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.

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.

For a wide field of view (FoV) wavefront coding athermalized infrared imaging system with a single decoding kernel, the off-axis aberration tends to cause artefacts. In order to correct off-axis aberration, many pieces of lenses will reduce the transmission efficiency and increase the weight and cost. To meet requirements for wide FoV, wide operating temperature and low weight of infrared imaging systems, this paper reports a wide-FoV wavefront coding athermalized infrared imaging system with a two-piece lens. Its principle, design, manufacture, measurement and performance validation are successively discussed. This paper constructs an optimization problem which maximizes the weighted mean of PSF consistency for both the FoV and operating temperature range. The two-piece lens contains four surfaces, where three aspheric surfaces are introduced to reduce optical off-axis aberrations and a cubic surface is introduced to achieve athermalization. The optical phase mask containing an aspheric surface and a cubic surface is manufactured by nano-metric machining of ion implanted material(NiIM). Experimental results validate that our wide-FoV wavefront coding athermalized infrared imaging system has a full FoV of 26.10° and an operating temperature over -20°C to +70°C.

A recent research has identified an inverse amplitude link between obliquity damping and short eccentricity amplification during Mid-Late Pleistocene based on LR04 δ18O and equatorial Pacific Site 846 sea surface temperature records, which is associated with the Earth's long-term cooling. In the present study, new evidence of this anticorrelation is presented from Antarctic D-CO2-CH4 records, global benthic-planktic δ18O, and regional (Atlantic, Pacific, Mediterranean, and Indian) climate-related proxies. Based on a critical review of theoretical constrains (Earth's oblateness changes and ice-volume phase lag in the obliquity band <5.0 kyr), this widespread and symmetric (bipolar) obliquity response damping has been interpreted as an effect of the obliquity-oblateness feedback, which could be the latent physical mechanism at the origin of the Mid-Pleistocene Transition (MPT). Indeed, results and considerations of the present work suggest that fast and positive/negative net variation of the Earth's oblateness in the obliquity band was controlled by dominant glacio-eustatic water mass component and, assuming a rapid response of the ice-volume to surface temperature changes, the mean obliquity lag response is estimated to be <5.0 kyr over the past 800 kyr. These elements may explain the interglacial/glacial damping observed in the obliquity response. The consolidation of the Earth's long-term icy-state in the subtrend IV, culminated with the post-MPT obliquity damping, might have contributed to the strengthening of the short eccentricity response by mitigating the obliquity ‘ice-killing’ during obliquity maxima (interglacials), favouring the obliquity-cycle skipping and a feedback amplified ice-growth in the short eccentricity band (obliquity damping hypothesis). This could suggest a different impact of the climate friction than what is generally believed, which is presumably the latent physical mechanism that triggers transient ‘competitive’ interaction between obliquity and short eccentricity started early during the Piacenzian.

This paper presents a numerical study on the vertical (axial) and lateral (flexure) behavior of CFST (Concrete-Filled Steel Tube) columns with active hoop prestress; this transverse prestressing effect is achieved by bolting together two steel half-tubes. This study refers to new construction only (i.e., no retrofit). 12 prototype CFST column specimens (segments) are analyzed. These specimens differ in the prestressing force (3 levels) and in the gravity loading ratio (4 levels); they are selected to represent typical ground columns of mid-rise buildings. The structural behavior of these column specimens is simulated with a nonlinear model implemented in Abaqus; the concrete and steel behavior are described with a damage-plasticity and a plasticity model, respectively. Finally, the interface concrete-steel interaction is represented by a hard (compression-only) surface-to-surface contact model. The calculations involve three consecutive loading steps: (i) transverse prestress, (ii) axial force, and (iii) lateral loading (shear force and bending moment). The structural behavior of the CFST columns is deeply examined and discussed; the results show that their axial-flexural capacity is adequate. Noticeably, it is concluded that the overall benefits of prestressing the columns are only modest. Preliminary studies on the aforementioned mid-rise buildings equipped with the CFST columns show that their gravity and wind capacities are largely enough; conversely, their seismic capacity is sufficient for moderate seismic ground motions only.

To address the issue of low prediction accuracy for sub-grid scale turbulence-radiation interaction (SGS-TRI) characteristics, which impacts the precision of remote infrared signal calculations for gas jet, a term for correlation of radiation-participating component fluctuations with temperature fluctuation was introduced into the current sub-grid Snegirev equation; Simultaneously, we established a new method that is more convenient in complex projects for solving the dynamic sub-grid model coefficients, e.g., dynamic Smagorinsky model and temperature variance model. The validity of these algorithms was confirmed through direct numerical simulations (DNS) for isotropic turbulence and test data of high-temperature air jet flow. The Multi-Scale Multi-Group Wide-Band (MSMGWB) k-distribution model was used to calculate radiative transfer in strongly non-isothermal and inhomogeneous media such as the remote infrared sensing of jet plume scenario considering influences of TRI and SGS-TRI. The study for axisymmetric jet plume revealed that the TRI accounts for more than 1/3 of total remote infrared signal in the 3-5micron band, with the SGS-TRI effect contributing more than 10%. Additionally, this influence was found to further increase with increasing detection distances.

The nutritional composition of food for animal production can be completed using olive tree and plant by-products due to their high contents of bioactive compounds, such as pentacyclic triterpenes. Here, we present a novel application of near-infrared spectroscopy (NIRS) for the prediction of the total or individual [maslinic acid (MA), oleanolic acid (OA), and uvaol (UO)] pentacyclic triterpene concentrations in a feed additive obtained from a plant mixture. The oxygen radical absorbance capacity of these types of samples demonstrated the existence of a high antioxidant capacity. The conventional determination methods of pentacyclic triterpenes concentration are costly, labor-intensive, and not practical for analyzing several lines within a limited timeframe at the factory level. The optimal regression model developed in our work demonstrated high correlation values for the calibration and validation sets, along with a high residual prediction deviation value. The NIRS method can be applied directly to dried powder and avoids extraction and high-performance liquid chromatography (HPLC) analysis. Our results also demonstrate that NIRS can accurately quantify pentacyclic triterpenes even at low concentrations in food additives. It can be used at the factory level to directly determine the concentrations in the additive powder obtained at the same time that the powder is produced.

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.

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.

A non-destructive analysis of proximate parameters (moisture content, MC; volatile matter, VM; fixed carbon, FC; and ash content) for a variety of chipped and ground biomasses was investigated through a combination of thermogravimetric analysis (TGA) and near-infrared spectroscopy (NIRS) with partial least squares regression (PLSR). In this study, the thermogravimetric method was employed as a tool to determine the proximate analysis data of biomass samples through TG and DTG curves, which were generated by tracking changes in biomass mass loss over time or temperature. NIRS non−destructively scanned the chipped biomass in diffuse reflectance while ground biomass using transflectance mode, covering the wavenumber range of 3594.87 to 12,489.48 cm−1. The PLSR−based models: Full−PLSR, genetic algorithm (GA)−PLSR, successive projection algorithm (SPA)−PLSR, the spectral multi−preprocessing (MP) PLSR− 5 range method, and the MP PLSR−3 range method were developed. Model performance was assessed, compared, and selected based on the coefficient of determination in the prediction set (R2P), root mean square error in the validation set (RMSEP), and the ratio of prediction to deviation (RPD). Based on the comprehensive analysis of model performance, MC and FC models of chip biomass have demonstrated satisfactory performance which makes it applicable cautiously in any application, including research. The optimum models for MC and FC in chip biomass were constructed using GA−PLSR with 2nd derivative and Full−PLSR with a constant offset, resulting in R2P values of 0.8654 and 0.8773, RMSEP values of 0.85% and 2.12%, and RPD values of 2.9 and 3.0, respectively. Other parameters such as MC and FC in ground biomass as well as VM and ash content in both chip and ground biomass were found suitable for rough screening. The sensitivity of the selected model was assessed by calculating the limit of quantification (LOQ), and the results indicated that the calculated LOQ values for VM in both chip and ground biomass and FC in chip biomass are lower than the minimum reference values used during model development, signifying a high level of sensitivity. However, for the remaining parameters, the LOQ values surpass the established minimum reference value, suggesting the model could not predict the future samples with reference value lower than the calibration range. Nevertheless, enhancing the models by incorporation an ample number of representative biomass samples and consistently validating the models with unknown samples is imperative to ensure accurate predictions.

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.

N-maleimide-derivatized phospholipids are often used to facilitate protein anchoring to membranes. In autophagy studies, this is applied to the covalent binding of Atg8, an autophagy protein, to a phosphatidylethanolamine (PE) in the nascent autophagosome. However, the question remains on how closely the N-maleimide PE derivative (PE-mal) mimicks the native PE in the bilayer. In the present paper, spectroscopic and calorimetric techniques have been applied to vesicles containing either PE or PE-mal (together with other phospholipids) to compare the properties of the native and derivatized forms of PE. According to differential scanning calorimetry, and to infrared spectroscopy, the presence of PE-mal did not perturb the fatty acyl chains in the bilayer. Fluorescence spectroscopy and microscopy showed that PE-mal did not alter bilayer permeability either. However, fluorescence emission polarization of Laurdan and DPH probes indicated an increased order, or decreased fluidity, in the bilayers containing PE-mal. In addition, infrared spectral data from the phospholipid phosphate region revealed a PE-mal-induced conformational change of the polar heads, accompanied by an increased hydration. Globally considered, the results suggest that PE-mal would be a reasonable substitute for PE in model membranes containing reconstituted proteins.

Leakages in the water distribution networks (WDNs) are real problems for utilities and other governmental agencies. Timely leak detection and location identification has been a challenge. In this paper, an integrated approach to geospatial and infrared image processing method was used for robust leak detection. The method combines drops in flow, pressure, and chlorine residuals to determine potential water leakage locations in the WDN using Geographic Information System (GIS) techniques. GIS layers were created from the hourly values of these three parameters for the city of Sharjah provided by Sharjah Electricity, Water and Gas Authority (SEWA). These layers are then analyzed for locations with dropped values of each of the parameters and are overlaid with each other. In the case where there were no overlaying locations between flow and pressure, further water quality analysis was avoided, assuming no potential leak. In the case where there are locations with drops in flow and pressure layers, these overlaying locations are then examined for drops in chlorine values. If overlaying locations are found, then these regions are considered potential leak locations. Once potential leak locations are identified, a specialized remote sensing technique can be used for precise leak location. This study also demonstrated the suitability of using an infrared camera for leak detection in a laboratory-based setup. This paper concludes that the following methodology can help water utility companies in the timely detection of leaks, saving money, time, and effort.

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.

Infrared and visible image fusion technologies are used to characterize the same scene by diverse modalities. However, most existing deep learning-based fusion methods are designed as symmetric networks, which ignore the differences between modal images and lead to the source image information loss during feature extraction. In this paper, we propose a new fusion framework for the different characteristics of infrared and visible images. Specifically, we design a dual-stream asymmetric network with two different feature extraction networks to extract infrared and visible feature maps respectively. The transformer architecture is introduced in the infrared feature extraction branch, which can force the network to focus on the local features of infrared images while still obtaining their contextual information. And the visible feature extraction branch uses residual dense blocks to fully extract the rich background and texture detail information of visible images. In this way, it can provide better infrared targets and visible details for the fused image. Experimental results on multiple datasets indicate that DSA-Net outperforms state-of-the-art methods in both qualitative and quantitative evaluations. In addition, we also apply the fusion results to the target detection task, which indirectly demonstrates the fusion performances of our method.

Infrared detection plays an important role in the military, aerospace, and other fields, which has the advantages of all-weather, high stealth, and strong anti-interference. However, infrared dim small target detection suffers from complex backgrounds, low signal-to-noise ratio, blurred targets with small area percentages, and other challenges. In this paper, we proposed a multiscale YOLOv5-AFAM algorithm to realize high-accuracy and real-time detection. Aiming at the problem of target intra-class feature difference and inter-class feature similarity, the Adaptive Fusion Attention Module - AFAM was proposed to generate feature maps that are calculated to weigh the features in the network and make the network focus on small targets. This paper proposed a multiscale fusion structure to solve the problem of small and variable detection scales in infrared vehicle targets. In addition, the downsampling layer is improved by combining Maxpool and convolutional downsampling to reduce the number of model parameters and retain the texture information. For multiple scenarios, we constructed an infrared dim and small vehicle target detection dataset, ISVD. The multiscale YOLOv5-AFAM was conducted on the ISVD dataset, compared to YOLOv7, mAP@0.5 achieves a small improvement while the parameters are only 17.98% of it. By contrast with the YOLOv5s model, mAP@0.5 was improved by 4.3% with a 6.6% reduction in the parameters. Experiments results demonstrate that the multiscale YOLOv5-AFAM has a higher detection accuracy and detection speed on infrared dim and small vehicles.

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.

This study looks at the application of delayed action Passive InfraRed (PIR) sensors in the control of water use for urinal flushing. In this we briefly review the literature on urinal controls before reviewing four different approaches to PIR urinal controls. Existing literature discusses some of the pros and cons of different types of urinal control. However, the literature doesn’t consider the marked differences that can occur within individual approaches, based on the way controls operate. This study was initiated at the University of Surrey during 2019 following a water saving audit, in an attempt to bring down what had been identified as one of the largest users of water. This paper therefore aims to identify the most effective way to reduce water consumption of urinal systems, through retro-fitting PIR control systems within the variety of settings found across university campuses. This paper also reviews the different reductions achieved over periods of different levels of use, linked to term time, holidays, Covid-19 related lockdowns, and the ‘new normal’. It found that grouped delayed action flushing was the most effective form of urinal control for reducing water use. The delayed action, grouped PIR sensors, achieved between, a 59-64% adjusted reduction against the control group during non-Covid19 periods, and a 35% reduction against the control group during lockdowns.

Enzymatically active nanocomposites are a perspective class of bioactive materials that finds their application in numerous fields of science and technology ranging from biosensors and therapeutic agents to industrial catalysts. Key properties of such systems are their stability and activity under various conditions, the problems that are addressed in any research devoted to this class of materials. Сomprehension ща the principles that affect these properties play the most important role in the development of the field, especially when it takes to a new class of bioactive systems. Recently, a new class of enzymatically doped magnetite-based sol-gel systems emerged and paved the way for a variety of potent bioactive magnetic materials with improved thermal stability. Such systems already showed themself as perspective industrial and therapeutic agents, but are still under intense investigation and many aspects are still unclear. Here we made a first attempt to describe the interaction of biomolecules with magnetite-based sol-gel materials and to investigate facets of protein structure rearrangements occurring within the pores of magnetite sol-gel matrix using dedicate Fourier-transform infrared spectroscopy.

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.

Lanthanide-doped upconversion nanoparticles (UCNPs) are promising bioimaging nanoprobes due to their excellent photostability. As one of the most commonly-used lanthanide activators, Tm3+ ions have perfect ladder-type electron configuration and can be directly excited by bio-friendly near-infrared-II (NIR-II) wavelengths. Here, the emission characteristics of Tm3+-doped nanoparticles under laser excitations of different near-infrared-II wavelengths were systematically investigated. The 1064 nm, 1150 nm and 1208 nm lasers are proposed to be three excitation strategies with different response spectra of Tm3+ ions. Particularly we found that 1150 nm laser excitation enables intense three-photon 475 nm emission, which is nearly 100 times stronger than that excited by 1064 nm excitation. We further optimized the luminescence brightness after investigating the luminescence quenching mechanism of bare NaYF4:Tm (1.75%) core. After growing inert shell, ten-fold increase of emission intensity was achieved. Combining the advantages of NIR-II wavelength and the higher-order nonlinear excitation, a promising facile excitation strategy was developed for the application of thulium-doped upconversion nanoparticles in single nanoparticle imaging and cancer cell microscopic imaging.

Synthesis and characterization of anhydrous LiZn(IO₃)₃ powders prepared from an aqueous solution are reported. Morphological and compositional analyses were carried out by scanning electron microscopy and energy dispersive X-ray measurements. The synthesized powders exhibit a needle-like morphology after annealing at 400°C. A crystal structure for the synthesized compound has been proposed from powder X-ray diffraction and density-functional theory calculations. Rietveld refinements led to a monoclinic structure, which can be described with space group P21, number 4, and unit-cell parameters a = 21.874(9) Å, b = 5.171(2) Å, c = 5.433(2) Å, and beta = 120.93(4)º. Density-functional theory calculations supported the same crystal structure. Infrared spectra were also collected and the vibrations associated to the different modes discussed. The non-centrosymmetric space group determined for this new polymorph of LiZn(IO₃)₃, the characteristics of its infrared absorption spectrum, and the observed second harmonic generation suggest a promising infrared non-linear optical material.

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.

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.

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.

The reliable online analysis of volatile compounds in exhaled breath remains a challenge as a plethora of molecules occur in different concentration ranges (i.e. ppt to %), and need to be detected against an extremely complex background matrix. While this complexity is commonly addressed by hyphenating a specific analytical technique with appropriate preconcentration and/or preseparation strategies prior to detection, we herein propose the combination of three analytical tools based on truly orthogonal measurement principles as an alternative solution: field-asymmetric ion mobility spectrometry (FAIMS), Fourier-transform infrared (FTIR) spectroscopy-based sensors utilizing substrate-integrated hollow waveguides (iHWG), and luminescence sensing (LS). These three tools have been integrated into a single compact analytical platform suitable for online exhaled breath analysis. The analytical performance of this prototype system was tested via artificial breath samples containing nitrogen (N2), oxygen (O2), carbon dioxide (CO2) and acetone as a model volatile organic compound (VOC) commonly present and detected in breath. Functionality of the combined system was demonstrated by detecting these analytes in their respectively breath-relevant concentration range and mutually independent of each other generating orthogonal yet correlated analytical signals. Finally, adaptation of the system towards the analysis of real breath samples during future studies is discussed.

One of the foundations of Smart Cities is mobility. To manage the efficiency of urban mobility, continuous recording of traffic flows is essential. The road traffic parameters in the cross section of the road network can be registered with the use of various measurement technologies. This is indirectly due to the complexity of the phenomenon of vehicle traffic and the disturbances in physical quantities generated as a result of it. Traffic data carries: sound waves, vibrations, optical waves and other media. The characteristics of these physical media, properly recorded and mathematically transformed, may constitute a superposition of the position of individual vehicles moving in the traffic flow. Moreover, these waves interfere with each other depending on the observed parameters of the movement of individual vehicles at different places of the network. In this context, information obtained from various media is the overall view of traffic. This article focuses on acquiring infrared traffic data. The movement of each vehicle, regardless of its type, is inextricably linked to infrared radiation, the distribution changing as a function of the road network. Each vehicle is a source of heat and therefore also emits infrared radiation (it heats up gradually while driving, with small temperature fluctuations). Therefore, the temperature of a moving vehicle is generally different from the background (road network) temperature. This article presents a successful attempt to analyze the infrared radiation accompanying road traffic in a selected cross section of the road. In order to register infrared radiation in the traffic stream, a prototype of a measuring system consisting of a pyrometer commonly used in thermometers (cost about 10 € and less) was used. Measurements of infrared radiation of the vehicle stream obtained in this way were compared with the measurement data of handheld traffic recorders, the so-called mechanical vehicle counters. The comparison of the results from both recording methods allows for the formulation of further research hypotheses towards extended research in the field of infrared radiation of the traffic stream. In this regard, it is important to research with various disturbances in the measurements that come from other heat sources located in the road network and its immediate vicinity. This study is important from the point of view of smart cities and C-ITS systems (mainly V2I communication). The registration of data describing the traffic flow with the use of a single pyrometer, instead of an expensive IR camera, is a cheap alternative in modern city information networks. This allows you to create intelligent sensor networks using connectivity based on, e.g., mesh networks. Such activities are possible with costs for one measuring point reduced by one or two orders of magnitude compared to the costs incurred with the use of IR cameras ( € 1000, € 10,000 and more). In the current situation of the energy crisis, the search for innovative solutions with lower energy demand is very important in the Smart City policy.

Nepal, primarily an agricultural country, heavily relies on agricultural residue and fuelwood for daily energy requirements. In 2022, total energy consumption was 640 PJ, with traditional sources accounting for 64.17%, and fuelwood comprising 58.53% of total fuel consumption. The estimated potential supply of agricultural residue is 26 million tonnes, yielding about 442 million GJ of energy in 2021. Biomass trading often emphasizes volume or weight, necessitating a rapid and non-destructive assessment of energy properties for mutual benefit, aiding in the identification, management, and utilization of biomass sources. In this study, 200 biomass samples were collected in two batches (126 and 74 samples) from various locations in Nepal. Using Partial Least Squares Regression (PLSR), a model was developed correlating higher heating value (HHV) from a bomb calorimeter and spectral data from Fourier Transform near-infrared spectroscopy (FT-NIRS) (3595 – 12,489 cm-1) sensor. PLSR models incorporated raw spectra, eight preprocessing techniques, the multi-preprocessing five-range method, and a genetic algorithm. Outliers in the first batch were identified, and the first batch divided into an 80% calibration set and a 20% validation set, while the second batch was designated as an unknown sample set. The optimum PLSR model, utilizing first derivative preprocessing, improved accuracy by 6.77%, with coefficients of determination in the calibration set, validation set, and unknown set as 0.9694, 0.9578, and 0.8089, respectively. Root mean square errors were 132.4790 J/g, 189.4800 J/g, and 360.8845 J/g for the calibration set (RMSEC), validation set (RMSEP), and unknown set (RMSEUN), respectively. The prediction to deviation ratio (RPD) for the validation set and unknown set was 4.9 and 2.4, respectively. The cross validation model of combined sample data of every sets showed the R2CV of 0.95 and RPDCV of 4.6 indicating the model could serve as a reliable and swift non-destructive alternative for evaluating biomass HHV using NIRS and ready for updating for industrial use. However, incorporating a larger number of representative samples is crucial to enhance accuracy and develop a more comprehensive global model for predicting biomass HHV.

Microplastics have been detected as widespread in an aquatic environment at the microscale, also known as plastic debris. They have continuously increased due to the increase in population, production of synthetic plastics and poor waste management. They are ubiquitous and slowly degrade in soil and water. They are emerging contaminants that have received attention from research communities and public audiences over the last few years. They have high stability and can absorb several other pollutants like heavy metals, pesticides, etc. After entering the environment, they can accumulate and persist for a long time. They can create a serious threat to freshwater ecosystems and human health. These particles can cause physical damage to freshwater organisms. Raman spectroscopy, Fourier-transform infrared spectroscopy, visual identification, density separation, microscopic method, chemical method, thermos-analytical method and hyperspectral imaging method are the commonly known approaches for identification and quantification of microplastics. The noticed concentration of microplastics depends on the analysis method, sampling location and technique. The authors reviewed the sources, health impact, transport and treatment of microplastics in freshwater environments in detail. This study will provide the baseline data for the researchers to do more research on microplastic pollution in the future.

The flour milling industry, a vital component of global food production, is undergoing a transformative phase driven by the integration of smart devices and advanced technologies. This transition promises improved efficiency, quality, and sustainability in flour production. Accurate estimation of protein, moisture and ash content in wheat grains and flour is of paramount importance due to their direct impact on product quality and compliance with industry standards. This paper explores the application of Near-Infrared (NIR) spectroscopy as a non-destructive, efficient, and cost-effective method for measuring the aforementioned essential parameters in wheat and flour, by investigating the effectiveness of a low-cost handle NIR spectrometer. Furthermore, a novel approach using Fuzzy Cognitive Maps (FCMs) is proposed to estimate protein, moisture and ash content in grain seeds and flour, marking the first known application of FCMs in this context. Our study includes an experimental setup that assesses different types of wheat seeds and flour samples and evaluates three NIR pre-processing techniques to enhance parameter estimation accuracy. The results indicate that low-cost NIR equipment can contribute to the estimation of the under-study parameters.

The mechanism of anionic/cationic combination collector ST-8 for the efficient separation of spodumene and feldspar was investigated by flotation tests, surface tension measurements, zeta potential measurements and infrared spectroscopy analysis. The flotation results indicated that when the optimum molar combination of sodium oleate and dodecylamine was 6:1-10:1 and pH=8.5 or so, the recovery of spodumene reached a maximum of 80% and that of feldspar reached about 24.5%. Zeta potential measurements showed that the negative shift of the kinetic potential on the surface of spodumene was much stronger than that on the surface of feldspar, indicating that the adsorption capacity of the combined collector on spodumene surface and feldspar surface was greatly different. From the surface tension as well as the synergistic parameters, there was a strong mutual attraction between dodecylamine and sodium oleate, and there was a significant synergistic effect between them. Infrared spectroscopy indicated that the combined collector chemisorbed on the mineral surface, and the intensity of the absorption peak after the action of spodumene was much stronger than that after the action of feldspar. The solution chemistry of the combined collector revealed that at pH 8.5, the positively charged ions RNH3+ and (RNH3+)22+ in dodecylamine and the negatively charged RCOO- and (RCOO-)22- ions in oleic acid form highly reactive complexes to interact with the mineral surface by electrostatic gravitation, thus achieving efficient separation of spodumene and feldspar. Finally, from the closed circuit test results, a flotation index of 6.34% Li2O grade and 88.51% Li2O recovery was obtained.

Food safety and quality are the first steps in the food chain. This work reports a low-cost and easy-to-use optoelectronic measurement system for improving feed chain quality and safety, based on near-infrared spectrometry (NIRS) technology. This is a significant challenge for dairy farm technicians and producers who need rapid and reliable knowledge of forage quality on their farms. In most cases, instrumentation suitable for these specifications is expensive and difficult to operate. The core of the measurement system is Texas Instruments´ NIRscan Nano Evaluation Module (EVM) spectrometer. This module has a large sensing area and high resolution suitable for forage samples. To evaluate the feasibility of the prototype to analyze agrifood samples, different ways of presenting the sample, intact or ground, were tested. The final objective of the research is not just to check the efficiency of the proposed system. It is also to determine the measurement system characteristics and how to improve them.

Background: In recent years, the assessment and guidance of cardiopulmonary resuscitation (CPR) quality using noninvasive and invasive monitoring techniques have been increasingly recommended. In this study, we introduce a new physiological monitoring system that simultaneously measures arterial pressure (AP), venous pressure (VP), and cerebral tissue oxygen saturation (SctO2) during CPR. Methods: This prospective observational study was conducted at a single center (Kagoshima City Hospital). Adult out-of-hospital patients aged ≥18 years with an intravenous femoral arterial line, venous line, and SctO2 monitor were included. We measured femoral arterial pressure (FAP) and femoral venous pressure (FVP) invasively if catheters were immediately inserted into the femoral artery and vein for potential candidates who require interventions such as extracorporeal cardiopulmonary resuscitation but did not receive such interventions as a result. Results: We observed several representative cases that provided the following insights: We presented several cases, including two patients with significant increases in FVP and low SctO2 values, and in both cases, return of spontaneous circulation (ROSC) was not achieved. In contrast, we also presented two cases in which CPR resulted in higher FAP compared to FVP and an upward trend in SctO2 values was observed, and both instances achieved ROSC. Conclusion: We presented a simultaneous physiological monitoring system that can monitor AP, VP, and SctO2 during CPR. Further case accumulations will be necessary to assess the variations in hemodynamic status during CPR and the association between each hemodynamic status and outcomes after cardiac arrest.

Hypoxic-ischemic encephalopathy (HIE) secondary to perinatal asphyxia occurs when the brain does not receive enough oxygen and blood. A surrogate marker for ‘intact survival’ is necessary for the successful management of HIE. The severity of HIE can be classified based on clinical presentation, including presence of seizures, using a clinical classification scale called Sarnat staging; however, Sarnat staging is subjective and the score changes over time. Furthermore, seizures are difficult to detect clinically and are associated with a poor prognosis. Therefore, a tool for continuous monitoring on the cot side is necessary, for example, electroencephalogram (EEG) that non-invasively measures the electrical activity of the brain from the scalp. Then, multimodal brain imaging, when combined with functional near-infrared spectroscopy (fNIRS), can capture the neurovascular coupling (NVC) status. In this study, we first tested the feasibility of a low-cost EEG-fNIRS imaging system to differentiate between normal, hypoxic, and ictal states in a perinatal ovine hypoxia model. Here, the objective was to evaluate a portable cot side device and autoregressive (ARX) modelling to capture the perinatal ovine brain states during a simulated HIE injury. So, ARX parameters were tested with a linear classifier using a single differential channel EEG, with varying states of tissue oxygenation detected using fNIRS, to label simulated HIE states in a perinatal ovine hypoxia model. Then, we showed the technical feasibility of the low-cost EEG-fNIRS device and ARX modeling with support vector machine classification for a human HIE case series with and without sepsis. The classifier trained with the ovine hypoxia data labelled ten severe HIE human cases (with and without sepsis) as “hypoxia” group and the four moderate HIE human cases as the “control” group. Furthermore, we showed the feasibility of experimental modal analysis (EMA) based on the ARX model to investigate the NVC dynamics using EEG-fNIRS joint-imaging data that differentiated six severe HIE human cases without sepsis from four severe HIE human cases with sepsis. In conclusion, our study showed the technical feasibility of EEG-fNIRS imaging, ARX modeling of NVC for HIE classification, and EMA that may provide a biomarker to detect sepsis effects on the NVC in HIE.

Diffuse optical tomography (DOT) is an optical medical imaging method that can assess the structural properties of tissues and their functional characteristics, such as hemoglobin concentration, water content, as well as lipid concentration, via three-dimensional image reconstruction. This paper presents the theoretical basis and working principle of diffuse optical tomography. The paper explains how the optical properties of tissue can be imaged by photon migration techniques based on diffusion theory. The author presents the reasoning for near-infrared (NIR) imaging as the most effective technique in terms of quantitative recovery of spectroscopic optical parameters. This work also listed various advantages, applications, and challenges of DOT. The paper also briefly discusses current progress in near-infrared medical imaging and its future direction.

This work presents the first evidence that dissolved globular proteins change the arrangement of hydrogen bonds in water, with different proteins showing quantitatively different effects. Using ATR-FTIR (Attenuated Total Reflection – Fourier Transform Infrared) spectroscopic analysis of OH-stretch bands, we obtain quantitative estimates of the relative amounts of the previously reported four subpopulations of water structures coexisting in a variety of aqueous solutions. Where solvatochromic dyes can measure the properties of solutions of non-ionic polymers, the results correlate well with ATR-FTIR measurements. In protein solutions to which solvatochromic dye probes cannot be applied, NMR (Nuclear Magnetic Resonance) spectroscopy was used for the first time to estimate the hydrogen bond donor acidity of water. We found strong correlations between the solvent acidity and arrangement of hydrogen bonds in aqueous solutions for several globular proteins. Even quite similar proteins are found to change water properties in dramatically different ways.

This study was carried out to determine the effects of apple vinegar and sodium diacetate addition on the aerobic stability of fermented high moisture maize grain (HMM) silage after opening. In the study, the effect of three different levels (0, 0.5, 1.0%) of apple vinegar (AV) and sodium diacetate (SDA) supplementation to fermented HMM at two different storage conditions (27-29°C, 48% Humidity; 35-37°C, 26% Humidity) were investigated. The material of the study was fermented rolled maize grain with 62% moisture content stored for about 120 days. Silage samples were subjected to aerobic stability test with 3 replicates for each treatment group. Wendee and microbiological analyses were made at 0, 2, 4, 7, and 12 days. Meanwhile, samples were displayed in the T200 IR brand thermal camera. According to the thermogram results, 1% SDA addition positively affected HMM silages at the second and fourth days of aerobic stability at both storage conditions (p<0.05). Aerobic stability and infrared thermography analysis indicated that 1% AV, 0.5%, and 1% SDA additions to HMM silages had promising effects. Due to our results, we concluded that thermal camera images might be used as an alternative quality indicator for silages in laboratory conditions.

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.

Background: Post-operative pain and swelling are frequently observed after sinus lift procedures. The aim of the present study was a clinical evaluation of swelling and pain of two sinus lift different flaps technique measured through visual analogue scale (VAS), verbal rating scale (VRS) and infrared thermal imaging (i.e., thermography). Methods: A randomized controlled trial was conducted in fifteen patients treated for a total of 30 sinus randomly allotted into two groups. In patients of Group I, a triangular flap was performed, while in Group II modified triangular flap without anterior release was performed. Postoperative pain was scored by VAS and inflammation was recorded by VRS at 2, 4, 6 and 14 days. The facial temperature was recorded before and after sinus augmentation, at 2, 4, 6 day intervals for the first week and at 14 day, to check the course of healing. Results: In group I the pain intensity was recorded at 2 days after the surgery with a score of 38.67 ± 6.4 mm. Swelling was higher at 2 and 4 day, and it was absent at day 6. Facial temperature difference before and after the procedure was 3 (4.737 °C ± 0.37). In Group II the pain was lower than in Group I (p < 0,05). Swelling scored 2 on the first and second day, and it was reduced at day 4. After the second day the difference of temperature reduced significantly compared to day of surgery (0.77 °C), at 2 and 4 days no difference was registered. Conclusion: In conclusion based on the outcome of the present study, modified triangular flap can be used with success in sinus augmentation procedures and is to be preferred to trapezoidal flap.

The StarDICE experiment strives to establish an instrumental metrology chain with a targeted accuracy of 1 mmag in griz bandpasses to meet the calibration requirements of next-generation cosmological surveys. Atmospheric transmission stands out as a significant source of systematic uncertainty. Specifically, gray extinction induces spurious variations of photometric fluxes. We propose a solution relying on an uncooled long-wave infrared thermal camera to evaluate it. This type of camera can provide real-time insights into atmospheric conditions and detect cirrus clouds. However, achieving accurate measurements with thermal imaging systems necessitates prior calibration due to the influence of temperature-induced effects, which compromise their spatial and temporal precision. Moreover, these systems cannot provide scene radiance values in physical units by default. This study introduces a new calibration process utilizing a tailored forward modeling approach. The method is applied to an uncooled FLIR Tau2 thermal camera. It incorporates sensor, housing, flat-field support, and ambient temperatures, along with raw digital response, as input data. Experimental measurements are conducted inside a climatic chamber, with the camera imaging a thermoregulated blackbody source. Results demonstrate the calibration effectiveness, achieving precise radiance measurements with a temporal pixel root-mean-square error (RMSE) of 0.09 W/m2/srand residual spatial noise of 0.03 W/m2/sr.

Focusing on the problem of identifying and classifying aero-engine models, this paper measures the infrared spectrum data of aero-engine hot jets using a telemetry Fourier transform infrared spectrometer. Simultaneously, infrared spectral data sets with the six different types of aero-engines are created. For the purpose of classifying and identifying infrared spectral data, a CNN architecture based on the continuous wavelet transform peak seeking attention mechanism (CWT-AM-CNN) is suggested. This method calculates the peak value of middle wave band by continuous wavelet transform, and the peak data is extracted by the statistics of the wave number locations with high frequency. Attention mechanism is used for the peak data, and the attention mechanism is weighted to the feature map of the feature extraction block. The training set, vali-dation set and prediction set are divided in the ratio of 8:1:1 for the infrared spectral data sets. For three different data sets, CWT-AM-CNN proposed in this paper is compared with the classical classifier algorithm based on CO2 feature vector and the popular AE, RNN and LSTM spectral processing networks. The prediction accuracy of the proposed algorithm in the three data sets is as high as 97%, and the lightweight network structure design not only guarantees high precision, but also has a fast running speed, which can realize the rapid and high-precision classification of the infrared spectral data of the aero-engine hot jets.

The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions. The study developed a novel method for evaluating the freshness of citrus fruits by integrating near-infrared spectroscopy with the nonlinear data processing capabilities of a BP neural network. This approach utilizes specific wavelength analysis to distinguish between fresh and non-fresh fruits effectively. Advanced preprocessing techniques are employed to remove spectral anomalies, enhancing the network's ability to accurately identify crucial quality indicators like sugar content. Concurrently, an experiment utilising a MATLAB-based BP neural network optimised the number of hidden layer nodes, identifying 61 as optimal. This configuration achieved impressive metrics, including a mean squared error of 0.0025665 and a root mean squared error of 49.8214, over 1000 training iterations with an 80% learning rate. The model demonstrated a high accuracy rate of 97.6275%, confirming its precision and reliability in assessing citrus freshness. This synergy between advanced neural network processing and spectroscopic techniques marks a significant advancement in agricultural quality assessment, setting new standards for speed and efficiency in data processing.

In this study, a new diphosphate compound, Ag0.2Na0.6FeP2O7, was synthesized using a solid-state reaction method and extensively studied using various techniques. The compound's structure was confirmed through X-ray diffraction analysis, revealing a single-phase monoclinic structure with the P21/C space group. The electrical behavior of the compound was investigated, and its conductivity followed Jonscher's universal law. The correlated barrier hopping (CBH) model explained the temperature-dependent ac conductivity and parameter "s" well. The study also explored the contribution of single-polaron and bipolaron hopping to the a.c. conductivity and discussed the ionic conductivity in relation to the structural characteristics of the sample. Overall, this investigation significantly enhances our comprehension of the distinctive electrical properties of Ag0.2Na0.6FeP2O7 and its potential practical applications.

Recently, polyacetylene (PA) is receiving a renewed scientific attention due to its electrical properties potentially useful for energy applications (e.g., fabrication of electrodes for batteries and supercapacitors) and unique functional characteristics (e.g., flame retardant, oxygen scavenger, EMI-shielding, etc.). This chemical compound can be obtained in form of a polyacetylene-PVOH copolymers simply by chemical dehydration of poly(vinyl alcohol) (PVOH), which is a very common type of polymer, widely used in packaging and other technological areas. This very inexpensive chemical reaction for the large-scale synthesis of PA/polyvinylenes has been investigated by reacting PVOH with fuming sulfuric acid at room temperature. In this process, PVOH, shaped in form of film, is dipped in fuming sulfuric acid (i.e., H₂SO₄ at 95-97%) and, after complete chemical-dehydration, it has been mechanically removed from the liquid phase by using a nylon sieve. The reduction process leads to a substantial PVOH film conversion to PA as it has been proved by infrared spectroscopy (ATR-mode). Indeed, the ATR spectrum of the reaction product included all characteristic absorption bands of PA. The reaction product has been also characterized by UV-Vis spectroscopy in order to evidence the presence in the structure of conjugated carbon-carbon double bonds of various lengths. Differential scanning calorimetry (DSC) and thermogravimetric analysis have been used respectively to investigate the PA solid-state cis-trans isomerization and thermal stability in air and nitrogen, respectively.

Environmental factors, such as drought-stress, significantly impact maize growth and productivity worldwide. To improve yield and quality, effective strategies for early detection and mitigation of drought-stress in maize are essential. This paper presents a detailed analysis of three imaging trials conducted to detect drought-stress in maize plants using an existing, custom-developed, low cost, high throughput phenotyping platform. We propose a pipeline for early detection of water stress in maize plants using a Vision Transformer classifier and analysis of distributions of near-infrared (NIR) reflectance from the plants. We also explored suitable regions on the plant that are more sensitive to drought-stress and show that the region surrounding the youngest expanding leaf (YEL) and the stem can be used as a more consistent alternative to analysis involving just the YEL. Our results show good separation between well-watered and drought-stressed trials for two out of the three imaging trials both in terms of classification accuracy from data-driven features as well as through analysis of histograms of NIR reflectance.

Infrared small target detection (ISTD) plays a crucial role in both civilian and military applications. The detection of small targets against dense cluttered backgrounds remains a challenging task, requiring the collaboration of false alarm source elimination and target detection. Existing approaches primarily focus on modeling targets while often neglecting false alarm sources. To address this limitation, we propose a Target and False Alarm Collaborative Detection Network to leverage the information provided by false alarm sources and the background. Firstly, we introduce a False Alarm Source Estimation Block (FEB) that estimates potential interferences present in the background by extracting features on multiple scales and employing stepwise upsampling for feature fusion. Subsequently, we propose a framework that employs multiple FEBs to eliminate false alarm sources across multiple scales. Finally, a Target Segmentation Block (TSB) is introduced to accurately segment the targets to produce the final detection result. Experiments on public datasets demonstrate that, compared to other methods, our model achieves high accuracy in segmenting targets while being able to extract false alarm sources which can be used for further studies.

Antarctica and its surrounding environment are considered untouched and thought that it is free from microplastics (MPs) pollution. However, recent studies and science projects have reported MPs in both water and sediment in the South Polar Regions. The reports state that the MP’s pollution occurs in this region due to fishing, tourism, and research activities by the nearby countries and nature's circulation is also part of it. The Antarctic Treaty System (ATS) has received attention on MP’s pollution and initiated research on it. MPs are tiny plastic particles with a size of less than 5 mm. It has two types, 1. Primary MPs have been manufactured directly for various applications like cosmetics and scrubbing etc 2. The secondary MPs are generated by photochemical degradation of large plastics.Although several studies have been done there is a quite gap in our understanding of the concentration, characteristics, and impact of plastics on the ecosystem of the Antarctic Region. The impact of MP’s pollution in this region may be very high. The presence of MP is a serious issue that is affecting not only the aquatic environment but also humans. It is an alarming situation that causes environmental damage. The main objective of this paper is to review MP's introduction, occurrence, sources, harmful effects, and detection methods. This review highlighted the various methodologies and analyses like density separation, microscope observation of MP’s properties Fourier-transform infrared spectroscopy (FTIR), and Raman spectrometer respectively, and urged for more research in the future and gave several recommendations to maintain the pristine region near Antarctica.

Amorphous germanium films on non-refractory glass substrates were annealed by ultrashort near-infrared (1030 nm, 1.4 ps) and mid-infrared (1500 nm, 70 fs) laser pulses. Crystallization of germanium irradiated at a laser energy density (fluence) range from 25 to 400 mJ/cm2 under single-shot and multi-shot conditions was investigated using Raman spectroscopy. The dependence of the fraction of the crystalline phase on the fluence was obtained for picosecond and femtosecond laser annealings. The regimes of almost complete crystallization of germanium films over the entire thickness are obtained (from the analysis of Raman spectra with excitation of 785 nm laser). The possibility of scanning laser processing is shown, which can be used to create films of micro and nanocrystalline germanium on flexible substrates.

Silicon-based photodetectors, as low-cost and environmentally friendly optical sensors, are attractive. Also, their compatibility with complementary metal-oxide-semiconductor (CMOS) technology is advantageous for the development of silicon photonics systems. However, extending optical responsivity of silicon-based photodetectors to the mid-infrared (mid-IR) wavelength range remains challenging. In developing mid-IR infrared Schottky detectors, nanoscale metals are critical. Nonetheless, one key factor is the Fermi level pinning effect at the metal/silicon interface and the presence of metal-induced gap states (MIGS). Here, we demonstrate the utilization of the passivated surface layer on semiconductor materials as an insulating material in metal-insulator-semiconductor (MIS) contacts to mitigate the Fermi level pinning effect. The removal of Fermi level pinning effectively reduces the Schottky barrier height by 12.5% to 16%. The demonstrated devices exhibit a high responsivity of up to 234 μA/W at a wavelength of 2 μm, 48.2 μA/W at 3 μm, and 1.75 μA/W at 6 μm. The corresponding detectivities at 2 and 3 μm are 1.17×10^8 cm Hz^1/2 W^-1 and 2.41×10^7 cm Hz^1/2 W^-1, respectively. The expanded sensing wavelength range contributes to the application development of future silicon photonics integration platforms.

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.

Individual differences in the responsiveness of the brain to transcranial electrical stimulation (tES) is increasingly demonstrated in large variability in the tES effects. Anatomically detailed computational brain models have been developed to address this variability; however, static brain models are not ‘realistic’ in accounting for the dynamic state of the brain. Therefore, human-in-the-loop optimization is proposed in this perspective article based on an extensive systems analysis of the tES neurovascular effects. First, modal analysis was conducted using a physiologically detailed neurovascular model that found stable modes in the 0 Hz to 0.05 Hz range for the pathway for vessel response through the smooth muscle cells, measured with functional near-infrared spectroscopy (fNIRS). tES effects in the 0 Hz to 0.05 Hz range can also be measured with functional magnetic resonance imaging (fMRI)-tDCS data with a maximum TR=10sec. Therefore, we investigated an open-source fMRI-tDCS dataset that used a TR=3.36sec. We found that both the anodal tDCS condition and sham tDCS condition had similar Finite Impulse Response at the region of interest underlying the anode and a remote location, which indicated a global hemodynamic effect of sham tDCS beyond the intended transient sensations. Here, transient sensations can have arousal effects on the hemodynamics so we conducted a healthy case series for black box modeling of fNIRS-pupillometry of short-duration tDCS effects. The block exogeneity test rejected the claim that tDCS is not a 1-step Granger-cause of the fNIRS total hemoglobin changes (HbT) and pupil dilation changes (p<0.05). Also, grey-box modeling using fNIRS of the tDCS effects in chronic stroke showed HbT response to be significantly different (paired-sample t-test, p<0.05) between the ipsilesional and the contralesional hemisphere for primary motor cortex tDCS and cerebellar tDCS which was subserved by the smooth muscle cells. Here, our perspective is that various physiological pathways subserving tES effects can lead to state-trait variability that can be challenging for clinical translation. Therefore, we conducted a case study on human-in-the-loop optimization using our reduced dimension model and a stochastic, derivative-free Covariance Matrix Adaptation Evolution Strategy. Future studies need to investigate human-in-the-loop optimization of tES for reducing inter-subject and intra-subject variability in tES effects.

In this contribution, we present a high-speed multiplex grating spectrometer based on a spectral coding approach that is founded on principles of compressive sensing. The spectrometer employs a single-pixel InGaAs detector to measure the signals encoded by an amplitude spatial light modulator (digital micromirror device, DMD). This approach leads to a speed advantage and multiplex sensitivity advantage atypical for standard dispersive systems. Exploiting the 18.2 kHz pattern rate of the DMD, we demonstrate 4.2 ms acquisition times for full spectra with a bandwidth of 450 nm (5250 cm-1 – 4300 cm-1; 1.9 µm – 2.33 µm). Due to the programmability of the DMD, spectral regions of interest can be chosen freely, thus reducing acquisition times further, down to the sub-millisecond regime. The adjustable resolving power of the system accessed by means of computer simulations is discussed, quantified for different measurement modes, and verified by comparison with a state-of-the-art Fourier-transform infrared spectrometer. We show measurements of characteristic polymer absorption bands in different operation regimes of the spectrometer. The theoretical multiplex advantage of 8 was experimentally verified by comparison of the noise behavior of the spectral coding approach and a standard line-scan approach.

The absolute absorption cross section of the ethyl peroxy radical, C2H5O2, in the Ã←X ̃ electronic transition with the peak wavelength at 7596 cm-1, has been determined by the method of dual wavelengths time resolved continuous wave cavity ring down spectroscopy. C2H5O2 radicals were generated from pulsed 351 nm photolysis of C2H6/Cl2 mixture in presence of O2 and detected on one of the CRDS paths. Two methods have been applied for the determination of the C2H5O2 absorption cross section: (i) based on Cl-atoms being converted alternatively to either C2H5O2 by adding C2H6 or to hydro peroxy radicals, HO2, by adding CH3OH to the mixture, whereby HO2 was reliably quantified on the second CRDS path in the 21 vibrational overtone at 6638.2 cm-1 (ii) based on the reaction of C2H5O2 with HO2, measured under either excess HO2 or under excess C2H5O2 concentration. Both methods lead to the same peak absorption cross section of C2H5O2,7596 cm-1 = (1.0±0.2) × 10-20 cm2. The rate constant for the cross reaction between of C2H5O2 and HO2 has been measured to be (6.5±1.6) × 10-12 cm3 molecule-1 s-1.

A focal plane array (FPA) detector was used for hyperspectral imaging in the infrared (IR) spectral region using thermal and synchrotron light sources. FPA Fourier-transform IR (FTIR) imaging microspectroscopy will be able to monitor real time changes at specific absorption bands when combined with high brightness synchrotron source. In this study, several types of samples with unique structural motifs were selected and used for assessing the capability of the FPA-FTIR imaging technique. It was shown that the time required for polariscopy at IR wavelengths can be substantially reduced by the FPA-FTIR imaging approach. By using natural and laser fabricated polymers with sub-wavelength features, alignment of absorbing molecular dipoles was revealed as well as higher order patterns (laser fabricated structures). Micro-spectroscopy of absorber orientation reveals alignment patterns even when they are not spatially resolved.

In this work performance improvements are described of a low power consumption non-dispersive infrared (NDIR) methane (CH₄) gas sensor using customised optical thin film bandpass filters (BPF). BPF’s shape the spectral characteristic of the combined mid infrared III-V based light emitting diode (LED)/ photodiode (PD) light source/detector optopair, enhancing NDIR CH₄ sensor performance. The BPF, deposited using a novel microwave plasma assisted pulsed DC sputter deposition process, is deposited at room temperature directly onto the temperature sensitive PD heterostructure. BPF’s comprise germanium (Ge) and niobium pentoxide (Nb₂O₅) alternating high and low refractive index layers respectively. Two different optical filter designs are progressed; with BPF bandwidths (BWs) of 160 nm and 300 nm. Comparison of modelled and measured NDIR sensor performance is described, highlighting maximized signal to noise ratio (SNR) and minimized cross talk performance benefits. BPF spectral stability for various environmental temperature and humidity conditions is demonstrated.

Clay ball is a pavement surface defect which refers to a clump in which clay or dirt is mixed with hot asphalt mixture. Clay ball is typically caused by a combination of aggregate contamination of clay or soil, high aggregate moisture, and low production temperature at the asphalt plant. It usually appears a few weeks or months after paving under traffic load, after being liquefied and knocked from the pavement surface. Clay balls can be the source of potholing, raveling, and other issues such as moisture infiltration and reduced ride quality. This paper presents an investigation of the clay balls on US-31 one winter after construction in Hamilton County, Indiana. In order to understand the pavement condition, their severity was measured using both visual observation and infrared image collection system. In addition, a clay ball distribution pattern, its density, and cores condition were evaluated. A precipitation effect on clay ball formation was investigated for finding a cause of the clay balls. The investigation found that infrared image collection system was appropriate in detecting the clay balls. The clay balls were elliptic in shape with 1 inch to 4 inches in diameter, and the maximum clay ball depth is almost penetrating the entire surface course. It was also found that the asphalt paving on the raining days or right after raining could increase the potential of clay balls. Monitoring of aggregate moisture during construction on or after raining days should be able to reduce the risk of clay balls.

This article discusses the results of fluid inclusions study in barite collected at the Jan Mayen vent field area (Troll Wall and Perle &amp; Bruce) and Loki’s Castle vent field of the Mohns Ridge segment of the Arctic Mid-Ocean Ridge. Three mafic-hosted volcanogenic massive sulfide deposits were examined within the active vent fields that adequately correspond to geological settings of ultraslow-spreading ridges and PT-conditions. Hydrothermal sediments were investigated to determine the temperature and salinity of the fluids responsible for barite precipitation. The hydrothermal origin of the barite was confirmed by its morphology. Fluid inclusions are two-phase and homogenized into the liquid phase on heating at temperatures below 287ºC. The salt concentration in fluids trapped in inclusions is 2.6–4.4 wt.% NaCl eq. The crystallization temperatures varied from 276ºС to 119ºС and from 307ºС to 223ºС for the Jan Mayen and Loki’s Castle vent fields, respectively. The data obtained allowed us to confirm evidence of fluid phase separation in the hydrothermal systems and to expand our knowledge of the temperature and salinity of mineral fluids previously known from recent direct measurements during the cruises within the G.O. Sars research vessel. The fluid inclusions data obtained from barites emphasize the fluid features characteristic of volcanogenic massive sulfide deposits, the similarities and differences for the studied hydrothermal sites, and allow comparisons with similar products from other active hydrothermal systems.

The success of near-infrared spectroscopy (NIRS) analysis hinges on the precision and robustness of the calibration model. Shallow learning (SL) algorithms like partial least squares discriminant analysis (PLS-DA) often fall short in capturing interrelationships between adjacent spectral variables, and the analysis results are easily affected by spectral noise, which dramatically limits the breadth and depth application of NIRS. Deep learning (DL) methods, with their capacity to discern intricate features from limited samples, are progressively integrated into NIRS. In this paper, two discriminant analysis problems including wheat kernels and Yali pears were used as examples, and several representative calibration models to research the robustness and effectiveness of the model. Additionally, this article proposed a near-infrared calibration model, which is based on gramian angular difference field and coordinate attention convolutional neural networks (G-CACNN). The research results show that, compared with SL, spectral preprocessing has a smaller impact on the analysis accuracy of consensus learning (CL) and DL, and the latter has the highest analysis accuracy in the modeling results of using the original spectrum. The accuracy of G-CACNN in two discrimination tasks is 98.48% and 99.39%, respectively. Finally, This research compared the performance of various models under noise to evaluate the robustness and noise resistance of the proposed method.

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.

The administration of regional neuraxial analgesia for pain relief during labor is widely recognized as a safe and effective method involving medication delivery into the epidural or subarachnoid space in the lower back. This study proposes an innovative semantic image segmentation methodology emphasizing enhanced interpretability using convolutional Random Fourier Features and layer-wise weighted class-activation maps tailored explicitly for foot segmentation in regional analgesia monitoring. Namely, our contribution is twofold: i) a novel Random Fourier Features layer is introduced to deal with image data to enhance three well-known architectures (FCN, UNet, and ResUNet); ii) three novel quantitive measures are presented to evaluate the interpretability of a given deep learning model devoted to segmentation tasks. Our approach is rigorously evaluated on a demanding dataset of foot thermal images from pregnant women who received epidural anesthesia. Its small size and considerable variability characterize the dataset. Our validation results demonstrate that the proposed methodology not only achieves competitive foot segmentation performance but also significantly enhances the explainability of the process, rendering it well-suited for applications such as epidural insertion during labor.

The increasing regulatory pressure to monitor and reduce GHG emissions and air pollutants requires cost-effective methods for their surveillance. The most common technique used for scientific investigations on gas concentration monitoring in barns are accurate but expensive and with a complex maintenance. This research study analysed the potential use of a low-cost portable measurement devices for the measurement of ammonia (NH3) and carbon dioxide (CO2) concentrations in an open dairy barn. A comparison between gas concentrations acquired at different heights from the floor by using portable devices and those acquired by a photoacoustic infrared multigas spectroscope (i.e., reference measurement) in the same sampling locations was carried out to determine the precision of the low-cost portable devices. The performance of low-cost portable devices was statistically analysed by the application of the one-way analysis of variance, correlation analysis and regression analysis. The results showed a significant difference between gas concentration values at various heights from the floor for both NH3 and CO2. The correlations between concentrations acquired by the low-cost portable device and the INNOVA were statistically significant (r=0.83; P&lt;0.001) for gas concentrations monitored at 0.4 m from the floor. Compared with the reference measurement device, the low-cost devices were effective in the monitoring of NH3 concentrations at 0.40 m from the floor though it underestimated them in the barn at increasing of the height from the floor, whereas the device was not adequate for CO2 concentrations. In detail, the relative measurement error of the low-cost devices compared to INNOVA was reduced close to the floor during NH3 concentration measurements. Within these limitations, this device could be useful for monitoring NH3 concentration in the barn and to assess variation in NH3 concentrations mainly related to the animal occupied zone. Further efforts are needed in this field of research to identify low-cost device that could simplify emission estimation from open dairy barns.

In this study, divalent Yb2+-doped silica fiber was fabricated using rod-in-tube technology. The fiber core of Yb2+-doped silica glass was prepared by high-temperature melting technology under vacuum conditions. The spectroscopic properties of the Yb2+-doped glass and fiber were studied. The experiments indicate that divalent Yb2+-doped glass has a high quantum efficiency and superbroadband fluorescence in the visible region with an excitation wavelength of 405 nm. In additionally, the results suggest that Yb2+-doped fiber has a potential for application in visible fiber laser and fiber amplification.

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.

High-resolution laboratory-based thermal infrared spectroscopy is an up-and-coming tool in the field of geological remote sensing. Its spatial resolution allows for detailed analyses at centimeter to sub-millimeter scale. However, this increase in resolution creates challenges with sample characteristics such as grain size, surface roughness and porosity that can influence the spectral signature. This research explores the effect of rock sample surface preparation on the TIR spectral signatures. We applied three surface preparation methods (split, saw and polish) to determine how the resulting differences in surface roughness affects both the spectral shape as well as the spectral contrast. The selected samples are a pure quartz sandstone, a quartz sandstone containing a small percentage of kaolinite, and an intermediate-grained gabbro. To avoid instrument or measurement type biases we conducted measurements on three TIR instruments, resulting in directional hemispherical reflectance spectra, emissivity spectra and bi-directional reflectance images. Surface imaging and analyses were performed with scanning electron microscopy and profilometer measurements. We demonstrate that surface preparation affects the TIR spectral signatures influencing both the spectral contrast as well as the spectral shape. The results show that polished surfaces predominantly display a high spectral contrast while the sawed and split surfaces display up to 25% lower reflectance values. Furthermore, the sawed and split surfaces display spectral signature shape differences at specific wavelengths, which we link to mineral transmission features, surface orientation effects and multiple reflections in fine-grained minerals. Hence, the influence of rock surface preparation should be taken in consideration to avoid an inaccurate geological interpretation.

Blood perfusion quantification is important vital parameters in different diagnostic procedure, using infrared thermography imaging; it is reliable to use this technique as non-contact, non-invasive blood flow measurement method. Therefore, we developed a measurement protocol for blood flow over the arm's anterior surface. By using the superficial brachial and radial veins to be monitored under the impact of cold-excitation of (2 °C to 5 °C), the blood perfusion signal was detected using thermal imager of long-wave infrared spectral range (LWIR, 7μm - 14 μm). The simulation of Penne's bioheat transfer equation was performed to be compared with results obtained from the infrared thermography. Furthermore, the proposed blood flow monitoring using external adjusting of the excitation temperature, by using (cold-compress, or cold air-stream) applied to the region under testing. The signal detected resembles to the hemodynamic pulse of the superficial veins, in the definition of systolic and diastolic phases of the cardiac cycle. Moreover, statistical analysis applied to the BFIRT signals from 24 subjects to estimate the skin's mean temperature after recovery from the thermal excitation.

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.

Land Surface Temperature (LST) is routinely retrieved from remote sensing instruments using semi-empirical relationships between top of atmosphere (TOA) radiances and LST, using ancillary data such as total column water vapor or emissivity. These algorithms are calibrated using a set of forward radiative transfer simulations that return the TOA radiances given the LST and the thermodynamic profiles. The simulations are done in order to cover a wide range of surface and atmospheric conditions and viewing geometries. This work analyses calibration strategies, considering some of the most critical factors that need to be taken into account when building a calibration dataset, covering the full dynamic range of relevant variables. A sensitivity analysis of split-windows and single channel algorithms revealed that selecting a set of atmospheric profiles that spans the full range of surface temperatures and total column water vapor combinations that are physically possible seems beneficial for the quality of the regression model. However, the calibration is extremely sensitive to the low-level structure of the atmosphere indicating that the presence of atmospheric boundary layer features such as temperature inversions or strong vertical gradients of thermodynamic properties may affect LST retrievals in a non-trivial way. This article describes the criteria established in the EUMETSAT Land Surface Analysis – Satellite Application Facility to calibrate its LST algorithms applied both for current and forthcoming sensors.

Novel double-impurity doping process for silicon (Si) surfaces was developed, utilizing nanosecond-laser melting of a top 11-nm thick gold (Au) film and a Si wafer substrate in a laser plasma-activated liquid-nitrogen (LN) environment. Scanning electron microscopy revealed the fluence- and exposure-independent surface micro-spike topography, while energy-dispersive x-ray spectroscopy identified minor Au (0.05 at. %) and major N (1-2 at. %) dopants, localized within a 0.5-m thick surface layer, and slight surface post-oxidation of the micro-relief (oxygen (O), 1.5-2.5 at. %). X-ray photoelectron spectroscopy identified bound surface (SiNx) and bulk doping chemical states of the introduced nitrogen (10 at. %), metallic (&lt;0.01 at. %) and cluster (&lt;0.1 at. %) forms of gold dopant, and evaluated their depth distributions, strongly affected by the competition between gold dopant in its marginal local concentrations and other more abundant dopants (N, O). 532-nm Raman micro-spectroscopy indicated the slight reduction of the crystalline order revealed in the second-order Si phonon band, nanoscale dimensions of the resolidified Si nano-crystallites envisioned by the main Si optical-phonon peak, negligible a-Si abundance and a low-wavenumber peak of Si3N4 structure. In contrast, Fourier-transform infrared (FT-IR) 2D-mapping exhibited the only broad structureless absorption bands in the range of 600-5500 cm-1 related to dopant absorption and light-trapping in the surface micro-relief. The room-temperature electrical characteristics of the laser-double-doped Si layer – carrier mobility of 1050 cm2/Vs and background carrier sheet concentration 21010 cm-2 (bulk concentration 1014-1015 cm-3) – are superior regarding previously reported parameters of nitrogen-implanted/annealed Si samples. This novel facile double-element laser-doping procedure paves the way toward local mask-less on-demand introduction of multiple intra-gap intermediate donor and acceptor bands in Si, providing its related multi-wavelength IR photoconductivity for optoelectronic applications.

This study explored the antimicrobial potential of Piper betle L. (PBL) and Piper nigrum L. (PNL) extracts against MRSA. Plant parts including stem, leaf, and fruit were extracted using aquadest, methanol, and hexane, resulting in 18 distinct extracts. FT-IR combined with cluster analysis (CA) categorized the extracts, and anti-MRSA activity was assessed through the paper disk dif-fusion method. The most potent extracts were further analyzed using GC-MS to identify bioac-tive compounds. Additionally, molecular docking studies were conducted for MRSA protein targets (4DKI, 6H5O, and 4CJN). The hexane extract of PNL and the aqueous extract of PBL fruit showed the strongest inhibitory effects. GC-MS identified Piperine (14.22%) and Diisooctyl phthalate (14.67%) as major compounds, with Piperolein B, Piperanine, Beta-caryophyllene ox-ide, and Alpha-caryophylladienol as minor compounds in the hexane extract of PNL, while hy-droxychavicol (81.89%) and Chavibetol (12.01%) were predominant in the aquadest extract of PBL. Molecular docking revealed that Piperolein B and Piperine had strong binding affinities to MRSA proteins 4DKI, 6H5O, and 4CJN, comparable to ciprofloxacin. In conclusion, this study confirms the potential of PBL and PNL as sources of novel anti-MRSA agents, supporting fur-ther research to develop new therapies.

Infrared and visible light image fusion technology integrates image feature information from two different modalities to generate an image that integrates complementary information from the source images. However, in low-light scenarios, the degradation of lighting in visible light images leads to existing fusion methods being unable to effectively extract texture detail information from the scene. At this time, the target prominence information provided by infrared images alone is far from sufficient. To address this challenge, this paper proposes a lightweight infrared and visible light image fusion method based on low-light enhancement, named LLE-Fuse. Firstly, the method improves the MobileOne Block by embedding the Sobel operator in the Edge-MobileOne Block to perform feature extraction and downsampling on the source images. Then, intermediate features of different scales are obtained and fused through a cross-modal attention fusion module, followed by image reconstruction using a decoder with upsampling. Next, the CLAHE algorithm is used for image enhancement of infrared and visible light images, and the enhanced loss guides the network model to learn the capability of low-light enhancement. Finally, upon completion of the network training, the method optimizes the Edge-MobileOne Block into the direct connection structure of MobileNetV1 through structural re-parameterization, effectively reducing the computational resource consumption of the model network. Through extensive experimental comparisons, this paper's method demonstrates excellent performance in both visual appeal and evaluation metrics, providing satisfactory fusion results even in low-light scenarios.

Infrared thermography is a contactless imaging technique, useful to measure surface temperature at distance which has been widely applied in animal production. The possible applications within the pig husbandry include sow heat detection, reduction of the incidence of neonatal mortality, recognition of febrile state caused by pathogens, study of metabolic efficiency, evaluation of stress caused by heat, by constraints, or by aggressive interaction and meat quality prediction. Even if this technique could be easily influenced by environmental factors, the continuous improvement in the procedures and the decrease in the cost of the equipment will allow in the next future new and more applications in swine farming.

Today, there are commercially available high-performance electric wheelchairs, capa-ble of responding to strong limitations. However, despite these advancements, there is a significant portion of the population with severe disabilities who cannot access this mobility solution due to safety reasons. Indeed, either due to motor issues preventing reliable control of the wheelchair's commands, or associated visual or cognitive pathologies, the use of an electric wheelchair by these individuals would put them at risk and could also constitute a danger to those around them. This is why our research team is working on the development of augmented wheelchairs that allow a greater autonomy. We are mainly developing a system compatible with all electric wheel-chairs on the market, capable of making them anti-collision and anti-fall on uneven ground (sidewalks, stairs, etc.). In this article, we present our solution regarding the anti-collision function, in particular the sensors used to do this, as well as the strategy. We will focus on the shortcomings and weaknesses of various types of sensors and demonstrate how, by combining them in a certain way, we can develop a collision avoidance device capable of managing the diversity of everyday life situations. The two types of sensors that will be combined here are an in-house developed special near infrared sensor from the laboratory and a commercially available ultrasonic sensor. We will present the internal operation of the sensors, their limitations, experimental tests involving integration with the wheelchair, and the structure of the complete device.

Water pollution is the main problem in the current scenario which is being faced by mankind due to the industrial and commercial effects. Industrial effluents contain heavy metals and inorganic ions like nitrates and phosphate ions. These ions are being included in waterways through industrial runoff. When the concentration of these hazardous pollutants increases in aquatic flora and fauna causes severe health problems. So remediation of these toxicants from water reserves is of chief importance. Conventional processes along with modern processes are being used for the treatment of industrial wastes. Photo-degradation is a modern method used for the degradation of inorganic ions due to its environmentally friendly nature and cost-effectiveness. In a recent study Aluminum oxide (Al2O3) nanoparticles were synthesized via a green approach at different pH by using Cymbopogan citratus (lemongrass) plant extract and these nanoparticles were used for the removal of inorganic ions from wastewater. The nanoparticles of different pH showed different % degradation of the inorganic ions in acidic medium removal of inorganic waste was less, in basic media % degradation was more as compared to acidic media but maximum % degradation was observed in neutral media like that average % degradation 91% and 81% was observed for nitrate and phosphate ions. The maximum absorbance occurred at 212nm and Al2O3 nanoparticles showed absorbance from 210nm-217nm. The R2 values of pseudo1st, 2nd, and BMG models were 0.956, 0.837, 0.7007 0.95, 0.896, and 0.756 for the nitrate and phosphate ions. Characterizations of synthesized particles were investigated by using UV-visible spectroscopy and other analytical methods like X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). The statistical analyses were done using Simple Linear regression (SLR).

Definitions of bronchopulmonary dysplasia (BPD) or post-prematurity respiratory disease (PPRD) aim to stratify the risk of mortality and morbidity, with an emphasis on long-term respiratory outcomes. There is no univocal classification of BPD, due to its complex multifactorial nature and the substantial heterogeneity of clinical presentation. Currently, there is no definitive cure available for extremely premature very-low-birth-weight infants with BPD, and challenges in finding targeted preventive therapies persist. However, innovative stem cells-based postnatal therapies targeting BPD-free survival are emerging, which are likely to be offered in the first few days of life to higher-risk subpopulations of premature infants. Hence the need for easy-to-use noninvasive tools for a standardized, precise and reliable BPD assessment at a very early stage, to support clinical decision-making and to predict the response to treatment. In this non-systematic review, we present an overview of strategies for monitoring preterm infants with early and evolving BPD-PPRD, and make some remarks on future prospects, with a focus on near-infrared spectroscopy (NIRS).

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.

Virgin coconut oil (VCO) is a functional food with important health benefits. Its economic interest encourages fraudsters to deliberately adulterate VCO with cheap and low-quality vegetable oils for financial gain, causing health and safety problems for consumers. In this context, there is an urgent need for rapid, accurate and precise analytical techniques to detect VCO adulteration. In this study, the use of Fourier Transform Infrared (FTIR) spectroscopy combined with Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) methodology was evaluated to verify purity or adulteration of VCO with reference to several low-cost commercial oils such as sun-flower, maize and peanut oils. Control charts were developed to assess the purity of oil samples using MCR-ALS scores values calculated from a data set of pure and adulterated oils. In addition, quantification models were developed using MCR-ALS with correlation constraint for adulterated coconut oil to assess the blend composition. Different data pre-treatment strategies were tested in order to best extract the information contained in the sample fingerprints, and the calibration models were optimised using a genetic algorithm (GA) to select the most important variables. The models gave satisfactory results in external validation procedure, with absolute errors of less than 4.6 % for samples adulterated with sunflower, maize and peanut oils.

Prereactive complexes in noncovalent organocatalysis are sensitive to the relative chirality of the binding partners and to hydrogen bond isomerism. Both effects are present when a transiently chiral alcohol docks on a chiral α-hydroxy ester, turning such 1:1 complexes into elementary, non-reactive model systems for chirality induction in the gas phase. With the help of linear infrared and Raman spectroscopy in supersonic jet expansions, conformational preferences are investigated for benzyl alcohol in combination with methyl lactate, also exploring p-chlorination of the alcohol and the achiral homolog methyl glycolate to identify potential London dispersion and chirality effects on the energy sequence. Three of the four combinations prefer barrierless complexation via the hydroxy group of the ester (association). In contrast, the lightest complex shows predominantly insertion into the intramolecular hydrogen bond, like the analogous lactate and glycolate complexes of methanol. The experimental findings are rationalized with computations and a uniform helicality induction in the alcohol by the lactate is predicted, independent on insertion into or association with the internal lactate hydrogen bond. p-Chlorination of benzyl alcohol has a stabilizing effect on association, because the insertion motif prevents a close contact between the chlorine and the hydroxy ester. After simple anharmonicity and substitution corrections, the B3LYP-D3 approach offers a fairly systematic description of the known spectroscopic data on alcohol complexes with α-hydroxy esters.

In this research, the other reasonable explanations for the cosmic microwave background radiation is revealed. Due to the microwave resolution, it very roughly shows the image of galaxies in the universe. Moreover, the intensity measurement on each pixel of the image is the sum of the incident microwaves from different directions, so the microwave image cannot represent the microwave sources clearly far away from the Earth. Hence, we propose a simulation after removing several strongest microwave sources, the remaining microwave radiation sources can establish a very uniform intensity distribution over a range of several ten light years. On the other hand, Sloan Digital Sky Survey reveals 200 million galaxies in the universe and, in fact, only to eliminate the contributions of all galaxies from the microwave image is impossible. The way to further obtain the fine-scale structure by only removing the few strongest microwave sources as the foreground effect will keep the other contributions from all the rest galaxies and stars. Therefore, the Cosmic Microwave Background cannot be uniquely explained the radiation which was left after the initial formation of the universe. Moreover, it is the mainly residual radiation from the un-calculated galaxies and inaccurate estimation of the microwave source strength.

Various products of the Integrated Multisatellite Retrievals for GPM (IMERG) and passive mi-crowave (PMW) sensors are assessed with respect to near-surface wet-bulb temperature (Tw), precipitation intensity, and surface type (i.e., with and without snow and ice on the surface) over the CONUS and using Stage-IV product as reference precipitation. IMERG products include precipitation estimates from infrared (IR), combined PMW, and their combination. PMW products generally have higher skills than IR over snow- and ice-free surfaces. Over snow- and ice-covered surfaces (1) PMW products (except AMSR-2) show a higher correlation coefficient than IR, (2) IR and PMW precipitation products tend to overestimate precipitation, but at colder temperatures (e.g., Tw<-10oC) PMW products tend to underestimate and IR product continues to show large overestimations, and (3) PMW sensors show higher overall skill in detecting precipitation oc-currence, but not necessarily at very cold Tw. The results suggest that the current approach of IMERG (i.e., replacing PMW with IR precipitation estimates over snow- and ice-surfaces) may need to be revised.

Asphalt cement will not last for a long time as the world encounters a diminishment in the crude oil. For sustainable, flexible pavement development, new resources can provide a contribution to replace it partially or entirely. In this study, asphalt was partially and entirely replaced by guayule resin as a bioresource by-product, extracted during the guayule natural rubber production. Crumb rubber modifier (CRM) was used as an asphalt enhancer. The Superpave grading system was followed at high, intermediate, and low temperatures to evaluate such innovative binder for rutting, fatigue, and thermal cracking, respectively, in addition to viscosity. Therefore, the original, short-term aging and long-term aging were simulated using tank, rolling thin film oven, and pressure aging vessel materials. Additionally, component analysis using Fourier-transform infrared spectroscopy was provided to link the rheological properties with the chemical changes. Outcomes showed a relatively much lower viscosity of guayule in the same high-temperature asphalt grade indicating savings in plant energy consumption and reduced environmental emissions. CRM enhanced guayule, but not as much as asphalt, proven by polymeric component migration through liquid binder. This enhancement was reflected in the rheological performance besides other factors. As-received guayule seems to have high oxygen content proven by strong absorption peak intensities of oxidative bonds (e.g., Carbonyl and sulfoxide). Such pre-oxidation was negatively reflected in the intermediate- and low-temperature performance of guayule and guayule-based binders. However, the investigated guayule had potential to compensate for asphalt replacement in the presence of CRM by 23–42% by weight of blend.

Land Surface Temperature is a one of the key variable of Global climate changes and model which estimate radiating budget in heat balance as control of climate model. It is a major influenced factor by the ability of the surface emissivity. In this study, were used Landsat 8 satellite image that have Operational Land Imager and Thermal Infrared Sensor to calculate Land Surface Temperature through geospatial technology over Ampara district, Sri Lanka. The Land Surface Temperature was estimated with respect to Land Surface Emissivity and Normalized Difference Vegetation Index values determined from the Red and Near Infrared channels. Land Surface Emissivity was processed directly by the thermal Infrared bands. Pixels based calculation were used to effort at LANDSAT 8 images that thermal Band 10 various dates in this study. The results were achievable to compute Normalized Difference Vegetation Index, Land Surface Emissivity, and Land Surface Temperature with applicable manner to compare with land use/ land cover data. It determines and predicts the changes of surface temperature to favorable to decision making process for the society. Study area faces seasonal drought in Sri Lanka, the prediction method that how land can be efficiently used with the present condition. Therefore, the Land Surface Temperature estimation can prove whether new irrigation systems for agricultural activities or can transformed source of energy into useful form that introducing solar hubs for energy production in future.

A tabletop system can facilitate multi-user collaboration in a variety of settings including small meetings, group work, and education and training exercises. The ability of identifying the users touching the table and their positions can promote collaborative work among participants, so methods have been studied that involve the attaching of sensors to the table or chairs or to the users themselves. An effective method of recognizing user actions without placing a burden on the user would be some type of visual process, so the development of a method that processes multi-touch gestures by visual means is desired. This paper describes the development of a multi-touch tabletop system using infrared image recognition for user position identification and presents the results of touch-gesture recognition experiments and a system usability evaluation. Using an FTIR touch panel and infrared light, this system picks up the shadow area of the user’s hand by infrared camera in relation to user touch operations and estimates user position by image recognition. The multi-touch gestures prepared for this system include an operation to change the direction of an object to face the user and a copy operation in which two users generate duplicates of an object. The average recognition rate of the change-direction gesture and copy gesture were found to be 96% and 85%, respectively. In addition, the system usability evaluation revealed that prior learning was easy and that system operations could be easily performed.

The weakness of the dispersion-managed link combined with optical phase conjugation to compensate for optical signal distortion caused by chromatic dispersion and nonlinear Kerr effect of standard single mode fiber is its limited structural flexibility. We propose dispersion map that can simultaneously compensate for the distorted wavelength division multiplexed signal while increasing the configurational flexibility. Each residual dispersion per span (RDPS) in the former half of the proposed link is randomly determined, and in the latter half, the arrangement order of RDPS is the same as or inverted in the former half. We confirm that the dispersion maps in which the RDPS distribution pattern in the latter half is opposite to the arrangement order in the former half are more effective in compensation, and rather, the compensation effect is better than in the dispersion map of the conventional scheme. The notable result of this paper is that the increase of flexibility can be achieved through random arrangement of RDPS in the former half, and the compensation improvement can be achieved by through inverse arrangement in the latter half which make the distribution profile of each half link roughly symmetric with respect to the midway optical phase conjugator.

Aiming at solving difficulties related to aero-engine classification and identification, the infrared spectra of aero-engine hot jets are measured by telemetry Fourier transform infrared spectrometer, and a six types aero-engine hot jet spectrum data set is generated. The spectrum data set were randomly sampled at a ratio of 8:1:1 to generate training set, validation set and prediction set. In this paper, a Peak Finding Siamese Convolutional Neural Network (PF-SCNN) is designed to match and classify spectrum data. In the first place, the matching pairs of training set and validation set are made independently with the labels of positive and negative samples creating. The spectrum feature extraction and distance similarity calculation are carried out by the Siamese convolutional neural network (SCNN). When entering the prediction phase, the prediction set and training set are integrated to predict in the trained model. The prediction set labels are ultimately determined by matching them with the training set labels having the highest similarities to ensure accurate predictions. To improve the operation efficiency of the SCNN, a peak finding method is introduced to extract the spectrum peaks. Peak positions of high frequency are counted, while the peak data are extracted. The peak data are used to take the place of the original data for training and prediction. The performance measures are defined as Accuracy, Precision, Recall, Confusion matrix and F1-score, and the prediction accuracy was up to 99%. The experimental results indicate that the proposed PF-SCNN is suitable for our spectral dataset and can complete the task of infrared spectrum classification of aero-engine hot jets.

Colloidal quantum dots (CQDs) are valuable for their potential applications in optoelectronic devices. However, they are susceptible to thermal degradation during processing and while in use. Mitigating thermally induced sintering, which leads to absorption spectrum broadening and undesirable changes to thin film electrical properties, is necessary for the reliable design and manufacture of CQD-based optoelectronics. Here, low temperature metal-oxide atomic layer deposition (ALD) was investigated as a method for mitigating sintering while preserving the optoelectronic properties of mercury telluride (HgTe) CQD films. ALD-coated films are subjected to temperatures up to 160oC for up to 5 hours and alumina (Al2O3) is found to be most effective at preserving the optical properties, demonstrating the feasibility of metal-oxide infilling to protect against sintering. HgTe CQD film electrical properties were investigated before and after alumina ALD in-filling, which was found to increase the p-type doping and hole mobility of the films. The magnitude of these effects depended on the conditions used to prepare the HgTe CQDs. With further investigation into the interaction effects of CQD and ALD process factors, these results may be used to guide the design of CQD-ALD materials for their practical integration into useful optoelectronic devices.

Chitosan (CS) nanoparticles are under investigation for a wide range of applications in nanomedicine. The structural, molecular and physicochemical properties of chitosan nanoparticles have a direct bearing on their efficacy in drug conjugation and delivery, gene delivery, and tissue engineering. Herein we report on the structural, morphological and molecular properties of chitosan nanoparticles synthesized using the ionic gelation method with the intended purpose of being used for drug delivery. The chitosan nanoparticles (CS-NPs) were prepared in the 0.25 to 1.0% w/v concentration range, whereas separate samples of the 1.0% w/v CS-NPs were also functionalized with polyethylene oxide (PEO) alone and with a diblock copolymer of PEO and polypropylene glycol (PPG). The average size of the CS-NPs was determined from TEM imaging to be in the range of 11.3 to 14.8 nm. The XRD and TEM analyses show that the CS-NPs have semi-crystalline structure with the degree of crystallinity depending upon the nature of synthesis. Our results show that functionalizing the 1.0% w/v CS-NPs with PEO increases the degree of crystallinity whereas functionalizing with the diblock copolymer PEO - PPG results in a significant increase in their degree crystallinity. The CS/TPP concentration (CS:TPP fixed at 1:1 ratio) had no apparent effect on the degree of crystallinity of the CS-NPs. The results from the FTIR analysis confirm incorporation of TPP with CS in the CS-NPs. Additionally, FTIR analysis shows an enhancement of hydrogen bonding that is consistent with the increase in the degree of crystallinity of the CS-NPs.

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.

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.

The integration of spray drying and agglomeration offers significant advantages, such us continuous production with lower energy consumption. However, it is a knife-edge process with a narrow operating window and limited degrees of freedom that decide between successful agglomeration and fluidized bed blockage due to excessive moisture. In this contribution, factors influencing the spray-through agglomeration process of skim milk powder as a model system were investigated via a design of experiments. Three in-line monitoring methods were applied and tested to observe the most important parameters in the agglomeration process: product moisture and particle size distribution. Regarding the moisture content, a capacitive moisture sensor was calibrated with linear regression and a near-infrared sensor with partial least squares regression. Near-infrared spectroscopy was found to be the suitable method for determining moisture content, while the capacitive moisture sensor mainly provides information on the bulk density, the filling level or fluidization state in the fluidized bed. Additionally, particle size distribution data was extracted from the spectral data using in-line data of a spatial filter velocimetry probe in the fluidized bed. This opens the potential to monitor both parameters in real-time with a single non-invasive sensor.

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.

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.

Gastroenteropancreatic neuroendocrine neoplasms (GEP NENs) are rare cancers consisting of neuroendocrine carcinomas (NECs) and neuroendocrine tumors (NETs), which have been increasing in incidence in recent years. Few cell lines and pre-clinical models exist for the study of GEP NECs and NETs, limiting the ability to discover novel imaging and treatment modalities. To address this gap, we isolated tumor cells from cryopreserved patient GEP NECs and NETs and injected them into the flanks of immunocompromised mice to establish patient-derived xenograft (PDX) models. Two of 6 mice developed tumors (NEC913 and NEC1452). Over 90% of NEC913 and NEC1452 tumor cells stained positive for Ki67. NEC913 PDX tumors expressed neuroendocrine markers such as chromogranin A (CgA), synaptophysin (SYP), and somatostatin receptor-2 (SSTR2) whereas NEC1452 PDX tumors do not express SSTR2. Exome sequencing revealed loss of p53 and RB1 in both tumors. To demonstrate an application of these novel NEC PDX models for SSTR2-targeted peptide imaging, the NEC913 and NEC1452 cells were bilaterally injected into mice. Near infrared-labelled octreotide was administered and fluorescent signal was specifically observed for the NEC913 SSTR2 positive tumors. These 2 GEP NEC PDX models serve as valuable resource for GEP NEN therapy testing.

Background: Maladaptive neuroplasticity related learned response in substance use disorder (SUD) can be ameliorated using non-invasive brain stimulation (NIBS); however, inter-individual variability needs to be addressed for clinical translation. Objective: Our first objective was to develop a hypothesis for NIBS for learned response in SUD based on competing neurobehavioral decision systems model. Next objective was to conduct computational simulation of NIBS of cortico-cerebello-thalamo-cortical (CCTC) loop in cannabis use disorder (CUD) related dysfunctional “cue-reactivity” – a closely related construct of “craving” that is a core symptom. Our third objective was to test the feasibility of our neuroimaging guided rational NIBS approach in healthy humans. Methods: “Cue-reactivity” can be measured using behavioral paradigms and portable neuroimaging, including functional near-infrared spectroscopy (fNIRS) and electroencephalogram (EEG), metrics of sensorimotor gating. Therefore, we conducted computational simulation of NIBS, including transcranial direct current stimulation(tDCS) and transcranial alternating current stimulation(tACS) of the cerebellar cortex and deep cerebellar nuclei(DCN), of the CCTC loop for its postulated effects on fNIRS and EEG metrics. We also developed a rational neuroimaging guided NIBS approach for cerebellar lobule (VII) and prefrontal cortex based on healthy human study. Results: Simulation study of cerebellar tDCS induced gamma oscillations in the cerebral cortex while tTIS induced gamma-to-beta frequency shift. Experimental fNIRS study found that 2mA cerebellar tDCS evoked similar oxyhemoglobin(HbO) response in-the-range of 5x10-6M across cerebellum and PFC brain regions (=0.01); however, infra-slow (0.01–0.10 Hz) prefrontal cortex HbO driven(phase-amplitude-coupling, PAC) 4Hz, ±2mA (max.) cerebellar tACS evoked HbO in-the-range of 10-7M that was statistically different (=0.01) across those brain regions. Conclusion: Our healthy human study showed the feasibility of fNIRS of cerebellum and PFC as well as fNIRS-driven ctACS at 4Hz that may facilitate cerebellar cognitive function via the frontoparietal network. Future work needs to combine fNIRS with EEG for multi-modal imaging.

Obstructive sleep apnea (OSA) is a common sleep disorder, and continuous positive airways pressure (CPAP) is the most effective treatment. Poor adherence is one of the major challenges in CPAP therapy. The recent boom of wearable optical sensors measuring oxygen saturation makes the at-home multiple-night CPAP titrations possible, which may essentially improve the adherence of CPAP therapy by optimizing its pressure in a real-life setting economically. We tested whether the oxygen desaturations (OD) measured in the arm muscle (arm_OD) by gold-standard frequency-domain multi-distance near-infrared spectroscopy (FDMD-NIRS) changes with titrated CPAP pressures in OSA patients together with polysomnography. We found that the arm_OD (2.08 ± 1.23%, mean ± standard deviation) was significantly smaller (P-value &lt;0.0001) than the fingertip OD (finger_OD) (4.46 ± 2.37%) measured by polysomnography pulse oximeter. Linear mixed-effects models suggested that CPAP pressure was a significant predictor for finger_OD but not for arm_OD. Since FDMD-NIRS measures a mixture of arterial and venous OD, whereas fingertip pulse oximeter measures arterial OD, our results of no association between arm_OD and finger_OD indicate that the arm_OD mainly represented venous desaturation. Arm_OD measured by near-infrared optical sensors may be not a suitable indicator of the effectiveness of CPAP titration.

Cyclic peptides have higher stability and better properties as therapeutic agents than their linear peptide analogues. Consequently, intramolecular click chemistry is becoming an increasingly popular method for the synthesis of cyclic peptides from their isomeric linear peptides. However, assessing the purity of these cyclic peptides by mass spectrometry is a significant challenge, as the linear and cyclic peptides have identical masses. In this paper, we have evaluated the analytical capabilities of energy-resolved mass spectrometry (ER MS) and mid-infrared microscopy (IR) to address this challenge. On the one hand, mixtures of both peptides were subjected to Collision Induced Dissociation tandem mass spectrometry (CID MS/MS) experiments in an ion trap mass spectrometer at several excitation energies. Two different calibration models were used: a univariate model (at a single excitation voltage) and a multivariate model (using multiple excitation voltages). The multivariate model demonstrated slightly enhanced analytical performance, which can be attributed to more effective signal averaging when multiple excitation voltages are considered. On the other hand, IR microscopy was used for the quantification of the relative amount of linear peptide. This was achieved through univariate calibration, based on the absorbance of an alkyne band specific to the linear peptide, and through Partial Least Squares (PLS) multivariate calibration. The PLS calibration model demonstrated superior performance in comparison to univariate calibration, indicating that consideration of the full IR spectrum is preferable to focusing on the specific peak of the linear peptide. The advantage of IR microscopy is that it is linear across the entire working interval, from linear peptide molar ratios of 0 (equivalent to pure cyclic peptide) up to 1 (pure linear peptide). In contrast, the ER MS calibration models exhibited linearity only up to 0.3 linear peptide molar ratio. However, ER MS showed better performances in terms of limit of detection, intermediate precision and root-mean-square-error of calibration. Therefore, ER MS is the optimal choice for the detection and quantification of the lowest relative amounts of linear peptide.

Functional neurological disorder (FND) is a complex condition that involves a disruption in the brain's ability to properly integrate sensory input and prior expectations, leading to motor dysfunction in functional motor disorders (FMD). This study investigates the role of sensory attenuation and the sense of agency in FMD, focusing on how these mechanisms can be leveraged for therapeutic interventions. Sensory attenuation, the brain's ability to predict and reduce the intensity of self-generated sensory inputs, is impaired in FMD, leading to a reduced sense of agency. This impairment disrupts motor control and contributes to the perception of involuntary movements.We propose using a bipedal reaching virtual reality (VR) task combined with a haptic ankle device to explore and potentially rehabilitate these impairments. In this task, foot positions are represented by VR objects, with initial weighting favoring the unaffected limb. This weighting gradually shifts to the affected limb as its involvement increases under operant conditioning. The task occasionally introduces perturbations to probe motor-related processing and feedback correction, providing a sensitive measure of visuomotor control. Additionally, we incorporate neuroimaging techniques, specifically functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), to monitor brain activity and assess the effectiveness of these interventions. This approach aims to enhance understanding of the neural mechanisms underlying FND and develop targeted therapies that improve motor control by leveraging the interplay between sensory attenuation and amplification.Our findings could provide valuable insights into the treatment of FND, emphasizing the importance of feedback modulation and the integration of modern technologies like VR and haptic feedback in therapeutic interventions. This research has the potential to significantly improve the prognosis and quality of life for individuals with FND.

We explores the hydrodynamical limit of a homogeneous electron gas, considering the interplay between the infrared limit and phenomenological hydrodynamics. Our study involves interactions generated by non-linear coordinate transformations. The focus begins with an analysis of non-linear coordinate transformations and their impact on normal modes in a harmonic system. The Lagrangian for a harmonic oscillator is used as an illustrative example, highlighting the role of position-dependent effective mass in preventing certain coordinate values. Moving beyond single degrees of freedom, we extend the discussion to a system with several harmonic degrees of freedom, introducing a collective variable. The effective dynamics of this collective variable are defined through a Lagrange multiplier term, emphasizing linearity in the description. Our study extends to the Current Two-Point function (CTP) formalism, introducing matrices and their inverses, such as $\hD$ and $\hD^{-1}$. Further, we delve into the dynamics of current in a homogeneous electron gas at finite density and zero temperature. Green's functions are generated using a functional integral approach, and the effective action is constructed via functional Legendre transformation. The quadratic part of the generator functional is expanded to derive the effective action, leading to the Maxwell equation and the subsequent elimination of the photon field. The resulting effective action for current dynamics is expressed in terms of the Current Two-Point function, paving the way for further exploration of the hydrodynamical aspects of the electron gas. Our study combines theoretical developments and mathematical formalism to provide insights into the hydrodynamical behavior of homogeneous electron gases.

This study applied multiple scientific approaches to establish the significance of an old work of art, Red Guitar, by examining its historical origin and the color materials used in its creation. Furthremore, the study provides thus far unknown pieces of Olga Picasso's family history to be added to her biography. Scientific approaches included digital X-ray radiography, X-ray fluorescence spectroscopy, infrared Fourier transform spectroscopy, Raman spectroscopy, and elemental thermal conductivity analysis. This combination of techniques provided broad confirmation about the time when the painting was created. The work includes colors (white, black, blue, yellow, green, red, and brown/red) and prevalent use of lead-and iron-based historic pigments Chrome Yellow, Yellow Ochre and Red Ochre. It also documents the use of unconventional materials, the colorant Pigment red 4 and nitrocellulose. This investigation led to the conclusion that the art, Red Guitar, is genuine and in accord with Picasso’s work during the first two decades of the 20th century.

The ability to monitor Sprained Ankle Rehabilitation Exercises (SPAREs) in home environments can help therapists to ascertain if exercises have been performed as prescribed. Whilst wearable devices have been shown to provide advantages such as high accuracy and precision during monitoring activities, disadvantages such as limited battery life, users' inability to remember to charge and wear the devices are often the challenges for their usage. Also, video cameras, which are notable for high frame rates and granularity, are not privacy-friendly. This paper, therefore, proposes the use and fusion of unobtrusive and privacy-friendly sensing solutions for data collection and processing during SPAREs in home environments. Two Infrared Thermopile Array (ITA-32) thermal sensors and two Frequency Modulated Continuous Wave (FMCW) Radar sensors were used to simultaneously monitor 15 healthy participants during SPAREs which involved twisting their ankle in 4-fundamental movement patterns namely (i) extension, (ii) flexion, (iii) eversion and (iv) inversion. Experimental results indicated the ability to identify thermal blobs of participants performing the 4 fundamental movement patterns of the human ankle. Cluster-based analysis of data gleaned from the ITA-32 sensors and the FMCW Radar sensors indicated average classification accuracy of 96.9% with K-Nearest Neighbours, Neural Network, AdaBoost, Decision Tree, Stochastic Gradient Descent and Support Vector Machine, amongst others.

The effect of stress on task performance is complex, too much or too little negatively affects performance; and there exists an optimal level of stress to drive optimal performance. Task difficulty and external affective factors are distinct stressors that impact cognitive performance. Neuroimaging studies showed that mood affects working memory performance and the correlates are changes in haemodynamic activity in the prefrontal cortex (PFC). We investigate the interactive effects of affective states and working memory load (WML) on working memory task performance and haemodynamic activity using functional near-infrared spectroscopy (fNIRS) neuroimaging on the PFC of healthy participants. We seek to understand if haemodynamic responses could tell apart workload related stress from situational stress arising from external affective distraction. We found that the haemodynamic changes towards affective stressor and workload related stress were more dominant in the medial and lateral PFC respectively. Our study reveals distinct affective state-dependent modulations of haemodynamic activity with increasing WML in n-back tasks, which correlate with decreasing performance. The influence of negative affect on performance is greater at higher WML, and haemodynamic activity showed evident changes in temporal, and both spatial and strength of activation differently with WML.