bluuebunny/crossref_metadata_2025_split huggingface dataset:
Synergistic process interactions and morphological change in a river reach subject to multiple disturbances, the Laja River, Chile
Authors: Barahona José , Alcayaga Hernán , Caamaño Diego , Mao Luca , González Christian 
Published: April 2024

AbstractThis work studies the multiple sources of impacts and disturbances in the Laja River (Central Chile) and evaluates the changes in water and sediment flows and planimetric geomorphic changes.The disturbances sources correspond to hydroelectric plants, water withdrawals for irrigation and the sustained decrease trend in rainfall in the basin. The changes in the plan‐view shape of the river reach were quantified using remote sensing techniques, through a supervised classification of Landsat 5 TM and 8 OLI satellite images, identified Water (W), Islands and Riverine Vegetation (IRV) and Bars and Banks Without Vegetation (BBWV), obtained a Kappa index>0,83 for a period of 15 years (2006–2021).Compared with historical records, the period of analysis shows a decrease in annual rainfall by 17.5%. In addition, water withdrawals for irrigation have contributed to a 64% decrease in monthly stream discharge during the dry season. As a consequence of the decrease in annual rainfall and water withdrawals for irrigation, the sediment transport capacity has also decreased by 10.5%.The changes in morphological driving variables (stream flows and sediment transport regimes) have manifested themselves in morphological changes, where it was possible to establish that a change in the channel form occurred in the last 15 years, going from a river with a single channel to a braided one. An important vegetation establishment has accompanied this morphological change on both riverbanks and the central bars. The colonizing vegetation corresponds to fast‐growing non‐native species (Salix spp., Populus spp. and Alnus spp). A stabilization of the channel form is expected, consolidating itself as a braided section with alternating vegetated bars.
Study on the influence of different thermal coals on the mixed‐burning characteristics of ventilation air methane and pulverized coal
Authors: Hao Chaoyu , Liu Yufei , Chen Yankun , Zhang Yachao , Deng Cunbao 
Published: April 2024

AbstractTo effectively utilize ventilation air methane (VAM), it is proposed to pass it into the boiler of the coal‐fired power plant for mixed combustion. However, the different types of thermal coal utilized present distinct characteristics when mixed with VAM. In this paper, lignite, bituminous coal, and anthracite are selected to study the CH4 conversion rate, system ignition temperature, and NO emission characteristics of the VAM‐pulverized coal coupled system on a fluidized bed experimental platform. The experimental results show that the ignition temperatures of VAM are 748, 736, and 732 °C when the CH4 concentration is 0.25, 0.5, and 0.75%. After the addition of thermal coal, the ignition temperature decreased significantly. When the CH4 concentration is 0.25%, the ignition temperature of the lignite‐VAM system is the lowest, which is 450 °C. Anthracite has the strongest catalytic effect on CH4 combustion. When the heating rate is 5 °C /min, the reaction rate of CH4 is the fastest in the anthracite‐VAM coupled system. Under laboratory conditions, the peak NO concentration in the coal‐VAM coupled system was lignite > bituminous coal > anthracite at different CH4 concentrations, and the CH4 in VAM had a reducing action on the NO generated during mixed combustion, with a stronger reducing effect observed as the CH4 concentration increased. The results of this study can lay the foundation for the industrial application of the mixed combustion of VAM and thermal coal, and be of great significance for solving the practical problems caused by the change of boiler coal types. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.
Diverse response of shallow lake water levels to decadal weather patterns in a heterogeneous glacial Boreal Plains landscape
Authors: Leader S. , Kettridge N. , Hannah D. , Mendoza C. , Devito K. J. 
Published: April 2024

AbstractTo examine the relative controls of landscape and climate on spatial variability, we measured water level dynamics of shallow lakes over two decades that represent both the heterogeneity of surficial geology classifications, and thus the potential range in surface and groundwater connectivity, and the long‐term weather patterns of the Boreal Plain hydrogeoclimatic setting. Large ranges in shallow lakes water levels (between 0.25 and 2 m) were observed corresponding to extremes in precipitation relative to the long‐term mean precipitation over the study period. We found low concurrence in water level dynamics among four detailed study lakes that received the same meteorological weather signal, but were located in different surficial geology texture classifications that incorporated important landscape parameters associated with lake water balance and storage. Surficial geology classification alone did not, however, distinguish between different ranges in lake water level measured in a broader synoptic survey of 26 lakes across the region. Thus, simple surficial geology classifications cannot alone be applied to classify Boreal Plain lake water level dynamics and other controls, notably landscape position, must also be considered. We further show that inter‐annual variability in lake water levels was significantly greater than seasonal variability in this hydrogeoclimatic setting. This emphasizes the need for studies of sufficient length to capture weather extremes that include periods of wetting and drying, and demonstrates how observed magnitudes of water level variability, and lake function, can be an artefact of study length and initiation date. These findings provide a foundation to test and calibrate conceptual understanding of the wider controls of lake water levels to form holistic frameworks to mitigate ecological and societal impacts due to hydrological changes under climate and anthropogenic disturbance within and between hydrogeoclimatic settings.
Overcome Certification Challenges of AI Based Airborne Systems Using FAA Overarching Properties
Authors: Paramasivam Prameela , P Shashi Kumar , Paulraj Vasantha Selvi , Chandrashekar Rooparani 
Published: April 2024

AbstractTechnologies such as Artificial Intelligence encompassing Machine Learning, Deep Learning techniques have proved to be a game changer in multiple sectors. The use of these technologies in Aviation will prove to ensure higher rigor in predictive maintainability, air traffic management, safety assurance and airworthiness. Current traditional regulatory and industry standards have proved to be efficient and effective in providing highest level of safety. However, Certification of Avionics systems is becoming challenging because of increasing complexity of avionics systems. Current regulations and standards are very prescriptive and inadequate to support new technological trends. Civil Aviation Authorities (CAAs) are working on establishing the regulatory guidance around these technologies. This paper addresses the new strategy of employing Federal Aviation Administration (FAA) “Overarching Properties” ‐ a framework for demonstrating that a product has the qualities required to perform effectively and safely with the purpose of simplifying the certification process. This paper also covers the possible scenarios for establishing the safety net concept to enhance the determinism and airworthiness of AI based systems.
SASER in Practice: Early Insights from Two Case Studies Connected to Industry
Authors: Lenssen Jan A. , Ahmed Usama , Nizamis Kostas , Pessoa Marcus V. Pereira , Bonnema G. Maarten 
Published: April 2024

AbstractSystems Engineering (SE) research with Industry as a laboratory (IaL) has multiple benefits: it safe‐guards relevance, facilitates co‐design, and allows researchers to gain practical insights. However, it faces a challenge in aligning traditional academic research with fast‐paced industrial processes. The Spiral Approach for Systems Engineering Research (SASER) was recently proposed to address this challenge. In this paper, we demonstrate and discuss for the first time the application of SASER in the context of (1) the High‐tech industry, and (2) Small and Medium Enterprises (SMEs). Despite the context differences of the two case studies, SASER shows promising results for both the researchers and the industrial partners in keeping the partners interested, enabling shorter feedback loops, and preventing analysis paralysis. Even though there are just two case studies and this is an early stage research application of SASER, we firmly believe that it shows merit in facilitating SE academic research in the context of IaL.
AI enabled Digital Transformation of Space Test System
Authors: Kumar Prabhat , Kumar Deepak , Sridharan Goutham 
Published: April 2024

AbstractA proper timebound testing is a crucial step throughout the development lifecycle from design to launch of every successful spacecraft mission. Each mission is unique and complexity increases with the variety. Each test plan includes multiple different tests which need to be planned manually which is cumbersome and prone to human errors. Here we can use AI to be more proactive in recognizing these in the first phase of choosing the best recommended plans. Artificial Intelligence (AI) assisted Digital transformation improves testing efficiency by streamlining many of the traditionally time‐consuming processes. In this paper, we have proposed multiple AI based solutions (Test plan and procedure recommendation, space simulation lab health monitoring, Faster Anomaly resolution) which will enable satellite system and test team in better planning and execution of test lifecycle. Proactive health monitoring and prior prediction of probable failure inside simulation lab helps us in increasing overall availability of testing facility.
A Benchmark Example of Intelligent Traffic Management System using Artificial Intelligence
Authors: R Jyothi Priya , Paramasivam Prameela , Kanagaraj Thava Bharathi , Paramasivan Sivasankari 
Published: April 2024

AbstractThis paper describes a practical methodology for benchmarking the Intelligent Traffic Management System (ITMS) of Smart City Application. It explains the complete development of the application from defining requirements to verifying the software. The system design is demonstrated using MATLAB System Composer. To achieve the intelligent traffic system, we used the You Only Look Once (YOLO) v5 algorithm for vehicle detection. The other deep learning prediction logics are shown to realize the various features such as the optimal route detection, hi‐traffic detection of ITMS. Followed by the verification and structural coverage of Intelligent Traffic Management System is demonstrated with example.
Selecting a birth weight standard for an indigenous population in a LMIC: A prospective comparative study
Authors: Geerts Lut , Brink Lucy T. , Odendaal Hein J. 
Published: April 2024

AbstractObjectivesThe aim of the present study was to compare birth weight (BW) distribution and proportion of BWs below or above specified percentiles in low‐risk singleton pregnancies in healthy South African (SA) women of mixed ancestry with expected values according to four BW references and to determine the physiological factors affecting BW.MethodsThis was an ancillary study of a prospective multinational cohort study, involving 7060 women recruited between August 2007 and January 2015 in two townships of Cape Town, characterized by low socioeconomic status, and high levels of drinking and smoking. Detailed information about maternal and pregnancy characteristics, including harmful exposures, was gathered prospectively, allowing us to select healthy women with uncomplicated pregnancies without any known harmful exposures. In this cohort we compared the median BW and the proportion of BWs <P3, 5 and 10 or >P90, 95 and 97 according to four reference standards (INTERGROWTH‐21st, customized according to the method described by Mickolajczyk, Fetal Medicine Foundation and revised Fenton reference) with expected values. Appropriate parametric and nonparametric tests were used, and sensitivity analysis was performed for infant sex, first trimester bookings and women of normal body mass index (BMI). Multiple regression was used to explore effects of confounders. Written consent and ethics approval was obtained.ResultsThe cohort included 739 infants. The INTERGROWTH‐21st standard was closest for the actual BW‐distribution and categories. Below‐expected BW was associated with boys, younger, shorter, leaner women, lower parity and gravidity. Actual BW was significantly influenced by maternal weight, BMI, parity and gestational age.ConclusionOf the four references assessed in this study, the INTERGROWTH‐21st standard was closest for the actual BW distribution. Maternal variables significantly influence BW.
Intelligent MRI diagnosis of neurological alterations in infants from 4 to 12 months
Authors: Calderon‐Uribe Salvador , Morales‐Hernandez Luis A. , De Leo‐Jimenez Jose O. , Resendiz‐Ochoa Emmanuel , Toledano‐Ayala Manuel , Cruz‐Albarran Irving A. 
Published: April 2024

AbstractMagnetic resonance imaging is an essential tool for the identification of neurological problems since it provides relevant information on brain development. The aim of the present work was the detection of neurological alterations in newborns from 4 to 12 months of age by segmentation and analysis of lateral ventricles in magnetic resonance images. For this purpose, an automated deep approach based on U‐net is proposed to segment the cerebral ventricles of the newborn. Subsequently, for these regions, features were extracted based on the patient's clinical history and on the shape (area, roundness, normalized central moment, among others) and pixel intensity (mean gray value, contrast level, among others). Once the features were extracted, different types of intelligent models (Logistic Regression, k‐Nearest Neighbors (kNN), and a Convolutional Neural Network) were assessed to detect the presence of neurological alterations. The segmentation phase of the system was tested on 50 patients and the classification phase on 28 patients (11 healthy, 17 with neurological changes). The results show a DICE similarity coefficient of 0.89 and a volume ratio of 1.05 for the segmentation stage and an accuracy of 98%, precision of 100%, sensitivity of 92%, and specificity of 100% for the classification stage using kNN. The last one proved to be the most computationally feasible model, due to the time required for training and inference (0.36 s and 35.2e‐4 s, respectively), as well as the consumption of computational resources (0.1 GB RAM CPU). In conclusion, it is possible to detect neurological alterations in newborns aged 4 to 12 months by segmenting and classifying the lateral ventricles in magnetic resonance images, using image processing techniques, the U‐net, as well as the kNN algorithm. This proposed methodology could play an important role in the early diagnosis and treatment of neurological disorders.
Cytogenotoxic screening of the natural compound niga‐ichigoside F1 from Rubus imperialis (Rosaceae)
Authors: Almeida‐Terassi Laíza Moura , Castanha Ana Paula Martins , Alves Greice Rafaele , Cechinel‐Filho Valdir , Niero Rivaldo , O'Neill de Mascarenhas Gaivão Isabel , de Oliveira Liana Martins , de Lima Luan Vitor Alves , Mantovani Mário Sérgio , Maistro Edson Luis 
Published: April 2024

AbstractRubus imperialis Chum. Schl. (Rosaceae) have demonstrated some pharmacological activities, including gastroprotective action. However, genotoxic effects of R. imperialis extract was also reported. Since niga‐ichigoside F1 (NIF1) is a major compound of this plant species, and which has proven pharmacological properties, it is essential to investigate whether this compound is responsible for the observed toxicity. Therefore, the objective of this study was to analyze the effects of NIF1 on HepG2/C3A cells for possible cytogenotoxicity, cell cycle and apoptosis influence, and expression of genes linked to the DNA damage, cell cycle, cell death, and xenobiotic metabolism. The results showed no cytogenotoxic effects of NIF1 at concentrations between 0.1 and 20 μg/ml. Flow cytometry also showed no cell cycle or apoptosis disturbance. In the gene expression analysis, none of the seven genes investigated showed altered expression. The data indicate that NIF1 has no cytogenotoxic effects, and no interruption of the cell cycle, or induction of apoptosis, apparently not being responsible for the cytotoxic effects observed in the crude extract of R. imperialis.
Designing a deep hybridized residual and SE model for MRI image‐based brain tumor prediction
Authors: Saran Raj S. , Surendiran B. , Raja S. P. 
Published: April 2024

AbstractDeep learning techniques have become crucial in the detection of brain tumors but classifying numerous images is time‐consuming and error‐prone, impacting timely diagnosis. This can hinder the effectiveness of these techniques in detecting brain tumors in a timely manner. To address this limitation, this study introduces a novel brain tumor detection system. The main objective is to overcome the challenges associated with acquiring a large and well‐classified dataset. The proposed approach involves generating synthetic Magnetic Resonance Imaging (MRI) images that mimic the patterns commonly found in brain MRI images. The system utilizes a dataset consisting of small images that are unbalanced in terms of class distribution. To enhance the accuracy of tumor detection, two deep learning models are employed. Using a hybrid ResNet+SE model, we capture feature distributions within unbalanced classes, creating a more balanced dataset. The second model, a tailored classifier identifies brain tumors in MRI images. The proposed method has shown promising results, achieving a high detection accuracy of 98.79%. This highlights the potential of the model as an efficient and cost‐effective system for brain tumor detection.
Influence of female sex and double‐quadruple semitendinosus‐gracilis graft on the incidence of postoperative symptomatic cyclops lesions after ACL reconstruction
Authors: Arens Tabea , van Melick Nicky , van der Steen Maria C. , Janssen Rob P.A. , Bogie Rob 
Published: April 2024

AbstractPurposeRisk factors for the development of symptomatic cyclops lesion after anterior cruciate ligament reconstruction (ACLR) surgery are not entirely identified yet. This study aimed to investigate whether the choice of hamstring graft (semitendinosus‐gracilis; STG vs. semitendinosus; ST) affects the risk of developing a symptomatic cyclops lesion after ACLR.MethodsThis retrospective cohort study included 1416 patients receiving either an ST graft (n = 1209) or an STG graft (n = 207) ACLR with a follow‐up of at least 2 years. A persisting extension limitation was clinically determined, and cyclops lesions were confirmed by magnetic resonance imaging (MRI) and second‐look arthroscopy. Graft‐specific incidence of cyclops lesions was examined with χ2 test and combined with the factors number of graft bundles, graft diameter and sex evaluated with a binominal logistic regression model.ResultsIn total, 46 patients developed symptomatic cyclops lesions (3.2%), with 36 having ACLR with an ST graft (3.0%) and 10 with an STG graft (4.8%) (n.s). The mean time from ACLR to the second‐look arthroscopy for cyclops removal was 1.1 ± 0.6 years. Female patients were 2.5 times more likely to develop a cyclops lesion than male patients. Patients with an STG graft and larger graft diameters did not have a higher risk of developing cyclops lesions. Patients who received an STG graft with both tendons folded four times (double‐quadruple) had significantly higher risk of developing a cyclops compared with all other numbers of graft bundles combined (8.3%, respectively 3.0%; p = 0.014).ConclusionThis study could not prove an increased risk of developing a symptomatic cyclops lesion for patients with an STG graft compared with an ST graft used for ACLR. However, patients with a double‐quadruple ACLR had a higher percentage of cyclops lesions compared with all other numbers of graft bundles. Female sex was associated with an increased risk of developing cyclops lesions.Level of EvidenceLevel III.
A non‐grain production on cropland spatiotemporal change detection method based on Landsat time‐series data
Authors: He Tingting , Jiang Suqin , Xiao Wu , Zhang Maoxin , Tang Tie , Zhang Heyu 
Published: April 2024

AbstractGlobal food security is being threatened by the reduction of high‐quality cropland, extreme weather events, and the uncertainty of food supply chains. The globalization of agricultural trade has elevated the diversification of non‐grain production (NGP) on cultivated land to a prominent strategy for poverty alleviation in numerous developing nations. Its rapid expansion has engendered a multitude of deleterious consequences on both food security and ecological stability. NGP in China is becoming very common in the process of rapid urbanization, threatening national food security. To better understand the causal mechanisms and enable governments to balance food security and rural development, it is crucial to have a clear understanding of the spatiotemporal dynamics of NGP using remote sensing. Yet knowledge gaps remain concerning how to use remote sensing to track human‐dominated or ‐induced long‐term cultivated land changes. Our study proposed a method for detecting the spatiotemporal evolution of NGP based on Landsat time‐series data under the Google Earth Engine platform. This approach was proposed by (1) obtaining the union of cultivated lands from multiple landcover products to minimize the cultivated land omission, (2) constructing multi‐index dynamic trend rules for 3 representative types of NGP and obtaining results at the pixel level, while adopting the continuous change detection and classification algorithm to Landsat time series (1986–2022) to determine when the most recent change occurred, (3) minimizing the noise by object‐oriented land use–land cover classification and mode filter approaches, and (4) mapping the spatiotemporal distribution of NGP. The proposed methodology was tested in Jiashan, located in Zhejiang Province (eastern China), where NGP is widespread. We achieved a high overall accuracy of 95.67% for NGP type detection and an overall accuracy of 85.26% for change detection of time. The results indicated a continued increasing pattern of NGP in Jiashan from 1986 to 2022, with the cumulative percentage of NGP increasing from 0.02% to 20.69%. This study highlights the utilization of time‐series data to document essential NGP information for evaluating food security in China and the method is well‐suited for large‐scale mapping due to its automatic manner.
The clinical gaze: Its power and purpose in determining the patient experience and the value of interpersonal and technical skills in medicine
Authors: Khan Hamaad Ahmad 
Published: April 2024

AbstractCurrent health education typically prizes and prioritises a biomedical sensibility in clinical practice, often to the detriment of the patient experience. In exploring the impact of the clinical gaze on patient experiences of medical care, this article argues for integrating interpersonal and technical skills in modern medicine, emphasising the value of holistic and compassionate care.