Clearness Index Research Articles

Characterizing two hybrid exercise-cognitive training interventions with neurophysiological and behavioral indexes in post-stroke patients with cognitive dysfunction: a randomized controlled trial.

Combined exercise and cognitive training have been evidenced to be effective in cognitive and physical functions in post-stroke survivors. Recent interest has gradually shifted to technology-aided cognitive rehabilitation. However, clear neural makers or comprehensive behavioral indexes used for evaluating rehabilitation remain unexplored. The study aimed to examine the effects of two types of combined exercise-cognitive training on stroke patients with cognitive dysfunction, focusing on neural and behavioral markers. 39 patients were randomly assigned to sequential exercise-cognitive training, simultaneous exercise-cognitive training or active control groups and underwent 60 minutes/day training, 3 days/week, for 12 weeks. 29 patients ultimately completed the training. The markers/indexes included cognitive function, physical function, instrumental activities of daily living, and caregiver strain. Cognitive function included working memory task performance, neurophysiological markers, and cognitive indexes. The results indicated no d-prime difference between groups after the training. The simultaneous training demonstrated significant improvements in the neurophysiological marker of P300 and theta coherence compared to the other groups. Moreover, the simultaneous training also led to significant enhancements in physical function, as measured by the Rivermead Mobility Index, comparing to the other groups. Further analysis contrasting the two exercise-cognitive trainings revealed that improvements in cognition and multifaceted domains (i.e., instrumental activities of daily living and caregiver strain) were manifested in the simultaneous training. Together with the neural markers identified in the current interventions, the differential impacts of the two interventions indicates the potential of technology-driven and personalized rehabilitation in post-stroke patients.

Adaptive control systems for dual axis tracker using clear sky index and output power forecasting based on ML in overcast weather conditions

The use of artificial intelligence in renewable energy systems increases energy generation and improves energy system management. The control system of many solar trackers is designed for maximum radiation power conditions and shows decent performance indicators, but during rapidly changing weather conditions or cloudy days, the performance of the solar trackers is reduced due to moving parts and low irradiance. Some studies show that the horizontal configuration produces more energy with scattered solar radiation than solar tracking systems. This work shows the possibility of using solar tracking systems under different weather conditions and cloudy days. To achieve the goals, a new adaptive control system for dual-axis solar trackers with astronomical tracking was developed, which differs from traditional controls in the use of horizontal configurations under certain weather conditions. The assessment of spatio-temporal weather conditions was carried out using the Clear Sky Index (CSI) and was complemented by forecasting the panel's power output. The study found that at 0.4 CSI values, the horizontal configuration exhibits higher power output than solar tracking systems, providing the potential to use the threshold for adaptive control. The developed system is more efficient by 18.3 %, 14.9 %, and 10.01 % than the horizontal configuration, single-axis, and dual-axis solar trackers.

Modeling of Biologically Effective Daily Radiant Exposures over Europe from Space Using SEVIRI Measurements and MERRA-2 Reanalysis

Ultraviolet solar radiation at the Earth’s surface significantly impacts both human health and ecosystems. A biologically effective daily radiant exposure (BEDRE) model is proposed for various biological processes with an analytical formula for its action spectrum. The following processes are considered: erythema formation, previtamin D3 synthesis, psoriasis clearance, and inactivation of SARS-CoV-2 virions. The BEDRE model is constructed by multiplying the synthetic BEDRE value under cloudless conditions by a cloud modification factor (CMF) parameterizing the attenuation of radiation via clouds. The CMF is an empirical function of the solar zenith angle (SZA) at midday and the daily clearness index from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) measurements on board the second-generation Meteosat satellites. Total column ozone, from MERRA-2 reanalysis, is used in calculations of clear-sky BEDRE values. The proposed model was trained and validated using data from several European ground-based spectrophotometers and biometers for the periods 2014–2023 and 2004–2013, respectively. The model provides reliable estimates of BEDRE for all biological processes considered. Under snow-free conditions and SZA < 45° at midday, bias and standard deviation of observation-model differences are approximately ±5% and 15%, respectively. The BEDRE model can be used as an initial validation tool for ground-based UV data.

Scenario-based stochastic optimization on the variability of solar and wind for component sizing of integrated energy systems

The inherent intermittency and variability of renewable energy sources present significant challenges to the optimal design and implementation of integrated energy systems (IES). This paper introduces a novel stochastic optimization model that integrates advanced scenario generation and clustering algorithm for renewable energy sources within a multi-objective, bi-level optimization framework. Specifically, the clearness index is employed to represent the stochastic distribution of solar radiation intensity by beta distribution, while wind speed uncertainty is modeled seasonally using the Weibull distribution. Monte Carlo sampling with synchronous back substitution is applied for scenario generation and reduction of solar radiation and wind speed. To address the multi-objective evaluation, the analytic hierarchy process is utilized, and the joint optimization is achieved by combining a region contraction algorithm with stochastic programming. The proposed methodology is validated on an IES featuring various heating devices, incorporating uncertainties in both wind and solar energy. The results indicate that the absorption heat pump-based scheme achieves superior energy-saving performance, achieving an energy rate of 0.4724. Additionally, the compression heat pump-based scheme exhibits excellent economic efficiency and environmental sustainability, with a cost of energy of 0.3639 and a renewable fraction of 0.5536.

Comparison and Optimization of Light Use Efficiency-Based Gross Primary Productivity Models in an Agroforestry Orchard

The light-use efficiency-based gross primary productivity (LUE-GPP) model is widely utilized for simulating terrestrial ecosystem carbon exchanges owing to its perceived simplicity and reliability. Variations in cloud cover and aerosol concentrations can affect ecosystem LUE, thereby influencing the performance of the LUE-GPP model, particularly in humid regions. In this study, the performance of six big-leaf LUE-GPP models and one two-leaf LUE-GPP model were evaluated in a humid agroforestry ecosystem from 2018–2020. All big-leaf LUE-GPP models yielded GPP values consistent with that derived from the eddy covariance system (GPPEC), with R2 ranging from 0.66–0.73 and RMSE ranging from 1.81–3.04 g C m−2 d−1. Differences in model performance were attributed to the differences in the quantification of temperature (Ts) and moisture constraints (Ws) and their combination forms in the models. The Ts and Ws algorithms in the eddy covariance-light-use efficiency (EF-LUE) model well characterized the environmental constraints on LUE. Simulation accuracy under the common limitation of Ts and Ws (Ts × Ws) was higher than the maximum limitation of Ts or Ws (Min (Ts, Ws)), and the combination of the Ts algorithm in the Carnegie–Ames–Stanford Approach (CASA) and the Ws algorithm in the EF-LUE model was optimized in combination forms, thereby constraining LUE for GPP estimates (GPPBLO, R2 = 0.76). Various big-leaf LUE-GPP models overestimated or underestimated GPP on sunny or cloudy days, respectively, while the two-leaf LUE-GPP model, which considered the transmission of diffuse radiation and the difference in photosynthetic capacity of canopy leaves, performed well (R2 = 0.72, p < 0.01). Nevertheless, the underestimation/overestimation for shaded/sunlit leaves remained under different weather conditions. Then, the clearness index (Kt) was introduced to calculate the dynamic LUE in the big-leaf and two-leaf LUE-GPP models in the form of exponential or power functions, resulting in consistent performance even in different weather conditions and an overall higher simulation accuracy. This study confirmed the potential applicability of different LUE-GPP models and emphasized the importance of dynamic LUE on model performance.

Design-Optimization of Solar-Collector Tilt Angle for Annual-Maximum and Seasonal-Balanced Energy Received

Abstract A detailed research study was performed on the annual energy received by a fixed-tilt solar collector. A new clear-sky model of solar radiation was developed by combining existing clear-sky models. Then, this model, was used to perform optimizations with two different objectives. The first objective is to achieve the maximum-annual solar energy received by a solar panel, while the second is to balance its energy received so that it is more uniform over the course of a year. Each of these objectives have differing optimum tilt values, depending on the location and usage of a solar receiver system. The novelty of this investigation is the use of the aforementioned solar model with a unique algorithm for multiple optimizations for these two different objectives, which results in two different optimum tilt angles that are in turn different from the widely accepted rule-of-thumb that recommends an angle that is equal to the latitude. The solar radiation results obtained with the model used herein were experimentally confirmed through comparison with measured weather data for a number of locations with different sky clearness indexes as well as comparison with those of other studies found in the literature. A major contribution of this study are the plots and tables showing optimum tilt angles as estimated by the model as well as with empirical data. By selecting the appropriate tilt angle, a designer can now decide to either maximize annual electrical energy production or else to balance delivery of electricity over the year.

Effects of Biophysical Factors on Light Use Efficiency at Multiple Time Scales in a Chinese Cork Oak Plantation Ecosystem

Light use efficiency (LUE) characterizes the efficiency of vegetation in converting photosynthetically active radiation (PAR) into biomass energy through photosynthesis and is a critical parameter for gross primary productivity (GPP) in terrestrial ecosystems. Based on the eddy covariance measurements of a Chinese cork oak plantation ecosystem in northern China, the temporal variations in LUE were investigated, and biophysical factors were examined at time scales ranging from hours to years. Our results show that diurnal LUE first increased sharply before 8:30 and then decreased gradually until 12:00, thereafter increasing gradually and reaching the maximum value at sunset during the growing season. The daily and monthly LUE first increased and then decreased within a year and showed a substantial drop around June. The annual LUE ranged from 0.09 to 0.17 g C mol photon−1, and the multiyear mean maximal LUE was 0.30 g C mol photon−1 during 2006–2019. Only GPP (positive) and clearness index (CI) (negative) had consistent effects on LUE at different time scales, and the effects of the remaining biophysical factors on LUE were different as the time scale changed. The effects of air temperature, vapor pressure deficit, precipitation, evaporative fraction, and normalized difference vegetation index on LUE were mainly indirect (via PAR and/or GPP). When CI decreased, an increased ratio of diffuse PAR to PAR produced a more uniform irradiance in the canopy, which ultimately resulted in a higher LUE. Due to climate change in our study area, the annual LUE may decrease in the future but improving management practices may slow or even reverse this trend in the annual LUE in the studied Chinese cork oak plantation.

Estimation of Diffuse Solar Radiation in the City of Natal – RN Based on the KT Index

Objective: This study aimed to validate and compare three models for estimating diffuse solar radiation in the city of Natal - RN, using data from the SONDA Project, with a focus on developing and validating the Ubial model. Theoretical Framework: The research is based on solar radiation concepts, which are essential for optimizing photovoltaic systems. Accurate models for estimating diffuse radiation are necessary for the efficient performance of these systems, especially in locations like Natal - RN. Method: The methodology adopted a quantitative and comparative approach, analyzing the Erbs, Bourges, and Ubial models. Data from the SONDA Project were used, and the statistical analysis included evaluating the Mean Bias Error (MBE) and correlating the data with the clearness index (Kt). Results and Discussion: The Ubial model showed superior performance, with a negative MBE, indicating greater accuracy without overestimating the data. These results were discussed in light of the theoretical framework, highlighting the suitability of the Ubial model for the climatic conditions of Natal - RN. Research Implications: The implications of this research include the potential application of the Ubial model in other regions with similar climatic characteristics, contributing to the optimization of photovoltaic systems. Originality/Value: This study introduces an innovative model that incorporates additional variables, resulting in more accurate estimates of diffuse solar radiation, with a direct impact on the solar energy field.

Factors and quantitative impact on electrical yield in fishery complementary photovoltaic power plant under different cloud cover conditions

The electrical yield of fishery complementary photovoltaic (FPV) power plants can be self-sustained through aquaculture, offering certain advantages over land-mounted photovoltaic systems. However, electrical yield was affected by environmental factors. The screening of impactful factors and quantification of the relationship between electrical yield and these factors are difficult for FPV power plants because of lacking observation materials. Therefore, electrical yield and its main impactful factors under various cloud cover conditions through in-situ observations were investigated in this study. The results indicated that distinct variations in electrical yield and impactful factors with different clearness index. Downward shortwave radiation (DSR), panel temperature, and vapor pressure deficit (VPD) jointly impact electrical yield. When panel temperature is below 30 °C, electrical yield capacity increases by 10 kWh for every 1 W m⁻2 rise in DSR. Moreover, a 1 kPa rise in VPD results in an approximate 9.50 kWh increase in electrical yield for each 1 W m⁻2 rise in DSR. This study will be crucial for effective electrical yield management planning for aquaculture in FPV power plants.

Project SIGNAL: A Dashboard for Supporting Community Confidence in Making Data-Driven Decisions.

Data dashboards have emerged as critical tools for surveillance and informing resource allocation. Despite their utility and popularity during COVID-19, there is a growing need to understand what tools and training are tailored to nonprofit community-based organizations that may partner with public health officials. In June 2021, the Rhode Island Department of Health and Brown University partnered to create Project SIGNAL (Spatiotemporal Insights to Guide Nuanced Actions Locally), which utilizes spatiotemporal analytics to identify Rhode Island's largest disparities in COVID-19-related outcomes (eg, testing, diagnosis, vaccinations) at the neighborhood level. Results were hosted in an interactive online dashboard (signal-ri.org) designed using principles of the CDC Clear Communication Index. The target audience included a network of 15 geographic areas called Health Equity Zones, funded by the health department to provide critical grassroots public health programs to address social, health, and economic outcomes in their communities. To disseminate the dashboard, a 6-hour virtual workshop series was created to train leaders to use the dashboard and increase their confidence in understanding common public health data terminology and concepts and better prepare attendees for rapid decision making during future public health emergencies. The Project SIGNAL dashboard was launched in August 2022 and has been accessed over 7500 times. A total of 84 community leaders were trained to use this dashboard, increasing their confidence in applying common public health metrics to make decisions about their COVID-19-related activities. While several studies have outlined best practices for data dashboards, this is among the first to examine incorporating these practices into a spatiotemporal decision tool designed specifically for community organizations. Project SIGNAL demonstrates that by incorporating design best practices and pairing data dashboards with hands-on training, we can empower community leaders to utilize advanced spatiotemporal methods to identify health disparities and take localized action.

Seasonal, Decadal, and El Niño-Southern Oscillation-Related Trends and Anomalies in Rainfall and Dry Spells during the Agricultural Season in Central Malawi

As governments continue to address climate change when formulating policy, there remains a need to determine if such a change exists in the historical record to inform clear indices for monitoring the present climate for site-specific interventions. This study characterised trends and anomalies in rainfall and dry spells, providing local information often projected from satellites or regional data in data-scarce regions. From 1961 to 2007, daily rainfall records in Central Malawi were used to calculate indices for low-(Balaka), medium-(Bunda, Chitedze, KIA), and high-altitude (Dedza) sites, which were then subjected to Mann–Kendall’s, Cramer’s, and Spearman-Rho’s trend tests. Significant decreasing trends in terms of wet days and growing season length were evident across locations. Seasonal and extreme rainfall, dry spells, and inter-seasonal and near-decadal anomalies were not consistently or inevitably significant. Unexpectedly, rainfall anomalies were largest in Bunda and KIA, which have mild climatic regimes, while the lowest were in Balaka, a rainfall-averse zone. The relationship between El Niño-Southern Oscillation (ENSO) and extreme rainfall and dry spell events did not reach statistical significance. In conclusion, extreme precipitation and dry spell events show varied intensities and proportions rather than increased frequency. The disparate results largely justify the need for in-depth local-scale assessments for agroclimatic applications.

Evaluating Multi-Jurisdictional Enteric Illness Outbreak Messaging in Canada: A Content Analysis of Public Health Notices

ABSTRACT Effective risk communication during enteric illness outbreaks requires the provision of clear and consistent information to diverse audiences to reduce risk of exposure, inform behavior changes, and prevent illness. Most enteric illnesses are caused by pathogens transmitted through consumption of contaminated food or water, contact with animals, or person-to-person contact. When multi-jurisdictional outbreaks occur, the Public Health Agency of Canada posts web-based Public Health Notices (PHNs) to inform Canadians. This study evaluated the comprehensibility of PHNs to optimize federal risk communication approaches. Publicly available web-based PHNs (n = 42) from 2014–2022 were obtained. A codebook was developed using the Centers for Disease Control and Prevention’s (CDC) Clear Communication Index (CCI) and Health Belief Model (HBM) and systematically applied. A SMOG readability calculator was used to determine reading grade level. Descriptive statistics were calculated to summarize coded data. The average reading grade level was above Grade 12 (13.9 ± 1.1). PHNs communicated the nature of the risk (100%) and behavioral recommendations (96.5%) clearly. An active voice was sometimes used (61.9%), but numerical information was less commonly presented using best practices (38.1%). The HBM was fully incorporated in seven PHNs, with most PHNs using five of six constructs (66.7%). PHNs shared similar information in a consistent order (75.0%). Aligning PHNs to best practices in risk communication is recommended, including writing content at a reading grade level that supports comprehension by diverse audiences, following the CCI to increase clarity, including all HBM constructs to promote behavior change, and maintaining message consistency.

Understandability of passive smoking prevention leaflets in Japan

PurposeThere are few studies in Japan evaluating informational materials about passive smoking prevention with respect to health literacy. This study applied a range of health literacy assessment tools to assess the understandability of public-sector leaflets about passive smoking prevention.MethodsWe collected 26 anti-passive smoking leaflets published online by Japanese prefectural governments. We used three internationally recognized health literacy assessment tools to evaluate their understandability: the Clear Communication Index (CCI), the Suitability Assessment of Materials (SAM), and the Patient Education Materials Assessment Tool (PEMAT, further divided into scales for understandability and actionability).ResultsAlthough none of the assessed materials met the CCI's scoring threshold for “easy to understand,” the highest-scoring leaflet, which received a top-two ranking in three of the four scales (excluding the PEMAT understandability scale), was characterized by its conciseness and its easy-to-understand structure, including the use of checklists and flowcharts to engage reader interest. There was a significant correlation between CCI and SAM, and between SAM and the PEMAT understandability scale. However, the PEMAT actionability scale did not significantly associate with the other assessment scales.ConclusionsThe understandability of written informational materials about passive smoking prevention in Japan could be improved by referring to multiple assessment scales.

Improving Soybean Gross Primary Productivity Modeling Using Solar-Induced Chlorophyll Fluorescence and the Photochemical Reflectance Index by Accounting for the Clearness Index

Solar-induced chlorophyll fluorescence (SIF) has been widely utilized to track the dynamics of gross primary productivity (GPP). It has been shown that the photochemical reflectance index (PRI), which may be utilized as an indicator of non-photochemical quenching (NPQ), improves SIF-based GPP estimation. However, the influence of weather conditions on GPP estimation using SIF and PRI has not been well explored. In this study, using an open-access dataset, we examined the impact of the clearness index (CI), which is associated with the proportional intensity of solar incident radiation and can represent weather conditions, on soybean GPP estimation using SIF and PRI. The midday PRI (xanthophyll de-epoxidation state) minus the early morning PRI (xanthophyll epoxidation state) yielded the corrected PRI (ΔPRI), which described the amplitude of xanthophyll pigment interconversion during the day. The observed canopy SIF at 760 nm (SIFTOC_760) was downscaled to the broadband photosystem-level SIF for photosystem II (SIFTOT_FULL_PSII). Our results show that GPP can be accurately estimated using a multi-linear model with SIFTOT_FULL_PSII and ΔPRI. The ratio of GPP measured using the eddy covariance (EC) method (GPPEC) to GPP estimated using SIFTOT_FULL_PSII and ΔPRI exhibited a non-linear correlation with the CI along both the half-hourly (R2 = 0.21) and daily scales (R2 = 0.25). The GPP estimates using SIFTOT_FULL_PSII and ΔPRI were significantly improved by the addition of the CI (for the half-hourly data, R2 improved from 0.64 to 0.71 and the RMSE decreased from 8.28 to 7.42 μmol•m−2•s−1; for the daily data, R2 improved from 0.71 to 0.81 and the RMSE decreased from 6.69 to 5.34 μmol•m−2•s−1). This was confirmed by the validation results. In addition, the GPP estimated using the Random Forest method was also largely improved by considering the influences of the CI. Therefore, our findings demonstrate that GPP can be well estimated using SIFTOT_FULL_PSII and ΔPRI, and it can be significantly enhanced by accounting for the CI. These results will be beneficial to vegetation GPP estimation using different remote sensing platforms, especially under various weather conditions.

Techno-economic analysis and optimization of 50 MWe linear fresnel reflector solar thermal power plant for different climatic conditions

The climate change crisis is primarily associated with carbon dioxide emissions from fossil fuel-based electricity generation, which significantly contribute to the greenhouse gas effect. The implementation of renewable energy technologies can mitigate environmental impacts and promote a sustainable future. This study aimed to conduct a techno-economic feasibility analysis and optimize performance parameters for a 50 MWe capacity Linear Fresnel Reflector (LFR) concentrated solar power plant for nine stations under various climatic conditions. The analysis was conducted based on Levelized Cost of Electricity (LCOE) and Capacity Factor (CF) to select a suitable Heat Transfer Fluid (HTF) for each station, by varying Solar Multiple (SM) from 1.0 to 6.0 and adjusting Thermal Energy Storage (TES) hours from four to sixteen. The HTFs selected for economically feasible projects at different stations were Hitec Solar Salt, Hitec, and Therminol VP-1. The combination of SM and TES hours was optimized for economic feasibility, targeting a minimum CF of 36 % and a maximum LCOE of 13.0 ¢/kWh. The optimum SM and TES hours were found to range from 2.0 to 5.8 and from six to thirteen, respectively. An LCOE of less than 7.5 ¢/kWh and a CF of more than 80 % was achieved for two stations with SM of 6.0 and TES of 16 h using Therminol VP-1. Stations with annual mean direct normal irradiance values higher than 1500 kWh/m2 and clearness index values over 0.58 are suitable for commercial power plants. This study contributes to achieving the United Nation's sustainable development goals by promoting the use of renewable energy technologies.

Performance assessment of large-scale rooftop solar PV system: a case study in a Malaysian Public University

Adopting rooftop solar PV systems in various domestic and non-domestic sectors (including commercial, industrial, and agricultural) exhibits their commitment to green energy ventures. This study intends to evaluate the effectiveness of a grid-connected solar system that has been installed so far: a 6.9 MWp photovoltaic (PV) system implemented at University Tun Hussein Onn (UTHM) in Batu Pahat, Johor. This green energy system was installed as a part of the Self-Consumption (SELCO) program utilizing a Supply Agreement for Renewable Energy (SARE) contract. To assess the mounted energy system's efficiency, performance monitoring was carried out between December 2021 and November 2022. By evaluating the enactment of the 6.9 MWp solar system at UTHM, this present work has attempted to compile the achievement of implementation of self-consumption in terms of performance analysis and financial benefits to consumer. Based on International Electrotechnical Commission (IEC) 61724 standard, this study also investigates several performance characteristics of the PV system, such as final yield, clearness index (CI), array yield, PV module efficiency, inverter efficiency, reference yield, and capacity factor (CF) performance ratio (PR). Moreover, an assessment of Carbon Dioxide (CO2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${CO}_{2})$$\\end{document} avoidance was also conducted to quantify the amount of CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${CO}_{2}$$\\end{document} reduction achieved. In addition, the self-consumption ratio and self-sufficiency ratio pattern for solar power plant in UTHM has been assessed. The monthly clearness index at the project location from December 2021 to November 2022 ranges from 0.63 to 0.66. The PV system was monitored throughout this time and generated 8529 MWh of energy. The installed PV system has shown an efficiency of 11.86%, a final yield of 108.28%, and a PR of 0.78, respectively. Further, the installed 6.9 MWp PV system at UTHM will cut CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${CO}_{2}$$\\end{document} emissions by 5450 tons annually. The 6.9 MWp solar PV system is also estimated to reduce 25.1 RM mil in 25 years. Making the system larger to 6.9 MWp would reduce the consumption from the grid during the week, makes it possible to achieve self-consumption levels of up to 100% and self-sufficiency of this solar plant is relatively low ranges from 22 to 35%. The research finding bridges the gap between policy makers, investors, and the public regarding acceptance of the large-scale Self-Consumption (SELCO) and strengthen the strategic collaborations between solar service provider and educational bodies to support of the government’s agenda to achieve carbon emission intensity reduction in Malaysia.Graphical

Oral cancer: analysis of the clarity of publications in Instagram profiles of official health agencies in Brazil.

This article aims to analyze the clarity and engagement measures of publications on oral cancer in the Instagram profiles of official health agencies in Brazil. An infodemiological study was conducted with 81 profiles. Data collected concerned content classification, account and media, manner of addressing the topic, number of posts, likes, comments, views and hashtags. The clarity of the educational publications was assessed with the Brazilian version of the Clear Communication Index (BR-CDC-CCI). Data analysis involved Spearman's correlation and the Mann-Whitney test (α = 5%). A total of 775 publications on oral cancer were found. The average BR-CDC-CCI score was 69.8 (SD = 15.5). The clarity of the information was adequate in 9.5% of the educational publications. Positive correlations were found between the number of likes and engagement (comments [r = 0.49], views [r = 0.96]), number of hashtags (r = 0.13) and year of publication (r = 0.21). Publications from the Health Ministry had a significantly higher BR-CDC-CCI score compared to the other profiles. Publications on oral cancer were correlated with engagement, year of publication and number of hashtags. Public agencies increased publications to reach the population, but the clarity of the content was low.

Regressive and Spatio-Temporal Accessibility of Variability in Solar Energy on a Short Scale Measurement in the Southern and Mid Region of Mozambique

Solar energy reaching a horizontal surface can possess fluctuations that impact electricity generation at a solar plant. Despite this, energy access remains inadequate, particularly in rural areas, with an estimated 82% deficiency. This drives us to assess the regressive and spatial-temporal accessibility of solar energy in the southern and mid regions of Mozambique. This evaluation aims to determine the actual availability of energy for electrification purposes. Data on global horizontal irradiation from approximately 8 stations across all provinces in the specified regions, collected between 2012 and 2014 at intervals of 1 and 10 min, were analyzed using regression and correlation methods along with a specialized algorithm for classifying days based on clear sky index terms. The statistical analysis identified days with significant potential for energy accessibility, exceeding 50% of the average. The findings suggest a correlation coefficient of approximately 0.30 for energy and non-linear regression with clear sky index coefficients around 0.80. The method employed demonstrated accuracy when compared to theoretical simulations of the clear sky index in the region, indicating its potential applicability in other regions of interest.

Comparative Numerical Energy and Performance Analysis of Solar Photovoltaic and Solar Thermal Power Generation

This study analyses fixed and tracking solar photovoltaics and, solar thermal power. Performance in different locations was analysed for each configuration and technology using simulations based on a Typical Meteorological Year data. The study analysed a 25MWp solar photovoltaic and solar thermal power plants in each of the eleven selected locations in Zimbabwe. The performance of the solar photovoltaic and solar thermal power plants under different meteorological variables were assessed for all the selected locations. It was shown that different configurations together with different technologies have different conversion efficiencies. A high solar thermal conversion efficiency was found to be 18.718% in Gweru while it was 15.502% for solar photovoltaics in Mutare. The study also showed that highest insolation and clearness index values were found in Gweru. The average energy generated by the fixed photovoltaic collectors, tracking photovoltaic collectors and the Concentrating Solar Power plant were respectively 47.38GWh, 68.18GWh and 192.86GWh. There was a maximum percentage difference in the LCoE generated of 32.03% between the fixed PV collectors and the Concentrating Solar Power plant and a difference of 4.74% was realised between the tracking photovoltaics and Concentration Solar Power.

Adaptive Design of Solar-Powered Energy Systems Based on Daily Clearness State Evolution

The optimal designing of the hybrid energy system (HES) is a challenging task due to the multiple objectives and various uncertainties. Especially for HES, primarily powered by solar energy, the reference solar radiation data directly impact the result of the optimization design. To incorporate the stochastic characteristics of solar radiation into the sizing process, a data-driven stochastic modeling method for solar radiation is proposed. The method involves two layers of stochastic processes that capture the intraday variation and daily evolution of solar radiation. First, the clearness index (CI) is introduced to describe the radiation intensity at different times. Then, the daily clearness state (DCS) is proposed, based on the statistical indicators of the intraday CI. The Markov model is used to describe the stochastic evolutionary characteristics between different DCSs. The probabilistic distribution of the CI under different DCS is obtained based on the diffusion kernel density estimation (DKDE), which is used for the stochastic generation of the CI at various times of the day. Finally, the radiation profile required for the optimal design is obtained by the stochastic generation of the DCS sequences and the intraday clearness index under corresponding states. A case study of an off-grid solar-powered HES is provided to illustrate this methodology.