Ferticontrol”) of a “Vicon RS-EDW” spreader with variable rate application (VRA), making it compatible with the “InCommand” system from “Ag Leader”. The solution includes low-cost “Arduino” and “Raspberry Pi” microcontrollers and open-source software. The development shows that it is possible to implement a solution that is accessible to farmers in general. It also provides a niche business opportunity for young researchers to set up small technology-based enterprises associated with universities and research centers. These partnerships guarantee permanent innovation and represent a decisive step towards modern, technological, competitive, and sustainable agriculture.

In the past two decades, several floods have affected people and their properties in Burkina Faso, with the unprecedented flooding occurring in Ouagadougou in September 2009. So far, most studies have focused on Ouagadougou and surrounding localities and have paid little attention to other flood-prone regions in Burkina Faso. Specifically, there is a data and knowledge gap about the flood risk in the Haut-Bassins region, which in turn hampers the development of mitigation strategies and risk reduction measures in communities. This study demonstrates how data collected at the household level can be used to understand flood risk and its components at the village level in this data-scarce region. Using an indicator-based method, we analyze both flood risk and flood risk perception at the village level. Moreover, we determine the factors influencing flood risk perception at the household level using an ordered logit model. We find that 12 out of the 14 villages considered, experience high levels of flood risk. The management of runoff from the nearest urban areas and poorly designed civil engineering infrastructures, such as roads, were highlighted by the households as significant factors that increase their vulnerability. Hence, dependencies in space and between urban and rural areas may play a substantial role in modifying risk. The perceived flood risk consistently exceeds the estimated flood risk, with an insignificant positive correlation between both risk indices. Regression results indicate that flood risk perception is mainly influenced by informational and behavioral factors of the households. The findings of this study can provide valuable information to municipal and regional authorities involved in disaster risk management in the study area. Moreover, the method is transferable to other data-scarce regions

Droughts have negative impacts on agricultural productivity and economic growth. Effective monitoring and accurate forecasting of drought occurrences and trends are crucial for minimizing drought losses and mitigating their spatial and temporal effects. In this study, trend dynamics in monthly total rainfall time series measured at Cape Town International Airport were analyzed using Mann-Kendall (MK) test, Modified Mann-Kendall (MMK) and innovative trend analysis (ITA) were examined. Additionally, we utilized hybrid prediction method that combined the model with the complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique, the autoregressive integrated moving average (ARIMA) model, the long short-term memory (LSTM) network (i.e. CEEMDAN-ARIMA-LSTM) to forecast SPI values of 6-, 9-, and 12-months using rainfall data between 1995 and 2020 from Cape Town International Airport meteorological rainfall stations. The results demonstrate the following essential points: (1) The trend analysis results of the total monthly rainfall values indicate a significant decreasing trend with the negative z-score (MK= -3.7541 and MMK= - 4.0773). ITA also indicated a significant downwards trend of rainfall in the study area. (2) As the forecasting horizon increases, the prediction accuracy of the models and CEEMDAN-combined models gradually improves, reaching their optimum performance at the 12-month horizon. (3) CEEMDAN efficiently stabilizes time-series data, resulting in greater prediction accuracy in the hybrid model compared to individual models at all timescales. (4) The RMSE and R2 values for the hybrid CEEMDAN-ARIMA-LSTM model at a 12-month forecasting horizon are 0.042 and 0.995, demonstrating the applicability of this hybrid approach for drought forecasting.

Obesity poses significant challenges in rural communities worldwide due to limited resources and healthcare access. Practical, innovative, and culturally sensitive approaches are essential. Time-Restricted Eating (TRE), aligning food intake with circadian rhythms, emerges as a promising strategy. By restricting eating to specific time windows, TRE synchronizes with the body's internal clock, offering potential metabolic health benefits. This review examines TRE's potential in rural obesity management, highlighting its simplicity and scalability. While TRE may address rural health disparities, cultural considerations and challenges such as undernutrition must be acknowledged for effective implementation. Overall, TRE shows promise in improving metabolic health and well-being in rural populations.

Climate change is a global issue that draws widespread attention from the international society. As an important component of the climate system, the water cycle is directly affected by climate change. Thus, it is very important to study the influences of climate change on the basin water cycle with respect to maintenance of healthy rivers, sustainable use of water resources, and sustainable socioeconomic development in the basin. In this study, by assessing the suitability of multiple General Circulation Models (GCMs) recommended by the Intergovernmental Panel on Climate Change, Statistical Downscaling Model (SDSM) and Automated Statistical Downscaling model (ASD) were used to generate future climate change scenarios. These were then used to drive distributed hydrologic models (Variable Infiltration Capacity, Soil and Water Assessment Tool) for hydrological simulation of the Yangtze River and Yellow River basins, thereby quantifying the effects of climate change on the basin water cycle. The results showed that suitability assessment adopted in this study could effectively reduce the uncertainty of GCMs, and that statistical downscaling was able to greatly improve precipitation and temperature outputs in global climate mode. Compared to a baseline period (1961–1990), projected future periods (2046–2065 and 2081–2100) had a slightly decreasing tendency of runoff in the lower reaches of the Yangtze River basin. In particular, a significant increase in runoff was observed during flood seasons in the southeast part. However, runoff of the upper Yellow River basin decreased continuously. The results provide a reference for studying climate change in major river basins of China.

Despite ample warning and significant lead time, global society has not acted sufficiently to combat climate change, and global carbon dioxide emissions are now about 60% higher today than in 1990 [1]. This paper uses marine fisheries as an analog model to compare with the many similar management issues faced with global climate change. Both marine fisheries and global climate change are complex systems that have similar properties that make management very difficult. A simple fishery prototype model is used to educate citizens about similar issues of dynamic complexity found in climate change models. We highlight how our slow reaction time to global climate change can be thought of as a mismatch between our cognitive abilities and the nature of global collective problems. Understanding the history and system structure of marine fisheries can provide insight into managing global climate change. We suggest ways to improve communication between science and citizens for updating mental models and changing climate policy.

While increasing empirical evidence suggests that global time series forecasting models can achieve better forecasting performance than local ones, there is a research void regarding when and why the global models fail to provide a good forecast. This paper uses anomaly detection algorithms and Explainable Artificial Intelligence (XAI) to answer when and why a forecast should not be trusted. To address this issue, a dashboard was built to inform the user regarding (i) the relevance of the features for that particular forecast, (ii) which training samples most likely influenced the forecast outcome, (iii) why the forecast is considered an outlier, and (iv) provide a range of counterfactual examples to understand value changes, in the feature vector or the predicted value, can lead to a different outcome. Moreover, a modular architecture and a methodology were developed to iteratively remove noisy data instances from the train set, to enhance the overall global time series forecasting model performance. Finally, to test the effectiveness of the proposed approach, it was validated on two publicly available real-world datasets.

The monitoring of coastal waters using satellite data, from sensors including Sentinel-3 OLCI, has become a vital tool in the management of these water environments; especially when it comes to improving our understanding of the effects of climate change on these regions. In this study, Level-2 water products derived from different OLCI Sentinel-3 processors were validated against a comprehensive in situ dataset from the NW Baltic Sea proper region. The products validated were those of the regionally adapted Case-2 Regional Coast Colour (C2RCC) OLCI processor (v1.0 and v2.1), as well as the latest standard Level-2 OLCI case 2 (Neural Network) products from Sentinel-3’s processing baseline: Baseline Collection 003 (BC003), including “CHL_NN”, “TSM_NN”, and “ADG443_NN”. Furthermore, the effect of the current EUMETSAT system vicarious calibration (SVC) on the Level-2 water products was also validated. Results showed that the system vicarious calibration (SVC) reduces the reliability of the Level-2 OLCI products. For example, the application of these SVC gains to the OLCI data for the regionally adapted v2.1 C2RCC products resulted in RMSD increases of 36% for “conc_tsm”; 118% for “conc_chl”; 33% for “iop_agelb”; 50% for “iop_adg”; and 10% for “kd_z90max” using a ±3h validation window. The findings indicate that the current EUMETSAT SVC gains should be applied and interpreted with caution in the region of study at present. A key outcome of the paper recommends the development of a regionally specific SVC against AERONET-OC data for the region.

Reducing under-five mortality is a key indicator of a country's overall development, yet in Angola, understanding the factors contributing to hospital deaths in this vulnerable demographic remains incomplete despite improvements in healthcare infrastructure and public health policies. With one of the highest under-five mortality rates in Sub-Saharan Africa, Angola grapples with significant challenges, including malaria, malnutrition, pneumonia, neonatal conditions, and intestinal infectious diseases, as leading causes of death among children. This study aimed to identify factors associated with these hospital deaths in children aged 28 days to five years admitted to DBPH in Luanda between May 2022 and June 2023. Utilizing a hospital-based case-control design, a total of 1020 children were studied, with 340 cases of hospital deaths. Notably, distal and intermediate determinants emerged as primary predictors of hospital mortality, revealing significant associations with maternal education status, maternal alcohol consumption during pregnancy, short hospital stays (≤24h), poor nutritional status, short interbirth intervals, maternal age ≤19 years, and maternal age ≥35 years. These findings underscore the preventable nature of most under-five hospital deaths and emphasize the urgent need to address social inequities and enhance the quality of primary healthcare services to effectively mitigate child mortality.

Emerging molecular and computational techniques may allow construction and computational treatment of dynamic digital twins on the scale of populations to individuals. This approach may pave the way for predictive, preventive, and personalized treatment. Because digital twins are independent of geographical location, this may also contribute to global equitable health.

Different machine learning models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to predict the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best machine model was checked in two independent stations. The results obtained confirmed that the best ML methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 122.6·10kJ/(m2∙day) and 113.6·10kJ/(m2∙day), respectively, and predict conveniently for independent stations, 201.3·10kJ/(m2∙day) and 209.4·10kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

According to current forecasts, global heating is likely to exceed 2,8°C by the end of the century. This makes substantial adaptation measures necessary to secure a broad basis for livelihoods provision and the conservation of biodiversity. While the implementation of top-down and technocratic adaptation efforts is the majority, related adaptation shortcomings of socio-economic and ecological nature are becoming more and more apparent. Community-based adaptation (CBA) with its participatory, inclusive and needs-based bottom-up approach offers a promising and powerful alternative. This article uses a semi-systematic literature review approach to screen the current literature and identify core issues of CBA. Linking communality, locality, multidimensionality, power imbalances, transformative potential, localisation, the triad of adaptation metrics and adaptation with nature to corresponding potential actions for practical implementations provides a more holistic conceptualisation and broadens the horizons for further learnings, research and improved application.

Product attribute extraction is an growing field in e- commerce business, with several applications including product ranking, product recommendation, future assortment planning and improving online shopping customer experiences. Under- standing the customer needs is critical part of online business, specifically fashion products. Retailers uses assortment planning to determine the mix of products to offer in each store and channel, stay responsive to market dynamics and to manage inventory and catalogs. The goal is to offer the right styles, in the right sizes and colors, through the right channels. When shoppers find products that meet their needs and desires, they are more likely to return for future purchases, fostering customer loyalty. Product attributes are a key factor in assortment planning. In this paper we present PAE, a product attribute extraction algorithm for future trend reports consisting text and images in PDF format. Most existing methods focus on attribute extraction from titles or product descriptions or utilize visual information from existing product images. Compared to the prior works, our work focuses on attribute extraction from PDF files where upcoming fashion trends are explained. This work proposes a more comprehensive framework that fully utilizes the different modalities for attribute extraction and help retailers to plan the assortment in advance. Our contributions are three-fold: (a) We develop PAE, an efficient framework to extract attributes from unstructured data (text and images); (b) We provide catalog matching methodology based on BERT representations to discover the existing attributes using upcoming attribute values; (c) We conduct extensive experiments with several baselines and show that PAE is an effective, flexible and on par or superior (avg 92.5% F1-Score) framework to existing state-of-the-art for attribute value extraction task.

Tropical forest and savanna biomes are pivotal in the functioning of the Earth system. Tropical forests are one of the largest terrestrial biosphere carbon pools, whereas savannas exchange carbon between the biosphere and atmosphere via frequent and extensive landscape fires. Both are biodiverse and under increasing threat due to land clearing and anthropogenic climate change. Reliable mapping of tropical forest and savanna is essential to provide understanding of how anthropogenic impacts are affecting the extent of these biomes. Using Google Maps satellite imagery, we manually classified 24,239 random points as forest, savanna, or anthropogenic landscapes within the tropics. Because fire and climate are correlated, we developed separate geospatial models to rank the importance of climate, topography, and human influence on vegetation present. This modelling confirmed that those areas with more fires had lower probabilities of tropical forest, that forest was most likely in areas with high mean annual rainfall with little seasonal variation in precipitation, and that anthropogenic factors disrupt this environmental predictability. We found there were environments where tropical forest and savanna were equally probable are geographically restricted. These relationships suggest that future drier climates projected under anthropogenic climate change, combined with clearing and burning that have reduced tropical forest extent to a subset of its theoretical distribution, will lead to irreversible loss of tropical forests. Our modelling provides global mapping that can be used track further changes to distribution of rainforests.

This study introduces a method for quantitatively assessing the complexity and predictability of fish behavior in closed systems through the application of information entropy, offering a novel lens to understand how fish adapt to environmental changes. Utilizing simulations rooted in a random walk model for fish movement, we delve into entropy fluctuations under varying environmental conditions, including responses to feeding and external stimuli. Our findings underscore the utility of information entropy in capturing the intricacies of fish behavior, particularly highlighting the synchrony in collective actions and adaptations to environmental shifts. This research not only broadens our comprehension of fish behavior but also paves the way for its application in fields like aquaculture and resource management. Through our analysis, we discovered that smaller grid sizes in simulations capture detailed local fluctuations, while larger grids elucidate general trends, pinpointing a 2.5m grid as optimal for our study. Moreover, changes in swimming speeds and behavioral adaptations during feeding were quantitatively analyzed, with results illustrating significant behavior modifications. Additionally, employing a Gaussian Mixture Model helped to clarify the nuanced changes in fish behavior in response to altered light conditions, demonstrating the layered complexity of fish responses to environmental stimuli. This investigation confirms the efficacy of information entropy as a robust metric for evaluating fish shoal behavior, offering a fresh methodology for ecological and environmental studies, with promising implications for sustainable management practices.

Oscillations in global modes of variability (MoV) form global teleconnections that affect regional climate variability and modify the potential for severe and damaging weather conditions. Understanding the link between certain MoVs and regional climate can improve the ability to more accurately predict environmental conditions that impact human life and health. In this study, we explore the connection between different MoVs, including the Arctic Oscillation (AO), Eurasian teleconnection, Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), and El Niño Southern Oscillation (Nino34), with winter and summer climate in the High Mountain Asia (HMA) region, including geopotential height at 250 hPa (z250), 2-m air temperature (T2M), total precipitation (PRECTOT), and fractional snow cover area (fSCA). Relationships are explored for the same monthly period between the MoVs and the climate variables, but also using a lagged correlation analysis to investigate whether any relationship exists at different time lags. We find that T2M has a negative correlation with the Eurasian teleconnection in the Inner Tibetan Plateau and Central China in both winter and summer and a positive correlation in Western China in summer. PRECTOT has a positive correlation with all MoV in most regions in winter, especially with the IOD, and a negative correlation in summer, especially with the Eurasian teleconnection. Snow cover in winter is positively correlated with most indices throughout many regions in HMA, likely due to wintertime precipitation also being positively correlated with most indices. Generally, the AO and NAO show similar correlation patterns with all climate variables, especially in the winter, possibly due to their oscillations being so similar. Furthermore, the AO and NAO are shown to be less significant in explaining the variation in HMA climate compared to other MoVs such as the Eurasian teleconnection. Overall, our results pinpoint different time-windows and specific regions within HMA that exhibit high correlation between climate and MoVs, which might offer additional predictability of the MoVs as well as of climate and weather patterns in HMA and throughout the globe.

Switzerland, a wealthy country, has a cutting-edge healthcare system, yet per capita, it emits over one ton of CO2, ranking among the world's most polluting healthcare systems. To estimate the carbon footprint of the healthcare system of Geneva’s canton, we collected raw data on the activities of its stakeholders. Our analysis shows that when excluding medicines and medical devices hospitals are the main greenhouse gas emitter by far, accounting for 48% of the healthcare system’s emission, followed by nursing homes (20%), private practice (18%), analysis laboratories (7%), dispensing pharmacies (4%), the homecare institution (3%) and the ambulance services (

Agriculture is a vital component of Egypt's economy; therefore, using Digital Elevation Models (DEMs) in agricultural planning in Egypt has significant benefits regarding water management, site appropriateness assessment, flood risk mitigation, and infrastructure construction. It is also essential for planners to make more informed decisions, optimize resource allocation, and support sustainable farming practices. This research paper investigates the accuracy of obtaining DEM data from four free global models (STRM30, ALOS30, COP30, and TanDEM-X90). The global DEM data has been compared to an actual GNSS-RTK DEM data surveyed onsite for two agricultural block areas in Aswan, the southern Government of Egypt. The two blocks are a part of a national project. For Block I and II, the RMSE of the Model STRM30 was 2.92 m and 3.59 m, respectively, indicating a poorer solution. Regarding accuracy, the ALOS30 model ranks third, reporting an RMSE of 2.58 m for block II and 3.30 m for block I. COP30 has an RMSE value of 1.06 m for blocks I and II and.91 m overall. TanDEM-X90 is the most accurate model in this investigation; block I provided an RMSE of 0.90 m with an SD of 0.58 m (SD95% = 0.38 m). After removing the anomalies, the model's stated RMSE for block II was 0.34 m, with an SD value of 0.62 m and 1.03 m. According to the classification using machine learning algorithms, with an accuracy of 84.7% for block I and 85% for block II, TanDEM-X90 is the best solution.

Understanding the dynamics of Organic Carbon mineralization is fundamental in forecasting biosphere to atmosphere Net Carbon Ecosystem Exchange (NEE). With this perspective, we developed 3D-CMCC-PSM, a new version of the hybrid Process Based Model 3D‐CMCC FEM where also heterotrophic respiration (R_h) is explicitly simulated. The aim was to quantify NEE as a forward problem, by subtracting Ecosystem Respiration (R_eco) to Gross Primary Productivity (GPP). To do so, we developed a simplification of the Soil Carbon dynamics routine proposed in DNDC [1]. The method calculates decomposition as a function of soil moisture, temperature, state of the organic compartments, and relative abundance of microbial pools. Given the pulse dynamics of soil respiration, we introduced modifications in some of the principal constitutive relations involved in phenology and littering sub-routines. We quantified the model structure related uncertainty in NEE, by running our training simulations over 1000 random parameter-sets extracted from parameters distributions expected from literature. 3D-CMCC-PSM predictability was tested on independent time series for 6 Fluxnet sites. The model resulted in daily and monthly estimations highly consistent with the observed time series. It showed lower predictability in Mediterranean ecosystems, suggesting that it may need further improvements in addressing evapotranspiration and water dynamics.

Onchocerciasis, or river blindness, has historically been one of the most important causes of blindness worldwide, and a major cause of socio-economic disruption, particularly in sub-Saharan Africa. Its importance as a cause of morbidity and an impediment to economic development in some of the poorest countries in the world motivated the international community to implement several programs to control or eliminate this scourge. Initially, these involved reducing transmission of the causative agent Onchocerca volvulus through controlling the vector population. When ivermectin was found to be a very effective drug for treating onchocerciasis, the strategy shifted to mass drug administration (MDA) of endemic communities. In some countries, both vector control and ivermectin MDA have been used together. However, traditional vector control methods involve treating rivers in which the black fly vectors breed with insecticides, a process which is expensive, requires trained personnel to administer, and is ecologically harmful. In this review, we discuss recent research into alternatives to riverine insecticide treatment, which are inexpensive, ecologically less harmful and which can be implemented by the affected communities themselves. These can dramatically reduce vector densities and, when combined with ivermectin MDA, can accelerate the time to elimination when compared to MDA alone.

Since the 1960s, the countries of sub-Saharan Africa have been affected by an urban crisis due to the demographic explosion in cities and the resulting land pressures. These have resulted in the proliferation of spontaneous housing areas which question the future of urban vegetation and its degree of inclusion in city planning. To analyze the state of knowledge on the research already carried out on the development of urban landscape, particularly in sub-Saharan Africa, a literature review on urban forestry covering the period 1998-2022 was carried out in order to better orient future research works for a greater consideration to urban forestry and their contributions in combating the adverse effects of climate change. Out of a set of 110 scientific papers selected, 50 were considered relevant because of their direct link with urban forestry. From the analysis of this papers, a classification of the articles according to 05 themes was carried out. These are: management of green spaces in urban areas, functions of trees in an urban environment, development of urban forestry, characterization of urban tree flora and financing of urban forestry. Thus, “function of trees in an urban environment and the characterization of urban tree flora” are the most addressed respectively at 42% and 28%. “Financing urban forestry” is practically not addressed (2%). The analysis of the 50 selected articles reveals a significant geographic concentration of urban forestry research. Countries like South Africa, Kenya, and Ghana are more represented in the literature, while vast regions in Central and Western Africa (e.g., Democratic Republic of Congo, Chad, and Niger) have minimal or no documented research on urban forestry. Future research should address these underrepresented themes, as they are crucial for the long-term sustainability of urban forestry projects. Particularly, research on financing mechanisms is essential to unlock sustainable funding sources for urban forestry, a critical barrier to its expansion.

Background: This qualitative systematic review protocol aims to explore the experiences of African customers in white-owned restaurants and hotels in post-apartheid South Africa, focusing on the dynamics of service interactions and their implications for racial relations within the hospitality industry. Despite the end of apartheid in 1994, the echoes of historical racial disparities and power imbalances persist, potentially influencing customer experiences in this sector, particularly in establishments owned by individuals of European descent. In view of above, the research question for this study is “How do African customers in white-owned restaurants and hotels in post-apartheid South Africa perceive and experience service interactions, and what are the underlying factors influencing these perceptions, considering historical power imbalances, societal perceptions, lingering effects of apartheid, inclusive practices, and the varied experiences across different hospitality establishments? Study Objectives: Objectives include examining the dynamics of service interactions, identifying factors influencing perceptions and experiences, evaluating alignment with inclusive practices and post-apartheid ideals, and describing African customers' experiences regarding service. Methods: The study will employ the SPIDER Framework to craft a nuanced research question that seeks to understand how African customers perceive and experience service interactions in these settings, considering historical power imbalances, societal perceptions, the lingering effects of apartheid, inclusive practices, and varied experiences across different types of hospitality establishments. This approach aims to dissect the intricate interplay of historical, societal, and economic factors that shape these experiences, providing insights into the complexities of achieving a truly inclusive and equitable hospitality landscape in post-apartheid South Africa. Expected Contribution to knowledge and practice: This review will contribute to a deeper understanding of the challenges and opportunities facing the hospitality industry in fostering environments that reflect the nation's diversity and commitment to reconciliation and social cohesion. By highlighting the lived experiences of African customers and examining the extent to which white-owned establishments embody post-apartheid ideals, this study aims to offer valuable recommendations for industry practitioners and policymakers to promote cultural sensitivity, equity, and inclusivity, thus enhancing the role of the hospitality industry in South Africa's broader societal transformation.

Aim: Shockwaves have been felt all over the world as a result of war, inflation, food shortages, and the COVID-19 pandemic's long tail. The aim of the study is to correlate present global conflicts, pandemic and socio-economic crises with present healthcare sustainability, identifying possible threats and visualize future global health crises if all these catastrophes persist for long. Materials and Methods: PubMed, ALTAVISTA, Embase, Scopus, Web of Science, and the Cochrane Central Registers were prioritized to collect public health issues. Results: The global economic slowdown, sharp decline in financial asset values, decline in imports and exports, contraction of industrial production, rise in inflation, decrease in wages, rise in unemployment, damage to the social security caused by numerous natural calamities, human displacement due to pandemic and ongoing conflicts tear down not only the economic sector but also the health sector. It is already late for taking corrective measure by the participation every country, community or intelligence. Only healthcare issues, highlighted in this study, if focused in the highest platforms, could compel global leaders to forego conflicts and work together. Conclusion: The global health sector will be collapsed soon, if proper initiatives are not taken right now, which has already been grossly mistreated by the pandemic.

Abstract: A large number of collecting expeditions were launched in regions of ‘centers of diversity’ and hundreds of thousands of sample have been collected and stored in gene banks as ‘genetic resources’. So far, only a small number of the samples have been evaluated for their biotic and abiotic stress tolerance. Now, their time to become useful has come. A new global phenomenon has arisen – climate change. The crop genetic resources and their wild progenitors that have survived countless years of changing environment during the last 11,000 years could harbor genes that may be useful under the new growing conditions and environmental factors thrown up by climate change and global warming. With the deployment of modern bio-engineering techniques selected genes or gene fragments can be transferred from genetic resources to modern varieties of crop plants to make them well-prepared to mitigate the effects of global warming and climate change. The latter is the most serious issue facing plant breeders today. New pests and diseases could affect crop production. These review paper discusses various impacts and issues as a result of this phenomenon and suggest ways to safeguard our most important crops through better management of crop plant genetic resources in the near future.

Can investing in women’s agriculture increase productivity? This paper argues that it can. We assess climate impacts and gender bias on women’s production in the global South and North and challenge the male model of agricultural development to argue further that women’s farming approaches can be more sustainable. Level-based analysis (global, regional, local) draws on literature review, including authors’ published longitudinal field research in Ghana and the United States. Women farmers are shown to be undervalued and to work harder, with fewer resources, for less compensation; gender bias challenges are shared globally while economic disparities differentiate; breaches of distributive, gender, and intergenerational justices as well as compromise of food sovereignty affect women everywhere. We conclude that investing in women’s agriculture needs more than standard approaches of capital and technology investment. Effective ‘investment’ would include systemic interventions into agricultural policy, governance, education, and industry; be directed at men as well as women; and use gender metrics, e.g. quotas, budgets, vulnerability and impacts assessments, to generate assessment reports and track gender parity in agriculture. Increasing women’s access, capacity, and productivity cannot succeed without men’s awareness and proactivity. Systemic change can increase productivity and sustainability.

COVID-19 is the most severe health crisis of the 21st century. COVID-19 presents a threat to almost all countries world-wide. The restriction of human mobility is one of the strategies used to control the transmission of COVID-19. However, it has yet to be determined how effective this restriction is in controlling the rise in COVID-19 cases, particularly in major capital cities such as Jakarta, Indonesia. Using Facebook's mobility data, our study explores the impact of restricting human mobility on COVID-19 case control in Jakarta. Our main contribution is showing how the restriction of human mobility data can give important information about how COVID-19 spreads in different places. We proposed modifying a global regression model into a local regression model by accounting for the spatial and temporal interdependence of COVID-19 transmission across space and time. We applied Bayesian hierarchical Poisson spatiotemporal models with spatially varying regression coefficients. We estimated the regression parameters using an Integrated Nested Laplace Approximation. We found that the local regression model with spatially varying regression coefficients outperforms the global regression model based on DIC, WAIC, MPL, and R2 criteria for model selection. In Jakarta's 44 districts, the impact of human mobility varies significantly. The impacts of human mobility on the log relative risk of COVID-19 range from –4.445 to 2.353. The prevention strategy involving the restriction of human mobility may be beneficial in some districts but ineffective in others. Therefore, a cost-effective strategy had to be adopted.

Following the detection of highly pathogenic avian influenza (HPAI) virus in countries bordering Kenya to the west, we conducted surveillance among domestic and wild birds along the shores of Lake Victoria. In addition, between 2018 and 2020, we conducted surveillance among poultry and poultry workers in live bird markets and among wild migratory birds in various lakes that are resting sites during migration to assess introduction and circulation of avian influenza viruses in these populations. We tested 7,464 specimens (oropharyngeal and cloacal swabs) from poultry and 6,531 fresh fecal specimens from wild birds for influenza A viruses by real-time RT-PCR. Influenza was detected in 3.9% (n=292) of specimens collected from poultry and 0.2% (n=10) of fecal specimens from wild birds. On hemagglutinin subtyping, most of the influenza A positives from poultry (274/292, 93.8%) were H9. Of 34 H9 specimens randomly selected for further subtyping, all were H9N2. On phylogenetic analysis, these viruses were genetically similar to other H9 viruses detected in East Africa. Only two of the ten influenza A positive specimens from the wild bird fecal specimens were successfully subtyped; sequencing analysis of one specimen collected in 2018 was identified as low pathogenicity avian influenza H5N2 virus of the Eurasian lineage and the second specimen, collected in 2020, was subtyped as H11. A total of 18 OP/NS swabs from poultry workers with ARI (12%) were collected; none were positive for influenza A virus. We observed significant circulation of H9N2 influenza viruses in poultry in live bird markets in Kenya. During the same period, low pathogenic H5N2 virus was detected in a fecal specimen collected in a site hosting a variety of migratory and resident birds. Although HPAI H5N8 was not detected in this survey, these results highlight the potential for introduction and establishment of highly pathogenic avian influenza viruses in poultry populations and the associated risk of spillover to human populations.

The age-related loss of muscle mass and strength is referred to as “sarcopenia”. The combined effect of sarcopenia and osteoporosis is a devastating threat to the old adult, accounting for reduced mobility, risk for falls and subsequent fractures. In previous studies, we observed a high degree of inter-individual variability of paraspinal muscle fatty infiltration, one most relevant index of muscle wasting. This aspect solicited us to develop a computerized method to quantitatively characterize muscle fatty infiltration in aging and diseases. Magnetic resonance images of paraspinal muscles from 58 women of different age (age range 23-85 years) and physio-pathological status (healthy young, pre-menopause, post-menopause, and os-teoporosis), were used to set up the method. It is based on fractal analysis of lean muscle area (contractile muscle) to estimate muscle fatty infiltration. In particular, lacunarity was computed by parameter β from our hyperbola model function, used to fit the GBA (gliding box algorithm) curvilinear plot. Succolarity was estimated by parameter µ, for the four main directions, through an algorithm implemented on purpose. Results show that fractal lacunarity, by quantifying muscle fatty infiltration, can discriminate be-tween osteoporosis and healthy aging while succolarity can separate the other three groups showing similar lacunarity. Therefore, fractal features of contractile muscle, by measuring fatty infiltration, can represent good indices of sarcopenia in aging and disease.

Subnational governments play a key role responding to climate change risks in terms of policy strategies and instruments. This article analyzes how Brazilian municipal and state governments have developed and implemented public policies to mitigate and to adapt to climate change risks. We surveyed all cities’ and states’ climate policies within the country. The methodological approach includes five main points of analysis: 1. mitigation targets and intentions; 2. adaptation actions; 3. stakeholders’ participation; 4. policy implementation; 5. participation in networks related to climate change. Our results suggest that even though subnational climate policies in Brazil are isolated initiatives within the national context, they play an important role responding to climate change risks in different scales and levels. The strongest Brazilian policies with both mitigation and adaptation actions counted on previous mobilization for the climate issue involving different stakeholders from several segments of the society. These governments have also participated in transnational cooperation networks related to climate change.

The month of May 2024 was characterized by solar energetic particles events directed towards the Earth, especially the big event causing a strong terrestrial geomagnetic storm during the night from 10 to 11 May 2024, with auroras observed everywhere in Europe. This was the strongest storm for the last 20 years with a Dst < -400 nT. In the present work, we show with observations of GOES, PROBA-V/EPT and MetOP/MEPED that this exceptional event was associated with the injection of energetic protons in the proton radiation belt, with important consequences for the South part of the South Atlantic Anomaly (SAA). In addition, the geomagnetic storm caused by the solar eruption has had tremendous impacts on the electron radiation belts. Indeed, we show that for 0.3 to 1 MeV electrons, the storm led to a long lasting four belts configuration which was not observed before with EPT launched in 2013, until a smaller geomagnetic storm on June 15 took place. Moreover, for the first time since its launch, observations of the EPT show that ultra-relativistic electrons with E>2 MeV have been injected into the inner belt until L=2.4, violating the impenetrable barrier previously estimated to be located at L=2.8.

In Belgium, sex work was recently decriminalized to enhance street-based sex workers’ (SSWs) health. This qualitative study explores stakeholders' perceptions of the impact of the working environment on street-based sex workers’ occupational safety, health and well-being in Brussels. In-depth interviews with seven stakeholders directly involved with SSWs were conducted with data analyzed using qualitative content analysis. The analysis of study data resulted in one theme: “A power imbalance and a lack of concerted efforts among stakeholders are detrimental to the occupational safety, health, and well-being of street-based sex workers”. In Brussels, the majority of SSWs face various challenges due to structural vulnerabilities, thus limiting their choices regarding their work environment, and further compromising their health. Limited access to resources and severe constraints within their working environment contribute to a lack of safety and adverse health outcomes. A stigma mechanism fueled by ideology and personal interests prevent the enforcement of their rights regarding their working environment, safety and health promotion. The lack of involvement of key stakeholders, especially from the public sector, prevents the majority of SSWs in Brussels from benefiting from a sound working environment and the promotion of their health, safety and well-being.

In the recent past, the number of invasive species around the world has increased significantly due to increased globalization and climate change. Understanding biological invasions under climate change is crucial for sustainable conservation of biodiversity. The insect pest Pseudococcus viburni (Signoret) can cause damages to plants, resulting in leaf yellowing, leaf defoliation, and reduced plant growth. A serious attack by this insect post can even lead to the entire plant withering and dying, bringing huge economic and ecological losses. Despite these damages, the potential distribution range and management strategies for this insect pest are poorly understood. Based on historical distribution data and environmental factors, the present study predicted the potentially suitable areas for P. viburni spread under different climate change scenarios using MaxEnt (maximum entropy). The results showed that precipitation of the coldest quarter (Bio19), precipitation seasonality (Bio15), and mean temperature of the wettest quarter (Bio8) were the most important environmental factors determining the distribution of P. viburni. Under the current climate conditions, its potential suitable areas are southern China, the whole of Japan, North America (especially the eastern part of the United States), the southwestern part of South America, the Mediterranean coast and most of Europe, the central part of Africa i.e., the south of the Sahara Desert and most of the southern coast of Australia. The total area of habitats suitable for this insect pest is predicted to be increased in the future. In order to prevent P. viburni transmission and spread, there is a need to strengthen the monitoring and quarantine measures against this pest at the Southern ports.

This review examines from a services perspective strategies for preparedness and response to mental health impacts of three types of climate-related events: 1) acute climate-related events such as hurricanes, floods and wildfires, 2) sub-acute or long-term changes in the environment such as drought and heat stress; and 3) the existential threat of long-lasting changes, including higher temperatures, rising sea levels and a permanently altered and potentially uninhabitable physical environment. Strategies for acute events include development and implementation of guidelines and interventions for monitoring and treating adverse mental health outcomes and strengthening individual and community resilience, training of non-mental health professionals for services delivery, and the mapping of available resources and locations of at-risk populations. Additional strategies for sub-acute changes include advocacy for mitigation policies and programs and adaptation of guidelines and interventions to address the secondary impacts of sub-acute events such as economic loss, threats to livelihood, health and well-being, population and family displacement, environmental degradation and collective violence. Strategies for long-lasting changes include implementation of evidence-based risk communication interventions that address the existential threat of climate change, promoting the mental health benefits of environmental conservation, and promoting positive mental health impacts of climate change.

Mangrove forests store and sequester large area-specific quantities of blue carbon (C_org). Except for tundra and peatlands, mangroves store more C_org per unit area than any other ecosystem. Mean mangrove C_org stock is 738.9 Mg C_org ha⁻¹ and mean global stock is 6.17 Pg C_org, which equates to only 0.4–7% of terrestrial ecosystem C_org stocks but 17% of total tropical marine C_org stocks. Seagrasses sequester more C_org per unit area than mangroves (179.6 g C_org m⁻²·a⁻¹) but twice the C_org sequestered by mangroves globally (15 Tg C_org a⁻¹). Mangroves sequester only 4% (range 1.3–8%) of C_org sequestered by terrestrial ecosystems, indicating that mangroves are a minor contributor to global C storage and sequestration. CO₂ emissions from mangrove losses equate to 0.036 Pg CO₂-equivalents a⁻¹ based on rates of C sequestration but 0.088 Pg CO₂-equivalents a⁻¹ based on complete destruction for conversion to aquaculture and agriculture. Mangrove CO₂ emissions account for only 0.2% of total global CO₂ emissions but 18% of CO₂ emissions from the tropical coastal ocean. Despite significant data limitations, the role of mangrove ecosystems in climate change mitigation is globally insignificant but may be more significant and effective at the national and regional scale.

One of the most important sources of energy is the sun. Taiwan is located at north 22-25° latitude. Due to its proximity to the equator, it experiences only a small angle of sunlight incidence. Its unique geographical location which can obtain sustainable and stable solar resources. This study takes research on the forecast of solar radiation to maximize the benefits of solar power generation, and develops methods that can predict the future solar radiation pattern to help reduce the costs of solar power generation. This study builds supervised machine learning models, known as deep neural network (DNN) and long short-term memory neural network (LSTM). The hybrid supervised and unsupervised model, namely cluster-based artificial neural network (k-means clustering and fuzzy C-means clustering-based models), was developed. After establishing these models, the study evaluated their prediction results. For different prediction periods, the study selected the best-performing model based on the results and proposed combining them to establish a real-time updated solar radiation forecast system capable of predicting the next 12 hours. The study area covered Kaohsiung, Hualien, and Penghu in Taiwan. Data from ground stations of the Central Weather Administration, collected between 1993 and 2021, as well as the solar angle parameters of each station, were used as input data for the model. The results of this study show that different models have their advantages and disadvantages in predicting different future times. Therefore, the hybrid prediction system can predict future solar radiation more accurately than a single model.

There is growing awareness that “Nature-based Solutions” (NbS) can help to protect us from climate change impacts whilst slowing further warming, supporting biodiversity and securing ecosystem services. However, the potential of NbS to provide the intended benefits has not been rigorously assessed. There are concerns over their reliability and cost-effectiveness compared to engineered alternatives, and their resilience to climate change. Trade-offs can arise if climate mitigation policy encourages NbS with low biodiversity value, such as afforestation with non-native monocultures. This can result in maladaptation, especially in a rapidly changing world where biodiversity-based resilience and multifunctional landscapes are key. Here we highlight the rise of NbS in climate policy—focussing on their potential for climate change adaptation as well as mitigation—and discuss barriers to their evidence-based implementation. We outline the major financial and governance challenges to implementing NbS at scale, highlighting avenues for further research. As climate policy turns increasingly towards greenhouse gas removal approaches such as afforestation, we stress the urgent need for natural and social scientists to engage with policymakers. They must ensure that NbS can achieve their potential to tackle both the climate and biodiversity crisis while also contributing to sustainable development. This will require systemic change in the way we conduct research and run our institutions.

This cross-sectional study evaluated the validity of three alternative methods compared to the gold standard 24-h urine collection for estimating dietary sodium intake, a modifiable risk factor for hypertension, among middle-aged and older adults with elevated blood pressure. These included spot urine collection (using Kawasaki, Tanaka, and INTERSALT equations), 24-h dietary recall, and food frequency questionnaire responses, compared against 24-h urine collection in a subset of 65 participants (aged 50–75 years, 58.5 % women, 61.6 % hypertensive) from the DePEC-Nutrition trial. The validity of the methods was assessed using bias, Spearman correlation coefficient (SCC), intraclass correlation coefficient (ICC), and Bland-Altman analysis. Among the alternative methods, spot urine collection using the Kawasaki equation showed the strongest correlation (SCC 0.238; ICC 0.119, 95 % CI -0.079 to 0.323) but it exhibited a significant bias (1414 mg/day, p-value < 0.001) relative to 24-h urine collection. Conversely, dietary surveys had a smaller bias but wider limits of agreement. These findings underscore the complexities of accurately estimating dietary sodium intake using spot urine collection or dietary surveys in this specific population, suggesting that a combination or refinement of existing methodologies might improve accuracy. Further research with larger samples is necessary to develop more reliable methods for assessing sodium intake in this high-risk group.

In this study, we develop a stochastic partial differential equation (SPDE) model to assess the regional severity of temperature increases due to global warming. The model incorporates various stochastic processes to account for uncertainties such as solar explosions, economic upheavals, rising emissions, and irreversible changes in the climate system. Our base temperature model, influenced by latitude, is enhanced by these random factors to simulate realistic and diverse outcomes of climate change. Numerical simulations on a global grid provide a heatmap representing potential temperature increase severity across different regions. The results highlight the significant impact on tropical and polar regions, demonstrating the utility of SPDE models in understanding and predicting the complex dynamics of climate change. This approach offers valuable insights for policymakers and researchers aiming to develop adaptive strategies for mitigating the adverse effects of global warming.

An existing simplified simulation model of global climate, population, economy, and governance is extended, through statistical and other data analysis, to include several measures of human well-being and population displacement. The revised model is used to explore (in testing to the year 2060) vicious cycles and causal cascades that, some have warned, could lead to acceleration of climate change in the coming decades. Model scenario testing addresses two uncertainties, namely the strength of the effect of climate change on governance erosion, and the strength of the “heat trap” effect from physical tipping points such as ice loss and permafrost thaw. This testing indicates that a strong heat trap effect could very well accelerate climate change, but that even a strong governance erosion effect is unlikely to do so. Governance erosion would hurt human well-being but is probably not much of a climate threat per se. These results are tentative but point to the possibility of formally modeling causal cascades beyond what the larger climate models have done to date.

Cultures in Mediterranean climate zones (MCZs) around the world have long been reliant on groundwater and springs as freshwater sources. While their ecology and cultural sustainability are recognized as critically important, inter-relationships between springs and culture in MCZs has received little attention. Here we augmented a global literature review with case studies in MCZ cultural landscapes to examine the intensity of cultural and socio-economic impacts on springs ecohydrogeology. MCZs are often oriented on the west and southern coasts in tectonically active landscapes, which control aquifer structure, the prevalence of westerly winds, and aridity, and generally expose associated habitats to harsh, afternoon sunlight. Cultural appreciation and appropriation of springs ranges widely, from their use as subsitence supplies to profound traditions, such as Greco-Roman nymphs, and Asian and Abrahamic spiritual cleansing and baptism. Commoditization of water resources for agriculture, industry (e.g., mining, water bottling, geothermal resorts), and urban needs is placing ever-increasing, non-sustainable demands on aquifers and springs. When economic values approach or exceed cultural values, these irreplaceable aquatic resources are degraded and lost. Sustainable stewardship of springs and the aquifers that support them are a little-recognized, but central conservation challenge for modern society facing global climate changes.

Ecosystems are not merely vulnerable to climate change but, if sustainably restored and protected, are a major source of human resilience. Not only is the evidence-base for the importance of these “Nature-based Solutions” (NbS) growing rapidly, but NbS are featuring with increasing prominence in global climate change policy. Here we report on the prominence of NbS in the 141 adaptation components of the 167 Nationally Determined Contributions (NDCs) that were submitted to UNFCCC by all signatories of the Paris Agreement. In total, 103 nations include NbS in the adaptation component of their NDC, 76 nations include them in both their adaptation and mitigation component, and an additional 27 include them as part of their mitigation plans only. In other words, 130 nations—or 66% of all signatories to the Paris Agreement—have articulated intentions of working with ecosystems, in one form or another, to address the causes and consequences of climate change. However, commitments rarely translate into robust science-based targets. As climate pledges are revised in 2020, we urge the ecosystem science community to work closely with policymakers to identify meaningful adaptation targets that benefit both people and the ecosystems on which they depend.

COVID-19 vaccine hesitancy and its enablers would shape community uptake of non-covid vaccines such as oral cholera vaccine (OCV) in the post-COVID-19 era. This study assessed the impact of COVID-19 vaccine hesitancy and its drivers on OCV hesitancy in a cholera endemic region of the Democratic Republic of Congo. We conducted a community-based survey in Bukavu. The survey included characteristics, intention to take OCV and COVID-19, reasons for COVID-19 hesitancy, and thinking and feeling about COVID-19 vaccines. Poisson regression analyses were performed. Of the 1708 respondents, 84.66% and 77.57% were hesitant to OCV alone and to both OCV and COVID-19, respectively. Hesitancy to COVID-19 vaccines rose OCV hesitancy by 12% (crude Prevalence Ratio, [cPR]=1.12, 95%CI[1.03-1.21]). Independent predictors of OCV hesitancy were living in semi-urban area (adjusted Prevalence Ratio [aPR]=1.10, 95%CI[1.03-1.12]), religious refusal of vaccines (aPR=1.06, 95%CI[1.02-1.12]), concerns about vaccine safety (aPR=1.05, 95%CI[1.01-1.11]) and adverse effects (aPR=1.06, 95%CI[1.01-1.12]), as well as poor knowledge (aPR=1.07, 95%CI[1.01-1.14]). Conversely, the belief about COVID-19 vaccine effectiveness reduced OCV hesitancy (aPR=0.76, 95%CI[0.62-0.93]). COVID-19 vaccine hesitancy and its drivers exhibiting a significant domino effect on OCV uptake. Addressing vaccine hesitancy through community-based health literacy interventions would likely improve the introduction of novel non-COVID-19 vaccines in post-COVID-19 era.

P-band radar remote sensing applied during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission has shown great potential for estimation of root zone soil moisture. When retrieving the soil moisture profile (SMP) from P-band radar, a mathematical function describing the vertical moisture distribution is required. Because only a limited number of observations are available, the number of free parameters of the mathematical model must not exceed the number of observed data. For example, a second order polynomial that contains 3 free parameters was presumed based on in-situ SMP data. The polynomial is currently parameterized based on 3 backscatter observations provided by AirMOSS (i.e. one frequency at three polarizations of HH, VV and HV). In this paper, a more realistic, physically-based SMP model containing 3 free parameters is derived based on a solution to Richards’ equation for unsaturated flow in soils. Evaluation of the new SMP model based on both numerical simulations and measured data revealed that it exhibits greater flexibility for fitting measured and simulated SMPs than the currently applied polynomial. It is also demonstrated that the new SMP model can be reduced to a second order polynomial at the expense of fitting accuracy.

Climate change is a major development challenge to Ethiopia. Unless adaptation measures are widely implemented, climate change can set back development efforts and achievements by years. Recognizing this, the Government of Ethiopia (GoE) and civil society organizations have since recently been making considerable efforts to tackle the climate change problem. This paper documents the experience of CARE International in Ethiopia in facilitating bottom-up approaches to promote community-led disaster risk management and climate change adaptation planning through a participator scenario planning (PSP) methodology. PSP is a coordinated approach which leverages a variety of stakeholders' skills and mandates to explore potential climate change risks and their impacts, and then develop locally relevant and shared adaptation action plans that support livelihoods, social capital and ecosystem resilience. The approach has facilitated timely access to and communication of seasonal climate advisories which in turn is empowering communities to take advantage of opportunities that climate presents, which is a key part of adapting to climate change. The institutionalization of the approach by district and regional disaster prevention and food security coordination offices in the southern pastoral regions of Ethiopia encouraged integration of PSP into community livelihood adaptation and local government development planning, hence continuity of the process. Sustainability is expected to be fully achieved when local government planning processes recognize the importance of and provides resources for the participation of meteorological services and community forecasting experts to help refine plans on a seasonal basis, and for systems for dissemination of advisories. Key words: Climate Change, Disaster Risk Reduction, Adaptation, Pastoralism, Ethiopia,

The Bunyavirales order includes at least fourteen families with diverse but related viruses, which are transmitted to vertebrate hosts by arthropod or rodent vectors. These viruses are responsible for an increasing number of outbreaks worldwide and represent a threat to public health. Infection in humans can be asymptomatic, or it may present with a range of conditions from a mild, febrile illness to severe hemorrhagic syndromes and/or neurological complications. There is a need to develop safe and effective vaccines, a process requiring better understanding of the adaptive immune responses involved during infection. This review highlights the most recent findings regarding T cell and antibody responses to the five bunyavirus families with known human pathogens (Peribunyaviridae, Phenuiviridae, Hantaviridae, Nairoviridae, and Arenaviridae). Future studies that define and characterize mechanistic correlates of protection against bunyavirus infections or disease will help inform the development of effective vaccines.

At the end of the dry season in early March to early April each year, extensive agricultural biomass waste burnings occur throughout insular mainland Southeast Asia. During this biomass burnings period, smoke aerosols blanket the whole regions and were transported and dispersed by predominant westerly and south westerly winds to southern China, Taiwan and as far southern Japan and the Philippines. The extensive and intense burnings coincided with some wildfires in the forests due to high temperature making the region one of the global hot spots of biomass fires. In this study, we focus on the effect of pollutants emitted from biomass burnings in March 2019 at the height of the burnings period to the exposed population and their health impact. The Weather Research Forecast- Chemistry (WRF-Chem) model was used to predict the PM2.5 concentration over the simulating domain and health impacts are then assessed on the exposed population in the four countries of Southeast Asia, namely Thailand, Laos, Cambodia and Vietnam. Using the health impact based on log-linear concentration response function and Integrated Exposure Response (IER), the results show that at the peak period of the burnings from 13 to 20 March 2019, Thailand experienced the highest impact, with an estimated 2170 premature deaths. Laos, Vietnam, and Cambodia followed, with estimated mortalities of 277, 565, and 314 deaths respectively. However, when considering the impact per head of population, Laos exhibited the highest impact, followed by Thailand, Cambodia, and Vietnam. The results highlight the significant health impact and associated economic cost of agricultural waste burnings in Southeast Asia at the end of the dry season. And hence policy makers should take these into account to design measures to reduce the negative impact of widespread burnings on exposed population in the region.

Africa's health landscape is rapidly changing, requiring new solutions such as a focus on health literacy. However, there is currently a limited overview of the development and application of health literacy in African countries and societies. To inform progress, a scoping review was conducted, including a systematic literature search of research approaches, historical developments, geographic origins, target populations and settings, and topical interests. 336 articles were retrieved from six databases following PRISMA guidelines. Sources identified included reports, policy briefs, book chapters, and research publications. The analysis provided the most comprehensive overview of the current scope and scale of health literacy in Africa to date. The publications represented 38 of the 54 African Union countries and dated back to 2001, although most were published in the last decade. The content analysis identified 13 broad themes, including mental health, communicable diseases, non-communicable diseases, maternal health, digital health, information and communication, health care, prevention and health promotion, conceptual perspectives, cultural perspectives, outcomes and measurement. The stakeholder analysis revealed a wide range of actors involved in different settings, mostly in health care or community settings. These comprehensive and novel findings can be used to prioritize future actions for public and professional capacity building, policy development and improved practice to improve health literacy for all in Africa.

The bioindicator potential of seagrass ecosystems in coastal trace metal monitoring is low for tropical island ecosystems. This study evaluated the bioindicator potential of six seagrass species exposed to anthropogenic trace metal pollution in the Andaman and Nicobar Islands (ANI) of India. Sediment and seagrass biomass, samples were analyzed for trace metals (Co, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Zn) from four locations of ANI exposed to anthropogenic contamination. Geo accumulation Index values indicated moderate trace metal contamination in seagrass sediment. Seagrass trace metal accumulation from sediment was both species-specific and location- specific for ANI. Small seagrass species such as H. ovalis, H. beccarii and H. uninervis accumulated the maximum concentration of six trace metals (Cr, Cu, Fe, Ni, Pb, Zn) in their tissues compared to big seagrass species like T. hemprichii and E. acoroides of ANI. This study indicates leaves of small seagrass and roots of big seagrass species can serve as short- and long-term bioindicators respectively, of coastal trace metal contamination in tropical islands of ANI.

Drought monitoring and early detection have improved greatly in recent decades through the development and refinement of numerous indices and indicators. However, a lack of guidance, based on user experience, exists as to which drought monitoring tools are most appropriate in a given location. This review paper summarizes the results of targeted user engagement and the published literature to improve the understanding of drought across North America, and to enhance the utility of drought monitoring tools. Workshops and surveys were used to assess and make general conclusions about the perceived performance of drought indicators, indices and impacts information used for monitoring drought in the five main Köppen climate types (Tropical, Temperate, Continental, Polar Tundra, Dry) found across Canada, Mexico, and the United States. In Tropical, humid Temperate, and southerly Continental climates, droughts are perceived to be more short-term (less than 6 months) in duration rather than long-term (more than 6 months). In Polar Tundra climates, Dry climates, Temperate climates with dry warm seasons, and northerly Continental climates, droughts are perceived to be more long-term than short-term. In general, agricultural and hydrological droughts were considered to be the most important drought types. Drought impacts related to agriculture, water supply, ecosystem, and human health were rated to be of greatest importance. Users identified the most effective indices and indicators for monitoring drought across North America to be the U.S. Drought Monitor (USDM) and Standardized Precipitation Index (SPI) (or another measure of precipitation anomaly), followed by the Normalized Difference Vegetation Index (NDVI) (or another satellite-observed vegetation index), temperature anomalies, crop status, soil moisture, streamflow, reservoir storage, water use (demand), and reported drought impacts. Users also noted the importance of indices that measure evapotranspiration, evaporative demand, and snow water content. Drought indices and indicators were generally thought to perform equally well across seasons in Tropical and the colder Continental climates, but their performance was perceived to vary seasonally in Dry, Temperate, Polar Tundra, and the warmer Continental climates, with improved performance during warm and wet times of the year. The drought indices and indicators, in general, were not perceived to perform equally well across geographies. This review paper provides guidance on when (time of year) and where (climate zone) the more popular drought indices and indicators should be used. The paper concludes by noting the importance of understanding how drought, its impacts, and indicators are changing over time as the climate warms, and by recommending ways to strengthen the use of indices and indicators in drought decision-making.

Studies evaluating candidate tick-derived proteins as anti-tick vaccines in natural hosts have been limited due to high costs. To overcome this problem, animal models are used in immunization tests. The aim of this article was to review the use of rabbits as an experimental model for the evaluation of tick-derived proteins as vaccines. A total of 57 tick proteins have been tested for their immunogenic potential using rabbit as model for vaccination. The most commonly used rabbit breeds were New Zealand (73.8%), Japanese white (19%), Californians (4.8%) and Flemish lop-eared rabbit (2.4%). Anti-tick vaccines efficacy resulted in up to 99.9%. Haemaphysalis longicornis (17.9%) and Ornithodoros moubata (12.8%) were the most common tick model in vaccination trials. Experiments in rabbits have revealed that some proteins (CoAQP, OeAQP, OeAQP1, Bm86, GST-Hl, 64TRP, serpins and voraxin) can induce immune responses against various tick species. In addition, in some cases it was possible to determine that the vaccine efficacy in rabbits was similar to experiments performed in natural hosts (e.g. Bm86, IrFER2, RmFER2, serpins and serine protease inhibitor). In conclusion, results have shown that prior to performing anti-tick vaccination trials using natural hosts, rabbits can be used as suitable experimental models for these studies

Bread wheat (Triticum aestivum L.) is one of the world's most important staple food crop providing 20% global energy and dietary proteins. It is widely grown in sub-tropical and tropical areas and as such exposed to heat-stress especially at grain filling period (GFP). Global warming has further affected its production and productivity in these heat-stressed environments. We examined the effect of heat-stress on 18 morpho-physiological and yield-related traits in 96 bread wheat accessions. Heat susceptibility index (HSI <0.60) and yield stability (i <0.55) used as criteria for selecting the tolerant accessions. Heat-stress, imposed by delayed sowing, decreased crop growth and GFP, and as a result reduced morphological and yield-related traits, namely days to 50% anthesis, plant height, peduncle length, flag-leaf area, spike length, spikelets per spike, grain length and width, thousand grain weight (TGW), harvest index and yield. The reduction in the trait values was severe in susceptible accessions (48.2% yield reduction in IC277741) than the tolerant. Physiological traits like chlorophyll content, canopy temperature depression (CTD), normalized difference vegetation index (NDVI), plant waxiness and leaf rolling showed higher expression in the tolerant accessions under heat-stress. Scanning electron microscopy of matured wheat grains revealed ultrastructural changes in endosperm and aleurone cells caused due to heat-stress. The reduction of size and density of large starch granules is the major cause of yield and TGW decrease in the heat-stress susceptible lines. The most stable and high-yielding accessions namely IC566223, IC128454, IC335792, EC576707, IC535176, IC529207, IC446713 and IC416019 were identified as the climate-smart germplasm lines. Germplasm lines possessing desirable traits were selected as po-tential parents for the development of bi-parental and multi-parental populations.

Spatial pattern-oriented evaluations of distributed hydrological models have contributed towards an improved realism of hydrological simulations. This advancement was supported by the broad range of readily available satellite-based datasets of key hydrological variables, such as evapotranspiration (ET). At larger scale, spatial patterns of ET are often characterized by an underlying climate gradient, and with this study, we argue that gradient dominated patterns may hamper the potential of spatial pattern-oriented evaluation frameworks. We hypothesize that the climate control of spatial patterns of ET overshadows the effect model parameters have on the simulated variability. To solve this limitation, we propose a climate normalization strategy. This is demonstrated for the Senegal River basin as modeling case study, where the dominant north-south precipitation gradient is the main driver of the observed hydrological variability. Two multi-objective calibration experiments investigate the effect of climate normalization. Both calibrations utilize observed discharge (Q) in combination with remote sensing ET data, where one is based on the original ET pattern and the other utilizes the normalized ET pattern. We identify parameter sets that balance the tradeoffs between the two independent observations and find that the calibration using the normalized ET pattern does not compromise the spatial patern performance of the original pattern. However, vice versa, this is not necessarily the case, since the calibration using the original ET pattern showed a poorer performance for the normalized pattern. Both calibrations reached comparable performance of Q. With this study, we identified a general shortcoming of spatial pattern-oriented model evaluations using ET in basins dominated by a climate gradient, but we argue that this also applies to other variables such as, soil moisture or land surface temperature.

The development of plant-based meat analogues has become a significant challenge for the food industry in recent years due to the increasing demand for sustainable and healthier proteins in the context of a global protein transition. Plant-based meat analogues imitate the visual, textural, and chemical properties of traditional meat products and are required to closely resemble meat to appeal to consumers. In addition, consumers demand natural, clean-label, and nutritional and healthy products. To address these challenges, the food industry must develop highly healthy, nutritious, and E-number-free meat analogue products. Understanding the functionality of each ingredient and its role in the food matrix is crucial to being a key player in the innovation of the meat analogues market. This review provides updated information on the primary ingredients utilized for the development of plant-based burger meat alternatives and their functionality. The key components of meat analogue burgers are outlined, including plant proteins, binding agents, fats and oils, flavorings, colorings, preservatives, fortificants, and clean-label considerations.

Severe malaria is a major public health concern in Angola, particularly among adults, with limited data on clinical outcomes and manifestations. The study therefore assessed the clinical manifestations and outcome of severe Plasmodium falciparum malaria in adult patients admitted to Hospital Central Dr. António Agostinho Neto of Lubango, Angola. The study retrospectively reviewed the individual records of patients over 14 years old, admitted for severe malaria during the first trimester of 2021 and 2022. World Health Organization (WHO) criteria were used to define severity. A total of 640 patients were recorded into the study, 167 in 2021 and 473 in 2022, distributed across the departments; Intensive Care Unit (ICU; n=81), Medicine (MED; n=458) and Infectiology (INF; n=101). The median age was 26 years and 59.4% were males. The most common manifestation of severe malaria was renal impairment (37.4%). The overall mortality rate was 7% and decreased considerably from 10.2% in 2021 to 5.9% in 2022. The increase in malaria cases during 2022 with increased in prevalence of life-threatening signs like renal impairment, jaundice and impaired conscience, underscores the urgent need for enhanced malaria control and surveillance strategies to mitigate morbidity and mortality, highlighting the potential for antimalarial drug resistance.

Studies were initiated to find new effective fungicides to use under field conditions to identify novel options for improved disease management in sugar beet. Cercospora-leaf spot (CLS) of sugar beet, caused by Cercospora beticola Sacc., is a major foliar disease of sugar beet in all sugar beet growing areas, worldwide. The disease is now dominant in almost all sugar beet growing areas of Europe, including Hungary. The epidemic spread of this disease can cause up to 50% yield loss. The use of fungicides has been being an integral part of the control of CLS of sugar beet, due to lack of efficacy from non-chemical alternatives. In recent decades, the emergence of resistant strains in C. beticola Sacc. populations have reduced the efficacy of some fungicides (Quinoine inside Inhibitors (QoI) – FRAC Group 11; and DeMethylation-Inhibitors (DMI) – FRAC Group 3) because of repeated use over many uears. Resistance of CLS to fungicides belonging to different groups of active substances has been described in many countries of the world, including Hungary. The picolinamides are a new distinct group of fungal respiration inhibitors (Inhibition of Complex III) belonging to Quinoine inside Inhibitors (QiI). Two novel fungicides from the group of picolinamides (Qil – FRAC Group 21) – fenpicoxamid and florylpicoxamid– were tested and evaluated over two seasons, in vegetation period of 2020 and 2021 for controlling CLS of sugar beet in Hungary. Both fungicides were applied as straight formulated products at a range of dose rates, and they showed very effective control of CLS compared to the untreated control check plots and the reference fungicide product difenoconazole. The results consistently showed all tested dose rates of fenpicoxamid and florylpicoxamid provided effective control against CLS of sugar beet in addition to a clear dose response curve. Disease severity, in terms of area under the disease progress curve values (AUDPC), was significantly correlated with yield decrease, while no significant context existed between disease severity and sugar content of the roots. Additionally, the results showed in two investigated years, the efficacy of both picolinamide fungicides applied at 75 g ai/ha provided significantly better % control on CLS of sugar beet than the DMI fungicide difenoconazole applied at 100 g ai/ha dose rate. Fenpicoxamid is already registered in Europe in cereals and label extension is planned in sugar beet and other crops. Florylpicoxamid is not yet registered in Europe but is beginning to be approved in countries across the globe in a range of crops and is continuously under evaluation for potential markets.

The quality of photogrammetric-based derived products like orthophotos, digital terrain models (DTMs) and digital line maps as well as the global digital elevation models (DEM) are rigorously dependent on the accuracy of image orientation. This paper evaluates the vertical accuracy of aerial photogrammetric Digital Terrain Model (DTM), Shuttle Radar Topography Mission (SRTM), Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER), and TerraSAR-X's twin satellite of TanDEM-X (TDX) datasets against in-situ orthometric heights computed from ellipsoidal heights and the 2008 Earth Gravitational Model (EGM2008) derived geoid heights in Ethiopia. The quality of the four global digital elevation models was also validated against the aerial photogrammetric DTM measurements. Besides, the accuracies of the photogrammetric DTM and the four DEM products were checked for their compliance to the American Society for Photogrammetry and Remote Sensing (ASPRS) standards as well as the Ethiopian national vertical data evaluation standards. The study showed that the photogrammetric DTM is in a good agreement with the reference orthometric heights compared to SRTM, ASTER and TDX datasets. More precisely, the result has an absolute accuracy of 1.67 m at Linear Error (LE) 95% confidence level, while the absolute accuracy of SRTM3 arc seconds (SRTM3) at LE 90% (11.91 m) is better than its product specification (16 m). The absolute accuracy of SRTM1 arc second (SRTM1) (7.70 m at LE 90%) surpasses that of SRTM3, whereas the absolute accuracy of ASTER DEM is somehow below its product specification. TDX also has the same vertical accuracy (10.29 m at LE 90%) compared to its product specification (10 m). Furthermore, the vertical accuracy of the photogrammetric DTM meets the100 cm vertical accuracy of the 2015 ASPRS standard. However, it does not meet the Ethiopian national vertical data accuracy requirement standard, i.e., RMSEz of ± 0.45 m. In general, the photogrammetric DTM, SRTM1, and TDX have been proven a superior product over the SRTM3 and ASTER DEMs, and better to use these products for high-level precision and accuracy required applications.

Assessment of extreme events and climate change on reservoir inflow is important for water and power stressed countries. Projected climate is subject to uncertainties related to climate change scenarios and Global Circulation Models (GCMs’). Extreme climatic events will increase with the rise in temperature as mentioned in the AR5 of the IPCC. This paper discusses the consequences of climate change that include extreme events on discharge. Historical climatic and gauging data were collected from different stations within a watershed. The observed flow data was used for calibration and validation of SWAT model. Downscaling was performed on future GCMs’ temperature and precipitation data, and plausible extreme events were generated. Corrected climatic data was applied to project the influence of climate change. Results showed a large uncertainty in discharge using different GCMs’ and different emissions scenarios. The annual tendency of the GCMs’ is bi-vocal: six GCMs’ projected a rise in annual flow, while one GCM projected a decrease in flow. The change in average seasonal flow is more as compared to annual variations. Changes in winter and spring discharge are mostly positive, even with the decrease in precipitation. The changes in flows are generally negative for summer and autumn due to early snowmelt from an increase in temperature. The change in average seasonal flows under RCPs’ 4.5 and 8.5 are projected to vary from -29.1 to 130.7% and -49.4 to 171%, respectively. In the medium range (RCP 4.5) impact scenario, the uncertainty range of average runoff is relatively low. While in the high range (RCP 8.5) impact scenario, this range is significantly larger. RCP 8.5 covered a wide range of uncertainties, while RCP 4.5 covered a short range of possibilities. These outcomes suggest that it is important to consider the influence of climate change on water resources to frame appropriate guidelines for planning and management.

This study uses the Scopus and Web of Science databases to review the forecast of electricity consumption from 2015 to 2024. Based on the keywords used in the article title, which are associated with electricity consumption forecasts, the study retrieved 821 docu-ments for additional analysis using various techniques. For frequency analysis, we used Mi-crosoft Excel; for data visualisation, we used VOSviewer; and for citation metrics and analysis, we used Harzing's Publish or Perish. Standard bibliometric variables, including publication growth, authorship patterns, collaboration, prolific authors, national contribution, most active institutions, favourite journals, and highly cited articles, are used in this study's reporting of the findings. Our analysis indicates that over ten years, starting in 2015, publications on the forecasting of electricity consumption have continued to increase. China was the country that contributed the most to research on electricity consumption. Most of the research publications on electricity consumption were published in the Journal of Energy.

There is increasing evidence that the impact of climate change on the productivity of grasslands will at least partly depend on their biodiversity. A high level of biodiversity may confer stability to grassland ecosystems against environmental change, but there are also direct effects of biodiversity on the quantity and quality of grassland productivity. To explain the manifold interactions, and to predict future climatic responses, models may be used. However, models designed for studying the interaction between biodiversity and productivity tend to be structurally different from models for studying the effects of climatic impacts. Here we review the literature on the impacts of climate change on biodiversity and productivity of grasslands. We first discuss the availability of data for model development. Then we analyse strengths and weaknesses of three types of model: ecological, process-based and integrated. We discuss the merits of this model diversity and the scope for merging different model types.