bluuebunny/crossref_metadata_2025 huggingface dataset:
President’s Column: Maximizing Your Membership by Leveraging SPE’s Multimedia Content
Authors: Palisch Terry
Published: March 2024

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Editor’s Note: This month we are introducing a summary of the full transcript. We encourage you to listen to the podcast episode to hear the full conversation on your favorite podcast platform under “The SPE Podcast.”

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In this podcast episode, I am joined by Dana Otillio, vice president of marketing and communications at SPE, to discuss how members can maximize their membership by leveraging multimedia content. The episode covers various aspects, including SPE Live, webinars, online training, and the Distinguished Lecturer (DL) Program. The conversation delves into the history and evolution of SPE’s multimedia content and its impact on connecting members globally.

I introduce the episode by highlighting the goal of helping members understand how to get the most out of their SPE membership. As I like to do, I liken an SPE membership to a gym membership, emphasizing the importance of exploring all available offerings to create your energy future.

Our discussion delves into the birth of SPE Live during the pandemic, emphasizing its role in overcoming the challenges posed by the inability to hold in-person events. Its success in delivering timely, relevant technical knowledge led to it becoming a series on LinkedIn providing conversational content on global topics.

I also highlight its popularity and mention the addition of Tech Talks, sponsored content sessions that have garnered significant views. To date these have been very popular, streaming four to five SPE Lives a month and garnering 1,600 to 1,700 views live and on-demand per presentation. It’s one of our many growing outlets for multimedia.

Evolution of Multimedia Content

Our discussion then turns to the accessibility of multimedia content through the SPE Energy Stream website, launched in 2022. I describe how it is a one-stop shop for all multimedia content, easily accessible from the spe.org homepage. The website offers webinars, online training, SPE Live, and podcast episodes showcasing the organization’s commitment to providing a comprehensive multimedia experience. I like to think of SPE Energy Stream as the “OnePetro” for multimedia content.

The conversation explores the integration of the DL program with SPE Live, allowing DLs to reach a broader audience through live broadcasts. We take a moment to review the DL program’s significance in connecting with sections worldwide, and the addition of SPE Live as a feature expands the program’s reach.

We distinguish between SPE Live, which focuses on conversational content, and webinars, which are more technical, lasting 60 to 90 minutes. The availability of webinars on-demand and the opportunity to earn continuing education credits are emphasized. Online training courses, both SPE-developed and in partnership with IHRDC, are discussed as more extensive, allowing members to learn at their own pace. In 2019, SPE partnered with IHRDC to link their training content to SPE members. It’s offered through the SPE website and provides a reduced rate for our members.
MicroCT scanning and analysis in the zebrafish skeleton
Authors:   Arianna Ericka Gómez (1,2),   Phil Salmon (4), Baumgarten Nell , Rai Jyoti , Huber Philippe , Simmonds Susana P. , Yase Rohda A. , Reynolds Kiana A. , Tang W. Joyce , Kwon Ronald Young
Published: March 2024

Zebrafish and other small laboratory fish have emerged as important model systems for bone biomedical research. Micro-computed tomography (microCT) is a powerful approach to non-destructively visualize and quantify bone morphology, microarchitecture, and mineral density. While consensus methods for microCT scanning analysis have been established in other vertebrates including small rodents, the small size of zebrafish, their different anatomy, and their amenability to rapid-throughput and high-content approaches call for unique considerations. In this chapter, we describe key considerations for performing microCT scans in the zebrafish skeleton. We also describe different approaches for sample mounting, scan acquisition, and analysis that can be used depending on experimental objectives and outcomes. Finally, we describe a step-by-step protocol for microCT scanning and analysis of adult zebrafish using FishCuT, resulting in hundreds of descriptors of vertebral morphology and mineral density.
Geospatial Mapping Technique: A Study on Morphometry in the Gomti River Basin
Authors: Arvind Arvind , Kumar Surender Kumar Surender , Veer Vikas , Rohtash Rohtash , Anil Anil , Ripudaman Ripudaman
Published: March 2024

Geospatial application is a greater, recently emerging interdisciplinary technology with significant potential for studying geomorphic challenges. The study analysed the morphometric characteristics of the Gomti River basin in Uttar Pradesh, India, using topographical sheet and SRTM data. The linear (stream order and number, mean stream length and ratio, length of overland flow, bifurcation ratio and mean bifurcation ratio; areal (stream density, stream frequency, stream texture and ratio, form factor, elongated ratio, circulatory ratio, shape factor), and relief (basin relief and ratio, gradient ratio) aspects with asymmetric factors have been calculated using various methods. The catchment area of the Gomti River is 31009 km2, while its perimeter is 1435 km2. The results showed that the Gomati River has sub dendric to dendric drainage pattern followed with the natural gradient of the area. The low drainage density estimates of the basin also imply that it has a highly permeable subsurface covered by dense vegetation. The basin is substantially extended, as evidenced by the circularity ratio. The asymmetric factor analysis revealed the tilt and uplift of the Gomati Basin, indicating tectonic control over the basin, particularly near the confluence. The study's goal was to identify the major variables that contributed to the establishment of the watershed.
Antioxident and Anticancer Potency of Hydro-Alcoholic Ingudi (Balanites aegyptiaca Linn. Delile) Seed Extract
Authors: Kumar Sanjeev , Das Manoj Kumar , Singh Neelu , Pal Poonam , Tripathi Nancy , Sharma Rajesh , Jain Shreyans K. , Ranjan Amit , Singh Rajesh Kumar , Mishra Anurag
Published: March 2024

Balanites aegyptiaca is a well-known medicinal plant, which has been traditionally used by local communities to treat various human ailments such as antibacterial, skin diseases, wound healing, and antioxidant properties. This study, examined the phytochemical composition and cell toxicity of Balanites aegyptiaca using established methodologies. The main objectives of this research work are:
1. Evaluation of hydro-alcoholic seed extract in cell viability in breast cancer cell lines.
2. The study significance antioxidant activity.
3. Phytochemical analysis of Balanites aegyptiaca extract.
4. Anticancer activity of Balanites aegyptiaca extract.
The seeds were extracted using a hydro-alcoholic solvent (4:6 ratio), concentrated, and then lyophilized to obtain a dry crude extract. The radical scavenging capacity was determined using the DPPH method, while cytotoxicity was assessed through cell proliferation assays (MTT) involving MCF-7, MDA-MB-231, FaDu, and human PBMCs cells (2×103 cells). Our findings revealed that the hydro-alcoholic extract of the seeds exhibited significant antioxidant and anti-cancer properties. The novelty of this research work is the phytochemicals present in the plant extract that inhibit the migratory ability of breast cancer cells. The recommendations of this study can be adopted in the experimental and clinical trials.
Minimizing the training dataset for efficient crystal structure prediction by graph neural network
Authors: Lin Zijing , Li Chuannan , Liang Hanpu , Xu Si-Yuan , Zhang Xie , Wei Su-Huai
Published: March 2024

Machine learning has boosted the remarkable development of crystal structure prediction (CSP), greatly accelerating modern materials design.The state-of-the-art methods, however, rely on a giant training dataset, and there is a trend to keep increasing the dataset, as triggered by the great success of artificial intelligence models.Here, we show that a giant training dataset is not really necessary.Only around 100 carefully selected training samples for a target composition is already sufficient for highly accurate CSP, which is demonstrated in our newly developed CSP framework named SCCOP.This achievement stems from: (i) graph-deep-learning-based potential energy surface (PES) slicing, which classifies structures into different prototypes; and (ii) a multi-start optimization algorithm to divide-and-conquer the PES.
The accuracy and efficiency of our framework is validated on 27 typical compounds.The powerful capability of SCCOP is further demonstrated in two challenging problems: searching for the lowest-energy structures in boron allotropes and determining the structure of ordered vacancy compound CuIn5Se8.Our method discovers several new boron allotropes, and identifies the puzzling crystal structures of the ordered vacancy compound CuIn5Se8.This study establishes a new paradigm for CSP and offers a highly efficient and generally applicable CSP framework.
Urban Active Transportation Behaviour is Sensitive to the Fresh Start Effect
Authors: McGavock Jonathan , Fast Isaak , Sobhan Shamsia , Kl;aprat Nika , George Tyler , Vik Nils , Prowse Dan , Colllett Jacqueline
Published: March 2024

Municipal governments are investing billions of dollars to facilitate urban active transportation (AT), however, little is known about population-level AT behaviours. Triangulating real world data for cycling traffic (n=1.2M counts), corporate bicycle parking (n=56,307 counts), vehicular traffic (n=6.3M counts) and fitness centre attendance (n=563,290 counts) in a large Canadian city, we show a significant 20% decline in AT-based cycling traffic over the course of a work week, replicated over 5 consecutive years. The decline AT (-147 cyclists/day; 95% CI: -199.0 to -94.6 cyclists/day) and use of occupational bicycle parking (-12 cyclists/day; 95% CI: -17 to -7 cyclists/day) were similar to the weekly decline in fitness centre attendance. In contrast, daily vehicular traffic increased 7% from the beginning to the end of a work week (+2248 cars/day; 95% CI: 2022 to +3674 cars/day). This study provides population-level evidence that urban AT behaviour is susceptible to the “Fresh Start Effect” heuristic.
Industrial agglomeration, university-enterprise cooperation and enterprise innovation
Authors: li chaojing , Li Qiming
Published: March 2024

This paper constructs a fixed-effects model to investigate the relationship between industrial agglomeration, university-enterprise cooperation, and firm innovation using data from 2002–2019 on Chinese A-share listed companies in non-financial industries and the degree of regional agglomeration. The results of the benchmark regressions find that an increase in the level of regional industrial agglomeration reduces the innovation ability of firms, while university-enterprise cooperation enhances the innovation ability. At the same time, we find that university-enterprise cooperation plays a moderating role between industrial agglomeration and firm innovation, and university-enterprise cooperation can mitigate the decrease in innovation caused by industrial agglomeration. The above findings still hold after a series of robustness tests. Further, the results of heterogeneity tests show that the effects of industrial agglomeration and university-enterprise cooperation on firm innovation are influenced by the property rights and the location of the firm. The negative effect of industrial agglomeration on state-owned enterprises' innovation capability is more prominent in the eastern regions. However, the effect of university-enterprise cooperation on improving innovation capacity of non-state-owned enterprises is more significant in the western region.
A tension offloading patch mitigates dermal fibrosis induced by pro-fibrotic skin injections
Authors: Wan Derrick , Talbott Heather , Griffin Michelle , Mascharak Shamik , Parker Jennifer , Kuhnert Maxwell , Guo Jason , Deleon Nestor Diaz , Lavin Christopher , Abbas Darren , Guardino Nicholas , Morgan Annah , Valencia Caleb , Cotterell Asha , Longaker Michael
Published: March 2024

Skin fibrosis is a clinical problem with devastating impacts but limited treatment options. In the setting of diabetes, insulin administration often causes local dermal fibrosis, leading to a range of clinical sequelae including impeded insulin absorption. Mechanical forces are important drivers of fibrosis and, clinically, physical tension offloading at the skin level using an elastomeric patch significantly reduces wound scarring. However, it is not known whether tension offloading could similarly prevent skin fibrosis in the setting of pro-fibrotic injections. Here, we develop a porcine model using repeated local injections of bleomycin to recapitulate key features of insulin-induced skin fibrosis. Using histologic, tissue ultrastructural, and biomechanical analyses, we show that application of a tension-offloading patch both prevents and rescues existing skin fibrosis from bleomycin injections. By applying single-cell transcriptomic analysis, we find that the fibrotic response to bleomycin involves shifts in myeloid cell dynamics from favoring putatively pro-regenerative to pro-fibrotic myeloid subtypes; in a mechanomodulatory in vitro platform, we show that these shifts are mechanically driven and reversed by exogenous IL4. Finally, using a human foreskin xenograft model, we show that IL4 treatment mitigates bleomycin-induced dermal fibrosis. Overall, this study highlights that skin tension offloading, using an FDA cleared, commercially available patch, could have significant potential clinical benefit for the millions of patients dependent on insulin.
A Human Health Risk Assessment (HHRA) associated with gaseous pollutants in the ambient environment at Coimbatore City's High Traffic Road
Authors: karthikeyan Shivaranjani Salem , Arunachalam Gandhimathi
Published: March 2024

Adverse gaseous environmental pollutants are more prevalent due to the exponential rise of motorized road transportation, facilitated by the development of medical centers, IT sectors, and expanding urbanization. Therefore, to calculate the Air Quality Index, it is necessary to analyze and quantify the concentration of pollutants, including CO, NOX, SOX, and PM10, in the ambient air at the intersections of traffic zones along National Highway 544 (Avinashi Road), which runs through the city of Coimbatore, Southern part of India (11.0168°N, 76.9558°E). Additionally, it is essential to predict the dispersion of these pollutants using Quantum GIS. Between May and October of 2023, a Respirable dust sampler is used to gather ambient air samples at 15 locations based on zones with heavy traffic signals. Air quality is determined by comparing quantitative data with the National Ambient Air Quality Standards (NAAQs). The dispersion rate in terms of concentration of CO, NOX, and SOX in the sampling locations of National Highway 544 revealed that At the Sampling Location (SL5), the mean NOX concentration in May was higher (85 µg/m³) than in other sampling months. May 2023 observed a high mean SOX concentration value at SL7 (54 µg/m³), and NH 544's SL7 (2.14 mg/m3) had a significantly high mean CO concentration because of increased temperature. Comparing SL5, SL7, and SL12 to other sampling locations, these three spots rank lower in the Very Poor category mainly because of improper roadway alignment, traffic congestion, violations of traffic laws, poor vehicle maintenance, and end-of-life cars. The Hazard Quotient (HQ) for CO pollutants was less than 1, whereas the HQ for NOX and SOX was more significant than 1 for all exposed groups. In humans, low levels can result in hospitalization for respiratory infections and acute and obstructive lung diseases.
A low complexity contextual stacked ensemble-learning approach for pedestrian intent prediction
Authors: Chiang Chia-Yen , Jaber Mona , Slabaugh Gregory , Fathy Yasmin
Published: March 2024

Smart cities harness data and technology to enhance the sustainability and efficiency of urban areas and communities. Walking as a form of active travel is essential in promoting sustainable transport. It is thus crucial to accurately predict pedestrian crossing intention and avoid collisions, especially with the advent of autonomous and advanced driver-assisted vehicles. Current research leverages computer vision and machine learning advances to predict near-misses; however, this often requires high computation power to yield reliable results. In contrast, this work proposes a low-complexity ensemble-learning approach that employs contextual data for predicting the pedestrian's intent for crossing. Given a single scene, the pedestrian is first detected, and their image is then compressed using skeleton-isation, and contextual information is added into a stacked ensemble-learning approach. Our conducted experiments on different datasets achieve up to 15.8% gain in predictive accuracy compared to state-of-the-art approaches with 19.1% reduction in model complexity.
An Inner-Inter City Graph Neural Network for Predicting the Course of COVID-19 Cases
Authors: Ndiaye Moussa , Liu Tong , Cui Yangguang , Chen Jie , Yu Hang
Published: March 2024

Highly infectious diseases, for instance, the recent novel corona virus pandemic (COVID-19) have impacted millions of lives in the globe. From its early stage, it's clear that its transmission is closely related to the crowd mobility. However, research on the predictive power of city level mobility is lacking. To fill this gap, in this paper, we propose the Inner-Inter city Learner (IIL), a method based on the high correlation between human interaction and the new cases, to conduct short and midterm predictions of the COVID-19 cases at a city level. In term of design, IIL is composed of two main components: an inter city transmission learner and an inner city propagation pattern learner. In the first module, we specially generate graphs based on the mobility between cities. Using graph neural networks fed with the city level mobility graphs, the model learns how the transmission pattern in a city is impacted by its neighborhood cities. And in the second part, capitalizing in the highly contagious nature of the virus, we use the human interactions within the cities to capture the invariant features that directly correlate with the spread. In addition, to account for the limited inner mobility data, we employed the model agnostic meta learning to transfer the features common to all cities. We conduct various experiments and compare our methods with the state-of-art baselines. The results show the superiority of our method in various forecast horizons.
Optimization of tall fescue (Festuca arundinacea) callus induction and regeneration in culture medium containing ZnO and Ag nanoparticles
Authors: Moradi Meysam , amini fatemeh , Darbandi Ali Izadi
Published: March 2024

This experiment was conducted to optimize in vitro regeneration of tall fescue (Festuca arundinacea) and study of ZnO and Ag nanoparticles effects on it. The effect of six combinations of growth regulators of auxin (2.4-D) and cytokinin (BAP and Kinetin) on stem and seeds halved explant (Iranian and Foreign) was evaluated in a completely randomized design with three replications. To investigate the effect of nanoparticles on callus induction and regeneration, four concentrations of silver nanoparticles (0, 20, 40, 60 mg l-1) and zinc oxide nanoparticles (0, 25, 50, 100 mg l-1) in a completely randomized design with three replications were performed. The reults showed that the halved seed micro-sample, the Iranian ecotype, and the MS/2 culture medium had the best results for this part of experiment. The culture medium containing 1 mg l-1 2.4-D and 0.1 mg l-1 kinetin was the most suitable culture medium for callus formation, number of days to callus formation, callus length and width, callus perimeter and area, fresh and dry weight of callus. Also, the culture medium containing 0.9 mg l-1 2.4-D and 0.5 mg l-1 BAP had a higher amount than other culture mediums in terms of indirect and direct regeneration. In rooting medium with the concentration of 0.25 mg l-1 NAA and 0.1 mg l-1 2.4-D, the maximum and minimum root length and days to rooting were observed, respectively. The analysis of variance showed that the effect of silver nanoparticles and zinc oxide nanoparticles on the number of days to callus induction was significant. The effects of silver nanoparticles on the percentage of regeneration also were significant. The results of mean comparisons of silver nanoparticles showed that in the concentration of 20 mg l-1 callus formation was induced more rapidly. However, the treatment of 60 mg l-1 had the lowest average of callus induction. The results of the mean comparison showed the positive effect of zinc oxide nanoparticles on concentration of 25, 50, and 100 mg l-1 on callus formation rate compared to the non-application of zinc oxide nanoparticles.
Human limits in Machine Learning: Prediction of plant phenotypes using soil microbiome data
Authors: Aghdam Rosa , Tang Xudong , Shan Shan , Lankau Richard , Solis-Lemus Claudia
Published: March 2024

The preservation of soil health is a critical challenge in the 21st century due to its significant impact on agriculture, human health, and biodiversity. We provide the first deep investigation of the predictive potential of machine learning models to understand the connections between soil and biological phenotypes. We investigate an integrative framework performing accurate machine learning-based prediction of plant phenotypes from biological, chemical, and physical properties of the soil via two models: random forest and Bayesian neural network. We show that prediction is improved when incorporating environmental features like soil physicochemical properties and microbial population density into the models, in addition to the microbiome information. Exploring various data preprocessing strategies confirms the significant impact of human decisions on predictive performance. We show that the naive total sum scaling normalization that is commonly used in microbiome research is not the optimal strategy to maximize predictive power. Also, we find that accurately defined labels are more important than normalization, taxonomic level or model characteristics. In cases where humans are unable to classify samples accurately, machine learning model performance is limited. Lastly, we provide domain scientists via a full model selection decision tree to identify the human choices that optimize model prediction power.
Automated characterization of sudden cardiac death by using empirical mode decomposition and fuzzy entropy of ECG signals
Authors: Shi Manhong , Shi Yinuo , Zhou Wenkang Wenkang , Qi Xue
Published: March 2024

Early prediction of Sudden Cardiac Deaths (SCD), as a promising methodology to save lives threatened by SCD, has garnered significant attention from various countries. Various approaches has been proposed to forecast the occurrence of SCD, these approaches are limited either in terms of prediction time resolution or the length of early prediction time. In this work, we propose a novel methodology that combines Empirical Mode Decomposition (EMD) and Fuzzy Entropy (FuEn) using only one ECG beat with 651 data samples for the early anticipation of SCD. The experiment extracted a total of 652 features, consisting of 651 first-level intrinsic mode function (IMF) coefficients and 1 FuEn extracted from the first-level IMF coefficients. Subsequently, the Locality Preserving Projection (LPP) algorithm is applied to these features to reduce the number of features, the LPP features were ranked by various ways. Finally, the ranked features were fed to classifiers for discrimination between normal and prone-to-SCD subjects. Unlike other works where 1 min or 2 min signal analyzed as the prediction resolution is employed and the length of early prediction time is no more than 20 min, the proposed methodology analyzes each ECG beat for the SCD identification and is capable of predicting SCD up to 30 min earlier with the accuracy, specificity, and sensitivity of 98.5%, 98.4%, and 98.6% respectively. Therefore, the proposed methodology can be applied in portable medical sensors to real-time monitor conditions of person at risk of SCD in hospital settings or at home.
Interfering Factors in the Diagnosis of Senecavirus A
Authors: Júnior Antônio Augusto Fonseca , Laguarda-nascimento Mateus , Barbosa Aline Aparecida , Silva Valdênia Lopes , Carmagos Marcelo Fernandes
Published: March 2024

Senecavirus A (SVA) is an RNA virus that belongs to the genus Senecavirus within the family Picornaviridae. This study aimed to analyze factors that can influence the molecular diagnosis of Senecavirus A, such as oligonucleotides, RNA extraction methods, and RT-qPCR kits. Trizol, silica columns, and semi-automated extraction were compared, as well as different RT-qPCR kits. The study found significant variations that could impact the final diagnosis.
The secretory Inka cell of the Drosophila larval trachea has a molecular profile similar to that of neurons
Authors: Roeder Thomas , Bossen Judith , Niu Xiao , She Xin-Yu , Knop Mirjam , Hofbauer Benedikt , Tiedemann Lasse , Franzenburg Sören , Bruchhaus Iris , Kraus-Etchmann Susanne , Wegener Christian
Published: March 2024

The remarkable architecture of their respiratory system enables the unique metabolic performance that makes insects so successful. We performed single-cell RNAseq analysis of the larval tracheal system of Drosophila melanogaster and identified 17 cell populations that are part of or closely associated with the trachea. In addition to several groups of epithelial cells and different progenitor cell types, the secretory Inka cells showed unexpected properties. These cells express almost all the molecular elements of the presynaptic active zone and the neurotransmitter vesicle release machinery, both characteristic features of neurons. Some of these molecular elements are essential for their secretory function. In addition, we could molecularly map the different progenitor cell types and their different developmental stages. The data collected in this work represents a valuable resource for future studies focusing on this critical organ. The most important finding of this study is that Inka cells have neuron-like properties, which challenges the traditional picture of the distinction between neuronal and non-neuronal cells.
The Clinical Analysis of 20 Thymic Cysts
Authors: Gao Xufeng , Dong Yaolei , Song Xuanke
Published: March 2024

BackgroundCT screening for lung cancer has led to frequent findings of small anterior mediastinal masses. It is very hard to distinguish small thymomas from thymic cysts. The objective of this study was to discuss the diagnosistic methods and clinical features of thymic cyst.
Methods 20 patients who underwent sungical treatment and were diagnosed with thymic cyst between 2015 and 2020 were included. The patients included 14 men and 6 women aged 44 to 79 years (median 54 y). The operation was via a video-assisted thoracoscopic sungery(VATS) in 16 cases, a median sternotomy in 4 cases. Size, location, contour, shape, presence of calcification, and enhancement [Hounsfield units (HU)] were evaluated. And the study analyzed the misdiagnosis rates of thymic cysts with diameters ≤4cm and diameters >4cm.
Resuts All postoperative pathology was confirmed to be thymic cysts. All cases are located in the anterior superior mediastinum. Most of the thymic cysts have a maximum diameter of ≤4 cm, accounting for 70.0%, with a density (average CT value)＞20 Hu, and are easy to be misdiagnosed before operation. The preoperative misdiagnosis rate is about 85.7%. The thymic cyst which maximum diameter ＞4 cm , accounts for 30.0%, and the density (average CT value) ＜20 Hu, and it is also easy to misdiagnose. But the preoperative misdiagnosis rate is about 33.3%.
ConclusionThis study can be used to diagnosis thymic cysts in patients with small, asymptomatic thymic abnormalities on CT screening.
MRI-based Radiomics Signature for Screening Lymphovascular Invasion in Breast Cancer Patients
Authors: Zhang Cici , zhong Minzhi , liang zhiping , zhou jing , wang Kejian , bu jun
Published: March 2024

Objective
The early detection of lymphovascular invasion (LVI) is critical for the effective treatment of breast cancer (BC). This study aimed to investigate a non-invasive radiomics model based on MRI sequences for LVI screening in BC patients.
Methods
A total of 454 BC patients were enrolled in our study, with 150 in the LVI group and 304 in the non-LVI group. Radiomics features were extracted from MRI scans, including T2WI and DCE sequences, using LASSO analysis. Common machine learning algorithms (including LR, RF, KNN, SVM, GBDT, XGBoost, and LightGBM) were employed to construct radiomics signatures for assessing LVI status in BC patients.
Results
Eighteen radiomics features, 10 from DCE and 8 from T2WI, were retained to construct the radiomics signature. Among all the machine learning algorithms, the RF classifier model demonstrated superior performance in assessing the LVI status of BC patients, with an accuracy, sensitivity, and specificity of 63.32%, 74.47%, and 43.68%, respectively. The decision curve demonstrated significant clinical benefit of this model.
Conclusion
The radiomics-based RF model derived from MRI serves as a reliable indicator for identifying LVI status in BC, and holds great clinical utility for prompt intervention in invasive BC to improve the survival rate of BC patients.
The mediation and moderation effect of risk preferences on time preference and financial literacy
Authors: Mudzingiri Calvin , Sunge Regret , Katuka Blessing , Miya Nkopane Washington
Published: March 2024

This paper investigates the mediation and moderation effect of risk preference on time preferences and financial literacy. Existing studies show that people with higher levels of financial literacy are more likely to achieve better life outcomes. Better life outcomes depend on an individual’s knowledge and intertemporal choices executed in a context where inherent risk is prevalent. Regardless, existing studies directly evaluate the effects of financial literacy on intertemporal decisions. The current study brings in a different dimension by accounting for risk preferences' mediation and moderation role on financial literacy and time preferences. We collected data from 191 university students in South Africa using Multiple Price List (MPL) time preference and risk preference elicitation, a questionnaire, and a financial literacy test. A maximum likelihood estimation (MLE) of a pooled panel structural equation model (SEM) analysis was employed. The headline finding is that risk preferences fully mediate the impact of financial literacy on time preferences. The random effect panel regression on the moderation effect of time-risk preferences on financial literacy and financial literacy-risk preferences on time preferences confirms reverse causality between time preferences and financial literacy. The moderation effect of risk preferences on time preferences and financial literacy varies from one category of subjects to another. Risk aversion is associated with high levels of financial literacy.
JEL: C91; G4
The Complete Mitochondrial Genome of Schistura fasciolata: Phylogenetic and Evolutionary Implications within Tribe Nemacheilini
Authors: Luo Chao-yang , Zhao Cheng-fa , Gong You-jing , Hu Wen-xian , Mu Yuan
Published: March 2024

Background:The mitochondrial genome is widely used to understand the phylogeny and evolution of tribe Nemacheilini. This study aims to deepen the understanding of the mitochondrial genome ofSchistura fasciolata, and the evolutionary implications within Nemacheilini.Methods and Results:We conducted a comprehensive sequence analysis of the complete mitochondrial genome ofSchistura fasciolata, which is comprised of 16,570 base pairs including 37 genes: 13 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs), and two ribosomal RNA genes (rRNAs). The base composition analysis indicated an A+T content of 56.51% and a G+C content of 43.49%. Phylogenetic analysis based on the 13 PCGs revealed extensive polyphyly within Nemacheilini. Furthermore, accelerated evolutionary rates inND4,ND5, andCYTBgenes were observed inHomatulaandTroglonectes, suggesting an adaptation to specific environmental conditions.Conclusions:The mitochondrial genome analysis ofS. fasciolataprovides critical insights into the phylogeny and adaptive evolution within Nemacheilini. Our findings underscore the complex evolutionary dynamics within the tribe, with distinct evolutionary trajectories observed in mitochondrial genes among different genera.
In-vitro and in-silico investigation of α-amylase and α-glucosidase inhibition using Chamaecostus cuspidatus leaf for diabetes mellitus
Authors: Balaji Menaka Priya , Rajeswari V Devi
Published: March 2024

Chamaecostus cuspidatus is a mysterious plant with potential therapeutic properties for the amelioration of diabetic mellitus and related complications. This investigation aims to determine the potential phytochemicals through GCMS, FTIR, and NMR techniques. The objective is to perform in-vitro assays on the extract to evaluate its potential in various domains including antioxidant, antidiabetic, anti-inflammatory, antibacterial, and anticancer effects. The extract of methanol has demonstrated significant antioxidant properties due to its high phenolic content (3.523 mg GAE/g), flavonoid content (2.55 mg QE/g), and total tannin content (2.133 mg TAE/g). GC-MS analysis of leaf extract depicted the presence of 16 bioactive compounds among 9-octadecenamide, 7-nonenamide, 13-docosenamide, and hexadecanamide are enrich essential fatty acids. The NMR spectrum of 13C confirms the presence of functional bioactive compounds. This work includes molecular computational studies, specifically protein-ligand docking energy and ADMET analysis. Selected screened compounds including akuammilan-17-ol, nor-diazepam, 1,2-benzene dicarboxylic acid, and standard metformin were investigating the interaction against α-amylase and α-glucosidase. The ligand of nor-diazepam showed the highest binding affinity compared to akuammilan-17-ol, 1,2-benzene dicarboxylic acid, and metformin; therefore, nor-diazepam could be the focus of more in-depth research.
tinyHulk: Lightweight Annotation for Wearable Cognitive Assistance
Authors: Nguyen Chanh , Iyengar Roger , Dong Qifei , Blakley Jim , Satyanarayanan Mahadev
Published: March 2024

Wearable Cognitive Assistance (WCA) employs DNNs models to provide real time step-by-step guidance to end-users for specific tasks. Collecting and annotating training data for such DNNs is painful and takes massive human effort and time. In this work, we propose tinyHulk, an automatic annotation tool that reduces human time and effort for these labor-intensive annotation tasks. Further, the tool is equipped with the background replacement, an object-aware data augmentation mechanism to increase the size and diversity of the training set. Our experimental results show that using tinyHulk can save human time up to fourfold compared to using manual methods for labeling tasks. The computer vision models trained with data annotated and generated by tinyHulk achieve high accuracy, outperforming the models trained with real or synthetic data using conventional methods for annotation.
Enhancing Growth and Nutrient Uptake in Bouffegous Date Palm Variety With Seaweed Extracts and AMF/PGPR Combination in the Field.
Authors: NOGOT ABDELAAZIZ , Khardi Abdesalam , ABOUMADANE Hicham , Goutoutou Madiha , Jaiti Fatima
Published: March 2024

The utilization of seaweed extracts (SWE) and a combination of arbuscular mycorrhizal fungi (AMF) and plant growth-promoting rhizobacteria (PGPR) has received considerable attention in recent years due to their potential to ameliorate growth and nutrient uptake in diverse vegetable species. To promote sustainable agriculture in oasis ecosystems, particularly for the date palm that serves as a fundamental component of the economic and social development of oases, a study was conducted at a private palm farm in Tamassint, Errachidia province. The study aimed to evaluate the effects of seaweed extracts (SWE) and a combination of arbuscular mycorrhizal fungi (AMF) and plant growth-promoting rhizobacteria (PGPR) on the mineral uptake, physiological, and morphological growth parameters of the Bouffegous date palm cultivar. Seven treatments were used, which included SWE: Ascophyllum nodosum at 1% and 2%, SWE: Ecklonia maxima at 1% and 2%, 10g/palm and 20g/palm of a combination of AMF/PGPR, and a control group with no treatment. The results indicated that seaweed extracts enhanced growth rate parameters, nutrient uptake, chlorophyll content, and fluorescence activity in both growing seasons, whereas the AMF + PGPR combination only improved all the measured parameters in the second year of the experiment, except for the number of leaves per palm where no effects were observed during both seasons.
An Improved Simulation Based Method forSelection of Cell in Cellular Network
Authors: Popat Kalpesh
Published: March 2024

In Cellular networks, selection of cell is a process which will help in determining the exact location of the cellular phone. Various services related to mobile network are dependent on selection of cells only. Factors like radio propagation, directional beamforming, and handover processes play a significant role in determining the overall performance of the network. Researchers and network operators continuously work on optimizing cell selection strategies to minimize disruptions and provide high-quality service to end users. Cell Selection plays a vital role in the whole cellular network system. In this paper Cell selection performance has been evaluated by using 9 working simulation models with 9 different scenarios from simple and small network to high congestion mobile network. Qualnet Software is employed for conducting analyses that take into account factors such as the quantity of nodes, simulation duration, and the volume of calls between multiple mobile devices.
The impact of environmental regulations on enterprise production——Based on the production game model
Authors: Fan Xuecheng , Wang Xinxin , Xu Zeshui
Published: March 2024

As an integral part of the modern environmental governance system, environmental regulations have the potential to alter the dynamics of competition and cooperation, resulting in a phenomenon known as the “relatively better” effect. In light of this phenomenon, we have developed a static game model based on incomplete information to analyze its implications. Through the exploration of Nash equilibrium, we have discovered that the "relatively better" effect allows environmentally regulated clean enterprises to transfer a portion of their costs to polluting enterprises. Consequently, clean enterprises experience lower financing costs compared to their polluting counterparts under environmental regulation conditions. This conclusion is supported by empirical analysis conducted using Chinese enterprise data. Furthermore, our research reveals that appropriately designed environmental regulations, including the intensity of regulation and the establishment of emission rights trading systems, can contribute to improving social distribution.
Research on the quality evaluation method of copper and aluminum diffusion bonding
Authors: Li Shengtao , Hou Huaishu , Yun Han , Li Jinhao
Published: March 2024

The application of copper-aluminum welded pole pieces is extensive in the new energy manufacturing sectors, such as automotive batteries. Diffusion bonding is one of the main ways of copper-aluminum connection, which is very important for the safe use of the components in terms of the quality of its connection. First, a water-immersion ultrasonic inspection platform was built to carry out inspection research on internal defects in copper-aluminum welding. In order to address the problem of overlapping signal features at the interface, the signal is decomposed and reconstructed by applying the wavelet decomposition method using the water-immersion focusing transmission method. The reconstructed signal is then decomposed in three layers, and the energy features of the last layer are extracted to form energy feature vectors for the qualitative analysis of different defect types. Secondly, different welding area samples were prepared and the peak power spectral density was extracted as an interface characterization parameter. Tensile tests were conducted on samples with varying weld areas, and the measured maximum pull-out force was correlated with the characteristic parameters of ultrasonic testing. The results demonstrate the effective capability of wavelet packet analysis in identifying un-welded, weak bond, and micro-porous defects at the diffusion welding interface. A higher peak value of power spectral density indicates superior welding quality. Establishing a reasonable threshold for peak power spectral density can serve as a reliable criterion for assessing the condition of copper and aluminum diffusion bonding.
Exhaustive Investigation Into the Corrosion Resilience of Aluminium-silicon Carbide Metal Matrix Composites
Authors: Sriniva Bharath
Published: March 2024

In recent years, there has been a noteworthy surge in the fascination surrounding Metal Matrix Composites (MMCs), driven by their remarkable corrosion resilience and superior mechanical attributes. Notably, aluminum and its alloys have garnered substantial utilization across diverse industries, prominently in manufacturing and aerospace, owing to their trifecta of attributes - lightweight construction, heightened mechanical prowess, and corrosion-resistant characteristics. Aluminum, being highly amenable to alloying, has been synergistically blended with Silicon Carbide (SiC) in weight-proportional ratios of 12% and 13% in the present investigation, thereby elevating these already laudable attributes of the base material. The focal point of this study has been the evaluation of the corrosion resistance exhibited by these newly formulated MMCs. A salient methodology employed for this purpose was the utilization of a rigorous salt spray corrosion test, extending over a duration of 96 hours. The findings stemming from these corrosion assessments unequivocally substantiate the marked superiority in corrosion resistance exhibited by both MMC variants in comparison to their pure aluminum counterpart. This heightened corrosion resilience can be ascribed principally to the presence of SiC particulates within the MMCs, which function as formidable impediments, effectively curtailing the ingress and migration of corrosive agents towards the metal matrix. Moreover, the outcomes gleaned from this study offer valuable insights into the multifaceted nature of composite material corrosion behavior. It becomes apparent that various auxiliary factors exert a profound influence on the corrosion dynamics of these advanced materials.
Wear mechanisms on metal seal pairs surfaces in three-body abrasive environments: removal of asperity peaks
Authors: Wei Tangshengjie , Zhang Kai , Zhou Qin , Zhou Ziyi , Lv Minghao , Di Yunfei , Qin Kun
Published: March 2024

Surface roughness characteristics are considered to be important in influencing wear performance, with asperity peaks being among the more influential. Changes in the asperity peaks removal mechanism were explored at the microscopic level through friction wear experiments, revealing the reasons for changes in the macroscopic wear amount. The asperity peaks show different forms of removal under metal surface with different roughness characteristics. At low roughness it is mainly removed as a whole and at high roughness it is mainly removed partially. In addition, the ranges of roughness parameters Ra and Rpk, which have excellent wear performance, were determined. The research in this paper can provide assistance in designing the processing of metal surfaces to reduce wear loss.
Mammals’ use of forest-cashew-rice mosaics in West Africa is modulated by their trophic guild
Authors: Fernàndez Aina Rossinyol , Dabo Djunco , Silva Francisco dos Reis , Oliveira Raquel , Seck Sambú , Rainho Ana , Cabeza Mar , Palmeirim Ana Filipa
Published: March 2024

Over millennia, mammals have co-existed with humans across the forest-savannah mosaics characterising part of West Africa. While some savannahs have long been used for rice cultivation in Guinea-Bissau, both those and forests are now being rapidly converted into cashew monocultures. To understand how species cope with such land-use changes, we investigated how mammals make use of forest patches, cashew orchards and rice paddies over two contrasting periods: at the start and at the end of the rainy season. Using camera-trapping, we examined mammal diversity and trophic guild activity across seven landscapes, each comprising three sampling sites located on each of the habitat types in Northern Guinea-Bissau. Based on 937 records from 21 species, species richness and activity were similar across habitat types, but generally higher at the end-rainy season. Species composition differed between the two forestry habitats and rice paddies. At the guild-level, carnivores increased in activity in forests at the start-rainy season, activity decreased in cashew orchards at the end-rainy season for insectivores and at the start-rainy season for herbivores, and omnivores increased in activity in cashew orchards. The similar assemblage-wide diversity across habitat types seems to be enabled by the high habitat heterogeneity but likely offset by previous extinctions from the area. Yet, guild-level habitat use suggested that cashew orchards might not sustain functionally healthy mammal assemblages. Habitat change is expected to become more detrimental under drier climates. Limiting cashew expansion while preserving forests is essential to optimize the long-term conservation of mammal diversity in rural West Africa.
Research on YOLOv8 Object Detection Algorithm in UAV Scenarios
Authors: Li Shixin , Chen Fankai , Sun Zhongyuan , Zhu Zhiren , Zhou Liming , Tang Kaiwen
Published: March 2024

Aerial images captured by Unmanned Aerial Vehicles (UAVs) often exhibit characteristics such as high object density, small targets, and wide coverage, which lead to the occurrence of false positives and false negatives in existing object detectors. In order to improve detection accuracy, this study proposes an enhanced YOLOv8 object detection model. Firstly, the Deformable Convolutional Networks v2 (DConv2) is incorporated into the backbone's C2F module to expand the receptive field of the image and enhance the detection accuracy of small objects. Secondly, the Fousion Block module is employed in the neck network to increase network depth and strengthen feature fusion capability. Subsequently, the model incorporates up sampling operations and discards large object detection layers, further improving the detection progress for small objects. Finally, the Efficient Intersection over Union (ECIoU) loss function is adopted to replace the Complete Intersection over Union (CIoU) loss function, accelerating the model's convergence speed and enhancing object detection precision. The improved model is validated using the VisDrone2019-DET-train dataset, and the results demonstrate that the enhanced model Yolov8s_UAU achieves anmAP of 49.5%, representing a 7.3% improvement over traditional models. With the capability to maintain UAV detection speed, the proposed model can more accurately accomplish the detection tasks for small targets during UAV aerial photography processes.