Recent publications
Fine particulate matter (PM2.5) pollution is a major environmental challenge across the Middle East, including Iran. However, a substantial lack of knowledge exists regarding the linkage between aerosol trends, specific compounds, and their interrelation with emissions, mitigation strategies, and land changes. This research comprehensively evaluates the spatiotemporal trends of PM2.5 and its main precursors (SO2 and BC) concentrations in relation to LULC (Land-Use and Land-Cover) changes and mitigation policies in Iran during 1980–2023. Surface PM2.5 concentrations were estimated using five monthly MERRA-2 simulation datasets, including sea salt2.5, dust2.5, BC, OC, and SO4. The Evaluation of MERRA-2 PM2.5 against ground-based measurements confirmed that the MERRA-2 reanalysis data is ideal for monitoring PM2.5 patterns in Iran. Our trend analysis showed that dust dominates high PM2.5 concentrations in southwestern and southeastern Iran during summer, while anthropogenic aerosols (SO2 and BC) are the most significant contributors to PM2.5 in urban areas like Tehran in winter. Overall, a significant rise in aerosol occurred over Iran during 1980–2023, which reversed to a decreasing trend in PM2.5, BC and SO2 around 2006–2010. At the regional scale, aerosols variations were influenced by land-use changes, while urban and agricultural LULC changes being the primary contributors in dust-dominant regions, accounting for 38.1% and 26.4% of the variation, respectively. Our findings indicate that, although land-use changes initially influenced air pollution trends, recent clean-air policies have been essential in reducing emissions across major urban centers. Additionally, these trends in Iran align with or diverge from global patterns, reflecting the rise in industrial emissions across South Asia and contrasting with policy-driven decreases in developed regions such as Europe and North America, highlighting the urgent need for effective policies and land management to mitigate urban air pollution from diverse aerosol sources.
Image data augmentation constitutes a critical methodology in modern computer vision tasks, since it can facilitate towards enhancing the diversity and quality of training datasets; thereby, improving the performance and robustness of machine learning models in downstream tasks. In parallel, augmentation approaches can also be used for editing/modifying a given image in a context- and semantics-aware way. Diffusion Models (DMs), which comprise one of the most recent and highly promising classes of methods in the field of generative Artificial Intelligence (AI), have emerged as a powerful tool for image data augmentation, capable of generating realistic and diverse images by learning the underlying data distribution. The current study realizes a systematic, comprehensive and in-depth review of DM-based approaches for image augmentation, covering a wide range of strategies, tasks and applications. In particular, a comprehensive analysis of the fundamental principles, model architectures and training strategies of DMs is initially performed. Subsequently, a taxonomy of the relevant image augmentation methods is introduced, focusing on techniques regarding semantic manipulation, personalization and adaptation, and application-specific augmentation tasks. Then, performance assessment methodologies and respective evaluation metrics are analyzed. Finally, current challenges and future research directions in the field are discussed.
Tangible programming tools (TPTs) are promising teaching aids in programming courses due to their interactivity and ability to enhance early childhood students' computational thinking, spatial reasoning, and executive function skills. However, it remains unclear whether TPTs support these skills simultaneously. This study examines the impact of TPTs on enhancing cognitive thinking skills among students at different developmental stages, with a focus on early childhood education. A quasi-experimental study with pre-and post-tests was conducted involving 82 preschoolers aged 5-7. Participants were categorized into three developmental stages (beginner, intermediate, advanced) based on their prior programming experience. A TPT called "Bee-bot Brushing Challenge: A Computational Adventure" was employed during a STEM summer camp program. The findings revealed significant improvements in students' abstract thinking, problem decomposition, and spatial reasoning skills, particularly among beginners. Participants at intermediate levels showed notable improvement in algorithm design and efficiency. Results also indicate significant differences in cognitive flexibility and inhibitory control between groups, with advanced students outperforming beginners and intermediates while working memory remained unaffected. This research provides important evidence supporting the inclusion of TPTs in early childhood curricula to foster comprehensive cognitive development. It offers valuable insights for educators and policymakers in designing similar learning environments.
This review paper examines the evolution of shape factors for the bearing capacity of shallow foundations, with a specific focus on rectangular and circular footings. Through a critical examination of methodologies from early empirical approaches to the sophisticated analyses enabled by recent technological advancements, this paper highlights the transformative impact of computational modeling on the field. Specifically, the review utilizes 3D finite element and finite difference analyses to validate and recalibrate shape factors against modern and reliable data. The quantitative findings confirm the reliability of the shape factors developed by Zhu and Michalowski in 2005 through classical finite element analysis in Abaqus. Their factor, for example, was validated using Flac3D. Particularly notable is the finding that shape factors for circular footings can be effectively expressed by adjusting those for square footings using a simple geometric ratio, . This adjustment, based on the perimeter or area ratios of the two shapes, suggests a more efficient approach that challenges the necessity for distinct shape factors for different footing types. Additionally, the review highlights historical gaps such as non-documented factors from early empirical research, limitations due to the scale effects of small-scale tests, and assumptions supporting shape factors derived from limit analysis. It also emphasizes that depending on the aspect ratio of the footing and the friction angle of the soil, the percentage error in bearing capacity calculations using non-acceptable shape factors, including those adopted by various design standards, could be several tens of percentage units. Additionally, the review identifies a gap in current research regarding large-scale experimental validation of these computational models, pointing to future directions in experimental research.
This study analyzes the PM10 concentrations and their chemical composition, in terms of heavy and potential toxic elements (PTEs), from airborne dust samples collected in two cities (Zabol and Birjand) in east Iran during the dusty summer period. The sampling sites are located downwind of major dust sources in Central Asia and east Iran and the concurrent analysis allows to determine the impact of local dust upon a regional dusty background. PM10 samples in both locations were mainly composed by Al, followed by Fe, Ti and Ca, while lower concentrations were found for PTEs like Pb, Zn, As, Cr, Ni, Cd, which however, may cause important environmental pollution, with increased values of geo-accumulation Index (Igeo), enrichment factor (EF), Integrated Pollution Index (EPI) and Ecological Risk Index (ERI) under certain conditions. PM10 concentration was much higher in Zabol (471.5 µg m⁻³) compared to Birjand (102.7 µg m⁻³), while the latter exhibited higher fraction of heavy metals to PM10 mass due to increased anthropogenic emissions. Analysis of soil samples revealed similar chemical compositions, indicating that local deserts are the main source of airborne dust. The carcinogenic and non-carcinogenic health risks were assessed for three exposure pathways (inhalation, ingestion and dermal contact), separately for children and adults. Non-carcinogenic inhalation risks were very high (Hazard Index: HI > 1) both for children and adults (adult: 6.9; child: 5.2 in Birjand; adult: 7.6, child: 5.9 in Zabol), while ingestion also exhibits high health risks. High carcinogenic risks (> 10⁻⁴) were found for the ingestion and inhalation pathways in both cities, mainly from As and Cr. Carcinogenic and non-carcinogenic risks for dermal contact were below the acceptable limits, but both atmospheric environments pose serious hazards for human health, with more deleterious effects in Zabol.
Tunnel stability is a critical factor in complex geological conditions, particularly in rock masses with steeply dipping layers. Among widely used methods, the Convergence–Confinement Method (CCM), a prevalent two-dimensional (2D) approach, effectively captures the relaxation process preceding support installation. However, most studies focus on homogeneous or horizontally layered rock masses, often overlooking the influence of steeply dipping, and layered geological formations. This study investigates the influence of layer dip angle and layer position on the stress relaxation factor (λ) and tunnel deformation through parametric analysis. The results indicate that λ increases with steeper dip angles, such as 60° and 90°, and decreases as the layers are positioned farther from the tunnel center, for instance, two tunnel widths above or below. Tunnel deformation is highly influenced by these factors, and the optimized λ values allow the 2D Convergence–Confinement Method (CCM) predictions to closely correlate with 3D simulation results. These findings enhance the applicability of the Convergence–Confinement Method (CCM) for tunnel stability analysis in steeply dipping, layered rock masses.
Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that poses critical challenges in global healthcare due to its increasing prevalence and severity. Diagnosing AD and other dementias, such as frontotemporal dementia (FTD), is slow and resource-intensive, underscoring the need for automated approaches. Methods: To address this gap, this study proposes a novel deep learning methodology for EEG classification of AD, FTD, and control (CN) signals. The approach incorporates advanced preprocessing techniques and CNN classification of FFT-based spectrograms and is evaluated using the leave-N-subjects-out validation, ensuring robust cross-subject generalizability. Results: The results indicate that the proposed methodology outperforms state-of-the-art machine learning and EEG-specific neural network models, achieving an accuracy of 79.45% for AD/CN classification and 80.69% for AD+FTD/CN classification. Conclusions: These results highlight the potential of EEG-based deep learning models for early dementia screening, enabling more efficient, scalable, and accessible diagnostic tools.
A.V. Arhangelskii introduced the dimension Dind and various studies have been devoted to this topological dimension, investigating many of its properties, like the closed subspace and union theorems, and its behaviour in the class of all finite -spaces. In this paper, we extend these studies, investigating the dimension Dind for finite -spaces via Matrix Algebra. We give firstly new topological characterizations of Dind through minimal open sets, inserting new notions of families consisting of such open sets. Also, since the dimension Dind is an inductive dimension, based on the dimension Dind of special closed subsets, we study a matrix approach of these subsets, presenting an algorithm which finds their incidence matrices. The above investigations lead to new characterizations of Dind through incidence matrices, which finally provide an algorithmic procedure for the computation of Dind for finite -spaces.
We investigate the effects of the real exchange rate on the trade balance between the USA and China by using a median threshold in nonlinear modelling, controlling for several factors. Nonlinear ARDL models are estimated with zero threshold, as well as with median threshold and this is the first time in the USA-China literature of balance of trade. Quarterly data is employed, and the examined sample covers the period 1995-2023. It appears that the threshold choice matters when it comes to policy implications. Specifically, NARDL with the conventional zero-threshold analysis could lead to misleading policy proposals, especially when there are strongly unequal probabilities in the USD appreciation and depreciation regime. In our case, the traditional analysis based on zero threshold leads to diametrically opposite policy suggestions of what the alternative methodology of median threshold would suggest. We find that a nominal USD appreciation suggests a perpetual improvement of the US trade balance, and such an improvement could be further magnified if the US prices increase more rapidly than China’s prices (e.g. as in the period from 2020 onwards). Evidence also shows that the US balance of trade could be temporarily benefitted by medium or large USD depreciations in real terms, and such an improvement could be notably facilitated when the US-China prices’ differential is downward sloping (e.g. as in the 2006-2019 period).
In this paper, an active intelligent omni-surface (A-IOS) is deployed to aid uplink transmissions in a non-orthogonal multiple access (NOMA) system. In order to shelter the covert signal embedded in the superposition transmissions, a multi-antenna full-duplex (FD) receiver is utilized at the base-station to recover signal in addition to jamming the warden. With the aim of maximizing the covert rate, the FD transmit and receive beamforming, A-IOS refraction and reflection beamforming, NOMA transmit power, and FD jamming power are jointly optimized. To tackle the non-convex covert rate maximization problem subject to the highly coupled system parameters, an alternating optimization algorithm is designed to iteratively solve the decoupled sub-problems of optimizing the system parameters. The optimal solutions for the sub-problems of the NOMA transmit power and FD jamming power optimizations are derived in closed-form. To tackle the rank-one constrained non-convex fractional programming of the A-IOS beamforming and FD beamforming, a penalized Dinkelbach transformation approach is proposed to resort to the optimal solutions via semidefinite programming. Numerical results clarify that the deployment of the A-IOS significantly improves the covert rate compared with the passive-IOS aided uplink NOMA system. It is also found that the proposed scheme provides better covert communication performance with the optimized NOMA transmit power and FD jamming power compared with the benchmark schemes.
Background: Endometriosis significantly affects women’s quality of life, yet remains underrepresented in public
discourse. This study aimed to explore the lived experiences and challenges of women with endometriosis as expressed
on Twitter/X.
Methods: A total of 2000 tweets were collected between September 2023 to November 2023 using relevant hashtags.
Tweets were thematically analyzed using NVivo to identify recurring patterns in women’s experiences with endometriosis.
Results: Findings indicated that 35% of the tweets described chronic pain, difficulty completing daily tasks, and concerns
about infertility. In 30% of the tweets, women also discussed various treatments, including surgery, medication, and dietary
modifications, with a notable focus on weight loss and healthy eating. However, a problematic preoccupation with food and
weight was observed in discussions about dietary changes. A recurring theme observed in the remaining tweets was the
feeling of being dismissed by healthcare providers, with many addressing gender dynamics and bias in the medical field.
Conclusion: This study highlights the power of Twitter/X as a platform for raising awareness about endometriosis.
The findings underscore the need for improved healthcare services and the development of supportive communities for
women with the condition.
In this paper, we consider a novel hybrid reconfigurable intelligent surface (HRIS) consisting of active as well as passive reflecting elements mounted on unmanned aerial vehicle (UAV). The aim is to improve air-to-ground communication by assisting multiple users, while detecting several moving targets. We formulate an optimization model that account for statistical channel estimation errors (SCEEs) to concurrently fine-tune both active and passive phase-shift matrices, UAV trajectory and transmit beamformer for integrated sensing and communication (ISAC), while aiming to maximize the overall achievable sum-rate. Subsequently, we introduce an alternating optimization algorithm that employs a repetitive method to address the combinatorial nonconvex optimization problem and ultimately yielding a solution that is nearly optimal. Using Monte Carlo simulations, we showcase the superiority of the suggested approach in comparison to baseline schemes. Index Terms-Hybrid reconfigurable intelligent surface (HRIS), integrated sensing and communication (ISAC), un-manned aerial vehicle (UAV).
Background
The increasing awareness of the emotional consequences of emergency cesarean deliveries (C-sections) highlights their substantial role in fostering postpartum post-traumatic stress disorder (PTSD). This systematic review and meta-analysis aim to evaluate the prevalence and determinants of PTSD following emergency C-sections, as well as the implications of these events on maternal mental health and welfare.
Methods
Undertaking extensive searches of Scopus, PubMed, PsycINFO, and Google Scholar, we have incorporated studies published from 2013 onwards that examined the occurrence of PTSD following emergency C-sections. Our primary focus was on the prevalence of PTSD at 6 weeks and up to 12 months postpartum. To evaluate the quality of these studies, we employed the Newcastle-Ottawa Scale (NOS) and the CEBM Critical Appraisal Tools.
Findings
We included a total of 10 studies with 4,995 participants. The prevalence of PTSD following emergency C-sections ranged from 2.2 to 41.2%, compared to 0–20% in elective cesarean sections. A meta-analysis revealed a significant rise in the number of people with PTSD in the emergency C-section group compared to the elective C-section group six weeks after giving birth (OR = 2.74; 95% CI = 1.13 to 6.64; p = 0.03) and six weeks to 12 months later (OR = 3.68; 95% CI = 2.63 to 5.15; p < 0.00001). The emergency C-section group also had a higher PTSD prevalence compared to vaginal birth six weeks to 12 months after birth (OR 3.16; 95% CI 1.51 to 6.60; p = 0.02). Risk factors included poor social support, maternal and neonatal complications, and prior psychiatric history.
Conclusions
Emergency C-sections are significantly associated with an increased risk of postpartum PTSD, necessitating targeted psychological support and interventions. Future research should aim for standardized diagnostic criteria and explore the long-term psychological outcomes of emergency C-sections.
Plant factories with artificial lighting (PFALs) are a notable choice for urban agriculture due to the system’s benefits, where light can be manipulated to enhance the product’s yield and quality. Our objective was to test the effect of light spectra with different red-blue combinations and white light on the growth, physiology, and overall quality of three baby-leaf vegetables (green lettuce, kale, and pak choi) grown in a restaurant’s PFAL. Leaf mass per area was lower under the most blue-containing treatments in all species. The performance indices (PIabs and PItot) of the photosynthetic apparatus were lower under more red light with the exception of PIabs in pak choi. Total soluble solids accumulation was diminished under most of the blue-containing LEDs, while total phenolics and antioxidant activity were induced by red-blue environments rich in blue light. Moreover, chlorophyll and carotenoid accumulation was also enhanced under blue-rich light treatments. Nitrate content was the lowest under monochromatic blue in all species. Finally, the employees were asked about their views on the PFAL within the restaurant’s compounds and they expressed positive opinions. Overall, a light environment including red and blue wavelengths proved beneficial for baby leafy vegetable production in terms of yield and quality.
We examine how trade and policy uncertainty affect shipping freight rates, using a Bayesian Vector Autoregression (BVAR) model. Trade uncertainty has a strong effect on shipping costs, even though the effects become insignificant within a year. On the other hand, policy uncertainty has a slightly smaller initial effect but tends to have longer-lasting effects on shipping costs. Trade uncertainty tends to benefit European stocks, perhaps as investors may believe that consumers will shift to local companies, with the impact on US stocks also being (mildly) positive, despite the (lagged) deterioration in economic activity. Trade uncertainty tends to have a longer-lasting impact on GDP than policy uncertainty, given then known merits of comparative advantage, while the effect of policy uncertainty is higher in the European markets compared to the US ones.
Computer-Aided Design (CAD) programming can be integrated into many engineering procedures that ranges from design and assembly of mechanical parts and products, to manufacturing. This work demonstrates the advantages of the Application Programming Interface (API) of SolidWorks™ CAD software in terms of topology detection that can be utilized in many routine tasks during the development phase of a product. These characteristic processes are embedded into a tool that is able to automatically apply typical design features such as chamfer, fillet, and hole to the corresponding geometric object (edge or face). The tool operates with minimal input from the user, requiring only the name of the geometric object in the case of either the chamfer or fillet and the center coordinates, in addition to the name of the appropriate geometric object, in the case of the hole feature. It is noted that the code was written with the Visual Basic for Applications (VBA™) language and can be applied to any solid model.
Assembling multiple identical components with a base part is common in industrial processes. Therefore, assembly automation within the graphical environment constitutes a critical aspect of a system’s development stage. Present paper introduces the utilization of Computer-Aided Design (CAD)-based programming for the realization of automated assembly tasks. Traditional programming with the Visual Basic for Application (VBA™) language was employed for this purpose. Specifically, the API methods that are available in SolidWorks™ CAD system were utilized. The assembly process between a set of bolt-shaped components and an orthogonal solid was used to test the developed tool. API-based programming was employed to search for the appropriate geometric objects and handle the obtained data. The textual programming was preferred instead of the visual programming or the use of tools that involve Artificial Neural Networks (ANNs) and similar methods, for simplicity. Moreover, the traditional methods were used, since they require far less computational resources and do not require specialized tools at all. By combining the developed code with a graphical User Interface (UI), the tool becomes more convenient and practical, as well as more efficient.
Nowadays, automated G-code generation can be achieved with either free software that are available on the web or with specialized Computer-Aided Manufacturing (CAM) systems. In the first case, 3D parts cannot be directly handled, whereas in the second one, the purchase cost is usually very high. The present paper introduces a way of utilizing 3D Computer-Aided Design (CAD) systems to generate G-code for drilling operations. The idea is to supplement free CAM systems, enabling 3D part handling, without the need for specialized CAM software. The methods and functions available in the Application Programming Interface (API) of SolidWorks™ were combined to develop the code using the Visual Basic for Applications (VBA™) language. Specifically, the appropriate API methods were employed to identify the design features of the part, enabling the geometric parameters extraction. The aim of the tool is the Computer Numerical Control (CNC) code generation for the identified hole features or geometric surfaces that belong to holes. A User Interface (UI) was designed to allow the user to interact. Additionally, an embedded calculator was added for the automatic calculation of the cutting parameters (cutting speed, feed, and tool), with respect to the inputs.
Design based on Computer-Aided Design (CAD) programming is a technique that relies on the programming of typical CAD systems via an integrated interface. The purpose of this technique is to automate certain engineering tasks such as the part design, the assembly, the drawing preparation and the G-code generation. The present paper focuses on the development of a code that is able to automate the design process of a simple mechanical part, by programming with the Application Programming Interface (API) of SolidWorks™ CAD software. The paper presents the developed User Interface (UI) and the written code for the case study. In addition, it illustrates the process and visualizes the given results. Visual Basic for Applications (VBA™) programming language was implemented to develop the code, mostly because it requires objects and enables a graphical interaction with them. The final product represents a typical steel angle used in the industry, which can be designed in a variety of profile dimensions and lengths.
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