An overview of neuro-ophthalmic disorders at Jenna Ophthalmic Center, Baghdad, Iraq (2021-2022).

Neuro-ophthalmic disorders are often documented individually for each illness, with little data available on their overall incidence and pattern. The overall incidence of neuro-ophthalmic illnesses in Iraq is still not recorded. This study aimed to evaluate the clinical, demographic, and etiological features of patients seeking consultation at an Iraqi neuro-ophthalmology clinic. A prospective cross-sectional observational research was conducted at the Janna Ophthalmic Center in Baghdad, Iraq. The center serves a diverse patient population from various governorates. All newly diagnosed patients with neuro-ophthalmic disorders who visited the neuro-ophthalmological clinic, regardless of gender or age group, were included. The neuro-ophthalmologist established a diagnosis for each case by reviewing the patient's medical history, doing physical examinations, administering specific tests, and, in certain cases, using neuroimaging methods. The duration of the study was extended from March 2021 to November 2022. Among the 6440 patients evaluated, 613 cases were confirmed at the neuro-ophthalmology clinic. Ischemic optic neuropathy (NAION, AION, and PION) was the most prevalent diagnosis, accounting for 17.61% of newly reported cases in the field of neuro-ophthalmology. This was followed by sixth nerve palsy. Diabetes mellitus affected 42.7% of the cases, followed by hypertension, which affected 39.3% of the participants. The incidence of neuro-ophthalmic diseases tended to be high. Ischemic optic neuropathy and sixth nerve palsy, traumatic/compressive optic neuropathy, and papilledema were the most common neuro-ophthalmic disorders reported.

Machine learning-based country-level annual air pollutants exploration using Sentinel-5P and Google Earth Engine.

Climatic condition is triggering human health emergencies and earth's surface changes. Anthropogenic activities, such as built-up expansion, transportation development, industrial works, and some extreme phases, are the main reason for climate change and global warming. Air pollutants are increased gradually due to anthropogenic activities and triggering the earth's health. Nitrogen Dioxide (NO2), Carbon Monoxide (CO), and Aerosol Optical Depth (AOD) are truthfully important for air quality measurement because those air pollutants are more harmful to the environment and human's health. Earth observational Sentinel-5P is applied for monitoring the air pollutant and chemical conditions in the atmosphere from 2018 to 2021. The cloud computing-based Google Earth Engine (GEE) platform is applied for monitoring those air pollutants and chemical components in the atmosphere. The NO2 variation indicates high during the time because of the anthropogenic activities. Carbon Monoxide (CO) is also located high between two 1-month different maps. The 2020 and 2021 results indicate AQI change is high where 2018 and 2019 indicates low AQI throughout the year. The Kolkata have seven AQI monitoring station where high nitrogen dioxide recorded 102 (2018), 48 (2019), 26 (2020) and 98 (2021), where Delhi AQI stations recorded 99 (2018), 49 (2019), 37 (2020), and 107 (2021). Delhi, Kolkata, Mumbai, Pune, and Chennai recorded huge fluctuations of air pollutants during the study periods, where ~ 50-60% NO2 was recorded as high in the recent time. The AOD was noticed high in Uttar Pradesh in 2020. These results indicate that air pollutant investigation is much necessary for future planning and management otherwise; our planet earth is mostly affected by the anthropogenic and climatic conditions where maybe life does not exist.

Machine learning algorithms for high-resolution prediction of spatiotemporal distribution of air pollution from meteorological and soil parameters.

This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM2.5 forecasting maps.

Surface modification of hybrid composite multilayers spin cold spraying for biomedical duplex stainless steel.

The performance of biomaterials in biological systems is of critical significance for advancing biomedical implants. Duplex Stainless Steel alloys are the major biomaterials due to their significant characteristics. Many functional coatings are deposited on DSS alloy surfaces utilizing numerous surface coating techniques to improve their bioactivity and protect them from corrosion degradations. Coatings of titanium dioxide (TiO2), Hydroxyapatite (HA), and zinc oxide (ZnO) have received considerable attention in the field of surface bioactive modification of DSS alloy implants. The coating techniques play a key role in increasing the required biological characteristics of DSS alloys, such as biocompatibility, mechanical properties, and corrosion resistance. In this regard, HA-ZnO, HA-TiO2, and TiO2-ZnO from each coating group are divided into single, double, and triple layers. These coatings were prepared by cold spray and deposited on the surface of the DSS alloy, followed by a heat treatment at 250 °C. The surface morphology of coated surfaces was analyzed utilizing field emission scanning electron microscopy (FESEM), atomic force microscopic (AFM), microhardness test, corrosion test in Ringer solution, and antibacterial test. The coatings showed nano-scale surface morphology with advanced crystallization and homogeneous structures; in the corrosion characteristics utilizing potentiodynamic polarization, triple layers of HA-ZnO coatings displayed advanced nanostructures with higher hardness values (514.75HV). The antibacterial test showed the triple layers of HA-TiO2 and two layers of TiO2-ZnO sensitivity to positive bacteria.

Farasan Island of Saudi Arabia confronts the measurable impacts of global warming in 45 years.

Coastal vulnerability assessment is the key to coastal management and sustainable development. Sea level rise (SLR) and anthropogenic activities have triggered more extreme climatic events and made the coastal region vulnerable in recent decades. Many parts of the world also noticed increased sediment deposition, tidal effects, and changes in the shoreline. Farasan Island, located in the south-eastern part of Saudi Arabia, experienced changes in sediment deposition from the Red Sea in recent years. This study used Digital Shoreline Analysis System (DSAS) to delineate the shoreline changes of Farasan Island during 1975-2020. Multi-temporal Landsat data and DSAS were used for shoreline calculation based on endpoint rate (EPR) and linear regression. Results revealed an increase in vegetation area on the island by 17.18 km2 during 1975-1989 and then a decrease by 69.85 km2 during 1990-2020. The built-up land increased by 5.69 km2 over the study period to accommodate the population growth. The annual temperature showed an increase at a rate of 0.196 °C/year. The sea-level rise caused a shift in the island's shoreline and caused a reduction of land by 80.86 km2 during 1975-2020. The highly influenced areas by the environmental changes were the north, central, northwest, southwest, and northeast parts of the island. Urban expansion and sea-level rise gradually influence the island ecosystem, which needs proper attention, management, policies, and awareness planning to protect the environment of Farasan Island. Also, the study's findings could help develop new strategies and plan climate change adaptation.

Experimental data on compressive strength and ultrasonic pulse velocity properties of sustainable mortar made with high content of GGBFS and CKD combinations.

The development in the construction sector and population growth requires an increase in the consumption of construction materials, mainly concrete. Cement is the binder in concrete, so increasing cement production will increase the energy consumed, as well as in the emission of carbon dioxide. This harmful effect of the environment led to the search for alternative materials for cement, as the waste or by-products of other industries is a promising solution in this case. Among these common materials are ground granulated blast furnace slag (GGBS) and cement kiln dust (CKD). This dataset describes the compressive strength and ultrasonic pulse velocity of mortar consisted of high content of GGBS and CKD combinations as a partial substitute for cement (up to 80%) at the ages of 1, 2, 3, 7, 14, 21, 28, 56, 90 and 550 days. This dataset can help the researchers to understand the behaviour of GGBS and CKD in high replacement levels for cement during early (1 day) and later ages (550 days). According to this understanding, the authors believe that the data available here can be used to produce more environmentally friendly mortar or concrete mixtures by significantly reducing the amount of cement used by replacing it with waste or by-products of other industries.