UV Index Forecasting under the Influence of Desert Dust: Evaluation against Surface and Satellite-Retrieved Data

Distance-based integration method for human skin type identification

Although identifying human skin types is essential in dermatology, cosmetology, and facial recognition, the classification of human skin types is challenging due to the complex nature, varied characteristics, and the influence of external factors. Traditional methods for skin type identification often rely on subjective assessments, leading to inconsistent and inaccurate results. Therefore, this paper proposes a novel method named a distance-based integration method to identify skin types based on the Fitzpatrick skin scale, also known as the Fitzpatrick skin type. This study focuses on the objective distance measurement, integrated with the Fuzzy Analytic Hierarchy Process (AHP). The objective distance was utilized to determine the distance between each HEX color code for a clinical image and each target skin type. The Fuzzy AHP algorithm was employed to calculate the total score for each target class to identify human skin type. For this study, 1,022 images of human skin were used in the experiment. The results indicated that the proposed method achieved a high average accuracy of 93 %, precision of 80 %, and specificity of 96 %.

UV index-based model for predicting synthesis of (pre-)vitamin D3 in the mediterranean basin

The importance of solar radiation for the body's ability to synthesize Vitamin D3 is well documented, yet the precise amount of sun exposure required to avoid Vitamin D insufficiency is less clear. To address this knowledge gap, this study sought to utilize the sun in a suitable period at the optimum dose by utilizing numerical simulations to determine the amount of Vitamin D3 synthesis in the skin according to season, time of day, and geographical location in Turkey. The study was carried out in three stages; in the first stage, daily, monthly, and annual values were determined in cases where the solar zenith angle has the active UV-B wavelength. The second stage determined the level of Vitamin D that can be synthesized in all skin types at 25% solar radiation exposure. In the third stage, the sun exposure time required for 1000 International Units (IU) for all skin types was calculated. According to the analysis, the yearly period of active synthesis of D3 on Earth lasts from the beginning of March to the third week of October. During the day, it is between 10:00 and 16:00. For 1000 IU/day, the average annual estimated times (minutes) are 5.05 for Type I, 6.3 for Type II, 7.6 for Type III, 11.35 for Type IV, 15.15 for Type V, and 25.25 for Type VI. The results of this paper will impact awareness for academic-medical users.

Air pollution burden of disease over highly populated states in the Middle East

Background

Recent epidemiological research has proven that air pollution triggers the risk of morbidity and mortality due to respiratory and cardiovascular-related diseases. More specifically, fine particulate matter with a diameter of <2.5 μm (PM2.5) can penetrate deeply into the lung and bloodstream, causing critical adverse effects on human health.

Objective

It is found that there is inadequate published research related to the health impact of ambient air pollution in the Middle East region. Some states are well studied, while others are not. This work aims to evaluate the health impact of long-term exposure to PM2.5 in the nine most populated countries in the Middle East region, with a total population of about 363 million (in 2012).

Methods

In this study, the human health impacts in terms of total mortality and the estimated attributable proportion (AP) due to long-term exposure to ambient PM2.5 were estimated using the World Health Organization method and software (AirQ+).

Results

In 2012, the annual median PM2.5 concentrations ranged from 34 μg/m3 in Turkey and Syria to 108 μg/m3 in Saudi Arabia. The total estimated mortalities in the nine most populated countries in the Middle East due to long-term exposure to fine particulate matter was about 152,925 (half of which were residents in Egypt). Moreover, the relative risk (RR) was the highest for Saudi Arabia at 1.8031 and the lowest for Turkey and Syria at a value of 1.1553. The highest AP (central value) was 44.5% in Saudi Arabia, while the lowest was 13.4% in Turkey and Syria.

Conclusions

The results indicate a significant impact of air pollution due to long-term exposure to fine particles resulting in early mortality. This urges the collaboration between the governments and different sectors to adopt stringent regulations to control the anthropogenic sources related to traffic and industrial emissions in the Middle East in order to reduce the health burden of air pollution.

Optimization algorithms as training approach with hybrid deep learning methods to develop an ultraviolet index forecasting model

The solar ultraviolet index (UVI) is a key public health indicator to mitigate the ultraviolet-exposure related diseases. This study aimed to develop and compare the performances of different hybridised deep learning approaches with a convolutional neural network and long short-term memory referred to as CLSTM to forecast the daily UVI of Perth station, Western Australia. A complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is incorporated coupled with four feature selection algorithms (i.e., genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), and differential evolution (DEV)) to understand the diverse combinations of the predictor variables acquired from three distinct datasets (i.e., satellite data, ground-based SILO data, and synoptic mode climate indices). The CEEMDAN-CLSTM model coupled with GA appeared to be an accurate forecasting system in capturing the UVI. Compared to the counterpart benchmark models, the results demonstrated the excellent forecasting capability (i.e., low error and high efficiency) of the recommended hybrid CEEMDAN-CLSTM model in apprehending the complex and non-linear relationships between predictor variables and the daily UVI. The study inference can considerably enhance real-time exposure advice for the public and help mitigate the potential for solar UV-exposure-related diseases such as melanoma.

Aluminum environmental pollution: the silent killer

The concern about aluminum (Al) toxicity has been proven in various cases. Some cases are associated with the fact that Al is a neurotoxic substance that has been found in high levels in the brain tissues of Alzheimer's disease (AD), epilepsy, and autism patients. Other cases are related to infants, especially premature infants and ones with renal failure, who are at the risk of developing the central nervous system (CNS) and bone toxicity. This risk is a result of infants' exposure to Al from milk formulas, intravenous-feeding solutions, and possibly from aluminum-containing vaccinations. Furthermore, most antiperspirants contain  aluminum compounds that raise human exposure to toxic Al. This review paper is intended to discuss in detail the above concerns associated with aluminum, and hence urges the need for more studies exploring the effects of overexposure to Al and recommending mitigation actions.

Ecotoxico Linking of Phthalates and Flame-Retardant Combustion Byproducts with Coral Solar Bleaching

Persian Gulf coral reefs are unique biota communities in the global sunbelts in being able to survive in multiple stressful fields during summertime (>36 °C). Despite the high-growth emerging health-hazard microplastic additive type of contaminants, its biological interactions with coral-algal symbiosis and/or its synergistic effects linked to solar-bleaching events remain unknown. This study investigated the bioaccumulation patterns of polybrominated diphenyl ether (PBDE) and phthalate ester (PAE) pollutants in six genera of living/bleached corals in Larak Island, Persian Gulf, and their ambient abiotic matrixes. Results showed that the levels of ∑₁₈PBDEs and ∑₁₃PAEs in abiotic matrixes followed the order of SPMs > surface sediments > seawater, and the cnidarian POP-uptake patterns (soft corals > hard corals) were as follows: coral mucus (138.49 ± 59.98 and 71.57 ± 47.39 ng g^-1 dw) > zooxanthellae (82.05 ± 28.27 and 20.14 ± 12.65 ng g^-1 dw) ≥ coral tissue (66.26 ± 21.42 and 34.97 ± 26.10 ng g^-1 dw) > bleached corals (45.19 ± 8.73 and 13.83 ± 7.05 ng g^-1 dw) > coral skeleton (35.66 ± 9.58 and 6.47 ± 6.47 ng g^-1 dw, respectively). Overall, findings suggest that mucus checking is a key^/facile diagnostic approach for fast detection of POP bioaccumulation (PB) in tropical corals. Although studied corals exhibited no consensus concerning hazardous levels of PB (log BSAF < 3.7), our bleaching evidence showed soft corals as the ultimate "summer winners" due to their flexibility/recovering ability.

Predicting sun protection measures against skin diseases using machine learning approaches

BACKGROUND

The substantial growth rate of skin cancer has necessitated adequate protection from solar radiation. Consequently, analyzing sun protection practices is an imperative research area in dermatology and pharmacology.

AIMS

This paper aims to analyze public sun-protection manners in the Arabian Peninsula regions.

METHODS

A simple random survey was conducted to assess public sun protection manners. Artificial neural network (ANN) and support vector machine (SVM) were selected from several machine learning algorithms to create the models for predicting public sun protection measures based on the prediction accuracy. Model performances were evaluated based on several performance indicators depending on the confusion matrices and receiver operating characteristic curves.

RESULTS

51% of the respondents have a low level, and 49% have a high level of sun protection practices. The results showed that the SVM performed considerably amended than the ANN for predicting the response. The relative importance of the predictors for the best predictive SVM model was also analyzed. The predictors are ranked as: the number of times having sunburnt >gender > use seat belt while driving/riding a vehicle >considers the UV index for personal sun exposure >income based on the expenses >sports/exercise activities >consciousness of the chance for having sunburnt on extended exposure to the sun >age > having any skin problem >nationality > skin type.

CONCLUSION

These identified significant predictors might be considered for developing an effective policy to increase public consciousness using proper protection from solar radiation's detrimental effect to rule out skin diseases.

Outdoor scale-up of Leptolyngbya sp.: Effect of light intensity and inoculum volume on photoinhibition and -oxidation

The effect of light intensity and inoculum volume on the occurrence of photooxidation for Leptolyngbya sp. QUCCCM 56 was investigated, to facilitate the transition from small‐scale laboratory experiments to large‐scale outdoor cultivation. Indoor, the strain was capable of growing at light intensities of up to 5600 µmol photons/m²/s, at inoculation densities as low as 0.1 g/L (10% inoculation volume vol/vol). Levels of chlorophyll and phycocyanin showed a significant decrease within the first 24 h, indicating some level of photooxidation, however, both were able to recover within 72 h. When cultivated under outdoor conditions in Qatar during summer, with average peak light intensities 1981 ± 41 μmol photons/m²/s, the strain had difficulties growing. The culture recovered after an initial adaptation period, and clear morphological differences were observed, such as an increase in trichome length, as well as coiling of multiple trichomes in tightly packed strands. It was hypothesized that the morphological changes were induced by UV‐radiation as an adaptation mechanism for increased self‐shading. Furthermore, the presence of contaminating ciliates could have also affected the outdoor culture. Both UV and contaminants are generally not simulated under laboratory environments, causing a mismatch between indoor optimizations and outdoor realizations.

Atmospheric Dynamics and Numerical Simulations of Six Frontal Dust Storms in the Middle East Region

This study analyzes six frontal dust storms in the Middle East during the cold period (October–March), aiming to examine the atmospheric circulation patterns and force dynamics that triggered the fronts and the associated (pre- or post-frontal) dust storms. Cold troughs mostly located over Turkey, Syria and north Iraq played a major role in the front propagation at the surface, while cyclonic conditions and strong winds facilitated the dust storms. The presence of an upper-atmosphere (300 hPa) sub-tropical jet stream traversing from Egypt to Iran constitutes also a dynamic force accompanying the frontal dust storms. Moderate-Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations are used to monitor the spatial and vertical extent of the dust storms, while model (Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), Copernicus Atmospheric Monitoring Service (CAMS), Regional Climate Model-4 (RegCM4)) simulations are also analyzed. The WRF-Chem outputs were in better agreement with the MODIS observations compared to those of CAMS and RegCM4. The fronts were identified by WRF-Chem simulations via gradients in the potential temperature and sudden changes of wind direction in vertical cross-sections. Overall, the uncertainties in the simulations and the remarkable differences between the model outputs indicate that modelling of dust storms in the Middle East is really challenging due to the complex terrain, incorrect representation of the dust sources and soil/surface characteristics, and uncertainties in simulating the wind speed/direction and meteorological dynamics. Given the potential threat by dust storms, more attention should be directed to the dust model development in this region.

Sun Awareness and Sun Protection Practices

Background

The incidence of skin cancer has increased over the past few years, owing to excessive exposure to ultraviolet (UV) radiation. Over the past decade, protection against solar radiation has been highly encouraged in numerous public health education campaigns. The adopted sun protection practices include avoiding exposure to the sun, using protective clothing, and applying sunscreen. The aim of this paper was to analyze the awareness and attitudes towards sun protection practices in some regions of the Arabian Peninsula.

Objective

The main objective of this study is to estimate the level of sun protection practices and determine the factors that have a significant impact on the level of adoption of sun protection practices by residents in the Arabian Peninsula.

Methods

A 35-question self-reporting online questionnaire to evaluate the sun protection measures adopted by the residents was distributed in the Kingdom of Saudi Arabia and Kingdom of Bahrain. The survey was conducted from January 2018 to May 2019, and a total 830 responses were collected for analysis. The data were analyzed using the statistical software SPSS. The chi-squared test and Spearman's correlation were performed to determine the association of the sun protection level with factors concerning the socio-demographic characteristics, sun sensitivity, health-conscious lifestyle, adopted sun protection measures, and general awareness about the UV index, skin cancer, and vitamin D. Logistic regression classification was applied in developing the empirical model to predict the sun protection level of the residents.

Results

Based on the analysis, only 34% of the total 830 respondents use sunscreen and most of the sunscreen users are female. The three most popular sun protection practices are: 1) avoiding exposure to the sun during peak hours, 2) using protective clothing, and 3) using sunglasses. The results indicate that 22% of respondents use a low level of sun protection, 53% use a medium level of protection, while 25% of respondents use a high level of sun protection. Many of the respondents understand that exposure to the sun can cause skin cancer and skin aging, while 54% of the respondents still believe that a suntan can be healthy if sunburn is avoided. Thirty percent of the participants of this study are aware of the UV index, and only 20% of all respondents know the correct meaning of the UV index. The overall predictive accuracy of the constructed logistic regression model is 67%. Based on the results of the logistic regression model, the five most significant factors of the sun protection level are: 1) perception that the sun can cause skin aging, or not, 2) age, 3) awareness of the dangerous effects of sun exposure, 4) income with respect to expenses, and 5) taking the UV index into consideration during exposure to the sun.

Conclusion

Most of the respondents are aware about sun protection. However, the sun protection level of the residents is still inadequate. An efficient strategy of increasing the awareness of the effective use of sun protection systems must be established based on the significant factors to prevent skin cancer.