bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Method for Damage Prediction in BCF/PEEK Drilling Using an Innovative Drill Tool
Authors: Liu Yong , Zhu Shenao , Xu Guangyao , Zhu Meng , Pan Zitao 
Published: June 2024

Abstract

The effective design of tool geometry and optimization of process parameters play a pivotal role in mitigating damages such as tearing, burrs, and delamination during the drilling process of braided carbon fiber reinforced poly ether ether ketone (BCF/PEEK). This study introduces a novel method for selecting optimal drilling tools and damage prediction analysis in BCF/PEEK drilling. Firstly, a scale-span drilling finite element (FE) model is established based on the analysis of twist bit geometry and BCF/PEEK composition. Simulation and experimental validation identify damage causes in prefabricated holes. Subsequently, three innovative types of designed drilling tools are evaluated based on factors like hole morphology, thrust force, and delamination. Finally, Regression models are established to correlate damage factors, thrust force, and process parameters. The research findings indicate that the use of twist drill bits results in higher thrust forces, leading to delamination defects at hole exits. Conversely, employing a tapered drill-reamer could enhance the exit quality of prefabricated holes, consistently maintaining damage factors below 1.14 under identical process parameters. The proposed method effectively predicts the exceptional process parameters, with a maximum error of only 0.276% in the drilling of BCF/PEEK.
Level of leadership effectiveness and associated factors among managers in Amanuel Mental Specialized Hospital, Addis Ababa, Ethiopia, 2023. A mixed-methods study
Authors: Ayenew Surafel , Liyew Woldehawariate , Azale Yenew , Fejo Edao 
Published: June 2024

Abstract

Background: The capacity to shape workers' attitudes, convictions, and skills in order to accomplish company objectives is known as leadership. It is essential to whether organizational performance is successful or unsuccessful. To successfully and efficiently manage health service delivery improvements, healthcare institutions require strong leadership. On the other hand, insufficient data exists on the present state of healthcare leaders to support evidence-based decision-making. Thus, the purpose of this study is to evaluate healthcare leadership effectiveness and associated factors among managers employed by Amanuel Mental Specialized Hospital in Addis Ababa, Ethiopia.
Methods: An institutional-based cross-sectional study was employed from 01 August to 01 September/2023. Six key informant interviews (KIIs) and 80 healthcare managers in total were undertaken. A complete census was used for quantitative data and purposive sampling for the qualitative approach. A binary logistic regression model was fitted to identify the factors associated with healthcare leadership effectiveness. An adjusted odds ratio (AOR) with a 95% confidence interval (CI) and p-value less than 0.05 during multivariable logistic regression was used to declare the factors associated with the outcome variable.
Results: In this study, 30% (95% CI: 20 -40) of the participants had effective healthcare leadership practices. Responsibility and accountability (AOR = 8.022; 95% CI; 7.582, 9.923), resolving problems and conflicts (AOR = 5.813; 95% CI; 4.240, 13.679), use resources in the right way (AOR = 2.968, 95% CI; 1.623, 4.145) were positively associated with effective healthcare leadership.
Conclusions: The prevalence of effective leadership among managers in Amanuel Mental Specialized Hospital was low. Responsibility and accountability, using resources in the right way, and resolving problems and conflict were the factors affecting healthcare leadership effectiveness. It is recommended that the topic should be investigated further, including the perspectives of the workforce on successful leadership.
Fabrication of TiN-doped Ti nanocomposites with high strength and ductility by plasma-assisted ball milling and selective laser melting
Authors: Zang Chuanjun , Yin Jianhua , Hong Shaofeng , Zeng Meiqin , Lu Zhongchen 
Published: June 2024

Abstract

An efficient method to fabricate titanium nitride (TiN)-doped titanium (Ti) nanocomposites through a combination of plasma milling, plasma spheroidization, and selective laser melting (SLM) was developed to obtain Ti-based materials with high strength and ductility from commercially pure titanium (CP-Ti). Nitrogen plasma milling and plasma spheroidization were used to fabricate Ti-TiN nanocomposites, which were mixed with CP-Ti powder at a 1:9 ratio and printed by SLM forming. The resulting materials possessed a dual-scale morphology with both coarse lath-like and fine acicular-like grains. The Ti-TiN nanocomposites possessed higher hardness and tensile strength and lower ductility than those of the control sample without TiN. The mechanical properties of the SLM-printed Ti-TiN nanocomposites were improved compared with those of SLM-printed CP-Ti because of the TiN particles and resulting dual-scale structure. Nitrogen plasma milling provides a simple route to fabricate TiN nanophase-reinforced Ti-based nanocomposites suitable for SLM printing.
Quantum-inspired clustering with light
Authors: Varga Miguel , Bermejo Pablo , Pellicer-Guridi Rubén , Orús Román , Molina-Terriza Gabriel 
Published: June 2024

Abstract

This article introduces a novel approach to perform the simulation of a single qubit quantum algorithm using laser beams. Leveraging the polarization states of photonic qubits, and inspired by variational quantum eigensolvers, we develop a variational quantum algorithm implementing a clustering procedure following the approach proposed by some of us in SciRep 13, 13284 (2023). A key aspect of our research involves the utilization of non-orthogonal states within the photonic domain, harnessing the potential of polarization schemes to reproduce unitary circuits. By mapping these non-orthogonal states into polarization states, we achieve an efficient and versatile quantum information processing unit which serves as a clustering device for a diverse set of datasets.
A Fuzzy Based Aproach for Battery Controller for Microgrid
Authors: MITKARI MOHIT , BAWAGE ANKITA , PATE PAWAN , tawani lata 
Published: June 2024

Abstract

This paper presents a fuzzy-based approach for designing a charging-discharging controller for lithium-ion batteries in microgrid applications. The goal is to enhance the efficiency and performance of battery systems within microgrids. The proposed controller utilizes fuzzy logic techniques to handle uncertainties and imprecise information, providing robust and adaptive control in real-time scenarios. The controller's fuzzy rules consider factors such as battery state of charge, load demand, and renewable energy availability to determine optimal charging and discharging strategies. Simulation results demonstrate the effectiveness of the fuzzy-based controller in improving battery utilization, ensuring stable microgrid operation, and enhancing overall system performance. This research contributes to the advancement of battery control strategies in microgrids, promoting more efficient and sustainable energy management systems.
Use of Real-World Data and Machine Learning to Screen for Maternal and Paternal Characteristics Associated with Cardiac Malformations
Authors: Brown Jeremy , Huybrechts Krista , Straub Loreen , Heider Dominik , Bateman Brian , Hernandez-Diaz Sonia 
Published: June 2024

Abstract

Effective prevention of cardiac malformations, a leading cause of infant morbidity, is constrained by limited understanding of etiology. The study objective was to screen for associations between maternal and paternal characteristics and cardiac malformations. We selected 720,381 pregnancies linked to live-born infants (n=9,076 cardiac malformations) in 2011-2021 MarketScan US insurance claims data. Odds ratios were estimated with clinical diagnostic and medication codes using logistic regression. Screening of 2,000 associations selected 81 associated codes at the 5% false discovery rate. Grouping of selected codes, using latent semantic analysis and the Apriori-SD algorithm, identified elevated risk with known risk factors, including maternal diabetes and chronic hypertension. Less recognized potential signals included maternal fingolimod or azathioprine use. Signals identified might be explained by confounding, measurement error, and selection bias and warrant further investigation. The screening methods employed identified known risk factors, suggesting potential utility for identifying novel risk factors for other pregnancy outcomes.
Predicting Drug-Drug Interactions Using Knowledge Graphs
Authors: Farrugia Lizzy , Debattista Jeremy , Azzopardi Lilian M. , Abela Charlie 
Published: June 2024

Abstract

In the last decades, people have been consuming and combining more drugsthan before, increasing the number of Drug-Drug Interactions (DDIs). To pre-dict unknown DDIs, recently, studies started incorporating Knowledge Graphs(KGs) since they are able to capture the relationships among entities provid-ing better drug representations than using a single drug property. In this paper,we propose an end-to-end framework that integrates several drug features frompublic drug repositories into a KG and embeds the nodes in the graph using var-ious translation, factorisation and Neural Network (NN) based KG Embedding(KGE) methods. Ultimately, we use a Machine Learning (ML) algorithm thatpredicts unknown DDIs. Among the different translation and factorisation-basedKGE models, we found that the best performing combination was the ComplExembedding method with a Long Short-Term Memory (LSTM) network, whichobtained an F 1-score of 95.19% on a dataset based on the DDIs found in Drug-Bank version 5.1.8. This score is 5.61% better than the state-of-the-art modelDeepDDI. Additionally, we also developed a graph auto-encoder model that usesa Graph Neural Network (GNN), which achieved an F 1-score of 91.94%. Con-sequently, GNNs have demonstrated a stronger ability to mine the underlyingsemantics of the KG than the ComplEx model, and thus using higher dimensionembeddings within the GNN can lead to state-of-the-art performance.
Economic dispatch of multi-microgrids considering flexible load based on distributed consensus algorithm
Authors: Wu Xiaopeng , Li Shiwang , He Ping , Zhao Chen , Liu Mingyang 
Published: June 2024

Abstract

This paper investigates the economic dispatch (ED) problem of multi-microgrids considering the flexible loads based on distributed consensus algorithm. Firstly, the structure topology diagram of distributed generator nodes is designed based on the global interconnection of multi-microgrids, then the flexible load is considered as adjustable load and be added into the fixed loads. Next, a multi-microgrids economic dispatch (MMED) model which contained fixed and flexible loads is constructed with the goal of minimizes the operating cost of the entire system. The proposed MMED model is a two-layer coordinated optimization model. The power transmission cost between each microgrid is used as the multi-microgrids layer framework, and the internal operating costs of a single microgrid is the sub-microgrid layer framework. The distributed consensus algorithm is designed to solve the MMED problem with the considering of constraint condition of distributed generators in each microgrid. Finally, some examples illustrate that the proposed method can not only optimize the operation of each single microgrid, but also maintains the stability of multi-microgrids through the adjustment of flexible load, and reduces the total operating cost of the whole multi-microgrids system.
Design and testing of a microwave Doppler-based yield estimation system for machine-picked seed cotton
Authors: Li Yuhang , Feng Jingan , Zhang Feng , Diao Shengang , Chen Jing 
Published: June 2024

Abstract

Accurately obtaining crop yields is an important part of the precision agriculture technology system; with the increased mechanisation of cotton planting and harvesting, it has become particularly important to accurately obtain seed cotton yield data. In order to achieve accurate estimation of machine-picked seed cotton yield, this paper designs a machine-picked seed cotton yield estimation system based on microwave Doppler principle. Based on LabVIEW software, the signal acquisition circuit is designed; the power spectrum of the echo signal is estimated; the signal echo power is obtained; the relationship model between echo power and seed cotton mass is established by using multiple regression equations; and the reliability of the model is verified by using statistical methods. By adjusting the rotational speed of the fan, the wind speed at the inlet of the cotton pipeline was 10 m/s, 15 m/s and 20 m/s, and the yield estimation tests were carried out at the three wind speeds, and the data showed that the average absolute percentage errors of the estimation at the three wind speeds were 7.36%, 7.72% and 8.17%, and the mean squared errors were 2.699, 4.938 and 4.026, respectively. The test results show that the accuracy of seed cotton mass estimation under the three wind speeds basically meets the needs of the yield measurement system, and the systematic error of seed cotton yield estimation is the smallest when the wind speed is 10 m/s.
Social Outcomes of the Housing Market under Climate Change
Authors: Moshfegh Peyman Habibi , Naderpajouh Nader , Morshedi Mohamadali , Fallah-Fini Saeideh , Wong Woon-Weng 
Published: June 2024

Abstract

Climate change results in unequal social outcomes within housing markets. Here we use the Australian housing market data to examine the disparate social outcomes of the housing market under climate scenarios presented by the shared socio-economic pathways. Our results suggest that even sustainable pathways with expected societal benefits exacerbate social inequalities by disproportionately imposing financial burdens on low-income households and tenants. Specifically, we demonstrate how intervention mechanisms, including insurance premiums, taxes, and mortgage rates, can counterproductively deepen social inequalities by increasing unaffordability and homelessness. These results call for contextually defined housing policies that support targeted intervention mechanisms for just housing outcomes in the face of climate change.
iDIA-QC: AI-empowered Data-Independent Acquisition Mass Spectrometry-based Quality Control
Authors: Guo Tiannan , Gao Huanhuan , Zhu Yi , Wang Dongxue , Wang Guibin , Nie Zongxiang , Liang Shuang , Xie Yuting , Sun Yingying , Jiang Wenhao , Dong Zhen , Qian Liqin , Wang Xufei , Liang Mengdi , Chen Ming , Fang Houqi , Zeng Qiufang , Tian Jiao , Sun Zeyu , Xue Juan , Li Shan , Chen Chen , Liu Xiang , Lyu Xiaolei , Guo Zhenchang , Qi Yingzi , Wu Ruoyu , Du Xiaoxian , Tong Tingde , Kong Fengchun , Han Liming , Wang Minghui , He Fuchu , Wang He 
Published: June 2024

Abstract

Quality control (QC) in mass spectrometry (MS)-based proteomics is mainly based on data-dependent acquisition (DDA) analysis of standard samples. Here, we collected 2638 files acquired by data independent acquisition (DIA) and paired DDA files from mouse liver digests using 21 mass spectrometers across nine laboratories over 31 months. Our data demonstrated that DIA-based LC-MS/MS-related consensus QC metrics exhibit higher sensitivity compared to DDA-based QC metrics in detecting changes in LC-MS status. We then optimized 15 metrics and invited 21 experts to manually assess the quality of 2638 DIA files based on those metrics. Based on the annotation results, we developed an AI model for DIA-based QC in the training set of 2110 DIA files. This model predicted the liquid chromatography (LC) performance with an AUC of 0.91 and the MS performance with an AUC of 0.97 in an independent validation dataset (n = 528). Finally, we developed an offline software called iDIA-QC for convenient adoption of this methodology for LC-MS QC.
Water quality assessment of Loktak Lake, Manipur using Landsat 9 imagery
Authors: Singh Rajkumar Birendrakumar , Yurembam Ghanashyam Singh , Jhajharia Deepak , Kusre B. C. 
Published: June 2024

ABSTRACT

The role of freshwater lakes in providing water resources and supporting ecosystems is essential. Monitoring water quality using remote sensing (RS) technologies is crucial for sustainable management practices. A study on Loktak Lake was done using RS algorithms to predict post-monsoon water quality. The multiplication band model (B1 × B6) demonstrated a moderate correlation with dissolved oxygen (DO) values (mg/l) with (coefficient of determination, R2 = 0.47, root mean square error, RMSE = 0.23, and standard error of estimation, SEE = 0.23). The band combination (B2/B4) was strongly correlated with electrical conductivity (EC) values (μs/cm) (R2 = 0.60, RMSE = 9.44, and SEE = 9.69). For total dissolved solids (TDS) (mg/l), with an R2 = 0.61, RMSE = 5.95, and SEE = 6.09, Band 2 demonstrated a strong correlation between field values and satellite imagery. The post-monsoon water quality map of the lake indicates lower concentrations of DO, EC, and TDS on the western side and elevated values on the eastern side. The research concluded that RS algorithms can be effectively used to predict water quality parameters in Loktak Lake, specifically DO, EC, and TDS. The findings suggest that effective pollution management is needed on the western side of the lake.
Model parameter estimation with imprecise information
Authors: Rauch Wolfgang , Rauch Nikolaus , Kleidorfer Manfred 
Published: June 2024

ABSTRACT

Model parameter estimation is a well-known inverse problem, as long as single-value point data are available as observations of system performance measurement. However, classical statistical methods, such as the minimization of an objective function or maximum likelihood, are no longer straightforward, when measurements are imprecise in nature. Typical examples of the latter include censored data and binary information. Here, we explore Approximate Bayesian Computation as a simple method to perform model parameter estimation with such imprecise information. We demonstrate the method for the example of a plain rainfall–runoff model and illustrate the advantages and shortcomings. Last, we outline the value of Shapley values to determine which type of observation contributes to the parameter estimation and which are of minor importance.
Acetylresveratrol (AC-Res): An Evolving Frontier in Modulating Gene
Expression
Authors: Panigrahy Uttam Prasad , Buchade Rahul Subhash , Subhadr Sandhya , Narayanan Anoop Kumar , Gunjal Sachinkumar Dnyaneshwar , Selvakumari Ezhilarasan , Pandey Narendra Kumar , Wal Ankita 
Published: June 2024

Background:
Acetylresveratrol (AC-Res), to date, is a powerful stilbene phytoalexin
generated organically or as a component of a plant's defensive system, is a significant plant phenolic
chemical portion and is investigated as a therapy option for a number of disorders. Owing to its
inadequate stabilisation and considerable conformation rigidity, the utility of AC-Res as a medication
is limited.

Objective:
The current review article outlined the structure of AC-Res, their methods of activity,
and the latest technological progress in the administration of these molecules. It is conceivable to
deduce that AC-Res has a variety of consequences for the cellular functions of infected cells.

Methods:
The literature survey for the present article was gathered from the authentic data published
by various peer-reviewed publishers employing Google Scholar and PubMedprioritizing
Scopus and Web of Science indexed journals as the search platform focusing on AC-Res pharmacological
actions, particularly in the English language.

Result:
Despite its extensive spectrum of biological and therapeutic applications, AC-Res has become
a source of increasing concern. Depending on the researchers, AC-Res possesses radioprotective,
cardioprotective, neurological, anti-inflammatory, and anti-microbial potential. It also has
anti-cancer and antioxidant properties.

Conclusion:
To avoid non-specific cytotoxicity, optimization efforts are presently emphasizing
the possible usage of AC-Res based on nanocrystals, nanoparticles and dendrimers, and nanocrystals.
Finally, while using AC-Res in biology is still a way off, researchers agree that if they continue
to explore it, AC-Res and similar parts will be recognized as actual possibilities for a variety of
things in the next years.
Development of Recommendations for the Digital Sharing of Notes With Adolescents in Mental Health Care: Delphi Study
Authors: Nielsen Martine Stecher , Steinsbekk Aslak , Nøst Torunn Hatlen 
Published: June 2024

Abstract

Background
In many countries, health care professionals are legally obliged to share information from electronic health records with patients. However, concerns have been raised regarding the sharing of notes with adolescents in mental health care, and health care professionals have called for recommendations to guide this practice.

Objective
The aim was to reach a consensus among authors of scientific papers on recommendations for health care professionals’ digital sharing of notes with adolescents in mental health care and to investigate whether staff at child and adolescent specialist mental health care clinics agreed with the recommendations.

Methods
A Delphi study was conducted with authors of scientific papers to reach a consensus on recommendations. The process of making the recommendations involved three steps. First, scientific papers meeting the eligibility criteria were identified through a PubMed search where the references were screened. Second, the results from the included papers were coded and transformed into recommendations in an iterative process. Third, the authors of the included papers were asked to provide feedback and consider their agreement with each of the suggested recommendations in two rounds. After the Delphi process, a cross-sectional study was conducted among staff at specialist child and adolescent mental health care clinics to assess whether they agreed with the recommendations that reached a consensus.

Results
Of the 84 invited authors, 27 responded. A consensus was reached on 17 recommendations on areas related to digital sharing of notes with adolescents in mental health care. The recommendations considered how to introduce digital access to notes, write notes, and support health care professionals, and when to withhold notes. Of the 41 staff members at child and adolescent specialist mental health care clinics, 60% or more agreed with the 17 recommendations. No consensus was reached regarding the age at which adolescents should receive digital access to their notes and the timing of digitally sharing notes with parents.

Conclusions
A total of 17 recommendations related to key aspects of health care professionals’ digital sharing of notes with adolescents in mental health care achieved consensus. Health care professionals can use these recommendations to guide their practice of sharing notes with adolescents in mental health care. However, the effects and experiences of following these recommendations should be tested in clinical practice.