bluuebunny/crossref_metadata_2025_split huggingface dataset:
Synthesis of MFe2O4 (M=Ni, Co) Nanoparticles by a Bicontinuous Microemulsion Method for the Oxygen Evolution Reaction
Authors: Sultan Faiz , González Sepúlveda Guillermo E. , Medina Dora I. , Videa Marcelo , Sánchez‐Domínguez Margarita , Cholula‐Díaz Jorge L. 
Published: February 2024

AbstractDeveloping efficient and low‐cost electrocatalysts for the oxygen evolution reaction (OER) is crucial for sustainable hydrogen production through water splitting. In this study, CoFe2O4 and NiFe2O4 nanoparticles as electrocatalysts were prepared via an inexpensive method involving the use of bicontinuous microemulsions as nanoreactors. The crystalline structure, morphology, and elemental composition of the electrocatalysts were characterized by XRD, Raman spectroscopy, TEM, and EDS elemental mapping. The electronic structure and textural properties were examined using XPS and the nitrogen adsorption‐desorption method. The OER measurements were carried out in a standard three‐electrode system. CoFe2O4 demonstrated relatively higher OER catalytic activity than NiFe2O4 in 1 M KOH solution, with a smaller overpotential of 410 mV to achieve a current density of 10 mA cm−2 and a smaller Tafel slope of 80 mV dec−1. In contrast, NiFe2O4 offered a higher overpotential of 450 mV to reach the same current density. The superior performance of CoFe2O4 is ascribed to higher ECSA, better conductivity, and lower charge transfer resistance. However, both electrocatalysts showed stability up to more than three hours of continuous performance.
Synergizing edge computing and blockchain for cyber‐physical systems
Authors: Thakur Payal , Sehgal Vivek Kumar 
Published: February 2024

SummaryAs a foundational technology for managing decentralized systems like smart grids and healthcare systems, blockchain is attracting a lot of interest. However, owing to excessive resource requirements and low scalability with frequent‐intensive transactions, its use in resource‐constrained mobile devices is restricted. To let mobile devices offload processing tasks to cloud resources, edge computing can be used. For scalability and secure transactions, the integration with edge computing guarantees consistent access, distributed computing, and non‐compromised storage. To accomplish effective integration, critical challenges such as reliability, adaptability, and resource planning must be resolved. Despite these efforts, further study is still needed to address issues with confidentiality, flexibility, and reliability to develop a workable, secure decentralized data storage system. This study utilizes edge computing to build an Internet of Things (IoT) architecture that satisfies the safety and scalability necessary criteria for integration along with the integration of peer‐to‐peer and blockchain technologies. Already‐existing blockchain and related technologies have also been investigated to suggest solutions that address issues like secrecy, reliability, and scalability in order to successfully incorporate blockchain in IoT systems. Our experimental findings indicate that achieving low rates of stale blocks and promoting decentralization requires a meticulous selection of block sizes and generation intervals. It is essential to maintain block creation intervals as short as practically possible, and utilizing blocks with a size below 1 MB is advisable.
MAGIC‐DR: An interpretable machine‐learning guided approach for acute myeloid leukemia measurable residual disease analysis
Authors: Shopsowitz Kevin , Lofroth Jack , Chan Geoffrey , Kim Jubin , Rana Makhan , Brinkman Ryan , Weng Andrew , Medvedev Nadia , Wang Xuehai 
Published: February 2024

AbstractMultiparameter flow cytometry is widely used for acute myeloid leukemia minimal residual disease testing (AML MRD) but is time consuming and demands substantial expertise. Machine learning offers potential advancements in accuracy and efficiency, but has yet to be widely adopted for this application. To explore this, we trained single cell XGBoost classifiers from 98 diagnostic AML cell populations and 30 MRD negative samples. Performance was assessed by cross‐validation. Predictions were integrated with UMAP as a heatmap parameter for an augmented/interactive AML MRD analysis framework, which was benchmarked against traditional MRD analysis for 25 test cases. The results showed that XGBoost achieved a median AUC of 0.97, effectively distinguishing diverse AML cell populations from normal cells. When integrated with UMAP, the classifiers highlighted MRD populations against the background of normal events. Our pipeline, MAGIC‐DR, incorporated classifier predictions and UMAP into flow cytometry standard (FCS) files. This enabled a human‐in‐the‐loop machine learning guided MRD workflow. Validation against conventional analysis for 25 MRD samples showed 100% concordance in myeloid blast detection, with MAGIC‐DR also identifying several immature monocytic populations not readily found by conventional analysis. In conclusion, Integrating a supervised classifier with unsupervised dimension reduction offers a robust method for AML MRD analysis that can be seamlessly integrated into conventional workflows. Our approach can support and augment human analysis by highlighting abnormal populations that can be gated on for quantification and further assessment. This has the potential to speed up MRD analysis, and potentially improve detection sensitivity for certain AML immunophenotypes.
Fine‐needle aspiration diagnosis of secondary malignant tumors of the thyroid gland: A single‐institution experience of 17 cases
Authors: Zhang Wei , Lei Jianyuan , Chen Jie , Li Wensheng 
Published: February 2024

AbstractBackgroundFine‐needle aspiration (FNA) is the most commonly used preoperative pathological diagnostic tool for thyroid tumors. Secondary malignant tumors of the thyroid gland account for less than 3% of all thyroid malignancies. The aim of this study was to investigate the types and cytopathological features of secondary thyroid tumors, evaluate diagnostic pitfalls in FNA.MethodsCases of secondary thyroid tumors diagnosed in the Department of Pathology of Shaanxi Provincial People's Hospital were collected, and their clinical data, cytologic features, immunohistochemical results, and histopathological diagnoses were summarized.ResultsThe study included 17 cases (8 males and 9 females) with a mean age of 60.4 ± 9.4 years (range, 45–83 years). Six cases had a known history of primary malignancy prior to FNA aspiration diagnosis. The most common organs of origin were the lungs (5 cases, 3 adenocarcinoma, and 2 small‐cell carcinoma) and esophagus (5 cases, 3 squamous‐cell carcinoma, 1 adenocarcinoma, and 1 small‐cell carcinoma). The next most common was squamous‐cell carcinoma of the larynx (3 cases), and gastric tumor (2 cases), including 1 lymphoma and 1 adenocarcinoma. Cell blocks and immunohistochemistry were performed in 12 of these cases. Comparison of the impact of positive history and IHC availability on the accuracy of pathologic diagnosis showed that both were statistically significant.ConclusionFNA is an effective means of diagnosing secondary malignancies of the thyroid, in which knowledge of the patient's history of malignancy is essential, and the use of cell blocks and immunohistochemistry helps to clarify the pathological diagnosis.
Blood perfusion status is important in the prognosis of ventricular aneurysm complicated by ventricular septal rupture
Authors: Huang Siyuan , Zhang Shicheng , Song Yangwu , Feng Wei 
Published: February 2024

AbstractAimsDue to its low incidence, poor prognosis, and high mortality in the acute phase, the long‐term prognosis of the left ventricular aneurysm (LVA) complicated by ventricular septal rupture (VSR) has received little attention. This study focus on the long‐term prognosis of patients with LVA complicated by relatively stable VSR.Methods and resultsOver a decade of retrospection, 68 patients with both LVA and VSR were compared with 136 patients with LVA alone after propensity score matching. Patients with both LVA and VSR were further divided into two groups depending on whether pre‐operative intra‐aortic balloon pump (IABP) was used (23 pre‐operative IABP vs. 45 non‐pre‐operative IABP). The primary endpoint was defined as major adverse cardiovascular and cerebrovascular events, a composite endpoint including mortality, myocardial infarction, revascularization, stroke, and heart failure. Patients with both LVA and VSR were generally in a worse condition upon admission compared with those with LVA alone [percentage of patients in New York Heart Association IV: 42.6% (29/68) vs. 11.0% (15/136), P < 0.001]. Both pre‐operative and post‐operative IABP use rates were significantly higher in patients with both LVA and VSR than in patients with LVA alone [pre‐operative IABP use rates: 33.8% (23/68) vs. 0.74% (1/136), P < 0.001 and post‐operative IABP use rates: 33.8% (23/68) vs. 10.3% (14/136), P < 0.001]. No significant difference was observed in the primary endpoint between patients with both LVA and VSR and those with LVA alone (log‐rank test, P = 0.63, median follow‐up time 63 months). We further investigated the effect of pre‐operative IABP on the long‐term prognosis of patients with both LVA and VSR. Patients who applied pre‐operative IABP had a worse long‐term prognosis than those who did not (log‐rank test, P = 0.0011).ConclusionsThe long‐term prognosis of LVA combined with VSR was not inferior than LVA alone after surgery, but poor blood perfusion status was associated with a worse prognosis.
‘Dutch’ according to children and mothers: Nationality stereotypes and citizenship representation
Authors: de Bruijn Ymke , Yang Yiran , Mesman Judi 
Published: February 2024

AbstractThis research examines the endorsement of the nationality stereotype Dutch = White among children and associations with citizenship representations of their mothers (Study 1). Additionally, Study 2 explores how mothers include the concept of Dutch citizenship in the upbringing of their children. Study 1 shows that children (n = 197, 57% girls, 7–13 years old) from different ethnic‐racial backgrounds (White Dutch, Turkish‐Dutch, Black Dutch, Chinese‐Dutch) all endorsed the nationality stereotype and did so to a similar extent. Most mothers rated civic citizenship as more important than ethnic citizenship, but maternal citizenship representations were unrelated to child nationality stereotype. Study 2 shows that mothers often do not actively and consciously include the topic of Dutch citizenship in their upbringing, but might confirm the nationality stereotype in more implicit ways. Future studies are needed to examine how to work towards a more inclusive view of nationality among children in the Dutch context.
Performance evaluation and development of FE modeling for passive greenhouse solar dryer for potato chips drying
Authors: Kumar Lalan , Prakash Om , Ahmad Asim , Das Biplab , Brar L. S. 
Published: February 2024

AbstractFood losses and hunger around the world are majorly attributed to the lack of diverse food processing technologies and standardized methods. Solar energy is a potential solution for drying agricultural products, but traditional solar drying methods are not yet widely used. This study developed a finite element model using COMSOL Multiphysics to assess the performance of a passive greenhouse dryer for drying food products. The proposed model was used to simulate the drying of potato slices, and the results showed that the dryer was able to reduce the moisture content of the potato slices from 1 to 0.005 in 3 h. The drying efficiency was 20.52%, the relative humidity inside the drying chamber was 25.2%, and the energy and exergy efficiencies were 63.46% and 94.01%, respectively. Overall, the results of this study suggest that the passive greenhouse dryer is a suitable drying method for potato chips. The model developed in this study can be used to further optimize the design of the dryer and to evaluate its performance for other food products.
Cobalt chromite nanoparticle effects on kapok diesel emulsion performance and emission characteristics at various injection pressures
Authors: Anbarasan B. , Muralidharan K. , Sakthi Rajan C. , Rajkumar T. 
Published: February 2024

AbstractAlternative fuels derived from vegetable oil have great potential as diesel fuel replacements in the transportation and manufacturing sectors. The aim of this study is to use cobalt chromite nanoparticles as a fuel additive with biodiesel in engine and to experimentally investigate the influence of injection pressure on combustion parameters. As an addition, cobalt chromite nanoparticles are used with biodiesel made from kapok oil, which is blended with mineral diesel at a ratio of 20:80. The engine is operated at various injection pressures (200–260 bar) and with an 80 ppm nanoparticle concentration. The results have shown that the increased injection pressure caused by the use of nanoparticles enhances engine combustion properties, such as the peak pressure and the rate of heat release. The main purpose of this research was to investigate the effects of a CoCr2O4 + KME20 mix on a CI engine, with the hope of improving engine performance characteristics. This investigation examines the effects of varying test fuel injection pressures. The increased injection pressure of CoCr2O4 + KME20 resulted in better performance and combustion. The 240‐bar IP was shown to be superior to lower IPs because of its greater penetration length and more uniform formation. The IP rating of 240 bar represented a significant improvement over competing products with respect to emission controls. In addition to reducing our reliance on fossil fuels, this also prevents harmful chemicals from being released into the air.
Assessing the types of policy networks in policymaking: Empirical evidence from administrative reform in Italy
Authors: Capano Giliberto , Erittu Eleonora , Francisci Giulio , Natalini Alessandro 
Published: February 2024

AbstractPolicy networks can propose solutions (policy communities, and epistemic communities), defend specific instruments (instrument constituencies), and programmatically prioritize change or stability (programmatic groups). This paper focuses on two specific networks that have been present in 30 years of administrative reform in Italy, and it empirically assesses what type of network they are according to their origins, developments over time, membership and motivations to stay together, and role in the policymaking. This comparison, while improving the current understanding of the networking taking place in the Italian administrative reform, shows that if policy networks are very relevant in the policy process, it is analytically more fruitful and empirically more reliable to assess their characteristics empirically, rather than to assume their existence in advance (and make hypotheses on this basis) or to use the concept in a purely metaphorical manner.
Identifying causal mechanisms of unexpected policy change: Accumulated punctuation in the field of lobbying transparency in Germany
Authors: Schiffers Maximilian , Plümer Sandra 
Published: February 2024

AbstractRecent trends toward mechanistic approaches offer a new perspective in understanding policy change and stability. This paper analyzes causal mechanisms leading to unexpected policy change by using punctuated equilibrium theory. As empirical illustration, the paper presents a case study on the introduction of the German mandatory lobbying register in 2021 after a 16‐year‐long debate. Methodologically, the paper employs process tracing and qualitative content analysis to examine policy documents. We identify a combination of three mechanisms: end of a de‐thematization of the policy issue, growing dominance of the issue network favoring stricter transparency regulations, and issue validation through the accumulation of scandals. Thus, policy change results from the descend of policy actors defending the status quo while those advocating for change ascend to an influential position, and actively exploit focusing events as fertile ground for reform. The paper contributes to a refined theoretical understanding of the causal mechanisms of policy change.
Brief cognitive behavioural therapy for eating disorders symptomatology among a mixed sample of adolescents and young adults in primary care: A non‐randomised feasibility and pilot study
Authors: Hart Melissa , Hirneth Stephen , Mendelson Jane , Jenkins Laura , Pursey Kirrilly , Waller Glenn 
Published: February 2024

AbstractObjectiveBrief and accessible therapies for people with an eating disorder is an important health target. Ten‐session cognitive behavioural therapy (CBT‐T) is a brief treatment evaluated in people with a non‐underweight eating disorder. This study aimed to evaluate the feasibility and preliminary effectiveness of CBT‐T for young people in primary care.MethodThis cohort pilot study used group (adolescents vs. young adults) by time (over four time points) Generalised Linear Mixed Model analysis. Participants included 13–25‐year‐olds attending an early intervention mental health service, receiving 10 sessions of CBT‐T. Feasibility was assessed using recruitment, retention and satisfaction. Eating and other pathology measures were administered at baseline, weeks four and 10, and 12‐week follow‐up.ResultsOf the 63 commencing treatment, 38 completed 10 CBT‐T sessions (60%). Most (94%) reported high treatment satisfaction. Significant reductions in eating pathology, depression and stress were found. Age group did not yield differences in CBT‐T outcome, with large to very large effect sizes across outcome variables. Anxiety was associated with attrition.ConclusionThis study provides preliminary support for the use of CBT‐T in primary care, across adolescence and early adulthood. Findings require replication in other clinical settings and comparison to other clinical approaches and control populations.
Electricity price forecast in wholesale markets using conformal prediction: Case study in Mexico
Authors: De la Torre Jorge , Rodriguez Leticia R. , Monteagudo Francisco E. L. , Arredondo Leonel R. , Enriquez José B. 
Published: February 2024

AbstractIn recent years, machine and deep learning models have attracted significant attention for electricity price forecast in global wholesale electricity markets. Yet, a predominant focus on point forecast in most parts of literature limits the practical application of these models due to the absence of uncertainty quantification. In this study, we first perform an analysis of the electricity price trends in the Mexican wholesale electricity market to determine the influence of key variables. Using independent component analysis and wavelet coherence analysis, we were able to identify primary determinants influencing locational marginal electricity prices. Subsequently, we applied four different models covering the most important algorithms proposed in the literature for electricity price forecast. Our findings revealed that the most accurate forecasting results were achieved using a deep learning‐based method with a decision tree‐based model trailing closely. Finally, we incorporate conformal prediction for uncertainty quantification by calculating the prediction intervals with a target coverage level of 95%. The conformal prediction intervals provide a more comprehensive view of the possible future scenarios, enhancing economic efficiency, risk management, and decision‐making processes. This is particularly important because of the dynamic nature of electricity markets, where prices are strongly influenced by multiple factors.
Backstepping sliding mode control of stabilized platform for fully rotary steerable drilling system based on nonlinear disturbance observer
Authors: Li Shuoyu , Wang Yuelong , Wang Degui , Chen Jia , Yang Yi 
Published: February 2024

AbstractTo handle the disturbances and uncertainties issue in the control of the stabilized platform toolface angle, a composite control approach based on a backstepping sliding mode control (BSMC) method with a nonlinear disturbance observer (NDO) is proposed. First, a nonlinear mathematical model of the stabilized platform is established, and the NDO is employed to observe and compensate for the external disturbance. Then, the BSMC control approach based on the nonlinear mathematical model is designed to improve the robustness of the stabilized platform control system. The asymptotic stability of the designed controller and NDO convergence are mathematically verified using the Lyapunov theory. Furthermore, to address the dead zone torque caused by mechanical friction on the stabilized platform, which could lead to instability in the toolface angle, a novel dead zone torque estimation algorithm is presented. Finally, in comparison with other advanced control methods, simulation results show the superiority and robustness of the proposed scheme under complex disturbances from the downhole environment. And drilling simulation experiment results are provided to validate the robustness and adaptivity of the proposed method.
Support vector machine based fault detection in inverter‐fed electric vehicle
Authors: Mestha Soumya Rani , Prabhu Nagesh 
Published: February 2024

AbstractInverters play a prominent role in the power train system of electric vehicles (EVs). Devices in EV connected power system are threatened by faults due to the continuous working and varying speed range of motors in EVs. Hence, in the EV connected application, the detection of fault is essential since it secures the system from severe damage and dangerous operating conditions. This paper deals with fault detection in inverter‐fed EV using a dual‐tree complex wavelet transform (DTCWT) based squeeze net (SN) and optimized support vector machine (SVM). Due to the simple structure and high power density, most EV models on the market are equipped with induction motors. In the proposed work, the voltage, current, and speed signals are measured at different faulty conditions, and then the features are extracted through the DTCWT‐based SN. Extracted data are processed and classified through the sucker‐vulture optimization algorithm (SVOA) based SVM. In the proposed SVOA, the exploration phase of remora optimization algorithm is used for the exploitation phase of African vulture optimization algorithm (AVOA). Thus, the convergence speed of AVOA is improved. The proposed method is implemented in MATLAB/SIMULINK, and the results are used for different scenarios. The accuracy and F1‐score for the proposed methodology are attained as 99.92441% and 99.92441%. From the obtained results, it is clear that the proposed DTCWT‐based SN effectively detects the faults in the inverter.