Near-infrared spectroscopy (NIRS) has become a key modality in medical imaging, finding application in both brain and breast imaging. This paper discusses the current trends in NIRS for brain and breast imaging, exploring advances in multi-modal integration with modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). Challenges related to spatial resolution, depth sensitivity, and the impact of extracerebral tissues on signal specificity are examined. In addition, ongoing efforts to enhance hemodynamic measurements’ quantitative accuracy. Challenges, including limited spatial resolution and tissue heterogeneity, are discussed. The discussion extends to diffuse optical tomography and ongoing instrumentation development, clinical trials and studies validating NIRS diagnostic efficacy in breast imaging. The paper emphasizes the need for standardization, integration into routine clinical practice, and motivates for future work.

NIRS uses the relative absorption of light at 850nm and 760nm, to determine skeletal muscle oxygen saturation. Previous studies have used the ratio of both signals to report muscle oxygen saturation. Purpose: To evaluate the different approaches used to represent muscle oxygen saturation, and to evaluate the pulsations of the O₂heme and Heme signal. Method: Twelve participants, ages 20-29years were tested on the forearm flexor muscles using continuous wave NIRS at rest. Measurements were taken during 2-3mins rest, during physiological calibration (5-minuts Ischemia) and during reperfusion. Results: There was a significant difference in pulse size between O₂heme and Heme signal at the three locations (p < 0.05). Resting oxygen saturation was 58.8+9.2%, 69.6+3.9%, and 89.2+6.9% when calibrated using O₂heme, TSI, and Heme, respectively. Conclusion: The difference in magnitude of O₂heme and Heme pulse with each heartbeat might suggest different anatomical locations of these signals, which propose calibrating with just one of the signals instead of the ratio of both. Calculations of physiological calibration must account for increased blood volume in the tissue, because of the changes in blood volume which appear to be primarily from the O₂heme signal. Resting oxygen levels calibrated with Heme agrees with theoretical oxygen saturation.

Near-Infrared Spectroscopy (NIRS) has been used to measure muscle mitochondrial capacity. The current method requires as many as 22 short ischemic occlusions to generate a recovery curve for mitochondrial capacity. PURPOSE: To determine the effectiveness of using a 6-occlusion analysis protocol to study muscle mitochondrial capacity. METHOD: Two independent, unidentified data sets were analyzed (bicep n=48, forearm n=41) from previous studies using a NIRS device (Artinis, Ltd.). Both data sets had two recovery tests that included 22 ischemic occlusions. A recovery rate used to indicate mitochondrial capacity was calculated two different ways (simultaneously). Each sample was analyzed with a MATLAB program; with a curve-fit for the 22 ischemic occlusions and curve matching for the first six ischemic cuffs and an end resting value. The two resulting rate constants were compared using correlations, both for the two data sets, good and bad fitting data, using the best 5 of 6 points for the 6 cuff approach. RESULTS: The rate constants were not significantly different between the 22 cmuff and 6 cuff for the total data sets: bicep (1.43+0.32min^-1, 1.44+0.35min^-1, p=0.56), forearm (1.94+0.42min^-1, 1.95+0.44min^-1, p=0.76). The average bicep rate constants, when compared to each other, had an equation of y=1.07x-0.09, R²=0.90. The average forearm rate constants, when compared to each other, had an equation of 0.98x+0.02, R²=0.93. CONCLUSIONS: The 6-Cuff analysis provided the same results as the longer 22-cuff. The 6-cuff approach is both shorter in time and uses less ischemic occlusion periods, increasing the practicality of the NIRS mitochondrial capacity test.

Near-infrared spectroscopy (NIRS) was implemented to monitor the moisture content of broadleaf litters. Partial least-squares regression (PLSR) models, incorporating optimal wavelength selection techniques, have been proposed to better predict the litter moisture of forest floor. Three broadleaf litters were used to sample the reflection spectra corresponding the different degrees of litter moisture. Maximum normalization preprocessing technique was successfully applied to remove unwanted noise from the reflectance spectra of litters. Four variable selection methods were also employed to extract the optimal subset of measured spectra for establishing the best prediction model. The results showed that the PLSR model with the peak of beta coefficients method was the best predictor among all candidate models. The proposed NIRS procedure is thought to be a suitable technique for on-the-spot evaluation of litter moisture.

We attempted to apply the optoelectronic sensor entitled 'OPT101' in intensive care unit clinics, based on its optoelectronic response characteristics in near-infrared wavelength range and near-infrared spectroscopy principle. The successful novel applications in our lab include early-diagnosis and therapeutic effect tracking of thrombus, noninvasive monitoring of patients' shock severity, and fatigue evaluation. This study also expects further improvements of the detector in noninvasive clinical applications.

Cratylia argentea is a leguminous shrub with a great potential for livestock feeding in tropical areas. However, time consuming and labor-intensive methods of chemical analysis limits the understanding of its nutritive value. Near infrared spectroscopy (NIRS) is a low-cost technology that has been widely used in forage crops to fasten the assessment of its chemical composition. The objective of this study was to develop prediction models to assess crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF) and dry matter (DM) of Cratylia based on NIRS and partial least square analysis. A total of 155 samples were harvested at different maturity levels and used for model development, whereas 107 were used for calibration and 48 for external validation. The cross-validation presented Root Mean Square Error of Prediction of 0.77, 2.56, 3.43, and 0.42; a Ratio of Performance to Deviation of 4.8, 4.0, 3.8, and 3.4; an R2 of 0.92, 0.92, 0.87, and 0.84 for CP, NDF, ADF, and DM, respectively. Based on the obtained results, we conclude the ability to predict chemical parameters of Cratylia with the current model was accurate. This way, livestock producers and researchers may use it to fasten the assessment of Cratylia’s nutritive value.

A lead zirconate titanate [PZT;Pb(Zr_0.52Ti_0.48)O₃] layer embedded infrared (IR) detector decorated with wavelength-selective plasmonic crystals has been investigated for high-performance non-dispersive infrared (NDIR) spectroscopy. A plasmonic IR detector with an enhanced IR absorption band has been designed based on numerical simulations, fabricated by conventional microfabrication techniques, and characterized with a broadly tunable quantum cascade laser. The enhanced responsivity of the plasmonic IR detector at specific wavelength band has improved the performance of NDIR spectroscopy and pushed the limit of detection (LOD) by an order of magnitude. In this paper, a 13 fold enhancement in the LOD of a methane gas sensing using NDIR spectroscopy is demonstrated with the plasmonic IR detector.

The primary barrier to understanding visual and abstract information in STEM fields is representational competence the ability to generate, transform, analyze and explain representations. The relationship is known between the foundational visual literacy and the domain specific science literacy, however how science literacy is a function of science learning is still not well understood despite investigation across many fields. To support the improvement of students’ representational competence and promote learning in science, identification of visualization skills is necessary. This project details the development of an artificial neural network (ANN) capable of measuring and modeling visual science literacy (VSL) via neurological measurements using functional near infrared spectrometry (fNIRS). The developed model has the capacity to classify levels of scientific visual literacy allowing educators and curriculum designers the ability to create more targeted and immersive classroom resources such as virtual reality, to enhance the fundamental visual tools in science.

Introduction The monitoring of cerebral perfusion in patients with brain damage is a major clinical challenge. Monitoring cerebral oxygenation (rSO2) by NIRS was introduced at the turn of the 1980s, and many clinicians believed it was a valuable method for assessing cerebral perfusion and subsequent measures to optimize cerebral flow. The main problem of NIRS usage is the presence of the intermediate structures-skin, skull, meninges, cerebrospinal fluid-and their influence on the outcome of the test. Therefore, it seems that NIRS assessment performed on a patient during brain death attempts, can give an idea of the magnitude of the influence of these intermediate structures on the monitoring result. Case We present a retrospective analysis of cerebral oxygenation measurements of a patient undergoing a brain death diagnostic procedure. A clinical situation where the cerebral blood flow is stopped can give an idea of the specificity of the method, particularly the impact of the influence of intermediate structures on the monitoring outcome. In this case, the NIRS result is increased by the oxygenation of the patient before the apnoea test. The influence of chromophores in the tissues surrounding the CNS, reflections and scattering of the light wave spectrum have a very significant effect on the final result of cerebral saturation measurement. Discussion The majority of research observations describing changes in cerebral perfusion or its optimisation may be burdened by the problem described, i.e. the significant influence of measured oxygenation from intermediate structures. The specificity of NIRS to assess cerebral perfusion requires careful analysis. The therapeutic implications of monitoring cerebral oxygenation with NIRS are of great importance and, from the example presented and the literature provided, the method should be used with caution. It has been shown that in a patient with brain death, the result of NIRS oxygenation measurements depends on the surrounding structures of the brain.

Recognition, decoding and tracking of vortex patterns is of increasing importance in many fields reaching from astronomical observation of distant galaxies to turbulence phenomena in liquids or gases. Currently, coherent light beams with orbital angular momentum (OAM) are of particular interest for optical communication, metrology, micro-machining or particle manipulation. One common task is to identify characteristic spiral patterns in pixelated intensity maps at real-world signal-to-noise ratios. Recently, Fast Fourier Transform (FFT) was applied to the quantitative analysis of spiral interference patterns of OAM beams. Our study considers this approach in more detail. It is demonstrated that specific information can be extracted by combining conformal mapping by polar transform with determining one- or two-dimensional angular frequency spectra either in spatially fixed or scanning mode. Results of proof-of-principle experiments are presented.

In this study, the partial least squares regression (PLSR) models were developed using no pre-processing, traditional preprocessing, multi-preprocessing 5 range, multi-preprocessing 3 range, genetic algorithm (GA), and successive projection algorithm (SPA) to assess the higher heating value (HHV) and ultimate analysis of grounded biomass for energy usage employing near-infrared (NIR) spectroscopy. A novel approach was utilized based on the assumption that using multiple pretreatment methods across different sections in the entire NIR wavenumber range would enhance the performance of the model. The performance of the model obtained from 200 biomass samples for HHV and 120 samples for ultimate analysis was compared, and the best model was selected based on the coefficient of determination of validation set, root mean square error of prediction, and the ratio of prediction to deviation values. Based on model performance results, the proposed HHV model from GA-PLSR, and the N and O models from the mul-ti-preprocessing PLSR 5 range method could be used for most applications, including research, whereas the C and H models from GA-PLSR performance is fair and applicable only for rough screening. The overall findings highlight that the multi-preprocessing 5-range method, which was attempted as a novel approach in this study to develop the PLSR model, demonstrated better accuracy for HHV, C, N, and O, improving by 4.1839%, 8.1842%, 3.7587%, and 35.9404%, respec-tively. Therefore, it can be considered a reliable and non-destructive alternative method for rap-idly assessing biomass properties for energy usage and can also be used effectively in biomass trading. However, due to the smaller number of samples used in the model development, more samples are needed to update the model for a robust application.

In this study, we propose the Signal Integration Reconstruction Peak Detection (SIRPD) algorithm for reliable Cardiac Beat Interval (CBI) measurement using functional Near-Infrared Spectroscopy (fNIRS) signals. This algorithm enhances reliability by identifying a regular waveform in cerebral blood flow associated with heartbeats. The SIRPD algorithm assesses the Signal Quality Index (SQI) per channel, discards signals from unavailable channels, and interpolates oxygenated and total hemoglobin concentration signals, thereby boosting resolution and integrating the signal into a single channel. The integrated and smoothed signal is transformed to identify the most significant periodic waveform, from which the CBI is extracted. Validated with data from 20 healthy subjects who simultaneously recorded fNIRS and ECG signals, our findings confirm the equivalence of the CBI derived from ECG and calculated through the SIRPD algorithm from fNIRS (R^2 = .98, RMSE = .011, ICC = .991). We also extracted heart rate variability (HRV) features, confirming no significant difference in all features between ECG and fNIRS devices. Uniquely, this study regards the fNIRS heart signal not as an artifact source but as a valuable bio signal. The proposed method, which allows for the detection of cardiac beat intervals from cerebral blood flow oscillations, suggests the potential clinical applicability of fNIRS as an auxiliary indicator for various cardiovascular factors.

Peanuts, owing to their composition of complex carbohydrates, plant protein, unsaturated fatty acids, and essential minerals (magnesium, iron, zinc, and potassium), hold significant potential as a vital component of the human diet. Additionally, their low water requirements and nitrogen fixation capacity make them an appropriate choice for cultivation in adverse environmental conditions. The germination ability of seeds profoundly impacts the final yield of the crop, assessing seed viability of extreme importance. Conventional methods for assessing seed viability and germination are both time-consuming and costly. To address these challenges, this study investigated Visible-Near Infrared Spectroscopy (Vis/NIR) in the wavelength range of 500-1030 nm as a non-destructive and rapid method to determine the viability of two varieties of peanut seeds: North Carolina-2 (NC-2) and Spanish flower (Florispan). The study subjected the seeds to three levels of artificial aging through heat treatment, involving incubation in a controlled environment at a relative humidity of 85% and a temperature of 50°C over 24-hour intervals. The absorbance spectra noise was significantly mitigated and corrected to a large extent by combining the Savitzky-Golay (SG) and Multiplicative Scatter Correction (MSC) methods. To identify the optimal wavelengths for seed viability assessment, a range of meta-heuristic algorithms were employed, including world competitive contest (WCC), league championship algorithm (LCA), Genetics (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), imperialist competitive algorithm (ICA), learning automata (LA), heat transfer optimization (HTS), forest optimization (FOA), discrete symbiotic organisms search (DSOS), and cuckoo optimization (CUK). These algorithms offer powerful optimization capabilities for effectively extracting relevant wavelength information from spectral data. Results revealed that all the algorithms demonstrated remarkable accuracy in predicting the allometric coefficient of seeds, achieving correlation coefficients exceeding 0.985 and errors below 0.0036, respectively. In terms of execution time, the ICA (2.3635 seconds) and LCA (44.9389 seconds) algorithms exhibited the most and least efficient performance, respectively. Conversely, the FOA and the LCA algorithms excelled in identifying the least number of optimal wavelengths (10 wavelengths). Subsequently, the seeds were classified based on the wavelengths selected by the FOA (10 wavelengths) and (DSOS (16 wavelengths) methods, in conjunction with logistic regression (LR), decision tree (DT), Multilayer Perceptron (MP), support vector machine (SVM), k-nearest neighbor (K-NN), and NaiveBayes (NB) classifiers. The DSOS-DT and FOA-MP methods demonstrated the highest accuracy, yielding values of 0.993 and 0.983, respectively. Conversely, the DSOS-LR and DSOS-KNN methods obtained the lowest accuracy, with values of 0.958 and 0.961, respectively. Overall, our findings demonstrated that Vis/NIR spectroscopy, coupled with variable selection algorithms and learning methods, presents a suitable and non-destructive approach for detecting seed viability.

'Huangguan' pear has significant social and economic value, and phosphorus, as one of the three main mineral elements of plants, has an irreplaceable effect on the normal growth of 'Huangguan' pear trees. The objective of this study was to predict the content of phosphorus in the pulp and peel of ‘Huangguan’ pears nondestructively and conveniently by using near-infrared spectroscopy (900–1700 nm) technology. First, twelve algorithms are used to preprocess the original spectral data, and the partial least squares regression algorithm and the gradient boosting regression tree algorithm are used to build a full-band prediction model based on the original spectral data and the processed spectral data. The characteristic wavelengths were extracted using genetic algorithms, followed by establishing a characteristic wavelength prediction model. The prediction accuracy of the models was evaluated according to the coefficient of determination R² and the relative analysis error RPD. The study found that the best prediction model for predicting phosphorus content in the pulp of 'Huangguan' pear was MSC-GA-PLSR, which had R²=0.843 and RPD=1.857 in the modelling set and R²=0.989 and RPD=7.041 in the prediction set. The best prediction model for predicting phosphorus content in the peel of ‘Huangguan’ pear was SG+SNV+FD-GA-PLSR, which had R²=0.991 and RPD=7.470 in the modelling set and R²=0.974 and RPD=4.414 in the prediction set, and the effect was good and met expectations. The results demonstrated that near-infrared spectroscopy could successfully achieve nondestructive detection of phosphorus content in the pulp and peel of 'Huangguan' pears.

A major limitation of near-infrared spectroscopy (NIRS) is its high sensitivity to the scalp and low sensitivity to the brain of adult humans. In the present work we use multi-distance hyperspectral NIRS (hNIRS) to investigate the optimal source-detector distances, range of wavelengths, and analysis techniques to separate cerebral responses to 30-s breath holds (BHs) from the responses in the superficial tissue layer in healthy adult humans. We observed significant responses to BHs in the scalp hemodynamics. Cerebral responses to BHs were detected in the cytochrome C oxidase redox (rCCO) at 4 cm without using data from the short-distance channel. Using the data from the 1 cm channel in the two-layer regression algorithm showed that hemodynamic and rCCO responses also occurred at 3cm. We found that the waveband 700-900 nm was optimal for the detection of cerebral responses to BHs in adults.

The Global Burden of Disease Study (GBD 2019 Diseases and Injuries Collaborators) found that diabetes significantly increases the overall burden of disease, leading to a 24.4% increase in disability-adjusted life years. Persistently high glucose levels in diabetes can cause structural and functional changes in proteins throughout the body, and the accumulation of protein aggregates in the brain is associated with the progression of Alzheimer's Disease (AD).To address this burden of type 2 diabetes mellitus (T2DM), a combined aerobic and resistance exercise program was developed based on the recommendations of the American College of Sports Medicine. The prospectively registered clinical trials (https://www.clinicaltrials.gov/ct2/show/NCT04626453, https://www.clinicaltrials.gov/ct2/show/NCT04812288) involved two groups: an Intervention group of older sedentary adults with T2DM and a Control group of healthy older adults who could be either active or sedentary. The completion rate for the 2-month exercise program was high, with participants completing on an average of 89.14% of the exercise sessions. This indicated that the program was practical, feasible, and well-tolerated, even during the COVID pandemic. It was also safe, requiring minimal equipment and no supervision. The exercise instructions were easy to understand, making them suitable for older adults with cognitive decline.The near-infrared spectroscopy (NIRS) based brain and muscle oxygenation study provided evidence on brain overactivation among older adults with T2DM, supporting the compensatory theory. It also demonstrated that the 2-month combined exercise intervention effectively reduced brain overactivation and contributed to improved cognitive function. Operational modal analysis showed an exercise-related effect on the very low-frequency hemodynamic oscillations cluster, which may be associated with better vascular muscle and/or perivascular neurogenic regulation. Furthermore, we personalized the exercise duration and interval based on muscle oxygenation during physical tasks, leading to improvements in muscle oxidative capacity within just two months. This finding has practical implications for physical therapists, as they can target muscle oxygenation changes during physical tests to prescribe appropriate exercise doses for enhancing physical performance.

Abstract—Low-frequency Fahræus–Lindqvist-driven (not blood pressure-driven) oscillations in the small vessels are crucial because oscillations in small vessels support nutrient supply. Understanding of this is critical in type 2 diabetes mellitus (T2DM) to develop therapeutic measures to prevent Alzheimer's Disease Related Dementias: vascular factors contribute to cerebrovascular disease as well as mild cognitive impairment and dementia, which are predicted to affect 152 million people by 2050 (Alzheimer's Disease International London, UK, 2019). In this clinical study, we performed functional near-infrared spectroscopy (fNIRS) of the forehead to investigate the effect of the Mini-Cog with three-item recall test on the prefrontal cortex (PFC) activation and the relative oscillatory power in the 0.01–0.02-Hz (Fahræus–Lindqvist effect) and 0.021–0.052 Hz (smooth muscle autonomic innervation) frequency bands in elderly (60 years and older) T2DM and age-matched controls. We found a significant (p<0.01) difference in the PFC activation between elderly subjects with T2DM and age-matched elderly controls. Moreover, power spectral density (PSD) analysis revealed a significantly lower relative power in 0.021–0.052 Hz (smooth muscle autonomic innervation) frequency band in elderly subjects with T2DM during the Mini-Cog three-item recall test. Furthermore, a drop in the oscillatory power in the 0.01–0.02-Hz frequency band during Mini-Cog three-item recall test was found more pronounced in the elderly subjects with T2DM. Therefore, our study highlighted portable brain imaging to capture cerebrovascular reactivity to cognitive load that may provide a biomarker of cerebrovascular dysfunction in T2DM.Clinical Relevance— Our study establishes forehead portable brain imaging for monitoring cerebrovascular function in T2DM under cognitive load.

The ultimate analysis parameters including carbon (C), hydrogen (H), nitrogen (N), and oxygen (O) content in biomass was rarely found to be predicted by nondestructive tests until to date. In this research, we developed partial least squares regression (PLSR) models to predict the ultimate analysis parameters of chip biomass using near infrared (NIR) raw spectra of non-wood and wood samples from fast growing tree and agricultural residue and nine different traditional spectral preprocessing techniques. These techniques include first derivative (sd1), second derivative (sd2), constant offset, standard normal variate (SNV), multiplicative scatter correction (MSC), vector normalization, min-max normalization, mean centering, sd1 + vector normalization, and sd1 + MSC. Additionally, we employed a genetic algorithm (GA), successive projection algorithm (SPA), multi-preprocessing (MP) 5−range, and MP 3−range to develop a PLSR model for rapid prediction. A dataset consisting of 120 chip biomass samples was utilized for model development in which the samples was non-wood samples of 65-67% and wood samples was 33-35%, and the model performance were evaluated and compared. The selection of the optimum performing model was mainly based on criteria such as the coefficient of determination in the prediction set (R2P), root mean square error of the prediction set (RMSEP), and the ratio of prediction to deviation values (RPD). The optimal model for weight percentage (wt.%) of C was obtained using GA−PLSR, yielding R2P, RMSEP, and RPD values of 0.6954, 1.1252 wt.%, and 1.8, respectively. Similarly, for wt.% of O, the most effective model was obtained using the multi-preprocessing PLSR−5 range method with R2P of 0.7150, RMSEP of 1.3088 wt.%, and RPD of 1.9. For wt.% of N, the optimal model was obtained using the MP PLSR−3 range method, resulting in R2P, RMSEP, and RPD values of 0.6073, 0.1008 wt.%, and 1.6, respectively. However, wt.% of H model provided R2P, RMSEP, and RPD values of 0.5162, 0.2322 wt.%, and 1.5, respectively. Notably, the limit of quantification (LOQ) values for C, H, and O were lower than the minimum reference values used during model development, indicating a high level of sensitivity. However, the LOQ for N, exceeded the minimum reference value, implying the samples to be predicted by the model must be in the range of reference range in calibration set. By scatter plot analysis, the effect of combined non-wood and wood spectra of biomass chips on rapid prediction of ultimate analysis parameters using NIR spectroscopy was investigated. To include different species in a model, the species have to be not only in the different values of the constituents to make a wider range for robust model but also they must provide their trend line characteristics in the scatter plot i.e. correlation coefficient (R), slope and intercept (same slope and slope approached to 1 and intercept is same (no gap) and approached zero, high R approached to 1). The effect of the R, slope and intercept to obtain the better optimized model were studied. The results show that the different species affected model performance of each parameter prediction in a different manner and by scatter plot analysis which of these species were affecting the model negatively and how the model could be improved was indicated. This is the first time of the effect is studied by the principle of scatter plot.

A fast and effective determination method of different species of vegetable seeds oil is vital in the plant oil industry. The near-infrared reflectance spectroscopy (NIRS) method was developed in this study to massively analyze the oil and moisture contents of Camellia gauchowensis Chang and C. semiserrata Chi seeds kernels. In the prediction models of NIRS, the levels of accuracy obtained were sufficient for C. gauchowensis Chang and C. semiserrata Chi, the correlation coefficient of which oil were 0.983 and 0.962, respectively, while which of moisture were 0.937 and 0.907, respectively. The near infrared spectrum of crush seeds kernels was more precise compared to intact kernels. Based on the calibration models of the two Camellia species, the NIRS predictive oil contents of C. gauchowensis Chang and C. semiserrata Chi seeds kernels were 48.71 ± 8.94% and 31.71 ± 7.39%, respectively, and the NIRS predictive moisture contents were 4.39 ± 1.08% and 3.49 ± 0.71%, respectively. Compared with the traditional chemical measurement, the rapid, precise measurement of oil and moisture of C. gauchowensis Chang and C. semiserrata Chi seeds kernels can be actualized by NIRS method.

Crystalline silver thin layers were obtained by high-energy pulsed laser ablation without heating of the deposition substrate. The fluid Plateau-Rayleigh (PRI), Rayleigh – Taylor (RTI) and Richtmyer – Meshkov (RMI) instabilities, as well as crown splash induced during pulsed laser deposition (PLD) in high energy regime, resulting in ring and pearl shaped structures, offer the benefit of increased sorption surface. These morphological structures obtained for the silver thin layers make them of interest for catalytic applications. The study presented with this work addresses both the fundamental and applied issues on the morphological structures obtained for the silver thin layer and their catalytic function in organic processes. In this sense, the catalytic action of the thin silver layer was highlighted by modifications of the Reactive Blue 21 dye (C.I.) in aqueous solution with sodium bicarbonate. Specific investigations and analyzes were carried out by electron microscopy and elemental analysis (SEM-EDX), atomic force microscopy (AFM) and profilometry, mass spectrometry, ablation plasma diagnosis, diffractograms (XRD), as well as IR spectroscopy (FTIR). In addition to the experimental investigation and analyzes, simulation of the ionization energy threshold was conducted in COMSOL for complementary evaluation on the involved processes and phenomena.

Orientational dependence of the IR absorbing amide bands of silk is demonstrated from two orthogonal longitudinal and transverse microtome slices only $\sim 100$~nm thick. A scanning near-field optical microscopy (SNOM) which preferentially probes orientation perpendicular to the sample's surface was used. Spatial resolution of silk-epoxy boundary was defined with a $\sim 100$~nm resolution while the spectra were collected by a $\sim 10$~nm tip. Ratio of the absorbance of the amide-II C-N at 1512~cm$^{-1}$ and amide-I C=O $\beta$-sheets at 1628~cm$^{-1}$ showed sensitivity of SNOM to the molecular orientation. SNOM characterisation is complimentary to the far-field absorbance which is sensitive to the in-plane polarisation. Volumes with cross sections smaller than 100~nm can be characterised for molecular orientation. A method of absorbance measurements at four angles of slice cut orientation, which is equivalent to the four polarisation angles absorbance measurement is proposed.

Sepsis still remains one of the most difficult conditions to diagnose as it presents unspecific clinical signs and symptoms, which makes early detection crucial. Our study aimed to enhance the accuracy of sepsis diagnosis by integrating multimodal monitoring technologies with traditional diagnostic methods. The research involved 121 newborns, comprising 39 cases of late-onset sepsis, 35 cases of early-onset sepsis, and 47 control subjects. Biosignals including pulse oximetry (PO), near-infrared spectroscopy (NIRS), and skin temperature (ST) were continuously monitored and an algorithm was developed using Python for identifying early signs of sepsis. The model demonstrated the ability to detect sepsis 6 to 48 hours in advance with an accuracy rate of 87.67% ± 7.42%. Sensitivity and specificity were recorded at 76% and 90%, respectively, with NIRS and ST having the most significant impact on predictive accuracy. Despite the promising results, limitations such as sample size, data variability, and potential biases were noted. The findings highlight the critical role of non-invasive biosensing methods in conjunction with traditional biomarkers and cultures, offering a robust framework for early sepsis detection and improved neonatal care. Further studies are recommended to validate these results across diverse clinical settings.

Propolis is a bee product widely used as a dietary supplement and included in sweets or baby foods due to its well-known antioxidant and nutritional properties that are directly correlated with its phenolic composition. For this reason, this study analysed the total contents of flavones and flavonols, flavanones and dihydroflavonols, and the antioxidant capacity by using the methods of ABTS and linoleic acid/β-carotene in 99 samples of propolis from Spain and Chile. A rapid method was developed for quantifying these parameters in raw propolis using near infrared (NIR) spectroscopy with an optical fibre probe of remote reflectance applied directly to the ground up sample. The models developed allow the determination of the total of flavones and flavonols (0-183 mg rutin/ g propolis), of the total of flavanones and dihydroflavonols (9-109 mg pinocembrin/ g propolis extract), and the antioxidant capacity by the ABTS method (0-3212 nmolesTrolox/ mg of propolis) and of linoleic acid/β-carotene (22-86% inhibition). The NIR spectroscopy models were applied in external validation to different samples of the calibration group, which led to the conclusion that the methods developed provide significantly identical data to the initial chemical data of reference.

Gestational diabetes mellitus (GDM) is a hyperglycemia state that is typically diagnosed by an oral glucose tolerance test (OGTT), which is unpleasant, time-consuming, poorly reproducible, and tardy. The machine learning (ML) predictive models that have been proposed to improve GDM diagnosis are usually based on instrumental methods that take hours to get a result. Near-infrared (NIR) spectroscopy, a simple, fast and low-cost analytical technique has never been assessed for the prediction of GDM. This study aims to develop ML predictive models for GDM based on NIR spectroscopy, and to evaluate their potential as early detection or alternative screening tools according to their predictive power and time of analysis. Serum samples from the first (before GDM diagnosis) and the second (at the time of GDM diagnosis) trimester of pregnancy were analyzed by NIR spectroscopy. Four spectral ranges were considered, and 80 pretreatments were tested for each. NIR data-based models were built with single- and multi-block ML techniques. Every model was subjected to double cross-validation. The best first and second trimester models got areas under the receiver operating characteristic curve of 0.5768 ± 0.0635 and 0.8836 ± 0.0259, respectively. This is the first study reporting NIR spectroscopy-based methods for the prediction of GDM. The developed methods allow to predict GDM from 10 µL of serum in only 32 minutes. They are simple and fast, and have a great potential to be applied in clinical practice, especially as alternative screening tools to the OGTT at the time of GDM diagnosis.

We report on the phosphorescence of singlet oxygen photogenerated through a stimulated Raman process. Nanosecond radiation in the green spectral region focused on hexane induces a Raman transition of the dissolved in the solvent oxygen molecules toward the singlet oxygen state producing a Stokes signal in the near infrared. The excited oxygen relaxes to ground emitting an infrared photon at 1269 nm. While the Stokes signal’s wavelength changes with the pumping light’s wavelength, the wavelength of the phosphorescent photon remains unaltered. The result confirms previously reports on stimulated Raman excitation of singlet oxygen.

Multi-channel and multi-parameter near-infrared spectrometers (NIRS) have gradually become a new research direction and hot spot due to their ability to provide real-time, continuous, comprehensive indicators of multiple parameters. However, multi-channel and multi-parameter detection may lead to crosstalk between signals. There is still a lack of benchmarks on the evaluation of reliability, sensitivity, stability and response consistency of the NIRS instrument. In this study, a set of test methods (the ink drop test, multi-channel crosstalk test and multi-parameter crosstalk test) for analysis crosstalk and verifying the reliability of NIRS were conducted to test experimental verification on a multi-channel (8-channel), multi-parameter (4-parameter) NIRS instrument independently developed by our team. Results show that these tests can be used to analyze the signal crosstalk and verify the reliability, sensitivity, stability and response consistency in the NIRS instrument. This study contributes to the establishment of benchmarks for the NIRS instrument crosstalk and reliability testing. These novel tests have the potential to become the benchmark for the NIRS instrument reliability testing.

The near-infrared (NIR) organic dyes with strong ultrafast nonlinear optical (NLO) activities are of importance for various applications. However, such kinds of dyes are still scarce. In this work, we have compared the NLO properties of two NIR Aza-BODIPY derivatives, in which the strong electron-donating groups, namely 4-(N, N-dimethylamino) phenyl and 1-ethyl-1,2,3,4-tetrahydroquinoline groups, are connected with the cores of Aza-BODIPY. Z-Scan experimental results show that two Aza-BODIPY derivatives exhibit strong saturation absorption and large modulation depth under the excitation of femtosecond pulses at 800 nm. Under 1300 nm excitation, two derivatives exhibit strong nonlinear refraction. In addition, the Aza-BODIPY derivatives also display effective two-photon excited fluorescence emission in the wavelength range of 1200-1600 nm. Based on the experimental results, it is found that 1-ethyl-1,2,3,4-tetrahydroquinoline group can more effectively enhance the NLO properties of Aza-BODIPY derivatives compared with the 4-(N, N-dimethylamino) phenyl group, thus providing new possibilities for the design and development of NIR NLO materials.

Chloroquine was among the first of several effective drug treatments against malaria until the onset of chloroquine resistance. In light of diminished clinical efficacy of chloroquine as an antimalarial therapeutic, there is potential in efforts to adapt chloroquine for other clinical applications, such as in combination therapies and in diagnostics. In this context, we designed and synthesized a novel asymmetrical squaraine dye coupled with chloroquine (SQR1-CQ). In this study, SQR1-CQ was used to label live Plasmodium falciparum (P. falciparum) parasite cultures of varying sensitivities towards chloroquine. SQR1-CQ positively stained ring, mature trophozoite and schizont stages of both chloroquine–sensitive and chloroquine–resistant P. falciparum strains. In addition, SQR1-CQ exhibited significantly higher fluorescence, when compared to a chloroquine-BODIPY (borondipyrromethene) conjugate. We also achieved successful SQR1-CQ labelling of P. falciparum directly on thin blood smear preparations. Drug efficacy experiments measuring half-maximal inhibitory concentration (IC50) showed lower concentration of effective inhibition against resistant strain K1 by SQR1-CQ compared to conventional chloroquine. Taken together, the versatile and highly fluorescent labelling capability of SQR1-CQ and promising preliminary IC50 findings potentiates it to be further developed as a promising diagnostic bioimaging tool with drug efficacy against chloroquine-resistant P. falciparum.

This paper presents the design, fabrication and characterization of Schottky erbium/silicon photodetectors working at 1.55 µm. These erbium/silicon junctions are carefully characterized using both electric and optical measurements at room temperature. A Schottky barrier ΦB of ~673 meV is extrapolated; the photodetectors show external responsivity of 0.55 mA/W at room temperature under a 8 V of reverse bias applied. In addition, the device performance is discussed in terms of normalized noise and noise equivalent power. To the best of our knowledge, these are the first Er/Si photodetectors designed for operation in free space at 1.55 µm. The proposed devices will pave the way towards development of Er-based photodetectors and light sources to be monolithically integrated in the same silicon substrate and both operating at 1.55 µm.

Background: Vitamin B 12 deficiency has been implicated in endothelial dysfunction and cardiovascular disease via hyperhomocysteinemia. Coronary tortuosity (CorT) is a common coronary angiography finding. The etiology, clinical implication and long term prognosis are still not well clarified. This study was conducted with the aim to evaluate the relationship between CorT and vitamin B12. Subjects and Method: The medical records of consecutive patients, who underwent coronary angiography, were retrospectively reviewed. The study group consisted of 1624 patients. Taking into consideration the inclusion criteria, 212 patients with CorT and 210 patients with normal coronary angiographies (control group) were included in the study. Vitamin B12, other biochemical parameters, clinical and echocardiographic parameters, and CorT score were evaluated in all patients. CorT is defined as fixed 3 bends during both systole and diastole, with each bend ≥45 °. Results: Patients with CorT had higher prevalence of older, female gender, hypertension, current smoking. Vitamin B12 was significantly decreased in patient with CorT (134.7±47.8 vs 239.6±53.8 p<0.001). On multivariate analysis age, female gender, hypertension, diabetes mellitus and vitamin B12 were independent predictors for CorT (OR 1.56; 95% CI: 1.247–1.962; p < 0.001, OR 1.628; 95% CI: 1.376-2.048; p<0.001, OR 1.865; 95% CI: 1.387-2.695; p<0.001, OR 1.362; 95% CI: 1.184-1.726; p<0.001, OR 1.862; 95% CI: 1.486-2.674; p<0.001, respectively). Conclusion: In our study, we have founded a significant relationship between vitamin B12 deficiency and CorT.

Earth-abundant transition metals like iron, nickel, copper, molybdenum, and vanadium have been identified as essential constituents of the cellular gas metabolism in all kingdoms of life. Associated with biological macromolecules, gas-processing metalloenzymes (GPMs) are formed that catalyse a variety of redox reactions. This includes the reduction of O₂ to water by cytochrome c oxidase (‘complex IV’), the reduction of N₂ to NH₄ by nitrogenase, as well as the reduction of protons to H₂ (and oxidation of the later) by hydrogenase. GPMs perform at ambient temperature and pressure, in the presence of water, and often extremely low educt concentrations, thus serving as natural examples for efficient catalysis. Facilitating the design of biomimetic catalysts, biophysicist thrive to understand the reaction principles of GPMs making use of various techniques. In this perspective, I will introduce Fourier-transform infrared spectroscopy in attenuated total reflection configuration (ATR FTIR) for the analysis of GPMs like cytochrome c oxidase, nitrogenase, and hydrogenase. Infrared spectroscopy provides information about the geometry and redox state of the catalytic cofactors, the protonation state of amino acid residues, the hydrogen-bonding network, and protein structural changes. I developed an approach to probe and trigger the reaction of GPMs by gas exchange experiments, exploring the reactivity of these enzymes with their natural reactants. This allows recording sensitive ATR FTIR difference spectra with seconds time resolution. Finally yet importantly, infrared spectroscopy is an electronically non-invasive technique that allows investigating protein samples under biologically relevant conditions, i.e., at ambient temperature and pressure, and in the presence of water.

Green open spaces (GOS) are well known to be vital to enhancing the standard of living conditions in urban environments. The World Health Organization (WHO) says it is important to have at least nine square meters per citizen. The European Environment Agency (EEA) stipulates that 5000 square meters of green space will be reachable within 300 meters to residents. GOS pattern in Kyrenia Region is not adequate or successful in that regard. The process of urban planning has been neglected in the Kyrenia region and these areas have lost their significance and value. This research evaluates the level of accessibility of green spaces in Kyrenia Region. Three urban areas, Kyrenia, Karavas and Agios Epiktitos have been analyzed within this framework. In order to do that, GOS areas and their pattern are researched by deploying geographical information analysis. Accessibility to these areas is measured by using Euclidean and network distance analysis based on 300, 800 and 5000 meters. Mainly, Euclidean distance and network distance methods are measured from building units and results showing accessibility values are constructed. According to the results, network accessibility is very low within the study region. Additionally, Kyrenia, Karavas and Agios Epiktitos have shortage in GOS per capita. To offer effective strategies for urban planning, the factors influencing the accessibility and also the usage of GOS are addressed.

Despite the increasing popularity of liquid chromatography-mass spectrometry (LC-MS)-based lipidomics, there is a lack of accepted and validated method for lipid extract quality and quantity assessment prior to LC-MS. Fourier-Transform Infrared Spectroscopy (FTIR) has been reported for quantification of pure lipids, however, the sample complexity and purity complex lipid extract quantification in lipidomics experiments could be impact quantification accuracy. Here, we report comprehensive assessment of the sample matrix on the accuracy of lipid quantification using Attenuated Total Reflectance Fourier-Transform Infrared Spectroscopy (ATR-FTIR). Pure lipids are characterized by CH-and C=O-stretching vibrations on FTIR, with quantitative range of 40–3000 ng and a limit of detection of 12 ng. Sample extraction method and local baseline subtraction during FTIR spectral processing significantly impact lipid quantification by CH-stretching. To facilitate sample quality screening, we developed the Lipid Quality (LiQ) score from a spectral library of common contaminants, using a ratio of peak heights between CH-stretching vibrations maxima and the collective vibrations from amide/amine, CH-stretching minima and sugar moieties. We evaluated LiQ score as a rapid sample quality control method by comparing to total LC-MS intensity of targeted lipidomics of 107 human plasma lipid extracts. Exclusion of poor-quality samples based on LiQ score improved the correlation between FTIR and LC-MS quantification. Finally, the uncertainty of absolute quantification by FTIR was estimated using a 795 ng SPLASH LipidoMix standard to be &lt;10%. In summary, this study identified key parameters for accurate FTIR-based quantification of complex lipid mixture, and developed a rapid workflow requiring only 1 µL of MS-ready sample and &lt; 5 minutes for routine lipidomics sample quality and quantity assessment.

Being able to ascertain the physiological condition of the buds on a young apple tree before bud burst could help farmers manage their orchards more efficiently, especially if they could do so without destroying the buds in the process. The experiments carried out in this study were conducted with the aim of distinguishing shoot from non-shoot buds before bud burst using a visible/near-infrared spectrometer, a device that does not destroy the buds being tested. Tests on spring-planted (April 30, 2021) trees were conducted to check shoot and non-shoot bud physiology and the winter dormancy of young ‘Jonagold’, ‘Miyabi Fuji’ and ‘Orin’ apple trees. The light absorbance of the shoot buds before bud burst was much lower than the light absorbance of the non-shoot buds as checked on the visible/near-infrared spectrometer. The highest first factor effect was determined by a PCA test conducted on shoot and non-shoot ‘Jonagold’ buds (99.9%) at a range of 640-652 nm, ‘Miyabi Fuji’ buds (99.7%) at 654-680 nm and ‘Orin’ buds (99.6%) at 704-766 nm seven days before bud burst. We also found that the highest level of accuracy, using the Classifier analysis, between shoot and non-shoot ‘Jonagold’ buds (76.6%) was one day before bud burst, for ‘Miyabi Fuji’ buds (82.1%) it was three days before and for ‘Orin’ buds (76.3%) it was two days before. These findings suggest that growers can more effectively manage the development of the young trees in their orchards with a visible/near-infrared spectrometer.

In recent years graphene has attracted much interest due to its unique properties of flexibility, strong light-matter interaction, high carrier mobility and broadband absorption. In addition, graphene can be deposited on many substrates including silicon with which is able to form Schottky junctions opening the path to the realization of near-infrared silicon photodetectors based on the internal photoemission effect where graphene play the role of the metal. In this work, we review the very recent progress of the near-infrared photodetectors based on Schottky junctions involving graphene. This new family of device promises to overcome the limitations of the Schottky photodetectors based on metals showing the potentialities to compare favorably with germanium photodetectors currently employed in silicon photonics.

In the field of business environments, the integration of novel artificial intelligence (AI) techniques has been identified in the literature and theoretical discourse as an essential catalyst for enhancing the decision-making processes. In the contemporary landscape of digital transformation, the application of these techniques becomes essential, despite encountering various hurdles that impede their seamless integration into the rational decision-making process within e-government, which constitutes a critical component of digital government initiatives. This study explores whether similar advancements can be observed within the e-government sector and investigates the impact of using machine learning (ML) as a technique of artificial intelligence on rational decision-making (RDM). Employing an empirical research approach, the study utilized a quantitative methodology, relying on an electronically structured questionnaire survey administered to 163 employees in the e-government sector in Jordan through purposive random sampling. Data analysis was conducted using the SPSS v25 program, complemented by a media-tion analysis using AMOS v23 software. The research findings revealed a significant contribution of using machine learning to enhance rational decision-making and trust levels, with trust positively impacting RDM. Trust was identified as a beneficial mediator in the relationship between machine learning and rational decision-making. Despite certain limitations, this study's outcomes offer substantial insights for researchers and government policy-makers alike, contributing to the advancement of sustainable practices in the e-government domain.

The kinetic analysis of octogen coating with a polyurethane base containing hydroxyl-terminated polybutadi-ene (HTPB) was investigated using infrared spectroscopy. The coating process involved a solvent method, where octogen and liquid polyurethane were mixed, the solvent was evaporated, and curing took place at an elevated temperature. The ratio of HTPB to diisocyanate was equimolar. Initially, the curing process occurred in the solvent system, followed by further curing in the bulk system. The kinetic analysis was performed using a modified diffusion-autocatalytic model, which includes non-catalytic, autocatalytic, and diffusion compo-nents. This model was compared with others during the bulk reaction and proved to be effective in correcting errors, particularly in the gel time region. Thermodynamic parameters were evaluated using the Arrhenius and Eyring equations. The reaction rate was initially controlled by chemical reactivity, but after the gel time, diffusion became the controlling factor. In the HTPB-TDI system, both the non-catalytic and autocatalytic parts decreased with increasing temperature, while diffusivity increased. It is worth noting that diffusivity is temperature-dependent. Different diisocyanates, namely toluene diisocyanate (TDI), isophorone diisocyanate (IPDI), and hexamethylene diisocyanate (HMDI), were studied, revealing that HMDI exhibited higher reactivi-ty than TDI and IPDI. The catalyst effect on reaction rate HTPB-TDI system is investigated. Catalysts adding (0.1%) to HTPB-TDI system is really decrease their activation energy by efectivity DBTL>FeAA>TPB. Catalyst not change their diffusivity.

Nanotechnology has enabled the development of novel therapeutic strategies such as targeted nanodrug delivery systems, control and stimulus-responsive release mechanisms, and the production of theranostic agents. As a prerequisite for the use of nanoparticles as drug delivery systems, the amount of loaded drug must be precisely quantified, a task for which two approaches are currently used. However, both approaches suffer from the inefficiencies of drug extraction and of the solid-liquid separation process, as well as from dilution errors. This work describes a new, reliable, and simple method for direct drug quantification in polymeric nanoparticles using attenuated total reflection Fourier transform infrared spectroscopy, which can be adapted for a wide variety of drug delivery systems. Silk fibroin nanoparticles and naringenin were used as model polymeric nanoparticle carrier and drug, respectively. The specificity, linearity, detection limit, precision and accuracy of the spectroscopic approach were determined in order to validate the method. A good linear relation was observed within 0.00 to 7.89 % of naringenin relative mass with an R2 of 0.973. The accuracy was determined by the spike and recovery method. Results showed an average 104% recovery. The limit of detection and limit of quantification of the drug loading content were determined to be 0.3 and 1.0 %, respectively. The method's robustness is demonstrated by the notable similarities between the calibrations carried out in two different equipment and institutions.

In this work, a highly efficient, molybdenum disulfide (MoS2) based near infrared (NIR) heterojunction photodetector is fabricated on a Si substrate using a cost-effective and simple drop casting method. A non-stoichiometric and inhomogeneous MoS2 layer with a S/Mo ratio of 2.02 is detected using energy dispersive X-ray spectroscopy and field emission scanning electron microscope analysis. Raman shifts are noticed at 382.42 cm-1 and 407.97 cm-1, validating MoS2 thin film growth with a direct bandgap of 2.01 eV. The fabricated n-MoS2/p-Si photodetector is illuminated with a 785 nm laser at different intensities, and demonstrate the ability of the photodetector to work in both regions, the forward biased and reverse biased from above 1.5 V and less than -1.0 V. The highest responsivity, R is calculated to be 0.52 A/W while the detectivity D* is 4.08 x 10^10 Jones for an incident light intensity of 9.57 mW/cm2. The minimum rise and fall times are calculated as 1.77 ms and 1.31 ms for an incident laser power of 9.57 mW/cm^2 and 6.99 mW/cm^2 respectively at a direct current bias voltage of 10 V. The demonstrated results are promising for the low-cost fabrication of a thin MoS2 film for photonics and optoelectronic device applications.

The optical properties and physiology of biological tissue, as well as how near-infrared (NIR) light interacts with the tissue, both play a significant role in interpreting the tissue probing optical measurements, and in solving the inverse problem of near-infrared spectroscopy (NIRS)-based medical imaging modalities such as diffuse optical tomography and functional near-infrared spectroscopy. This paper discusses the optical properties of tissue, specifically in the NIR wavelength range, which influence NIRS measurements in NIRS-based medical imaging. There is an easy-to-understand explanation given in this paper of the NIR light-tissue interaction phenomenon. The mathematical explanation, the processes involved in the interaction, and the rationale for a few approximations are described. Various types of chromophores present in the tissue, their composition in the tissue, and how these chromophores overall affect the scattering and absorption of NIR light are presented. The absorption spectra of these chromophores are shown. Finally, the paper concludes with the author’s perspective on two NIRS-based medical imaging modalities, diffuse optical tomography, and functional near-infrared spectroscopy.

Infrared spectroscopy is an effective method for the determination of compositions and concentrations of liquids, with advantages of fast response, no-sampling, flexible in use and is able for on-line monitoring. However, for trace substances in drinking water, such as nitrates and heavy metals, infrared spectroscopy is not sensitive enough for the quantitative and qualitative measurement. In this study, we improved the sensitivity of infrared spectroscopy for nitrite determination by developing an ion-exchange-enhanced diffuse reflectance spectroscopy, which consist of an accessory based on ion-exchange resin for enrichment and a FTIR spectrometer for measurement. Using this method, the limit of detection for nitrate is 1.7 mg/L, which is enough for drinking water sensing. We also verified the quantitative measurement ability of the method. Furthermore, the limit of detection and quantitative measuring range could be adjusted by changing the mass of resin and adsorption time. This study demonstrated the method can be used to detect trace nitrites in drinking water, can be applied in the field, and is sensitive, rapid, and inexpensive with a wide dynamic range.

We propose a novel plasmon nano-optical antenna that consists of three vertically superimposed discs with different materials. Two symmetrical nanometer-scale Yagi antenna elements with gold-material are embedded into the surface of the middle disc with silicon dioxide. Based on plasmon theory and Yagi antenna principle, we explore the properties of the far-field radiation and near-field transmission of the antenna in near infrared band. It is found that the pattern of the xoy plane in the antenna can be adjusted in arbitrary direction. The far-field pattern and near-field transmission spectrum can be reconfigured by changing the angle a of the Yagi antenna elements in the middle disc. Moreover, we also discuss the influences of the widths and lengths of the elements on the near-field transmission spectrum when a is fixed at 0 degree.

Treatment with tumor-selective photosensitive substances, such as photodynamic therapy and photoimmunotherapy, has been reported to enable the visualization of tumor drug accumulation in real time and identify the required light irradiation dose by fluorescence imaging. However, fluorescence observation is difficult during treatments performed under gastrointestinal endoscopy because of the challenges associated with including the fluorescence filter in the camera part. To address this issue, this study developed a device that integrates a narrow camera and a laser diffuser to enable fluorescence imaging through a forceps port. This device was employed to demonstrate that a laser diffuser with an NIR fluorescence imaging sensor could be delivered through a 3.2-mm-diameter port. In addition, fluorescence images of Cetuximab-IR700 were successfully observed in two mice, and the fluorescence intensity confirmed that the fluorescence decayed within 330 s. This device is expected to have practical application as a tool to identify the optimal irradiation dose for tumor-selective photosensitive substances under endoscopy.

Phase imaging of biochemical samples has been demonstrated for the first time at the Infrared Microspectroscopy (IRM) beamline of the Australian Synchrotron using the usually discarded Near-IR (NIR) region of the synchrotron-IR beam. The synchrotron-IR beam at the Australian Synchrotron IRM beamline has a unique fork shaped intensity distribution as a result of the gold coated extraction mirror shape, which includes a central slit for rejection of the intense X-ray beam. The resulting beam configuration makes any imaging task challenging. For intensity imaging, the fork shaped beam is usually tightly focused to a point on the sample plane followed by a pixel-by-pixel scanning approach to record the image. In this study, a pinhole was aligned with one of the lobes of the fork shaped beam and the Airy diffraction pattern was used to illuminate biochemical samples. The diffracted light from the samples was captured using a NIR sensitive lensless camera. A rapid phase-retrieval algorithm was applied to the recorded intensity distributions to reconstruct the phase information corresponding to different planes. The preliminary results are promising to develop multimodal imaging capabilities at the IRM beamline of the Australian Synchrotron.

Photoacoustic spectroscopy allows the identification of specific molecules in gases. We evaluate the spectral resolution and detection limits for a PAS system in the broadband infrared wavelength region 3270 nm ≲ λ ≲ 3530 nm driven by a continuous wave optical parametric oscillator with P ≈ 1.26 W by measuring the absorption of diluted propane, ethane and methane test gases at low concentrations c ~ 100 ppm for ~1350 discrete wavelengths λ_i. The resulting spectra I_PAS(λ_i) were compared to the high resolution cross section data σ_FTIR obtained by Fourier Transform Infrared Spectroscopy from the HITRAN database. Deviations as little as 7.1(6)% for propane, 8.7(11)% for ethane and 15.0(14)% for methane with regard to the average uncertainty between I_PAS(λ_i) and the expected reference values based on σ_FTIR were recorded. The wavelengths λ_res of the characteristic absorption lines can be pinpointed with a high relative accuracy <5 × 10⁻⁵ corresponding to a resolution of λ_res ~ 0.16 nm. Detection limits range between 7.1 ppb (ethane) to 13.6 ppb (methane) coinciding with high experimental signal-to-noise ratios. Moreover, using EUREQA, an artificial intelligence program, simulated mixed gas samples at low limits of detection could be deconvoluted. These results justify a further development of PAS technology to support, e.g., biomedical research.

A tunable quantum cascade laser (QCL) spectrometer was used to develop methods for detecting and quantifying high explosives (HE) in soil based on multivariate analysis (MVA) and artificial intelligence (AI). For quantification, mixes of 2,4-dinitrotoluene (2,4-DNT) with concentrations from 0% to 20% w/w were investigated using three types of soils: bentonite, synthetic soil, and natural soil. A Partial least squares regression model was generated for predicting 2,4-DNT concentrations. To increase its selectivity, the model was trained and evaluated using additional analytes as interferences, including other HEs such as PETN, RDX, and TNT and non-explosives such as benzoic acid and ibuprofen. For detection, mixes of different explosives in soils were used to implement two AI strategies. In the first strategy, the spectra of the samples were compared with those of soils recorded in a database to identify the most similar soils based on QCL spectroscopy. Next, a Classical Least Squares preprocessing (Pre-CLS) was applied to soils spectra selected from the database. The parameter obtained based on the sum of the weights of Pre-CLS was then used to generate a simple binary discrimination model for distinguishing between contaminated and uncontaminated soils, achieving an accuracy of 0.877. In the second AI strategy, the same parameter was added to a principal component matrix obtained from spectral data of samples and used to generate multi-classification models based on different machine learning algorithms. A Random Forest model worked best with 0.997 accuracy and allowing to distinguish between soils contaminated with DNT, TNT, or RDX and uncontaminated soils.

Achieving global goals on sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require, among others, instantaneous access to information on food quality at key points within agri-food systems. Although stationary methods are usually used to quantify grain quality (wet-lab chemistry, benchtop NIR spectrometer); these do not suit many required user-cases, such as stakeholders in decentralized agri-food-chains that are typical for emerging economies. Therefore, we explored new technologies and models that might aid these particular user-cases. For this purpose, we generated the NIR spectra of 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, sorghum) with a standard benchtop NIR Spectrometer (DS2500, FOSS) and a novel mobile NIR-based sensor (HL-EVT5, Hone). We explored a range of classical deterministic and novel machine learning (ML)-driven models to build calibrations out of the NIR spectra. We were able to build relevant calibrations out of both types of spectra. At the same time, ML-based methods enhanced the prediction capacity of calibration models compared to classical deterministic methods. We also documented that the prediction of grain protein content based on NIR spectra generated by a mobile sensor (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the findings of this study lay the foundations on which to expand the utilization of NIR spectroscopy applications for agricultural research and development.

We present a robust, fresh-frozen approach to immunohistochemistry (IHC), without committing the tissue to IHC via fixation and cryopreservation while maintaining long-term storage, using LiCor-based infrared (IR) quantification for sensitive assessment of TH in immunoreacted mid-brain sections for quantitative comparison across studies. In fresh-frozen tissue stored up to 1 year prior to IHC reaction, we found our method to be highly sensitive to rotenone treatment in 3-month-old Sprague-Dawley rats, and correlated with a significant decline in rotarod laten-cy-to-fall measurement by approximately 2.5 fold. The measured midbrain region revealed a 31% lower TH signal when compared to control (p<0.01 by t test, n=5). Bivariate analysis of in-tegrated TH counts versus rotarod latency-to-fall indicates a positive slope and modest but sig-nificant correlation of R2=0.68 (p<0.05, n=10). These results indicate this rapid, instrument-based quantification method by IR detection successfully quantifies TH levels in rat brain tissue, while taking only 5 days from euthanasia to data output. This approach also allows for the identifica-tion of multiple targets by IHC with the simultaneous performance of downstream molecular analysis within the same animal tissue, allowing for the use of fewer animals per study.

Bacterial infections and resistance to antibiotic drugs represent the highest challenges to public health. The search for new and promising compounds with anti-bacterial activity is a very urgent matter. In order to promote the development of platforms enabling to the discovery of compounds with anti-bacterial activity, Fourier Transformed Mid-Infrared (FT-MIR) spectroscopy associated with the machine learning algorithms were used to predict the impact of compounds extracted from Cynara cardunculus against Escherichia coli cells. According to the plant tissue (seeds, dry and fresh leaves, and flowers) and the solvents (ethanol, methanol, acetone, ethyl acetate, and water), compounds with different compositions concerning, phenol content, antioxidant, and antimicrobial activity were obtained. Principal component analysis of spectra, allowed to discriminate compounds that inhibit the E. coli growth, according to the conventional assay. The supervised classification models enabled the prediction of the compound's impact on the E. coli growth, with the accuracy for the test data: Partial least squares-discriminant analysis of 94%; Support vector machines of 89%; k-Nearest neighbor of 72%; and a Backpropagation network of 100%. According to the promising results, the integration of FT-MIR spectroscopy with machine learning presents a high potential to promote the discovery of new compounds with antibacterial activity, thereby streamlining the drug exploratory process.

A tunable near-infrared surface plasmon resonance (SPR) biosensor based on gate-controlled graphene plasmons is investigated theoretically. The novel characteristics of chemical potential sensing make the proposed sensor promising in the application of ultra-sensitive and highly specific biosensing technology. The sensitivity of chemical potential sensing in wavelength interrogation mode can be calculated to be 1.5, 1.89, 2.29, 3.21, 3.73 and 4.68 nm/meV respectively at the central wavelength of 1100, 1200, 1310, 1550, 1700 and 1900 nm. The much smaller full width half maximum (FWHMs) comparing with that of 2D nanomaterial-enhanced metal SPR sensors indicates higher figure of merit. The sensitivity of chemical potential sensing in gate voltage interrogation mode also can be calculated to be 156.9822, 143.6147, 131.0779, 111.0351, 101.3415 and 90.6038 mV/meV respectively at the incident wavelength of 1100, 1200, 1310, 1550, 1700 and 1900 nm. It can be estimated theoretically that the limit of detection (LOD) in DNA sensing of the proposed sensor can reach femtomolar level and even attomolar level, comparable to and even lower than that of 2D nanomaterial-enhanced metal SPR sensors with AuNPs as a sensitivity enhancement strategy. The feasibility of preparation and operation of this new concept SPR biosensor is also analyzed and discussed.

porous silicon-based photodetectors have attracted more researches due to their high luminous efficiency, good stability and low cost. In this paper, a PbS quantum dots/porous silicon hybrid structure has been fabricated. The PbS quantum dots (QDs) partly infiltrated into the porous silicon (PSi) layer and partly deposited on its surface, which could increase the absorption of near-infrared wavelength range and extend the light absorption in silicon for wavelengths longer than 1100 nm. After that, A metal-semiconductor-metal (MSM) device is fabricated and its response spectrum could extend to the 1200 nm at -3 V. As a silicon-based photodetector (PD), one can envision its role for operation from visible light to short-wavelength infrared range.

Recently, the interest in silicon-based detectors capable of detecting single photons in the near-infrared is growing mainly due to LiDAR applications, autonomous driving in particular. Silicon single-photon avalanche diodes are one of the most interesting single-photon NIR technology available on the market, nevertheless, their efficiency is hindered by the low absorption coefficient of Si in the NIR. The idea is the integration of CMOS-compatible nanostructures, specifically, silver grating array supporting Surface Plasmons Polaritons (SPPs), to confine superficially the incoming NIR photons and therefore increase photons probability to generate an electron-hole pair. The plasmonic silver array is geometrically fine-tuned using time domain simulation software to achieve maximum detector performance at 950 nm. Then, the plasmonic silver array is integrated by means of the focused ion beam technique on the detector. Finally, the integrated detector is electro-optically characterized, demonstrating a quantum efficiency of 13 at 950 nm, 2,2 times more than the reference detector. This result suggests the production of a device capable of detecting single NIR photons, at a very low cost and compatible with CMOS, thus integrable on existing technology platforms.

Melanoma is a type of highly malignant and metastatic skin cancer. In situ molecular imaging of endogenous levels of the melanoma biomarker tyrosinase (TYR) may decrease the likelihood of mortality. In this study we proposed the weakly fluorescent probe 1-(4-(2-(4-(dicyanomethylene)-4H-chromen-2-yl)vinyl)phenyl)-3-(4-hydroxybenzyl)urea (DCM-HBU), which releases a strong red-shifted fluorescent signal after a TYR-mediated oxidation followed by hydrolysis of the urea linkage. The large Stokes shift of the dye is owed to the recovery of the intramolecular charge transfer (ICT) effect. The resulting probe derivate shows a highly ratiometric fluorescence output. Furthermore, the simultaneous excitation by two near-infrared (NIR) photons of the released DCM-NH2 fluorophore could avoid the usual drawbacks, such as cellular absorption, autofluorescence and light scattering, due to an usually short wavelength of the excitation light on biological systems, resulting in images with deeper tissue penetration. In addition, the probe is useful for the quantitative sensing of TYR activity in vivo, as demonstrated in zebrafish larvae. This new ratiometric two photon NIR fluorescent probe is expected to be useful for the accurate detection of TYR in complex biosystems at greater depths than other one-photon excited fluorescent probes.

Volcanic activity essentially consists of the transfer of heat from the Earth’ interior to the surface. The precise signature of this heat transfer relates directly to the processes underway at and within a particular volcano and this can be observed, at a safe distance, remotely, using infrared sensors that are present on Earth-orbiting satellites. For over 50 years, scientists have perfected this art using sensors intended for other purposes, and they are now in a position to determine the particular sort of activity that characterizes different volcanoes. This review will describe the theoretical basis of the discipline and then discuss the sensors available for the task and the history of their use. Challenges and opportunities for future development in the discipline are then discussed.

Omega-3 fatty acids (ω-3 FAs) are important fatty acids having the beneficial roles in human health including reducing blood pressure, lowering the risk of cardiovascular disease and exerting anti-inflammation activities. Omega-3 FAs were mainly found in fish oils, therefore, determination of these FAs is very important. This study highlighted the employment of FTIR spectroscopy combined with multivariate data analysis for determination of ω-3 FAs in fish oils. Fish oils were obtained from the extraction of corresponding fishes and subjected to purification. The oils were further subjected to FTIR spectroscopic measurement at mid infrared region (4000-450 cm-1). Fatty acid compositions of ω-3 FAs namely eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) were determined using gas chromatography with flame ionization (GC-FID), and the results from GC-FID were used as actual values. Two multivariate regressions along with wavenumbers regions or their combinations were optimized and compared to provide the best condition for prediction of EPA and DHA in fish oils. The results showed that partial least square regression (PLSR) was suitable for prediction of DHA applying the variable of absorbance values of the second derivative spectra, with the values of coefficient of determination (R2) of 0.9916 and 0.9316 in calibration and validation models, respectively. The values of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) obtained were 0.789 and 2.53. While, prediction of EPA was performed using principal component regression with R2 value of &gt; 0.72 and low values of RMSEC and RMSEC. It can be concluded that the combination of FTIR spectra and multivariate regression provides the effective tools and alternative GC-FID method for the prediction of EPA and DHA in fish oils.

Polyhedral Oligomeric Silsesquioxanes (POSS) are one of the smallest silica nano particles. The presence of ordered ladder-like or cage conformation in their structure leads to remarkable thermal stability far greater than that for a linear polysiloxane. Reinforcement with nano particles is one of the recently favored strategy used to enhance the performance of polymers materials. The cyclic silica structure present in POSS ensures excellent thermal and mechanical stability. As a reinforcing filler, POSS can enhance the resistance of polymer matrix against pyrolysis and corrosion. In this paper, amine-terminated POSS with ladder like structure was synthesized by condensation of hydrolyzed aminosilane coupling agent. The ladder-like structure of POSS severely limits the motion of the chain backbone. The absence of phase transition for the synthesized POSS was confirmed by differential scanning calorimetry (DSC). Thermogravimetric analysis (TGA) was utilized to evaluate the thermal stability of POSS. TGA data show that the ladder-like POSS reported here has high char yield of about 75 % in nitrogen atmosphere and 54% in air atmosphere at 800℃. The TGA derivative curves show that the degradation of ladder-like POSS involves multiple stages and that the degradation rate is directly proportional to the heating rate. With the aid of Fourier Transform Infrared Spectroscopy (FT-IR), the cyclic structure of Si-O-Si bonding was determined to be the predominant structure. By analyzing the FTIR spectra of the cured POSS residue obtained at the specified temperatures, the detailed degradation mechanism of POSS was inferred. From the FTIR spectra of the low temperature cured samples, it was shown that the terminal silanol group was eliminated below 400℃. Some intermediate polymeric siloxane structures were observed by means of FT-IR and they eventually transitioned into silica which is the final product of pyrolysis of POSS. The presence of the FTIR transmission peaks at 1000 and 1100 cm-1, due to asymmetrical vertical and horizontal stretching of Si-O-Si, were the key evidence for the inferred ladder-like structure of POSS.

Near-infrared (NIR) photodetectors (PDs) have attracted much attention in noninvasive medical diagnosis and treatment. Especially, the self-filtered NIR PDs are highly demandable for tremendous biomedical application due to their strong ability in wavelength discrimination. In this work, we designed and then fabricated a Si micro-holes array/Graphene (Si MHA/Gr) van der Waals (vdW) Schottky NIR photodiode by using PbS quantum dots (QDs) coating. The device exhibits a unique self-filtered NIR response with a responsivity of 0.7 A/W at -1 V and a response time of 61 μs, which are higher than that without PbS QDs coating and even most of previous Si/Gr Schottky photodiodes. The light trapping of the Si MHA and the PbS QDs coating can be attributed to the high responsivity of the vdW photodiode. Furthermore, the presented NIR photodiode can also be integrated in photoplethysmography (PPG) for real-time heart-rate (HR) monitoring. The extracted HR is in great accord with the value measured with the patient monitor by analyzing the Fourier transform of the stable and reliable fingertip PPG waveform, suggesting its potential for practical applications.

Abstract: The genus Acaryochloris is unique among phototrophic organisms due to the dominance of chlorophyll d in its photosynthetic reaction centres and light-harvesting proteins. This allows Acaryochloris to capture light energy for photosynthesis over an extended spectrum of up to ~760 nm in the near infra-red (NIR) spectrum. Acaryochloris sp. has been reported in a variety of ecological niches, ranging from polar to tropical shallow aquatic sites. Here, we report a new Acarychloris strain isolated from an NIR-enriched stratified microbial layer 4-6 mm under the surface of stromatolite mats located in the Hamelin Pool of Shark Bay, Western Australia. Pigment analysis, by traditional spectrometry/fluorometry, flow cytometry and spectral confocal microscopy identify unique patterns in pigment distribution that likely reflect niche adaption. For example, unlike the original A. marina species (type strain MBIC11017), this new strain, Acarychloris LARK001, shows little change in the chlorophyll d/a ratio in response to changes in light wavelength, displays a different Fv/Fm response and lacks detectable levels of phycocyanin. Indeed, 16S rRNA analysis supports the identity of the A. marina LARK001 strain as distinct from the A. marina HICR111A strain first isolated from Heron Island, previously found on the Great Barrier Reef, under coral rubble on the reef flat. Taken together, A. marina LARK001 is a new cyanobacterial strain adapted to the stromatolite matts in Shark Bay.

Conventional mechanical Fourier Transform Spectrometers (FTS) are able to simultaneously measure absorption and dispersion spectra of gas-phase samples. However, they usually need very long measurement times to achieve time-resolved spectra with a good spectral and temporal resolution. Here, we present a mid-infrared dual-comb-based FTS in an asymmetric configuration, providing broadband absorption and dispersion spectra with a spectral resolution of 5 GHz, a temporal resolution of 20 μs, and a total measurement time of a few minutes. We used the dual-comb spectrometer to monitor the reaction dynamics of methane and ethane in an electrical plasma discharge. We observed ethane/methane formation as a recombination reaction of hydrocarbon radicals in the discharge in various static and dynamic conditions. The results demonstrate a new analytical approach for measuring fast molecular absorption and dispersion changes and monitoring fast dynamics of chemical reactions, which can be interesting for chemical kinetic research and particularly for the combustion and plasma analysis community.

Infrared thermography has already been proven to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity and low cost. However, the thorniest issue for wider application of IRT is the quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Units (GRU) in composite material samples via pulsed thermography (PT). Carbon Fiber Reinforced Polymer(CFRP) embedded with flat bottom holes were designed via Finite Element Method (FEM) modeling in order to precisely control the depth and geometrics of the defects. The GRU model automatically quantified the depth of defects presented in the CFRP material. The proposed method evaluated the accuracy and performance of synthetic CFRP data from FEM for defect depth predictions.

The necessity for precise and current data concerning the dynamics of land cover change in Indonesia is crucial for efforts to reduce natural vegetation cover due to agricultural expansion. The functionality of monitoring systems that incorporate Terra-MODIS is currently compromised by the limited availability of data for the immediate future. This study seeks to assess the potential of VIIRS satellite imagery in developing an early warning system for monitoring vegetation cover change in Indonesia. The Normalized Differential Open Area Index (NDOAI) computed from the 8-day’ VIIRS data was employed to detect changes in vegetation cover based on pixel-by-pixel subtraction in the NDOAI data time series. Evaluating the pixel-level accuracy of change detection is complicated due to the fact that we evaluate a change map at a coarser resolution than the Landsat-based reference map. The results revealed that increasing the threshold percentage is associated with improved accuracy. In change detection, there is often a trade-off between accuracy and sensitivity. A threshold that is too low may result in false positives, while a threshold that is too high may lead to missed changes. This study demonstrates that when a threshold value of less than 20% is applied, Landsat can identify vegetation cover changes at an earlier stage. Conversely, when a threshold value greater than 20% is employed, VIIRS will detect the change 4.5 days earlier than Landsat. Additionally, VIIRS is capable of detecting changes 25.4 days and 54.8 days faster than Landsat, respectively, when using thresholds of 40% and 75%.

The aim of this work was to test polyamines as potential natural substrates of the Acinetobacter baumannii chlorhexidine efflux protein AceI using near-UV synchrotron radiation circular dichroism (SRCD) spectroscopy. The Gram-negative bacterium A. Baumannii is a leading cause of hospital-acquired infections and an important foodborne pathogen. A. Baumannii strains are becoming increasingly resistant to antimicrobial agents, including the synthetic antiseptic chlorhexidine. AceI was the founding member of the recently recognised PACE family of bacterial multidrug efflux proteins. Using the plasmid construct pTTQ18-aceI(His₆) containing the A. Baumannii aceI gene directly upstream from a His₆-tag coding sequence, expression of AceI(His₆) was amplified in E. coli BL21(DE3) cells. Near-UV (250-340 nm) SRCD measurements were performed on detergent-solubilised and purified AceI(His₆) at 20 °C. Sample and SRCD experimental conditions were identified that detected binding of the triamine spermidine to AceI(His₆). In a titration with spermidine (0-10 mM) this binding was saturable and fitting of the curve for the change in signal intensity produced an apparent binding affinity (K_D) of 3.97 +/- 0.45 mM. These SRCD results were the first experimental evidence obtained for polyamines as natural substrates of PACE proteins.

This paper proposed an optimal tracking control scheme through adaptive dynamic programming(ADP) for a class of partially unknown discrete-time nonlinear systems based on radial basis function neural network(RBF-NN). In order to acquire the unknown system dynamics, we use two RBF-NNs, the one is used to construct the identifier, and the another is used to directly approximate the steady-state control input, where a novel adaptive law is proposed to update neural network weights. While the optimal feedback control and the cost function are derived via feedforward neural networks approximating, it is proposed to regulate the tracking error, the critic network and the actor network are then trained online to obtain the solution of the associated Hamilton–Jacobi–Bellman (HJB) equation being built under the ADP framework. Simulations verify the effectiveness of the optimal tracking control technique using the neural networks.

The chiral optical Tamm state (COTS) is a special localized state at the interface of a handedness-preserving mirror and a structurally chiral medium such as a cholesteric liquid crystal or a chiral sculptured thin film. The spectral behavior of COTS, observed as reflection resonances, is described by the temporal coupled-mode theory. Mode coupling is different for two circular light polarizations because COTS has a helix structure replicating that of the cholesteric. The mode coupling for co-handed circularly polarized light exponentially attenuates with the cholesteric layer thickness since the COTS frequency falls into the stop band. Cross-handed circularly polarized light freely goes through the cholesteric layer and can excite COTS when reflected from the handedness-preserving mirror. The coupling in this case is proportional to anisotropy of the cholesteric and theoretically it is only anisotropy of magnetic permittivity that can ultimately cancel this coupling. These two couplings being equal results in a polarization crossover (the Kopp--Genack effect) for which a linear polarization is optimal to excite COTS. The corresponding cholesteric thickness and scattering matrix for COTS are generally described by simple expressions.

High-frequency near-infrared diode laser provides high peak output, low heat accumulation, and efficient biostimulation. Although these characteristics are considered suitable for osteoarthritis (OA) treatment, the effect of high-frequency near-infrared diode laser in in vitro or in vivo OA models has not yet been reported. Therefore, we aimed to assess the biological effects of high-frequency near-infrared diode laser irradiation on IL-1β-induced chondrocyte inflammation in an in vitro OA model. Normal Human Articular Chondrocyte-Knee (NHAC-Kn) cells were stimulated with human recombinant IL-1β and irradiated with high-frequency near-infrared diode laser (910 nm, 4 or 8 J/cm2). The mRNA and protein expression of relevant inflammation- and cartilage destruction-related proteins was analyzed. IL-1β treatment significantly increased the mRNA levels of IL-1β, IL-6, TNF-α, MMP-1, MMP-3, and MMP-13. High-frequency near-infrared diode laser irradiation significantly reduced the IL-1β-induced expression of IL-1β, IL-6, TNF-α, MMP-1, and MMP-3. Similarly, high-frequency near-infrared diode laser irradiation decreased the IL-1β-induced increase in protein expression and secreted levels of MMP-1 and MMP-3. These results highlight the therapeutic potential of high-frequency near-infrared diode laser in OA.

As an important organelle in eukaryotic cells, Golgi apparatus is responsible for processing and transporting proteins and lipids in cells. Precise monitoring the status of Golgi apparatus by targeting fluorescence imaging technology is of enormous importance but remains an attractive yet dramatically challenging task. In this study, we report the construction of the first Golgi apparatus targeted sensor with bright near-infrared fluorescence, termed as Golgi-Pdots. As a start point of our investigation, hydrophobic CDs with bright NIR fluorescence at 674 nm (fluorescence quantum yield : 12.18%), narrow emission band of 23 nm, and excellent stability were facilely prepared from Magnolia Denudata flowers through an ultrasonic method. Incorporating the CDs into a polymer matrix modified with Golgi-targeting molecules can produce the water-soluble Golgi-Pdots, which showed high colloidal stability and similar optical properties as compared to CDs. Further studies revealed that the Golgi-Pdots showed good biocompatibility and Golgi-targeting ability. Based on these fascinating properties, Golgi-Pdots have been successfully used for long term bioimaging of Golgi apparatus inside live cells.

Addressing within field spatial variability for nitrogen management to avoid over and under-use of nitrogen is crucial for optimizing crop productivity and ensuring environmental sustainability. In this study we investigated the economic, environmental and agronomic benefits of variable rate nitrogen application in potato (Solanum tuberosum). An online visible and near infrared (vis-NIR) spectroscopy sensor we utilized to predict soil moisture content (MC), pH, total organic carbon (TOC), extractable phosphorus (P), potassium (K), magnesium (Mg), and cation exchange capacity (CEC) using a partial least squares regression (PLSR) models. The crop's normalized difference vegetation Index (NDVI) from Sentinel-2 satellite images was incorporated to online measured soil data to derive fertility management zones (MZs) maps, after homogenous raster and clustering analyses. The MZs maps were categorized into high fertile (VR-H), medium-high fertile (VR-MH), medium-low fertile (VR-ML), and low fertile (VR-L) zones. A parallel strip experiment compared variable rate nitrogen (VR-N) with uniform rate (UR) treatments, adjusting nitrogen levels based on fertility zones as: 50% less for VR-H, 25% less for VR-MH, 25% more for VR-ML, and 50% more for VR-L zones compared to the UR treatment. Results showed that the VR-H zone received a 50% reduction in N fertilizer input and demonstrated a significantly higher crop yield compared to the UR treatment. This implies a potential reduction in negative environmental impact by lowering fertilizer costs while maintaining robust crop yields. In total, the VR -N treatment received an additional 1.2 Kg/ha of nitrogen input, resulting in a crop yield increase of 1.89 tons/ha. The relative gross margin for the VR-N treatment compared to the UR treatment 374.83 €/ha, indicating substantial profitability for the farmer. To further optimize environmental benefits and profitability, additional research is needed to explore site-specific application of all farm resources through precision agricultural technologies.

Fire detection from satellite sensors relies on an accurate estimation of the unperturbed state of a target pixel, from which an anomaly can be isolated. Methods for estimating the radiation budget of a pixel without fire depend upon training data derived from the location's recent history of brightness temperature variation over the diurnal cycle, which can be vulnerable to cloud contamination and the effects of weather. This study proposes a new method that utilises the common solar budget found at a given latitude in conjunction with an area's local solar time to aggregate a broad-area training dataset, which can be used to model the expected diurnal temperature cycle of a location. This training data is then used in a temperature fitting process with the measured brightness temperatures in a pixel, and compared to pixel-derived training data and contextual methods of background temperature determination. Results of this study show similar accuracy between clear-sky medium wave infrared upwelling radiation and the diurnal temperature cycle estimation compared to previous methods, with demonstrable improvements in processing time and training data availability. This method can be used in conjunction with brightness temperature thresholds to provide a baseline for upwelling radiation, from which positive thermal anomalies such as fire can be isolated.

In recent decades, with the ever-growing scale of video data, near-duplicate videos continue to emerge. Data quality issue caused by near-duplicate videos is becoming more and more prominent, which has affected the application of normal videos. Although current studies on near-duplicate video detection can be helpful to uncover data quality issues for videos, they still lack a process of automatic merging for the video data represented by high-dimensional features, which are difficult to automatically clean the near-duplicate videos to improve data quality for video datasets. At present, there are few studies on near-duplicate video data cleaning. The existing studies have the sensitive problems of video data orderliness and clustering initial center under a condition that prior distribution is unknown, which seriously affect the accuracy of near-duplicate video data cleaning. To address the above issues, an automatic near-duplicate video data cleaning method based on a consistent feature hash ring is proposed in this paper. First, a residual network with convolutional block attention modules, a long short-term memory deep network, and an attention model are integrated to construct an RCLA deep network with the multi-headed attention mechanism to extract spatiotemporal features of video data. Then, a consistent feature hash ring is constructed, which can effectively alleviate the sensitivity of video data orderliness while providing a condition of near-duplicate video merging. To reduce the sensitivity of the initial cluster centers to results of near-duplicate video cleansing, an optimized feature distance-means clustering algorithm is constructed by utilizing a mountain peak function on a consistent feature hash ring, which can implement automatic cleaning of near-duplicate video data. Finally, experiments are conducted based on a commonly used dataset named CC_WEB_VIDEO and a coal mining video dataset. Compared with some existing works, simulation results demonstrate that the performance of the proposed method.

The models of partial least squares (PLS) regression and artificial neural network (ANN) for evaluation of texture properties of cooked germinated brown rice (GBR) using the Fourier transform near infrared (NIR) spectra of uncooked whole grain combined with data separation methods, spectral pretreatment methods were investigated in this study. The ANN was outperformed in evaluation of hardness by back extrusion test of cooked GBR using the smoothing combined with standard normal variate pretreated NIR spectra in the range of 12,500-4,000 cm−1 of 188 whole grain samples where the calibration sample set was separated from prediction set by Kennard-Stone method where the best ANN model for hardness from hidden layers of 25 and 8 iteration time provided R2, r2, RMSEC, RMSEP, Bias and RPD of 0.9987, 0.9447, 0.1021 N, 0.7699 N, 0.0216 N and 4.3 respectively. The PLS regression of 64 sample KDML GBR group and 64 sample various variety GBR group, provided models for the hardness of the former and the toughness of the latter which developed by using 7506−5446.3, 4605.4−4242.9 cm−1 which included the amylose vibration band of 6834 cm−1 while toughness model was from 9403.8-6094.3 cm−1 where included 6834 and 8316 cm−1 vibration bands of amylose which influenced the texture of cooked rice. The PLS regression models for hardness and toughness were with the r2 of 0.85 and 0.82, and the RPD of 2.9 and 2.4, respectively. The ANN model for hardness of cooked GBR should be implemented to the practical use in GBR production factories for the quality assurance and further updating using more samples and several brands to obtain the robust models.

Recently Candida auris has emerged as a multi-resistant fungal pathogen, with a significant clinical impact, and has become a critical issue in WHO fungal priority list, due to its unique ability to persist for long time on human skin and hospital environments. It is therefore fundamental to reinforce hospital surveillance protocols to limit nosocomial outbreaks. The study purpose was the Fourier-Transform Infrared Spectroscopy (FT-IR) application to investigate the phylogenetic relationships between isolated strains within the C. auris outbreak at the university Intensive Care Unit of a Tertiary University hospital in Turin (Italy). In order to calculate a clustering cut-off, intra- and inter-isolates distance values were analysed. Data showed the presence of a major cluster Alfa and a minor cluster Beta with a defined C. auris clustering cut-off. The results were validated by an external C. auris strain and Principal Component and Linear Discriminant Analysis. The application of FT-IR technology had allowed to obtain important information about the phylogenetic relationships between the analysed strains, defining for the first time a “not WGS-based” clustering cut-off with a statistical-mathematical approach. FT-IR could represent a valid alternative to molecular methods for rapid and cost saving typing of C. auris strains with important clinical implications.

This article utilizes the Canny edge extraction algorithm based on contour curvature and the cross-correlation template matching algorithm to extensively study the impact of high-repetition-rate CO2 pulsed Laser on the target extraction and tracking performance of the infrared imaging detector. It establishes a quantified dazzling pattern for lasers on infrared imaging systems. By conducting laser dazzling and damage experiments, a detailed analysis of the normalized correlation between target and dazzling images is performed to quantitatively describe laser dazzling effects. Simultaneously, an evaluation system, including target distance and laser power evaluation factors, is established to determine the dazzling level and whether the target is recognizable. The research results reveal that laser power and target position are crucial factors affecting the detection performance of infrared imaging-guided weapon systems under laser dazzling. Different laser powers are required to successfully interfere with the recognition algorithm of infrared imaging-guided weapons at different distances. And laser dazzling will produce a lot of false edge information, which seriously affects the performance of pattern recognition algorithm. In laser damage experiments, the detector experienced functional damage, with a quarter of the image displaying as completely black. The energy density threshold required for functional damage to the detector is approximately 3 J/cm2. The dazzling assessment conclusions also apply to evaluating damage results. Finally, the proposed evaluation formula aligns with experimental results, objectively reflecting the actual impact of laser dazzling on the target extraction and tracking performance of infrared imaging systems. This study provides an in-depth and accurate analysis for understanding the influence of lasers on the performance of infrared imaging-guided weapons.

Diesel is consistently a very popular fuel for internal combustion engines. Its consumption in the European Union has been rising over recent years, as has the number of engines increasingly sensitive to fuel contamination. One of the most important parameters affecting diesel quality is water content. Although its exceedances of the standard’s permissible amount are not common, they carry the risk of serious damage to expensive, modern fuel injection systems. Current methods of measuring water content require both specialised personnel and equipment, as well as transporting the sample to a suitable laboratory. The research presented in this paper was aimed at verifying whether it would be possible to test the moisture content of diesel using impedance spectroscopy. Once refined, such measurement method could be used in situ. Analysing the properties of diesel is challenging, mainly because many types of the fuel are sold, the chemical composition of which is always a well-guarded manufacturer’s secret. The experimental results obtained on many types of commercially available diesel show that it is possible to use impedance spectroscopy to estimate the water content of different types of diesel fuel. The measurement accuracy achieved makes it possible to detect approaching or exceeding the permissible water content.

Leaf photosynthetic pigments play a crucial role in evaluating nutritional elements and physiological states. In facility agriculture, it is vital to obtain rapidly and accurately the pigment content and distribution of leaves to ensure precise water and fertilizer management. In our research, we utilized chlorophyll a (Chla), chlorophyll b (Chlb), chlorophyll (Chll), and carotenoid (Caro) as indicators to study the variations in leaf position of Lycopersicon esculentum Mill. Under 10 nitrogen concentration applications, a total of 2610 leaves (435 samples) were collected using visible-near infrared hyperspectral imaging (VNIR-HSI). In this study, a "coarse-fine" screening strategy was proposed by using competitive adaptive reweighted sampling (CARS) and iteratively retained informative variable (IRIV) algorithm to extract characteristic wavelengths. Finally, simultaneous and quantitative models were established using partial least squares regression (PLSR). The CARS-IRIV-PLSR was used to create models to achieve a better prediction effect. The coefficient determination (R2), root mean square error (RMSE), and ratio performance deviation (RPD) were predicted to be 0.8240, 1.43, 2.38 for Chla, 0.8391, 0.53, 2.49 for Chlb, 0.7899, 2.24, 2.18 for Chll, and 0.7577, 0.27, 2.03 for Caro, respectively. The combination of these models with the pseudo-color image allowed for a visual inversion of the content and distribution of pigment. These findings have important implications for guiding pigment distribution, nutrient diagnosis, and fertilization decisions in plant growth management.

A large core size and bending resistance are very important properties of mid-infrared energy transfer fibers, but large core sizes usually lead to a deterioration of bending properties. A negative-curvature nested node-free anti-resonant hollow-core fiber (AR-HCF) based on quartz is proposed. It was made by adding a nested layer to a previous AR-HCF design to provide an additional anti-resonance region while keeping the gap between adjacent tubes strictly correlated to the core diameter to produce a node-free structure. These features improve the fiber’s bending resistance while achieving a larger core diameter. The simulation results show that the radial air-glass anti-resonant layer is increased by the introduction of the nested anti-resonant tube, and the weak interference overlap between the fiber core and the cladding mode is reduced, so the fiber core’s limiting loss and the sensitivity to bending are effectively reduced. When the capillary wall thickness t of the fiber is 0.71 μm, the core diameter D is 70 μm, the ratio of the inner diameter of the cladding capillary to the core diameter d/D is 0.62, and the diameter of the nested tube is d0=29 μm, the fiber has a lower limiting loss at the wavelength of 2.79 μm, and the limiting loss is 3.28×10-4 dB/m. At the same time, the optimized structure also has good bending resistance. When the bending radius is 30 mm, the bending loss is only 4.72×10-2 dB/m. To our knowledge, this is the first research to achieve an anti-bending low-loss micro-structure hollow fiber with a bending radius of less than 30 mm in the 2.79 μm band. An anti-bending low-loss anti-resonant hollow-core fiber with this structure constitutes a reliable choice for the light guiding system of a 2.79 μm band erbium laser therapy instrument.

Saudi Arabia is a prominent producers of dates, with 1.6 million tons annually. There is a need to evaluate physical properties and quality of fruits non-destructively and then be modeled and predicted throughout the storage period. The aim of the current study was to generate a quality index (Qi) and Near-infrared spectra (NIR) models non-destructively to predict properties of Barhi dates fruits including objective and sensory evaluations. The engineering properties of Barhi dates were measured and modeled with quality index (Qi) based on NIR of fresh Barhi fruits (hardness, color, TSS, pH, and sensory evaluations) and during storage in cold, ambient, and controlled at-mosphere (CA) for up to four months. The prediction of Qi is non- destructively based on NIR utilizing PLSR and ANN data analysis. The results showed that the Qi generated corresponds with high precision to the characteristics of the examined fruits through the duration of the storage period with R2 of 0.96. The NIR spectrum proves to be an efficient method to evaluate the Barhi fruits' quality index. where ANN was found to be more suitable than PLSR analysis. Thus, NIR can be utilized to accurately predict the Qi of fruits quality effectively throughout the handling, processing, transporting, storage, and retail sector supply chain.

Fingerprints are essential for human identification and are valuable tools in criminal investigations. The pursuit of new materials for digital printing is expanding, with increasing interest in natural compounds such as bixin, sourced from annatto seeds. Despite its traditional use as a natural dye with medicinal properties, the potential of bixin in papilloscopy remains largely untapped. In this study, we meticulously extracted bixin from annatto seeds and meticulously developed composites incorporating zinc carbonate (bixin/ZnCO3) and kaolinite (bixin/kaolinite). UV-visible spectroscopy was used for characterization, and the extracted bixin showed absorption peaks at 429, 453, and 481 nm, which were very similar to standard peaks at 429, 457, and 487 nm. The two samples also had the same retention times (7.07 min) according to further liquid chromatography analysis. Sweat pores were easier to detect thanks to the effectiveness of the bixin/ZnCO3 and bixin/kaolinite composites in creating high contrast sebaceous and natural latent fingerprints. These results highlight the composites’ potential as novel and fascinating instruments for papilloscopy applications, which might also improve forensic investigations.

Directed energy deposition (DED) parameters were determined for near-β alloy Ti-55511 by employing statistical design of experiments (DOE) methods. Parameters resulting in fully dense freeform deposits were identified using two sequential DOEs. Single laser tracks were printed with several laser power, traverse rate, and powder feed rate settings in an initial DOE to identify promising build parameters. Capture efficiency and effective deposition rate were used to characterize and rank the single track deposits. The best parameters were then used to print solid cube with various X-Y and Z-overlaps (different hatch spacing, HS, and layer thickness, ZS) in a second DOE. Suitable deposition parameters were selected based on cube density and microstructure and were used to fabricate larger tensile samples for mechanical testing. Multiple parameter sets were found to provide dense Ti-55511 deposits with acceptable mechanical properties and parametric models showed statistical significance.

Purpose of review The forest industry has deployed ICTs to improve productivity of forest supply chains. In this sense, digitalization of data flows significantly impacts wood supply chains, from forest to mills, which must react flexibly to market fluctuations of forest products. The goal of this study was to conduct a literature review on data flow implementations in management systems of wood supply chains and to evaluate their applicability in supply chains to analyze the opportunities for improving them in practice. Recent findings We utilized the ScienceDirect database, Scopus and Web of Science in order to document data flows in systems and actual applications. Due to ongoing outsourcing, the wood procurement chains, and the wood supply chains were identified in the forest industry. Several different technologies can be implemented also in wood supply chains to digitalize the data flow depending on the specific needs of organizations. In this regard, digitalization of Big Data causes significant changes in elements of supply models that should be integrated and standardized in the optimization and simulation systems. Summary A modelling guide is suggested for accurate dynamic solution of problems of forest logistics. Then, the opportunities in digitalization for connecting different data flows, technologies, and stakeholders are discussed thoroughly. We conclude that data-driven dynamic optimization, and locally implemented digitalization contributes to wood supply modeling in the establishment of standards for cloud-based platforms at time-related and sequential measures which will ensure successful forest logistics through the planning and the monitoring of wood supply chains.

Background: Asthma is a widespread chronic respiratory disease that poses a significant public health challenge. The current study investigated the associations between air pollution and asthma severity among individuals residing near the Sohar industrial port (SIP) in Oman. Despite the presence of multiple major industrial complexes in Oman, limited knowledge regarding their impact on respiratory health is accredited. Hence, the primary objective of this study is to offer valuable insights into the respiratory health consequences of industrial air pollution in Al Batinah North. Methods: The state health clinics' records for patient visits related to asthma were collected for the timeframe spanning 2014 to 2022. Exposure was defined as the distance from SIP, Majan Industerial Area (MIA) and Sohar Industerial Zone (SIZ) to determine high, intermediate, and low exposure zones (12 km, respectively). Exposure effect modifications by age, gender, and smoking status were also examined. Results: The conducted cross-sectional study of 410 patients (46.1% males and 53.9% females) living in over 17 areas around SIP revealed that 73.2% of asthmatics were under 50 years old, with severity significantly associated with closeness to the port. Risk ratios were estimated to be (RR:2.42; CI95%: 1.01–5.78), (RR:1.91; CI95%: 1.01–3.6), and (RR:1.68; CI95%: 0.92–3.09) for SIP, MIP and SIZ areas, respectively, compared to the control area. Falaj Al Qabail (6.4 km) and Majees (6 km) had the highest number of asthma patients (N. 69 and N. 72), and highest percentages of severe asthma cases among these patients (28% and 24%) with significant risk ratios (RR:2.97; CI95%: 1.19–7.45 and RR:2.55; CI95%: 1.00–6.48), correspondingly. Moreover, severe asthma prevalence peaked in the 25-50 age group (RR:2.05; CI95%: 1.26–3.33), and this linkage between asthma and age was much more pronounced in males than females. Smoking and exposure to certain contaminant (dust and smoke) increased also the risk of severe asthma symptoms, but their effects were less important in the high risk zone, suggesting much more important risk factor. A neural network model accurately predicted asthma risk (94.8% accuracy), with proximity to Sohar Industrial Port as the most influential predictor. Conclusion: The study highlights the High asthma burden near Sohar Industrial Port, linked to port proximity and smoking and wind direction as major risk factors. These findings inform vital public health policies to reduce air pollution and improve respiratory health in the region, prompting national policy review.

The adsorption of surfaces exposed to sunlight results in increased temperature that can cause physical damage and increase in energy consumption. The infrared reflective coatings that keep objects cooler have significant benefits in a wide variety of application by reflecting the infrared lights, reducing the operating costs, improving energy efficiency of buildings and vehicles (roofs, walls and windows), extend the objects’ lifespan. Our research focused on the elaboration of coatings with minimum adsorption in the infrared wavelength range. This was achieved by production of coatings that have infrared transparent and infrared reflective ability. The infrared reflection and surface warm up was investigated in the function of concentration and composition of pigments in the coatings. With investigation of different coating compositions the pigments and the binding systems were optimized. The coatings with different compositions were characterized by total solar reflection in the UV, visible and infrared wavelength range as well as by infrared reflection. Different coatings were produced in RAL7016 anthracite green color but with much better infrared reflectance, transparency as well as with heat reflectance.

Antimony (Sb) has gained significance as a critical raw material (CRM) within the European Union (EU) due to its strategic importance in various industrial sectors, particularly in the textile industry for flame retardants and as a component of Sb-based semiconductor materials. Moreover, Sb is emerging as a potential alternative for anodes used in lithium-ion batteries, a key element in the Energy transition. This study focused on exploring the feasibility of identifying and quantifying Sb mineralizations through the spectral signature of soils using reflectance spectroscopy, a non-invasive remote sensing technique, and by employing deep learning algorithms such as Convolutional Neural Networks (CNNs). Common signal preprocessing techniques were applied to the spectral data, and the soils were analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Despite achieving high R-squared values, the study faces a significant challenge of generalization of the model to new data. Despite the limitations, this study provides valuable insights into potential strategies for future research in this field.

Axial resolution is one of the most important characteristics of a microscope. In all microscopes, a high axial resolution is desired in order to discriminate information efficiently along the longitudinal direction. However, when studying thick samples that do not contain laterally overlapping information, a low axial resolution is desirable as information from multiple planes can be recorded simultaneously from a single camera shot instead of plane-by-plane mechanical refocusing. In this study, we increased the focal depth of an infrared microscope non-invasively by introducing a binary axicon fabricated on barium fluoride substrate close to the sample. Preliminary results of imaging thick and sparse silk fibers showed an improved focal depth with a slight decrease in lateral resolution and increase in background noise.

Background: Visual acuity (VA) assessments are crucial in ophthalmology but traditionally rely on in-clinic evaluations. The emergence of telemedicine has spurred interest in creating dependable self-administered VA tests for use beyond standard clinical environments. This study evaluates the practicality and validity of a self-administered Near VA card test against traditional Snellen and Rosenbaum Pocket Vision Screener (RPVS) methods for home monitoring and enhancing clinical workflow. Methods: In a cross-sectional study a near VA card (Hadassah Self-Visual Acuity Screener-HSVA) was developed with written and videotaped instructions for self-use. Patients with a minimal best corrected VA (BCVA) of 1.0 LogMAR in at least one eye were recruited from Ophthalmology and Optometry clinics. Outcomes included mean BCVA difference between the self-administered and those obtained by the examiner, and correlations among BCVA values obtained by Snellen, RPVS, HSVA and previous distance BCVA according to the patient electronic medical records. Results: A total of 275 participants (mean age: 42.5±19.4 years, range: 18-89 years, 47% female) were included. Test-retest reliability analysis for the HSVA demonstrated very good correlation and repeatability (n=38 patients.; Rs=1.0, P

This study presents the utilization of the Microwave InfraRed Thermography (MIRT) technique to identify and analyze the defects in the carbon-fiber-reinforced polymer (CFRP) composite rein-forcement of concrete specimens. At first, a set of the numerical models was created, comprising of the broadband pyramidal horn antenna and the analyzed specimen. The utilization the system operating at a power of 1000W in a continuous mode, operating at frequency of 2.45 GHz was analyzed. The specimen under examination comprised a compact concrete slab that was covered with an adhesive layer, thereafter, topped with a layer of CFRP. An air gap represented a defect at the interface between the concrete and CFRP within the adhesive layer. In the modelling stage the study investigated three separate scenarios: a sample with no defects, a sample with a defect located at the center, and a sample with a numerous additional random defects located at the rim of the CFRP matte, to analyze the effect of the natural reinforcement degradation in this area. The next phase of the study involved conducting experiments to confirm the results obtained from nu-merical modeling. In the experiments the concrete sample aged for 10 years with the defect in the center and naturally developed defects at the CFRP rim was used. The study employed numerical modeling to explore the phenomenon of microwave heating in complex structures. The aim was to assess the chosen antenna design and identify the most effective experimental setup. These con-clusions were subsequently confirmed through experimentation. The observations made during the heating process were particularly remarkable, since they deviated from earlier studies that solely conducted measurements of the sample post-heating phase. The findings demonstrate that MIRT has the capacity to be employed as a technique for detecting flaws in concrete structures reinforced with CFRP.

Hyperspectral imaging data holds great potential for the different stages of the mining life cycle in active and post-mining environments. However, the technology has yet to reach the stage of large-scale industrial implementation and acceptance. While hyperspectral satellite imagery can achieve high spectral resolution, signal-to-noise ratio (SNR) and global availability with break-through satellite systems like EnMAP, EMIT and PRISMA, limited spatial resolution poses chal-lenges for sectors like mining, which require decimetre to centimetre scale resolution for applica-tions such as reconciliation, ore/waste estimates, geotechnical assessments and environmental monitoring. Hyperspectral imaging from drones (referred to herein as Uncrewed Aerial Systems; UASs) offers high spatial resolution data relevant to the camp/ mine scale, with the capability for frequent, user-defined re-visit times. This has been made possible by the miniaturization of hy-perspectral imaging systems. Collection of data in the visible to near and shortwave infrared (VNIR-SWIR) wavelength regions enables the detection of different minerals and surface altera-tion patterns potentially revealing crucial information for exploration, extraction, re-mining, waste remediation, and rehabilitation. In this paper, we provide a review of relevant studies de-ploying hyperspectral imaging in or applicable to the mining sector, especially for the use of hy-perspectral VNIR-SWIR Uncrewed Aerial Systems. Where required, we draw on previous in-sights derived from satellite or ground-based systems. We also discuss UAS survey planning, and sampling considerations for validation and interpretation.

A Cu₂ZnSnS₄ (CZTS) film with a thickness of approximately 1.5 μm was fabricated on a Mo-coated glass substrate by annealing a CZTS precursor fabricated from nanoparticle ink. The chemical states of the elements in the CZTS thin film in the depth direction were studied to identify the presence of secondary phases, which are detrimental to the performance of solar cells containing CZTS. X-ray diffraction was unable to detect any secondary phases in CZTS because of their small relative amount. Instead, X-ray photoelectron spectroscopy (XPS), which is highly sensitive to chemical state, was conducted at different depths in the CZTS film to further check the presence of secondary phases. XPS analysis revealed peaks shift consistent with the presence of secondary phases. For the CZTS film annealed in a S atmosphere at 575 °C for 3 h, the film surface consisted of a secondary-phase layer composed of CuS, ZnS, and SnSx (x=1 or 2) originating from the decomposition of CZTS. At depths below 80 nm, the film was pure CZTS. Formation of MoS2 at the CZTS–Mo interface was confirmed by XPS analysis of Mo and S.

There is increasing interest in the application of near-infrared (NIR) laser light for the treatment of various musculoskeletal disorders. The present study thoroughly examined the physical characteristics of laser beams from two different laser therapy devices that are commercially available for the treatment of musculoskeletal disorders. Then, these laser beams were used to measure the penetration depth in various biological tissues from different animal species. The key result of the present study was the finding that for all investigated tissues, most of the initial light energy was lost in the first one to two millimeters, more than 90% of the light energy was absorbed within the first ten millimeters, and there was hardly any light energy left after 15 – 20 mm of tissue. Furthermore, the investigated laser therapy devices fundamentally differed in several laser beam parameters that can have an influence on how light is transmitted through tissue. Overall, the present study showed that a laser therapy device that is supposed to reach deep layers of tissue for treatments of musculoskeletal disorders should operate with a wavelength between 800 nm and 905 nm, a top-hat beam profile, and it should emit very short pulses with a large peak power.

For the agricultural sector to develop sustainably in the future, progress toward more environmentally friendly technologies and methods is crucial. It is necessary to increase output while reducing the demand for energy, agrochemicals, and water resources. Although greenhouses can be utilized successfully for this purpose, significant technical advancements are required, especially when it comes to heating, to lower the use of fossil fuels and boost energy efficiency. Infrared waves and microwaves, for instance, can warm plants without having to heat the entire greenhouse volume, which takes a significant amount of energy to compensate for heat loss to the outdoor environment. In this paper, through a thorough examination of the state of the art, a general overview of novel greenhouse heating systems based on radiation is reported. First, infrared heating of greenhouses is analyzed, then the strengths and weaknesses of microwave and dielectric heating are discussed, and finally the use of microwaves for soil sterilization is examined. All outcomes suggest these irradiation-based technologies can contribute significantly to an agriculture that is energetically sustainable.

Phytochromes are biological photoswitches that translate light into a physiological function. Spec-troscopic techniques are essential tools in molecular research on these photoreceptors. This review is directed to summarize how resonance Raman and IR spectroscopy contributed to the under-standing of structure, dynamics, and reaction mechanism of phytochromes, outlining the substan-tial experimental and theoretical challenges and describing the strategies to master them. It is shown that the potential of the various vibrational spectroscopic techniques can be most efficient-ly exploited in integral approaches by combination with theoretical methods as well as other ex-perimental techniques.

Serological human birth cohort studies have identified maternal infection during pregnancy as a risk factor for development of disorders such as Autism Spectrum Disorder and schizophrenia in offspring. Similarly, in experiments using animal models, maternal immune activation (MIA) has been shown to alter neuroanatomical and behavioral development in offspring. This study employs magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) in conjunction with behavioral assays to refine our understanding of the impact of MIA on neurobiological development in exposed animals. On gestational day nine, pregnant dams were injected with either polyinosinic:polycytidylic acid (POL) to induce MIA or saline (SAL) as a control. Whole-brain MRI, localized proton MRS, and behavioral tests (open field, three chambered social approach, and prepulse inhibition) were acquired at two timepoints, during adolescence (postnatal day [PND] 35) and adulthood (PND 60). Whole-brain voxel-wise volumetric analyses revealed that MIA offspring exhibited altered volume in the hippocampus and caudate putamen (CPu) between adolescence and early adulthood. MRS data were assessed at each timepoint separately; MIA offspring during early adulthood but not adolescence exhibited trending reductions in γ-aminobutyrate (GABA) (p = 0.06) and myo-inositol (Ins) (p = 0.08) compared to saline controls. However, these metabolite differences did not reach levels of significance, even before multiple comparison corrections. Open field testing revealed that during adolescence, MIA offspring displayed a more anxious phenotype than controls wherein they spent less time in the anxiogenic center zone of the open field arena (p < 0.007), but this difference normalized by adulthood. There were no significant differences in sociability preference, novelty preference, or prepulse inhibition comparing the groups. Results suggest that early gestational exposure to MIA results in subtle neuroanatomical changes in the trajectories of development, trending behavioral changes in adolescent offspring, and slight neurochemical changes in young adult offspring. Maternal infection alone may not be enough; additional genetic or environmental risk factors may be required to elicit the more typical symptoms of neuropsychiatric disorders.

Given a fuzzy normed space $ \left( X,N \right) $‎, ‎we will introduce the notion of fuzzy near best approximation within a relative distance $ \rho \geq 0 $‎. ‎Some basic properties are characterized and also many examples for illustration are presented‎.

Decision tree algorithms have been among the most popular algorithms for interpretable (transparent) machine learning since the early 1980s. On the other hand, deep learning methods have boosted the capacity of machine learning algorithms and are now being used for non-trivial applications in various applied domains. But training a fully-connected deep feed-forward network by gradient-descent backpropagation is slow and requires arbitrary choices regarding the number of hidden units and layers. In this paper, we propose near-optimal neural regression trees, intending to make it much faster than deep feed-forward networks and for which it is not essential to specify the number of hidden units in the hidden layers of the neural network in advance. The key idea is to construct a decision tree and then simulate the decision tree with a neural network. This work aims to build a mathematical formulation of neural trees and gain the complementary benefits of both sparse optimal decision trees and neural trees. We propose near-optimal sparse neural trees (NSNT) that is shown to be asymptotically consistent and robust in nature. Additionally, the proposed NSNT model obtain a fast rate of convergence which is near-optimal up to some logarithmic factor. We comprehensively benchmark the proposed method on a sample of 80 datasets (40 classification datasets and 40 regression datasets) from the UCI machine learning repository. We establish that the proposed method is likely to outperform the current state-of-the-art methods (random forest, XGBoost, optimal classification tree, and near-optimal nonlinear trees) for the majority of the datasets.

Toby Walsh in “The Singularity May Never Be Near” gives six arguments to support his point of view that technological singularity may happen but that it is unlikely. In this paper, we provide analysis of each one of his arguments and arrive at similar conclusions, but with more weight given to the “likely to happen” probability.

Tengchongite is a uranyl molybdate uranium mineral and it was found and named by Chen 1985.No more scholars studied on tengchongite after Chen’s work, The identification of this mineral has only been confirmed by single crystal X-ray diffraction. In the paper, micro laser Raman spectroscopy and infrared spectroscopy are used to identify the spectroscopy properties of tengchongite. The studies fill in the gaps of more than 30 years in terms of the molecular spectroscopy research of tengchongite. The mineral has an ideal model of Ca(UO₂)₆(MoO₄)₂O₅•12H₂O its bands attributed to the vibrating units are clearly identified in the Raman spectrum. Symmetric stretching modes at 812 cm^-1 and 839 cm^-1 are assigned to ν1 (UO₂)²⁺ ,The ν3 antisymmetric stretching modes of (UO₂)²⁺ are observed at 896 cm^-1, Symmetric stretching modes at 419^-1 and 440 cm^-1 are assigned to ν2 (UO₂)²⁺ .Symmetric stretching modes at 919cm-1 are assigned to ν1(MoO₄)^2-,The ν3 antisymmetric stretching modes of (MoO₄)^2- are observed at 752 cm^-1, the in-plane ν2(MoO₄)^2-and the out-of-plane ν4(MoO₄)^2- bending modes are at 169 cm^-1 and 254 cm^-1. IR spectrum of tengchongite shows the major uranyl band at 858 cm^-1 and 693 cm^-1, Mo-O bonds are observed at about 985 cm^-1 and 780 cm^-1, and H₂O groups are present with a wide range peaks from 3100 cm^-1 to 3500 cm^-1 and 1647 cm^-1, and the band at 1432.4 cm^-1 is probably due to the stretching vibration hydroxyl (OH^-1), therefore, tengchongite contains may include hydroxyl and its chemical formula needs to be modified .

Modeling and design of fiber lasers facilitate the process of their practical realization. Of particular interest during the last few years is the development of lanthanide ion-doped fiber lasers which operate at wavelengths exceeding 2000 nm. There are two main host glass materials considered for this purpose, namely fluoride and chalcogenide glasses. Therefore, this study concerns comparative modeling of fiber lasers operating within the infrared wavelength region beyond 2000 nm. In particular, the convergence properties of selected algorithms, implemented within various software environments, are studied with a specific focus on the central processing unit (CPU) time and calculation residual. Two representative fiber laser cavities are considered: one is based on a chalcogenide-selenide glass step-index fiber doped with trivalent dysprosium ions whilst the other is a fluoride step-index fiber doped with trivalent erbium ions. The practical calculation accuracy is also assessed by comparing directly the results obtained from the different models.

The search for molecular biosignatures at the surface of Mars is complicated by an intense irradiation in the mid- and near- ultraviolet (UV) spectral range for several reasons: (i) many astrobiologically relevant molecules are electronically excited by efficient absorption of UV radiation and rapidly undergo photochemical reactions; (ii) even though the penetration depth of UV radiation is limited, aeolian erosion continually exposes fresh material to radiation; and (iii) UV irradiation generates strong oxidants such as perchlorates that can penetrate deep into soils and cause subsurface oxidative degradation of organics.As a consequence, it is crucial to investigate the effects of UV radiation on organic molecules embedded in mineral matrices mimicking the martian soil, in order to validate hypotheses about the nature of the organic compounds detected so far at the surface of Mars by the Curiosity rover, as well as organics that will be possibly found by the next rover missions Mars 2020 and ExoMars 2020. In addition, studying the alteration of possible molecular biosignatures in the martian environment will help to redefine the molecular targets for life detection missions and devise suitable detection methods.Here we report the results of mid-UV irradiation experiments of Mars soil analog samples obtained adsorbing relevant organic molecules on a clay mineral that is quite common on Mars, i.e. montmorillonite, doped with 1 wt% of magnesium perchlorate. Specifically, we chose to investigate the photostability of a plausible precursor of the chlorohydrocarbons detected on Mars by the Curiosity rover, namely phthalic acid, along with the biomarkers of extant life L-phenylalanine and L-glutamic acid, which are proteomic amino acids, and adenosine 5’-monophosphate, which is a nucleic acid component.We monitored the degradation of these molecules adsorbed on montmorillonite through in situ spectroscopic analysis, investigating the reflectance properties of the samples in the Near InfraRed (NIR) spectral region. Such spectroscopic characterization of molecular alteration products provides support for two upcoming robotic missions to Mars that will employ NIR spectroscopy to look for molecular biosignatures, through the instruments SuperCam on board Mars 2020, ISEM, Ma_Miss and MicrOmega on board ExoMars 2020.

Within the spectra of lanthanide atoms quite often overlapping spectral lines (blends) are observed. On the example of complicated blend situations within Pr I and La I it is shown that in such cases a combination of optogalvanic and laser-induced fluorescence detection is necessary to find all transitions contributing to the observed spectral signature.

This article reports new measurements of laser-induced plasma hypersonic expansion measurements of diatomic molecular cyanide (CN). Focused, high-peak power 1064-nm Q-switched radiation of the order of 1 TW/cm² generates optical breakdown plasma in a cell containing a 1:1 molar gas mixture of N₂ and CO₂ at a fixed pressure of 1.1 × 10⁵ Pascal and in a 100 ml/min flow of the mixture. Line-of-sight (LOS) analysis of recorded molecular spectra indicate the outgoing shockwave at expansion speeds well in excess of Mach number 5. Spectra of atomic carbon confirm an increased electron density near the shock wave, and equally, molecular CN spectra reveal higher excitation temperature near the shockwave. The results are consistent with corresponding high-speed shadow graphs obtained by visualization with an effective shutter speed of five n anosecond. In addition, LOS analysis and application of integral inversion techniques allow inferences about the spatio-temporal distribution of the plasma.

Infrared dim and small target detection is a key technology of various detection tasks. However, due to the lack of shape, texture, and other information, it is a challenging task to detect dim and small targets. Recently, since many traditional algorithms ingore global information of infrared images, they generate some false alarms in complicated environments. To address this problem, in this paper, a coarse-to-fine deep learning-based method is proposed to detect dim and small targets. Firstly, a coarse-to-fine detection framework integrating deep learning and background prediction is applied for detecting targets. The framework contains of coarse detection module and fine detection module. In coarse detection stage, Region Proposal Network (RPN) is employed to generate masks in target candidate regions. Then, for further optimize the result, inpainting is utilized to predict the background by the global semnantic of images. In this paper, inpainting algorithm with a mask aware dynamic filtering module is incorporated into the fine detection stage to estimate background in candidate target areas. Finally, compared with existing algorithms, the experimental results indicate that the proposed framework have effective detection capability and robustness for complex surroundings.