Hyperspectral imaging provides high-dimensional spectral information for material characterization and remote sensing analytics. However, deployment is constrained by hardware cost, inter-band redundancy, and the limited suitability of predominantly spatially oriented models for one-dimensional (1D) spectral signatures. This paper presents an integrated sensor-learning framework that combines data-driven wavelength selection with sequence-aware spectral classification to support compact multispectral sensing. sparse principal component analysis with strong $L_{1}$ regularization is used to identify informative wavelengths and derive reduced-band multispectral configurations that mitigate redundancy and the curse of dimensionality. A 1D recurrent multi-head convolutional network is then introduced, which integrates bidirectional recurrent encoding, multi-head self-attention, and hierarchical 1D convolutions to capture local spectral structure and long-range inter-band dependencies. In contrast to convolutional neural network (CNN), recurrent neural network (RNN) and Transformer variants designed for spatial (2D) feature learning or tokenized hyperspectral cubes, the proposed architecture targets reduced-band 1D spectral sequences and is suitable for computationally constrained operation. Experiments on Indian Pines and Houston2013 show that the proposed approach outperforms representative CNN-, RNN-, and transformer-based baselines and requires fewer spectral bands. Overall accuracy reaches 82.3% on Indian Pines and 88.6% on Houston2013.

The misrepresentation of timber provenance undermines global supply chain transparency and sustainable forest management practices. Although reference forensic approaches, such as stable isotope analysis and chemical and genetic approximations, are reliable, their high cost and complexity hinder their widespread adoption. This work evaluates the feasibility of a high-throughput framework for provenance classification that integrates VIS-NIR flame emission spectroscopy dynamics and an end-to-end deep learning model. Eucalyptus globulus samples were harvested from five distinct geographical sectors in Chile and subjected to controlled combustion. Combustion kinetic profiles were recorded using a VIS-NIR spectrophotometer and processed using Transformer architecture, benchmarked against Support Vector Machine (SVM) and XGBoost classifiers. The proposed model achieved a test accuracy and F1-score of 97.5%, significantly outperforming SVM (80.0%) and XGBoost (62.5%) baselines. Traditional models exhibited severe overfitting owing to static feature compression, whereas the Transformer successfully captured the transient temporal patterns of the combustion cycle. Interpretable feature attribution analysis using Shapley Additive exPlanations (SHAP) confirms that the classification relies on specific atomic emission lines (potassium doublet at 766.4 and 770.1 nm), validating the physico-chemical basis of the model. These findings confirm that treating wood provenance as a dynamic pattern recognition task yields superior robustness, providing a data-efficient basis for a compact optical sensor for traceability that complements existing timber tracing frameworks.

In this work, we present a method that reconstructs pixel-wise visible spectra from a trichromatic CMOS camera and derives temperature, local radiation, and sodium-emission maps in biomass pellet flames. Spectral recovery is performed with the Maloney-Wandell method using three principal components estimated from reference spectra; pixel spectra are accepted when the Goodness-of-Fit coefficient (GFC) exceeds 0.96. From the recovered spectra, flame temperature is estimated via two-color pyrometry in the visible band, and sodium is mapped by integrating the emission of Na centered at 589.4 nm. The setup was evaluated on laboratory flames of Pinus radiata pellets, acquiring 100 frames over a 10 s window, yielding spatially coherent temperature/radiation fields and sodium distributions consistent with simultaneously measured point spectra. The results are obtained under controlled laboratory conditions and rely on the two-color assumption of weak emissivity variation; absolute alkali quantification and industrial validation are left for future work.

# Background It is unknown if differences exist for normalized velocity (m/s) and step length (m) when measured using clinically accessible tools, such as the 10-Meter Walk Test (10MWT) and a timed gait analysis (TGA), and costly equipment, such as the GAITRite® electronic walkway system, in healthy adolescent athletes. # Purpose The purpose of this study was to compare normalized velocity and step length data using low- and high-tech equipment during single- and dual-task gait. The investigators hypothesized that there would be no significant differences between data collected using the 10MWT, TGA, and GAITRite®. # Study Design Cross-sectional, repeated-measures study design. # Methods A convenience sample of healthy male (n=23) and female (n=20) adolescent athletes aged 14-18 years were recruited from a local high school. A three-way mixed analysis of variance analyzed normalized velocity (m/s) and step length (m) data measured with the 10MWT, TGA, and GAITRite® while participants walked at a self-selected speed with and without a visuospatial cognitive task. All data were collected in the participants’ school setting. A three-way mixed ANOVA was used to analyze data. # Results No significant interactions between assessment tool, walk condition, or sex were found. A main effect of walk condition (p<0.0001) and sex (p<0.0004) was found for normalized velocity (i.e., females walked faster than males). Normalized velocity was also significantly decreased when measured with the 10MWT compared to the GAITRite® (p<0.007) and the TGA (p<0.03). # Conclusions Normalized velocity may be generalizable between the TGA and GAITRite®, but not the 10MWT. Therefore, the TGA may be a viable adjunct to current multimodal assessments of gait following concussion in the absence of costly equipment. # Level of Evidence Level 2

The energy emitted by a flame and its spectra provide critical information about fuels, such as energy type, temperature, and molecular properties. While traditionally used to analyze combustion, we introduce a spectral-time-based index, optical calorific power (OCP), to calculate the total energy released per mass unit (J/Kg), relating to the calorific value of wood-based fuels. Using novel optical variables and machine learning, we identified different wood species during the combustion of the wood samples. Homogeneous samples of four wood species (sapwood and heartwood) were ignited in a temperature-controlled furnace. Spectra were measured using a calibrated spectrophotometer in the 450-900 nm range with a 100ms integration time. Continuous and discontinuous spectral patterns were observed in all samples, used to calculate spectral-time-based optical variables, and separated using the AirPLS algorithm. Discontinuous spectra correlated with Na and K emissions (589.4 nm and 766.5 nm, respectively). Continuous spectra were analyzed to determine optical variables such as flame temperature (K), total continuous radiation (TCR, mu W/cm(2)), and total continuous energy (TCE, mu J). Five supervised classification models, XGBoost, LR, SVM, LDA, and RF, were trained using normalized physical parameters and optical variables with stratified cross-validation. The proposed optical variables yielded an identification accuracy of 93%, precision of 95%, and recall of 93% during combustion using the XGBoost method. These promising results demonstrate the potential of our method for accurately identifying wood species, while offering a cost-effective and novel alternative to NIR-SWIR reflectance spectral identification techniques.

The correct identification of minerals is crucial task for the exploration and exploitation of mineral resources, environmental monitoring, and industrial processes. In this article, we propose a hyperspectral imaging system and classification model to identify nine types of minerals. To accomplish this, we employed a hyperspectral shortwave infrared (SWIR) camera to capture hyperspectral images. We then introduce a convolutional neural network (CNN) architecture that considers only spectral data, complemented by a fully connected network for classification. To prevent overfitting, we implemented the dropout technique, which enables random deactivation of neurons during the backpropagation process. This results in improved performance during the training phase and a better generalization capacity. Training was optimized to minimize the categorical cross-entropy objective function, and the model was evaluated during training using an accuracy metric. Finally, we evaluated the results with the test data using accuracy, recall, and precision metrics, and achieved 98.52%, 98.25%, and 98.68%, respectively. Our source code is available at https://github.com/jcifuenr/Spec-CNN.

Flame spectroscopy is a technique widely used to analyze combustion processes.In a flame, energy is emitted across a wide spectral range, and its corresponding spectra can be classified in both, continuous and discontinuous behaviors. Optimizing combustion, has the potential to maximize efficiency, thus reducing fuel consumption and emissions of residual gases. This report shows the spectral emission of a flame emitted by combustion of pellets at different humidity contents, from a specific brand (here referred to as Alfa), typical in the Chilean market. To perform the spectral analysis, the HR-4000 spectrophotometer (Ocean Optics), previously calibrated in absolute radiance (in uW/nm·cm 2 ) was used. From the collected spectra, parameters such as flame temperature (in °C), total continuous radiation (in µW/cm2) and total continuous energy (in µJ) were estimated. The spectral behavior, at different humidity contents, shown different spectral patterns. Thus, in this report we introduce the optical calorific power (in J/kg), quantifying the energy provided by a pellet sample, and calculated from spectral results. For the Alfa brand, with a humidity content between a 6-8% we estimated an optical calorific power of 6.12 (J/kg) and with a humidity content of 13.6 %, an optical calorific power of 4.96 (J/kg). The different parameters proposed in this work hold promises for understanding biomass combustion phenomena, such as the dynamics of energy emission in flame distribution.

A flame emits energy over a wide spectral region, providing important information about the combustion process. Its associated spectra, classified into continuous and discontinuous features, provide information concerning the temperature of the soot particle and atomic or molecular reactions. However, because of the non-linear and turbulent nature of the flame, added to the high-dimensional nature of the spectral data and the overlap between continuous and discontinuous spectral emissions, complex methods are needed to obtain valuable information for combustion closed-loop control and optimization. In this paper, we propose a method for retrieval of hyperspectral flame images from Liquefied Petroleum Gas (LPG) fuel, using only images captured by a trichromatic camera, but combined with a spectral recovery method. The hyperspectral flame data recovered was used to estimate indexes, such as the flame temperature (in K), the local emitted radiance (at the pixel level) and the global radiance (both in μW/cm2), thus performing a dynamic, spectral and spatial combustion diagnosis in different air/fuel ratios. Our method was validated by performing measurements with a calibrated spectrophotometer, achieving small errors in the retrieval procedure (less than 1%) and in the temperature estimation (less than 4%). The results show that our approach can play an essential role in flame sensing, providing an important tool for monitoring or controlling combustion processes.

Background Adolescent athletes aged 10 to 19 years are at the highest risk of experiencing sport-related concussions (SRCs). Despite the known deficits and battery of assessments following concussion, postural stability during dual-task gait remains understudied in this population. Purpose The purpose of this study was to evaluate the dual-task cost (DTC) in adolescents with an acute or chronic SRC compared to reference values from healthy athlete peers for spatiotemporal parameters of gait during walking with and without a concurrent visuospatial memory task presented on a hand-held tablet. Researchers hypothesized that adolescents during the acute phase of concussion would be likely to experience a greater DTC compared to healthy peers in at least one spatiotemporal parameter of gait when walking within the dual-task paradigm. Study Design Cross-sectional, observational cohort design Methods Adolescents with concussion were recruited to participate. Subjects were divided into acute and chronic categories based on significant differences in the neuropsychological function after a period of 28 days. They walked at a self-selected speed along the 5.186-meter GAITRite® Walkway System with and without a concurrent visuospatial cognitive task presented on a hand-held tablet. Outcomes included normalized velocity (m/s), step length (m), and double limb (DLS) and single limb support (SLS) (defined as the percent of a gait cycle [%GC]). The data were then compared to the previously published reference values established using the same methods in the healthy athlete participants for all spatiotemporal parameters of gait. Results Data was collected on 29 adolescent athletes with SRC. Among males (15.53+/-1.12 years) with SRC, 20% of acute and 10% of chronic cases experienced a greater DTC compared to healthy athlete reference values. A similarly increased DTC was experienced by 83% of acute and 29% of chronic SRC cases for females (15.58+/-1.16 years). Conclusions Adolescent athletes with concussion may continue demonstrating deficits in gait capabilities even in the chronic phase, and compensatory gait strategies were not the same between males and females. Dual-task cost assessment using the GAITRite® may be a valuable adjunct to comprehensive analysis of gait following SRC. Level of Evidence 2

Flame reconstruction, an indispensable tool in combustion diagnostics, improves the understanding of the radiative characteristics of the flame and can be derived from the flame emission spectrum. This paper investigates the feasibility of spectral reconstruction from low resolution sensor data, namely from digital colour cameras capturing visible light (RGB). Two spectral reconstruction techniques are evaluated: the Maloney-Wandell method, which requires an accurate characterisation of the spectral sensitivity function (SSF), and a kernel-based regression method, which avoids the need for an explicit characterisation of the SSF. The study focuses on flames generated in industrial environments, including the combustion of hydrocarbons and copper concentrates. This research contributes to the advancement of spectral reconstruction techniques for industrial combustion monitoring and process optimisation, because although the results show that both methods effectively reconstruct spectral emissions, kernel-based regression proves to be a cost- effective and practical solution for industrial environments as it does not require characterisation of the SSF, which is a difficult task in practice.

Objective: The purpose of this study was to explore the utilization of the Y Balance Test (YBT) alongside the Balance Error Scoring System (BESS) during examination of healthy adolescent athletes (14-18 year old) as well as those with acute and chronic concussion. Design: A repeated-measures study of balance in a cross-sectional convenience sample of adolescents participating in high-school athletics. Setting: Data were collected on healthy athletes in their school setting for comparison purposes and on concussed athletes in the physical therapy rehabilitation center at the hospital. Participants: Participants were a convenience sample of male and female athletes between the ages of 14 to 18 year old [180 healthy (111 male, 69 female) and 44 (28 male, 16 female) with concussion]. Assessment of Risk Factors: All participants were cleared for participation by preparticipation examination or by the treating sport medicine physician. Main Outcome Measures: Healthy athletes performed the YBT, a dynamic assessment of balance. Athletes with concussion also performed the BESS, a static assessment of balance. Results: Means for each YBT reach direction were statistically different for both healthy males and females (P < 0.05). Within both the acute and chronic subsets of the concussed sample, some participants performed over the median value for the BESS but not the YBT. Conclusions: These data may suggest that dynamic balance testing in conjunction with static balance testing could be valuable in both the acute and chronic phases of concussion to ensure a comprehensive assessment of the necessary balance skills for athletic play.

A method is proposed to validate the flame temperature calculated with the two-color pyrometer method. The temperature is obtained from the estimated spectra, us- ing the Maloney & Wandell method, and compared with measurements from a type K thermocouple.

A flame emits energy over a wide spectral region, and its associated spectra contain both continuous and discontinuous components, closely related to the combustion process current state. However, the non-linear and turbulent nature of the flame, high dimensionality of the spectral data, and the overlap between continuous and discontinuous spectral emissions requires the use of complex method to obtain valuable information for combustion closed loop-control and optimization. This work analyzes the spectral emission of the flame emitted by LPG flames to compute high resolution spectra combining the Maloney-Wandell spectral recovery method and field information of flame images with low spectral resolution. Then, we obtain different variables such as the flame temperature (in K) and local radiance (in μW=cm2), performing a dynamic, spectral and spatial combustion diagnostics.

In this paper, we present a method for hyperspectral retrieval using multispectral satellite images. The method consists of the use of training spectral data with a compressive capability. By using principal component analysis (PCA), a proper number of basis vectors are extracted. These vectors are properly combined and weighted by the sensors’ responses from visible MODIS channels, achieving as a result the retrieval of hyperspectral images. Once MODIS channels are used for hyperspectral retrieval, the training spectra are projected over the recovered data, and the ground-based process used for training can be reliably detected. To probe the method, we use only four visible images from MODIS for large-scale ash clouds’ monitoring from volcanic eruptions. A high-spectral resolution data of reflectances from ash was measured in the laboratory. Using PCA, we select four basis vectors, which combined with MODIS sensors responses, allows estimating hyperspectral images. By comparing both the estimated hyperspectral images and the training spectra, it is feasible to identify the presence of ash clouds at a pixel-by-pixel level, even in the presence of water clouds. Finally, by using a radiometric model applied over hyperspectral retrieved data, the relative concentration of the volcanic ash in the cloud is obtained. The performance of the proposed method is compared with the classical method based on temperature differences (using infrared MODIS channels), and the results show an excellent match, outperforming the infrared-based approach. This proposal opens new avenues to increase the potential of multispectral remote systems, which can be even extended to other applications and spectral bands for remote sensing. The results show that the method could play an essential role by providing more accurate information of volcanic ash spatial dispersion, enabling one to prevent several hazards related to volcanic ash where volcanoes’ monitoring is not feasible.

The purpose of this study was to assess the mortality of severe pancreatitis, according to the origin of the disease. Methods: Between 2009 to 2017, a retrospective cohort of 53 patients was recruited, and data was grouped by the origin of the pancreatitis, surgical procedure requirement as management strategy for severe pancreatitis, and relevant clinical data. Results were associated with patient survival. For statistical analyses, p values < 0.05 were considered significant in all analyses. Results: 62% of the patients were male, with a median age of 59 years. The median Marshall score was 8. In 25 patients (47%), the origin of pancreatitis was biliary. 8 patients were admitted for hypertrigliceridemic pancreatitis. The survival proportion at 8 years was 53.4%. 19 patients died, 3 were excluded from the analysis (cause of death not related to pancreatitis). Patients were grouped according to origin in Biliary Pancreatitis and Non Biliary Pancreatitis. When overall survival of both groups was compared, no statistically differences were found (p = 0.3). When cause related deathswere analysed, 8 (47%) were from biliary origin. 13 patients required a surgical procedure (including ERCP, biliary drainage or necrosectomy), 6 of which died (46%). Conclusions: In severe pancreatitis, the global mortality remains high (32%), with no differences according to the origin of the disease (32% of mortality in the biliary group). Necrosectomy procedure was required in 6 patients, 4 from the biliary group. Biliary does not seem to have a more benign outcome.

Visible spectral emissions emitted by different biomass species in a combustion process were measured and analyzed. By applying spectral techniques, it is possible to characterize and to monitor the combustion process, identifying different species.

Background: Xanthogranulomatous cholecystitis (XGC) is usually diagnosed in patients with chronic cholecystitis, which is the most frequent clinical form of presentation. Represents a surgical challenge, since can mimick gallbladder cancer, and requires histopathological confirmation for accurate diagnosis. The aim of this study was to characterize the clinical presentation, radiological features, surgical approach in a cohort of patients after elective or emergent cholecystectomy, in a chilean hospital. Method: Retrospective descriptive study using clinical data obtained from medical records of 112 consecutive patients, who underwent surgical treatment for cholelitiasis, with histopathologic diagnosis confirming XGC. Results: Between 2002–2016, 112 patients were diagnosed with XGC. 43.75% were man and 56.25% women, with a median age of 55.5 years. 43.8% were admitted elective surgery. Most frequent imagenological findings were signs of chronic cholecistytis. The suspicion of gallbladder cancer was described in 10 patients (9%). Laparoscopic approach was achieved in 88 patients (78.6%), with a conversion to open approach in 22 patients (19.6%), mostly due to technical difficulty. None of the patients was diagnosed with gallbladder cancer. Conclusions: XGC remains a clinical entity that requires accurate preoperative assessment, proper informed consent to the patient about surgical approaches, intraoperative complications, and the underling suspicion of gallbladder cancer. Preoperative diagnosis is difficult, particularly under emergency diagnosis.

Background: The purpose of this study is to report the clinical features and surgical outcomes in a series of patients undergoing to surgical procedure for Pancreatic Insulinoma (PI) Method: Retrospective case series of patient admitted and resolved surgically with diagnosis by PI in surgery department of Barros Luco Hospital, between 2007 and 2017. The variables was development: age, sex, clinics, diagnosis exams, surgery procedure, surgical time, distribution of injuries and postoperative result were recorded. Descriptive statistics were used. Results: 11 consecutive patients were studied (54% women) with median age of 45 years (21 – 81 years). All cases had symptoms derived from the autonomic nervous system and glycemia in fasting altered in the range of hypoglycemia (<60 mg/dl). The most sensitive test was pancreatic magnetic resonance (85%). The surgery was laparoscopic in 3 cases (enucleation). 54% presented morbidity, 1 of them with Clavien Dindo >III. There was no mortality in this serie. Conclusion: PI is a differencial diagnoses for hypoglycemia, with low incidence. The outcomes of our study are consistent with those reported in the literature.

This paper reviews the historiographical production published in the Cuadernos Journal (National University of Jujuy) throughout the last fifty issues, and generates, based on such trajectory in the study about the past, a set of readings about the recurrent orientations. Thus, the review work provides specific guidelines with regard to aspects related to spatial and temporal dimensions, as well as preferences concerning topics and ways to approach positions.

In this work we analyze the spectral emission of the flame emitted by burning biomass, of different species of wood such as Cypress, Fig, Olive and Pine. The analysis was carried out to distinguish the different species of wood sensing the compounds of Sodium and Potassium emitted at 582 nm and 780 nm. Also, a spectral analysis carried out for samples of wood at different contents of water. The results show that is possible make a difference between the biomass species, and a new technique can be implemented that will allow the estimation of water content in wood using the emission spectrum of the burned wood.