bluuebunny/crossref_metadata_2025_split huggingface dataset:
Self‐Attention Mechanisms‐Based Laryngoscopy Image Classification Technique for Laryngeal Cancer Detection
Authors: Kang Yi‐Fan , Yang Lie , Hu Yi‐Fan , Xu Kai , Cai Lan‐Jun , Hu Bin‐Bin , Lu Xiang 
Published: November 2024

ABSTRACTBackgroundThe early diagnosis of laryngeal cancer (LCA) is crucial for prognosis, driving our search for an accurate, precise, and sensitive deep learning model to assist in LCA detection.MethodsWe collected 5768 laryngoscopic images from 1462 patients and created the intelligent laryngeal cancer detection system (ILCDS) based on Swin‐Transformer. Following training and validation, we assessed the ILCDS performance on the internal and external test sets and compared it with previous convolutional neural network (CNN) models and three professional laryngologists.ResultsThe ILCDS outperformed the six CNNs, with the highest accuracy of 92.78% and an area under the curve (AUC) of 0.9732. Despite a slight drop in performance on external sets, the ILCDS maintained the best superiority, with 85.79% accuracy and an AUC of 0.9550. Surpassing professional laryngologists, the ILCDS achieved 92.00% accuracy.ConclusionsThe ILCDS offers high accuracy and stability for LCA detection, reducing the burden on laryngologists.
Depression detection in dementia: A diagnostic accuracy systematic review and meta analysis update
Authors: Atchison Kayla , Nazir Alaia , Wu Pauline , Seitz Dallas , Watt Jennifer A. , Goodarzi Zahra 
Published: November 2024

AbstractBackgroundDepression is common in persons with dementia and is often under‐detected and under‐treated. It is critical to understand which available tools accurately detect depression in the context of dementia.MethodsWe updated our systematic review completed in 2015. The search strategy of our original review was replicated in Medline, Embase, and PsycINFO. Studies describing the use of a tool to identify depression in persons with dementia, compared to a criterion standard, and reporting diagnostic accuracy outcomes were included in the review update. Pooled prevalence estimates of major depression and pooled estimates of diagnostic accuracy outcomes (i.e., sensitivity [SN], specificity [SP]) for tools were calculated.ResultsThree studies were included of the 8980 returned from the database search and were added to the prior 20 articles from the 2015 review. The Cornell Scale for Depression in Dementia (CSDD), Geriatric Depression Scale (GDS)−15 item, Neuropsychiatric Inventory‐Depression items (NPI‐D), and Depression in Old Age Scale (DIA‐S) were evaluated in the three studies. Two new studies were added to the existing pooled prevalence estimate of major depression (29%, 95% confidence interval [CI] = 21.6%–36.5%, n = 17) and pooled diagnostic accuracy estimate for the CSDD at the best cut‐off (SN = 0.83, 95% CI = 0.74–0.90; SP = 0.81, 95% CI = 0.69–0.89). New pooled diagnostic accuracy estimates were completed for the CSDD (cut‐off ≥12) (SN = 0.61, 95% CI = 0.42–0.77; SP = 0.83, 95% CI = 0.76–0.88), GDS‐15 (best cut‐off) (SN = 0.65, 95% CI = 0.40–0.83; SP = 0.72, 95% CI = 0.55–0.85), and Montgomery Asberg Depression Rating Scale (MADRS) (best cut‐off) (SN = 0.77, 95% CI = 0.67–0.85; SP = 0.68, 95% CI = 0.60‐0.75).ConclusionsThe CSDD continues to have the most evidence for depression case finding in persons living with dementia. The CSDD and Hamilton Depression Rating Scale have the highest sensitivities and may be recommended for use over other common tools like the GDS‐15 and MADRS. Newly identified tools like the NPI‐D and DIA‐S require further study before they can be recommended for use in practice.
Modeling of Heat Transfer and Airflow Inside Evacuated Tube Collector With Heat Storage Media: Experimental Validation Powered by Artificial Neural Network
Authors: Elbrashy Amr , Elgohr Abdelrahman , Elshennawy Abdullah , Rashad Maher , El‐fakharany Magda 
Published: November 2024

ABSTRACTSolar air heater (SAH) is a widely employed technology for harnessing solar thermal energy in numerous applications. Contemporary research optimizes designs within identical spatial constraints to maximize energy output. However, there is a recognized need to conduct analytical validation to stimulate the experimental setup and formulate an artificial neural network (ANN) model to govern and predict the operation system. This investigation involved developing and assessing an evacuated tube solar air heater (ETSAH) integrated with annulus‐filled heat storage media. Furthermore, this study introduced an ANN model and analytical solution to predict performance parameters, representing a noteworthy contribution. The proposed ANN model achieved its optimal validation performance with a mean square error of 5.4018 × 10−6 after 11 epochs within 31 of training. Also, correlation findings show that the optimal architecture of a feed‐forward backpropagation is achieved when the 5‐40‐40‐40‐2 model architecture is used. In this case, there are five neural nodes in the input layer that represent timing, temperature, radiation, and airflow rate. The power output of the ETSAH device shows a strong correlation with the flow rate, reaching its peak at 0.05 kg/s with a value of 2261 W and dropping to 368 W at 0.006 kg/s. Correspondingly, the greatest energy efficiency was measured at airflow rates of 0.05, 0.01, and 0.006 kg/s, accounting for 48.38%, 27.32%, and 19.65%, respectively.
Comparative Analysis of Heat Exchanger Models for Phase Change Material Melting Process: Experimental and Numerical Investigation
Authors: Saleh Hussein Hatem , Mussa Munther Abdullah 
Published: November 2024

ABSTRACTThermal energy storage systems using PCM offer promising solutions for efficient thermal applications. This study aims to provide valuable insights into the PCM melting process and compare the thermal performance of different heat exchanger models. The experimental rig is carefully designed to simulate a shell and tube heat exchanger with five longitudinal copper fins at specific conditions. Three distinct models of the heat exchanger, labeled Models A, B, and C, were examined for their heat transfer efficiency during the melting process. Numerical simulations were conducted for the three models and compared with experimental results. Model A represented the benchmark model. It had uniform fin length, location, and angle. Model B was the modified design aimed at enhancing melting progress. These modifications included a longer lower fin, a shorter side fin compared to the reference, and a lower fin angle to optimize heat transfer performance. Model C incorporates further design modifications, with a longer lower fin, a shorter top fin, and different lower fin location. Numerical simulations and experimental observations revealed significant differences in heat transfer performance among the three models. The average heat transfer rates, measured up to the critical decline point, are crucial parameters for practical applications. A comparison among the three models showed that Model B surpassed the average heat transfer rate up to the critical point of Models A and C by 10% and 4%, respectively, demonstrating its superior practical applicability.
Optimization of Thermal Performance by Enhancing Natural Convection of Diverse Configurations of Fin Heat Sink Filled With Nano‐Enhanced Phase Change Materials: A Numerical Study
Authors: Omi Mallik Nadim Arman 
Published: November 2024

ABSTRACTElectronic components require faster propagation of heat to prevent overheating as well as to maintain a stable operating condition. Incorporating phase change materials (PCMs) in fin heat sinks (FHSs) can be a viable solution to enhance natural convection. In the present work, an extensive investigation has been carried out on three types (square, circular, and triangular) of FHSs filled with PCMs to find the best combinations for the thermal cooling of a heat sink. To increase the thermal conductivity of the PCMs, Cu–water nanofluid is added. The effects of three types (paraffin wax, stearic acid, and polyethylene glycol) of PCMs have been studied. Nondimensional equations have been solved numerically by using Galerkin's finite element method. A parametric investigation has been conducted by varying the Rayleigh number within the range of 103–106 to calculate the Nusselt number. Results reveal that a heat sink with PCMs can reduce the temperature rise by 6.41% and increase the Nusselt number by 4.6% compared to a heat sink without PCMs. As the height of the fins increases from 10 to 16 mm, thermal efficiency increases. Moreover, square FHSs show the best thermal performance by reducing the temperature rise by as much as 13.41% compared to circular and triangular FHSs. Therefore, this comparative study shows that the dimensional configurations of FHSs with varying PCMs have a great impact on thermal performance and thus provide one the opportunity to obtain an effective arrangement of heat sinks.
Submarine Groundwater Discharge at a Mega‐Tidal Beach
Authors: Craddock Raymond D. , Mohammed Aaron A. , Tamborski Joseph J. , Kurylyk Barret L. 
Published: November 2024

ABSTRACTTidally influenced groundwater systems in coastal environments represent important mixing zones of fresh groundwater and circulating seawater, manifesting as submarine groundwater discharge (SGD). Water circulation induced by tidal pumping enhances the exchange of chemicals between aquifers and coastal waters and thereby influences the biogeochemistry of coastal zones. Here, we report the results of an SGD field study conducted at a steep, mega‐tidal sand and gravel beach along the Canadian coast of the Bay of Fundy, a region with the world's highest tides (semi‐diurnal tidal ranges exceeding 10 m). Several physical and geochemical measurement techniques were employed to document the spatiotemporal SGD variability. SGD was directly sampled from seepage meters installed over multiple tidal cycles and two summer campaigns. SGD rates were estimated from tracer mass balances for radon (August 2020) and radium isotopes (July 2021) over multiple tidal cycles. Tidally averaged SGD estimates from seepage meters ranged from 12 to 87 cm d−1, with an average of 42 cm d−1, while radon tracing yielded a tidally averaged rate of 86 cm d−1. SGD estimates from radium tracing ranged from 23 to 43 cm d−1 along the shoreline and 6 to 71 cm d−1 offshore, depending on the estimated residence times. Radionuclide analyses of seepage meter waters suggest that the residence time of seawater circulation through the aquifer is less than 1 day. SGD measurements in mega‐tidal settings are rare, and the results suggest that the combination of the steep slopes, highly permeable sediments and high tidal range drive very high seepage rates for diffusive SGD. Salinity gradients in the intertidal zone demonstrate that SGD is primarily comprised of circulated seawater with negligible fresh groundwater. Although the freshwater proportion of SGD is relatively low, the large volumetric rates of total SGD can still contribute large amounts of terrestrially derived and remineralized nutrients to coastal waters.
Antimony Flux and Transport Dynamics in a Mining‐Impacted River Is Linked to Catchment Hydrodynamics and Climate Oscillations
Authors: Wichman Gretchen , Johnston Scott G. , Maher Damien T. 
Published: November 2024

ABSTRACTWe investigate how seasonal flow variations and a climatic regime that is dominated by the El Niño–Southern Oscillation (ENSO) influence Sb flux dynamics in an Australian river impacted by mining. Sampling (n = 496) spans a hydrologically complex 7‐year period of drought, bushfires and floods from 2016 to 2023, during which 17% of samples exceeded the Sb drinking water guideline concentration (3 μg L−1). Aqueous Sb (SbAq) concentration–discharge (C–Q) relationships are non‐continuous/non‐linear across the flow range, with chemodynamic behaviour at moderate flows reflecting hydrological connection to the primary Sb‐source area combined with variable dilution. In contrast chemostatic behaviour occurred at extreme low and high flows, reflecting hydrological disconnection from the source area and persistent dilution, respectively. SbAq was significantly positively correlated (p < 0.01, Spearman's ρ = 0.58) with a Q index representing the proportional contribution of sub‐catchment flow from the mineral‐field area, suggesting sufficient localised rainfall in the Sb mining‐impacted sub‐catchment contributes to downstream peaks in SbAq concentrations. Aqueous and particulate Sb (SbP) annual loads (La) during the study period spanned 24–5174 and 1.2–2820 kg, respectively and were strongly flow dependant with extreme interannual variability reflecting dry and wet years. We extrapolate daily load‐daily discharge (Ld–Qd) relationships for SbAq and SbP to estimate Ld over a 53‐year period (1970–2023) of continuous Q data (mean total Sb La = 1865 kg ± [SE] 247). Positive correlations between the annual Southern Oscillation Index and both Sb La (p < 0.05) and proportional SbP La over 53 years suggests ENSO fluctuations influence annual Sb transport dynamics. Upstream SbP load estimates correspond with downstream estimates of coastal floodplain sedimentary Sb mass, with approximately 10%–45% of the estimated SbP exported downstream since approximately 1880 accumulated on the Macleay coastal floodplain. Data suggest at current rates of export, complete flushing‐leaching of mine tailings‐derived Sb from the upper Macleay catchment may take in the order approximately 600–1000 years.
Subsurface Sediment Transport in the Shallow Vadose Zone of Fine‐Textured Soils With Heterogenous Preferential Flows
Authors: Ford William , Williams Mark , Mumbi Rose 
Published: November 2024

ABSTRACTSubsurface sediment transport in tile‐drained landscapes occurs through macropores; however, little is known regarding how heterogeneous preferential flows influence fluxes. We performed laboratory rainfall simulations on 10 intact core lysimeters from a tile‐drained field in Indiana, USA to study the impacts of surface and subsurface erosion on sediment leachate in heterogeneous preferential flow paths. Seven rainfall simulations were conducted to assess the impact of rainfall intensity on the leachate of surface eroded sediments (three events), and the impact of antecedent conditions on subsurface eroded sediments (four events). Cumulative sediment yield, linear mixed effects modelling, and hysteresis analyses were performed for all events. Results were presented in a series of four case studies. Results showed that surface sediment leachate concentration and yield were tightly linked to the filtration capacity of lysimeters, with more than 2/3rd of sediment originating from a single lysimeter, despite similar flow leachate volumes from each. Rainfall intensity significantly impacted the transport of surface eroded sediment at the highest intensity. Subsurface sediment erosion from undisturbed macropores was low compared to surface soils, but we found contrasting controls on sediment concentrations at low and high antecedent moistures that were equally important to sediment leachate yields. Disturbed macropores produced comparable sediment yields to surface erosion and behaved similarly to soil pipes in terms of erosion mechanics. Hysteresis results generally highlighted contrasting results for surface and subsurface sources but suggest that the prominence of slow flow, low‐concentration leachate sources can alter the interpretation of results in field‐scale applications. Our findings underscore an array of processes and pathways for sediment transport in the shallow vadose zone, and results will be useful for evaluating new model formulations.
YOLOv7‐XAI: Multi‐Class Skin Lesion Diagnosis Using Explainable AI With Fair Decision Making
Authors: Veeramani Nirmala , Jayaraman Premaladha 
Published: November 2024

ABSTRACTSkin cancer, a prevalent and potentially life‐threatening condition, demands accurate and timely detection for effective intervention. It is an uncontrolled growth of abnormal cells in the human body. Studies are underway to determine if a skin lesion is benign (non‐cancerous) or malignant (cancerous), but the biggest challenge for a doctor is determining the type of skin cancer. As a result, determining the type of tumour is crucial for the right course of treatment. In this study, we introduce a groundbreaking approach to multi‐class skin cancer detection by harnessing the power of Explainable Artificial Intelligence (XAI) in conjunction with a customised You Only Look Once (YOLOv7) architecture. Our research focuses on enhancing the YOLOv7 framework to accurately discern 8 different skin cancer classes, including melanoma, basal cell carcinoma, and squamous cell carcinoma. The YOLOv7 model is the robust backbone, enriched with features tailored for precise multi‐class classification. Concurrently, integrating XAI elements, Local Interpretable Modal‐agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) ensures transparent decision‐making processes, enabling healthcare professionals to interpret and trust the model's predictions. This innovative synergy between YOLOv7 and XAI heralds a new era in interpretability, resulting in high‐performance skin cancer diagnostics. The obtained results are 96.8%, 94.2%, 95.6%, and 95.8%, evaluated with popular quantitative metrics such as accuracy, precision, recall, and F1 score, respectively.
Artificial intelligence in the diagnosis of cerebrovascular diseases using magnetic resonance imaging: A scoping review
Authors: Wang Yituo , Zhang Zeru , Peng Ying , Chen Silu , Zhou Shuai , Liu Jiqiang , Gao Song , Zhu Guangming , Han Cong , Wu Bing 
Published: November 2024

AbstractThe field of radiology is currently undergoing revolutionary changes owing to the increasing application of artificial intelligence (AI). This scoping review identifies and summarizes the technical methods and clinical applications of AI applied to magnetic resonance imaging of cerebrovascular diseases (CVDs). Preferred Reporting Items for Systematic reviews and Meta‐Analyses extension for Scoping Reviews was adopted and articles listed in PubMed and Cochrane databases from January 1, 2018 to December 31, 2023, were assessed. In total, 67 articles met the eligibility criteria. We obtained a general overview of the field, including lesion types, sample sizes, data sources, and databases and found that nearly half of the studies used multisequence magnetic resonance as the input. Both classical machine learning and deep learning were widely used. The evaluation metrics varied according to the five main algorithm tasks of classification, detection, segmentation, estimation, and generation. Cross‐validation was primarily used with only one third of the included studies using external validation. We also illustrate the key questions of the CVD research studies and grade the clinical utility of their AI solutions. Although most attention is devoted to improving the performance of AI models, this scoping review provides information on the availability of algorithms, reliability of external validations, and consistency of evaluation metrics and may facilitate improved clinical applicability and acceptance.
Bonferroni‐Type Tests for Return Predictability With Possibly Trending Predictors
Authors: Astill Sam , Harvey David I. , Leybourne Stephen J. , Taylor A. M. Robert 
Published: November 2024

ABSTRACTThe Bonferroni
test is widely used in empirical studies investigating predictability in asset returns by strongly persistent and endogenous predictors. Its formulation, however, only allows for a constant mean in the predictor, seemingly at odds with many of the predictors used in practice. We establish the asymptotic size and local power properties of the
test, and the corresponding Bonferroni
‐test, under a local‐to‐zero specification for a linear trend in the predictor, revealing that size and power depend on the magnitude of the trend for both. To rectify this, we develop with‐trend variants of the operational Bonferroni
and
tests. However, where a trend is not present in the predictor, we show that these tests lose (both finite sample and asymptotic local) power relative to the extant constant‐only versions of the tests. In practice, uncertainty will necessarily exist over whether a linear trend is genuinely present in the predictor or not. To deal with this, we also develop hybrid tests based on union‐of‐rejections and switching mechanisms to capitalise on the relative power advantages of the constant‐only tests when a trend is absent (or very weak) and the with‐trend tests otherwise. A further extension allows the use of a conventional
‐test where the predictor appears to be weakly persistent. We show that, overall, our recommended hybrid test can offer excellent size and power properties regardless of whether or not a linear trend is present in the predictor, or the predictor's degrees of persistence and endogeneity. An empirical application illustrates the practical relevance of our new approach.JEL Classifications: C22, C12, G14
Synergistic Effect of Salinomycin With Budesonide on TNBC Regression via EMT Reversal and Autophagy Induction
Authors: Sarkar Shilpi , Ghosh Siddhartha Sankar 
Published: November 2024

ABSTRACTTriple‐negative breast cancer (TNBC) poses a significant clinical challenge due to its aggressive nature, lack of specific therapeutic targets, and drug resistance. Chemotherapy resistance in TNBC is largely driven by the abnormal activation of epithelial‐to‐mesenchymal transition (EMT) and the associated cancer stem cell‐like characteristics. The combination of multiple chemotherapeutic drugs has shown promise as a treatment approach for TNBC. This study evaluates the efficacy of a novel combination therapy involving the anti‐inflammatory drug Budesonide and Salinomycin, which targets cancer stem cells. Co‐administration of Budesonide and Salinomycin demonstrated a synergistic effect in inhibiting TNBC cell growth by activating the intrinsic apoptosis pathway. It induced a 2‐ to 3‐fold increase in intracellular reactive oxygen species (ROS) generation and a 25%–30% rise in mitochondrial membrane depolarization. Additionally, extensive signaling studies revealed that the co‐treatment specifically targeted multiple signaling nodes, limiting downstream crosstalk. The combination also enhanced autophagic activity by inhibiting the AKT/mTOR pathway and reduced cell migration and stemness by suppressing the EMT process. Therefore, the combination of Budesonide and Salinomycin offers a novel therapeutic approach for TNBC.
Lactoperoxidase Inhibition of Celecoxib Derivatives Containing the Pyrazole Linked‐Sulfonamide Moiety: Antioxidant Capacity, Antimicrobial Activity, and Molecular Docking Studies
Authors: Bayrak Songül , Gerni Serpil , Öztürk Cansu , Almaz Züleyha , Bayrak Çetin , Kılınç Namık , Özdemir Hasan 
Published: November 2024

ABSTRACTCelecoxib derivatives that contain the pyrazole‐linked sulfonamide moiety were synthesized, and the in vitro inhibitory impacts of the aforesaid compounds against the lactoperoxidase (LPO) enzyme were researched. To this end, LPO was purified using the affinity chromatography technique with a yield of 12.63% (319.23‐fold). The results showed that the aromatic pyrazole compound (compound 1) containing 2,3‐dimethoxyphenyl functional groups was the most effective LPO inhibitor with a Ki value of 3.2 ± 0.7 nM and noncompetitive inhibition type. The second section of the study tested the previously synthesized compounds to reveal their antioxidant and antimicrobial properties. The above‐mentioned compound also displayed high activity levels compared to standard antibiotics and antifungals, while all other compounds also showed antibacterial activity. In the three antioxidant methods we used, the compound with 2,5‐dimethoxy phenyl groups obtained from the reaction of the aromatic pyrazole compound with propionic anhydride in the presence of NEt3 displayed the highest activity. Furthermore, molecular docking and molecular mechanics studies were conducted to complement and validate the experimental findings. The results obtained from these computational analyses are highly consistent with the experimental data.