Search Results (5,268)

Daily actions are influenced by sensory information. Several studies have investigated the multisensory integration of multiple sensory modalities, known as crossmodal perception. Recently, visual–olfactory crossmodal perception has been studied using objective physiological measures rather than subjective evaluations. This study focused on sensing in the orbitofrontal cortex (OFC), which responds to visual and olfactory stimuli, and may serve as a physiological indicator of perception. Using near-infrared spectroscopy (NIRS), we analyzed the emotions evoked by combinations of colored light and fragrance with a particular focus on the lateralization of brain function. We selected pleasant and unpleasant fragrances from some essential oils, paired with colored lights that were perceived as either harmonious or disharmonious with the fragrances. NIRS measurements were conducted under the four following conditions: fragrance-only, colored light-only, harmonious crossmodal, and disharmonious crossmodal presentations. The results showed that the left OFC was activated during the crossmodal presentation of a harmonious color with a pleasant fragrance, thereby evoking pleasant emotions. In contrast, during the crossmodal presentation of a disharmonious color with an unpleasant fragrance, the right OFC was activated, suggesting increased displeasure. Additionally, the lateralization of brain function between the left and right OFC may be influenced by ‘pleasure–displeasure ’ and ‘crossmodal perception–multimodal perception’. Full article

The crop yield in commercial fields is a very important parameter for farmers. The use of Precision Agriculture tools has been shown to improve rice crop yields. One of these tools is remote sensing on satellite platforms. Sentinel-2 provides free data on reflectance at different wavelengths. Focusing on commercial farms, correlations between the yield and satellite reflectance were studied over several years and locations for ‘JSendra’ rice crops. Four years of yield maps for 706 ha composed the database. Mid tillering-MT, panicle initiation-PI and grain filling-GF reflectance values and Vegetation Indices (VIs) were used. At MT, correlations with the yield were variable (0.23–0.70). At PI, correlations with the yield increased in NIR (0.39–0.85), but the other regions and VIs experienced a decrease. Visible bands and B05 Red Edge were significantly correlated with each other; similarly, B08 NIR was highly correlated with B06, B07, and B8A; SWIR bands were correlated with each other but not with the yield. At GF, the previous pattern was similar. Substantial limitations in estimating yield variability directly from reflectance or VIs were discussed. Two periods were established. The first is designing strategies to increase NIR and decrease red reflectance from MT to PI. The second is avoiding the relationship between crop greenness and NIR from PI to harvest. NIR was a better variable than VIs, but the single use of this band is challenging. Future recommendations focus on the visible–NIR collinearities to interpret differences between years or locations. Full article

Nanostructured hybrid materials based on the combination of semiconductor QDs and GO are promising candidates for different optoelectronic and catalytic applications and being able to produce such hybrid materials in solution will expand their possible range of applications. In the current work, capping ligand-exchange procedures have been developed to replace the lead oleate normally found on the surface of PbS QDs synthesized by the popular hot-injection method. After the capping ligand-exchange process, the QDs are water soluble, which makes them soluble in most GO solutions. Solution-processed nanostructured hybrid materials based on GO flakes decorated with ligand-exchanged (EDT, TBAI and L-Cysteine) PbS QDs were synthesized by combining PbS QDs and GO solutions. Afterward, the resulting hybrid materials were thoroughly characterized by means of FTIR, XPS, Raman, UV-Vis-NIR and photoluminescence spectroscopy, as well as SEM and TEM techniques. The results indicate a clear surface chemistry variation in the capping ligand-exchanged PbS QDs, showing the presence of the exchanged ligand molecules. Thin films from the solution-processed nanostructured hybrid materials were deposited by the spin coating technique, and their optoelectronic properties were studied. Depending on the capping ligand molecule, the photoresponse and resistance of the thin films varied; the sample with the EDT ligand exchange showed the highest photoresponse and the lowest resistance. This surface chemistry had a direct effect on the charge carrier transfer and transport behavior of the nanostructured hybrid materials synthesized. These results show a novel and accessible route for synthesizing solution-processed and affordable nanostructured hybrid materials based on semiconductor QDs and GO. Additionally, the importance of the surface chemistry displayed by the PbS QDs and GO was clearly seen in determining the final optoelectronic properties displayed by their hybrid materials. Full article

Soil nitrogen (N) is a crucial nutrient for agricultural productivity and ecosystem health. The accurate and timely assessment of total soil N is essential for evaluating soil health. This study aimed to determine the impact of bootstrapping techniques on improving the predictive accuracy of indirect total soil N in conventional wheat fields in Al-Muthanna, Iraq. We integrated a novel methodological framework that integrated bootstrapped and non-bootstrapped total soil N data from 110 soil samples along with Landsat 9 imagery on the Google Earth Engine (GEE) platform. The performance of the proposed bootstrapping-enhanced random forest (RF) model was compared to standard RF models for soil N prediction, and outlier samples were analyzed to assess the impact of soil conditions on model performance. Principal components analysis (PCA) identified the key spectral reflectance properties that contribute to the variation in soil N. The PCA results highlighted NIR (band 5) and SWIR2 (band 7) as the primary contributors, explaining over 91.3% of the variation in soil N within the study area. Among the developed models, the log (B5/B7) model performed best in capturing soil N (R² = 0.773), followed by the ratio (B5/B7) model (R² = 0.489), while the inverse log transformation (1/log (B5/B7), R² = 0.191) exhibited the lowest performance. Bootstrapped RF models surpassed non-bootstrapped random forest models, demonstrating enhanced predictive capability for soil N. This study established an efficient framework for improving predictive capacity in areas characterized by limited, low-quality, and incomplete spatial data, offering valuable insights for sustainable nitrogen management in arid regions dominated by monoculture systems. Full article

Total soluble solids (TSSs) serve as a crucial maturity indicator and quality determinant in apricots, influencing harvest timing and postharvest management decisions. This study develops an advanced framework integrating adaptive boosting (Adaboost) ensemble learning with high-frequency spectral variables selected by uninformative variable elimination (UVE) for the rapid non-destructive detection of fruit quality. Near-infrared (NIR) spectra (1000~2500 nm) were acquired and then preprocessed through robust principal component analysis (ROBPCA) for outlier detection combined with z-score normalization for spectral pretreatment. Subsequent data processes included three steps: (1) 100 continuous runs of UVE identified characteristic wavelengths, which were classified into three levels—high-frequency (≥90 times), medium-frequency (30–90 times), and low-frequency (≤30 times) subsets; (2) the development of the base optimal partial least squares regression (PLSR) models for each wavelength subset; and (3) the execution of adaptive weight optimization through the Adaboost ensemble algorithm. The experimental findings revealed the following: (1) The model established based on high-frequency wavelengths outperformed both full-spectrum model and full-characteristic wavelength model. (2) The optimized UVE-PLS-Adaboost model achieved the peak performance (R = 0.889, RMSEP = 1.267, MAE = 0.994). This research shows that the UVE-Adaboost fusion method enhances model prediction accuracy and generalization ability through multi-dimensional feature optimization and model weight allocation. The proposed framework enables the rapid, non-destructive detection of apricot TSSs and provides a reference for the quality evaluation of other fruits in agricultural applications. Full article

With advances in optical satellite remote sensing, urban flood mapping (UFM) leveraging water’s distinct spectral characteristics for water identification is preferred and has gained more attention. PlanetScope’s daily 3 m resolution imagery enables detailed and time-sensitive flood monitoring. Unlike machine learning, which requires extensive training data, thresholding methods offer a faster and more adaptable solution for binary classification. Three global (Yen’s, Otsu’s, Isodata) and three local (Niblack, Sauvola, Gonzalez) thresholding methods, with their parameters optimized for each case study, were assessed in this study. Additionally, a hybrid approach was proposed and evaluated. In this approach, local thresholds are computed for each pixel, using the respective local thresholding method. Then, a global threshold is derived by calculating the simple arithmetic mean of all these local thresholds. This global threshold is subsequently applied across the entire image to perform a binary classification, distinguishing flooded from non-flooded areas. To enhance water detection, we also evaluated 26 remote sensing indices. Each was computed using two formulations—the normalized difference and the ratio—where at least one of the eight PlanetScope bands was NIR or RedEdge to enhance water detection. We tested this methodology on three flooding events with different water coverage scenarios in Brazil (34% water coverage), the USA (11%), and Australia (21%). The model performance was validated using the Matthews correlation coefficient (MCC) and the Fowlkes–Mallows index (FMI). The results demonstrated that combining PlanetScope imagery with carefully selected remote sensing indices and thresholding techniques enhances efficient UFM. The hybrid methods outperformed the others by capturing local variations while maintaining global consistency, with the MCC and the FMI exceeding 0.9. The indices incorporating NIR and RedEdge, particularly NDRE, achieved the highest accuracy. However, each flood event was best classified by a different combination of method and index, indicating that it is important to carefully select the appropriate remote sensing indices and thresholding techniques for each specific case. This framework provides a fast, effective solution for UFM, adaptable to diverse urban environments and flood conditions. Full article

Near-Infrared Spectroscopy (NIRS) is a non-invasive technique that measures the oxygenation variations of brain tissue in response to different stimuli. It has many advantages such as being easy to use, portable, and non-invasive. Several studies over the years have demonstrated the usefulness of NIRS in neurological and neurodegenerative diseases. NIRS remains relatively underutilized in clinical practice. The aim of this brief review was to describe the use of NIRS in neurological and neurodegenerative diseases and how its use can modify clinical, therapeutic, and rehabilitative approaches. A total of 54 relevant articles were selected from the PUBMED research database related to the diagnostic and prognostic role of fNIRS in the main neurological and neurodegenerative diseases; significant outcomes have been reported in a descriptive form with careful considerations. In addition, we excluded studies using fNIRS in co-registration with other neurophysiological techniques. The use of NIRS should be applied even in the field of neurological and neurodegenerative diseases; in dementia, NIRS can aid in differential diagnosis and predict possible evolutions from Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) stage; in stroke, it plays an important role especially in the post-acute phase, giving information about the patient’s chances of recovery; in Parkinson’s Disease (PD), the results showed the important role of cognitive aspects; in epilepsy, NIRS can localize the epileptic focus or potentially predict seizure onset. Full article

Impeded by the limited light penetration of photodynamic therapy (PDT) to tissues and the hypoxic environment of solid tumors, the clinical therapeutic efficacy and application are below expectations. In this study, a glutathione (GSH)-responsive nano-photosensitizer, based on the chlorquinaldol (CQD)-loaded iron-containing nanorod composed of meso-tetra (4-carboxyphenyl) porphyrin (TCPP), was prepared to serve as the laser-ignited ferroptosis sensitizer to improve the tumoricidal effect of PDT. In the tumor microenvironment (TME) with elevated GSH levels, therapeutic cargos and ferrous ions are released and are accompanied by the degradation of the nano-photosensitizer and GSH exhaustion. This not only increases liable iron pool (LIP) accumulation by the released ferrous ions but also decreases glutathione peroxidase 4 (GPX4) activity by GSH exhaustion. Simultaneously, GSH exhaustion disrupts intracellular redox homeostasis, heightening NIR light irradiation-triggered photosensitive oxidative stress. Moreover, the released CQD elevates the level of intracellular reactive oxygen species (ROS), enabling the nanorods to gain an oxygen radical generation ability and enhancing the photosensitive oxidative therapeutic efficacy. Strikingly, CQD exacerbates the downregulation of GPX4 expression to promote the accumulation of lipid peroxides. Therefore, we herald a new paradigm for synergistically potentiating PDT based on the “all-in-one” nano-photosensitizer through the multi-pronged upregulation of ferroptosis sensitivity. Full article

Substantial amounts of nitrogenous (N) compounds, as well as bisphenol A (BPA) and bisphenol S (BPS), contribute to the impurities of pharmaceutical contamination (PC) in wastewater, which have detrimental effects on the environment, humans, and aquaculture. The anammox processes is primarily used to treat wastewater contamination, in which certain microbial communities play a crucial role. In this regard, the present study focuses on microbial communities and the functional genes involved in the anammox process. Further, the current study highlights the secondary (biological) and tertiary (advanced) methods; these techniques are more effective solutions for PC treatment. Anammox bacteria are the primary drivers of the wastewater’s ammonium and nitrite removal process. However, overall, 25 anammox species have been recognized between five important genera, including Anammoxoglobus, Anammoximicrobium, Brocadia, Kuenenia, and Jettenia, which are mainly found in activated sludge and marine environments. The group of bacteria called anammox has genes that encode enzymes such as hydrazine synthase (HZS), hydrazine dehydrogenase (HDH), nitrite oxidoreductase reductase (NIR), hydroxylamine oxidoreductase (HAO), and ammonium monooxygenase (AMO). The anammox process is responsible for developing about 30% to 70% N gases worldwide, making it a critical component of the nitrogen cycle as well. Therefore, this review paper also investigates the pathways of hydrazine, an intermediate in the anammox process, and discusses the potential way to significantly decrease the N-compound contamination from wastewater systems and the environmental effects of determined organic contaminants of BPA and BPS. Full article

Individuals experiencing high levels of stress face significant impacts on their overall well-being and quality of life. Electrical stimulation techniques have emerged as promising interventions to address mental stress, depression, and anxiety. This systematic review investigates the impact of different electrical stimulation approaches on these types of disorders. The review synthesizes data from 30 studies, revealing promising findings and identifying several research gaps and challenges. The results indicate that electrical stimulation has the potential to alleviate symptoms of anxiety, depression, and tension, although the degree of efficacy varies among different patient populations and modalities. Nevertheless, the findings also underscore the necessity of standardized protocols and additional research to ascertain the most effective treatment parameters. There is also a need for integrated methodologies that combine hybrid EEG-fNIRS techniques with stress induction paradigms, the exploration of alternative stimulation modalities beyond tDCS, and the investigation of the combined effects of stimulation on stress. Despite these challenges, the growing body of evidence underscores the potential of electrical stimulation as a valuable tool to manage mental stress, depression, and anxiety, paving the way for future advancements in this field. Full article

Dual-mode photodetectors (DmPDs) have attracted considerable interest due to their ability to integrate multiple functionalities into a single device. However, 2D material/InP heterostructures, which exhibit built-in electric fields and rapid response characteristics, have not yet been utilized in DmPDs. In this work, we fabricate a high-performance DmPD based on a graphene/InP Van der Waals heterostructure in a facile way, achieving a broadband response from ultraviolet-visible to near-infrared wavelengths. The device incorporates two top electrodes contacting monolayer chemical vapor deposition (CVD) graphene and a bottom electrode on the backside of an InP substrate. By flexibly switching among these three electrodes, the as-fabricated DmPD can operate in a self-powered photovoltaic mode for energy-efficient high-speed imaging or in a biased photoconductive mode for detecting weak light signals, fully demonstrating its multifunctional detection capabilities. Specifically, in the self-powered photovoltaic mode, the DmPD leverages the vertically configured Schottky junction to achieve an on/off ratio of 8 × 10³, a responsivity of 49.2 mA/W, a detectivity of 4.09 × 10¹¹ Jones, and an ultrafast response, with a rising time (τ_r) and falling time (τ_f) of 2.8/6.2 μs. In the photoconductive mode at a 1 V bias, the photogating effect enhances the responsivity to 162.5 A/W. This work advances the development of InP-based multifunctional optoelectronic devices. Full article

Dust is one of the most important problems of human societies in arid and semi-arid areas. This study analyzed the rising and propagation of the dust storm occurring from 21 to 23 May 2016 in Isfahan province (Central Iran) by using the WRF-Chem and HYSPLIT models. The dust storm was visualized using visible imagery and coarse-mode aerosol optical depth data from satellite sensor data, and dust emission and transport were simulated for Central Iran by using WRF-Chem with the AFWA and GOCART schemes. The results show that the dust concentration in Sistan and Baluchistan province and the Persian Gulf was as high as 2000 µg/m³, and both schemes estimate the highest amount of dust emissions from the central parts of Iran and the eastern part of Isfahan province. PM10 data of Yazd station was used to verify the model outputs, which showed that the AFWA dust scheme has a higher correlation coefficient with observations (0.62) than the GOCART dust scheme. This case study suggests that WRF-Chem dust schemes simulate dust rising and propagation in Central Iran with reasonably good reliability, though further determination and enhancement are still required for an accurate prediction of dust concentration and extents. Full article

This study presents the evaluation of tools for weed analysis and management to support agroecological practices in organic farming, emphasizing agriculture digitalization, and remote sensing. The main aim was to provide techniques for monitoring and predicting weed spread using multispectral satellite and drone data, without the use of chemical inputs. Key findings indicate that VV and VH channels of Sentinel-1 and B2, B3, B4, and B8 channels of Sentinel-2 are not different regarding tillage, herbicide use, or sowing density. However, RE and NIR channels of drone detected significant variations and proved effectiveness for weediness monitoring. The NIR channel is sensitive to agrotechnical factors such as cultivation type, making it valuable for field monitoring. Correlation and regression analyses revealed that B2, B3, B8 channels of Sentinel-2, and RE and NIR drone channels are the most reliable for predicting weed levels. Conversely, Sentinel-1 showed limited predictive utility. Random effect models confirmed that Sentinel-2 and drone channels can accurately account for site characteristics and timing of weed proliferation. Taken together, these tools provide effective organic weed monitoring systems, enabling rapid identification of problem areas and adjustments in agronomic practices. Full article

This paper proposes a novel hybrid approach that combines unsupervised feature extraction through clustering and unsupervised feature selection for data reduction, specifically targeting high-dimensional data. The proposed method employs K-means clustering for feature extraction, where cluster membership serves as a new feature representation, capturing the inherent data characteristics. Subsequently, the K-Nearest Neighbors (KNN) and Random Forest algorithms are utilized for supervised feature selection, identifying the most relevant feature to enhance model performance. This hybrid approach leverages the strengths of both unsupervised and supervised learning techniques. The new algorithm was applied to 13 different tabular datasets, with 9 datasets showing significant improvements across various performance metrics (accuracy, precision, recall, and F1-score) in both KNN and Random Forest models, despite substantial feature reduction. In the remaining four datasets, we achieved substantial dimensionality reduction with only negligible performance decreases. This improvement in performance while reducing dimensionality highlights the potential of the proposed method within the procedure, where datasets are treated without prior knowledge or assumptions. The proposed method offers a promising solution for handling high-dimensional data, enhancing model performance while maintaining interpretability and ease of integration within the proposed frameworks, with the ability to be irrespective of supervised or unsupervised designation datasets while reducing the dependency on a target or label features. Full article

Accurate detection of textile composition is a major challenge for textile reuse/recycling. This paper investigates the feasibility of identification of textile materials using a Near Infra-Red (NIR) camera. A transportable metric has been defined which could be capable of identification and distinction between cotton and polyester. The NIR camera provides a single data value in the form of the “intensity” of the exposed light at each pixel across its 2D pixel array. The feasibility of textile material identification was investigated using a combination of various statistical methods to evaluate the output images from the NIR camera when a bandpass filter was attached to the camera’s lens. A repeatable and stable metric was identified and was shown to be independent of both the camera’s exposure setting and the physical illumination spread over the textiles. The average value of the identified metric for the most suitable bandpass filter was found to be 0.68 for cotton, with a maximum deviation of 2%, and 1.0 for polyester, with a maximum deviation of 1%. It was further shown that carbon black dye, a known challenge in the industry, was easily detectable by the system, and, using the proposed technique in this paper, areas that are not covered by carbon black dye can be identified and analysed. Full article