bluuebunny/crossref_metadata_2025_split huggingface dataset:
Semantic congruency modulates the speed-up of multisensory responses
Authors: Roberts Kalvin , Jentzsch Ines , Otto Thomas U. 
Published: January 2024

AbstractResponses to multisensory signals are often faster compared to their unisensory components. This speed-up is typically attributed to target redundancy in that a correct response can be triggered by one or the other signal. In addition, semantic congruency of signals can also modulate multisensory responses; however, the contribution of semantic content is difficult to isolate as its manipulation commonly changes signal redundancy as well. To disentangle the effects of redundancy and semantic congruency, we manipulated semantic content but kept redundancy constant. We presented semantically congruent/incongruent animal pictures and sounds and asked participants to respond with the same response to two target animals (cats and dogs). We find that the speed-up of multisensory responses is larger for congruent (e.g., barking dogs) than incongruent combinations (e.g., barking cats). We then used a computational modelling approach to analyse audio-visual processing interferences that may underlie the effect. Our data is best described by a model that explains the semantic congruency modulation with a parameter that was previously linked to trial sequence effects, which in our experiment occur from the repetition/switching of both sensory modality and animal category. Yet, a systematic analysis of such trial sequence effects shows that the reported congruency effect is an independent phenomenon. Consequently, we discuss potential contributors to the semantic modulation of multisensory responses.
The moderating effect of fluid overload on the relationship between the augmentation index and left ventricular diastolic function in patients with CKD
Authors: Han Byoung-Geun , Pak Daewoo , Kim Jae-Seok , Sohn Yujin 
Published: January 2024

AbstractIncreased vascular stiffness, fluid overload, and left ventricular diastolic dysfunction (LVDD) are common in patients with chronic kidney disease (CKD). We investigated the potential moderating effect of volume status in the relationship between arterial stiffness and left ventricular (LV) diastolic function in non-dialysis patients with stage 5 CKD. The radial augmentation index at a heart rate of 75 beats/min (rAIx75), overhydration/extracellular water (OH/ECW), and E/e´ ratio were concurrently measured in 152 consecutive patients. Each of these parameters reflects the status of vascular stiffness, fluid balance, and LV diastolic function, respectively. Hierarchical regression analysis demonstrated a significant interaction effect of OH/ECW for all patients (P = 0.015), even after controlling for confounders. In separate analyses, this interaction effect was particularly significant in women (P = 0.010), whereas its significance in patients with diabetes was marginally significant (P = 0.062). Our study suggested that fluid overload could be one of the more aggravating factors of LVDD in patients with CKD who have increased arterial stiffness. Therefore, it is advisable to conduct simultaneous assessments of vascular stiffness, fluid balance, and LV function, particularly in the specific groups mentioned earlier. Our results may serve as evidence applicable to patients with chronic heart failure.
Risk factors of unintentional piecemeal resection in endoscopic mucosal resection for colorectal polyps ≥ 10 mm
Authors: Ishikawa Tsubasa , Okimoto Kenichiro , Matsumura Tomoaki , Ogasawara Sadahisa , Fukuda Yoshihiro , Kitsukawa Yoshio , Yokoyama Yuya , Kanayama Kengo , Akizue Naoki , Iino Yotaro , Ohta Yuki , Ishigami Hideaki , Taida Takashi , Tsuchiya Shin , Saito Keiko , Kamezaki Hidehiro , Kobayashi Akitoshi , Kikuchi Yasuharu , Tada Minoru , Shiko Yuki , Ozawa Yoshihito , Kato Jun , Yamaguchi Taketo , Kato Naoya 
Published: January 2024

AbstractThis study aimed to investigate the lesion and endoscopist factors associated with unintentional endoscopic piecemeal mucosal resection (uniEPMR) of colorectal lesions ≥ 10 mm. uniEPMR was defined from the medical record as anything other than a preoperatively planned EPMR. Factors leading to uniEPMR were identified by retrospective univariate and multivariate analyses of lesions ≥ 10 mm (adenoma including sessile serrated lesion and carcinoma) that were treated with endoscopic mucosal resection (EMR) at three hospitals. Additionally, a questionnaire survey was conducted to determine the number of cases treated by each endoscopist. A learning curve (LC) was created for each lesion size based on the number of experienced cases and the percentage of uniEPMR. Of 2557 lesions, 327 lesions underwent uniEPMR. The recurrence rate of uniEPMR was 2.8%. Multivariate analysis showed that lesion diameter ≥ 30 mm (odds ratio 11.83, 95% confidence interval 6.80–20.60, p < 0.0001) was the most associated risk factor leading to uniEPMR. In the LC analysis, the proportion of uniEPMR decreased for lesion sizes of 10–19 mm until 160 cases. The proportion of uniEPMR decreased with the number of experienced cases in the 20–29 mm range, while there was no correlation between the number of experienced cases and the proportion of uniEPMR ≥ 30 mm. These results suggest that 160 cases seem to be the minimum number of cases needed to be proficient in en bloc EMR. Additionally, while lesion sizes of 10–29 mm are considered suitable for EMR, lesion sizes ≥ 30 mm are not applicable for en bloc EMR from the perspective of both lesion and endoscopist factors.
Leveraging opposition-based learning for solar photovoltaic model parameter estimation with exponential distribution optimization algorithm
Authors: Kullampalayam Murugaiyan Nandhini , Chandrasekaran Kumar , Manoharan Premkumar , Derebew Bizuwork 
Published: January 2024

AbstractGiven the multi-model and nonlinear characteristics of photovoltaic (PV) models, parameter extraction presents a challenging problem. This challenge is exacerbated by the propensity of conventional algorithms to get trapped in local optima due to the complex nature of the problem. Accurate parameter estimation, nonetheless, is crucial due to its significant impact on the PV system’s performance, influencing both current and energy production. While traditional methods have provided reasonable results for PV model variables, they often require extensive computational resources, which impacts precision and robustness and results in many fitness evaluations. To address this problem, this paper presents an improved algorithm for PV parameter extraction, leveraging the opposition-based exponential distribution optimizer (OBEDO). The OBEDO method, equipped with opposition-based learning, provides an enhanced exploration capability and efficient exploitation of the search space, helping to mitigate the risk of entrapment in local optima. The proposed OBEDO algorithm is rigorously verified against state-of-the-art algorithms across various PV models, including single-diode, double-diode, three-diode, and photovoltaic module models. Practical and statistical results reveal that the OBEDO performs better than other algorithms in estimating parameters, demonstrating superior convergence speed, reliability, and accuracy. Moreover, the performance of the proposed algorithm is assessed using several case studies, further reinforcing its effectiveness. Therefore, the OBEDO, with its advantages in terms of computational efficiency and robustness, emerges as a promising solution for photovoltaic model parameter identification, making a significant contribution to enhancing the performance of PV systems.
Optimistic framing increases responsible investment of investment professionals
Authors: Daugaard Dan , Kent Danielle , Servátka Maroš , Zhang Lyla 
Published: January 2024

AbstractThe global warming crisis is unlikely to abate while the world continues to collectively fund the extraction and burning of fossil fuels. Carbon divestment is urgently needed to ward off the impending climate emergency. Yet responsible investments still only account for a modest share of global assets. We conduct an incentivized artefactual field experiment to test whether framing divestment as a social norm, communicating it by a person with perceived credibility and expertise (a messenger), and highlighting optimistic attributes bolster responsible investment. Our subjects are investment professionals who have significant influence over the allocation of funds. We provide evidence that optimistic framing increases responsible investment. Assuming a comparable effect size, the observed increase would represent a $3.6 trillion USD global shift in asset allocations.
Debulking corneal biopsy with tectonic amniotic membrane transplantation in refractory clinically presumed fungal keratitis
Authors: Eleiwa Taher K. , Youssef Gehad H. , Elsaadani Ibrahim Abdelkhalik , Abdelrahman Samar N. , Khater Ahmed A. 
Published: January 2024

AbstractThe treatment of fungal keratitis (FK) is challenging due to the subacute indolent course, and initial misdiagnosis. In this retrospective case series, we highlight both the diagnostic and therapeutic roles of corneal biopsy together with amniotic membrane transplantation (AMT) in patients with refractory clinically presumed FK. Debulking biopsy and tectonic AMT were performed during the initial presentation. Biopsy specimens were sent for KOH smears and cultures. After KOH smears confirmed the presence of fungal elements, topical voriconazole 1% was prescribed for the first 72 h then tailored according to the clinical response and the culture results. The outcome measures were complete resolution of infection and restoration of corneal integrity. Cases associated with culture proven bacterial keratitis were excluded. Twelve cases were included in the study. KOH smears confirmed the presence of fungal growth in all specimens. Cultures grew Aspergillus in 6/12 cases, sensitive to voriconazole (5/6) and amphotericin (3/6); Fusarium (4/12), sensitive to both voriconazole and amphotericin; and no growth in 2/12 cases. Amphotericin 0.15% eye drops were added to the 7 cases with proven sensitivity and to the remaining 2 culture negative cases. Gradual resolution of infection was seen in all cases after 35.6 ± 7.8 days. In FK, a debulking biopsy simultaneously with AMT help decrease the microbial load, suppress the inflammatory process, support the corneal integrity, confirm the presence of fungal pathogen.
Infrared-reflective ultrathin-metal-film-based transparent electrode with ultralow optical loss for high efficiency in solar cells
Authors: Perrakis George , Tasolamprou Anna C. , Kakavelakis George , Petridis Konstantinos , Graetzel Michael , Kenanakis George , Tzortzakis Stelios , Kafesaki Maria 
Published: January 2024

AbstractIn this work we study in-depth the antireflection and filtering properties of ultrathin-metal-film-based transparent electrodes (MTEs) integrated in thin-film solar cells. Based on numerical optimization of the MTE design and the experimental characterization of thin-film perovskite solar cell (PSC) samples, we show that reflection in the visible spectrum can be strongly suppressed, in contrast to common belief (due to the compact metal layer). The optical loss of the optimized electrode (~ 2.9%), composed of a low-resistivity metal and an insulator, is significantly lower than that of a conventional transparent conductive oxide (TCO ~ 6.3%), thanks to the very high transmission of visible light within the cell (> 91%) and low thickness (< 70 nm), whereas the reflection of infrared light (~ 70%) improves by > 370%. To assess the application potentials, integrated current density > 25 mA/cm2, power conversion efficiency > 20%, combined with vastly reduced device heat load by 177.1 W/m2 was achieved in state-of-the-art PSCs. Our study aims to set the basis for a novel interpretation of composite electrodes/structures, such as TCO–metal–TCO, dielectric–metal–dielectric or insulator–metal–insulator, and hyperbolic metamaterials, in high-efficiency optoelectronic devices, such as solar cells, semi-transparent, and concentrated systems, and other electro-optical components including smart windows, light-emitting diodes, and displays.
Multi scale-aware attention for pyramid convolution network on finger vein recognition
Authors: Zhang Huijie , Sun Weizhen , Lv Ling 
Published: January 2024

AbstractIn recent years, biometrics has been the most popular style of personal identification. The finger vein is an intrinsic and stable trait, and with the ability to detect liveness, it receives academic and industry attention. However, convolution neural networks (CNNs) based finger vein recognition generally can only cover a small input region by using small kernels. Hence, the performance is poor, facing low-quality finger vein images. It is a challenge to effectively use the critical feature of multi-scale for finger veins. In this article, we extract multi-scale features via pyramid convolution. We propose scale attention, namely, the scale-aware attention (SA) module, which enables dynamic adjustment of the weight of each scale to information aggregation. Utilize the complementation of different scale detail features to enhance the discriminativeness of extracted features, thus improving the finger vein recognition performance. In order to verify the present method’s efficiency, we carried out experiments on two public data sets and one internal data, and the wide range of experimental results proves the proposed method’s effectiveness.
Embedding and cross-sectioning as a sample preparation procedure for accurate and representative size and shape measurement of nanopowders
Authors: Mrkwitschka Paul , Rühle Bastian , Kuchenbecker Petra , Löhmann Oliver , Lindemann Franziska , Hodoroaba Vasile-Dan 
Published: January 2024

AbstractReliable measurement of the size of polydisperse, complex-shaped commercial nanopowders is a difficult but necessary task, e.g., for regulatory requirements and toxicity risk assessment. Suitable methods exist for the accurate characterization of the size of non-aggregated, stabilized, spherical and monodisperse nanoparticles. In contrast, industrial nanoscale powders usually require dedicated sample preparation procedures developed for the analysis method of choice. These nano-powders tend to agglomerate and/or aggregate, a behavior which in combination with an innate broad particle size distribution and irregular shape often significantly alters the achievable accuracy of the measured size parameters. The present study systematically tests two commercially available nanoscale powders using different sample preparation methods for correlative analysis by scanning electron microscopy, dynamic light scattering, Brunauer–Emmet–Teller method and differential mobility analysis. One focus was set on the sample preparation by embedding nanoparticles in carbon-based hot-mounting resin. Literature on this topic is scarce and the accuracy of the data extracted from cross sections of these particles is unclearly stated. In this paper systematic simulations on the deviation of the size parameters of well-defined series of nanoparticles with different shapes from the nominal value were carried out and the contributing factors are discussed.
Identification of influential weather parameters and seasonal drought prediction in Bangladesh using machine learning algorithm
Authors: Al Mamun Md. Abdullah , Sarker Mou Rani , Sarkar Md Abdur Rouf , Roy Sujit Kumar , Nihad Sheikh Arafat Islam , McKenzie Andrew M. , Hossain Md. Ismail , Kabir Md. Shahjahan 
Published: January 2024

AbstractDroughts pose a severe environmental risk in countries that rely heavily on agriculture, resulting in heightened levels of concern regarding food security and livelihood enhancement. Bangladesh is highly susceptible to environmental hazards, with droughts further exacerbating the precarious situation for its 170 million inhabitants. Therefore, we are endeavouring to highlight the identification of the relative importance of climatic attributes and the estimation of the seasonal intensity and frequency of droughts in Bangladesh. With a period of forty years (1981–2020) of weather data, sophisticated machine learning (ML) methods were employed to classify 35 agroclimatic regions into dry or wet conditions using nine weather parameters, as determined by the Standardized Precipitation Evapotranspiration Index (SPEI). Out of 24 ML algorithms, the four best ML methods, ranger, bagEarth, support vector machine, and random forest (RF) have been identified for the prediction of multi-scale drought indices. The RF classifier and the Boruta algorithms shows that water balance, precipitation, maximum and minimum temperature have a higher influence on drought intensity and occurrence across Bangladesh. The trend of spatio-temporal analysis indicates, drought intensity has decreased over time, but return time has increased. There was significant variation in changing the spatial nature of drought intensity. Spatially, the drought intensity shifted from the northern to central and southern zones of Bangladesh, which had an adverse impact on crop production and the livelihood of rural and urban households. So, this precise study has important implications for the understanding of drought prediction and how to best mitigate its impacts. Additionally, the study emphasizes the need for better collaboration between relevant stakeholders, such as policymakers, researchers, communities, and local actors, to develop effective adaptation strategies and increase monitoring of weather conditions for the meticulous management of droughts in Bangladesh.
Evaluating the effect of maternal non-communicable disease on adverse pregnancy outcomes and birthweight in Pakistan, a facility based retrospective cohort study
Authors: Wainwright Esther , Sheikh Irfan , Qureshi Rahat , Yousuf Sana , Khan Raheela , Elmes Matthew 
Published: January 2024

AbstractNon-communicable diseases (NCDs) claim 74% of global lives, disproportionately affecting lower and middle-income countries like Pakistan. NCDs may increase the risk of preterm birth (PTB), caesarean section (CS), and low birthweight. This study aims to determine whether the high prevalence of NCDs in Pakistan play a role in the high rates of preterm births, and CS. This retrospective cohort study from Aga Khan University Hospital, Pakistan, investigated effects of pre-existing NCDs on pregnancy outcomes of 817 pregnant women. Medical records were used to generate odds ratios for the risk of PTB, labour outcome and birthweight in women with type 1 and type 2 diabetes, hypertension, asthma and thyroid disorders. Multinomial logistic regression and general linear models were used to adjust for confounding variables using IBM SPSS Statistics (v27). Type 2 diabetes significantly increased the risk of PTB and elective CS (both P < 0.05). Elective CS was significantly increased by hypertension and asthma (both, P < 0.05). Surprisingly, asthma halved the risk of PTB (P < 0.05), while type 1 diabetes significantly increased birthweight from 2832 to 3253g (P < 0.001). In conclusion, pre-existing NCDs increase the risk of negative pregnancy outcomes, including PTB, elective CS and birthweight. Asthma, however reduced PTB and justifies further investigation.
WNTA5-mediated miR-374a-5p regulates vascular smooth muscle cell phenotype transformation and M1 macrophage polarization impacting intracranial aneurysm progression
Authors: Li Zengshi , Huang Junqiang , Yang Lijian , Li Xi , Li Wei 
Published: January 2024

AbstractmiR-374a-5p expression and localization in intracranial aneurysm (IA) tissues were detected, and its correlation with vascular smooth muscle cells (VSMCs) and macrophage markers was analyzed. Using platelet-derived growth factor-BB (PDGF-BB) induced VSMC model, elastase-induced IA rat model. Subsequently, miR-374a-5p was knocked down or overexpressed. We investigated the effects of miR-374a-5p on phenotypic conversion, and in vivo experiments were also carried out to verify the findings. The targeted relationship between miR-374a-5p and WNTA5 was analyzed. The effect of WNT5A inhibition on VSMC phenotypic transformation and THP-1-derived macrophage polarization was explored. Clinical studies have shown that miR-374a-5p was upregulated in IA patients. miR-374a-5p was negatively correlated with SM22α, α-SMA, CD206, and positively correlated with CD86. In vitro experiments showed that knocking down miR-374a-5p reversed the promotion of SM22α and α-SMA expression by PDGF-BB, while overexpression of miR-374a-5p had the opposite effect. In addition, knocking down miR-374a-5p also reversed the decrease in Calponin, TIMP3, TIMP4, and IL-10 levels caused by PDGF-BB, and further reduced the levels of MMP1, MMP3, MMP9, IL-1β, IL-6, and TNF-α. These findings were further validated in vivo. In IA rats, there were notable increases in both systolic and diastolic blood pressure, along with an elevated M1/M2 ratio and the occurrence of vascular lesions. However, these symptoms were improved after knocking down miR-374a-5p. Furthermore, miR-374a-5p could target the WNT signals (WNT2B, WNT3, and WNT5A). miR-374a-5p regulated the VSMC phenotypic conversion and M1 macrophage polarization by targeting WNT5A, thereby impacting the progression of IA.
Forest carbon removal potential and sustainable development in Japan
Authors: Zhang Bingqi , Imbulana Arachchi Janaki , Managi Shunsuke 
Published: January 2024

AbstractForests play a crucial role in mitigating climate change and reducing emissions as a major carbon sink. However, its value in removing carbon dioxide (CO2) from the atmosphere is always underestimated in natural capital (NC) accounting and sustainability assessments. This study predicted Japan’s forest CO2 removal by afforestation and forest management and its monetary value until 2042 from national to gridded level, with statistical data and complementary satellite data products, and explored how that CO2 removal will contribute to sustainable development under the inclusive wealth (IW) framework. The results show that: (1) the annual CO2 removal by forests has the potential to offset 15.3% of the emission and increase NC by 6.8% in Japan, significantly contributing to carbon neutrality and IW growth; (2) the total CO2 removal in exiting forests will peak at around 2030 and then decrease, but expanding afforestation could offset that decrease in later years; (3) the spatial distribution patterns of IW and forest CO2 removal are opposite. This indicates a national carbon trading market could create new wealth for rural communities where vast forests exist, and then effectively balance the inequal urban–rural development in Japan. The explicit spatial information of this study could provide valuable information for differentiating policy priorities of forestry planning and sustainable development in different local communities.