bluuebunny/crossref_metadata_2025_split huggingface dataset:
Investigation of Improved Energy Dissipation in Stepped Spillways Applyinga Bubble Image Velocimetry
Authors: Mikalsen Lars Marius , Thorsen Kasper Haugaard , TÜRKBEN Asli BOR , Lia Leif 
Published: June 2024

Abstract

This study focuses on the skimming flow regime, two-phase air-water flow conditions, and simple measures for improving energy dissipation, and aims to optimize the design, operation, and modeling of stepped spillways. The experiments were carried out at the Norwegian Hydraulic Laboratory at the Norwegian University of Science and Technology in Trondheim. Velocity field measurements were made in two physical models of different scales, conducted in two different rectangular flumes. For the first scenario, a 1:50 scale spillway model was used, and for the second, a larger model, 1:17 scale was built with planks and marine plywood with a crest of extruded polystyrene (XPS). Crest splitters were installed to further increase energy dissipation, and the hydraulic jump method was applied to measure the residual energy level. Flow properties were investigated the flow properties with the Bubble Image Velocimetry (BIV) technique, which tracks air bubbles. Experimental results show that BIV is remarkably successful for studying turbulent two-phase air-water flow. Additionally, two experiments were carried out with and without splitters on the ogee crest with ADV, revealing how the splitters changed the velocity profile above the crest. The study found crest splitters to be a practical and cost-effective measure to improve energy dissipation, in addition to reducing the length of inception and cavitation potential.
Multi-label Emotion Classification on Social Media Comments using Deep learning
Authors: Bayu Yeshimebet , tegegn tesfa 
Published: June 2024

Abstract

Social media is an online platform that people use to develop social networks or relationships with others. Every day, millions of people use different social media to express their thoughts, emotions, and experiences. An emotion is a complex psychological event that involves a mixture of reactions occurring in the human body and brain, usually triggered by a mental content(Almeida et al., 2018). Multi-label text emotion classification is the problem that aims to identify all possible emotions from a given text that best represents the author's mental state. Many researches have been done on text emotion classification in English, Arabic, and Chinese language. However, most of them focus on single-label emotion classification which is unable to identify all present emotions in the given instance. To the best of our knowledge, there is no research conducted on multi-label emotion classification for Amharic text. In addition to this, there is no available dataset to conduct multi-label emotion classification research. These reasons motivate us to do research on multi-label Amharic text emotion classification. for this research we collected 18000 datasets from different social platforms YouTube, Facebook, and Twitter. The dataset is annotated by psychologists and other professionals. We use word2vec and one hot encoding to prepare the feature vector. We train and test four deep-learning approaches such as LSTM, BILSTM, CNN, and GRU. We perform the experiment by feeding one hot encoding and word2vec features to these for deep learning models and achieve the best accuracy with one hot vector. We achieve test accuracy of 53.1%, 54.5%, 54%, and 39.7% for LSTM, BILSTM, CNN, and GRU respectively. For the future we conduct this research using a large dataset with transformer models (BRT, ROBERTA, and XLNET) and test the performance of these models on Amharic text multi-label emotion classification.
Reservoir evaluation utilizing petrophysical analysis of well logging data validated by core sample in Sitra field, Abu EL-Gharadig Basin, Western Desert, Egypt
Authors: Mohamed Hadeel , MABROUK Walid M. , METWALLY Ahmed 
Published: June 2024

Abstract

In the northern section of the Western Desert, there are many extremely profitable petroleum and natural gas deposits in the Abu EL-Gharadig Basin. This study aims to highlight the hydrocarbon potential of Abu Roash F Formation, which stands for high organic content unconventional tight reservoirs, and Abu Roash G Formation which stands for conventional sand reservoirs, in Sitra field located in the central-western part of the Abu EL-Gharadig Basin. The research employed well-log data from four wells to ascertain petrophysical properties combined with core samples of two wells for a comprehensive examination and description of lithology. Initially, we commenced the execution of petrophysical analysis, encompassing log quality control procedures. Subsequently, we identified and revealed zones of interest and hydrocarbon indicators in both formations. Additionally, we ascertained the three most influential parameters, namely shale Volume, effective Porosity, and water saturation, which serve as defining factors for reservoir quality. Subsequently, an examination of the core samples, which encompassed lithologic description, lithofacies analysis, paleoenvironmental interpretation, petrographic analysis, and porosity assessment is conducted. For the sake of a more accurate interpretation, we conclude our research with Cartographic maps created to evaluate the geographical distribution of hydrocarbon potential based on petrophysical characteristics, Distribution of the net-to-gross ratio among wells by correlating the litho-saturation models (rock models) for the four wells. The foregoing results declare that The Abu Roash F carbonate-rich rocks are a contender for unconventional tight oil reservoir potential with thin secondary porosity and high organic content with total organic matter (TOC) values up to 3.04 wt%, which normally requires a kind of hydraulic fracturing for prospective oil extraction, Furthermore, the upper section of Abu Roash G formation, particularly in well sitra8-03, has highly favorable conventional reservoir characteristics.
The solar FUV-UV spectrometer flight experiment onboard high-altitude balloon
Authors: WEI Fei , ZHANG Xuanyi 
Published: June 2024

Abstract

An experiment measuring the solar far-ultravoilet-ultraviolet (FUV-UV) irradiance with spectral-resolution bettern than 0.1 nm in the wavelength range between 170 to 400 nm, was carried out by the “HongHu-6” high-altitude balloon that flight to the bottom region of the near-space in September 2022. This experiment was based on the fact that solar FUV-UV penetrates through a complex cross-section window of the upper atmosphere, from outer space to the near space. The solar FUV-UV deposits energy in the upper atmosphere, which provides a key to answer scientific questions on the most important energy contributor to overall heating sources of the near space and how the near-space environment responds to solar activities. In the wavelength band between 150 to 210 nm, irradiance maps from active regions of the solar corona, the comparative small cross-section of molecular oxygen allows certain wavelengths of the band to arrive at altitudes between 20 and 30 km above the ground, indicating solar flares could directly impact the bottom region of the near space. The solar UV irradiance in wavelength 170–400 nm is absorbed by the upper atmosphere as a function of wavelength, and deposits energy vertically in the lower regions of the near space. This experiment provides precise experimental data to assess the top-down energy input to the lower regions of the near-space. The solar FUV-UV spectrometer (SUVS) is a compact instrument based on improved Roland circle optics to adapt to the “HongHu-6” balloon payload platform. In this paper, we introduce the scientific goals of the solar FUV-UV spectrum measurement experiment, information on the SUVS instrument, preflight calibration, and the first results from the flight data.
Predicting current and future habitats of Adjutants under climate change for implementing strategic conservation measures in South and Southeast Asian landscapes
Authors: Abedin Imon , Mukherjee Tanoy , Singha Hilloljyoti , Go Yunji , Kang Hye-Eun , Kim Hyun-Woo , Kundu Shantanu 
Published: June 2024

Abstract

The storks (Ciconiiformes: Ciconiidae) represent a captivating avian group distinguished by their heavy-wading nature, long legs, elongated necks, and robust bills. The South and Southeast Asian region boasts the most diverse population of storks, necessitating immediate conservation efforts to preserve their habitats and shield them from the escalating threats of global warming. Within the genus Leptoptilos, three distinct species exist, two of which—the Greater Adjutant (Leptoptilos dubius) and the Lesser Adjutant (Leptoptilos javanicus)—have garnered attention as threatened according to the IUCN Red List. Nevertheless, recent strides in localized conservation efforts have prompted the tagging off these imperiled species to the ‘Near-Threatened’ category, citing population upswings. However, the assessment overlooks the crucial aspects like ramifications of climatic shifts and anthropogenic-induced habitat fragmentation. Hence, this study endeavors to assess climatic impacts via an ensemble approach to species distribution modeling. The findings unveil alarming trends for both adjutants across South and Southeast Asia. The Greater Adjutant (GA) is projected to undergo a severe decline of over 95% across all future scenarios (SSP245 and SSP585 in both time periods) from its current suitable extent of 38,686 km2, which represents only 5.91% of its total extent. On the contrary, the Lesser Adjutant (LA) experiences a spatial relocation towards Southeast Asia under the SSP245 and SSP585 scenarios, resulting in a decline of over 20% from its present suitable range of 239,490 km2, which accounts 22.59% of its IUCN range. Furthermore, the resulting habitat fragmentation, propelled by climatic alterations, is severe, with the GA losing numerous viable patches entirely and the LA experiencing fragmentation in its habitat. Furthermore, given the overlapping ranges of both adjutant species, the current scenario yields a niche overlap value of 0.370. Therefore, the present study advocates for the reassessment of both GA and LA, urging their IUCN assessment under threatened category. Furthermore, strategic conservation measures are proposed in this study, involving local communities, non-governmental organizations, and governmental entities, to safeguard these remarkable avian species.
Remaining useful life prediction of ultrasonic motor based on nonlinear stochastic degradation process
Authors: Gao Yuepeng , Yao Zhiyuan , Yang Lin 
Published: June 2024

Abstract

In this paper, the useful life prediction of traveling wave ultrasonic motor is taken as the research object to explore the estimation method of motor life and analyze the main mechanisms affecting the life of motors. The degradation pattern of motor performance is stochastic and nonlinear, and two modes of remaining useful life prediction models were established based on Wiener process theory. The parameters of the model were estimated based on Maximum Likelihood Estimation and multidimensional search optimization method. The performance of each degradation model was evaluated by introducing Akai Information Criterion and error evaluation method. Through the construction of an ultrasonic motor life test system and the life test, experimental data that can reflect the degradation of motor performance were obtained. The feasibility of the proposed method was verified using simulated signals and experimental datasets of motors. Compared with the existing methods on remaining useful life prediction, the modeling approach proposed has its uniqueness and superiority.
Knowledge Graph Decision Transformer:Conditional sequence decision-making model for Offline RL integrated with knowledge graph
Authors: He Shan , Lu Zhaoyang 
Published: June 2024

Abstract

Decision Transformer is one of the representative algorithms in the field of offline reinforcement learning for conditional sequence modeling. It can learn from past decisions and predict future action strategies, performing well in most tasks. However, recent research indicates its limitations in concatenating different trajectory segments, meaning it struggles to utilize suboptimal trajectories effectively. This paper combines Decision Transformer with knowledge graph link prediction, leveraging the strengths of both methods to enhance decision-making in complex environments. While Decision Transformer provides a robust framework for handling sequential data, knowledge graph link prediction offers structured representations of entity relationships and predicts potential links between entities, allowing the model to infer unobserved state transitions. By integrating knowledge graph predictions into the Decision Transformer model, we can enhance decision accuracy by leveraging additional contextual and relational information. This synergy enables more effective utilization of available data and better adaptation to diverse scenarios. Through experiments and evaluations, we have demonstrated the effectiveness of this integration approach in improving the performance of offline reinforcement learning models across various tasks and domains.
Mechanism of Rhubarb and Agastache rugosa in the treatment of renal cell carcinoma based on network pharmacology and experimental validation
Authors: Li Jiaqing , Yuan Ling , Zhang Lei , Jiang Wenjie , Jiao Taiqiang , Yang Niu , Dou Hongli , Nan Yi 
Published: June 2024

Abstract

Purpose Network pharmacology and molecular docking were uesd to forecast the related effect targets and potential signal pathways of Rhubarb and Agastache rugosa in curing renal cancer. Methods Searching the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP) to screen the effective ingredients and targets of Rhubarb and Agastache rugosa, the differential genes related to renal cancer were obtained by searching GEO database. Construct the regulatory networks and protein-protein interaction(PPI) networks using Cytoscape 3.9.1 software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed based on Metascape database, the gene set was analyzed by GSEA. The active components and core genes were molecular docked, and the core genes were analyzed for clinical correlation and immune infiltration. Finally, in order to verify the results of network pharmacological analysis, we performed cell experiments with 786-O cells and ACHN cells in vitro. Results The top six core genes, EGFR, HSP90AA1, MMP9, KDR, CA9, and LDHA, were identified by network pharmacological analysis; and Rhubarb and Agastache rugosa was predicted to play a role in the PI3K/Akt/mTOR pathway through central carbon metabolism in cancer. Cellular experiments showed that Rhubarb and Agastache rugosa restrained the proliferation of 786-O and ACHN cells, induced apoptosis, arrested the cell cycle, and reduced the colony forming ability of cells. qRT-PCR results showed that the expression of core targets of EGFR, HSP90AA1, MMP9, KDR, CA9, and LDHA were significantly down-regulated. Western blot results showed that the protein expression levels of EGFR, p-PI3K, PI3K, p-Akt/Akt, and p-mTOR/mTOR were significantly down-regulated. Discuss The core targets in the effective components of Rhubarb and Agastache rugosa may be to inhibit the development and proliferation of renal carcinoma cells through the PI3K/Akt/mTOR pathways.
Low temperature exposure decreases Cry1Ac insecticidal endotoxin content in cotton seeds
Authors: Chen Yuan , Dong Shu , Dai Yuyang , Zhang Xiang , Chen Dehua , Chen Yinglong , Liu Zhenyu 
Published: June 2024

Abstract

Background
Sudden temperature drops, resulting from extreme weather events, often occur during the boll-setting period of cotton in Xinjiang, China, causing decreased expression of Bacillus thuringiensis (Bt) insecticidal proteins in cotton bolls. The precise threshold temperatures and durations that lead to significant changes in Cry1Ac endotoxin levels under low temperatures remain unclear. To address this, we investigated the effects of different temperatures and stress durations on Cry1Ac endotoxin levels in cotton bolls. In 2020–2021, two Bt transgenic cotton cultivars, conventional Sikang1 and hybrid Sikang3, were selected as experimental materials. Various low temperatures (ranging from 16 to 20°C) with different durations (12h, 24h and 48h) were applied during the peak boll-setting period.
Results
As the temperature decreased, the Cry1Ac endotoxin content in the boll shell, fiber, and seed exhibited a declining trend. Moreover, the temperature causing a significant change in Cry1Ac endotoxin content increased with the prolonged duration of low-temperature stress. Among the components of cotton bolls, seeds were most affected by low-temperature stress, with the threshold temperature for a significant reduction in Cry1Ac endotoxin content ranging from 17°C to 19°C. Correlation analysis indicated that low temperatures led to a decrease in protein synthesis capacity and an increase in degradation ability, resulting in reduced Cry1Ac endotoxin content. Pathway analysis revealed that both amino acid and peptidase had significant negative effects on Cry1Ac endotoxin content.
Conclusion
In summary, when the daily average temperature was ≤ 19°C, implementing cultural practices to reduce free amino acid content and peptidase activity could serve as effective cold defense strategies for Bt cotton production.
Balanced Iterative Reduction and Clustering Using Hierarchies (BIRCH) methods to Prioritize and reduce Test Suites
Authors: Prabhakar Prajith , Venkatesh Yokesh , Prasanth A , Sathish N 
Published: June 2024

Abstract

Throughout testing, there is an abundance of duplicate test cases to ensure that the new code will not impact the program that has previously been tested with every possible combination. Essential as it may be, software testing may be expensive, especially when test cases are not based on real-world events. By combining state-of-the-art Balanced Iterative Reducing and Clustering Using Hierarchies (BIRCH) techniques with the Fruit Fly optimization (FFO) methodology, we can use fewer test cases to reduce challenging time while still providing accurate results and making effective use of resources. BIRCH does away with the original data points and instead creates clustered summaries from them. The BIRCH-FFO clustering method produced a full test suite with full path coverage while significantly reducing the number of experimental cases.
Energy efficient clustering routing algorithm based on improved FCM
Authors: Sun Qian , Meng Xiangyue , Zhao Zhiyao , Xu Jiping , Zhang Huiyan , Wang Li , Yu Jiabin , Liu Guoxin 
Published: June 2024

Abstract

Wireless sensor networks (WSNs) play a crucial role in the Internet of Things (IoT). The sensor nodes(SNs) in WSNs are powered by batteries, making energy efficiency and network lifetime key issues in WSNs research. Cluster-routing algorithms are a focal point for addressing energy efficiency challenges. Selecting cluster heads (CHs) based on clustering algorithms can reduce the energy consumption of SNs and enhance overall network stability and sustainability. This paper introduces a method for selecting the number of clusters (\({N_C}\)) and CHs based on fuzzy clustering. The fuzzy C-means (FCM) clustering algorithm requires pre-setting the number of clusters, with no inclusion of CHs information in the output after running the algorithm. The number of clusters and selection of CHs were determined using the elbow rule and scoring criteria for CHs selection. The performance of the network under different monitoring areas is simulated and analyzed in this paper. Experiments demonstrate that the proposed algorithm outperforms existing algorithms in terms of network energy consumption and lifetime.
Knockdown of lncRNA MEG3 promotes damage of vascular endothelial cells induced by vibration
Authors: Hu Xiuwen , Zeng Fanfei , Chen Ziyu , Hu Kuan , Chen Qingsong , Xia Yun , Yang Hongyu 
Published: June 2024

Abstract

Hand–arm vibration syndrome (HAVS) is caused by long-term exposure to hand-transmitted vibration (HTV), and its pathogenesis has not been elucidated fully. We explored the molecular mechanism of HAVS and provided clues and a theoretical basis for the early prevention and treatment of HAVS. After vibration, samples were collected from the plasma of human workers, plasma of rat tails, and human umbilical vein endothelial cells (HUVECs). ELISAs were used to measure the expression of vasoactive factors. Cell Counting Kit-8 and electron microscopy were used to detect cell damage. Flow cytometry was employed to detect apoptosis. Real-time reverse transcription-polymerase chain reaction was used to measure the expression of long non-coding RNAs (lncRNAs). Western blotting was used to measure the expression of apoptosis-related proteins. Vibration could cause cell damage, apoptosis, and changes in the expression vasoactive factors and lncRNAs. The lncRNA maternally expressed gene 3 (MEG3) had a significant regulatory effect on cell damage, apoptotic proteins, and vascular regulatory factors in the HUVEC damage induced by vibration, as shown by the further decrease in viability and aggravation of injury after knockdown of MEG3 expression in HUVECs treated with vibration. Expression of vasoactive factors and apoptosis-related proteins was changed after interfering with MEG3 expression. In conclusion, vibration can affect the expression of vasoactive factors and lncRNA, and cause damage to vascular endothelial cells. MEG3 may be involved in the inflammatory damage to vascular endothelial cells induced by vibration.
Usangu rainfall variability analysis: Unveiling spatio-temporal trend, distribution and concentration of rainfall in a semi-arid region
Authors: Kavishe Rosemary , Mulungu Deogratias , Alexander Augustina 
Published: June 2024

Abstract

Rainfall unpredictability in semi-arid climates affects the quality and quantity of water available for socioeconomic activities, ecological maintenance and developmental projects. Understanding rainfall characteristics such as trend, distribution, and concentration is important for managing water resources and planning to mitigate impacts from rainfall uncertainty. This study was conducted in the Usangu catchment located in a semi-arid environment with three competing users agricultural land upstream, and the National Park and hydropower plant. A multidisciplinary approach that integrated statistical methods, hydrological analysis and geographic information system (GIS) tools was used to unveil a spatio-temporal trend and variability of annual rainfall in Usangu. The results indicated a significant trend for all stations except Mafinga station. The highest decrease was at Kimani (S=-6.13) and the highest increase at Matamba (S = 2.93). There is a distinct distribution of rainfall between the highlands (800-1,200 mm/year) and lowlands (500–800 mm/year). Rainfall is concentrated in short periods indicated by higher precipitation concentration index (PCI) values ranging from (18.8 to 23.6). There is less dispersion of rainfall at station N based on the coefficient of variation (CV = 17.1%) and high dispersion at Kimani (CV = 33.6%) but the majority of the catchment experienced consistent rainfall patterns. According to the standardised anomaly index (SAI), oftentimes the catchment experienced normal conditions (32 years out of 42 years) which accounts for 77%. Also, extreme drought conditions are more dominant in the lowlands than in the highlands. The results of this study are useful for long-term planning of water resources utilization in Usangu.