Search Results (5,459)

Freshwater surimi typically exhibits poor gel-forming capability and is prone to gel deterioration, limiting its applications in food products. This study successfully prepared silver carp surimi gels with improved gel strength and water-holding capacity (WHC) using carboxymethyl konjac glucomannan (CKGM) as a functional modifier. Furthermore, the regulatory mechanism of CKGM with different degrees of substitution (DS) on the gel properties of silver carp surimi was systematically investigated. Results demonstrated that DS significantly influenced gel strength, WHC, and microstructure. CKGM (DS = 0.21%) substantially enhanced the gel strength and WHC through strengthened hydrophobic interactions and hydrogen-bond networks. However, CKGM with a higher DS (0.41%) induced a steric hindrance effect, decreasing elastic modulus and WHC and resulting in a more porous gel network. Raman spectroscopy analysis revealed that CKGM facilitated the conformational transition of myofibrillar proteins from α-helix to β-sheet, thereby improving the density of the gel network. The study provides theoretical foundations and technical guidance for the quality improvement of surimi products. Full article

This study comprehensively compares the performance of two non-destructive testing (NDT) techniques—active thermography (AT) and digital shearography (DS)—for identifying various damage types in composite structures. Three distinct composite specimens were inspected: a carbon-fiber-reinforced polymer (CFRP) plate with flat-bottom holes, an aluminum honeycomb core sandwich plate with a circular skin-core disbond, and a CFRP plate with two low-energy impacts damage. The research highlights the significant role of post-processing methods in enhancing damage detectability. For AT, algorithms such as fast Fourier transform (FFT) for temperature phase extraction and principal component thermography (PCT) for identifying significant temperature components were employed, generally making anomalies brighter and easier to locate and size. For DS, a novel band-pass filtering approach applied to phase maps, followed by summing the filtered maps, remarkably improved the visualization and precision of damage-induced anomalies by suppressing background noise. Qualitative image-based comparisons revealed that DS consistently demonstrated superior performance. The sum of DS filtered phase maps provided more detailed and precise information regarding damage location and size compared to both pulsed thermography (PT) and lock-in thermography (LT) temperature phase and amplitude. Notably, DS effectively identified shallow flat-bottom holes and subtle imperfections that AT struggled to clearly resolve, and it provided a more comprehensive representation of the impacts damage location and extent. This enhanced capability of DS is attributed to the novel phase map filtering approach, which significantly improves damage identification compared to the thermogram post-processing methods used for AT. Full article

As veganism becomes more popular, the number of vegan pregnant women and children is steadily increasing. During vegan pregnancy and early childhood, there is a high risk for nutrient deficiencies that may impair child development. External factors, such as healthcare advice, social networks, and social environments, that affect the diet of vegan pregnant women, parents, and their children, as well as their approach towards dietary supplementation, have not yet been investigated. Various sources of information, combined with a lack of expertise, sparse food and nutritional health literacy, and qualitatively heterogeneous information provision by medical experts, unsettle vegan pregnant women and parents and affect their dietary choices and potentially the health of their children. The VedieS study aims to investigate potential connections between external influences and associated impacts on a vegan diet and the intake of dietary supplements (DS) of pregnant women and children. Two surveys are being conducted within the study: one targeting 1000 vegan pregnant women and parents, and another targeting 60 experts in each of five healthcare groups: gynecologists, pediatricians, general practitioners, pharmacists, and dietitians. This study is the first to examine how socio-economic, social, and further informational factors influence dietary practices during vegan pregnancy and childhood. It highlights the need for reliable, expert-led guidance, as current information sources are often inconsistent and may put these vulnerable groups at risk. Full article

Background: Inflammation abrogates cellular organization and tissue homoeostasis, resulting in redness, swelling, heat, pain, and loss of function. A model of carrageenan-induced paw edema (CIE) is commonly utilized to test anti-inflammatory substances. Based on the ability of Lepisanthes alata (LA), a tropical plant that is rich in phytochemicals like polyphenols, this study assessed the optimal dose and the health benefits of LA in rats that had been induced with carrageenan to develop paw swelling. Methods: Twenty-four male Wistar rats were divided into four groups to which carrageenan was administered, after which, distilled water at oral dose (C + DW), sodium diclofenac 25 mg/kg (C + DS), LA extract in 250 mg/kg (C + LA250), and 500 mg/kg (C + LA500) was given, respectively. Paw edema was assessed in 24 h. Pain was assessed using the Rat Grimace Scale (RGS), cytokines, antioxidant activity, and tissue changes. Results: LA at 250 and 500 mg/kg significantly decreased paw edema and inflammatory markers in the results of both studies. Remarkably, LA 250 mg/kg significantly decreased RGS scores as well as IL-1β, TNF-α, and histological inflammation but had a positive effect on T-SOD levels. Conclusions: LA extract, especially at 250 mg/kg, shows potent anti-inflammatory, analgesic, and antioxidant properties in CIE rats. Full article

Background and Objectives: Galectin-3 (Gal-3), a pro-inflammatory cytokine, has been implicated in atherosclerosis and adverse cardiovascular outcomes. While its role in coronary artery disease (CAD) is increasingly recognized, its association with systemic atherosclerosis remains underexplored. Objective: To investigate serum Gal-3 levels in patients with CAD and evaluate correlations between CAD severity and extra-coronary atherosclerotic involvement (carotid, femoral, and radial territories). Materials and Methods: We prospectively enrolled 56 patients with CAD undergoing coronary angiography (42.8% with acute-ACS; 57.2% with chronic coronary syndromes-CCS). Gal-3 levels were measured within 24 h of admission. Atherosclerosis severity was assessed angiographically and through vascular ultrasound of the carotid, femoral, and radial arteries. Patients were stratified by median Gal-3 levels, and clinical follow-up was performed at 1 and 3 months. Results: Gal-3 levels were significantly higher in CAD vs. controls (20.7 vs. 10.1 ng/mL; p < 0.00001) and in ACS vs. CCS (22.18. vs. 17.93 ng/mL; p = 0.019). Gal-3 correlated positively with culprit lesion diameter stenosis (DS) (R = 0.30; p = 0.023) and maximum severity of additional treated lesions (R = 0.62; p = 0.006). Gal-3 also correlated positively with carotid plaque thickness (R = 0.32; p = 0.016), while patients with Gal-3 levels above the median showed increased median values for femoral plaque thickness (32.4 vs. 26.45 mm, p = 0.046). No correlation was found with radial artery calcification. Gal-3 showed moderate discrimination for ACS (AUC = 0.685; cut-off 20.18 ng/mL). On multivariate analysis age, DS, and ACS presentation were independent predictors of Gal-3 above 19.07 ng/mL. Conclusions: Gal-3 levels are elevated in ACS and correlate with atherosclerotic burden, particularly in coronary, carotid, and femoral territories. These findings support Gal-3 as a potential marker of lesion severity and systemic vascular involvement, highlighting its possible role in risk stratification and the monitoring of atherosclerotic disease progression. This study provides integrated insights into the impact of Gal-3 across multiple vascular beds by assessing them concurrently within the same patient cohort. Full article

This research examines the traffic disruptions resulting from the collapse of the Francis Scott Key Bridge in Baltimore, utilizing advanced econometric methods and real-time ClearGuide data. Employing Fixed Effects (FEs), Mixed Effects (MEs), Difference-in-Differences (DiDs), and stratified regression models, the study uniquely examines the impacts of congestion across Immediate, Fall, and Winter periods, distinctly separating AM and PM peak patterns. Significant findings include severe PM peak congestion, up to four times greater than AM peak congestion, particularly on critical corridors such as the Harbor Tunnel Thruway northbound and MD-295 northbound. Initial route-level impacts were heterogeneous, gradually becoming uniform as the network adapted. The causal DiD analysis provides strong evidence that increased congestion is causally linked to proximity to the collapse. It is anticipated that incorporating the suggested framework will yield insightful information for stakeholders and decision-makers, such as targeted freight restriction, peak-hour dynamic pricing, corridor-specific signal adjustments, and investments in real-time traffic monitoring systems to strengthen transportation network resilience. Full article

Background/Objectives: Accurate assessment of speech intelligibility is necessary for individuals with motor speech disorders. Transcription or scaled rating methods by naïve listeners are the most reliable tasks for these purposes; however, they are often resource-intensive and time-consuming within clinical contexts. Automatic speech recognition (ASR) systems, which transcribe speech into text, have been increasingly utilized for assessing speech intelligibility. This study investigates the feasibility of using an open-source ASR system to assess speech intelligibility in Hebrew and English speakers with Down syndrome (DS). Methods: Recordings from 65 Hebrew- and English-speaking participants were included: 33 speakers with DS and 32 typically developing (TD) peers. Speech samples (words, sentences) were transcribed using Whisper (OpenAI) and by naïve listeners. The proportion of agreement between ASR transcriptions and those of naïve listeners was compared across speaker groups (TD, DS) and languages (Hebrew, English) for word-level data. Further comparisons for Hebrew speakers were conducted across speaker groups and stimuli (words, sentences). Results: The strength of the correlation between listener and ASR transcription scores varied across languages, and was higher for English (r = 0.98) than for Hebrew (r = 0.81) for speakers with DS. A higher proportion of listener–ASR agreement was demonstrated for TD speakers, as compared to those with DS (0.94 vs. 0.74, respectively), and for English, in comparison to Hebrew speakers (0.91 for English DS speakers vs. 0.74 for Hebrew DS speakers). Listener–ASR agreement for single words was consistently higher than for sentences among Hebrew speakers. Speakers’ intelligibility influenced word-level agreement among Hebrew- but not English-speaking participants with DS. Conclusions: ASR performance for English closely approximated that of naïve listeners, suggesting potential near-future clinical applicability within single-word intelligibility assessment. In contrast, a lower proportion of agreement between human listeners and ASR for Hebrew speech indicates that broader clinical implementation may require further training of ASR models in this language. Full article

Penicillium astrolabium is a primary pathogenic fungus that causes grape blue mold during postharvest, leading to substantial losses in the grape industry. Nevertheless, hypovirulence-associated mycoviruses can attenuate the virulence of postharvest grape-rot pathogens, thereby offering a promising biocontrol tool. Characterizing the mycovirus repertoire of P. astrolabium is imperative for grape protection, yet remains largely unexplored. Here, we screened six strains harboring viruses in 13 P. astrolabium isolates from rotted grapes. Using high-throughput sequencing, four novel dsRNA viruses and two +ssRNA viruses were identified from the six P. astrolabium strains. The dsRNA viruses belonged to two families—Chrysoviridae and Partitiviridae—and were designated to Penicillium astrolabium chrysovirus 1 (PaCV1), Penicillum astrolabium partitivirus 1′ (PaPV1′), Penicillum astrolabium partitivirus 2 (PaPV2), and Penicillum astrolabium partitivirus 3 (PaPV3). For the +ssRNA viruses, one was clustered into the Alphaflexiviridae family, while the other one was clustered into the Narnaviridae family. The two +ssRNA viruses were named Penicillium astrolabium alphaflexivirus 1 (PaAFV1) and Penicillium astrolabium narnavirus 1 (PaNV1), respectively. Moreover, several viral genomic contigs with non-overlapping and discontinuous sequences were identified in this study, which were probably representatives of five viruses from four families, including Discoviridae, Peribunyaviridae, Botourmiaviridae, and Picobirnaviridae. Taken together, our findings could expand the diversity of mycoviruses, advance the understanding of mycovirus evolution in P. astrolabium, and provide both potential biocontrol resources and a research system for dissecting virus–fungus–plant interactions. Full article

Background: Down syndrome (DS) is a genetic disorder characterized by an extra copy of chromosome 21, often leading to intellectual disabilities, developmental delays, and an increased risk of various comorbidities, including thyroid dysfunction and mental health disorders. The relationship between thyroid dysfunction and mood disorders, particularly depression in DS populations, requires further investigation. Objective: This study aims to investigate the presence of a correlative relationship between hypothyroidism and depression in 178,840 individuals with DS, utilizing data from the National Inpatient Sample (NIS) to determine if those with comorbid hypothyroidism exhibit higher rates of depression compared to their counterparts without hypothyroidism. Methods: A retrospective analysis of the 2016–2019 NIS dataset was conducted, focusing on patients with DS, hypothyroidism, and depression diagnoses. The diagnoses were determined and labeled based on ICD-10 codes associated with NIS datapoints. Survey-weighted linear regression analyses were employed to assess the association between hypothyroidism and depression within the DS cohort, adjusting for demographic factors such as age, gender, and race. Results: This study found that individuals with DS exhibit a significantly higher prevalence of hypothyroidism (29.88%) compared to the general population (10.28%). Additionally, individuals with DS and comorbid hypothyroidism demonstrated a higher prevalence of depression (8.67%) compared to those without hypothyroidism (3.00%). These findings suggest a significant association between hypothyroidism and increased depression risk among individuals with DS. However, the overall prevalence of depression in DS (4.69%) remains substantially lower than in the general population (12.27%). Conclusions: This study highlights the importance of considering hypothyroidism as a potential contributor to depression in individuals with DS. Further research is needed to explore the underlying mechanisms of this association and potential screening and management strategies to address thyroid dysfunction and its potential psychiatric implications in DS. Full article

Soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), is a major pest of soybean fields. While high-temperature stress induced by global warming can initially suppress aphid populations, these pests may eventually adapt, leading to more severe infestations and crop damage. Heat shock proteins (HSPs), which are upregulated in response to heat stress to protect aphid development, also confer tolerance to other abiotic stressors, including insecticides. To investigate the role of HSPs in insecticide resistance in A. glycines, we analyzed the expression profiles of three AgHsp90 genes (AgHsp75, AgHsp83, and AgGrp94) following exposure to high temperatures and insecticides. Functional validation was performed using RNA interference (RNAi) to silence AgHsp90 genes. Our results demonstrated that AgHsp90 genes were significantly upregulated under both heat and insecticide stress conditions. Furthermore, after feeding on dsRNA of AgHsp90 genes, mortality rates of A. glycines significantly increased when exposed to imidacloprid and lambda-cyhalothrin. This study provides evidence that AgHsp90 genes play a crucial role in mediating thermal tolerance and insecticide resistance in A. glycines. Full article

Since its publication in the 1960s, the Kalman Filter (KF) has been a powerful tool in optimal state estimation. However, the KF and most of its variants have mainly focused on the state estimation of smooth systems. In this work, we propose a new algorithm called the Discrete Unilateral Constrained Extended Kalman Filter (DUCEKF) that expands the capabilities of the Extended Kalman Filter (EKF) to a class of hybrid mechanical systems known as systems with unilateral constraints. Such systems are non-smooth in position and discontinuous in velocity. Lyapunov stability theory is invoked to establish sufficient conditions for the estimation error stability of the proposed algorithm. A comparison of the proposed algorithm with the EKF is conducted in simulation through a case study to demonstrate the superiority of the DUCEKF for the state estimation tasks in this class of systems. Simulations and an experiment were developed in this case study to validate the performance of the proposed algorithm. The experiment was conducted using electronic hardware that consists of an Embedded System (ES) called “Mikromedia for dsPIC33EP” and an external DAC-12 Click board, which includes a Digital-to-Analog Converter (DAC) from Texas Instruments. Full article

Concentration polarization (CP) is one of the inherent problems that lowers the operating performance of forward osmosis (FO) membranes. Therefore, a quantitative evaluation of CP is vital to understand its impact on the FO process. This study systematically investigated the influences of different CPs on the osmotic pressure drop across the membrane under different conditions by using the water transmission coefficient, η_WT, defined as the ratio of the measured water flux to the theoretical water flux. The results showed that η_WT decreased with an increase in the concentration gradient between the draw solution (DS) and the feed solution (FS) under different conditions. The proportions of osmotic pressure drop caused by dilutive internal concentration polarization (ICP) increased, while those caused by concentrative external concentration polarization (ECP) decreased, in different types of DSs in FO mode. Both ECP and ICP were found to be capable of reducing osmotic pressure. However, the internal CP had the dominant influence. To better understand the adverse effects of CP, using an organic FS provided greater insight than using deionized (DI) water as the FS. As the FS concentration increased, the water flux reduced, and the adverse effects of CP worsened. CaCl₂ led to a greater reduction in water transfer efficiency than NaCl when used as the DS. In comparison to FO mode, pressure-retarded osmosis (PRO) mode led to greater pure water flux and flux decline. In FO mode, both the proportion of dilutive ICP and the η_WT decreased, while the proportion of concentrative ECP increased over time. However, in PRO mode, the proportions of dilutive ECP and concentrative ICP increased, and η_WT gradually decreased. Dilutive ICP had a significant negative effect on osmotic pressure in the former, while dilutive ECP was dominant in the latter. Full article

This study proposes a novel hybrid forecasting approach designed explicitly for long-horizon financial time series. It incorporates LMD (Local Mean Decomposition), SD (Signal Decomposition), and sophisticated machine learning methods. The framework for the NASDAQ Composite Index begins by decomposing the original time series into stochastic and deterministic components using the LMD approach. This method effectively separates linear and nonlinear signal structures. The stochastic components are modeled using ARIMA to represent linear temporal dynamics, while the deterministic components are projected using cutting-edge machine learning methods, including XGBoost, Random Forest (RF), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs). This study employs various statistical metrics to evaluate the predictive ability across both short-term noise and long-term trends, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Directional Statistic (DS). Furthermore, the Diebold–Mariano test is used to determine the statistical significance of any forecast improvements. Empirical results demonstrate that the hybrid LMD–ARIMA–SD–XGBoost model consistently outperforms alternative configurations in terms of prediction accuracy and directional consistency. These findings demonstrate the advantages of integrating decomposition-based signal filtering with ensemble machine learning to improve the robustness and generalizability of long-term forecasting. This study presents a scalable and adaptive approach for modeling complex, nonlinear, and high-dimensional time series, thereby contributing to the enhancement of intelligent forecasting systems in the economic and financial sectors. As far as the authors are aware, this is the first study to combine XGBoost and LMD in a hybrid decomposition framework for forecasting long-horizon stock indexes. Full article

This study proposes the Weight-Incorporating A* (WIA*) algorithm for mobile robot path planning. The WIA* algorithm integrates three weight factors into the Conventional A* cost function: an Obstacle Collision (OC) weight factor for collision avoidance, a Path Distance (PD) weight factor for path length optimization, and a Driving Suitability (DS) weight factor for environmental considerations. Experimental validation was conducted using nine 2D grid maps and a 3D virtual environment. The results show that WIA* achieved zero obstacle collisions compared to an average of 9.11 collisions with Conventional A*. Although WIA* increased path length by 12.69%, it reduced driving suitability cost by 93.88%, achieving zero cost in six out of nine test environments. The algorithm demonstrates effective collision-free path generation while incorporating environmental factors for practical mobile robot navigation. Full article

The present study aimed to optimize amaranth microgreen production by evaluating key factors such as the seeding density (SD), substrate type (ST), electrical conductivity (EC), and the application intervals of the nutrient solution. A split-plot experimental design was employed, with three EC levels (tap water at 0.3 dS m⁻¹) and nutrient solutions at 1.0 (50% half-strength) and 2.0 dS m⁻¹ (100% full-strength) assigned to the main plots. The subplots combined two ST (coconut fiber and phenolic foam) with four SD (25, 50, 75, and 100 g m⁻²). Two experiments were conducted using this setup, varying the application intervals of water or nutrient solutions for either two or four hours. Asteca amaranth microgreens were cultivated for eight days (a total of 10 days from sowing). The traits analyzed were seedling height (SH), seedling fresh matter (SFM), SFM yield (SFMY), seedling dry matter (SDM), SDM yield (SDMY), water content in seedling, and water productivity of SFM. The results showed that using a half-strength nutrient solution was sufficient for amaranth production compared to using water alone. Coconut fiber outperformed phenolic foam across all evaluated parameters. Based on these findings, we recommend cultivating amaranth microgreens at a SD of 80 g m⁻² on coconut fiber substrate using a nutrient solution of 1.0 dS m⁻¹ EC applied at 2 h intervals. Full article