Search Results (129)

Background/Objectives: Leishmaniasis constitutes one of the most fatal parasitic diseases globally, adversely impacting the health of individuals residing in both intertropical and temperate zones. In these geographical areas, the administration of treatment is often inconsistent and largely ineffective with the available pharmaceuticals, as these exhibit more pronounced side effects than the therapeutic advantages they purport to provide. Methods: Consequently, the current investigation seeks to engage in molecular modeling of novel pharmacological candidates incorporating 1,2,3 disubstituted triazole moieties, coordinated with Cu^II metal centers, in pursuit of promising bioactive properties. Results: Two complexes were prepared and X-ray analysis revealed a comparable structural configuration surrounding the copper (II) atom. The planar square coordination geometry was elucidated through the assessment of the ${\tau }_4=0$ (tau four) parameters. The comprehensive characterization encompasses HRMS-ESI (+), NMR, elemental analyses, mid-infrared, and UV-vis spectroscopic techniques. Time-dependent density functional theory (TD-DFT) analyses will substantiate the findings obtained through UV-vis spectroscopy. Crucially, the biological assays against Leishmania (L.) amazonensis revealed that Complex 1 exhibited outstanding potency against the intracellular amastigote form, demonstrating a half-maximal inhibitory concentration (IC₅₀) of 0.4 µM. This activity was 6-fold higher than that of amphotericin B (IC₅₀ = 2.5 µM) and 33-fold higher than pentamidine (IC₅₀ = 13.3 µM). Furthermore, Complex 1 showed a promising selectivity index (SI = 9.7) against amastigotes, surpassing the reference drugs and meeting the criteria for a lead compound. While less active on promastigotes, both complexes demonstrated high stability in DMSO solution, a prerequisite for biological testing. Conclusions: These results unequivocally identify Complex 1 as a highly promising candidate for the development of new antileishmanial therapies, warranting further in vivo studies. Full article

Globally, lakes are increasingly recognized as sensitive indicators of climate change and ecosystem stress. Qaraoun Lake, Lebanon’s largest artificial reservoir, is a critical resource for irrigation, hydropower generation, and domestic water supply. Over the past 25 years, satellite remote sensing has enabled consistent monitoring of its hydrological and environmental dynamics. This study leverages the advanced cloud-based processing capabilities of Google Earth Engine (GEE) to analyze over 180 cloud-free scenes from Landsat 7 (Enhanced Thematic Mapper Plus) (ETM+) from 2000 to present, Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) from 2013 to present, and Landsat 9 OLI-2/TIRS-2 from 2021 to present, quantifying changes in lake surface area, water volume, and pollution levels. Water extent was delineated using the Modified Normalized Difference Water Index (MNDWI), enhanced through pansharpening to improve spatial resolution from 30 m to 15 m. Water quality was evaluated using a composite pollution index that integrates three spectral indicators—the Normalized Difference Chlorophyll Index (NDCI), the Floating Algae Index (FAI), and a normalized Shortwave Infrared (SWIR) band—which serves as a proxy for turbidity and organic matter. This index was further standardized against a conservative Normalized Difference Vegetation Index (NDVI) threshold to reduce vegetation interference. The resulting index ranges from near-zero (minimal pollution) to values exceeding 1.0 (severe pollution), with higher values indicating elevated chlorophyll concentrations, surface reflectance anomalies, and suspended particulate matter. Results indicate a significant decline in mean annual water volume, from a peak of 174.07 million m³ in 2003 to a low of 106.62 million m³ in 2025 (until mid-November). Concurrently, pollution levels increased markedly, with the average index rising from 0.0028 in 2000 to a peak of 0.2465 in 2024. Episodic spikes exceeding 1.0 were detected in 2005, 2016, and 2024, corresponding to documented contamination events. These findings were validated against multiple institutional and international reports, confirming the reliability and efficiency of the GEE-based methodology. Time-series visualizations generated through GEE underscore a dual deterioration, both hydrological and qualitative, highlighting the lake’s growing vulnerability to anthropogenic pressures and climate variability. The study emphasizes the urgent need for integrated watershed management, pollution control measures, and long-term environmental monitoring to safeguard Lebanon’s water security and ecological resilience. Full article

In this paper, the design of a mid-infrared four-band ultra-narrowband wave-absorbing sensor based on the localized equi-excited exciton resonance of graphene metamaterials is presented. The designed super-surface unit has a geometrically symmetric structure and is insensitive to incident light sources with different polarization directions. The absorbing sensor has four resonant wavelengths located at λ₁ = 3.172 μm, λ₂ = 3.525 μm, λ₃ = 3.906 μm, and λ₄ = 4.588 μm, with absorption efficiencies of 99.94%, 99.46%, 99.55%, and 98.16%, respectively. In addition, the dynamic tuning of the resonant wavelength and absorption efficiency can be realized by changing the gate voltage or through chemical doping of graphene. Moreover, the wave-absorbing performance can maintain stable absorption over a wide range of incidence angles from 0 to 50°. Finally, the wave-absorbing sensor was subjected to different ambient refractive indices, and the refractive index sensitivities corresponding to the four resonant wavelengths were obtained as 587.5 nm/RIU, 700.0 nm/RIU, 850.0 nm/RIU, and 900.0 nm/RIU, with FOM values of 48.96 RIU⁻¹, 58.34 RIU⁻¹, 53.13 RIU⁻¹, and 28.13 RIU⁻¹, respectively, all of which have superior sensing characteristics. Therefore, this paper enriches the variety of mid-infrared absorber sensors and has a broad application prospect in the fields of wave absorption, sensing, and detection. Full article

To explore the potential of new information transmission windows, this work presents a broadband plasmonic filter based on gold-deposited silicon photonic crystal fiber (PCF) operating in mid-infrared regime numerically, using the finite element method (FEM). The simulation results indicate that the interaction between the high-refractive-index pure silicon material and the gold layer can cause a shift of the resonance central point to the mid-infrared band, which provides the prerequisite for mid-infrared filtering. When the cladding holes’ diameter is 1.3 µm, the inner holes’ diameter is 1.04 µm, the diameter of the holes located on both sides of the core region is 2.08 µm, the gold-coated holes’ diameter is 2.08 µm, the lattice constant is 2 µm, and the gold thickness is 50 nm, this PCF can operate in the mid-infrared band near the central wavelength of 3 µm. The 1 mm long PCF polarizer exhibits a maximum extinction ratio (ER) of −43.5 dB at 3 µm and a broad operating bandwidth of greater than 820 nm with ER better than −20 dB. Additionally, it also possesses high fabrication feasibility. This in-fiber polarization filter, characterized by its comprehensive performance and ease of fabrication, aids in exploring the development potential of high-speed and large-capacity modern communication networks within new optical bands and contributes to new photonic computing and sensing. Full article

The growing demand for compact, low-power infrared (IR) sensors necessitates advanced solutions for on-chip spectral selectivity, particularly for integration with Thermal Metal-Oxide-Semiconductor (TMOS) devices. This paper investigates the design and analysis of CMOS-compatible metal–insulator–metal (MIM) metamaterial absorbers tailored for selective absorption in the long-wave infrared (LWIR) region. We present a design methodology utilizing an equivalent-circuit model, which provides intuitive physical insight into the absorption mechanism and significantly reduces computational costs compared to full-wave electromagnetic simulations. An important rule in this design methodology is demonstrating how the resonance wavelength of these absorbers can be precisely tuned across the LWIR spectrum by engineering the geometric parameters of the top metallic patterns and, critically, by optimizing the dielectric substrate’s refractive index and thickness, which assist in designing small period MIM absorber units which are important in infrared thermal sensor pixels. Our results demonstrate that the resonance wavelength of these absorbers can be precisely tuned across the LWIR spectrum by engineering the geometric parameters of the top metallic patterns and by optimizing the dielectric substrate’s refractive index and thickness. Specifically, the selection of silicon as the dielectric material, owing to its high refractive index and low losses, facilitates compact designs with high-quality factors. The transmission line model provides intuitive insight into how near-perfect absorption is achieved when the absorber’s input impedance matches the free-space impedance. This work presents a new approach for the methodology of designing MIM absorbers in the mid-infrared and long-wave infrared (LWIR) regions, utilizing the intuitive insights provided by equivalent circuit modeling. This study validates a highly efficient design approach for high-performance, spectrally selective MIM absorbers for LWIR radiation, paving the way for their monolithic integration with TMOS sensors to enable miniaturized, cost-effective, and functionally enhanced IR sensing systems. Full article

Background: The variation in sheep milk composition is closely related to the sheep’s metabolic status. This study aimed to analyze the milk composition and serum metabolic characteristics of Xinggao sheep during different lactation periods and to evaluate the association between milk quality traits and body metabolism. Methods: Eighteen intensively reared ewes were divided into three groups: an early lactation group (MA), a mid-lactation group (MB), and a late lactation group (MC). Milk components were detected by infrared spectroscopy, and the ewes’ serum metabolomic characteristics were detected by liquid chromatography–mass spectrometry (LC-MS). K-means correlation analysis revealed that the milk fat percentage was positively correlated with L-aspartic acid and negatively correlated with citrulline levels. Random forest analysis for metabolite importance ranking showed that methionine sulfoxide and methionine exhibited high mean decrease accuracy and mean decrease Gini index values. Results: The milk composition results showed that, compared with MA, the milk fat content and total solids in MB and MC were significantly higher, while the freezing point in the MC was significantly lower. Metabolomic studies showed that 207, 210, and 238 differential metabolites were identified in the comparisons of MA vs. MB MA vs. MC, and MB vs. MC, respectively, and these metabolites were mainly enriched in the pyrimidine metabolism, arachidonic acid metabolism, and arginine biosynthesis pathways. Evaluation of metabolite importance using random forest models revealed that 27 metabolites, including 2-Arachidonyl glycerol ether, methionine, and methionine sulfoxide, showed a high mean decrease accuracy and mean decrease Gini index. Correlation analysis revealed that milk fat percentage and total solids were positively correlated with 11 metabolites, including citrulline, phenylalanine, and octadecylamine, and negatively correlated with isoproterenol, cortisol, and kynurenic acid. The freezing point was positively correlated with cortisol, isoproterenol, and kynurenic acid and negatively correlated with aldosterone, dehydroepiandrosterone, and betaine. Conclusions: This study showed that there were significant differences in the milk composition and metabolites of Xinggao sheep during different lactation periods, highlighting the impact of lactation stage on milk composition and production performance. We recommend developing targeted nutritional strategies based on the specific metabolic profiles of different lactation periods to optimize the feeding management and nutritional regulation of Xinggao sheep. Full article

Climate change research is increasingly important in regions vulnerable to extreme hydrometeorological events like droughts, which pose significant socio-economic and environmental challenges. This study examines future variability of meteorological drought in northernmost South America using the Standardized Precipitation Index (SPI) and precipitation projections from CMIP6 models. We first evaluated model performance by comparing historical simulations with observational data from the Climate Hazards Group InfraRed Precipitation with Station dataset for 1981–2014. Among the models, CNRM-CM6-1-HR was selected for its superior accuracy, demonstrated by the lowest errors and highest correlation with observed data—specifically, a correlation coefficient of 0.60, a normalized root mean square error of 1.08, and a mean absolute error of 61.37 mm/month. Under SSP1-2.6 and SSP5-8.5 scenarios, projections show decreased rainfall during the wet months in the western Perijá mountains, with reductions of 3% to 26% between 2025 and 2100. Conversely, the Sierra Nevada of Santa Marta is expected to see increases of up to 33% under SSP1-2.6. During dry months, northern Colombia and Venezuela—particularly coastal lowlands—are projected to experience rainfall decreases of 10% to 17% under SSP1-2.6 and 13% to 20% under SSP5-8.5. These areas are likely to face severe drought conditions in the mid and late 21st century. These findings are essential for guiding water resource management, enabling adaptive strategies, and informing policies to mitigate drought impacts in the region. Full article

Advancements in remote sensing technology have enabled the acquisition of high spatial and radiometric resolution imagery, offering abundant and reliable data sources for forest fire monitoring. In order to explore the ability of Sustainable Development Science Satellite 1 (SDGSAT-1) in wildfire monitoring, a systematic and comprehensive study was proposed on smoke detection during the wildfire early warning phase, fire point identification during the fire occurrence, and burned area delineation after the wildfire. The smoke detection effect of SDGSAT-1 was analyzed by machine learning and the discriminating potential of SDGSAT-1 burned area was discussed by Mid-Infrared Burn Index (MIRBI) and Normalized Burn Ratio 2 (NBR2). In addition, compared with Sentinel-2, the fixed-threshold method and the two-channel fixed-threshold plus contextual approach are further used to demonstrate the performance of SDGSAT-1 in fire point identification. The results show that the average accuracy of SDGSAT-1 fire burned area recognition is 90.21%, and a clear fire boundary can be obtained. The average smoke detection precision is 81.72%, while the fire point accuracy is 97.40%, and the minimum identified fire area is 0.0009 km², which implies SDGSAT-1 offers significant advantages in the early detection and identification of small-scale fires, which is significant in fire emergency and disposal. The performance of fire point detection is superior to that of Sentinel-2 and Landsat 8. SDGSAT-1 demonstrates great potential in monitoring the entire process of wildfire occurrence, development, and evolution. With its higher-resolution satellite imagery, it has become an important data source for monitoring in the field of remote sensing. Full article

The present research investigated the effect of moderate heat treatment (65 °C for 5 min) and calcium enrichment (10 mM CaCl₂) on the quality of gels formed by lemon juice at 30 °C for 180 min. Raw milk, calcium-fortified raw milk, heated milk, and calcium-fortified heated milk were used. Rheological measurements showed that the addition of calcium to milk significantly improved the elastic modulus (G’), which passed from 21.2 Pa to 80.18 Pa. However, the combination of heat treatment and calcium produced weaker gels with G’ = 3.71 Pa. Turbiscan analysis revealed higher instability in calcium-fortified heated milk samples that have high Turbiscan Stability Index (TSI) values. Mid-infrared spectral regions (3000–2800 cm⁻¹, 1700–1500 cm⁻¹, and 1500–900 cm⁻¹) and fluorescence spectroscopy indicated some structural changes in protein–water, protein–protein, and protein–lipid interactions depending on coagulation conditions. Principal component analysis (PCA) applied to the fluorescence and MIR datasets allowed the differentiation of gel samples depending on heat treatment and calcium addition. Scanning electron microscopy (SEB) indicated dense and uniform gels produced with calcium-enriched raw milk and porous structures with heated and calcium-enriched milk. These results reveal new information on how thermal treatment and calcium supplementation affect protein network structure formation and the gel microstructure during lemon juice-induced coagulation. Full article

Driven by the rising demand for digitalization and intelligent development of infrared payloads, next-generation systems must be developed within compressed timelines. High-precision digital modeling and simulation techniques offer essential data sources but often falter in complex space-based scenarios due to the limited availability of infrared characteristic data, hindering evaluation of the payload effectiveness. To address this, we propose a digital imaging simulation and verification (DISV) model for high-fidelity infrared image generation and closed-loop validation in the context of cloud–sea target detection. Based on on-orbit infrared imagery, we construct a cloud cluster database via morphological operations and generate physically consistent backgrounds through iterative optimization. The DISV model subsequently calculates scene infrared radiation, integrating radiance computations with an electron-count-based imaging model for radiance-to-grayscale conversion. Closed-loop verification via blackbody radiance inversion is performed to confirm the model’s accuracy. The mid-wave infrared (MWIR, 3–5 µm) system achieves mean square errors (RSMEs) < 0.004, peak signal-to-noise ratios (PSNRs) > 49 dB, and a structural similarity index measure (SSIM) > 0.997. The long-wave infrared (LWIR, 8–12 µm) system yields RMSEs < 0.255, PSNRs > 47 dB, and an SSIM > 0.994. Under 20–40% cloud coverage, the target radiance inversion errors remain below 4.81% and 7.30% for the MWIR and LWIR, respectively. The DISV model enables infrared image simulation across multi-domain scenarios, offering vital support for optimizing on-orbit payload performance. Full article

Anti-resonant hollow-core fibers have exhibited excellent performance in applications such as high-power pulse transmission, network communication, space exploration, and precise sensing. Employing anti-resonant hollow-core fibers instead of light guiding arms for transmitting laser energy at the 2.79 μm band can significantly enhance the flexibility of medical laser handles, reduce system complexity, and increase laser transmission efficiency. Nevertheless, common anti-resonant hollow-core fibers do not have the ability to maintain the polarization state of light during laser transmission, which greatly affects their practical applications. In this paper, we propose a polarization-maintaining anti-resonant hollow-core fiber applicable for transmission at the mid-infrared 2.79 μm band. This fiber features a symmetrical geometric structure and an asymmetric refractive index cladding composed of quartz and a type of mid-infrared glass with a higher refractive index. Through optimizing the fiber structure at the wavelength scale, single-polarization transmission can be achieved at the 2.79 μm wavelength, with a polarization extinction ratio exceeding 1.01 × 10⁵, indicating its stable polarization-maintaining performance. Simultaneously, it possesses low-loss transmission characteristics, with the loss in the x-polarized fundamental mode being less than 9.8 × 10⁻³ dB/m at the 2.79 µm wavelength. This polarization-maintaining anti-resonant hollow-core fiber provides a more reliable option for the light guiding system of the 2.79 μm Er; Cr: YSGG laser therapy device. Full article

Recently, sensing for biomolecules has become increasingly popular in the fields of environmental monitoring, personal health, and food safety. Plasmonic biosensors have been a powerful tool due to their high sensitivity and label-free operation. However, when it comes to molecules with different kinds and concentrations, detection technology and data processing remain a challenging task. In this study, we investigate the qualitative and quantitative detection of two kinds of biomolecules in the mid-infrared region simultaneously by the utilization of a plasmonic sensor. The strong coupling between each plasmonic resonance and the corresponding molecular vibration is found to significantly enhance the absorption signal of molecules, and the obtained Rabi splitting is not only a proof of molecular existence but also an indicator of molecular concentration. However, the amount of the molecular solution with a background refractive index in turn affects the plasmonic resonance position. In more general situations, it is not easy to achieve the match between plasmonic resonance and molecular resonance, and thus the quantitative detection by the Rabi splitting depth is not always feasible. Hence, we propose a machine learning algorithm called principal component analysis (PCA), providing a versatile approach for analyzing the proportion of each molecule in the mixture. Our work opens up new routes in noninvasive optical sensing and the integration of AI-driven data analysis further strengthens its potential for real-world applications. Full article

The seeds of Cichorium glandulosum Boiss. et Huet. (CS) are known for various effects. However, the research on the establishment of quality evaluation standards for CS is currently limited. Therefore, this study employed Ultra Performance Liquid Chromatography–Tandem Mass Spectrometry (UPLC-MS/MS) to analyze the components of CS. Forty-nine compounds were identified through manual analysis and database comparison. The components were then verified using HPLC and standards. Additionally, 19 batches were collected to establish the fingerprint chromatogram. Five major chemical components were selected for subsequent analysis. MIR, combined with three variable selection algorithms and three preprocessing methods, was used to build prediction models. For the three indexes of Chlorogenic Acid, 1,4-Dicaffeoylquinic Acid, and 1,5-Dicaffeoylquinic Acid, the R² values for both the training set and test set were above 0.9, the RPD values were all greater than 2.5, and the RER values were greater than 10. This indicated that the combination of mid-infrared spectroscopy and chemometrics had excellent model applicability and prediction performance for these three indexes. A quality evaluation system has been initially established, laying a foundation for research on quality evaluation of CS. Full article

Accurate cover crop biomass estimation is critical for evaluating their ecological benefits. Traditional methods, like destructive sampling, are labor-intensive and time-consuming. This study investigates the application of unmanned aerial vehicle (UAV)-mounted multispectral sensors to estimate biomass in oats, Austrian winter peas (AWP), turnips, and a combination of all three crops across six experimental plots. Five spectral images were collected at two growth stages, analyzing band reflectance, nine vegetation indices, and canopy height models (CHMs) for biomass estimation. Results indicated that most vegetation indices were effective during mid-growth stages but showed reduced accuracy later. Stepwise multiple linear regression revealed that combining the normalized difference red-edge (NDRE) index and CHM provided the best biomass model before termination (R² = 0.84). For bitemporal images, green reflectance, CHM, and the ratio of near-infrared (NIR) to red achieved the best performance (R² = 0.85). Cover crop species also influenced the model performance. Oats were best modeled using the enhanced vegetation index (EVI) (R² = 0.86), AWP with red-edge reflectance (R² = 0.71), turnips with NIR, GNDVI, and CHM (R² = 0.95), and mixed species with NIR and blue band reflectance (R² = 0.93). These findings demonstrate the potential of high-resolution multispectral imaging for efficient biomass assessment in precision agriculture. Full article

The reflectance (R) of linear and circular micro-gratings on c-plane sapphire Al₂O₃ ablated by a femtosecond (fs) laser were spectrally characterised for thermal emission $\propto (1-R)$ in the mid-to-far infrared (IR) spectral range. An IR camera was used to determine the blackbody radiation temperature from laser-patterned regions, which showed (3–6)% larger emissivity dependent on the grating pattern. The azimuthal emission curve closely followed the Lambertian angular profile $\propto cos{\theta }_a$ at the 7.5–13 μm emission band. The back-side ablation method on transparent substrates was employed to prevent debris formation during energy deposition as it applies a forward pressure of >0.3 GPa to the debris and molten skin layer. The back-side ablation maximises energy deposition at the exit interface where the transition occurs from the high-to-low refractive index. Phononic absorption in the Reststrahlen region 20–30 μm can be tailored with the fs laser inscription of sensor structures/gratings. Full article