bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mapping acceptance and commitment therapy outcomes in the context of infertility: a systematic review
Authors: Barbosa Carolina , Santos Susana , Pedro Juliana 
Published: November 2023

AbstractEvidence suggests that individuals with infertility are confronted with psychosocial challenges. This has led to a significant number of studies highlighting the subsequent negative effects on mental health. Acceptance and Commitment Therapy (ACT) has already been established as having a beneficial effect on psychological issues related to other health conditions. Since infertility is a relevant stressor, ACT was also expected to have a protective effect on the mental health of individuals with this disease. A systematic review of the literature was conducted to identify studies assessing Infertility and ACT. Empirical and quantitative studies were considered when they assessed mental health variables and ACT hexagon model components in patients with infertility. Out of the 137 studies initially identified, six met the eligibility criteria and were included in the review. The samples were composed of women (three) and couples (three). Since ACT variables appear to be associated with better mental health outcomes, couples with infertility were expected to benefit from intervention addressing ACT components. Implications for future research include the need for a distinction between ACT variables, the use of validated assessment methods and the empirical examination of predictors of changes in ACT variables.
Further basic evidence for the dark-ego-vehicle principle: Higher pathological narcissism is associated with greater involvement in feminist activism
Authors: Krispenz Ann , Bertrams Alex 
Published: November 2023

AbstractAccording to the dark-ego-vehicle principle (DEVP), individuals with so-called dark personalities (e.g., individuals with high narcissistic traits) are attracted to political and social activism not for the achievement of prosocial goals but to repurpose the activism to satisfy their specific ego-focused needs. In this pre-registered study, we aimed at replicating and extending previous empirical evidence for the DEVP by examining the associations of pathological narcissism with involvement in feminist activism. A diverse US sample (N = 458) completed online measures of the Pathological Narcissism Inventory and several covariates (i.e., altruism, self-identification as a feminist, and age). Paralleling previous research, higher pathological narcissistic grandiosity was found to be statistically significantly related to greater involvement in feminist activism. Unexpectedly, gender did not moderate this relationship. Also, higher pathological narcissism was related to stronger self-identification as a feminist; however, pathological narcissistic grandiosity explained some variance in the involvement in feminist activism over and above feminist self-identification. In exploratory secondary analyses, we found that higher pathological narcissism was associated with specific feminist conversational interaction behaviors (e.g., correcting other’s non-feminist language). The limitations (e.g., the relevance of other dark personality traits beyond narcissism) and the theoretical implications for the DEVP are discussed. Overall, the findings of the present study are further support for the DEVP.
Exploiting response surface D-optimal design study for preparation and optimization of spanlastics loaded with miconazole nitrate as a model antifungal drug for topical application
Authors: Ibrahim Mervat Shafik , Elkady Omar A. , Amer Mai A. , Noshi Shereen H. 
Published: November 2023

Abstract
Purpose
Skin fungal infections are widely spreading worldwide and are considered a main cause of skin, mucous membranes, and systemic diseases. In an approach to enhance the topical delivery of miconazole nitrate (MZN) as a poorly permeable antifungal agent, spanlastics nanocarriers as a type of elastic vesicles were adopted in the current work.

Methods
MZN spanlastics were prepared and optimized according to a D-optimal response surface design to investigate the influence of formulation variables, edge activator (EA) percentage, EA type on particle size (PS), and drug entrapment efficiency percentage (% EE) as dependent variables. The spanlastics optimized formula (F7) was further assessed for its elasticity and physico-pharmaceutical properties before being incorporated into a gel. The F7 gel formula was also examined for its physical properties, in vitro release, in vitro antifungal activity against Candida albicans (ATCC® 10231), and ex vivo skin deposition studies. The results of the F7 gel formula were compared to the F7 aqueous dispersion.

Results
The D-optimal design revealed that F7, developed using Tween 60 as EA and Span 60 at a weight ratio 2:8, is the optimized formula. F7 was an elastic, spherical, non-aggregated vesicle with an average PS of 210 nm and a drug entrapment efficiency of 90%. The drug was present in an amorphous form within the vesicles. The gel form of F7 showed a prolonged drug release behavior relative to the solution form, where 75% of the drug was released over 10 h for the former and 5 h for the latter. The antifungal study revealed a significant (p < 0.05) increase in the zone of inhibition of Candida albicans (ATCC® 10231) demonstrated by spanlastics compared to MZN suspension at the same concentration level. MZN suspension showed cytotoxic activity at a concentration of 20 μg/mL and above; the incorporation of the drug in spanlastics dispersion or gel form increased the cell viability percentage. The skin deposition studies showed that F7 deposition in the dermal layer, where deep skin infections occur, is 164-folds that of the plain drug.

Conclusions
The results confirm the potential application of MZN-spanlastics to treat deeply seated skin fungal infections.
β-Arrestin2 promotes docetaxel resistance of castration-resistant prostate cancer via promoting hnRNP A1-mediated PKM2 alternative splicing
Authors: Zhou Yuhao , Li Fei , Zou Bangyu , Zhou Xiaofeng , Luo Lianmin , Dong Sicheng , He Zhiqing , Zhang Zhixiong , Liao Liqiong , Liu Hongxing , Cai Chao , Gu Di , Duan Xiaolu 
Published: November 2023

Abstract
Purpose
To investigate the influence of β-arrestin2 on the docetaxel resistance in castration-resistant prostate cancer (CRPC) and elucidate the underlying molecular mechanisms.

Methods
PC3 and DU145 cells with stable β-arrestin2 overexpression and C4-2 cells with stable β-arrestin2 knockdown, were constructed via using lentivirus and puromycin selection. MTT and colony formation assays were carried out to investigate the effect of β-arrestin2 expression on the docetaxel resistance of CRPC cells. Glycolysis analysis was used to assess the glycolytic capacity modulated by β-arrestin2. GO enrichment analysis, gene set enrichment analysis and Spearman correlation test were carried out to explore the potential biological function and mechanism via using public data from GEO and TCGA. The expressions of PKM2, Phospho-PKM2, Phospho-ERK1/2 and hnRNP A1 were detected by western blot. Functional blocking experiments were carried out to confirm the roles of PKM2 and hnRNP A1 in the regulation of β-arrestin2’s biological functions via silencing PKM2 or hnRNP A1 expression in cells with stable β-arrestin2 overexpression. Finally, nude mice xenograft models were established to confirm the experimental results of cell experiments.

Results
β-Arrestin2 significantly decreased the sensitivity of CRPC cells to docetaxel stimulation, through enhancing the phosphorylation and expression of PKM2. Additionally, β-arrestin2 increased PKM2 phosphorylation via the ERK1/2 signaling pathway and induced PKM2 expression in a post-transcriptional manner through an hnRNP A1-dependent PKM alternative splicing mechanism, rather than by inhibiting its ubiquitination degradation.

Conclusion
Our findings indicate that the β-arrestin2/hnRNP A1/PKM2 pathway could be a promising target for treating docetaxel-resistant CRPC.
MXene Enhanced 3D Needled Waste Denim Felt for High-Performance Flexible Supercapacitors
Authors: Fan Wei , Wang Qi , Rong Kai , Shi Yang , Peng Wanxi , Li Handong , Guo Zhanhu , Xu Ben Bin , Hou Hua , Algadi Hassan , Ge Shengbo 
Published: November 2023

AbstractMXene, a transition metal carbide/nitride, has been prominent as an ideal electrochemical active material for supercapacitors. However, the low MXene load limits its practical applications. As environmental concerns and sustainable development become more widely recognized, it is necessary to explore a greener and cleaner technology to recycle textile by-products such as cotton. The present study proposes an effective 3D fabrication method that uses MXene to fabricate waste denim felt into ultralight and flexible supercapacitors through needling and carbonization. The 3D structure provided more sites for loading MXene onto Z-directional fiber bundles, resulting in more efficient ion exchange between the electrolyte and electrodes. Furthermore, the carbonization process removed the specific adverse groups in MXenes, further improving the specific capacitance, energy density, power density and electrical conductivity of supercapacitors. The electrodes achieve a maximum specific capacitance of 1748.5 mF cm−2 and demonstrate remarkable cycling stability maintaining more than 94% after 15,000 galvanostatic charge/discharge cycles. Besides, the obtained supercapacitors present a maximum specific capacitance of 577.5 mF cm−2, energy density of 80.2 μWh cm−2 and power density of 3 mW cm−2, respectively. The resulting supercapacitors can be used to develop smart wearable power devices such as smartwatches, laying the foundation for a novel strategy of utilizing waste cotton in a high-quality manner.
Electrolyte Design for Low-Temperature Li-Metal Batteries: Challenges and Prospects
Authors: Sun Siyu , Wang Kehan , Hong Zhanglian , Zhi Mingjia , Zhang Kai , Xu Jijian 
Published: November 2023

AbstractElectrolyte design holds the greatest opportunity for the development of batteries that are capable of sub-zero temperature operation. To get the most energy storage out of the battery at low temperatures, improvements in electrolyte chemistry need to be coupled with optimized electrode materials and tailored electrolyte/electrode interphases. Herein, this review critically outlines electrolytes’ limiting factors, including reduced ionic conductivity, large de-solvation energy, sluggish charge transfer, and slow Li-ion transportation across the electrolyte/electrode interphases, which affect the low-temperature performance of Li-metal batteries. Detailed theoretical derivations that explain the explicit influence of temperature on battery performance are presented to deepen understanding. Emerging improvement strategies from the aspects of electrolyte design and electrolyte/electrode interphase engineering are summarized and rigorously compared. Perspectives on future research are proposed to guide the ongoing exploration for better low-temperature Li-metal batteries.
Determination of the minimum number of specimens required for laboratory testing of the shear strength of rock joints
Authors: Yong Rui , Song Jiamin , Wang Changshuo , Luo Zhanyou , Du Shigui 
Published: November 2023

AbstractThe shear strength of rock joints is critical in determining rock mass stability. However, the reliability of shear strength determined by testing only a few rock joint specimens is questionable due to the inherent heterogeneity of rock joints. This study aimed to investigate shear strength heterogeneity and determine the required minimum number of specimens (RMN) for obtaining reliable values. Specimens were extracted from various positions within a natural rock joint, and their roughness and shear strength variations were examined. A significant correlation between shear strength heterogeneity and joint roughness was found. Monte Carlo simulation was used to simulate engineering practice sampling procedures. The effectiveness of specimen numbers under different normal stresses was evaluated using relative error and corresponding probability. Results showed that the RMN depended on acceptable relative error, specified probability, and normal stress. For acceptable relative errors of 5% to 10% and corresponding probabilities of 85–95%, the RMN exhibits a decrease with an increase in normal stress, while it demonstrates an increase when considering acceptable relative error and probability requirements. A smaller RMN was needed under high normal stress for the same rock joint, demonstrating a decrease in shear strength heterogeneity with increasing normal stress. However, commonly used specimen numbers like 3 or 5 did not always align with the recommended RMN. The results also emphasize the significance of considering rock material properties when determining the RMN. The proposed method provides a quantitative and reliable tool for determining the RMN, considering the heterogeneity of rock joints.
Shrinkage estimation with logarithmic penalties
Authors: Kubokawa Tatsuya 
Published: November 2023

AbstractIn this paper, we have developed a novel approach for deriving shrinkage estimators of means without assuming normality. Our method is based on the equation of the first-order condition with a logarithmic penalty, and it introduces both one-step and two-step shrinkage estimators. The one-step estimator closely resembles the James–Stein estimator, while the differentiable two-step estimator exhibits similar performance to the positive-part Stein estimator. Although the latter does not satisfy Baranchik’s conditions, both estimators can be demonstrated to be minimax under normality assumptions. Furthermore, we have extended this method to handle cases involving an unknown scale. We have successfully applied this approach to the simultaneous estimation of Poisson means.
Calf Venous Compliance During Hypotension After an Acute bout of Aerobic Exercise in Humans
Authors: Endo Masako Yamaoka , Kanda Masako , Fukuba Yoshiyuki , Kashima Hideaki , Oue Anna , Miura Akira 
Published: November 2023

Abstract
Purpose
Post-exercise hypotension (PEH) following prolonged dynamic exercise is induced by an increase in systemic vascular conductance via skeletal muscle vasodilation, which may occur not only in the arteries but also in the veins, and the vasodilated regions may contribute to greater venous pooling in the exercised limbs. However, the contribution of venous distention to PEH is unclear. Therefore, we aimed to evaluate venous compliance in previously exercised muscles in a preliminary study.

Methods
Seven participants performed a single 60-min session of upright cycle ergometry at 60% of heart rate reserve. Calf venous compliance was measured using venous congestion plethysmography before and 45 min following exercise in Trial 1. Stroke volume and femoral arterial blood flow were determined using ultrasonography at the same time points in Trial 2. Heart rate and blood pressure were monitored during both trials.

Results
Mean arterial pressure significantly decreased between the start and ~ 30 min following the end of exercise (82 ± 6 mmHg vs. 76 ± 6 mmHg, respectively, in Trial 1; 79 ± 6 mmHg vs. 74 ± 5 mmHg, respectively, in Trial 2; P < 0.05). Pre- and post-exercise stroke volume did not significantly differ. Blood flow and vascular conductance in the femoral artery significantly increased from 641 ± 84 mL/min and 8.13 ± 0.79 mL/min/mmHg pre-exercise to 773 ± 121 mL/min and 10.55 ± 1.00 mL/min/mmHg post-exercise, respectively (P < 0.05). Pre- and post-exercise calf venous compliance did not significantly differ.

Conclusion
Our findings demonstrate that vasodilation in the active limb increases during exercise, but the resulting PEH does not affect venous compliance.
Tiling and PCA Strategy for Clustering-Based High-Dimensional Gaussian Filtering
Authors: Oishi Sou , Fukushima Norishige 
Published: November 2023

AbstractEdge-preserving filtering is an essential tool for image processing applications and has various types of filtering. High-dimensional Gaussian filtering (HDGF) supports a wide range of edge-preserving filtering. This paper approximates HDGF by clustering with Nyström approximation, tiling, and principal component analysis (PCA) to accelerate HDGF. Also, we compare it with the conventional HDGF approximations and clarify its effective range. We accelerate HDGF by clustering-based constant-time algorithm, which has O(K) order for convolution, where K is the number of clusters. First, we perform PCA for dimensionality reduction and then cluster signals with k-means++. HDGF is decomposed to Gaussian filtering by approximate eigenvalue decomposition of Nyström approximation using the clusters. The Gaussian filtering is performed in a constant-time algorithm. The process is further accelerated by the tiling strategy cooperating with PCA. In our experimental results, we compared three approximated HDGFs: clustering-based HDGF, permutohedral lattice, and Gaussian KD-tree. Also, we evaluated six types of high dimensional signals: RGB, RGB-IR, RGB-D, flash/no-flash, hyperspectral image, and non-local means. The proposed clustering-based HDGF was effective for low/middle-dimensional cases: RGB, RGB-IR, RGB-D, flash/no-flash, and hyperspectral images. Also, tiling with PCA strategy is effective for the conventional permutohedral lattice and Gaussian KD-tree. In the approximation of HDGF, the clustering-based HDGF is the better solution for low/middle-dimensional signals. For the higher-dimensional case of non-local means filtering, the conventional HDGF of the permutohedral lattice with the proposed PCA tiling is effective. Our code and dataset are available at https://norishigefukushima.github.io/TilingPCA4CHDGF/.
Predicting rice diseases using advanced technologies at different scales: present status and future perspectives
Authors: Li Ruyue , Chen Sishi , Matsumoto Haruna , Gouda Mostafa , Gafforov Yusufjon , Wang Mengcen , Liu Yufei 
Published: November 2023

AbstractThe past few years have witnessed significant progress in emerging disease detection techniques for accurately and rapidly tracking rice diseases and predicting potential solutions. In this review we focus on image processing techniques using machine learning (ML) and deep learning (DL) models related to multi-scale rice diseases. Furthermore, we summarize applications of different detection techniques, including genomic, physiological, and biochemical approaches. In addition, we also present the state-of-the-art in contemporary optical sensing applications of pathogen–plant interaction phenotypes. This review serves as a valuable resource for researchers seeking effective solutions to address the challenges of high-throughput data and model recognition for early detection of issues affecting rice crops through ML and DL models.
Line Planning for Different Demand Periods
Authors: Schiewe Alexander , Schöbel Anita , Sieber Linda 
Published: November 2023

AbstractLine planning as one of the first planning stages in public transport is a well-researched topic. Nearly all models start with the assumption that the demand for public transport is known and fixed. For real-world applications this assumption is not practicable, since there are different demands depending on the period of the day and the day of the week, e.g., the high demand in morning traffic differs from the demand during a week-day, or from the low demand on Sunday’s afternoons, or at night. Planning lines for different demand periods comes with two conflicting goals: On the one hand, the line concept should be adapted as good as possible to the respective demand. On the other hand, the lines should be as similar as possible for different demand periods, e.g., the line plan for Sunday afternoon should be related to the one on Monday morning. In this paper, we show that line planning for different demand periods can be modeled and solved: We introduce the multi-period line planning problem which is to find optimized line concepts for each demand period which are similar (enough) to each other. To this end, we discuss three different approaches to define the (dis)similarity between line concepts. These are frequency-based concepts, and concepts taking the number of different lines and the shape of the lines into account. For the latter, we use Wasserstein distances for modeling the similarity between two line concepts. We show that for all these similarity measures the line planning problem can be formulated as an integer linear program and solved efficiently. Our experiments furthermore show the differences of the resulting line concepts, and that the similarity of line concepts between different demand periods and the quality of the line concept are conflicting goals.
Simulating Gaussian boson sampling quantum computers
Authors: Dellios Alexander S. , Reid Margaret D. , Drummond Peter D. 
Published: November 2023

AbstractA growing cohort of experimental linear photonic networks implementing Gaussian boson sampling (GBS) have now claimed quantum advantage. However, many open questions remain on how to effectively verify these experimental results, as scalable methods are needed that fully capture the rich array of quantum correlations generated by these photonic quantum computers. In this paper, we briefly review recent theoretical methods to simulate experimental GBS networks. We focus mostly on methods that use phase-space representations of quantum mechanics, as these methods are highly scalable and can be used to validate experimental outputs and claims of quantum advantage for a variety of input states, ranging from the ideal pure squeezed vacuum state to more realistic thermalized squeezed states. A brief overview of the theory of GBS, recent experiments, and other types of methods are also presented. Although this is not an exhaustive review, we aim to provide a brief introduction to phase-space methods applied to linear photonic networks to encourage further theoretical investigations.
Performance enhancement and environmental analysis of vapor compression refrigeration system with dedicated mechanical subcooling
Authors: Solanki Naveen , Arora Akhilesh , Singh Raj Kumar 
Published: November 2023

AbstractThe primary focus of this study is on the energy, exergy, and environmental (3E) analysis of a dedicated mechanical subcooled vapor compression refrigeration (DMS-VCR) system for applications involving commercially available water chillers that employ R134a (in both subcooler and main cycle). For a cooling capacity of 100-kW water chillers, the mathematical model of the DMS-VCR system is built to determine the performance parameter of the system. The DMS-VCR system reduces electricity usage by 15.52% and increase in COP by 9.5%, which results in a significant reduction in CO2 emissions of about 15.55%. When compared to equivalent vapor compression refrigeration system (VCRS), the system’s exergetic efficiency is also increased by 8%. Since the computer simulation results will undoubtedly give design engineers a better option, the subcooling and superheating of the vapor compression refrigeration system become alluring in this study. Consequently, the DMS-VCR system performs better as per the combined 3E study.