bluuebunny/crossref_metadata_2025_split huggingface dataset:
From Psychological Mechanisms to Policy Interventions: Integrating SOR Framework and Secondary Risk Theory for Mitigating Vaccine Hesitancy
Authors: Peng Rui , Peng Zongchao 
Published: February 2025

Abstract

Vaccination has emerged as the most anticipated intervention to mitigate the spread of the virus. However, vaccine hesitancy remains a pervasive issue, and few studies have explored the link between external policy intervention and psychological factors and the impact on vaccine hesitancy. This study categorizes policy interventions into three types: media interventions, reminder interventions, and service interventions, and proposes integrating the Stimulus-Organism-Response (SOR) framework into the secondary risk theory to explore the integration of external policy intervention and psychological factors in the receivers’ willingness and reactions (n = 6532). Using a survey dataset collected in China, the study employs structural equation modeling (SEM) to analyze the relationships between these intervention strategies, individual risk cognitions, and COVID-19 vaccine hesitancy. The results show that media interventions from formal news sources, reminders from family members, and educational campaigns about vaccines significantly enhance vaccination willingness. Conversely, reminders via telephone and material incentives have little to no positive effect. The study also finds that vaccine confidence and the perception of vaccine risk significantly influence vaccination willingness, while the perception of pandemic risk does not. These findings provide valuable insights for policymakers in designing effective intervention strategies to combat vaccine hesitancy and promote vaccination during the pandemic.
Existing demand-side climate change mitigation policies neglect avoid options
Authors: Brad Alina , Schneider Etienne , Dorninger Christian , Haas Willi , Hirt Carolin , Wiedenhofer Dominik , Gingrich Simone 
Published: February 2025

Abstract

Demand-side options are increasingly recognized for their potential to mitigate climate change while reducing reliance on novel carbon dioxide removal. However, systematic analyses of implemented demand-side mitigation policy mixes remain scarce, compromising assessment and exploration of effective and feasible demand-side policies. Here, we provide a multilevel analysis of the evolution, composition, and foci of demand-side mitigation policy mixes in the transport and housing sector from 1995 to 2024, focusing on the EU, the federal Austrian and two provincial levels (Vienna, Lower Austria). Our high-resolution policy database features 351 demand-side measures, systematically classified according to policy target, instrument type, and the avoid-shift-improve framework. We find that existing policy mixes heavily rely on shift and improve measures, critically neglecting mitigation potentials of avoid options as well as certain policy areas. This suggests an urgent need to broaden demand-side policy mixes and explore strategies that increase the political feasibility of avoid options.
Analyzing the Impact of Economic Growth, FDI and Energy Use on CO2 Emission in Kenya: An ARDL Approach
Authors: Oluwaseun Ayodele 
Published: February 2025

Abstract

This study estimates the effects of Gross Domestic Product (GDP), population, renewable energy consumption, fossil fuels, and foreign direct investment (FDI) on Kenya's carbon emissions between 1972 and 2021. This investigation makes use of the “Autoregressive Distributed Lag (ARDL)” method, which is grounded in the theoretical framework as the “Stochastic Impacts by Regression on Population, Affluence, and Technology” model known as (STIRPAT) model. The ARDL bound test and structural break test were also used in the study. According to our preliminary results, the data exhibits long-run cointegration; as a result, the uses of ARDL, which is adept at handling both short- and long-term effects, is essential. This study lends credence to earlier research by demonstrating that a rise in Kenya's GDP and population can result in an increase in that country's CO2 emissions. Kenya may reduce its damaging carbon dioxide emissions by transitioning to renewable energy sources. All estimates place the impacts of GDP growth and population growth at parity. Achieving Kenya's sustainable development goals will require substantial investment in the country's energy infrastructure, making this analysis potentially useful in planning and establishing strategies for future financial funding in the energy sector. For ARDL, the effects of fossil fuels are negative but insignificant. FDI has an insignificant but positive effect on the environment. Based on these findings, policymakers can make informed decisions to sustainable use of renewable energy.
Water supply from groundwater: new solutions for a battered-and-bruised Ukraine
Authors: Sudakov Andrii , Pavlychenko Artem , Hapich Hennadii , Isakova Mariia , Shumov Andrii 
Published: February 2025

ABSTRACT
We present improved technology for alternative water supplies for Ukraine during martial law and post-war reconstruction. Sugar-bonded gravel filters for well screens reduce the consumption of material and time for their transportation to the site, and avoid sanding and the formation of voids, thereby reducing hydraulic resistance. Our results facilitate on-site production of block gravel filters using sugar as a binding agent for fine-and-medium-grained sands that constitute the effective component of the filterblock. We explore the constraints on installing such a filter to depths as great as 200 m.
Advances in Molecular Docking Techniques for Targeting Protein Misfolding in Neurodegenerative Diseases
Authors: Singh Kuldeep , Gupta Jeetendra Kumar , Narayan Shiv , Rani Ketki , Jain Divya , Porwal Prateek , Sharma Mukesh Chandra , Kumar Shivendra 
Published: February 2025

Abstract:
Neurodegenerative diseases, such as Alzheimer’s, Parkinson’s, and Huntington’s disease,
represent a significant global health challenge with limited therapeutic options. Protein
misfolding and aggregation, a common pathological hallmark in these disorders, have emerged
as promising targets for therapeutic intervention. Molecular docking techniques have played a
pivotal role in the identification and design of small molecules that can modulate protein misfolding,
offering new hope for effective treatments. This review provides an overview of recent
advancements in molecular docking techniques for targeting protein misfolding in neurodegenerative
diseases. We discuss the principles and methodologies behind molecular docking, including
various scoring functions and algorithms employed for accurate ligand-protein interactions.
Additionally, we explore the use of molecular dynamics simulations and machine learning
approaches to enhance the precision of docking studies. Furthermore, we highlight case studies
and success stories where molecular docking has contributed to the discovery of potential drug
candidates for neurodegenerative diseases. These include compounds that inhibit amyloid-β aggregation
in Alzheimer’s disease, α-synuclein oligomerisation in Parkinson’s disease, and mutant
huntingtin aggregation in Huntington’s disease. We also discuss the problems and restrictions
of molecular docking related to neurodegenerative diseases, such as how to accurately
show the flexibility of proteins and why docking results need to be confirmed by experiments.
We also discuss the structural biology methods, such as cryo-electron microscopy and X-ray
crystallography, and how these techniques might help in improving molecular docking studies.
The Molecular Mechanism of a Complex1-Induced Apoptosis in Cancer Cells of the Esophagus
Authors: Liu Zhi-Qiang , luo Jun-rui , Yao Xin , wang Zhen-hui , hao Shuang-ying , Li Ming-Xue , Zhang Hong 
Published: February 2025

Background:
Esophageal Cancer (EC) is a commonly occurring cancer of
the digestive tract. The bismuth compounds from thiosemicarbazones have been
observed to be active against cancer cells. However, a synthetic nine-coordinate
bismuth (III) complex (complex 1) has never been assessed so far for its anticancer in
the esophageal squamous cell carcinoma cell line (EC109).

Objective:
This study aimed to investigate the apoptosis effect of a complex1 in the
EC109 cells.

Methods:
EC109 cells were treated with complex1. The MTT assay was employed
to assess the viability of EC109 cells; the changes in apoptotic and morphological
characteristics, reactive oxygen species (ROS) generation, and mitochondrial
membrane potential (MMP) were examined. The expression levels of proteins
associated with apoptosis were assessed using western blotting.

Results:
Complex1 was found to inhibit the growth of EC109 cells, exhibiting an IC50
of 0.654 μM through apoptosis depends upon complexation with bismuth(III). In
addition, cells exposed to complex1 exhibited a significant increase in the level of
intracellular ROS through the suppression of the antioxidant system and caused a
reduction in mitochondrial membrane potential(MMP). Co-treatment with N-acetyl-Lcysteine(
NAC), an antioxidant agent prevented accumulation of ROS and cell death.
Complex1 also led to enhanced Bax expression, and reduced Bcl-2 expression in
EC109 cells, thereby enhancing caspase-3/9 activity.

Conclusion:
Our study confirmed that complex1 induced apoptosis via enhancing the
generation of ROS along with a decline in levels of antioxidant enzymes, subsequently
causing MMP loss.
Insight into Controllable Preparation of Graphene Quantum Dots by Gaseous Detonation
Authors: Cheng Saiwei , He Chuang , Hu Yin , Lin Weijun , Xu Weikai , Liu Chang , Yan Honghao 
Published: February 2025

Background:
The gaseous detonation method is a promising method for the rapid and
large-scale preparation of graphene quantum dots (GQDs). However, the relationship between detonation
parameters and the morphological and optical properties of GQDs remains unclear and needs
further investigation.

Methods:
This study systematically investigates the influence of detonation parameters—
specifically, the initial temperature, molar ratio of H2 to O2, and the amount of the precursor (benzoic
acid, BA)—on the morphological and optical properties of GQDs. An orthogonal experiment was
conducted with initial temperature, H2 to O2 molar ratio, the amount of BA as factors, and particle
size, crystallinity, and photoluminescence quantum yield (PLQY) as indicators.

Results:
Findings indicate that increasing the initial temperature and the molar ratio of H2 to O2
increases the particle size, crystallinity, and PLQY of the GQDs. In contrast, the amount of BA has
minimal influence on these properties. Additionally, detonation parameters had no significant effect
on the group types, ultraviolet-visible (UV-vis) absorption, and photoluminescence (PL) emission
spectra of GQDs. The optimal preparation conditions are when the initial temperature T0 is 393.15
K, the molar ratio of H2 to O2 is 2:1, the amount of BA is 4 g, and the PLQY of the prepared GQDs
is maximized.

Conclusion:
By altering the detonation parameters, the particle size, crystallinity, and PLQY of
GQDs can be finely controlled. These findings are highly significant for achieving the controllable
preparation of GQDs while providing guidance for the industrial preparation of GQDs with controllable
morphologies and optical properties via gaseous detonation.

other:
Future work will focus on optimizing these parameters to maximize the efficiency and quality of GQD production and exploring the application potential of GQDs in various industrial fields.
In vitro Evaluation of the Synergistic Anti-inflammatory Activities of the Combined Hydroalcoholic Extracts from Mentha Pulegium L. and Pimpinella Anisum L.
Authors: Zatla Amina Tabet , Hammoudi Amina , El Amine DIB Mohammed , Perard Joëlle 
Published: February 2025

Background:
Chronic inflammatory diseases are rising, driving the search for effective natural treatments. Mentha pulegium L. and Pimpinella anisum L. (anise) exhibit notable anti-inflammatory properties individually, but their combined effects are less stud-ied. This research evaluates the in vitro synergistic anti-inflammatory activities of their hydroalcoholic extracts. Phytochemical analysis confirmed the presence of flavonoids, tannins, and polyphenols in both extracts. Individually, they demonstrated significant ac-tivity (78.5% and 72.3%, respectively, at 10 g/L) compared to Diclofenac (62.3%). Their combination achieved 88.6% inhibition at the same concentration. These findings high-light their potential as natural anti-inflammatory agents.

Introduction:
The rise in chronic inflammatory diseases has increased interest in natural anti-inflammatory treatments. Mentha pulegium L. and Pimpinella anisum L. are well-known for their anti-inflammatory potential, attributed to their bioactive compounds like flavonoids and polyphenols. While the individual effects of these plants are established, their combined use is underexplored. This study evaluates the in vitro synergistic anti-inflammatory activity of hydroalcoholic extracts from these plants, aiming to offer an ef-fective natural therapeutic alternative.

Methods:
Hydroalcoholic extracts were prepared by maceration of both plants. Anti-in-flammatory activity was assessed using the protein denaturation method with Diclofenac as a standard.

Results:
Phytochemical analysis identified flavonoids, alkaloids, tannins, glycosides, and polyphenols in Mentha pulegium, with an absence of saponins. Pimpinella anisum con-tained flavonoids, tannins, heterosides, and polyphenols. The extracts exhibited strong anti-inflammatory activity individually (78.5% and 72.3%, respectively, at 10 g/L) and even higher inhibition (88.6%) when combined, surpassing Diclofenac (62.3%).

result:
The phytochemical analysis of the hydroalcoholic extract of Mentha pulegium L. revealed a notable presence of flavonoids, alkaloids, tannins, glycosides, and polyphenols but the absence of saponins, In the hydroalcoholic extract of Pimpinella anisum L., significant quantities of flavonoids, tannins, heterosides, and polyphenols were identified. The results showed that the hydroalcoholic extract of Mentha pulegium L. and Pimpinella anisum L. has a very good effect, with percentages of 78.5% and 72.3%, respectively, at a concentration of 10 g/L compared to Diclofenac (62.3%). However, the combination of this two hydroalcoholic extracts demonstrated a high inhibition (88.6%) at the same concentration (10 g/L).

Conclusion:
The combination of Mentha pulegium and Pimpinella anisum extracts sig-nificantly enhanced anti-inflammatory activity compared to individual extracts, under-scoring their potential as natural therapeutic alternatives to conventional treatments.
1-Deoxynojirimycin Ameliorates Diabetic Liver Injury by Regulating AMPK/SIRT1 and Oxidative Stress in db/db Mice
Authors: Li Yao , Cao Yu , Tian Yun-Yuan , Chen Wen-Wen , Wang Juan , Zhang Meng-Meng , Wang Si-Wang , Xie Yan-Hua 
Published: February 2025

Background:
Patients with diabetic liver injury are in the dilemma of lowering glucose
and protecting liver function. This study aimed to uncover the protective effect and mechanism of
1-deoxynojirimycin (1-DNJ), an alpha-glucosidase inhibitor, against diabetic liver injury.

Methods:
The db/db mice were gavaged with 25 mg/kg, 50 mg/kg, and 100 mg/kg of 1-DNJ for 8
weeks. At the end of the administration, the serum and liver were isolated for further detection.
The biochemical indices including TC, TG, LDL-C, AST, ALT, and TBiL were detected in
serum. The livers were further analyzed with H&E, oil red, and Masson staining, and the amount
of ROS in the liver was detected with probe dihydroethidium. Western blot was used to analyze
the levels of proteins involved in fibrosis, oxidative stress, and AMPK/SIRT1 signaling pathway
in the liver.

Results:
1-DNJ administration reduced body weight, liver coefficient, and TC, TG, LDL-C, AST,
ALT, and TBiL in db/db mice. H&E and oil red staining showed that 1-DNJ ameliorated hepatocellular
ballooning degeneration and lipid deposition in the liver. Moreover, 1-DNJ reduced the hepatic
collagen fiber deposition and the protein expression of α-SMA and Collagen I. Further assays
revealed that 1-DNJ treatment reduced the ROS level, up-regulated the proteins expression of
SOD2, HO-1, NQO-1, p-AMPK/AMPK, p-ACC/ACC, and SIRT1 proteins, and down-regulated
the expression of SREBP-1 and SCD-1 proteins in the liver.

Conclusion:
1-DNJ improves liver function, lipid deposition, and fibrosis of diabetic liver injury
in db/db mice by regulating the AMPK/SIRT1 pathway to improve glucose-lipid metabolism and
oxidative stress.
An Integrated PneumoNet Model for the Early Detection of Pneumonic Lungs in Chest X-ray Radiographs using Pre-trained Xception, VGG16, and VGG19
Authors: Sharma Shagun , Guleria Kalpna , Dogra Ayush 
Published: February 2025

Background:
Pneumonia is a respiratory infection that inflames the air sacs in one or both
lungs, potentially filling them with fluid. This condition leads to symptoms, such as cough with
phlegm, fever, and difficulty in breathing. The accuracy of pneumonia detection via chest X-ray
interpretation is heavily reliant on the experience and expertise of radiologists. In low-resource areas,
the absence of radiologists and advanced diagnostic facilities hampers the timely and accurate diagnosis
of pneumonia.

Methods:
This work proposes the use of deep learning models for efficient feature extraction and
classification in the early prediction of pneumonia from chest X-ray images. This work employs
convolutional neural networks (CNN) and four pre-trained models, MobileNetV2, VGG16, VGG19,
and Xception. The performance of each model was evaluated, and the top-performing models, namely,
Xception, VGG16, and VGG19, were combined to form an integrated model named PneumoNet,
which was configured with 20 epochs, a batch size of 32, and a learning rate of 0.01.

Results:
The integrated PneumoNet model demonstrated improved performance in the early prediction
of pneumonia. The model achieved significant enhancements in accuracy, precision, recall, F1-
score, and loss values. Specifically, the model reached 100% accuracy, 1.00 precision, 1.00 recall,
1.00 F1-score, and a loss value of 1.32E-21.

Conclusion:
The implementation of the PneumoNet model has identified the most prominent outcomes
for the early prediction of pneumonia in chest X-ray images. The configuration and optimization
of the model have resulted in superior performance metrics, addressing the limitations of traditional
diagnostic methods, particularly in low-resource environments.
DE “COITEIRA” A “CANGACEIRA”
Authors: Moura Francisco Henrique Oliveira , Câmara Yls Rabelo , Medeiros Karina Mandetta 
Published: February 2025

O objetivo deste estudo é analisar como o cangaço desempenha um papel determinante no eixo narrativo em Dôra, Doralina, de Rachel de Queiroz, e como também o encontramos em Memorial de Maria Moura, da mesma autora. O cangaço, considerado banditismo social, ocorreu no Sertão nordestino brasileiro entre fins do século XIX e início do XX, tendo Lampião como figura máxima. Em Dôra, Doralina, os vestígios do cangaço são marcantes, principalmente em Belmiro, jagunço e fugitivo que, ao aparecer baleado e delirante na fazenda Soledade, passa a contar com o apoio de Maria das Dores, narradora, protagonista e herdeira de Soledade. Em Memorial de Maria Moura, a protagonista ingressa no cangaço para sobreviver a um universo patriarcal e conservador. Os eventos que a envolvem são determinantes para que ela, antes sinhazinha, torne-se liderança de um bando de jagunços. Comparando as obras, é possível inferir que Moura teve sua inserção no cangaço devido às injustiças sociais marcantes no contexto feminino à época, nessa região, enquanto com Belmiro, é a falta de perspectivas o preponderante. Partindo da relação Literatura e sociedade, observamos como Rachel de Queiroz utiliza suas memórias do cangaço, condição social que fez parte de seu tempo e espaço, para ficcionalizar.
Information Technology Expertise and Environmental, Social, and Governance Activities
Authors: Walton Stephanie , Zhang Yiyang 
Published: February 2025

SYNOPSIS
One impediment to environment, social, and governance (ESG) activities is the acquisition and promulgation of high-quality ESG information within a firm. One way to potentially advance the role of information technology (IT) surrounding ESG activities is through the IT expertise of executives and board of directors. Greater IT expertise could strategically improve information collection and quality within the firm, benefiting the ESG information environment by reducing information asymmetry and information processing costs. Through facilitating greater availability of ESG information, we expect and find that IT expertise is positively associated with ESG activities. We find that CEO IT expertise is positively associated with governance activities, whereas having a technology committee on the board is positively associated with environment, social, and governance activities. Our study provides a greater understanding of the benefits of IT expertise on ESG reporting.
Evaluating Pre-Analytical Errors with Six Sigma and Pareto in A Clinical Laboratory in Lebanon
Authors: Al Akoum Hayat , Awad Khodor , Yassine Haytham , Hamdan Ali 
Published: February 2025

The Pre-Analytical (Pre-A) phase represents up to 70% of total errors in the testing process. Six Sigma is a statistical approach used to detect and eliminate defects that impact the process. This study aimed to assess the performance of the Pre-A process according to the Six Sigma methodology at a clinical laboratory in South Lebanon. This observational study was performed over 5 months (January to May 2024). A total of 8,845 urine and stool samples, 59,497 blood samples, and 21,013 test requisition forms were screened for the most common possible Pre-A errors (hemolyzed sample, insufficient sample, missing information). Percentages of rejected samples were calculated using Microsoft Excel 2010. The Westgard online formula was used to compute Sigma as well as Six Sigma levels and defects per million opportunities for the total and each Pre-A error. A Pareto chart was also constructed based on the total number of errors and cumulative percentage. On blood sampling and test request forms, the overall rate of total critical Pre-A errors were 2.99% and 1.01%, respectively, with a Sigma level of 3.6. According to this graph, there were two most common Pre-A mistakes over the 5 months namely insufficient sample (1.18%, Sigma level: 3.8) and hemolyzed sample (0.76%, Sigma level: 4). In conclusion, the majority of the Six Sigma levels of Pre-A errors were within an acceptable range. Planned regulatory training and regular observation should be a part of the laboratory management strategy to reduce the rate of Pre-A errors and improve laboratory performance.