Search Results (45)

Concrete mix design plays a pivotal role in ensuring the mechanical performance, durability, and sustainability of construction projects. However, the nonlinear interactions among the mix components challenge traditional approaches in predicting compressive strength and optimizing proportions. This study presents a two-stage hybrid framework that integrates deep learning with reinforcement learning to overcome these limitations. First, a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model was developed to capture spatial–temporal patterns from a dataset of 1030 historical concrete samples. The extracted features were enhanced using an eXtreme Gradient Boosting (XGBoost) meta-model to improve generalizability and noise resistance. Then, a Dueling Double Deep Q-Network (Dueling DDQN) agent was used to iteratively identify optimal mix ratios that maximize the predicted compressive strength. The proposed framework outperformed ten benchmark models, achieving an MAE of 2.97, RMSE of 4.08, and R² of 0.94. Feature attribution methods—including SHapley Additive exPlanations (SHAP), Elasticity-Based Feature Importance (EFI), and Permutation Feature Importance (PFI)—highlighted the dominant influence of cement content and curing age, as well as revealing non-intuitive effects such as the compensatory role of superplasticizers in low-water mixtures. These findings demonstrate the potential of the proposed approach to support intelligent concrete mix design and real-time optimization in smart construction environments. Full article

Moringa peregrina (Forssk.) Fiori is a tropical tree in southern Iran known as the most important natural coagulant in the world. Today, plant tissue culture is a new method that has a very high potential to produce valuable medicinal compounds on a commercial level. Advances in in vitro cultivation methods have increased the usefulness of plants as renewable resources. In this study, in addition to the phytochemical analysis of the extract of M. peregrina using HPLC, the interaction effect of different concentrations of aqueous extract of M. peregrina (0, 1, 1.5, and 3 mg/L) in two types of MS and ½ MS basal culture media over three weeks on the in vitro growth of Staurogyne repens (Nees) Kuntze was studied. The amounts of quercetin, gallic acid, caffeic acid, and myricetin in the aqueous extract of M. peregrina were 64.9, 374.8, 42, and 4.6 mg/g, respectively. The results showed that using M. peregrina leaf aqueous extract had a positive effect on the length of the branches, the percentage of green leaves, rooting, and the fresh and dry weight of S. repens samples. The highest increase in growth indices was observed in the MS culture medium supplemented with 3 mg/L of M. peregrina leaf aqueous extract after three weeks of cultivation. Of course, this effect was significantly greater in the MS medium and at higher concentrations compared to the ½ MS medium. Three weeks after cultivation at a concentration of 3 mg/L of the extract, the length of the S. repens branches was 5.3 and 1.8 cm in the two basic MS and ½ MS culture media, and the percentage of green leaves was 14 and 4 percent, respectively. Also, rooting was measured at 9.6 and 3.6 percent, fresh weight at 6 and 1.4 g, and dry weight at 1.1 and 0.03 g, respectively. Therefore, adding M. peregrina leaf aqueous extract as a stimulant significantly increased the in vitro growth of S. repens. Full article

Resistive gas sensors are among the most widely used sensors for the detection of various gases. In this type of gas sensor, the gas sensing capability is linked to the surface properties of the sensing layer, and accordingly, modification of the sensing surface is of importance to improve the sensing output. Plasma treatment is a promising way to modify the surface properties of gas sensors, mainly by changing the amounts of oxygen ions, which have a central role in gas sensing reactions. In this review paper, we focus on the role of plasma treatment in the gas sensing features of resistive gas sensors. After an introduction to air pollution, toxic gases, and resistive gas sensors, the main concepts regarding plasma are presented. Then, the impact of plasma treatment on the sensing characteristics of various sensing materials is discussed. As the gas sensing field is an interdisciplinary field, we believe that the present review paper will be of significant interest to researchers with various backgrounds who are working on gas sensors. Full article

The development of low-cost and low-power gas sensors for reliable NO₂ gas detection is important due to the highly toxic nature of NO₂ gas. Herein, initially, SnO₂ nanowires (NWs) were synthesized through a simple vapor–liquid–solid growth mechanism. Subsequently, different amounts of SnO₂ NWs were composited with MoS₂ nanosheets (NSs) to fabricate SnO₂ NWs/MoS₂ NS nanocomposite gas sensors for NO₂ gas sensing. The operation of the sensors in self-heating mode at 1–3.5 V showed that the sensor with 20 wt.% SnO₂ (SM-20 nanocomposite) had the highest response of 13 to 1000 ppb NO₂ under 3.2 V applied voltage. Furthermore, the SM-20 nanocomposite gas sensor exhibited high selectivity and excellent long-term stability. The enhanced NO₂ gas response was ascribed to the formation of n-n heterojunctions between SnO₂ NWs and MoS₂, high surface area, and the presence of some voids in the SM-20 composite gas sensor due to having different morphologies of SnO₂ NWs and MoS₂ NSs. It is believed that the present strategy combining MoS₂ and SnO₂ with different morphologies and different sensing properties is a good approach to realize high-performance NO₂ gas sensors with merits such as simple synthesis and fabrication procedures, low cost, and low power consumption, which are currently in demand in the gas sensor market. Full article

Accurate spatiotemporal monitoring and modeling of soil moisture (SM) is of paramount importance for various applications ranging from food production to climate change adaptation. This study deals with modeling SM with the random forest (RF) algorithm using datasets comprising multispectral data from Sentinel-2, Landsat-8/9, and hyperspectral data from the CoSpectroCam sensor (CSC, licensed to AgriWatch BV, Enschede, The Netherlands) mounted on an unmanned aerial vehicle (UAV) in Iran. The model included nine bands from Landsat-8/9, 11 bands from Sentinel-2, and 1252 bands from the CSC (covering the wavelength range between 420 and 850 nm). The relative feature importance and band sensitivity to SM variations were analyzed. In addition, four indices, including the perpendicular index (PI), ratio index (RI), difference index (DI), and normalized difference index (NDI) were calculated from the different bands of the datasets, and their sensitivity to SM was evaluated. The results showed that the PI exhibited the highest sensitivity to SM changes in all datasets among the four indices considered. Comparisons of the performance of the datasets in SM estimation emphasized the superior performance of the UAV hyperspectral data (R² = 0.87), while the Sentinel-2 and Landsat-8/9 data showed lower accuracy (R² = 0.49 and 0.66, respectively). The robust performance of the CSC data is likely due to its superior spatial and spectral resolution as well as the application of preprocessing techniques such as noise reduction and smoothing filters. The lower accuracy of the multispectral data from Sentinel-2 and Landsat-8/9 can also be attributed to their relatively coarse spatial resolution compared to the CSC, which leads to pixel non-uniformities and impurities. Therefore, employing the CSC on a UAV proves to be a valuable technology, providing an effective link between satellite observations and ground measurements. Full article

Hydrogen (H₂) gas, with its high calorimetric combustion energy and cleanness, is a green source of energy and an alternative to fossil fuels. However, it has a small kinetic diameter, with high diffusivity and a highly explosive nature. Hence, the reliable detection of H₂ gas is essential in various fields such as fuel cells. Herein, we decorated ZnO nanoparticles (NPs) with Pd noble metal NPs, using UV irradiation to enhance their H₂ gas-sensing performance. The synthesized materials were fully characterized in terms of their phases, morphologies, and chemical composition. Then, the sensing layer was deposited on the electrode-patterned glass substrate to make a transparent sensor. The fabricated transparent gas sensor was able to detect H₂ gas at various temperatures and humidity levels. At 250 °C, the sensor exhibited the highest response to H₂ gas. As a novelty of the present study, we successfully detected H₂ gas in mixtures of H₂/benzene and H₂/toluene gases. The enhanced H₂ gas response was related to the catalytic effect of Pd, the formation of heterojunctions between Pd and ZnO, the partial reduction of ZnO to Zn in the presence of H₂ gas, and the formation of PdH_x. With a high performance in a high response, good selectivity, and repeatability, we believe that the sensor developed in this study can be a good candidate for practical applications where the detection of H₂ is necessary. Full article

Extracellular vesicles (EVs), a diverse group of cell-derived exocytosed particles, are pivotal in mediating intercellular communication due to their ability to selectively transfer biomolecules to specific cell types. EVs, composed of proteins, nucleic acids, and lipids, are taken up by cells to affect a variety of signaling cascades. Research in the field has primarily focused on stem cell-derived EVs, with a particular focus on mesenchymal stem cells, for their potential therapeutic benefits. Recently, tissue-specific EVs or cell type-specific extracellular vesicles (CTS-EVs), have garnered attention for their unique biogenesis and molecular composition because they enable highly targeted cell-specific communication. Various studies have outlined the roles that CTS-EVs play in the signaling for physiological function and the maintenance of homeostasis, including immune modulation, tissue regeneration, and organ development. These properties are also exploited for disease propagation, such as in cancer, neurological disorders, infectious diseases, autoimmune conditions, and more. The insights gained from analyzing CTS-EVs in different biological roles not only enhance our understanding of intercellular signaling and disease pathogenesis but also open new avenues for innovative diagnostic biomarkers and therapeutic targets for a wide spectrum of medical conditions. This review comprehensively outlines the current understanding of CTS-EV origins, function within normal physiology, and implications in diseased states. Full article

Resistive gas sensors, which are widely used for the detection of various toxic gases and vapors, can be fabricated in planar and tubular configurations by the deposition of a semiconducting sensing layer over an insulating substrate. However, their deposition parameters are not often optimized to obtain the highest sensing results. Here, we have investigated the effect of deposition variables on the H₂ gas sensing performance of commercially available SnO₂ particles on tubular alumina substrate. Utilizing a tubular alumina substrate equipped with gold electrodes, we varied the number of deposited layers, rotational speed of the substrate, and number of rotations of the substrate on the output of the deposited sensor in terms of response to H₂ gas. Additionally, the effect of annealing temperatures (400, 500, 600, and 700 °C for 1 h) was investigated. According to our findings, the optimal conditions for sensor fabrication to achieve the best performance were the application of one layer of the sensing material on the sensor with ten rotations and a rotation speed of 7 rpm. In addition, annealing at a lower temperature (400 °C) resulted in better sensor performance. The optimized sensor displayed a high response of ~12 to 500 ppm at 300 °C. This study demonstrates the importance of optimization of deposition parameters on tubular substrates to achieve the best gas sensing performance, which should be considered when preparing gas sensors. Full article

Cutaneous leishmaniasis (CL) is a common illness that causes skin lesions, principally ulcerations, on exposed regions of the body. Although neglected tropical diseases (NTDs) are typically found in tropical areas, they have recently become more common along Africa’s northern coast, particularly in Libya. The devastation of healthcare infrastructure during the 2011 war and the following conflicts, as well as governmental apathy, may be causal factors associated with this catastrophic event. The main objective of this study is to evaluate alternative diagnostic strategies for recognizing amastigotes of cutaneous leishmaniasis parasites at various stages using Convolutional Neural Networks (CNNs). The research is additionally aimed at testing different classification models employing a dataset of ultra-thin skin smear images of Leishmania parasite-infected people with cutaneous leishmaniasis. The pre-trained deep learning models including EfficientNetB0, DenseNet201, ResNet101, MobileNetv2, and Xception are used for the cutaneous leishmania parasite diagnosis task. To assess the models’ effectiveness, we employed a five-fold cross-validation approach to guarantee the consistency of the models’ outputs when applied to different portions of the full dataset. Following a thorough assessment and contrast of the various models, DenseNet-201 proved to be the most suitable choice. It attained a mean accuracy of 0.9914 along with outstanding results for sensitivity, specificity, positive predictive value, negative predictive value, F1-score, Matthew’s correlation coefficient, and Cohen’s Kappa coefficient. The DenseNet-201 model surpassed the other models based on a comprehensive evaluation of these key classification performance metrics. Full article

Mesenchymal stem/stromal cells (MSCs) are considered a valuable option to treat ocular surface disorders such as mustard keratopathy (MK). MK often leads to vision impairment due to corneal opacification and neovascularization and cellular senescence seems to have a role in its pathophysiology. Herein, we utilized intrastromal MSC injections to treat MK. Thirty-two mice were divided into four groups based on the exposure to 20 mM or 40 mM concentrations of mustard and receiving the treatment or not. Mice were clinically and histopathologically examined. Histopathological evaluations were completed after the euthanasia of mice after four months and included hematoxylin and eosin (H&E), CK12, and beta-galactosidase (β-gal) staining. The treatment group demonstrated reduced opacity compared to the control group. While corneal neovascularization did not display significant variations between the groups, the control group did register higher numerical values. Histopathologically, reduced CK12 staining was detected in the control group. Additionally, β-gal staining areas were notably lower in the treatment group. Although the treated groups showed lower severity of fibrosis compared to the control groups, statistical difference was not significant. In conclusion, it seems that delivery of MSCs in MK has exhibited promising therapeutic results, notably in reducing corneal opacity. Furthermore, the significant reduction in the β-galactosidase staining area may point towards the promising anti-senescence potential of MSCs. Full article

Crop classification using remote sensing data has emerged as a prominent research area in recent decades. Studies have demonstrated that fusing synthetic aperture radar (SAR) and optical images can significantly enhance the accuracy of classification. However, a major challenge in this field is the limited availability of training data, which adversely affects the performance of classifiers. In agricultural regions, the dominant crops typically consist of one or two specific types, while other crops are scarce. Consequently, when collecting training samples to create a map of agricultural products, there is an abundance of samples from the dominant crops, forming the majority classes. Conversely, samples from other crops are scarce, representing the minority classes. Addressing this issue requires overcoming several challenges and weaknesses associated with the traditional data generation methods. These methods have been employed to tackle the imbalanced nature of training data. Nevertheless, they still face limitations in effectively handling minority classes. Overall, the issue of inadequate training data, particularly for minority classes, remains a hurdle that the traditional methods struggle to overcome. In this research, we explore the effectiveness of a conditional tabular generative adversarial network (CTGAN) as a synthetic data generation method based on a deep learning network, for addressing the challenge of limited training data for minority classes in crop classification using the fusion of SAR-optical data. Our findings demonstrate that the proposed method generates synthetic data with a higher quality, which can significantly increase the number of samples for minority classes, leading to a better performance of crop classifiers. For instance, according to the G-mean metric, we observed notable improvements in the performance of the XGBoost classifier of up to 5% for minority classes. Furthermore, the statistical characteristics of the synthetic data were similar to real data, demonstrating the fidelity of the generated samples. Thus, CTGAN can be employed as a solution for addressing the scarcity of training data for minority classes in crop classification using SAR–optical data. Full article

Owing to their large surface area, two-dimensional (2D) semiconducting nanomaterials have been extensively studied for gas-sensing applications in recent years. In particular, the possibility of operating at room temperature (RT) is desirable for 2D gas sensors because it significantly reduces the power consumption of the sensing device. Furthermore, RT gas sensors are among the first choices for the development of flexible and wearable devices. In this review, we focus on the 2D MXenes used for the realization of RT gas sensors. Hence, pristine, doped, decorated, and composites of MXenes with other semiconductors for gas sensing are discussed. Two-dimensional MXene nanomaterials are discussed, with greater emphasis on the sensing mechanism. MXenes with the ability to work at RT have great potential for practical applications such as flexible and/or wearable gas sensors. Full article

Metal oxide nanowires (NWs) with a high surface area, ease of fabrication, and precise control over diameter and chemical composition are among the best candidates for the realization of resistive gas sensors. Among the different techniques used for the synthesis of materials with NW morphology, approaches based on the vapor–liquid–solid (VLS) mechanism are very popular due to the ease of synthesis, low price of starting materials, and possibility of branching. In this review article, we discuss the gas-sensing features of metal oxide NWs grown by the VLS mechanism, with emphasis on the growth conditions and sensing mechanism. The growth and sensing performance of SnO₂, ZnO, In₂O₃, NiO, CuO, and WO₃ materials with NW morphology are discussed. The effects of the catalyst type, growth temperature, and other variables on the morphology and gas-sensing performance of NWs are discussed. Full article

Background: Evidence on the genetics of functional seizures is scarce, and the purpose of the current scoping systematic review is to examine the existing evidence and propose how to advance the field. Methods: Web of science and MEDLINE were searched, from their initiation until May 2023. The following key words were used: functional neurological disorder(s), psychogenic neurological disorder(s), functional movement disorder(s), psychogenic movement disorder(s), functional seizures(s), psychogenic seizure(s), nonepileptic seizure(s), dissociative seizure(s), or psychogenic nonepileptic seizure(s), AND, gene, genetic(s), polymorphism, genome, epigenetics, copy number variant, copy number variation(s), whole exome sequencing, or next-generation sequencing. Results: We identified three original studies. In one study, the authors observed that six (5.9%) patients with functional seizures carried pathogenic/likely pathogenic variants. In another study, the authors observed that, in functional seizures, there was a significant correlation with genes that are over-represented in adrenergic, serotonergic, oxytocin, opioid, and GABA receptor signaling pathways. In the third study, the authors observed that patients with functional seizures, as well as patients with depression, had significantly different genotypes in FKBP5 single nucleotide polymorphisms compared with controls. Conclusion: Future genetic investigations of patients with functional seizures would increase our understanding of the pathophysiological and neurobiological problems underlying this common neuropsychological stress-associated condition. Full article

Today, resistance gas sensors which are mainly realized from metal oxides are among the most used sensing devices. However, generally, their sensing temperature is high and other materials with a lower operating temperature can be an alternative to them. Graphene and its derivatives with a 2D structure are among the most encouraging materials for gas-sensing purposes, because a 2D lattice with high surface area can maximize the interaction between the surface and gas, and a small variation in the carrier concentration of graphene can cause a notable modulation of electrical conductivity in graphene. However, they show weak sensing performance in pristine form. Hence, doping, and in particular N doping, can be one of the most promising strategies to enhance the gas-sensing features of graphene-based sensors. Herein, we discuss the gas-sensing properties of N-doped graphene and its derivatives. N doping can induce a band gap inside of graphene, generate defects, and enhance the conductivity of graphene, all factors which are beneficial for sensing studies. Additionally, not only is experimental research reviewed in this review paper, but theoretical works about N-doped graphene are also discussed. Full article