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IntroductionDetection of tree leaf diseases plays a crucial role in the horticultural field. These diseases can originate from viruses, bacteria, fungi, and other pathogens. If proper attention is not given, these diseases can drastically affect trees, reducing both the quality and quantity of yields. Due to the importance of quince in Iran's export market, its diseases can cause significant economic losses to the country. Therefore, if leaf diseases can be automatically identified, appropriate actions can be taken in advance to mitigate these losses. Traditionally, the identification and detection of tree diseases rely on experts' naked-eye observations. However, the physical condition of the expert such as eyesight, fatigue, and work pressure can affect their decision-making capability. Today, deep convolutional neural networks (DCNNs), a novel approach to image classification, have become the most crucial detection method. DCNNs improve detection or classification accuracy by developing machine-learning models with many hidden layers to extract optimal features. This approach has significantly enhanced the classification and identification of diseases affecting plants and trees. This study employs a novel CNN algorithm alongside two pre-trained models to effectively identify and classify various types of quince diseases.Materials and MethodsImages of healthy and diseased leaves were acquired from several databases. The majority of these images were sourced from the Agricultural Research Center of Isfahan Province in Iran, supplemented by contributions from researchers who had previously studied in this field. Other supporting datasets were obtained from internet sources. This study incorporated a total of 1,600 images, which included 390 images of fire blight, 384 images of leaf blight, 406 images of powdery mildew, and 420 images of healthy leaves. Of all the images obtained, 70%, 20%, and 10% were randomly selected for the network's training, validation, and testing, respectively. Image flipping, rotation, and zooming were applied to augment the training dataset. In this research, a proposed convolutional neural network (CNN) combined with image processing was developed to classify quince leaf diseases into four distinct classes. Three CNN models, including Inception-ResNet-v2, ResNet-101, and our proposed CNN model, were investigated, and their performances were compared using essential indices including precision, sensitivity, F1-score, and accuracy. To optimize the models’ performance, the impact of dropout with a 50% probability and the number of neurons in the hidden layers were examined. Our proposed CNN model consists of an architecture with four convolutional layers, with 224 × 224 RGB images as input to the first layer, which has 16 filters, followed by additional convolutional layers with 32, 64, and 128 filters respectively. Activation functions of ReLU combined with max-pooling were used at each convolutional layer, and Softmax activation was applied in the last layer of the neural network to convert the output into a probability distribution.Results and DiscussionThree confusion matrices based on the test dataset were constructed for all the CNN models to compare and evaluate the performance of the classifiers. The indices obtained from the confusion matrices indicated that Inception-ResNet-v2 and ResNet-101 achieved accuracies of 79% and 72%, respectively. While all models exhibited promising efficiency in classifying leaf diseases, the proposed shallow CNN model stood out with an impressive accuracy of 91%, marking it as the most effective solution. The comprehensive results indicate that the optimized CNN model, featuring four convolutional layers, one hidden layer with 64 neurons, and a dropout rate of 0.5, outperformed the transfer learning models.ConclusionThe findings of this study demonstrate that our developed proposed CNN model provides a high-performance solution for the rapid identification of quince leaf diseases. It excels in real-time detection and monitoring, achieving remarkable accuracy. Notably, it can identify fire blight and powdery mildew with a precision exceeding 95%.

Historical literature may constitute a valuable source of information to reconstruct sea-level changes. Here, historical documents and geological records have been combined to reconstruct Caspian sea-level (CSL) changes during the last millennium. In addition to a comprehensive literature review, new data from two short sediment cores were obtained from the south-eastern Caspian coast to identify coastal change driven by water-level changes and to compare the results with other geological and historical findings. The overall results indicate a high-stand during the Little Ice Age, up to −21 m (and extra rises due to manmade river avulsion), with a −28 m low-stand during the Medieval Climate Anomaly, while presently the CSL stands at −26.5 m. A comparison of the CSL curve with other lake systems and proxy records suggests that the main sea-level oscillations are essentially paced by solar irradiance. Although the major controller of the long-term CSL changes is driven by climatological factors, the seismicity of the basin creates local changes in base level. These local base-level changes should be considered in any CSL reconstruction.

Abstract Background This study aims to examine vessel density changes in the optic nerve and macula following silicone oil removal (SOR) surgery in eyes with rhegmatogenous retinal detachment (RRD) at different time points by Optical Coherence Tomography Angiography (OCTA) in compared to the contralateral eye. Methods A total of 43 eyes from 43 patients with silicone oil in their eyes for 3–9 months underwent OCT-A using AngioVue and optic disc-associated vessel density (VD) and thickness, macular-associated VD and thickness, Foveal avascular zone (FAZ) area, FAZ perimeter (PERIM), Acircularity index (AI), vessel density within a 300 μm wide region of the FAZ were compared between eyes. OCTA scans were performed one week before SOR and one month and three months after SOR. Results The mean age of participants was 52.8 years (SD = 15.85) and a median visual acuity was 0.8 (range: 0.5-1.0). Notably, male participants constituted 67.4% of the sample. The preoperative mean value BCVA (logMAR) of patients was 0.73, and 3 months post-oil removal was 0.7727. Regarding optic disc parameters, RNFL thickness and vessel density (VD) measurements Peripapillary, whole disc, inside disc, and Disc Angio (superior, Nasal, inferior, temporal) did not change. In analyzing macular thickness parameters, all of them (Whole and Fovea, parafoveal, and Perifovea) remained unchanged. Examining macular vessel density parameters revealed no significant changes across superficial and deep retinal layers. Finally, the comparison of the foveal avascular zone (FAZ) area and flow density (FD) parameters demonstrated consistent measurements with non-significant alterations observed in FAZ size (p = 0.6) and FD values (p = 0.49) over the monitored duration. Conclusion There was no change in peripapillary VD and macular vessel density of the superficial capillary plexus (SCP) and deep capillary plexus (DCP) after silicone oil removal. FAZ and full retinal thickness remained stable 3 month after SOR. Clinical trial number: Not applicable.

Climate change is a serious issue that we must face. It requires action that goes beyond just scientific methods and includes insights from religious teachings. The Holy Qur'an, which often mentions weather and climate, hasn't been fully explored by religious scholars in this area. These texts show weather as a sign of God's power, and as a way to show kindness or punishment. This study suggests a fresh examination of the Qur'an's climate-related stories, looking for links with past weather events and God's messages. It argues that Islamic principles could help us address the climate crisis and encourages religious scholars to apply these ideas to current environmental issues. By examining the Qur'an's descriptions of climate across different regions and times, we aim to connect ancient warnings with today's scientific understanding. The paper calls for a combined approach, where religious perspectives enhance scientific research, leading to a sustainable way of living.

Background: Prolonged labor is associated with various maternal and neonatal complications. This study aims to investigate the relationship between 25-hydroxyvitamin D3 levels and pain intensity and duration of labor stages in primiparous women. Materials and Methods: This cross-sectional study was conducted in Iran from November 2021 to January 2022 and comprised primiparous women who were in active labor after a term pregnancy (37–42 weeks). Five milliliter of blood was taken from each subject and centrifuged for the measurement of vitamin D level using the enzyme-linked immunosorbent assay method. The High-Performance Liquid Chromatography (HLPC) method was used to measure 25-OH vitamin D. In addition, through history, examination, and investigations, the subjects were evaluated according to the pain intensity and duration of the first (active phase) and second stages of labor. Results: The results of the Pearson correlation test indicated a significant relationship between vitamin D and active phase duration (r = 0.64, p = 0.012), second stage duration (r = 0.73, p = 0.001), pain intensity of the active phase (r = 0.61, p = 0.022), and pain intensity of the second stage (r = 0.65, p = 0.026). According to the analysis of variance table, based on vitamin D, there were statistically significant differences between the groups in terms of the active phase duration, second stage duration, pain intensity of the active phase, and that of the second stage of labor (p < 0.05). Conclusions: Low levels of vitamin D may influence the progress of labor and increase the rate of prolonged labor.

Abstract Fuchs Uveitis Syndrome (FUS), also known as Fuchs Heterochromic Iridocyclitis, is a chronic form of uveitis characterized by mild inflammation primarily affecting one eye. This study aimed to investigate the clinical and epidemiological features of FUS in an Iranian population. A retrospective analysis was conducted on 466 patients diagnosed with FUS at an ophthalmology center affiliated with Isfahan University of Medical Sciences between 2003 and 2021. The Kimura et al. criteria were used for FUS diagnosis. Demographic data, clinical characteristics, misdiagnosed cases, concurrent diseases, and associated ocular findings were analyzed. The study included 507 eyes of 466 FUS patients, with a mean age of 34.01 ± 11.25 years. Iris atrophy, keratic precipitates, and vitritis were common clinical findings. Heterochromia was an infrequent feature. Initial misdiagnosis occurred in 13 patients, with pars planitis being the most common incorrect diagnosis. Toxoplasmosis and multiple sclerosis were common concurrent diseases. Pediatric FUS cases were noted, possibly attributed to early-onset manifestations. Differences in clinical characteristics were observed when compared to other populations. This study provides insights into the clinical and epidemiological aspects of FUS in an Iranian population. Variations in clinical features, misdiagnosis patterns, and concurrent diseases were noted. Attention to specific clinical parameters can aid in accurate FUS diagnosis. Understanding these differences contributes to a better understanding of FUS presentation and its relationship with other diseases.

Water and energy are two strategic drivers of sustainable development, intimately interlaced and vital for a secure future of humanity. Given that water resources are limited, whereas global population and energy demand are exponentially growing, the competitive balance between these resources, referred to as the water-energy nexus, is receiving renewed focus. The desalination industry alleviates water stress by producing freshwater from saline sources, such as seawater, brackish or groundwater. Since the last decade, the market has been dominated by membrane desalination technology, offering significant advantages over thermal processes, such as lower energy demand, easy process control and scale-up, modularity for flexible productivity, and feasibility of synergic integration of different membrane operations. Although seawater reverse osmosis (SWRO) accounts for more than 70% of the global desalination capacity, it is circumscribed by some significant technological limitations, such as: (i) the relatively low water recovery factor (around 50%) due to the negative impact of osmotic and polarization phenomena; (ii) an energy consumption in the range of 3–5 kWh m ^−3 , still far from the theoretical energy demand (1.1 kWh m ^−3 ) to produce potable water from seawater (at 50% water recovery factor). Ultimately, desalination is an energy intensive practice and research efforts are oriented toward the development of alternative and more energy-efficient approaches in order to enhance freshwater resources without placing excessive strain on limited energy supplies. Recent years have seen a relevant surge of interest in membrane distillation (MD), a thermally driven membrane desalination technology having the potential to complement SWRO in the logic of Process Intensification and Zero Liquid Discharge paradigm. Due to its peculiar transport mechanism and negligibility of osmotic phenomena, MD allows high-quality distillate production (theoretically, non-volatile species are completely rejected) with a recovery factor of up to 80% at a relatively low operative temperature (typically 60 °C–80 °C). Although low operative temperatures make MD technology attractive for renewable power applications (e.g. solar thermal, wind or geothermal energy sources) or for efficient exploitation of low-grade or waste heat streams, the low energy efficiency intrinsically due to heat losses—and specifically to temperature polarization—has so far hindered the application at industrial scale. Nowadays, photothermal materials able to absorb and convert natural or artificial irradiation into heat have gained great attention, demonstrating the potential to mitigate the ‘anthropic’ energy input to MD and to mitigate the impact of thermal inefficiencies. On this road, a step-change improvement in light-to-heat conversion is expected through high-throughput computational screening over thermoplasmonic materials based on electronic and optical properties of advanced materials including novel topological phases of matter used as nanofillers in polymeric membranes. Coherently with the concept of Circular Economy, waste hypersaline solutions rejected from desalination process (referred as ‘brine’) are now the subject of valorization activities along two main exploitation routes: (1) recovery of valuable minor and trace metals and minerals, with special focus on critical raw materials (including, among others, Mg, Na, Ca, K, Sr, Li, Br, B, and Rb); (2) production of salinity gradient power (SGP) renewable energy resulting from the recovery of the Gibbs energy of mixing (mainly represented by the entropic contribution) of two solutions having different ionic concentration. The exciting new frontier of sustainable mining of seawater concentrates is accelerating the appearance of a plethora of innovative membrane materials and methods for brine dehydration and selective extraction of trace ions, although under the sword of Damocles represented by cost feasibility for reliable commercial application. On the other hand, among several emerging technologies, reverse electrodialysis (SGP-RED) was already proven capable—at least at the kW scale–of turning the chemical potential difference between river water, brackish water, and seawater into electrical energy. Efforts to develop a next generation of ion exchange membranes exhibiting high perm-selectivity (especially toward monovalent ions) and low electrical resistance, to improve system engineering and to optimize operational conditions, pursue the goal of enhancing the low power density so far achievable (in the order of a few W per m ^2 ). This Roadmap takes the form of a series of short contributions written independently by worldwide experts in the topic. Collectively, such contributions provide a comprehensive picture of the current state of the art in membrane science and technology at the water-energy nexus, and how it is expected to develop in the future. In addition, this Roadmap acknowledges the challenges and advances in membrane systems, particularly emphasizing the interplay of material innovation and system optimization, which collectively contribute to advancing the desalination field within the water-energy nexus framework.

Main protease (Mpro) of SARS-CoV-2 is considered one of the key targets due to its role in viral replication. The use of traditional phytochemicals is an important part of complementary/alternative medicine, which also accompany the concept of temperament, where it has been shown that hot medicines cure cold and cold medicines cure hot, with cold and hot pattern being associated with oxidative and anti-oxidative properties in medicine, respectively. Molecular docking in this study has demonstrated that a number of anti-oxidative and hot temperament-based phytochemicals have high binding affinities to SARS-CoV-2 Mpro, both in the monomeric and dimeric deposited states of the protein. The highest ranking phytochemicals identified in this study included savinin, betulinic acid and curcumin. Complexes of savinin, betulinic acid, curcumin as well as Nirmatrelvir (the only approved inhibitor, used for comparison) bound to SARS-CoV-2 Mpro were further subjected to molecular dynamics simulations. Subsequently, RMSD, RMSF, Rg, number of hydrogen bonds, binding free energies and residue contributions (using MM-PBSA) and buried surface area (BSA), were analysed. The computational results suggested high binding affinities of savinin, betulinic acid and curcumin to both the monomeric and dimeric deposited states of Mpro, while highlighting the lower binding energy of betulinic acid in comparison with savinin and curcumin and even Nirmatrelvir, leading to a greater stability of the betulinic acid-SARS-CoV-2 Mpro complex. Overall, based on the increasing mutation rate in the spike protein and the fact that the SARS-CoV-2 Mpro remains highly conserved, this study provides an insight into the use of phytochemicals against COVID-19 and other coronavirus diseases.

AbstractThe Caspian Sea, the Earth’s largest lake, straddles the continents of Asia and Europe and has historically served as both a barrier and a conduit for human development. The maritime activities within the Caspian Sea have been profoundly shaped by its dynamic sea-level fluctuations, coastal geomorphology, and climatic conditions as well as the socioeconomic and geopolitical situation. Despite a rich history of navigation and numerous related monuments, there has been a scarcity of research on marine archaeological evidence assessing the influence of historical environmental and socioeconomic shifts on the development of maritime activities in the Caspian Sea. In this study, we employed geohistorical, geoarchaeological, and archaeological methodologies to unearth evidence of maritime activities along the Caspian’s southern coast. We investigated 12 archaeological sites and historical shipwrecks, unveiling their maritime historical and archaeological significance. Our findings indicate that there are two distinct patterns of port migration. The transverse migration of ports has predominantly occurred in reaction to swift changes in Caspian Sea level. Additionally, we have identified a long-term migration of ports from the eastern to the western extremities of the southern coast, driven by geopolitical, socioeconomic, and environmental transformations. Drawing on historical and archaeological insights, and considering contemporary global initiatives aimed at forging east–west and north–south connections through the Caspian Sea, as well as the ongoing climatic changes affecting the Caspian Sea level, we anticipate a forthcoming wave of port migrations along its coastlines. [ABSTRACT FROM AUTHOR]

Background: Eye health is essential for quality-of-life. The present study aimed to design an eye care behaviors assessment instrument for the student community and evaluate its psychometric properties. Methods: The present mixed-method cross-sectional study was conducted in two sections using Creswell and Plano Clark methods for instrument development. The study was conducted in Isfahan, Iran, in 2021. The first section (textual analysis and qualitative research) explained and developed the instrument's fundamental items. This section included in-depth, semistructured interviews with 21 students and eight experts. The second section measured the psychometric properties of the instrument. Twenty students assessed the instrument's qualitative and quantitative face validity in this section. The instrument's content validity ratio (CVR) and content validity index (CVI) were evaluated. In addition, exploratory factor analysis (performed by 251 students) was used to establish construct validity. Internal and test–retest reliability was determined using Cronbach's alpha and intraclass correlation coefficients (ICC), respectively. Results: During face and content validity assessment, a 37-item questionnaire was finalized. Exploratory factor analysis led to the identification of three factors, including “examinations and glasses-related behaviors,” “symptom-related behaviors,” and “screen-related behaviors.” The three extracted factors accounted for 37.9% of the variance. Cronbach's alpha was equal to 0.874 when evaluating internal consistency, and the ICC value for the total score of the questionnaire was 0.885 (0.810–0.941), indicating excellent test–retest reliability. Conclusions: These results demonstrate the questionnaire's validity and reliability. This instrument assesses the prevalence of university students' most significant eye health risk behaviors. Consequently, it helps prevent eye problems.