bluuebunny/crossref_metadata_2025_split huggingface dataset:
Strain to shine: stretching-induced three-dimensional colloidal lattice transformations and optical transitions
Authors: Zhao Qibin , An Tong , Jiang Xinyu , Gao Feng , Schäfer Christian , Qiu Junjun , Nan Shi , Song Xiaokun , Zhang Manyao , Finlayson Chris , Zheng Xuezhi , Li Xiuhong , Tian Feng , Zhu Bin , Sui Tan , Han Xianhong , Baumberg Jeremy , Fan Tongxiang 
Published: October 2023

Abstract

Combining three-dimensional colloidal lattices with soft polymer matrices imbues these materials with strain resilience and responsive properties1–4, holding promise for various applications5,6. Understanding how they deform under strain is crucial, yet current knowledge is confined to one- or two-dimensional simplifications7–9. Here we provide three-dimensional analytical models and experimental results for colloidal-assembled polymeric opals with close-packed lattices subjected to uniaxial stretching. By extending the investigation to strains of up to 200%, we elucidate distinct deformation principles and pathways for three-dimensional elastic colloidal lattices, contrasting with those of atomic lattices. We detail the evolution of lattice transformations and optical transitions that occur during material stretching in distinct directions, presenting an unprecedented narrative from a three-dimensional perspective. The stretched lattices display unusual optical properties exclusive to three-dimensional structures. Notably, stretching beyond a critical 40% strain threshold in particular directions reveals previously hidden Bragg reflections inside the opals, resulting in retro-reflective colours that are far brighter than surface diffraction, surpassing their original bandgap limit. We stretch green opals to show red and redshift colours when tilted. Our findings revolutionize the transformative mechanics in elastic colloidal lattices, showcasing a new route to achieve exceptional performance and novel structures with controlled lattice transformations10,11.
Does Increased Muscle Mass Associate with Reduced Small Cell Lung Cancer Risk? Evidence from a Mendelian Randomization Study
Authors: Chen Xinqiao , Li Li , Song Wei , Lu Jin , Chen Naifei , Li Junxin , Liu Xiangliang , Chen Xiao , Cui Jiuwei 
Published: October 2023

Abstract
Observational studies suggest an association between reduced appendicular lean mass and increased lung cancer risk, but confounding biases exist. This study aimed to investigate the causal relationship between appendicular lean mass and small cell lung cancer risk using Mendelian randomization analysis. A two-sample Mendelian randomization study was conducted using summary-level data from genome-wide association studies on appendicular lean mass (205,513 Europeans) as exposure and small cell lung cancer (174,006 Europeans) as the outcome. 251 genetic variants robustly associated with appendicular lean mass were selected as instrumental variables. Inverse-variance weighted, MR-Egger regression, and other methods were used to estimate causality. The inverse-variance weighted method provided evidence for a causal association between increased appendicular lean mass and reduced small cell lung cancer risk (OR 0.530, 95% CI 0.305–0.923, P = 0.026). Sensitivity analyses indicated no pleiotropy or heterogeneity. Increased appendicular lean mass appears causally associated with reduced small cell lung cancer risk, adding credibility to previous observational evidence. These findings provide preliminary insight into lung cancer pathogenesis and prevention from a nutritional and metabolic perspective. Further research is warranted to elucidate underlying mechanisms.
Hexane extract of Persea Schiedeana Ness as green corrosion inhibitor for the brass immersed in 0.5 M HCl
Authors: BustosRivera-Bahena Genoveva , Ramírez-Arteaga A. M. , Saldarriaga-Noreña Hugo A. , Larios-Gálvez A.K. , González-Rodríguez José G. , Romero-Aguilar M. , López-Sesenes R. 
Published: October 2023

Abstract

The hexane extract of Persea Schiedeana Ness (PSN) was analyzed as corrosion inhibitor for the brass surface immersed in 0.5 M HCl. Fourier-transform infrared spectroscopy and a gas chromatographic (GC) and mass spectrometric (MS) were used to identify the PSN extract´s functional groups and compound constituents. The functional groups identified were CH3 and CH2 functional alkyl groups, C = O stretching vibration of aldehydes, ketones, and carbonyl groups found in flavonoids and fatty acids. The GC-MS determined the presence of fatty acids in the PSN extract, where palmitic acid, oleic acid, and ethyl oleate were the major constituents. Electrochemical characterizations were conducted to observe the effect of the hexane extract of PSN as corrosion inhibitor on the brass surface. The Rp and Rn calculated from EIS and ENA give the same behavior with magnitude values near each one. Based on the OCP behavior, it was determined that the PSN works as a mix inhibitor affecting both anodic and cathodic reactions. The corrosion current density (Icorr) determined with the Potentiodynamic test suggests that the extract of PSN reduces the corrosion rate of the brass with efficiencies above 90% for all concentrations. The efficiency obtained for each PSN concentration was attributed to forming a corrosion scale of CuO and Cu2O, which reacted with the carboxyl group to form copper carboxylates on the metal surface.
Knowledge, attitudes and practices regarding antibiotic use and resistance among pharmacists in the Banadir region of Mogadishu Somalia
Authors: Mohamed Mohamed Abdelrahman , Tahlil Abdullahi Ahmed , Osman Marian Muse , Gedi Saido , Ali Bisma , Muhumed Mukhtar Bulale , Mohamed Ahmed Adam , Fiidow Osman Abubakar , Abatcha Mustapha Goni , Osman Abdinasir Yusuf , Ahmed Abdifatah Diriye 
Published: October 2023

Abstract
Background
Globally, antibiotic resistance poses a public health concern and adversely impacts treatment, elevates mortality risk, prolongs morbidity, and extends hospital stays and healthcare costs. Dispensing antibiotics without prescriptions contributes to this issue, with pharmacists playing a crucial role in ensuring safe antibiotic use.
Methods
The purpose of the study was to evaluate pharmacists' knowledge, attitudes, and practices regarding antibiotic use and antibiotic resistance in Mogadishu, Somalia, from January to March 2023. The pharmacists in Mogadishu, Somalia, were recruited from January to March 2023 using a self-administered questionnaire that was pilot-tested by health professionals who approached all pharmacies in each of the 18 districts. Respondents were selected by a convenience sampling method. The data were analysed using the Statistical Package for Social Science (SPSS).
Results
Out of 410 participants, 81 (90.2%) were male, and the majority of the respondents, 275 (67%), were within the age group of 19–29 years.A total of 334(81.5%) of the participants had a bachelor's degree. Only 81 (19.8%) of the participants studied pharmacy. Regarding the overall knowledge of antibiotic use and resistance, 283 (69%) of the respondents had good knowledge. In addition, 195 (47.6%) of the participants also reported having an inadequate attitude. Moreover, inadequate practices related to the use of antibiotics were also reported among 198 (48.3%) of the participants.
Conclusion
It is the first study to examine knowledge, attitudes, and practices concerning antibiotic use and antibiotic resistance in Mogadishu. The results of this study indicated that pharmacists in Mogadishu had good knowledge, but a poor attitude and practice regarding the use of antibiotics and the development of antibiotic resistance. In order to control antibiotic resistance and improve antibiotic use in Somalia, pharmacists must be trained and made aware of the importance of antimicrobial stewardship and antimicrobial resistance.
Hydrochemical Characteristics Assessment of Groundwater Quality, Mathura District, Uttar Pradesh, India
Authors: Kushwah Vinod Kumar , Singh Kunwar Raghvendra 
Published: October 2023

Abstract
In this research, the quality of the groundwater was investigated in the surrounding areas of Mathura city, India. Piper diagrams were used to identify hydrochemical facies and chemical datasets were also employed in different mathematical calculations (SAR, %Na, and USSL diagrams) to determine acceptable quality of drinking consumption and irrigation use of groundwater in the Mathura region was. Multivariate statistical methods such as Discriminant analysis (DA) Cluster analysis (CA) and applied for the observed datasets collected from Goverdhan, Radhakund, Barsana, Nandgoan, Kosikalan, and Chaumuhan. 19 different groundwater sampling sites—including hand pumps, tube wells, and borings were collected and analyzed. Analysis of 18 distinct groundwater quality indicators, including Temperature (T), pH, turbidity (tur), Electrical conductivity (EC), etc. The Piper diagram's conclusion shows that the main ions Na+, K+, SO42−, Cl− predominate whereas ion like Ca2+, Mg2+, HCO3− are significantly unsuitable. Most of the groundwater samples fall in the category of C4S4 based on USSL diagram, which denotes high sodium and salinity water. Most of the sampling locations had SAR and %Na values that are above the permitted limit, so requiring a suitable drainage and water management plan for the research region. Cluster analysis grouped nineteen sampling locations into 3 clusters based on group of similarities of the characteristics they possess. The first, second and third clusters represented moderately polluted, more polluted sites and less polluted sites. Water quality metrics provided 100% accurate assignments because of DA which aid in evaluating the variables that differentiate the perceived groups.
A Systematic Review on Airlines Industries based on Sentiment Analysis and Topic Modeling
Authors: Ha Khan Md , Naseem Usman , Keya Ashfia Jannat , Maitra Shithi , Mithu Kamruzzaman , Alam Md. Golam Rabiul 
Published: October 2023

Abstract
One of the most important things that managers need is feedback from customers about the quality of the products and services their firms offer. Getting feedback from customers is now typically done via online channels including social media, instant messaging, and review websites. However, due to the massive volume, a powerful tool is required for processing the data received from various sources. Sentiment analysis is a form of NLP that can instantly determine if a user is feeling positive, negative, or neutral towards a given topic. The researcher uses topic modeling to iteratively pick a subset of topics that best describes the whole. Many studies, some using topic modeling, have been conducted on the topic of the airline business over the past few years, with the majority of them focusing on sentiment analysis in a particular area or region. This article provides a comprehensive study of the airline industry in terms of sentiment analysis and topic modeling using machine learning and deep learning techniques so that customers may learn more about the most reputable and convenient airlines in the world. In this review paper, we enlisted overall 84 articles in different years and provide a statistical analysis of 60 articles based on sentiment analysis and topic modeling focusing on different types of machine learning and deep learning. The most important tasks involved in sentiment analysis of airline industries are discussed, and it is determined that sentiment analysis may be performed on various languages. In addition, a summary of popular datasets, essential characteristics of those datasets, machine learning and deep learning models applied to those datasets, accuracy acquired from those datasets, and a comparison of different machine and deep learning models are included in the survey. The fundamental objective of this study is to draw attention to the efficacy of machine and deep learning architectures in solving issues of various airline industries involving sentiment analysis and topic modeling.
Effect of sanitary and phytosanitary measures on cocoa exports : does the quality of governance matter ?
Authors: NGATSI Eric Brice FOSSO 
Published: October 2023

Abstract
This paper contributes to the ongoing debate on the effects of institutional differences and sanitary and phytosanitary (SPS) measures on agricultural exports. Specifically, we first assess the effects of SPS measures and governance quality on cocoa exports. We then analyse how governance quality, measured by governance distance, modifies the effect of SPS measures on cocoa exports. The empirical analysis is conducted by applying Poisson Pseudo-Maximum Likelihood (PPML) and Heckman estimators to a gravity model on a panel of cocoa importing countries from the world's top 10 producers. Our results show that an increase in SPS measures reduces cocoa exports. Similarly, an increase in bilateral governance distance reduces cocoa exports. Furthermore, the interaction between governance distance and SPS measures is positively correlated with cocoa exports. This means that the quality of governance reduces the impact of SPS measures on cocoa exports. Cocoa producing countries targeting markets in developed countries, which are considered to be high value-added markets, but located in countries with low institutional quality, need to increase their level of compliance with SPS measures according to the requirements of importing countries.
Parent-Child Relationship Measures and Pre-Post Treatment Changes for a Clinical Preschool Sample Using DC:0-3R
Authors: Baans Nicole Elli Ursula , Janßen Marius , Müller Jörg Michael 
Published: October 2023

Abstract
Background
To reduce psychopathologies in children, various treatment approaches focus on the parent-child relationship. Disruptions in the parent-child relationship are outlined in the most recently revised versions of the Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood (DC:0-3R/DC:0–5). The measures used to assess the parent-child relationship include the Parent-Infant Relationship Global Assessment Scale (PIRGAS) and the Relationship Problems Checklist (RPCL), which cover, e.g., essential concepts like over- or underinvolvement of the caregiver. However, not much is known about the cross-sectional and predictive value of PIRGAS and RPCL scores at admission to discharge, namely whether changes in these scores are correlated with child and maternal psychopathologies and changes through treatment.
Methods
Based on clinical records of 174 preschool-aged children of the Family Day Hospital, we report related basic descriptive data and changes from admission to discharge for the parent-child relationship, child behaviour, and maternal psychopathology. We used a Pearson correlation or a point-biserial correlation to describe the associations and performed a paired t-test to examine differences before and after measurement.
Results
Our results show overall improvements in our parent-child relationship measures and in child and maternal psychopathology. However, we observed little or no correlation between the parent-child relationship measures and child or maternal psychopathology.
Conclusions
We highlight potential drawbacks and limitations of the two relationship measures used that may explain the results of this study on the associations between the variables assessed. The discussion emphasizes the assessment of DC:0-3R/DC:0–5, which are popular in clinical practice for economic reasons.
Geodetically convex sets in the Heisenberg group Hn, n ≥ 1
Authors: Prajapat Jyotshana V. , Varghese Anoop 
Published: October 2023

Abstract
A geodetically convex set in the Heisenberg group Hn, n ≥ 1 is defined to be a set with the property that a geodesic joining any two points in the set lies completely in it. Here we classify the geodetically convex sets to be either an empty set, a singleton set, an arc of a geodesic or the whole space Hn. We also show that a geodetically convex function on Hn is a constant function. These results generalize the known results of H1 to higher dimensional Heisenberg group.
Deep learning assisted identification of ATP5J and ALDH1A2 combination in RNA-sequencing data as a novel specific potential diagnostic biomarker in prostate cancer
Authors: Sokhangouy Saeideh Khorshid , Zeinali Mohsen , Nazari Elham 
Published: October 2023

Abstract
Prostate cancer is ranked as the second most prevalent disease among men globally. The timely diagnosis of this cancer is crucial in reducing morbidity rates. Unfortunately, due to the limitations of current diagnostic methods, which often lack specificity and accuracy, prostate cancer is frequently diagnosed at advanced stages, leading to less effective treatment strategies. Therefore, our primary objective was to identify valuable diagnostic biomarkers through the application of bioinformatics and artificial intelligence. To achieve this goal, we utilized three prostate cancer expression datasets, aiming to pinpoint differentially expressed genes (DEGs) associated with prostate cancer. Subsequently, we harnessed deep learning, a subset of artificial intelligence, to unveil the most significant genes from the pool of 3875 common DEGs implicated in prostate cancer's pathogenesis. The deep learning model's performance was evaluated using six key metrics: Mean Squared Error (MSE) with a value of 0.03, R-squared (R²) at 0.83, Area Under the Curve (AUC) of 0.97, Accuracy at 87.7%, Root Mean Squared Error (RMSE) of 0.18, and Precision-Recall AUC (PR-AUC) at 0.93, demonstrating the model's exceptional performance. Furthermore, gene enrichment analysis shed light on ten candidate genes with pivotal roles in prostate cancer development. Additionally, Protein-Protein Interaction (PPI) network analysis revealed ATP5J, GJA1, AMACR, and B3GAT1 as hub genes, with AMACR and B3GAT1 exhibiting an intriguing interaction. Further validation through Receiver Operating Characteristic (ROC) analysis of the ten key genes identified by deep learning unveiled ATP5J, ALDH1A2, and AMACR as promising diagnostic biomarkers for prostate cancer. Notably, the combined use of ATP5J and ALDH1A2 demonstrated remarkable accuracy, with an accuracy rate of 0.75, sensitivity of 0.73, and specificity of 0.71, comparable to common prostate cancer diagnostic biomarkers such as PSA, PCA3, and PHI. The validation of these biomarkers in prostate cancer was carried out using the PCaDB database, lending support to the potential clinical utility of these markers. In conclusion, our findings underscore the importance of further research and validation to establish the clinical applicability of ATP5J and ALDH1A2 as promising diagnostic biomarkers for prostate cancer, offering a potential breakthrough in early detection and more effective management of this prevalent disease.
WITHDRAWN: The evaluation of monthly precipitation and average temperature by using innovative graphical and statistical trend approaches: Palu station example
Authors: Acar Ramazan 
Published: October 2023

Abstract
Climate change occurring worldwide due to global warming affects rainfall, flow, humidity and temperature, etc. in many regions. It has begun to significantly change the regimes of hydro-meteorological parameters. It is extremely important to analyze the future changes of these parameters, which significantly affect the management of water resources, ecosystems and agricultural activities. In this study, the temporal variability of monthly total precipitation (1965–2012) and monthly average temperature (1965–2020) data of Palu station located within the borders of Elazığ province in Turkey was investigated. Mann-Kendall (MK) and Spearman's Rho (SR) test statistics were used to statistically determine the trends of temperature and precipitation data. In addition to these methods, analyzes were carried out with three innovative trend methods that are capable of interpreting trends both statistically and visually. These innovative trend methods are Innovative Polygon Trend Analysis (IPTA), Innovative Şen Test (Şen-ITA) and trend analysis with the combination of Wilcoxon test and scatter diagram (CWTSD). In addition, precipitation and temperature data were subjected to regression analysis separately and sets of equations were obtained and the decrease-increase amounts were determined in mm and ⸰C. In particular, the IPTA method was applied separately for the arithmetic mean and standard deviation, and the transitions between the increasing and decreasing regions of the trends on a monthly basis were determined and the slopes and lengths of the trends resulting from these transitions were calculated. With the application of the IPTA method, an increasing trend in some months and a decreasing trend in other months were detected for the two parameters. In addition, when the results of precipitation and temperature data were examined, it was seen that there was no single or regular polygon in the IPTA graphs, so both parameters varied over the years and did not exhibit a homogeneous behavior. In the MK test, SR test, Wilcoxon test statistics, Şen-ITA and NO-ITA methods, apart from some months in which increasing and decreasing trends were detected, months in which no trend could be detected, that is, there was no trend, were also observed. When the results obtained from the analysis of two parameters from the relatively new CWTSD method are examined, they show strong consistency with the results of the MK test, SR test and Şen-ITA methods. Advantages such as performing numerical and visual trend analysis with the CWTSD method and determining the trends of data with low-medium-high values have shown that this method can be used as an alternative to the MK test, SR test and Şen-ITA methods that are widely used in the literature. Finally, the IPTA method was evaluated to be more sensitive in determining trends on a monthly basis compared to other methods used in the study..
Comparative analysis of detected ionospheric anomalies with earthquake magnitudes: A case study of the earthquakes of March 4, 2021.
Authors: Thomas Jewel E. , Nathaniel Ekong U. , George Nyakno J. , Ekanem Aniekan M. , Akpan Samuel S. 
Published: October 2023

Abstract
Comparative analysis of detected ionospheric anomalies with earthquake magnitudes was carried out on two seismic events of March 4, 2021, ( M7.3 Gisborne and M8.1 Kermadec Earthquakes) to establish the link between observed perturbations and event size. GPS-TEC data were downloaded 20 days pro and 5 days post the events from stations within the epicenter of respective earthquakes. The data were statistically analyzed to detect unusual variations within the investigative window. The investigation revealed that the M7.3 Gisborne EQ recorded two variations on -19 and -4 days; both being positive perturbations. However, five anomalies were detected for the M8.1 Kermadec EQ on -15, -11, -5, -3 and -2 days. With exception of the irregularities on -3 days, all others were positive anomalies. The observed anomalies were subjected to further scrutiny using the geomagnetic indices of Kernnifzer digit (kp) and disturbance storm time (Dst.) for geomagnetically induced perturbations while sunspot number and F10.7cm were to monitor solar activities within this same period. Gisborne EQ measured 100% seismo-induced variations while Kermadec EQ had 80% seismogenic anomalies. The study clearly showed an increased in detected anomalies with earthquake magnitudes.