bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessment of Difference in Structure from Motion for Stockpile Volume Estimation using UAV Approach
Authors: Halim Ahmad Eizat Abdul , Rahman Abdul Aziz Ab , Maulud Khairul Nizam Abdul , Nekmat Muhammad Salihin , Mukhlisin Muhammad 
Published: September 2023

Abstract
The software for processing the photogrammetry software is increasingly capable of producing a high-quality 3D model DEM, DSM, and DTM from an aerial photo and measuring the volume of material. Nowadays, UAV technology has been rapidly used for stockpile volume calculation for the mining and construction industry. The purpose of this study is to compare image processing for the stockpile volume calculation using three different software for the UAV with the conventional method GPS as a reference. The result obtained using GPS is processed using CIVIL3D and act as a base measurement of the stockpile volume to compare result obtain from photogrammetry software which is Pix4D, Agisoft, and SimActives Correlator3D. The analysis of this study compared 3D model for volume calculation from a photo image for each photogrammetry software selected and obtained the result volume calculation for photogrammetry with the accuracy achieving below 3% error from the referenced volume of the stockpile. 12 GCP were placed around the study area to test the difference between the numbers of GCP processing for the volume stockpile results. The image acquisition is taken at 10 meter altitude and 20 meter altitude using UAV Phantom 4 pro and set with Pix4Dcapture circular mission. The processing method is SfM in the 3 photogrammetric selected that have 4 types of processing for each software, 10 meter altitude and 20 meter altitude with 4 GCP and 12 GCP. 12 results of the 3D model and volume of the stockpile was obtained. As for the volume results, the Pix4D 10 meter 4 GCP result obtained the closest volume to the reference. The point cloud generated is also stable based on the cloud compare volume density result. Overall, the study can conclude that not only the altitude of flight affected, and the type of software used is affecting the number of point clouds generated but also the number of GCP.
Marine Habitat Mapping using Multibeam Echosounder Survey and Underwater Video Observations: A Case Study from Tioman Marine Park
Authors: Muhamad Muhammad Abdul Hakim , Hasan Rozaimi Che , Said Najhan Md , Said Mohd Shahmy Mohd , Razali Raiz 
Published: September 2023

AbstractIn recent years, there has been an increasing trend of utilizing high-resolution multibeam echosounder (MBES) datasets and supervised classification via machine learning to create marine habitat maps. The purpose of current study was threefold: (1) to extract bathymetric and backscatter derivatives from a multibeam dataset, (2) to measure the correlation between bathymetric and backscatter derivatives, and (3) to generate a marine habitat map using the Random Forest (RF). Tioman Marine Park (TMP), which is situated Southeast China Sea. MBES surveyed area are encompassed an area of 406 km² and served as the location for the study. Based on results and analysis, fourteen (14) derivative were derived from bathymetry map and backscatter mosaic. The second step involved integrating variables and a total of 152 of habitat ground-truth data were used, derived from underwater imageries, and sediment samples, into an RF model to generate a map of the marine habitat. Based on marine habitat map, six habitat classes including sand, rock, gravel and sand, coral rubble, coral and rock, and coral were classified. The distribution of coral habitat was found to be correlated with the depth of the bathymetry in the shallow water region. Therefore, the study has reached the conclusion that the integration between MBES derivatives, ground-truth data, and RF machine learning algorithm is an effective in classifying the distribution of marine habitats, specifically the coral habitat.
Conceptual Framework for Maximizing Service Catchment Area Between Rail Transit and Feeder Service Using Spatial Temporal Regression Technique
Authors: Halim Maisarah Abdul , Rosni Nur Aulia , Tsong Teh Bor 
Published: September 2023

Abstract
The move towards a more sustainable transport mode is crucial in the Klang Valley by incorporating feeder services to rail network stations. Feeder services will complement the expanding rail transits network and deliver connectivity for areas not covered within rail stations. Past literatures found that feeder services were ineffective during rail breakdown as feeder service was not going or stopping to where it was intended to. Feeder service can be allocated more effectively if we have better idea of targeted service area. Urban planners now have access to a variety of urban analytics because of technological advancements and the rise of smart cities. Data generated are produced across numerous locations and at diverse time scales, providing a significant possibility for data mining to uncover insightful information when both space and time are considered. This study will attempt to assess the community’s accessibility to rail transit through feeder service in Klang Valley. In achieving this a conceptual framework is developed within the objectives of this study which are firstly, to identify built environment factors that influence ridership around transit stations. Secondly, to determine the spatial -temporal relationship between built environment and transit ridership of feeder service and rail transits. Next, the station-level spatial and temporal variability of ridership data between feeder service to rail network is examined. Finally, as the main finding of this study, a framework in maximizing a service catchment transportation service for feeder service and rail transit will be developed. In doing that, the conceptual framework approach is by addressing numerical analytical technique in geographic data, by using spatial temporal regression. This study aspires to provide planning departments and transit agencies with useful guidance to execute targeted policies and develop a sustainable and efficient feeder service coverage area to support rail transits by looking at both spatial and temporal scales.
Analyzing the Impact of Land Surface Temperature Due to Urbanization Using Landsat 8 Satellite Image
Authors: Idris Muhammad Ajmal A’imullah Bin , Hashim Nurhanisah , Khalid Nafisah 
Published: September 2023

Abstract
Over the past years, global warming has become one of the enormous issues the world has faced. Malaysia is also not excluded from this worldwide problem. Due to rapid urbanization and deforestation, the temperature is getting warmer day by day. One of the dangerous effects that can be occurred were heatwaves. This phenomenon can even cause death due to heatstroke. This study aims to analyse the temporal changes in Land Surface Temperature due to Land Cover changes using multispectral Landsat 8 satellite images. The study area is Puncak Alam, Selangor where thermal images and landcover classification images were produced. By using Landsat 8 TIRS, analysing the land surface temperature of the study area can be achieved. The output was landcover classification images using Support Vector Machine algorithm and thermal images for year 2014 and 2021. From the result, it can be analysed that, the study area had been experiencing deforestation due to urbanization over the years. This causes an increase in land surface temperature in 2021 compared to 2014. The increase in land surface temperature percentage was obvious in class range 20°C to 25°C to a hotter range of 27.5°C to 32.5°C by almost 15% to 30% increase. The forest and urban area which show the most deforestation area has experienced an increase in land surface temperature to 27.5°C - 30°C by 44%.
Coastlines estimation and erosion rate assessment in Tuba Island, Langkawi using remotely-sensed digital imageries analysis
Authors: Adnan Nor Aizam , Norazman Najiehah , Maulud Khairul Nizam , Mokhtar Ernieza Suhana , Yusoff Zaharah Mohd 
Published: September 2023

Abstract
Malaysia’s coastline is constantly exposed to ocean threats, resulting in coastal erosion and sea-level rise. Pulau Tuba’s is a part of Pulau Langkawi which known as a popular tourist destination in Malaysia. This study focuses on using remote sensing and Geographical Information System (GIS) tools to examine changes along the coastline of Pulau Tuba, Langkawi, during a six-year period. In this research, changes to the coastline were analyzed using Sentinel-2 imagery from 2016 to 2021, as a result of the comparison of coastline extraction methods. Coastline extraction methods used in this study include 3x3-5x5 and 7x7 edge detection filtering techniques. Image classifier of Maximum Likelihood (ML) and Support Vector Machine (SVM) were also used to quantify the estimated coastlines length. Digital Shoreline Analysis System (DSAS) was used to calculate coastline erosion rate from Sentinel-2 images. The comparison between method to extract coastline revealed that the best window size of edge detection image filtering method is 7x7 and SVM outperform MLC classifier. The erosion rate as estimated from DSAS statistical calculation also highlighted that come parts along the Pulau Tuba coastlines experiencing alarming rate of coastal erosion. The authority needs to plan for soft and hard structure to mitigate the impact of coastal erosion in the future.
The seasonal Antarctic sea ice concentration anomalies related to the Atlantic Niño index
Authors: Yu Lejiang , Zhong Shiyuan , Vihma Timo , Sui Cuijuan , Sun Bo 
Published: September 2023

Abstract
Antarctic sea ice concentration anomalies (ASICA) have been found to be linked to sea surface temperature anomalies in tropical oceans. However, it is not clear whether and how ASICA is linked to the Atlantic Niño mode (ANM). This study demonstrates a significant relationship between ASICA and ANM. The relationships vary by season, with a peak in austral winter and a secondary one in spring. Significant sea ice anomalies associated with a positive phase of ANM are mostly negative in austral winter and spring, and mostly positive in austral summer and autumn. This teleconnection is established by atmospheric wavetrains that are excited over the tropical southwestern Pacific and Indian Oceans and the southern Atlantic Oceans and propagate over the Southern Ocean. These wavetrains induce anomalous near-surface circulations, which generate dynamic and thermodynamic forcing on sea ice, resulting in the observed ASICA patterns. The absence of El Niño Southern Oscillation weakens the connection.
Becky Thatcher’s Literary Half-life: Appropriating Mark Twain’s Good Girl
Authors: Shannon Edward A 
Published: September 2023

Abstract
Mark Twain’s Becky Thatcher features very little in The Adventures of Tom Sawyer and Adventures of Huckleberry Finn. She mostly vanishes after Tom Sawyer, and even in that novel she speaks fewer than a thousand words of dialogue. She disappears from Twain’s work almost completely after Huckleberry Finn, where she receives a single mention. An insignificant character to academics, Becky appears in the scholarly record little more than she had in Twain’s fiction. This essay explores Becky Thatcher’s outsized role in cinematic, literary, and other adaptations across the twentieth and twenty-first centuries. Becky Thatcher’s literary half-life extends to clothing racks and gift shops, as well as in cinematic and literary adaptations and appropriations. These transformations map racial and sexual anxieties of several American generations. Minor though she may seem, Becky looms large in films, fiction, restaurants, tourist attractions, and all the ephemera produced from the marketing of successful fictional characters in a capitalist media landscape.
Hidradenitis suppurativa: new insights into disease mechanisms and an evolving treatment landscape
Authors: Krueger James G , Frew John , Jemec Gregor B E , Kimball Alexa B , Kirby Brian , Bechara Falk G , Navrazhina Kristina , Prens Errol , Reich Kristian , Cullen Eva , Wolk Kerstin 
Published: September 2023

Abstract
Hidradenitis suppurativa (HS), also known as acne inversa, is a chronic disabling and debilitating inflammatory disease with a high unmet medical need. The prevalence of HS reported in most studies is 1–2%, although it is likely to be under-reported and estimates vary globally owing to variance in data collection methods, ethnicity, geographical location and under-diagnosis. HS is characterized by persistent, painful cutaneous nodules, abscesses and draining tunnels commonly affecting the axillary, anogenital, inguinal and perianal/gluteal areas. Over time, chronic uncontrolled inflammation results in irreversible tissue destruction and scarring. Although the pathophysiology of HS has not been fully elucidated, the tumour necrosis factor (TNF)-α and interleukin (IL)-17 pathways have an important role, involving multiple cytokines. Currently, treatment options include topical medications; systemic therapies, including repeated and/or rotational courses of systemic antibiotics, retinoids and hormonal therapies; and various surgical procedures. The anti-TNF-α antibody adalimumab is currently the only biologic approved by both the US Food and Drug Administration and the European Medicines Agency for HS; however, its efficacy varies, with a clinical response reported in approximately 50% of patients in phase III trials. HS is a rapidly evolving field of discovery, with a diverse range of agents with distinct mechanisms of action currently being explored in clinical trials. Several other promising therapeutic targets have recently emerged, and agents targeting the IL-17 and Janus kinase (JAK)/signal transducer and activator of transcription (STAT) pathways are the most advanced in ongoing or completed phase III clinical trials. Alongside limited therapeutic options, significant challenges remain in terms of diagnosis and disease management, with a need for better treatment outcomes. Other unmet needs include significant diagnostic delays, thus missing the therapeutic ‘window of opportunity’; the lack of standardized outcome measures in clinical trials; and the lack of established, well-defined disease phenotypes and biomarkers.
Machine learning to predict outcomes following endovascular abdominal aortic aneurysm repair
Authors: Li Ben , Aljabri Badr , Verma Raj , Beaton Derek , Eisenberg Naomi , Lee Douglas S , Wijeysundera Duminda N , Forbes Thomas L , Rotstein Ori D , de Mestral Charles , Mamdani Muhammad , Roche-Nagle Graham , Al-Omran Mohammed 
Published: September 2023

Abstract

Background
Endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA) carries important perioperative risks; however, there are no widely used outcome prediction tools. The aim of this study was to apply machine learning (ML) to develop automated algorithms that predict 1-year mortality following EVAR.

Methods
The Vascular Quality Initiative database was used to identify patients who underwent elective EVAR for infrarenal AAA between 2003 and 2023. Input features included 47 preoperative demographic/clinical variables. The primary outcome was 1-year all-cause mortality. Data were split into training (70 per cent) and test (30 per cent) sets. Using 10-fold cross-validation, 6 ML models were trained using preoperative features with logistic regression as the baseline comparator. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Model robustness was evaluated with calibration plot and Brier score.

Results
Some 63 655 patients were included. One-year mortality occurred in 3122 (4.9 per cent) patients. The best performing prediction model for 1-year mortality was XGBoost, achieving an AUROC (95 per cent c.i.) of 0.96 (0.95–0.97). Comparatively, logistic regression had an AUROC (95 per cent c.i.) of 0.69 (0.68–0.71). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.04. The top 3 predictive features in the algorithm were 1) unfit for open AAA repair, 2) functional status, and 3) preoperative dialysis.

Conclusions
In this data set, machine learning was able to predict 1-year mortality following EVAR using preoperative data and outperformed standard logistic regression models.
Unlocking the secret diversity of Anadenanthera: insights from molecular genetics of four evolving species
Authors: Mangaravite Érica , da Silveira Thamyres Cardoso , Vinson Christina Cleo , Bueno Marcelo Leandro , Silva Roberta dos Santos , Carniello Maria Antonia , Veldman Joseph W , Garcia Magali Gonçalves , de Oliveira Luiz Orlando 
Published: September 2023

Abstract
The understanding of genetic diversity and evolutionary relationships within the genus Anadenanthera Speg. (Fabaceae) is limited. Brenan’s classification recognized four species, but Altschul’s classification currently recognizes only two species, each with two varieties. Both classifications were based solely on morphology. We analysed molecular data from 283 specimens using 12 SSRs and 186 specimens using ITS sequence data. The results showed that neither SSR-based genotyping nor ITS-based phylogeny supported Altschul’s classification. Instead, they provided evidence of four reproductively isolated taxa and a single clade corresponding to one of Altschul’s varieties. Additionally, ecological niche modelling (ENM) investigated the influence of past climate on the geographic distribution of Anadenanthera. The ENM results suggest that the ‘varieties’ may have occupied different regions of South America with distinct paleoclimates. On the basis of these findings, the authors suggest recognizing four species, as per Brenan’s original classification.
“We are disposable”: precarity, mobility, and inequity in higher education’s gig academy
Authors: Stewart Nicole K , Adams Philippa R , Bin Quader Shams 
Published: September 2023

Abstract
The COVID-19 pandemic amplified inequities for contingent scholars in the neoliberal gig academy. In this article, we document the struggles of three early career scholars, all contingent instructors, researchers, or both, working at multiple institutions in higher education (HE). Through critical collaborative autoethnography we follow our experiences through the pandemic with a focus on the ‘return to campus’ semester in fall 2021. We forge a critique and activist stance against the structural problem of precarity in HE using our dialogues and vignettes to highlight our experiences around precarity, mobility, and systemic inequities.
Characterizing brain stage-dependent pupil dynamics based on lateral hypothalamic activity
Authors: Takahashi Kengo , Sobczak Filip , Pais-Roldán Patricia , Yu Xin 
Published: September 2023

Abstract
Pupil dynamics presents varied correlation features with brain activity under different vigilant levels. The modulation of brain dynamic stages can arise from the lateral hypothalamus (LH), where diverse neuronal cell types contribute to arousal regulation in opposite directions via the anterior cingulate cortex (ACC). However, the relationship of the LH and pupil dynamics has seldom been investigated. Here, we performed local field potential (LFP) recordings at the LH and ACC, and whole-brain fMRI with simultaneous fiber photometry Ca2+ recording in the ACC, to evaluate their correlation with brain state-dependent pupil dynamics. Both LFP and functional magnetic resonance imaging (fMRI) data showed various correlations to pupil dynamics across trials that span negative, null, and positive correlation values, demonstrating brain state-dependent coupling features. Our results indicate that the correlation of pupil dynamics with ACC LFP and whole-brain fMRI signals depends on LH activity, suggesting a role of the latter in brain dynamic stage regulation.
Differences in atrial substrate localization using late gadolinium enhancement-magnetic resonance imaging, electrogram voltage, and conduction velocity: a cohort study using a consistent anatomical reference frame in patients with persistent atrial fibrillation
Authors: Nairn Deborah , Eichenlaub Martin , Müller-Edenborn Björn , Huang Taiyuan , Lehrmann Heiko , Nagel Claudia , Azzolin Luca , Luongo Giorgio , Figueras Ventura Rosa M , Rubio Forcada Barbara , Vallès Colomer Anna , Westermann Dirk , Arentz Thomas , Dössel Olaf , Loewe Axel , Jadidi Amir 
Published: September 2023

Abstract

Aims
Electro-anatomical voltage, conduction velocity (CV) mapping, and late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) have been correlated with atrial cardiomyopathy (ACM). However, the comparability between these modalities remains unclear. This study aims to (i) compare pathological substrate extent and location between current modalities, (ii) establish spatial histograms in a cohort, (iii) develop a new estimated optimized image intensity threshold (EOIIT) for LGE-MRI identifying patients with ACM, (iv) predict rhythm outcome after pulmonary vein isolation (PVI) for persistent atrial fibrillation (AF).

Methods and results
Thirty-six ablation-naive persistent AF patients underwent LGE-MRI and high-definition electro-anatomical mapping in sinus rhythm. Late gadolinium enhancement areas were classified using the UTAH, image intensity ratio (IIR &gt;1.20), and new EOIIT method for comparison to low-voltage substrate (LVS) and slow conduction areas &lt;0.2 m/s. Receiver operating characteristic analysis was used to determine LGE thresholds optimally matching LVS. Atrial cardiomyopathy was defined as LVS extent ≥5% of the left atrium (LA) surface at &lt;0.5 mV. The degree and distribution of detected pathological substrate (percentage of individual LA surface are) varied significantly (P &lt; 0.001) across the mapping modalities: 10% (interquartile range 0–14%) of the LA displayed LVS &lt;0.5 mV vs. 7% (0–12%) slow conduction areas &lt;0.2 m/s vs. 15% (8–23%) LGE with the UTAH method vs. 13% (2–23%) using IIR &gt;1.20, with most discrepancies on the posterior LA. Optimized image intensity thresholds and each patient’s mean blood pool intensity correlated linearly (R2 = 0.89, P &lt; 0.001). Concordance between LGE-MRI-based and LVS-based ACM diagnosis improved with the novel EOIIT applied at the anterior LA [83% sensitivity, 79% specificity, area under the curve (AUC): 0.89] in comparison to the UTAH method (67% sensitivity, 75% specificity, AUC: 0.81) and IIR &gt;1.20 (75% sensitivity, 62% specificity, AUC: 0.67).

Conclusion
Discordances in detected pathological substrate exist between LVS, CV, and LGE-MRI in the LA, irrespective of the LGE detection method. The new EOIIT method improves concordance of LGE-MRI-based ACM diagnosis with LVS in ablation-naive AF patients but discrepancy remains particularly on the posterior wall. All methods may enable the prediction of rhythm outcomes after PVI in patients with persistent AF.