bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Universal Parallel Simulation Framework for Energy Pipeline Networks on High-performance Computers
Authors: Han Pu , Hua Haobo , Wang Hai , Xue Fei , Wu Changmao , Shang Jiandong 
Published: July 2023

Abstract

Energy distribution networks represent crucial infrastructures for modern society, and various simulation tools have been widely used by energy suppliers to manage these intricate networks. However, simulation calculations include a large number of fluid control equations, and computational overhead limits the performance of simulation software. This paper proposes a universal parallel simulation framework for energy pipeline networks that takes advantages of data parallelism and computational independence between network elements. A non-pipe model of an energy supply network is optimized, and the input and output of the network model in the proposed framework are modified, which can reduce the development burden during the numerical computations of the pipeline network and weaken the computational correlation between different simulated components. In addition, independent computations can be performed concurrently through periodic data exchange procedures between component instances, improving the parallelism and efficiency of simulation computations. Further, a parallel water pipelines network simulation computing paradigm based on a heterogeneous computer hardware architecture is used to evaluate the proposed framework’s performance. A series of tests are conducted to verify the accuracy of the proposed framework, and simulation errors of less than 5% are achieved. The results of multi-threaded simulation experiments have demonstrated the feasibility of the proposed framework in a parallel computing approach. Moreover, an Advanced Micro Devices (AMD) Deep Computing Unit (DCU)-parallel program is implemented into a water supply network simulation system; the computational efficiency of this system is compared with that of its serial counterpart. The experimental results show that the proposed framework is appropriate for high-performance computer architectures, and the 18x speed-up ratio demonstrates that the parallel program based on the proposed universal framework outperforms the serial program. That provides the basis for the application of pipe network simulation on high performance computers.
Relationship Between Nonperfusion Area from Widefield Optical Coherence Tomography Angiography and Macular Vascular Parameters in Diabetic Retinopathy
Authors: Konno Ami , Ishibazawa Akihiro , Pretto Lucas , Shimouchi Akito , Omae Tsuneaki , Song Young-Seok 
Published: July 2023

Abstract
Purpose
To evaluate the relationship between the nonperfusion area (NPA) from wide optical coherence tomography angiography (OCTA) and macular vascular parameters in diabetic retinopathy (DR).
Methods
In total, 51 eyes from 51 patients with proliferative DR (PDR) or moderate/severe non-PDR were included. Widefield OCTA using the Zeiss Plex Elite 9000 was performed. A semi-automatic algorithm calculated the percentages of the NPA within the total image. Macular OCTA (3 × 3 mm and 6 × 6 mm area) was scanned using the RTVue-XR avanti. Vessel density (VD) was automatically separated into the superficial (SCP) and deep capillary plexus (DCP), and foveal avascular zone (FAZ) measurements were computed according to the parafoveal (1–3 mm) and perifoveal (3–6 mm) regions.
Results
A negative correlation was found between the average VD of the SCP and DCP obtained 3 × 3 mm and 6 × 6 mm area and the NPA. Multiple regression analysis revealed that the temporal–perifoveal region most negatively correlated with the NPA (r = − 0.55, p < 0.0001). No correlation was found between FAZ measurements and DR severity (area, p = 0.07; perimeter, p = 0.13).
Conclusion
Diabetic macular nonperfusion was significantly associated with the NPA from widefield OCTA. In particular, the temporal–perifoveal DCP disorder may be a sensitive indicator of wide NPA.
Nullspaces yield new explicit Runge--Kutta pairs
Authors: VERNER JAMES HAMILTON 
Published: July 2023

Abstract
Sixty years ago Butcher [1] characterized a natural tabulation of the or-
der conditions for Runge{Kutta methods as an isomorphism from the set
of rooted trees having up to p nodes, and provided examples of explicit
and implicit methods of several orders. Within a few years. Fehlberg
[3] derived pairs of explicit methods of successive orders that could be
implemented eciently by using the dierence of each pair of estimates
to control the local error. Unfortunately, Fehlberg's pairs were decient
for quadrature problems. Subsequently, this author [5],[6] derived para-
metric families of explicit Runge{Kutta pairs of increasing orders 6 to 9
that avoided this problem altogether. These, and most known explicit
methods, have been derived by exploiting certain 'simplifying conditions'
suggested by Butcher [1] that imposed constraints on subsets of the co-
ecients, and thereby simplied the solution of the order conditions for
moderate to high order methods.
'Test 21', a MAPLE program developed recently by Butcher [2], was
applied to derive known 13-stage pairs of orders 7 and 8. Unexpectedly,
results of this application revealed the existence of some previously un-
known methods - ie. some that satised most, but not all, of the previously
known simplifying conditions. This present study develops formulas for
directly computing exact coecients of these new pairs together with oth-
ers lying within this new parametric family of (13,7-8) pairs. While the
best of these new pairs falls short of the best of pairs already known, the
properties discovered might be utilized to precisely characterize recently
reported higher order methods found using other approaches by Khashin
[4] and Zhang[7], and possibly lead to nding other Runge{Kutta and
related yet unknown methods.
CurvAGN: Curvature-based Adaptive Graph Neural Networks for Predicting Protein-Ligand Binding Affinity
Authors: Wu First Jianqiu , Chen Second Hongyang , Cheng Third Minhao , Xiong Fourth Haoyi 
Published: July 2023

Abstract
Accurately predicting the binding affinity between proteins and ligands is crucial for drug discovery. Recent advances in graph neural networks (GNNs) have made significant progress in learning representations of protein-ligand complexes to estimate binding affinities. To improve the performance of GNNs, there frequently needs to look into protein-ligand complexes from geometric perspectives. While the "off-the-shelf" GNNs could incorporate some basic geometric structures of molecules, such as distances and angles, through modeling the complexes as homophilic graphs, these solutions seldom take into account the higher-level geometric attributes like curvatures and homology, and also heterophilic interactions.To address these limitations, we introduce the Curvature-based Adaptive Graph Neural Network (CurvAGN). This GNN comprises two components: a curvature block and an adaptive attention guided neural block (AGN). The curvature block encodes multiscale curvature informaton, then the AGN, based on an adaptive graph attention mechanism, incorporates geometry structure including angle, distance, and multiscale curvature, long-range molecular interactions, and heterophily of the graph into the protein-ligand complex representation. We demonstrate the superiority of our proposed model through experiments conducted on the PDBbind-V2016 core dataset.
Predicting mechanoregulatory responses in bone during breast cancer metastasis: A Finite Element Analysis
Authors: Verbruggen Anneke S.K. , McCarthy Elan C. , Dwyer Roisin M. , McNamara Laoise M. 
Published: July 2023

Abstract
Breast cancer metastasises to bone in 70–80% of patients with advanced disease. Bone cells contribute to tumour metastasis by activating bone resorption, which releases biochemical factors that stimulate tumour cell proliferation. The local mechanical environment of bone tissue is altered during early metastasis, prior to the formation of overt osteolytic metastasis. According to mechanoregulation theory, these changes might activate mechanobiological responses in bone cells and thereby contribute to osteolytic resorption. However, whether mechanobiological responses of bone cells drive osteolysis during metastasis is unknown. The objective of this study was to apply a computational mechanoregulation framework to predict how early changes in the bone mechanical environment contribute to osteolysis. Subject-specific finite element (FE) models were developed to predict the mechanical environment within bone tissue during early stage metastasis (3 weeks post-inoculation). We then applied a mechanoregulation algorithm to predict changes in bone tissue density as a function of the evolving mechanical environment due to tumour invasion. Substantial bone loss was predicted in the greater trochanter region, which coincides with experimental reports of regional bone loss in this animal model. Moreover, application of the mechanoregulation algorithm predicted that the mechanical environment evolved in a similar manner to that predicted through subject-specific finite element (FE) models. This is the first study to implement a computational mechanoregulation framework to predict the development of osteolysis. Our findings support the hypothesis that early changes in the physical environment of bone tissue during metastasis may elicit mechanobiological cues for bone cells and activate osteolytic destruction.
Message-In-A-Bottle: Engaging Stories Around Sustainable and Safe Wine Products
Authors: Papadakos Panagiotis , Chrysakis Ioannis , Patkos Theodore , Flouris Giorgos , Samaritakis Giorgos , Angelakis Dimitris , Basina Nena , Tsampanaki Nikoleta , Pratikaki Ariadne , Baritakis Pavlos , Loulakakis Ilias , Gouma Maria , Kioupakis Akis , Apostolidi Christina , Lyrarakis Bart 
Published: July 2023

Abstract
The wine industry has evolved thanks to the introduction of digital technologies in every aspect of the wine production chain and the emerging need of the food industry for qualitative, sustainable, and safe products. As a result, the incorporation of digital services that facilitate access to related data of wine products is crucial for wine enterprises to increase their competitiveness, customer loyalty, and market share in this highly competitive domain. In this work, we present the Message-in-a-Bottle(MiB) ecosystem, which exploits multi-dimensional and multi-sourced data for creating engaging and interactive stories around wine labels. We especially focus on the sustainability and safety issues in the wine industry and showcase how MiB addresses them. The ecosystem is developed in the context of the MiB project∗ and has already started to be available in the market through the Lyrarakis† wine enterprise.
Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks
Authors: Pourkeyvan Alireza , Safa Ramin , Sorourkhah Ali 
Published: July 2023

Abstract
Early diagnosis of mental disorders and intervention can facilitate the prevention of severe injuries and the improvement of treatment results. Using social media and pre-trained language models, this study explores how user-generated data can be used to predict mental disorder symptoms. Our study compares four different BERT models of Hugging Face with standard machine learning techniques used in automatic depression diagnosis in recent literature. The results show that new models outperform the previous approach with an accuracy rate of up to 97%. Analyzing the results while complementing past findings, we find that even tiny amounts of data (Like users’ bio descriptions) have the potential to predict mental disorders. We conclude that social media data is an excellent source of mental health screening, and pre-trained models can effectively automate this critical task.
Tailor-made Tenorite (CuO) Interface Films for Enhanced Photocatalysis: An Improved Dip-Coating Approach with Enhanced Surface Topography and Hydrophobicity
Authors: Althamthami Mohammed , Hachmi Guettaf Temam EI , Temam Hachemi Ben , Hasan Gamil Gamal , Rahmane Saâd , Gasmi Brahim 
Published: July 2023

Abstract
Semiconductor films are essential in photocatalysis applications; however, the controlled production of certain films remains challenging and inefficient. Previous studies have mainly focused on deposition processes, heating rates, and doping of semiconductor oxides. In this paper, we propose a novel approach to fabricating tenorite (CuO) semiconductor films with varying concentrations (0.01, 0.02, 0.04, 0.06, and 0.1 g/ml) using a dip-coating technique. We investigate the effects of contact angles, 3D surface topography, and film thickness on the photoactivation properties, as these factors have received limited attention in previous research. The results demonstrate that higher-concentration tenorite films exhibit rougher surfaces, increased hydrophobicity, improved light-harvesting ability, enhanced charge separation, and higher active oxygen output. This approach has significant implications for tenorite film manufacturing and the efficient photocatalytic removal of organic contaminants with minimal environmental impact. Our study examines concentration variation in tenorite thin films produced through sol-gel processes and dip-coating. Wettability tests show a 21.47% improvement in the 0.1 g/ml film surface under indirect sunlight compared to darkness. Surface morphology analysis reveals an increased presence of grains with higher concentrations. Transmittance rates at 600 nm range from 0.02–90.94%. The direct optical bandgaps range from 2.74 to 1.21 eV, while the indirect bandgaps remain unaffected. The photocatalytic efficiency against dyes (MB) was affected by the concentration, crystal phase, size, thickness, wettability, surface roughness, and direct band-gap. These tenorite thin films demonstrate exceptional photocatalytic properties, being highly efficient, environmentally friendly, reusable, and stable, making them suitable for practical applications.
CPSF6 promotes the Warburg effect and angiogenesis leading to tumor progression via c-Myc/ FBW7/ VEGF/ PD-L1 signaling axis with its siRNA synergistic potential with Sorafenib as a therapeutic target for liver cancer therapy.
Authors: Sim Deok Yong , Lee Hyo–Jung , Ahn Chi-Hoon , Park JiEon , Park Su-Yeon , Shim Bum-Sang , Kim Bonglee , Kim Sung-Hoon 
Published: July 2023

Abstract
Background
Though CPSF6 was known to act as an oncoprotein via glycolysis in several cancers, the oncogenic mechanism of CPSF6 was not explored in association with the Warburg effect and angiogenesis in hepatocellular carcinoma (HCC) to date.
Methods
MTT assay, colony formation assay, cell cycle analysis and microarray were conducted in CPSF6 depleted HCC cells. Expression of CPSF6 was evaluated in HCC tissues, cell lines and by The Cancer Genome Atlas (TCGA) analysis, while molecular mechanism of CPSF6 was assessed by Western blotting, Immunofluorescence and Immunoprecipitation. The growth of Hep3B cells was monitored in BALB/c orthotopic and xenograft tumor models with Immunohistochemistry analysis. Also, tube formation assay, CAM assay and ELISA were performed for angiogenesis by CPSF6.
Results
CPSF6 was overexpressed in HCC tissues with poor survival rates compared to normal tissues. Hence, CPSF6 depletion suppressed the viability and colonies, induced apoptosis via PARP cleavages and increased sub-G1 population in Hep3B and Huh7 cells. Also, CPSF6 enhanced the stability of c-Myc via their binding through nuclear colocalization as an upstream of c-Myc, mainly in nucleoplasm. Furthermore, CPSF6 depletion activated phosphorylation of c-Myc (T58) for c-Myc degradation and inactivated c-Myc (S62), which was disturbed by FBW7 depletion or proteosomal inhibitor MG132. Additionally, CPSF6 depletion suppressed the Warburg effect by inhibition of glucose, HK2, PKM2, LDH and lactate, showed synergistic effect with Sorafenib in Hep3B cells and also inhibited angiogenesis by tube formation and CAM assays along with decreased expression and production of VEGF. Notably, CPSF6 depletion attenuated PD-L1 along with the increase of CD4/CD8 cell percentage in the splenocytes of BALB/c nude mice bearing Hep3B cells. Consistently, CPSF6 depletion reduced the growth of Hep3B cells in BALB/c mice in orthotopic and xenograft tumor models with inhibition of tumor microenvironment associated proteins by immunohistochemistry.
Conclusions
These findings suggest that CPSF6 enhances the Warburg effect for immune escape and angiogenesis, leading to cancer progression via c-Myc/ FBW-7/ VEGF/PD-L1 signaling axis with its siRNA synergistic effect with Sorafenib as a molecular target of liver cancer therapy.
Trial registration: retrospectively registered
the Properties and Fabrication of Al 2 O 3 Porous Ceramics by Protein Foaming Method
Authors: Li Xinyue , Jiao Hua , Zhao Kang 
Published: July 2023

Abstract
In this paper, Al2O3 porous ceramics were designed and fabricated by egg albumen foaming method, in which the egg albumen was selected as a green binder and foaming agent. The effect of sucrose on the stabilization of egg albumen foam was studied, and the sintering system was determined according to the incineration of proteins during the sintering process, and finally the preparation process of porous ceramics was optimized. Al2O3 ceramic with a porosity of up to 82% and a compressive strength of up to 30 MPa has been successfully fabricated.
TKMBR: Temporal Knowledge Graph-based Multi-Behavior Recommendation for E-commerce
Authors: Zhang Xiaoman , Cheng Yang , Bi Xuehua , Yu Guanglei , Cao Ruyi , Zhang Linlin 
Published: July 2023

Abstract
Striving to enhance predictive performance by leveraging auxiliary behaviors, multi-behavior recommendation models have emerged in the realm of e-commerce. These models aim to address the diversity and effectiveness of interactive behaviors. While some methods have shown promising effects, they still exhibit certain limitations, such as overlooking dynamic nature of user interactions. In this paper, we present TKMBR, a multi-behavior recommendation framework based on a temporal knowledge graph in e-commerce. TKMBR incorporates a temporal knowledge graph to capture the temporal dynamics of user behaviors, which allows for the identification of underlying temporal patterns and the capturing of evolving user preferences over time. To augment the understanding of user preferences, heterogeneous signals are integrated and an item-side information knowledge graph is constructed based on various user-item interactions. Moreover, contrastive learning tasks are employed to alleviate the issue of data sparsity. Finally, we evaluate the performance of our approach on two representative recommendation datasets using standard metrics with HR and NDCG. Experimental results demonstrate the effectiveness of TKMBR in improving recommendation quality.
Grey Wolf Optimizer based Resource Allocation and Optimization Algorithm in Cloud Computing Environment
Authors: Mandal Shrabanti 
Published: July 2023

Abstract

The Internet of Things (IoT) is a scattered structure where every item links with internet for communication. The IoT structure is composed by several resources and gateways or cluster heads. So in this environment, IoT resource allocation and scheduling task is a crucial challenge. The resource allocation and resource scheduling process are responsible to perform mapping between IoT resources and gateways and also deals with optimized resource allocation at gateways. In the real scenario, each gateway may face heavy traffics and that may create the overhead so allocatingand scheduling resources manually is not a feasible solution. Therefore, in this research work we introduce a hybrid approach to optimize the resources so that we can achieve the minimum transmission cost. The proposed approach is based on Grey Wolf Optimizer (GWO) algorithm. The GWO has been designed by motivation from the civic activity specially searching and hunting prey of grey wolves. The introduced hybrid GWO has been tested on the several benchmark functions and got the satisfied result.
WITHDRAWN: Acoustically Levitated Fuel Droplet Evaporation at High Temperatures: An Experimental Investigation
Authors: Pang Bin , Yang Guangcan , Liu Xiaoxin , Huang Yu , Li Wanli , He Yongqing , Shi Zhongyuan , Yang Zhaochu , Dong Tao 
Published: July 2023

Abstract
Fuel droplet evaporation characteristics are critical for fuel engine efficiency, energy conservation, and emission reduction. This paper presents experimental studies on fuel droplet evaporation with ultrasonic standing wave levitation and high-speed camera to explore the influences of temperatures and droplet properties, including the initial diameter and droplet component. The evaporation of fuel droplets such as hexadecane and diesel are recorded over a temperature range of 150°C to 550°C, with an initial droplet equivalent diameter of 0.10–0.30 mm. Evaporation rate is positively correlated with temperature and may vary by 15–71% between hexadecane and diesel droplets.
Intensive antibiotic treatment of sows with parenteral crystalline ceftiofur and tulathromycin alters the composition of the nasal microbiota of their offspring
Authors: Bonillo-Lopez Laura , Obregon-Gutierrez Pau , Huerta Eva , Correa-Fiz Florencia , Sibila Marina , Aragon Virginia 
Published: July 2023

Abstract
Nasal microbiota plays an important role in animal health and the use of antibiotics is one of the major factors that influence its composition. Here, we study the consequences on piglets’ nasal microbiota when an intensive antibiotic treatment was applied to sows and/or their piglets. Four pregnant sows received a treatment of ceftiofur and tulathromycin (CTsows) while two other sows received only crystalline ceftiofur (Csows). Sow treatments were performed at D-4, D3 and D10 for ceftiofur and D-3, D4 and D11 for tulathromycin. Half of the piglets born to CTsows were treated at birth with ceftiofur. Nasal swabs were taken from piglets at 22-24 days of age and bacterial load and nasal microbiota composition were defined by 16s rRNA gene qPCR and amplicon sequencing. Antibiotic treatment of sows reduced their nasal bacterial load, as well as in their offspring, indicating a reduced bacterial transmission from the dams. In addition, nasal microbiota composition of the piglets exhibited signs of dysbiosis, showing unusual taxa. The addition of tulathromycin to the ceftiofur treatment seemed to enhance the effect on the microbiota diversity by diminishing some bacteria commonly found in the piglets’ nasal cavity, such as Glaesserella, Streptococcus, Prevotella, Staphylococcus and several members of the Ruminococcaceae and Lachnospiraceae families. The additional treatment of piglets with ceftiofur resulted in no further effect beyond the treatment of the sows. Altogether these results suggest that intensive antibiotic treatments in sows, especially the double antibiotic treatment, disrupt the nasal microbiota of their offspring and highlight the importance of sow-to-piglet microbiota transmission.