Search Results (8,641)

Efficient and accurate panel assignment is critical in expert and peer review processes. Traditional methods—based on manual preferences or Heuristic rules—often introduce bias, inconsistency, and scalability challenges. We present an automated framework that combines transformer-based document similarity modeling with optimization-based reviewer assignment. Using the all-mpnet-base-v2 from model (version 3.4.1), our system computes semantic similarity between proposal texts and reviewer documents, including CVs and Google Scholar profiles, without requiring manual input from reviewers. These similarity scores are then converted into rankings and integrated into an Integer Linear Programming (ILP) formulation that accounts for workload balance, conflicts of interest, and role-specific reviewer assignments (lead, scribe, reviewer). The method was tested across 40 researchers in two distinct disciplines (Chemical Engineering and Philosophy), each with 10 proposal documents. Results showed high self-similarity scores (0.65–0.89), strong differentiation between unrelated fields (−0.21 to 0.08), and comparable performance between reviewer document types. The optimization consistently prioritized top matches while maintaining feasibility under assignment constraints. By eliminating the need for subjective preferences and leveraging deep semantic analysis, our framework offers a scalable, fair, and efficient alternative to manual or Heuristic assignment processes. This approach can support large-scale review workflows while enhancing transparency and alignment with reviewer expertise. Full article

(1) Background: The concentration of lamotrigine, an antiepileptic drug very often used in bipolar disorder, is most often determined in the blood, with many inconveniences. An alternative may be to use saliva as a diagnostic material for this purpose. The development of a method to determine lamotrigine in saliva as a biological material significantly improves patient comfort during sampling. The developed method uses solid-phase extraction for the isolation of the drug from saliva for the first time. (2) Methods: This study aimed to develop a method to determine lamotrigine in saliva using solid-phase extraction (SPE) for isolation and liquid chromatography with a diode array detector (LC-DAD) for quantitative analysis. (3) Results: The method was validated by determining its linearity in the concentration range 10–2000 ng/mL (R² > 0.99), and the intra- and inter-day precision expressed as coefficient of variation (CV%) did not exceed 15%. (4) Conclusions: The developed method was used to determine the salivary concentration of lamotrigine in patients treated with the studied compound, confirming its usefulness in bipolar disorder (BD). Full article

Objective: The objective of this study was to assess changes in body composition, with a specific focus on fat mass (FM) and fat-free mass (FFM), in obese adults with type 2 diabetes (T2D) treated with once-weekly (OW) subcutaneous (s.c.) semaglutide. Methods: This was a single-center, 12-month, real-world, ambispective study (6-month prospective and 6-month retrospective). Body composition parameters were assessed via segmental multifrequency bioelectrical impedance analysis (SMF-BIA). Results: A total of 117 patients with DM2, with a median age of 56 years, a median HbA1c level of 9.4%, and a median body weight of 102.5 kg, were included in the study. The median body weight, body fat mass, and visceral fat significantly decreased at 6 months, with values of −9.3, −7.5, and −1.8 kg, respectively. There were further reductions from 6 to 12 months, albeit at a slower rate. The median skeletal muscle mass significantly decreased at 6 months (−1.2 kg), although no further significant reductions were observed at 12 months. Conclusions: OW s.c. semaglutide for 12 months significantly improved body composition parameters, mainly at the expense of fat mass loss, with the preservation of skeletal muscle mass. These changes are clinically meaningful, since they impact general metabolic health and are associated with improvements in metabolic control and clinical parameters associated with renal and CV risks, as well as presumable improvements in quality of life. Full article

Compared to traditional real-valued neural networks, which process only amplitude information, complex-valued neural networks handle both amplitude and phase information, leading to superior performance in polarimetric synthetic aperture radar (PolSAR) image classification tasks. This paper proposes a multi-scale feature extraction (MSFE) method based on a 3D complex-valued network to improve classification accuracy by fully leveraging multi-scale features, including phase information. We first designed a complex-valued three-dimensional network framework combining complex-valued 3D convolution (CV-3DConv) with complex-valued squeeze-and-excitation (CV-SE) modules. This framework is capable of simultaneously capturing spatial and polarimetric features, including both amplitude and phase information, from PolSAR images. Furthermore, to address robustness degradation from limited labeled samples, we introduced a multi-scale learning strategy that jointly models global and local features. Specifically, global features extract overall semantic information, while local features help the network capture region-specific semantics. This strategy enhances information utilization by integrating multi-scale receptive fields, complementing feature advantages. Extensive experiments on four benchmark datasets demonstrated that the proposed method outperforms various comparison methods, maintaining high classification accuracy across different sampling rates, thus validating its effectiveness and robustness. Full article

Restaurants and open markets generate considerable quantities of organic waste. Converting these residues into poultry feed ingredients offers a sustainable disposal route. This study aimed to evaluate the nutritional and sensory viability of a novel feed complement formulated from Bonito fish meal (Sarda chiliensis chiliensis) and Única potato peel flour (Solanum tuberosum L. cv. Única). This study was conducted in three phases: (i) production and nutritional characterization of the two by-product flours; (ii) formulation of a 48:52 (w/w) blend, incorporated into broiler diets at 15%, 30%, and 45% replacement levels over a 7-week trial divided into starter (3 weeks), grower (3 weeks), and finisher (1 week) phases; and (iii) assessment of growth performance (weight gain, final weight, and feed conversion ratio), followed by a sensory evaluation of the resulting meat using a Check-All-That-Apply (CATA) analysis. The Bonito fish meal exhibited 50.78% protein, while the Única potato peel flour was rich in carbohydrates (74.08%). The final body weights of broiler chickens ranged from 1872.1 to 1886.4 g across treatments, and the average feed conversion ratio across all groups was 0.65. Replacing up to 45% of commercial feed with the formulated complement did not significantly affect growth performance (p > 0.05). Sensory analysis revealed that meat from chickens receiving 15% and 45% substitution levels was preferred in terms of aroma and taste, whereas the control group was rated higher in appearance. These findings suggest that the formulated feed complement may represent a viable poultry-feed alternative with potential sensory and economic benefits, supporting future circular-economy strategies. Full article

Environmental sustainability and responsible resource utilization are critical global challenges. In this work, we present a sustainable and circular-economy-based approach for synthesizing CuFe₂O₄ nanoparticles by directly utilizing copper oxide minerals sourced from Chilean mining operations. Copper sulfate (CuSO₄) was extracted from these minerals through acid leaching and used as a precursor for nanoparticle synthesis via both chemical co-precipitation and microwave-assisted methods. The influence of different precipitating agents—NaOH, Na₂CO₃, and NaF—was systematically evaluated. XRD and FESEM analyses revealed that NaOH produced the most phase-pure and well-dispersed nanoparticles, while NaF resulted in secondary phase formation. The microwave-assisted method further improved particle uniformity and reduced agglomeration due to rapid and homogeneous heating. Electrochemical characterization was conducted to assess the suitability of the synthesized CuFe₂O₄ for supercapacitor applications. Cyclic voltammetry (CV) and galvanostatic charge–discharge (GCD) measurements confirmed pseudocapacitive behavior, with a specific capacitance of up to 1000 F/g at 2 A/g. These findings highlight the potential of CuFe₂O₄ as a low-cost, high-performance electrode material for energy storage. This study underscores the feasibility of converting primary mined minerals into functional nanomaterials while promoting sustainable mineral valorization. The approach can be extended to other critical metals and mineral residues, including tailings, supporting the broader goals of a circular economy and environmental remediation. Full article

In the process of plant protection for fruit trees using rotary-wing UAVs, challenges such as droplet drift, insufficient canopy penetration, and low agrochemical utilization efficiency remain prominent. Among these, the uncertainty in the spatio-temporal distribution of downwash airflow is a key factor contributing to non-uniform droplet deposition and increased drift. To address this issue, we developed a wind field numerical simulation model based on an improved hierarchical leaf density model to clarify the spatio-temporal characteristics of downwash airflow, the scale of turbulence regions, and their effects on internal canopy airflow under varying flight altitudes and different rotor speeds. Field experiments were conducted in orchards to validate the accuracy of the model. Simulation results showed that the average error between the simulated and measured wind speeds inside the canopy was 8.4%, representing a 42.11% reduction compared to the non-hierarchical model and significantly improving the prediction accuracy. The coefficient of variation (CV) was 0.26 in the middle canopy layer and 0.29 in the lower layer, indicating a decreasing trend with an increasing canopy height. We systematically analyzed the variation in turbulence region scales under different flight conditions. This study provides theoretical support for optimizing UAV operation parameters to improve droplet deposition uniformity and enhance agrochemical utilization efficiency. Full article

Carbon monoxide (CO) is a major toxic gas emitted from vehicle exhaust, industrial processes, and incomplete fuel combustion, posing serious environmental and health risks. Catalytic oxidation of CO into less harmful CO₂ is an effective strategy to reduce these emissions. In this study, we investigated the catalytic performance of platinum (Pt) single atoms doped on C₃N monolayers with various vacancy defects, including single carbon (C_V) and nitrogen (N_V) vacancies, using density functional theory (DFT) calculations. Our results demonstrate that Pt@N_V-C₃N exhibited the most favorable catalytic properties, with the highest O₂ adsorption energy (−3.07 eV). This performance significantly outperforms Pt atoms doped at other vacancies. It can be attributed to the strong binding between Pt and nitrogen vacancies, which contributes to its excellent resistance to Pt aggregation. CO oxidation on Pt@N_V-C₃N proceeds via the Eley–Rideal (ER2) mechanism with a low activation barrier of 0.41 eV for the rate-determining step, indicating high catalytic efficiency at low temperatures. These findings suggest that Pt@N_V-C₃N is a promising candidate for CO oxidation, contributing to developing cost-effective and environmentally sustainable catalysts. The strong binding of Pt atoms to the nitrogen vacancies prevents aggregation, ensuring the stability and durability of the catalyst. The kinetic modeling further revealed that the ER2 mechanism offers the highest reaction rate constants over a wide temperature range (273–700 K). The low activation energy barrier also facilitates CO oxidation at lower temperatures, addressing critical challenges in automotive and industrial pollution control. This study provides valuable theoretical insights for designing advanced single-atom catalysts for environmental remediation applications. Full article

This study investigates the delineation of management zones (MZs) to support site-specific crop management by simplifying within-field variability in irrigated (54.6 ha) and rainfed (7.9 ha) sorghum and soybean fields in Brazil. Historical yield, apparent soil electrical conductivity (ECa) at 0.75 m and 1.50 m, and terrain data were analyzed using multivariate statistics to define MZs. Two clustering methods—fuzzy c-means (FCM) and hierarchical clustering—were compared for variance reduction effectiveness. Rainfed areas showed greater spatial variability (yield CV 9–12%; ECa CV 20–27%) than irrigated fields (yield CV < 7%; ECa CV ~5%). Principal component analysis (PCA) identified subsoil ECa and elevation as key variables in irrigated fields, while surface ECa and topography influenced rainfed variability. FCM produced more homogeneous zones with fewer classes, especially in irrigated fields, whereas hierarchical clustering better detected outliers but required more zones for similar variance reduction. Yield correlated strongly with slope and moisture in rainfed systems. These results emphasize aligning MZ delineation with production system characteristics—enabling variable rate irrigation in irrigated fields and promoting moisture conservation in rainfed systems. FCM is recommended for operational efficiency, while hierarchical clustering offers higher precision in complex contexts. Full article

This study aims to characterize short-wave infrared (SWIR) reflectance spectra at cranial (at the scalp overlying the frontal cortex and the temporal bone window) and extracranial (biceps and triceps) sites in patients with multiple sclerosis (MS) and age-/sex-matched controls. We sought to identify the diagnostic accuracy of wavelength-specific patterns in distinguishing MS from normal controls and spectral markers associated with disability (e.g., Expanded Disability Status Scale scores). To achieve these objectives, we employed a multi-site SWIR spectroscopy acquisition protocol that included measurements from traditional cranial locations as well as extracranial reference sites. Advanced spectral analysis techniques, including wavelength-dependent absorption modeling and machine learning-based classification, were applied to differentiate MS-related hemodynamic changes from normal physiological variability. Classification models achieved perfect performance (accuracy = 1.00), and cortical site regression models showed strong predictive power (EDSS: R²_CV = 0.980; FSS: R²_CV = 0.939). Variable Importance in Projection (VIP) analysis highlighted key wavelengths as potential spectral biomarkers. This approach allowed us to explore novel biomarkers of neural and systemic impairment in MS, paving the way for potential clinical applications of SWIR spectroscopy in disease monitoring and management. In conclusion, spectral analysis revealed distinct wavelength-specific patterns collected from cranial and extracranial sites reflecting biochemical and structural differences between patients with MS and normal subjects. These differences are driven by underlying physiological changes, including myelin integrity, neuronal density, oxidative stress, and water content fluctuations in the brain or muscles. This study shows that portable spectral devices may contribute to bedside individuation and monitoring of neural diseases, offering a cost-effective alternative to repeated imaging. Full article

The division of the flood season is of great significance for the precise operation of water conservancy projects, flood control and disaster reduction, and the rational allocation of water resources, alleviating the contradiction of the uneven spatial and temporal distribution of water resources. The single weighting method can only determine the weight of the flood season division indicators from a certain perspective and cannot comprehensively reflect the time-series attributes of the indicators. This study proposes a Flood Season Division Model based on the Goose Optimization Algorithm and Minimum Deviation Combined Weighting (FSDGOAMDCW). The model uses the Goose Optimization Algorithm (GOA) to solve the Minimum Deviation Combination model, integrating weights from two subjective methods (Expert Scoring and G1) and three objective methods (Entropy Weight, CV, and CRITIC). Combined with the Set Pair Analysis Method (SPAM), it realizes comprehensive flood season division. Based on daily precipitation data of the Nandujiang River (1961–2022), the study determines its flood season from 1 May to 30 October. Comparisons show that: ① GOA converges faster than the Genetic Algorithm, stabilizing at T = 5 and achieving full convergence at T = 24; and ② The model’s division results have the smallest Intra-Class Differences, avoiding indistinguishability between flood and non-flood seasons under special conditions. This research aims to support flood season division studies in tropical islands. Full article

This study arises from the search for non-invasive diagnostic alternatives for equine gastric ulceration (EGUS), which is prevalent, clinically variable and only confirmed by gastroscopy. The aim is to quantify five salivary biomarkers (IL1-F5, PIP, CA VI, serotransferrin, albumin) under clinical conditions by validated assays and analyse their diagnostic value. Horses were grouped in No EGUS (neither clinical signs of EGUS nor gastric lesions), EGUS non-clinical (apparently no clinical signs of EGUS but with gastric lesions), and EGUS clinical (obvious clinical signs of EGUS and with gastric lesions). The concentration of 5 analytes could be quantified using sandwich ELISA assays, with high precision (CV: 6.79–12.38%) and accuracy (>95%). Mean salivary levels of IL1-F5, CA-VI, serotransferrin and albumin were significantly higher in EGUS clinical horses compared to No EGUS horses, whereas PIP showed no statistical significance. EGUS non-clinical horses showed statistical differences with No EGUS horses for PIP and albumin. In addition, IL1-F5, CA-VI, serotransferrin and albumin showed moderate accuracy to distinguish between No EGUS and EGUS clinical horses (AUC ≥ 0.8), with sensitivity and specificity greater than 77% and 65%, respectively. Therefore, these biomarkers could be a promising starting point for screening horse that might have EGUS in practice. Full article

Koshu wine, produced from the indigenous Japanese grape Vitis vinifera L. cv. Koshu exhibits a lower pH than other white wines, hindering malolactic fermentation (MLF) by lactic acid bacteria (LAB). Here, we aimed to isolate LAB strains capable of performing MLF under these challenging conditions to improve wine quality. Sixty-four Oenococcus oeni and one Lactobacillus hilgardii strain were isolated from Koshu grapes and wines that had undergone spontaneous MLF. MLF activity was assessed under varying pH, SO₂, and ethanol conditions in modified basal medium (BM) and Koshu model wine media. Expression of stress-related genes was analyzed using real-time PCR. Carbon source utilization was evaluated via API 50CH assays. All isolates degraded malic acid and produced lactic acid at 15 °C and pH 3.2 in BM without reducing sugars. Seven strains, all identified as O. oeni, demonstrated MLF activity at pH 3.0 in modified BM lacking added reducing sugars or tomato juice. Six wine-derived strains tolerated up to 12% ethanol, whereas the grape-derived strain was inhibited at 10%. In a synthetic Koshu wine model (13% ethanol, pH 3.0), wine-derived isolates exhibited higher MLF activity than commercial starter strains. In high-performing strains, mleA was upregulated, and most isolates preferred fructose, arabinose, and ribose over glucose. These findings suggest that indigenous O. oeni strains from Koshu wine possess unique stress tolerance and metabolic traits, making them promising candidates for region-specific MLF starter cultures that could enhance Koshu wine quality and terroir expression. Full article

Rootstocks are widely used in viticulture as an agronomic measure to cope with biotic and abiotic stresses. In winegrapes, the aroma is one of the major factors defining the quality of grape berries and wines. In the present work, the grape aroma and wine aroma of Cabernet Sauvignon (CS) grafted on three rootstocks were investigated to inform the selection of rootstocks to utilize. 1103P, 5A, and SO₄ altered the composition of aromatic volatiles in CS grapes and wines. Among them, 5A and SO₄ had less effect on green leaf volatiles in the berries and wines, while 1103P increased green leaf volatile concentrations, up-regulating VvADH2 expression in both vintages. VvLOXA, VvLOXC, VvHPL1, VvADH1, VvADH2, and VvAAT were co-regulated by vintage and rootstock. Orthogonal partial least squares regression analysis (OPLS-DA) showed that the differential compounds in CS/1103P and CS berries were dominated by green leaf volatiles. Furthermore, the concentrations of 1-hexanol in the CS/1103P wines were significantly higher than in the other treatments in the two vintages. 1103P altered the expression of genes in the LOX-HPL pathway and increased the concentration of grape green leaf volatiles such as 1-hexanol and 1-hexanal, while vine vigor also affected green leaf volatile concentrations, the combination of which altered the aromatic composition of the wine and gave it more green flavors. Full article

Background: COVID-19-associated coagulopathy (CAC) is a complex condition, with high rates of thrombosis, high levels of inflammation markers and hypercoagulation (increased levels of fibrinogen and D-Dimer), as well as extensive microthrombosis in the lungs and other organs of the deceased. It resembles, without being identical, other coagulation disorders such as sepsis-DIC (SIC/DIC), hemophagocyte syndrome (HPS) and thrombotic microangiopathy (TMA). Platelets (PLTs), key regulators of thrombosis, inflammation and immunity, are considered an important risk mediator in COVID-19 pathogenesis. Platelet index MPV/PLT ratio is reported in the literature as more specific in the prognosis of platelet-related systemic thrombogenicity. Studies of MPV/PLT ratio with regards to the severity of COVID-19 disease are limited, and there are no references regarding this ratio to the outcome of COVID-19 disease at specific time points of hospitalization. The aim of this study is to evaluate the relationship of COVID-19 mortality with the red cell distribution width–coefficient of variation (RDW-CV), platelets and MPV/PLT ratio parameters. Methods: Values of these parameters in 511 COVID-19 hospitalized patients were recorded (a) on admission, (b) as mean values of the 1st and 2nd week of hospitalization, (c) over the total duration of hospitalization, (d) as nadir and zenith values, and (e) at discharge. Results: As for mortality (survivors vs. deceased), statistical analysis with ROC curves showed that regarding the values of the parameters on admission, only the RDW-CV baseline was of prognostic value. Platelet parameters, absolute number and MPV/PLT ratio had predictive potential for the disease outcome only as 2nd week values. On the contrary, with regards to disease severity (mild/moderate versus severe/critical), only the MPV/PLT ratio on admission can be used for prognosis, and to a moderate degree. On multivariable logistic regression analysis, only the RDW-CV mean hospitalization value (RDW-CV mean) was an independent and prognostic variable for mortality. Regarding disease severity, the MPV/PLT ratio on admission and RDW-CV mean were independent and prognostic variables. Conclusions: RDW-CV, platelets and MPV/PLT ratio hematological parameters could be of predictive value for mortality and severity in COVID-19 disease, depending on the hospitalization timeline. Full article