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We present a deep learning-based approach for accurate bone segmentation directly from routine T1-weighted MRI scans, with the goal of enabling MRI-only virtual surgical planning in head and neck oncology. Current workflows rely on CT for bone modeling and MRI for tumor delineation,
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We present a deep learning-based approach for accurate bone segmentation directly from routine T1-weighted MRI scans, with the goal of enabling MRI-only virtual surgical planning in head and neck oncology. Current workflows rely on CT for bone modeling and MRI for tumor delineation, introducing challenges related to image registration, radiation exposure, and resource use. To address this, we trained a deep neural network using CT-based segmentations of the mandible, cranium, and inferior alveolar nerve as ground truth. A dataset of 100 patients with paired CT and MRI scans was collected. MRI scans were resampled to the voxel size of CT, and corresponding CT segmentations were rigidly aligned to MRI. The model was trained on 80 cases and evaluated on 20 cases using Dice similarity coefficient, Intersection over Union (IoU), precision, and recall. The network achieved a mean Dice of 0.86 (SD ± 0.03), IoU of 0.76 (SD ± 0.05), and both precision and recall of 0.86 (SD ± 0.05). Surface deviation analysis between CT- and MRI-derived bone models showed a median deviation of 0.21 mm (IQR 0.05) for the mandible and 0.30 mm (IQR 0.05) for the cranium. These results demonstrate that accurate CT-like bone models can be derived from standard MRI, supporting the feasibility of MRI-only surgical planning.
Full article
Background: Multiple sclerosis is a multifactorial neurodegenerative disease characterized by autoimmune and inflammatory processes. Despite advancements in disease-modifying therapies, multiple sclerosis remains challenging due to its complex pathophysiology and variable clinical presentation. Current therapies focus on managing inflammation and promoting immunosuppression but do
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Background: Multiple sclerosis is a multifactorial neurodegenerative disease characterized by autoimmune and inflammatory processes. Despite advancements in disease-modifying therapies, multiple sclerosis remains challenging due to its complex pathophysiology and variable clinical presentation. Current therapies focus on managing inflammation and promoting immunosuppression but do not achieve complete symptom regression or enhance remyelination. Emerging therapies, such as Peroxisome Proliferator-Activated Receptor gamma (PPARγ) agonists and Bruton tyrosine kinase (BTK) inhibitors, show promise in modulating inflammation and targeting immune cells. Innovative approaches like human fetal neural precursor cells (hfPNCs) and mesenchymal stem cell transplantation are being explored to reduce neural inflammation and improve neuroprotection. Early diagnosis and intervention are crucial for managing multiple sclerosis effectively and preventing progression to severe forms and permanent disability. Therapeutic education for individuals with multiple sclerosis and their caregivers is essential, emphasizing the need for clear, reliable information to support disease management and improve quality of life.Objectives: This review provides an up-to-date overview of multiple sclerosis pathophysiology, current treatments, and emerging therapies, aiming to enhance the knowledge base of healthcare professionals and researchers, facilitating informed decision-making and contributing to ongoing research efforts.
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This study introduces and evaluates stochastic models to describe Bitcoin price dynamics at different time scales, using daily data from January 2019 to December 2024 and intraday data from 20 January 2025. In the daily analysis, models based on are introduced to capture
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This study introduces and evaluates stochastic models to describe Bitcoin price dynamics at different time scales, using daily data from January 2019 to December 2024 and intraday data from 20 January 2025. In the daily analysis, models based on are introduced to capture long memory, paired with both constant-volatility (CONST) and stochastic-volatility specifications via the Cox–Ingersoll–Ross (CIR) process. The novel family of models is based on Generalized Ornstein–Uhlenbeck processes with a fluctuating exponential trend (GOU-FE), which are modified to account for multiplicative fBm noise. Traditional Geometric Brownian Motion processes (GFBM) with either constant or stochastic volatilities are employed as benchmarks for comparative analysis, bringing the total number of evaluated models to four: GFBM-CONST, GFBM-CIR, GOUFE-CONST, and GOUFE-CIR models. Estimation by numerical optimization and evaluation through error metrics, information criteria (AIC, BIC, and EDC), and 95% Expected Shortfall (ES95) indicated better fit for the stochastic-volatility models (GOUFE-CIR and GFBM-CIR) and the lowest tail-risk for GOUFE-CIR, although residual analysis revealed heteroscedasticity and non-normality. For intraday data, Exponential, Weibull, and Generalized Gamma Autoregressive Conditional Duration (ACD) models, with adjustments for intraday patterns, were applied to model the time between transactions. Results showed that the ACD models effectively capture duration clustering, with the Generalized Gamma version exhibiting superior fit according to the Cox–Snell residual-based analysis and other metrics (AIC, BIC, and mean-squared error). Overall, this work advances the modeling of Bitcoin prices by rigorously applying and comparing stochastic frameworks across temporal scales, highlighting the critical roles of long memory, stochastic volatility, and intraday dynamics in understanding the behavior of this digital asset.
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The current study examines the feasibility of Ultrasint PP nat 01 polypropylene material in powder bed fusion through powder characterisation. The results obtained are also deemed to be pertinent when developing or validating analytical and numerical models of Polymer Laser Sintering, which were
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The current study examines the feasibility of Ultrasint PP nat 01 polypropylene material in powder bed fusion through powder characterisation. The results obtained are also deemed to be pertinent when developing or validating analytical and numerical models of Polymer Laser Sintering, which were not within the scope of this paper. The following critical characteristics were examined: powder morphology, powder particle size distribution (PSD), bulk density, tapped density, melt flow index, thermal characteristics of the material, degree of crystallinity, and optical properties. Ultrasint PP nat 01 powder has a PSD in the range of 20–80 µm, which is within the recommended particle size distribution. The Hausner ratio, tapped density, and bulk density of the material were calculated and measured as 1.230 ± 0.05, 0.455 ± 0.02 g/cm3, and 0.370 ± 0.03 g/cm3, respectively. The melt flow index of Ultrasint PP nat 01 was measured as 15.8 g/10 min. The initial melting point of the material was determined to be 133.8 °C. The powder used had a relatively high sintering window of 30.7 °C, a degree of crystallinity of around 31.8%, and a high thermal stability of around 461.52 °C. The material was found to attain full fusion of particles at around 170 °C. Fourier Transform Infrared Spectroscopy indicated that Ultrasint PP nat 01 powder has poor radiation absorption, but high transmission properties.
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Background: The COVID-19 pandemic disrupted essential health services globally, including contraceptive provision. This study examined barriers to contraceptive access in Nigeria during the national lockdown and lessons for future health crisis preparedness. Methods: A cross-sectional online survey of 1273 respondents was conducted during
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Background: The COVID-19 pandemic disrupted essential health services globally, including contraceptive provision. This study examined barriers to contraceptive access in Nigeria during the national lockdown and lessons for future health crisis preparedness. Methods: A cross-sectional online survey of 1273 respondents was conducted during the COVID-19 lockdown. Descriptive statistics and multivariate logistic regression were used to identify predictors of unmet contraceptive need. Online convenience sampling may limit representativeness. Results: Fear of contracting COVID-19 at health facilities (76.6%), closure of drug and chemist shops (53.7%), movement restrictions (48.4%), and inability to reach healthcare providers (43.5%) were the most reported barriers. Adults aged 26–33 years (AOR = 2.00, 95% CI: 1.05–3.73), those married or cohabiting (AOR = 3.87, 95% CI: 2.58–5.68), and Yoruba respondents (AOR = 1.70, 95% CI: 1.04–2.58) were significantly more likely to report unmet need. Tertiary education (AOR = 0.28, 95% CI: 0.13–0.55) and rural residence (AOR = 0.57, 95% CI: 0.37–0.86) were protective factors. Conclusion: COVID-19-related restrictions exposed systemic weaknesses in Nigeria’s contraceptive delivery. Addressing fragile supply chains, strengthening community-based alternatives, and embedding reproductive health into emergency preparedness plans will be critical to building resilient systems for future crises.
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Microalgae are photosynthetic microorganisms that could be used as potential microbial cell factories by directly converting CO2 into valuable bioproducts and biofuels. This study aims to improve the production of biofuel from the isolated green alga Chlorella sp., in terms of an
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Microalgae are photosynthetic microorganisms that could be used as potential microbial cell factories by directly converting CO2 into valuable bioproducts and biofuels. This study aims to improve the production of biofuel from the isolated green alga Chlorella sp., in terms of an increase in its lipid content and its conversion to fatty acid methyl esters (FAMEs) when the cells are grown under the influence of phosphorus (P) limitation and heavy metal addition. The results show that the highest content of lipids, at 68.9%, was achieved within one day under 0% P with a 17 µM cobalt addition. Moreover, supplementation with a low Pb concentration increased cell growth even under P limitation, but under this condition, its lipid content was decreased after seven days of growth. The lipids of Chlorella sp. were transesterified to produce FAMEs. The overall biodiesel properties of the obtained FAMEs were of acceptable quality according to the standards (ASTM and EN). Additionally, the energy conversion from light energy to lipids was shown to be in the range of 10–16% conversion efficiency within seven days. Hence, the physiological modification of Chlorella sp. culture by phosphorus limitation coupled with the addition of a low concentration of heavy metals enabled the improvement of lipid content, with the subsequent transesterification resulting in the production of biodiesel with acceptable quality.
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Microplastics (MPs) are ubiquitous and persistent contaminants of the marine environment, but a clear understanding of their cycling, fate, and impacts in coastal zones is lacking. In this study, large MPs (1–5 mm) were sampled spatially and temporally from the strandline of a
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Microplastics (MPs) are ubiquitous and persistent contaminants of the marine environment, but a clear understanding of their cycling, fate, and impacts in coastal zones is lacking. In this study, large MPs (1–5 mm) were sampled spatially and temporally from the strandline of a macrotidal, sandy beach (Polzeath) in southwest England. MPs encompassing a diversity of sources were categorised by morphology (foams, nurdles, biobeads, fragments, fibres, films) and quantified by number and mass, with a selection analysed for polymer type. A total of about 17,600 particles of around 350 g in mass were retrieved from 30 samples over a period of five months, with an abundance ranging from 35 and 2048 per m2. The space- and time-integrated average mass of MPs on the beach strandline was about 2 kg and was dominated (>90%) by fragments, nurdles, and biobeads of polyethylene or polypropylene construction. Nurdles, biobeads, fragments, and, to a lesser extent, fibres were correlated with strandline seaweed abundance, which itself was correlated with previous storm activity. Relationships with seaweed abundance were also supported by visible associations of these MP morphologies with macroalgal deposits through entanglement and adhesion. These observations, coupled with a lack of MPs below the sand’s surface (50 cm depth), suggest that the majority of MPs are transported from an offshore stock with floating organic debris, resulting in a transitory strandline repository and a habitat enriched with small plastics.
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Without changing any of its constituents, tyre pyrolysis oil energy (TPOE) has frequently been subjected to Diesel-RK (D-RK) analyses in diesel engines in an effort to serve as a substitute for diesel fuel. Environmentally beneficial TPOE features, such as biodegradability, renewability, and ease
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Without changing any of its constituents, tyre pyrolysis oil energy (TPOE) has frequently been subjected to Diesel-RK (D-RK) analyses in diesel engines in an effort to serve as a substitute for diesel fuel. Environmentally beneficial TPOE features, such as biodegradability, renewability, and ease and safety of handling, are highly sought after. In addition to its beneficial aspects, TOPE also has drawbacks. The BTE and SFC of performance metrics, as well as the smoke and NOx of emission parameters of alternative fuel, do not meet the emission limits specified by regulatory authorities. Nano-additions have been shown to be effective for boosting fuel quality for improved performance and production characteristics. In this study, TPOE–diesel blends are blended with ceramic oxide (CeO2 of 50 and 100 ppm) nanoparticles and subjected to a performance and production investigation of engine working physiognomies in diesel engines. For the blend TPOE10CDF80 + D, the numerical results show a positive outcome of a 1.0% rise in BTE, a 2.0% decrease in SFC, a 17.7% decrease in smoke emission, and an 18.2% increase in NOx emission as compared to diesel fuel (CDF).
Full article
To support the development, testing, and operations of the apex experiments flown on-board the MAPHEUS-8 and -10 missions, a series of service module simulators and mission support tools have been developed and improved over the years. With each generation, a more generalized approach
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To support the development, testing, and operations of the apex experiments flown on-board the MAPHEUS-8 and -10 missions, a series of service module simulators and mission support tools have been developed and improved over the years. With each generation, a more generalized approach has been taken, which allowed simulating not only sounding rocket service module payload interfaces but also the Astrobotic Peregrine Moon Lander and the Swedish Space Corporation Suborbital Express experiment interfaces. This study is part three of a three-part series describing the apex Mk.2/Mk.3 experiments, open-source ground segment, and service module simulator.
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Mitigating heat generation in electric vehicle (EV) batteries is crucial for safety, operational efficiency, and battery lifespan. Liquid-cooled cold plates are widely used; however, comparative studies of channel geometries are often hindered by inconsistent experimental conditions. This study systematically compares six cold plate
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Mitigating heat generation in electric vehicle (EV) batteries is crucial for safety, operational efficiency, and battery lifespan. Liquid-cooled cold plates are widely used; however, comparative studies of channel geometries are often hindered by inconsistent experimental conditions. This study systematically compares six cold plate configurations under identical cross-sectional areas and uniform thermal boundary conditions. These controls isolate the effect of geometry on performance. Computational fluid dynamics (CFDs) was used to evaluate six configurations, derived from three main channel layouts (serpentine with eight U-turns, serpentine with six U-turns, and elliptical) and two cross-sectional shapes (circular and square). The serpentine square-tube design with eight U-turns exhibited the lowest thermal resistance (0.0159 K/W). The circular-tube variant achieved the most uniform temperature distribution (TUI > 0.53). The six U-turn circular-tube configuration demonstrated the lowest pressure drop (11.7 kPa). The results indicate that no single design optimizes all performance metrics, highlighting trade-offs between cooling effectiveness, temperature uniformity, and hydraulic efficiency. By isolating geometric variables, this study offers targeted design recommendations for engineers developing battery thermal management systems (BTMS).
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Electrified roads represent an emerging transportation solution in the context of global energy transition. These systems enable vehicles equipped with roof-mounted pantographs to draw power from overhead contact lines while in motion, allowing continuous energy replenishment. The effectiveness of this energy transfer—namely, the
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Electrified roads represent an emerging transportation solution in the context of global energy transition. These systems enable vehicles equipped with roof-mounted pantographs to draw power from overhead contact lines while in motion, allowing continuous energy replenishment. The effectiveness of this energy transfer—namely, the quality of pantograph–catenary interaction—is significantly influenced by the pantograph's equivalent mechanical parameters. This study develops a three-dimensional overhead catenary model and a five-mass pantograph model tailored to electrified roads. Under conditions of road surface irregularities, it investigates how variations in equivalent pantograph parameters affect key contact performance indicators. Simulation results are used to identify a new set of equivalent pantograph parameters that significantly improve the overall quality of pantograph–catenary interaction compared to the baseline configuration. Sensitivity analysis further reveals that, under road-induced excitation, pan-head stiffness is the most critical factor affecting contact performance, while pan-head damping, upper frame stiffness, and upper frame damping show minimal influence. By constructing a coupled dynamic model and conducting parameter optimization, this study elucidates the role of key pantograph parameters for electrified roads in determining contact performance. The findings provide a theoretical foundation for future equipment development and technological advancement.
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Metabolic imaging is undergoing a fundamental transformation. Traditionally confined to static representations of metabolite distribution through modalities such as PET, MRS, and MSOT, imaging has offered only partial glimpses into the dynamic and systemic nature of metabolism. This Perspective envisions a shift toward
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Metabolic imaging is undergoing a fundamental transformation. Traditionally confined to static representations of metabolite distribution through modalities such as PET, MRS, and MSOT, imaging has offered only partial glimpses into the dynamic and systemic nature of metabolism. This Perspective envisions a shift toward dynamic metabolic intelligence—an integrated framework where real-time imaging is fused with physics-informed models, artificial intelligence, and wearable data to create adaptive, predictive representations of metabolic function. We explore how novel technologies like hyperpolarized MRI and time-resolved optoacoustics can serve as dynamic inputs into digital twin systems, enabling closed-loop feedback that not only visualizes but actively guides clinical decisions. From early detection of metabolic drift to in silico therapy simulation, we highlight translational pathways across oncology, cardiology, neurology, and space medicine. Finally, we call for a cross-disciplinary effort to standardize, validate, and ethically implement these systems, marking the emergence of a new paradigm: metabolism as a navigable, model-informed space of precision medicine.
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To address the issue of poor yaw stability in distributed-drive electric pickup trucks at medium-to-high speeds, particularly under the influence of continuously varying tire forces and road adhesion coefficients, a novel Kalman filter-based method for estimating the road adhesion coefficient, combined with a
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To address the issue of poor yaw stability in distributed-drive electric pickup trucks at medium-to-high speeds, particularly under the influence of continuously varying tire forces and road adhesion coefficients, a novel Kalman filter-based method for estimating the road adhesion coefficient, combined with a Tube-based Model Predictive Control (Tube-MPC) algorithm, is proposed. This integrated approach enables real-time estimation of the dynamically changing road adhesion coefficient while simultaneously ensuring vehicle yaw stability is maintained under rapid response requirements. The developed hierarchical yaw stability control architecture for distributed-drive electric pickup trucks employs a square root cubature Kalman filter (SRCKF) in its upper layer for accurate road adhesion coefficient estimation; this estimated coefficient is subsequently fed into the intermediate layer’s corrective yaw moment solver where Tube-based Model Predictive Control (Tube-MPC) tracks desired sideslip angle and yaw rate trajectories to derive the stability-critical corrective yaw moment, while the lower layer utilizes a quadratic programming (QP) algorithm for precise four-wheel torque distribution. The proposed control strategy was verified through co-simulation using Simulink and Carsim, with results demonstrating that, compared to conventional MPC and PID algorithms, it significantly improves both the driving stability and control responsiveness of distributed-drive electric pickup trucks under medium- to high-speed conditions.
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Assessing soil and water quality in irrigated farming is vital for sustainable agriculture management. Low-quality irrigation water, particularly in semi-arid regions, poses environmental challenges and leads to soil salinization. This study was conducted in the Jedaida district, Manouba province, NE Tunisia. Forty-three soil
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Assessing soil and water quality in irrigated farming is vital for sustainable agriculture management. Low-quality irrigation water, particularly in semi-arid regions, poses environmental challenges and leads to soil salinization. This study was conducted in the Jedaida district, Manouba province, NE Tunisia. Forty-three soil and water samples were collected to develop indices for assessing soil quality. Sixteen indicators were selected using principal component analysis (PCA) for the minimum soil data set (MSD), including electrical conductivity, sand, organic soil carbon, and pH. The linear method shows a correlation with physical and chemical properties, classifying Jedaida soils into three quality metrics: good, moderate, and poor. The non-linear method displays the lowest indicator contribution in Zahira soils, followed by Mansoura soils (high and moderate). MSD combined with linear scoring is the most acceptable method of assessing the soil quality index (SQI). Water quality indices (WQIs) identify the suitability of irrigation. The results show a Kelly’s ratio > 1, a sodium adsorption ratio (SAR) > 10, and a sodium soluble percentage (SSP) varying from 40 to 60%. This highlights the negative effects of long-term irrigation with poor-quality water on soil health. Accordingly, groundwater was found to be unsuitable for irrigating surface soils. This finding emphasizes the importance of selecting suitable irrigation water to ensure soil quality.
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Petru Octavian Drăgoescu, Bianca Liana Grigorescu, Andreea Doriana Stănculescu, Andrei Pănuș, Nicolae Dan Florescu, Monica Cara, Maria Andrei, Mihai Radu, George Mitroi and Alice Nicoleta Drăgoescu
Background and Objectives: The severe systemic response to urinary tract infections known as urosepsis is associated with significant morbidity and mortality rates. The neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) are simple blood tests that could be useful in predicting the outcome
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Background and Objectives: The severe systemic response to urinary tract infections known as urosepsis is associated with significant morbidity and mortality rates. The neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) are simple blood tests that could be useful in predicting the outcome of sepsis. Materials and Methods: A prospective observational study was conducted at a tertiary care hospital, where our team studied 223 patients with urosepsis. The patients underwent Sepsis-3 criteria-based urosepsis and septic shock stratification followed by survivor and non-survivor classification. Clinical scores (Sequential Organ Failure Assessment-SOFA, National Early Warning Score-NEWS), laboratory markers (NLR, PLR, PCT-procalcitonin), and patient outcomes were then analysed. Results: An admission NLR ≥ 13 was a strong predictor of septic shock (adjusted Odds Ratio (OR) 2.10, 95% Confidence Interval (CI) 1.25–3.54) and in-hospital mortality (adjusted OR 2.45, 95% CI 1.40–4.28). While the prognostic value of the PLR remained moderate, the NLR demonstrated superior predictive power. As easily measurable biomarkers, the NLR and PLR provide valuable information to help clinicians identify at-risk patients during the early stages of urosepsis. Conclusions: The NLR is an independent predictor with high predictive value for both septic shock and mortality, performing as well as established clinical scores. The combination of these parameters with clinical assessments could lead to better early decisions and improved outcomes for patients with urosepsis.
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The growing accumulation of waste lubricating oil presents serious environmental issues, calling for sustainable management solutions. This research discusses the creation of FeNi/TiO2 nanocatalysts that were synthesized through an eco-friendly method utilizing grape seed extract (GSE) as a natural reducing agent for
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The growing accumulation of waste lubricating oil presents serious environmental issues, calling for sustainable management solutions. This research discusses the creation of FeNi/TiO2 nanocatalysts that were synthesized through an eco-friendly method utilizing grape seed extract (GSE) as a natural reducing agent for the catalytic pyrolysis of waste lubricating oil. The nanocatalyst was produced using the microemulsion technique and refined via Response Surface Methodology (RSM) to optimize its catalytic performance. Pyrolysis was carried out at 400 °C, leading to a significant conversion of waste oil into valuable fuel. The FeNi/TiO2 nanocatalyst exhibited exceptional capabilities in facilitating the breakdown of heavy hydrocarbons into lighter fuel fractions while reducing unwanted byproducts. GC-MS analysis demonstrated the prevalence of C6–C20 hydrocarbons in the pyrolysis oil, underscoring its potential as a high-quality alternative fuel similar to traditional diesel. This study aids in the progress of environmentally sustainable waste-to-energy technologies, offering a promising pathway for effective fuel production and hazardous waste management.
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Background and Objectives: Oral squamous cell carcinoma (OSCC) is characterized by a high propensity for cervical lymph node metastasis, which remains a strong predictor of patient outcome. Despite advances in management, the prognosis for OSCC has not significantly improved, and the identification
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Background and Objectives: Oral squamous cell carcinoma (OSCC) is characterized by a high propensity for cervical lymph node metastasis, which remains a strong predictor of patient outcome. Despite advances in management, the prognosis for OSCC has not significantly improved, and the identification of reliable predictors for occult lymph node metastasis (OLNM) in clinically node-negative (cN0) patients is crucial for optimizing treatment strategies. Lymphovascular density (LVD) immunohistochemically assessed by podoplanin (D2-40) has been proposed as a potential biomarker for regional metastasis, but its prognostic value remains controversial. This study aimed to evaluate the prognostic significance of intratumoral (ILVD) and peritumoral lymphovascular density (PLVD) for OLNM in OSCC. Materials and Methods: A retrospective analysis was conducted on 43 cN0 patients with primary OSCC who underwent surgical resection and elective neck dissection (END) at a tertiary care cancer center. LVD was assessed by immunohistochemical staining for podoplanin (D2-40) in both intratumoral and peritumoral regions. Clinicopathological data were collected and statistically analyzed. Results: In observed cohort peritumoral LVD was significantly higher than intratumoral LVD. PLVD was also significantly higher in early-stage tumors (pT1/pT2) compared to advanced stages (pT3/pT4). Higher ILVD was significantly associated with the presence of OLNM. Neither ILVD nor PLVD demonstrated a statistically significant influence on overall survival, although a trend toward poorer outcomes was observed in patients with higher ILVD. Conclusions: ILVD was significantly associated with occult nodal metastasis, whereas PLVD was not. However, neither LVD parameter independently predicted overall survival. Results suggest that ILVD may serve as a useful marker for identifying cN0 OSCC patients at higher risk for occult metastasis.
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Background and Objectives: Hepatic steatosis is associated with an increased risk of liver-related morbidity and mortality. Although numerous studies have reported associations between depression, obesity, metabolic syndrome, and cardiovascular disease, the relationship between depression and hepatic steatosis has not yet been fully
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Background and Objectives: Hepatic steatosis is associated with an increased risk of liver-related morbidity and mortality. Although numerous studies have reported associations between depression, obesity, metabolic syndrome, and cardiovascular disease, the relationship between depression and hepatic steatosis has not yet been fully elucidated. Moreover, obesity is a shared risk factor for hepatic steatosis and depression; however, few studies have adequately adjusted for obesity as a potential confounder. In this study, we investigated the association between depression and hepatic steatosis stratified by obese and non-obese status. Materials and Methods: This study used data from the Korean National Health and Nutrition Examination Survey, conducted by the Korean Ministry of Health and Welfare between 2010 and 2019, which was a cross-sectional and nationally representative study of non-institutionalized civilians using a stratified, multistage, clustered probability sampling design. Multivariate logistic regression analyses were conducted to evaluate the association between depression and hepatic steatosis in the groups stratified by obese status. Results: Of 80,861 participants, data from 45,307 were included in the analysis. The prevalence of non-obese and obese hepatic steatosis was 3.1% and 19.3%, respectively, and the prevalence of diagnosed depression was 4.6%. Individuals with hepatic steatosis showed less favorable metabolic profiles, including higher rates of diabetes and elevated liver enzyme levels. Those with depression were older, predominantly female, and had lower socioeconomic status. After fully adjusting for confounding factors, multivariate logistic regression analysis showed that non-obese hepatic steatosis was significantly associated with an increased risk of depression, and obese hepatic steatosis was significantly associated with suicidal ideation and attempts. Conclusions: This study suggests a significant association between depression and hepatic steatosis with and without obese status. Given the significant impact of hepatic steatosis on depression outcomes, healthcare providers should screen patients with hepatic steatosis for depression and provide appropriate treatment as needed.
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Nicoly Farias Gomes, Maria Vitória Neves de Melo, Maria Eduarda Gonçalves de Oliveira, Gledson Luiz Pontes de Almeida, Kenny Ruben Montalvo Morales, Taize Cavalcante Santana, Héliton Pandorfi, João Paulo Silva do Monte Lima, Alexson Pantaleão Machado de Carvalho, Rafaella Resende Andrade, Marcio Mesquita and Marcos Vinícius da Silva
Estimated population growth and increased demand for food production bring with them the evident need for more efficient and sustainable production systems. Because of this, computer vision plays a fundamental role in the development and application of solutions that help producers with the
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Estimated population growth and increased demand for food production bring with them the evident need for more efficient and sustainable production systems. Because of this, computer vision plays a fundamental role in the development and application of solutions that help producers with the issues that limit livestock production in Brazil and the world. In addition to being stressful for the producer and the animal, the conventional pig weighing system causes productive losses and can compromise meat quality, being considered a practice that does not value animal welfare. The objective was to develop a computational procedure to predict the live weight of pigs in the growth and finishing phases, through the volume of the animals extracted through the processing of 3D images, as well as to analyze the real and estimated biometric measurements to define the relationships of these with live weight and volume obtained. The study was conducted at Roçadinho farm, in the municipality of Capoeiras, located in the Agreste region of the state of Pernambuco, Brazil. The variables weight and 3D images were obtained using a Kinect®—V2 camera and biometric measurements of 20 animals in the growth phase and 24 animals in the finishing phase, males and females, from the crossing of Pietrain and Large White, totaling 44 animals. To analyze the images, a program developed in Python (PyCharm Community Edition 2020.1.4) was used, to relate the variables, principal component analyses and regression analyzes were performed. The coefficient of linear determination between weight and volume was 73.3, 74.1, and 97.3% for pigs in the growing, finishing, and global phases, showing that this relationship is positive and satisfactorily expressed the weight of the animals. The relationship between the real and estimated biometric variables had a more expressive coefficient of determination in the global phase, having presented values between 77 and 94%.
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Aguiar Afonso Mariano, Gabriel Brandão das Chagas, Larissa Alves Rodrigues, Andreza de Brito Leal, Michel Cavalheiro da Silveira, Maurício de Oliveira, Antonio Costa de Oliveira, Luciano Carlos da Maia and Camila Pegoraro
A rice grain’s proximate composition determines its nutritional potential. Macronutrient quantification is essential to identify superior genotypes and direct breeding efforts to reach more people who are vulnerable. Conventional methods to determine proximate composition are highly accurate; however, they remain time-consuming, costly, and
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A rice grain’s proximate composition determines its nutritional potential. Macronutrient quantification is essential to identify superior genotypes and direct breeding efforts to reach more people who are vulnerable. Conventional methods to determine proximate composition are highly accurate; however, they remain time-consuming, costly, and destructive. Near-infrared (NIR) spectroscopy enables proximate composition analysis in a non-destructive, rapid, inexpensive, and practical manner, providing results similar to well-established conventional methods. This study aimed to evaluate the feasibility of NIRs-based selection to identify more nutritious rice genotypes. A collection of 155 rice genotypes grown in Southern Brazil was used. After harvest, grains were hulled, polished, and milled. NIRs was used to determine moisture, starch, protein, fat, ash, and fiber contents in rice flour. It was possible to differentiate genotypes with higher and lower levels of the investigated components. Similar and distinct values were observed in comparison to other studies, indicating the accuracy of NIRs and the effect of genotype and environment, respectively. Starch is correlated negatively with protein and fat, preventing the identification of genotypes with high levels of these three components. PCA enabled the separation of the genotypes but highlighted the complexity of sample distribution. NIRs is an effective and accurate method to determine the proximate composition of rice, enabling the selection of more nutritious genotypes.
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This exploratory study investigates how youths aged 18–25 perceive and prioritize elements of the Integrated Digital Storytelling for Social Media (IDSM) framework in cultural heritage tourism contexts, addressing critical gaps between theoretical frameworks and contemporary social media engagement requirements. Through purposive sampling at
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This exploratory study investigates how youths aged 18–25 perceive and prioritize elements of the Integrated Digital Storytelling for Social Media (IDSM) framework in cultural heritage tourism contexts, addressing critical gaps between theoretical frameworks and contemporary social media engagement requirements. Through purposive sampling at cultural heritage tourism sites in Bangkok, Thailand, questionnaires were distributed to 100 participants to examine their preferences for cultural tourism video content and validate framework elements. Cultural authenticity emerged as the paramount consideration among participants, while traditional storytelling elements demonstrated sustained relevance when adapted for social media contexts. Youth participants preferred authentic mobile phone recordings over professional production, with optimal video durations and caption-dependent storytelling for mobile consumption. TikTok emerged as the primary motivational platform despite moderate usage frequency patterns. This exploratory study contributes preliminary empirical assessment of an integrated framework specifically designed for social media applications in cultural heritage tourism contexts. The findings provide evidence-based guidelines to help practitioners develop platform-optimized content strategies that effectively engage youth audiences while maintaining cultural authenticity.
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The buffer zone of a historical or cultural heritage always surrounds the core protected area of the heritage. Currently, there are no mature or universally applicable methods for buffer zone delineation. This study examines the historical and cultural village, Huitong Village in Zhuhai,
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The buffer zone of a historical or cultural heritage always surrounds the core protected area of the heritage. Currently, there are no mature or universally applicable methods for buffer zone delineation. This study examines the historical and cultural village, Huitong Village in Zhuhai, Guangdong, as the research area. From the perspective of cultural geography, this study explores a new framework for delineating buffer zones with three steps. This paper proceeds from two points: the integrity of the water system and the shared value of mankind in the visual corridors. The conclusions of this study are as follows: (1) this study shows the feasibility of our proposed buffer zone delineation method based on cultural geography; (2) the delineation method of buffer zone in this study can serve as a reference method of value comparison for historical space protecting planning at different scales.
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The Rotunda in Thessaloniki, Greece, preserves in its interior a magnificent wall mosaic assemblage of unique inspiration and beauty. Thirty-six (36) glass tesserae, blue, green, yellow, brown, black, gold and silver in color, were examined for the first time via UV-Vis reflectance spectroscopy,
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The Rotunda in Thessaloniki, Greece, preserves in its interior a magnificent wall mosaic assemblage of unique inspiration and beauty. Thirty-six (36) glass tesserae, blue, green, yellow, brown, black, gold and silver in color, were examined for the first time via UV-Vis reflectance spectroscopy, scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS) and X-ray diffraction (XRD) analysis, in order to investigate the base glass composition, and their technological and morphological features. Despite the heterogeneity observed in the glass composition, the results indicated similarities with other Early Christian and Byzantine wall mosaics in the use of colorants, opacifiers and decolorizers. Cobalt, copper, iron and manganese along with lead and tin compounds are responsible for the blue, green, yellow, brown and black colors. Tin-based opacifiers and bone ash contribute to the glass opacity. The use of different glass recipes and opacifiers in the Rotunda’s assemblage reflects the transition from the Roman glass tradition to the Byzantine glass production of the fourth and the fifth century in the eastern Mediterranean.
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Within the field of permanent magnet synchronous motor sensorless speed control systems, we present a novel scheme with a Multi-dimensional Taylor Network (MTN)-based nonlinear observer as the core, supplemented by two auxiliary MTN modules to realize closed-loop control: (1) MTN Model Identifier: Provides
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Within the field of permanent magnet synchronous motor sensorless speed control systems, we present a novel scheme with a Multi-dimensional Taylor Network (MTN)-based nonlinear observer as the core, supplemented by two auxiliary MTN modules to realize closed-loop control: (1) MTN Model Identifier: Provides real-time PMSM nonlinear dynamic feedback for the observer; (2) MTN Adaptive Inverse Controller: Compensates for load disturbances using the observer’s estimated states. The study focuses on optimizing the MTN observer to address key limitations of existing methods (high computational complexity, lack of stability guarantees, and low estimation accuracy). Compared with the neural network observer, this MTN-based scheme stands out due to its straightforward structure and significantly reduced approximately 40% computational complexity. Specifically, the intricate calculations and high resource consumption typically associated with neural network observers are circumvented. Subsequently, by leveraging Lyapunov theory, an adaptive learning rule for the MTN weights is meticulously devised, which seamlessly bridges the theoretical proof of the nonlinear observer’s stability. Simulation results demonstrate that the proposed MTN observer achieves rapid convergence of speed and position estimation errors (with steady-state errors within ±0.5% of the rated speed and ±0.02 rad for rotor position) after a transient period of less than 0.2 s. Even when stator resistance is increased by tenfold to simulate parameter variations, the observer maintains high estimation accuracy, with speed and position errors increasing by no more than 1.2% and 0.05 rad, respectively, showcasing strong robustness. These results collectively confirm the efficacy and practical value of the proposed scheme in PMSM sensorless speed control.
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We present a hybrid parallel scheme for efficiently solving Caputo time-fractional partial differential equations (CTFPDEs) with integer-order spatial derivatives on multicore CPU and GPU platforms. The approach combines a second-order spatial discretization with the time-stepping scheme and employs MATLAB parfor parallelization
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We present a hybrid parallel scheme for efficiently solving Caputo time-fractional partial differential equations (CTFPDEs) with integer-order spatial derivatives on multicore CPU and GPU platforms. The approach combines a second-order spatial discretization with the time-stepping scheme and employs MATLAB parfor parallelization to achieve significant reductions in runtime and memory usage. A theoretical third-order convergence rate is established under smooth-solution assumptions, and the analysis also accounts for the loss of accuracy near the initial time caused by weak singularities inherent in time-fractional models. Unlike many existing approaches that rely on locally convergent strategies, the proposed method ensures global convergence even for distant or randomly chosen initial guesses. Benchmark problems from fractional biological models—including glucose–insulin regulation, tumor growth under chemotherapy, and drug diffusion in tissue—are used to validate the robustness and reliability of the scheme. Numerical experiments confirm near-linear speedup on up to four CPU cores and show that the method outperforms conventional techniques in terms of convergence rate, residual error, iteration count, and efficiency. These results demonstrate the method’s suitability for large-scale CTFPDE simulations in scientific and engineering applications.
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