Spectral Signatures Research Articles

Floating litter detection at the estuary of Puducherry using Sentinel‐2 data and machine learning model

Marine Floating Litter (MFL) comprises synthetic polymers or plastic that are purposefully tossed out or inadvertently mislaid along rivers, drains, beaches, or other coastal environments that connect the marine ecosystem. Given the adverse impact of the chemicals involved in plastic manufacturing on the marine environment, including marine biota, the need to locate, track, and remove the floating plastic has become demanding. Increasing focus has been placed on remote sensing for its multispectral imaging with high resolution and its capability to monitor large areas systematically. Compared to old-fashioned in-situ observation, satellite imaging products are pecuniary and non-chronophagous. Therefore, the current study exploited the full functionality of open-source Sentinel-2 satellite-retrieved data to detect MFL. The Union Territory of Puducherry (Southern region of India) is the study location. Field measurements were collected from the Ariyankuppam River and the Chunnambar River. A spectral signature profile was created to distinguish other floating debris from floating plastic. The Floating Debris Index (FDI) and Plastic Index (PI) were modeled to validate the performance. It has been proven that FDI performs better at detecting MFL than PI. A Random Forest machine learning model was applied to classify water, plastic debris, and slurry debris. The overall observation of the model details the distribution percentage and accuracy assessment of the proposed model. The accuracy of classifying water, slurry debris, and plastic debris are 64.91 %, 61.6 %, and 96.71 %, respectively, indicating room for improvement in classifying water and slurry debris. The proposed model is used for the first time at the Union Territory of Puducherry and is significantly more efficient compared to in-situ observation in terms of time, cost, and workforce.

Transmission Spectroscopy of the Habitable Zone Exoplanet LHS 1140 b with JWST/NIRISS

LHS 1140 b is the second-closest temperate transiting planet to Earth with an equilibrium temperature low enough to support surface liquid water. At 1.730 ± 0.025 R ⊕, LHS 1140 b falls within the radius valley separating H2-rich mini-Neptunes from rocky super-Earths. Recent mass and radius revisions indicate a bulk density significantly lower than expected for an Earth-like rocky interior, suggesting that LHS 1140 b could be either a mini-Neptune with a small envelope of hydrogen (∼0.1% by mass) or a water world (9%–19% water by mass). Atmospheric characterization through transmission spectroscopy can readily discern between these two scenarios. Here we present two JWST/NIRISS transit observations of LHS 1140 b, one of which captures a serendipitous transit of LHS 1140 c. The combined transmission spectrum of LHS 1140 b shows a telltale spectral signature of unocculted faculae (5.8σ), covering ∼20% of the visible stellar surface. Besides faculae, our spectral retrieval analysis reveals tentative evidence of residual spectral features, best fit by Rayleigh scattering from a N2-dominated atmosphere (2.3σ), irrespective of the consideration of atmospheric hazes. We also show through Global Climate Models (GCMs) that H2-rich atmospheres of various compositions (100×, 300×, 1000× solar metallicity) are ruled out to >10σ. The GCM calculations predict that water clouds form below the transit photosphere, limiting their impact on transmission data. Our observations suggest that LHS 1140 b is either airless or, more likely, surrounded by an atmosphere with a high mean molecular weight. Our tentative evidence of a N2-rich atmosphere provides strong motivation for future transmission spectroscopy observations of LHS 1140 b.

Traversing a kinetic pole during inflation: primordial black holes and gravitational waves

We consider an inflationary kinetic function with an integrable polethat is traversed during inflation.This scenario leads to enhanced spectra of primordial scalar inhomogeneities with detectable signals:formation of primordial black holes(that could explain Dark Matter) andscalar-induced gravitational waves (that couldreproduce the recent Pulsar Timing Array observation, or predict signals in future detectors such as LISA or ET).Spectral signatures depend on whether the inflaton mass dimensionat the pole is above or below 2.Values mildly below 2 allow a big power spectrum enhancement with a mild tuning.Finally, we discuss the possibility that a kinetic pole can arise as anomalousdimension of the inflaton due to quantum effects of Planckian particles that becomelight at some specific inflaton field value.

Effect of Boko Haram Insurgency on Irrigation Activities Using Field and Earth Observation Data in Part of Northeastern Nigeria

The Boko HaramInsurgency in Nigeria has affected the socio-economic activities in most parts of the northeastern part of the country, leading to a low supply of food from agricultural output and increased poverty. Therefore, the main objective of this paper is to analyse the effect of the Boko Haram insurgency on irrigation activities in Kukuwa village. Data was sourced for interviews of 240 respondents and Landsat8_OLI for 2014 and Sentinel 2 for 2015 to 2019. The images were converted to NDVI and used to determine the spectral signatures for each crop using Spectral Matching Techniques (SMTs). The result indicated the majority of the farmers irrigated their farms as > 2 ha (69%) before the crisis, 2 ha (87%) during the crises and > 2 ha (62%) after the crisis, and the majority hired farmers for labour. The intensity of the irrigation was reduced during the crises and it increased after the crises. The result of the SMTs in 2014 indicated a high spectral signature for irrigation activities, with 227.07ha (29.6%) for tomatoes. In 2015 and 2016, low spectral signatures for crops were observed for 541.69ha (69.80%) and 519.5ha (66.94%), respectively. While the spectral result in 2017, 2018, and 2019 indicates a good appreciation of irrigation activities, which shows an increase in most of the dominant crops cultivated in the area, such as tomato, onion, watermelon, and pepper. Among the breakthroughs of this paper is the analysing the reflected signatures from the classified image for different crops through irrigation activities during and after the insurgency in the area. The accuracy assessment indicated fairly good with Landsat but very good with the sentinel images. The study suggests further research into the effect of boko haram insurgency on irrigation activities using field and earth observation data in part of northeastern Nigeria.

Revisited UV- spectra of chlorohydroxoaurate anions

The most important ionic precursor of gold, [AuCl4]-, is used in aqueous solution leading to chlorohydroxoaurates species, [AuCl4-x(OH)x]- (x = 1–4) due to partial hydrolysis. Their UV spectral signatures are still relatively unknown though very useful in many domains of application. Individual spectra of each of them are determined for the first time thanks to a thorough experimental investigation comprising the range 200–250 nm, surpringly ignored up to now. New isosbestic points useful for species partition analysis are evidenced. Electronic transition attribution is obtained from quantum chemical calculations based on TD-DFT. The prediction of the experimental blueshifted bands of the [AuCl4-x(OH)x]-1 anions was possible only after applying energy corrections calibrated on the full UV range two-band spectrum of the [AuCl4]- complex.

Emergent quantum phase transition of a Josephson junction coupled to a high-impedance multimode resonator

The physics of a single Josephson junction coupled to a resistive environment is a long-standing fundamental problem at the center of an intense debate, strongly revived by the advent of superconducting platforms with high-impedance multimode resonators. Here we investigate the emergent criticality of a junction coupled to a multimode resonator when the number of modes is increased. We demonstrate how the multimode environment renormalizes the Josephson and capacitive energies of the junction so that in the thermodynamic limit the charging energy dominates when the impedance is larger than the resistance quantum and is negligible otherwise, independently from the bare ratio between the two energy scales and the compact or extended nature of the phase of the junction. Via exact diagonalization, we find that the transition surprisingly stems from a level anticrossing involving not the ground state, but the first excited state, whose energy gap vanishes in the thermodynamic limit. We clarify the nature of the two phases by pointing at a different behavior of the ground and excited states and we show that at the transition point the spectrum displays universality not only at low frequencies. In agreement with recent experiments, we reveal striking spectral signatures of the phase transition.

Detection of Na in the atmosphere of the hot Jupiter HAT-P-55b

The spectral signatures of optical absorbers, when combined with those of infrared molecules, play a critical role in constraining the cloud properties of exoplanet atmospheres. We aim to use optical transmission spectroscopy to confirm the tentative color signature previously observed by multiband photometry in the atmosphere of hot Jupiter HAT-P-55b. We observed a transit of the HAT-P-55b with the OSIRIS spectrograph on the Gran Telescopio Canarias (GTC). We created two sets of spectroscopic light curves, using the conventional band-integrated method and the newly proposed pixel-based method, to derive the transmission spectrum. We performed Bayesian spectral retrieval analyses on the transmission spectrum to interpret the observed atmospheric properties. The transmission spectra derived from the two methods are consistent, both spectrally resolving the tentative color signature observed by MuSCAT2. The retrievals on the combined OSIRIS and MuSCAT2 transmission spectrum yield a detection of Na at 5.5σ and a tentative detection of MgH at 3.4σ. The current optical-only wavelength coverage cannot constrain the absolute abundances of the atmospheric species. Space-based observations covering the molecular infrared bands or ground-based high-resolution spectroscopy are needed to further constrain the atmospheric properties of HAT-P-55b.

Self-Association and Microhydration of Phenol: Identification of Large-Amplitude Hydrogen Bond Librational Modes.

The self-association mechanisms of phenol have represented long-standing challenges to quantum chemical methodologies owing to the competition between strongly directional intermolecular hydrogen bonding, weaker non-directional London dispersion forces and C-H⋯π interactions between the aromatic rings. The present work explores these subtle self-association mechanisms of relevance for biological molecular recognition processes via spectroscopic observations of large-amplitude hydrogen bond librational modes of phenol cluster molecules embedded in inert neon "quantum" matrices complemented by domain-based local pair natural orbital-coupled cluster DLPNO-CCSD(T) theory. The spectral signatures confirm a primarily intermolecular O-H⋯H hydrogen-bonded structure of the phenol dimer strengthened further by cooperative contributions from inter-ring London dispersion forces as supported by DLPNO-based local energy decomposition (LED) predictions. In the same way, the hydrogen bond librational bands observed for the trimeric cluster molecule confirm a pseudo-C3 symmetric cyclic cooperative hydrogen-bonded barrel-like potential energy minimum structure. This structure is vastly different from the sterically favored "chair" conformations observed for aliphatic alcohol cluster molecules of the same size owing to the additional stabilizing London dispersion forces and C-H⋯π interactions between the aromatic rings. The hydrogen bond librational transition observed for the phenol monohydrate finally confirms that phenol acts as a hydrogen bond donor to water in contrast to the hydrogen bond acceptor role observed for aliphatic alcohols.

Spatio-temporal assessment of soil salinization utilizing remote sensing derivatives, and prediction modeling: Implications for sustainable development

This study aims to investigate the combined use of multi-sensor datasets (Landsat 4–5 &8 OLI satellite imagery, spatial resolution = 30 m) coupled with field studies to evaluate spatio-temporal dynamics of soil salinization along the coastal belt in West Bengal, India. This study assesses soil salinization by mapping the salinity and electrical conductivity of saturation extract (ECe) and utilizing spectral signatures for estimating soil salinity. The SI change (%) was analyzed (2021–1995), categorizing increases in salinity levels into 5%, 10%, and 50% changes possibly due to salt encrustation on the soil layers. The land use land cover (LULC) change map (2021–1995) demonstrates that the study area is continuously evolving in terms of urbanization. Moreover, in the study area, soil salinity ranges from 0.03 ppt to 3.87 ppt, and ECe varies from 0.35 dSm−1 to 52.85 dSm−1. Additionally, vulnerable saline soil locations were further identified. Classification of soil salinity based on ECe reveals that 26% of samples fall into the non-saline category, while the rest belong to the saline category. The Spectral signatures of the soil samples (n = 19) acquired from FieldSpec hand spectrometer show significant absorption features around 1400, 1900, and 2250 nm and indicate salt minerals. The results of reflectance spectroscopy were cross-validated using X-ray fluorescence and scanning electron microscopy. This study also employed partial least square regression (PLSR) approach to predict ECe (r2 = 0.79, RMSE = 3.29) and salinity parameters (r2 = 0.75, RMSE = 0.51), suggesting PLSR applicability in monitoring salt-affected soils globally. This study’s conclusion emphasizes that remote sensing data and multivariate analysis can be crucial tools for mapping spatial variations and predicting soil salinity. It has also been concluded that saline groundwater used for irrigation and aqua-cultural activities exacerbates soil salinization. The studywill help policymakers/farmers identify the salt degradation problem more effectively and adopt immediate mitigation measures.

Volume of detonation determination based on gaseous products of energetic materials by time resolved LIPS combined with schlieren image

The safe and fast measurement of volume of detonation (VoD) is always a hard task for macroscale explosion even though it is one of the important parameters to evaluate the explosion performance. Therefore, a promising technology to determine the VoD is highly desirable for evaluation of energetic materials. Herein, a new method of VoD determination based on gaseous products via small dose energetic materials by time correlated laser induced plasma spectroscopy (LIPS) combined with schlieren image was proposed. Hydrodynamics of products after laser ablation on a time scale ranging from microsecond to millisecond was investigated. Based on the analysis of hydrodynamics of products after laser ablation, the effective spectra of gaseous products of each energetic material are obtained. Subsequently, a high-accuracy quantitative analysis model of VoD based on gaseous products using the method of principal component analysis - partial least squares (PCA-PLS) with small sample modeling algorithm has been developed( R2>0.96). The VOD model accurately predicts the detonation parameters with the average relative error of test set (ARET) < 3% and the maximum relative error of test set (MRET) < 5%. Moreover, the results without spectra selection of the relative error of blind data show the max relative error is less than 7%. The results of variable importance in projection (VIP) identification indicate a robust association between the spectral signatures of carbon (C), nitrogen (N), hydrogen (H), oxygen (O) and VoD. Furthermore, the N lines exert the most substantial influence on the VoD model. This method provides a new safe and fast determination technology for the evaluation of VoD and clarification of the related mechanism.

Discrimination of Fe-Ni-Laterites from Bauxites Using a Novel Support Vector Machines-Based Methodology on Sentinel-2 Data

Currently, the global shift towards green energy is at the forefront of efforts introducing a new era, thus rendering exploration for critical raw materials essential. To this purpose, the utilization of advanced machine learning methods in remote sensing has emerged as a rapid and cost-effective approach. This study proposes a new methodology, utilizing Sentinel-2 satellite data, to distinguish ferronickel (Fe-Ni-) laterite from bauxite across pre-mining, mining, and post-mining occurrences worldwide. Both ores contain mineral raw materials such as nickel, iron, cobalt, and alumina and their discrimination is generally macroscopically challenging, especially when their locations are often in geographical proximity. The proposed method is based on Support Vector Machines (SVM) classification using spectral signatures of known Fe-Ni-laterite and bauxite-bearing pixels in Greece, Cuba, and Jamaica. The highest classification accuracies are obtained by combining 𝑏12 with 𝑏6 or 𝑏7 spectral bands. Comparisons with specific ore mineralogies show that 𝑏6 and 𝑏7 are strongly linked to the ferric phase, while 𝑏12 is mainly associated with the argillic mineralogies, the latter probably being the key discriminating factor between the two ores. From laboratory chemical analyses, we also establish that 𝑏12 and 𝑏6 or 𝑏7 are strongly associated with Al2O3 and Fe2O3 content correspondingly. The proposed method is accurate, it has reduced prospection costs, and it can facilitate the initial screening of broad areas by automatically characterizing whether an ore is bauxite or Fe-Ni-laterite. This underscores the methodology’s significance in ore differentiation and exploration within the context of green energy endeavors.

The potentials of nonlinear polarization with hyperspectral imaging of RNA for hepatocellular carcinoma early diagnosis

BackgroundMost cancers acquire numerous genetic changes in proto-oncogenes as well as tumor-suppressor genes. Cancer's early diagnosis remains a challenge. Recently, nonlinear polarization has revealed the potential as a promising tool for early cancer diagnosis. Laser-induced nonlinear polarization can offer a novel fingerprint signature.MethodsIn this study, nonlinear polarization was adopted for the characterization of both DNA and RNA samples from healthy volunteers. Total DNA and RNA were illuminated with a 656-nm LED source, and the resonance frequencies (scattered and re-emitted signals) were captured and recorded using a hyperspectral camera.ResultsChanges in signal frequency as well as phase shift offered a potent means to differentiate DNA (control) from RNA (control). DNA (control) demonstrated characteristic resonance frequencies that differ from total RNA (control) at the 2nd, 3rd, 4th, and 5th harmonics. While DNA demonstrated a phase shift dominating at 0.88 GHz, RNA dominates at 0.106 GHz. The resonance spectral signature of RNA samples from people with hepatocellular carcinoma (HCC) was compared to that of RNA (control). RNA (HCC) demonstrated distinctive frequency signals at 0.014, 0.021, 0.032, and 0.072 GHz. These characteristics feature could facilitate early HCC diagnosis. While RNA (control) dominates at 0.014 and 0.072 MHz, RNA (HCC) dominates at 0.021 and 0.032.ConclusionAs far as we are aware, this is the initial investigation into the use of simple nonlinear polarization to generate spectral fingerprinting signatures of total DNA and RNA. Furthermore, RNA mutations due to HCC were identified via characteristic nonlinear spectral signature.

Spectral library of gemological minerals from NE Brazil: A reference to aid the reflectance spectra-based discrimination of gem-quality materials

We report a spectral library of gem-quality minerals from NE Brazil and imitation material as a strategic, rapid, nondestructive, and relatively cost-effective tool to be used as a reference to aid in discriminating gemological materials. Our study used spectra data collected by ASD-FieldSpec spectroradiometers (Standard Resolution and High-Resolution Next Generation) for 42 gem-quality mineral samples of beryl, emerald, elbaite tourmaline, euclase, vesuvianite, and apatite crystals from the Borborema Province. In addition, five emerald imitations and nine faceted emeralds from unknown provenances were also used to compose the spectral dataset. The main metals- and molecules-related absorption features were interpreted to show the similarities and differences of the spectral signatures from the minerals and imitation samples. In addition, the principal component analysis (PCA) was applied for data dimensionality reduction of the normalized reflectance spectra (n = 115), and the significance of PCs (99% of variance) was interpreted to highlight the main mineral groups. FreeViz algorithm was employed as an alternative for efficient visualization of PCs’ axes, showing a suitable discrimination of the samples. For example, the clustering of (a) beryl and emerald samples, (b) iron-rich elbaites (blue and green colors), and (c) Cu- and Mn-rich elbaites (Paraíba tourmaline and green varieties) was revealed, summarizing the similarities already expected from the qualitative spectral signature inspections and automating the discrimination of main mineral groups. Beyond introducing a spectral library of gem materials from NE Brazil, the methodology supports studies of gem classification and the authenticity assessment of diverse gem materials. The methodology can also be expanded to other reflectance datasets of gem-quality minerals worldwide.

Implications to basin evolution from the interpretation of superficial and buried geological features from remote sensing and magnetic data sets, Lower and Middle Benue Trough, Nigeria

The Lower and Middle Benue Trough is an intra-continental rifted basin with most rocks concealed by thick sediments and vegetation. Geological field mapping is very difficult resulting in high uncertainties. Remote sensing and geophysical techniques can assist in refining lithological boundaries and detecting buried geological features such as intrusive bodies, which can be very useful in interpreting the structural history of a basin. The main aim of our study is to re-process and interpret aeromagnetic and multispectral satellite datasets to understand the evolution of the Benue Trough. We used high-resolution aeromagnetic data and, Landsat 8 and ASTER data sets to interpret surface and near-surface lithologies, particularly igneous bodies, within the Benue Trough, Nigeria, with the aim of better constraining the basin evolution in time and space. A matched bandpass filter was applied to the aeromagnetic data to separate shallow/near-surface (residual) anomalies from deep (regional) anomalies. The result of the residual anomaly was further filtered by derivative-based filters to delineate surface/near-surface igneous bodies. The satellite remote sensing datasets were processed and enhanced, and diagnostic mineral spectral signatures related to geology were interpreted. A spectral angle mapping (SAM) supervised classification was used to map various lithologies and their boundaries. The delineated igneous bodies (volcanic rocks, plugs, dykes, bosses, and sills) are concentrated along the SE margin of the Trough where they intruded along a major NE-SW trend; but changes in the intrusion style are interpreted to relate to specific stages in the basin history. We suggest that the initial magma emplacement was controlled by structures mostly along the SE margin of an asymmetric basin, but the rifting locus later migrated towards the centre of the present basin.

Cross-modal and multimodal data analysis based on functional mapping of spectral descriptors and manifold regularization

Multimodal manifold modeling methods extend spectral geometry-aware data analysis to incorporate learning from multiple related and complementary modalities. However, most methods assume an equal number of homogeneous data samples in each modality and partial correspondences between modalities as prior knowledge. This work introduces two new multimodal modeling methods. The first method introduces a comprehensive framework for handling multimodal information in heterogeneous data without requiring specific prior knowledge. To achieve this, we begin by extracting local descriptors using spectral graph wavelet signatures (SGWS) to identify manifold localities. Then, we propose a manifold regularization framework that incorporates functional mapping between SGWS descriptors (FMBSD) to determine pointwise correspondences. The second method involves manifold regularized multimodal classification based on pointwise correspondences (M2CPC). This method addresses the challenge of multiclass classification in multimodal heterogeneous data by determining correspondences between modalities using the FMBSD method. Experimental results evaluating the FMBSD method on three widely used cross-modal retrieval datasets and evaluating the M2CPC method on three benchmark multimodal multiclass classification datasets demonstrate their effectiveness and superiority over state-of-the-art methods.

Microplastic Analysis in Soil Using Ultra-High-Resolution UV–Vis–NIR Spectroscopy and Chemometric Modeling

The study of microplastics (MPs) in soils is impeded by similarities between plastic and non-plastic particles and the misidentification of MP by current analytical methods such as visual microscopic examination. Soil MPs pose serious ecological and public health risks because of their abundance, persistence, and ubiquity. Thus, reliable identification methods are badly needed for scientific study. One possible solution is UV–Vis–NIR spectroscopy, which has the ability to rapidly identify and quantify concentrations of soil microplastics. In this study, a full-range, field portable spectrometer (350–2500 nm) with ultra-high spectral resolution (1.5 nm, 3.0 nm, and 3.8 nm) identified three types of common plastics: low-density polyethylene (LDPE), polyvinyl chloride (PVC), and polypropylene (PP). Three sets of artificially MP-treated vermiculite soil samples were prepared for model prediction testing and validation: 150 samples for model calibration and 50 samples for model validation. A partial least square regression model using the spectral signatures for quantification of soil and MP mixtures was built with all three plastic polymers. Prediction R2 values of all three polymers showed promising results: polypropylene R2 = 0.943, polyvinyl chloride R2 = 0.983, and polyethylene R2 = 0.957. Our study supports previous work showing that combining ultra-high-resolution UV–Vis–NIR spectrometry with quantitative modeling can improve the accuracy and speed of MP identification and quantification in soil.

Exploring Electrocortical Signatures of Gait Adaptation: Differential Neural Dynamics in Slow and Fast Gait Adapters.

Individuals exhibit significant variability in their ability to adapt locomotor skills, with some adapting quickly and others more slowly. Differences in brain activity likely contribute to this variability, but direct neural evidence is lacking. We investigated individual differences in electrocortical activity that led to faster locomotor adaptation rates. We recorded high-density electroencephalography while young, neurotypical adults adapted their walking on a split-belt treadmill and grouped them based on how quickly they restored their gait symmetry. Results revealed unique spectral signatures within the posterior parietal, bilateral sensorimotor, and right visual cortices that differ between fast and slow adapters. Specifically, fast adapters exhibited lower alpha power in the posterior parietal and right visual cortices during early adaptation, associated with quicker attainment of steady-state step length symmetry. Decreased posterior parietal alpha may reflect enhanced spatial attention, sensory integration, and movement planning to facilitate faster locomotor adaptation. Conversely, slow adapters displayed greater alpha and beta power in the right visual cortex during late adaptation, suggesting potential differences in visuospatial processing. Additionally, fast adapters demonstrated reduced spectral power in the bilateral sensorimotor cortices compared with slow adapters, particularly in the theta band, which may suggest variations in perception of the split-belt perturbation. These findings suggest that alpha and beta oscillations in the posterior parietal and visual cortices and theta oscillations in the sensorimotor cortex are related to the rate of gait adaptation.

Progressive gap-filling in optical remote sensing imagery through a cascade of temporal and spatial reconstruction models

Filling gaps caused by thick cloud cover or sensor malfunctions has always posed a significant challenge in the preprocessing of optical remote sensing images. The concept of similar pixels, derived from spatial and temporal similarities in remote sensing scenes, has been widely embraced and extensively applied, leading to the development of various gap-filling models. However, in complex scenarios characterized by spatiotemporally heterogeneous surfaces and extensive missing areas, current models often produce noticeable noise-like artifacts, distorted spectral signatures, and unreliable spatial textures. To address this challenge, we propose a simple yet effective similar-pixel-based approach called progressive gap-filling through the cascading temporal and spatial framework (PGFCTS). Drawing inspiration from the negative correlation between spatial distances and the robustness of similar pixels, we employ a progressive gap-filling scheme to ensure that similar pixels are spatially close to the target pixel. This significantly enhances the effectiveness of similar pixels and improves the accuracy of the reconstruction model. Moreover, unlike traditional methods that integrate spatial and temporal information in parallel, our approach integrates these two sources in a cascading manner. Initially, a temporal model is used to obtain preliminary results with fine texture details, followed by a spatial model to enhance spectral fidelity. Through tests on two gap-filling missions and comparisons with seven classical methods, results demonstrate that PGFCTS effectively eliminates noise-like artifacts, faithfully restores spectral features, and accurately reproduces spatial details. Quantitative assessment reveals that PGFCTS consistently outperforms other methods, securing the best scores. Importantly, our method maintains its superiority over extended time intervals between the target and reference images. In summary, PGFCTS emerges as an effective solution for filling missing gaps and reproducing surface information, thereby enhancing the usability of optical images.

Consistency of the Signature of Phonotraumatic Vocal Hyperfunction Across Different Ambulatory Voice Measures.

Although different factors and voice measures have been associated with phonotraumatic vocal hyperfunction (PVH), it is unclear what percentage of individuals with PVH exhibit such differences during their daily lives. This study used a machine learning approach to quantify the consistency with which PVH manifests according to ambulatory voice measures. Analyses included acoustic parameters of phonation as well as temporal aspects of phonation and rest, with the goal of determining optimally consistent signatures of PVH. Ambulatory neck-surface acceleration signals were recorded over 1 week from 116 female participants diagnosed with PVH and age-, sex-, and occupation-matched vocally healthy controls. The consistency of the manifestation of PVH was defined as the percentage of participants in each group that exhibited an atypical signature based on a target voice measure. Evaluation of each machine learning model used nested 10-fold cross-validation to improve the generalizability of findings. In Experiment 1, we trained separate logistic regression models based on the distributional characteristics of 14 voice measures and durations of voicing and resting segments. In Experiments 2 and 3, features of voicing and resting duration augmented the existing distributional characteristics to examine whether more consistent signatures would result. Experiment 1 showed that the difference in the magnitude of the first two harmonics (H1-H2) exhibited the most consistent signature (69.4% of participants with PVH and 20.4% of controls had an atypical H1-H2 signature), followed by spectral tilt over eight harmonics (73.6% participants with PVH and 32.1% of controls had an atypical spectral tilt signature) and estimated sound pressure level (SPL; 66.9% participants with PVH and 27.6% of controls had an atypical SPL signature). Additionally, 77.6% of participants with PVH had atypical resting duration, with 68.9% exhibiting atypical voicing duration. Experiments 2 and 3 showed that augmenting the best-performing voice measures with univariate features of voicing or resting durations yielded only incremental improvement in the classifier's performance. Females with PVH were more likely to use more abrupt vocal fold closure (lower H1-H2), phonate louder (higher SPL), and take shorter vocal rests. They were also less likely to use higher fundamental frequency during their daily activities. The difference in the voicing duration signature between participants with PVH and controls had a large effect size, providing strong empirical evidence regarding the role of voice use in the development of PVH.

Identifying the origin of Yemeni green coffee beans using near infrared spectroscopy: a promising tool for traceability and sustainability

Yemeni smallholder coffee farmers face several challenges, including the ongoing civil conflict, limited rainfall levels for irrigation, and a lack of post-harvest processing infrastructure. Decades of political instability have affected the quality, accessibility, and reputation of Yemeni coffee beans. Despite these challenges, Yemeni coffee is highly valued for its unique flavor profile and is considered one of the most valuable coffees in the world. Due to its exclusive nature and perceived value, it is also a prime target for food fraud and adulteration. This is the first study to identify the potential of Near Infrared Spectroscopy and chemometrics—more specifically, the discriminant analysis (PCA-LDA)—as a promising, fast, and cost-effective tool for the traceability of Yemeni coffee and sustainability of the Yemeni coffee sector. The NIR spectral signatures of whole green coffee beans from Yemeni regions (n = 124; Al Mahwit, Dhamar, Ibb, Sa’dah, and Sana’a) and other origins (n = 97) were discriminated with accuracy, sensitivity, and specificity ≥ 98% using PCA-LDA models. These results show that the chemical composition of green coffee and other factors captured on the spectral signatures can influence the discrimination of the geographical origin, a crucial component of coffee valuation in the international markets.