bluuebunny/crossref_metadata_2025 huggingface dataset:
Detection of cardiac amyloidosis using machine learning on routine echocardiographic measurements
Authors: Chang Rachel Si-Wen , Chiu I-min , Tacon Phillip , Abiragi Michael , Cao Louie , Hong Gloria , Le Jonathan , Zou James , Daluwatte Chathuri , Ricchiuto Piero , Ouyang David
Published: December 2024

Background

Cardiac amyloidosis (CA) is an underdiagnosed, progressive and lethal disease. Machine learning applied to common measurements derived from routine echocardiogram studies can inform suspicion of CA.

Objectives

Our objectives were to test a random forest (RF) model in detecting CA.

Methods

We used 3603 echocardiogram studies from 636 patients at Cedars-Sinai Medical Center to train an RF model to predict CA from echocardiographic parameters. 231 patients with CA were compared with 405 control patients with negative pyrophosphate scans or clinical diagnosis of hypertrophic cardiomyopathy. 19 common echocardiographic measurements from echocardiogram reports were used as input into the RF model. Data was split by patient into a training data set of 2882 studies from 486 patients and a test data set of 721 studies from 150 patients. The performance of the model was evaluated by area under the receiver operative curve (AUC), sensitivity, specificity and positive predictive value (PPV) on the test data set.

Results

The RF model identified CA with an AUC of 0.84, sensitivity of 0.82, specificity of 0.73 and PPV of 0.76. Some echocardiographic measurements had high missingness, suggesting gaps in measurement in routine clinical practice. Features that were large contributors to the model included mitral A-wave velocity, global longitudinal strain (GLS), left ventricle posterior wall diameter end diastolic (LVPWd) and left atrial area.

Conclusion

Machine learning on echocardiographic parameters can detect patients with CA with accuracy. Our model identified several features that were major contributors towards identifying CA including GLS, mitral A peak velocity and LVPWd. Further study is needed to evaluate its external validity and application in clinical settings.
Dark energy stars with cosmological constant as a black-hole mimickers
Authors: Azman Muhamad Ashraf , Yusof Norhasliza , Kassim Hasan Abu , Algaba Juan Carlos Marcos
Published: December 2024

In this study, we investigated an interior solution of a static spherically symmetric dark energy star model incorporating anisotropic fluid and a state parameter of the type cosmological constant, achieved through parametrization of the Finch and Skea metric function. The motivation for this study stems from the lack of investigation of dark energy star models in relation to BHs in the previous studies, and from recent proposals suggesting that dark energy might originate inside BHs. This opens up the opportunity to explore the dark energy star model and establish its relationship with BHs. The structural profiles of the models, including mass function, energy density, compactness, surface redshift, and local acceleration due to gravity, were investigated. Analysis involving the matching of the exterior Schwarzschild vacuum solution to the interior spacetime at a junction interface was also explored. The results demonstrate that this solution is free of singularities, possessing outward gravitational repulsion with an infinite property near the surface boundary. Notably, the model exhibited an infinite redshift surface, and a compactness of one half, thus evading the Buchdahl limit. It fulfills the energy conditions except for the strong energy conditions and remains in a state of static equilibrium, upheld by both hydro-static and anisotropic forces. Numerical values of physical properties for various types of astrophysical BH candidates have been determined. Overall, the obtained model is physically unique and represents the most compact and extreme model of dark energy stars. Some features of this model resemble those of BHs, rendering it indistinguishable from BHs.
An inquisitive study on photophysical properties of few modified indole derivatives
Authors: Banana Tejendra , Rajput Swati Singh , Chandravanshi Neelam , Alam Md Mehboob
Published: December 2024

Indole is an interesting molecule that exists in nature as a part of several important biomolecules including melatonin — a chemical that maintains the circadian rhythm in human body, serotonin — a chemical carrying messages through nerve cells, and tryptophan — an amino acid. It has a benzene ring fused with a pyrrole ring. Similar molecules containing cycloheptene ring fused with pyrrole — called azulene, cyclohepta[b]pyrrole and dihydrocyclohepta[b]pyrrole — also exist. It would be very interesting to see the effect on different properties of the above-mentioned biomolecules, if indole is replaced by azulene or cyclohepta[b]pyrrole or dihydro-cyclohepta[b]pyrrole. This paper precisely investigates such interesting substitutions. We replaced indole in the three biomolecules by azulene, cyclohepta[b]pyrrole, and dihydrocyclohepta[b]pyrrole and studied the corresponding change in acid-dissociation constant, static polarizabilities, first and second hyperpolarizabilities, absorption spectra, fluorescence, charge-transfer, and nature of the first two singlet, and the first two triplet states, using the state-of-the-art (time-dependent) density functional theory. The results are compared with the values obtained for the indole analogues calculated at the same level of theory.
APPLIED LINGUISTICS-DRIVEN ARTIFICIAL INTELLIGENCE APPROACH FOR SENTIMENT ANALYSIS AND CLASSIFICATION ON SOCIAL MEDIA
Authors: ALHASHMI ASMA A. , ALSHAMMARI ALYA , SAEED MUHAMMAD KASHIF , ALZAIDI MUHAMMAD SWAILEH A. , ALANAZI FUHID , SAYED AHMED
Published: December 2024

Sentiment analysis (SA) is an essential application of machine learning (ML) and natural language processing (NLP) that comprises the automatic extraction of opinions or sentiments presented in textual data. By leveraging methods to distinguish the expressive nature conveyed in written content, SA permits businesses and research workers to gain valuable insights into social media discourse, customer feedback, and public reviews. In the field of SA, the synergy of Applied Linguistics and Artificial Intelligence (AI) has led to a robust method that goes beyond conventional methods. By incorporating linguistic principles into AI methods, this interdisciplinary collaboration allows a more nuanced perception of human sentiments expressed in language. Applied Linguistics offers the theoretical basis for understanding the details of pragmatics, semantics, and linguistic structures, while AI algorithms leverage this knowledge for analyzing large datasets with notable accuracy. This study presents an Applied Linguistics-driven Artificial intelligence Approach for SA and Classification (ALAIA-SAC) system in social media. The primary intention of the ALAIA-SAC technique is to apply an attention mechanism with a fractal hyperparameter-tuned deep learning (DL) method for identifying sentiments. In the ALAIA-SAC technique, data preprocessing takes place in several stages to convert the input data into a compatible format. In addition, the TF-IDF model could be employed for the word embedding method. The self-attention directional long short-term memory (SBiLSTM) model is used for sentiment classification. Finally, the SBiLSTM model’s hyperparameter selection is performed using a Fractal Pelican optimization algorithm (FPOA). The experimentation results of the ALAIA-SAC method are assessed under two benchmark datasets. The comparative study of the ALAIA-SAC technique exhibited a superior accuracy value of 99.17% and 99.39% under Twitter US Airlines and IMDB datasets.
Supramolecular interaction between N-methylfulleropyrrolidine and porphyrazine studied in solution
Authors: Das Goutam Krishna , Nayak Subrata , Bhattacharya Shalmali , Banerjee Shrabanti , Paul Suravi , Bhattacharya Sumanta
Published: December 2024

Supramolecular interaction of N-methylfulleropyrrolidine (A) with a free-base porphyrazine (H2Pz) is investigated in toluene. The interaction between A and H2Pz is favored by charge transfer (CT), as the CT absorption band for the A-H2Pz system is located at 533 nm. Utilizing the CT absorption maxima, some important physicochemical parameters are evaluated in the present work. The binding constant (K) of the A-H2Pz system is determined both from UV-vis (KUV-vis = 29640 dm3· mol−1) and steady-state fluorescence measurements (Kfluorescence = 15440 dm3 · mol−1). Lifetime measurement measures the value of the rate constant of charge separation and quantum yield of charge separation of the A-H2Pz system, which supports the rationale of CT at the ground state. Hybrid-DFT calculations by the B3LYP method, extend very good support in favor of the K value of the A-H2Pz system in terms of the heat of formation value, i.e., 0.57 kcal · mol−1, and it provides excellent corroboration between the experimentally obtained CT energy (i.e., 2.325 eV) and the theoretically obtained EHOMO – ELUMO value of the A-H2Pz system (EHOMO – ELUMO = 2.330 eV). The present study envisages that H2Pz may be suitably employed as an effective receptor for functionalized fullerene molecules in the near future.
KEY COMPETENCES FOR THE ADOPTION OF AI-BASED INNOVATIONS IN ORGANISATIONS
Authors: BAUMGARTNER MARCO , HORVAT DJERDJ , KINKEL STEFFEN , KICK ELENA
Published: December 2024

Successfully adopting AI and realising its full innovation potential requires different competences within a company. We identified five clusters, namely, AI decision-making, AI utilisation, AI foundational, AI development and leadership & moderation competences, as the basis for our AI competence framework, combining 35 individual competences. Based on a quantitative survey of 215 companies, we determined the importance of these competences for the successful adoption of AI innovations and their current availability within companies. According to our findings, AI foundational competences play a particularly critical role compared to the other competence clusters, which are considered important but comparatively rarely available. Furthermore, our analyses show that companies with higher levels of AI foundational, AI development, and AI utilisation competences have significantly higher AI innovation capabilities. Again, in particular AI foundational competences seem to fertilize the capabilities to identify appropriate AI use cases, to make decisions about AI innovation adoption, to successfully integrate AI into internal processes, and to use the AI innovation effectively within the organisation. Our findings thus enrich the theoretical discourse on competences for organisational adoption of AI innovations and guide practitioners in taking action to develop the necessary competences.
NAVIGATING SUCCESS: ANALYSING THE INFLUENCE OF VALUE ON SMALL AND MEDIUM ENTERPRISES’ PERFORMANCE ACROSS SELECTED BUSINESS MODEL CONFIGURATIONS
Authors: Molina-Castillo Francisco-Jose , Lopez-Nicolás Carolina , Meroño-Cerdán Angel-Luis
Published: December 2024

The number of studies around the business model of small and medium enterprises (SMEs) has increased during the last decade in several fields such as entrepreneurship or strategic management. Despite the increasing attention, there are still scarce contributions around the factors that better fit with a specific business model configuration. This research develops two studies to gain a better understanding of this phenomenon. First, multiple qualitative case study research provides insights into the value dimensions of different business model innovation configurations (evolutionary, adaptive, focussed and complex). Second, an extensive quantitative study drawing on 1,328 European SMEs offers valuable findings on the impact of value capture, value creation and value proposition on business model innovation performance for each type of business model considered, from evolutionary to complex business models. The discussions offer recommendations on specific dimensions depending on the type of business model configuration adopted by the firm.
AI-driven optimization in 3D printing: Reducing time and material consumption through machine learning models
Authors: Nguyen Phuong Dong , Dao Manh Binh , Nguyen Thanh Q.
Published: December 2024

This paper investigates the impact of various 3D printing parameters on two critical resources: printing time and plastic material consumption. Through a series of experiments, the paper aimed to optimize these resources by adjusting the relevant parameters. First, by incorporating multiple algorithmic models, our stacking artificial intelligence model achieved superior prediction accuracy and a more comprehensive evaluation of the parameters. This novel approach amalgamates machine learning models to identify the optimal configuration or leverage the strengths of various models. Second, the proposed method paves the way for developing automated tools that simplify the configuration of the printer parameters, making 3D printing more accessible. This innovation allows users with limited expertise to focus on essential aspects of 3D printing, reducing the process to a few elementary steps. Third, our findings enable 3D printing entities to offer advanced estimates of printing time and filament usage, particularly for large-scale projects. With a prediction accuracy of 96.19% based on the R2 score, the proposed method enables users to plan uninterrupted printing sessions more effectively and optimize both print time and resource consumption according to their specific preferences. Lastly, mastering the printing parameters that affect time and material consumption is crucial to producing high-quality 3D models efficiently. This optimization reduces plastic waste and environmental pollution while increasing profits and operational efficiency. In general, through these contributions, our study advances the field of 3D printing by providing practical tools and insights that enhance both the efficiency and accessibility of the technology.
Validity-Preserving Delta Debugging
via
Generator Trace Reduction
Authors: Ren Luyao , Zhang Xing , Hua Ziyue , Jiang Yanyan , He Xiao , Xiong Yingfei , Xie Tao
Published: December 2024

Reducing test inputs that trigger bugs is crucial for efficient debugging. Delta debugging is the most popular approach for this purpose. When test inputs need to conform to certain specifications, existing delta debugging practice encounters a validity problem: it blindly applies reduction rules, producing a large number of invalid test inputs that do not satisfy the required specifications. This overall diminishing effectiveness and efficiency becomes even more pronounced when the specifications extend beyond syntactical structures. Our key insight is that we should leverage input generators, which are aware of these specifications, to generate valid reduced inputs, rather than straightforwardly performing reduction on test inputs. In this article, we propose a generator-based delta debugging method, namely GReduce, which derives validity-preserving reducers. Specifically, given a generator and its execution, demonstrating how the bug-inducing test input is generated, GReduce searches for other executions on the generator that yield reduced, valid test inputs. The evaluation results on five benchmarks (i.e., graphs, DL models, JavaScript programs, SymPy, and algebraic data types) show that GReduce substantially outperforms state-of-the-art syntax-based reducers including Perses and T-PDD, and also outperforms QuickCheck, SmartCheck, as well as the state-of-the-art choice-sequence-based reducer Hypothesis, demonstrating the effectiveness, efficiency, and versatility of GReduce.
Arabic App Reviews: Analysis and Classification
Authors: Aljeezani Othman , Alomari Dorieh , Ahmad Irfan
Published: December 2024

User opinions and feedback on mobile applications are crucial for application developers, offering insights into issues like bugs, popular features, and enhancement requests. Given the vast number of feedback for each app, it is impractical for developers to manually extract valuable information. To better understand and analyze user opinions, developers can benefit from automatic sentiment analysis and classification of app reviews. Existing research has primarily focused on reviews written in English, with some studies addressing sentiment analysis of Arabic reviews but overlooking the classification task. Given the widespread use and complexity of Arabic compared to English, our work investigates both sentiment analysis and classification of Arabic app reviews. We introduce the AURA (
A
pp
U
ser
R
eview in
A
rabic) dataset. AURA dataset has two versions: AURA-Sentiment with 29,700 labeled reviews for sentiment analysis, and AURA-Classification with 2,900 labeled reviews for classification. Using these datasets, we applied deep learning (DL) and natural language processing (NLP) techniques for analyzing and classifying Arabic app reviews. Leveraging the MarBert model, we achieved an F1-score of 0.89 for sentiment analysis and an F1-score of 0.62 for a four-class classification problem. Our findings provide valuable insights and suggest directions for future research.
Improving multi-hop network with Blockchain
Authors: Tokunaga Junpei , Ebara Hiroyuki
Published: December 2024

Mobile ad-hoc network (MANET) is a self-organized system comprised of mobile wireless nodes, and MANET allows one to instantly build a network between terminals and the entire of area by multi-hop connection. MANET are expected to provide a means of communication in crowded event venues and post-disaster environments. It also supports applications that require network communication because it does not require a contract with a telecommunications carrier. However, the MANET relay node is in the state of a volunteer providing a communication environment to maintain the network, and there is no reward for continuing to forward other people’s data without sending. In some cases, a function to monitor transfer results can be provided. In this case, since MANET are distributed environments, a centralized monitoring function is not always reliable. In this paper, we propose a protocol that can manage the transfer record even in a network composed of unreliable nodes by using a blockchain that can realize a distributed ledger even between unreliable nodes. Since the protocol uses smart contracts such as Ethereum, a data structure in which messages are concatenated with hashes is adopted to reduce the gas equivalent to the execution fee.
Classification via Two-Way Comparisons
Authors: Chrobak Marek , Young Neal E.
Published: December 2024

Given a weighted, ordered query set

\(Q\)

and a partition of

\(Q\)

into classes, we study the problem of computing a minimum-cost decision tree that, given any query

\(q\in Q\)

, uses equality tests and less-than tests to determine

\(q\)

's class. Such a tree can be faster and smaller than a conventional search tree and smaller than a lookup table (both of which must identify

\(q\)

, not just its class). We give the first polynomial-time algorithm for the problem. The algorithm extends naturally to the setting where each query has multiple allowed classes.
Coccoliths as Recorders of Paleoceanography and Paleoclimate over the Past 66 Million Years
Authors: Bolton Clara T. , Stoll Heather M.
Published: December 2024

Coccolithophores are a major group of oceanic calcifying phytoplankton, and their calcite skeletal remains, termed calcareous nannofossils, are a major component of deep-sea sediments accumulating since the Jurassic. Coccolithophores play a role in both the biological pump and the carbonate pump, exporting organic and inorganic carbon, respectively, out of the surface ocean. This means that they are key responders to and recorders of ocean carbon cycle and climate changes over geological and shorter timescales, and studying these responses can help elucidate the uncertain fate of calcifying phytoplankton under projected climate change scenarios. Here, we review established and emerging approaches for reconstructing (a) mixed-layer ocean temperature, (b) marine productivity, and (c) aspects of the ocean carbon cycle, using calcareous nannofossils from deep-sea sediments. For each parameter, we discuss the different proxies that have been proposed, based on abundance or species composition, inorganic geochemistry, and/or coccolith morphology, and explore their applications and limitations in Cenozoic paleoceanography.

▪

Calcareous nannofossils can be used to reconstruct upper ocean conditions and changes over centennial to million-year timescales.

▪

Key coccolith-based proxies for temperature, productivity, and the carbon cycle are reviewed.

▪

Approaches based on assemblages, geochemistry, and morphology provide novel insights into the evolution and adaptation of coccolithophores and past climate.
Network Pharmacology and Bioinformatics Analysis on the Underlying Mechanisms of Matrine Against Uterine Corpus Endometrial Carcinoma
Authors: Cheng Sijin , Xu Yanan , Lei Feiyang , Yang Mingrui , Ma Cuiyan
Published: December 2024

Background: Uterine corpus endometrial carcinoma (UCEC) ranks as a predominant tumor within the female reproductive system. Matrine, the chief active constituent of Compound Kushen Injection, exhibits antibacterial, antiviral, anti‐inflammatory, and antitumor effects. On the basis of previous research studies, this study utilized bioinformatics and network pharmacology approaches to investigate matrine’s anti‐UCEC potential, establishing a theoretical groundwork for subsequent mechanistic studies.

Objective: Differentially expressed genes (DEGs) from the TCGA‐UCEC database were processed using R packages. Potential targets of matrine were identified via PubChem, SwissTargetPrediction, and BindingDB. The hub genes were identified through cytoHubba from the overlapping genes of matrine targets and UCEC DEGs. These genes were then subjected to cancer immune infiltration analysis, assessed with the R package “ggbeeswarm,” and validated by molecular docking studies.

Results: We identified 28 potential drug–disease interaction genes and narrowed down to 10 hub genes. Notably, CDH1 and EPCAM were significantly differentially expressed between tumor and normal samples. Their expression levels correlated positively with multiple immune cells types. Molecular docking revealed significant interactions between CDH1, EPCAM, and matrine.

Conclusions: These findings suggest that matrine’s anti‐UCEC activity may involve interactions with CDH1 and EPCAM. Advancing UCEC therapy based on these interactions could improve tumor diagnosis and treatment, including for UCEC.
Evaluation of Air Quality in a Primary School Classroom During Wintertime
Authors: Dovrou Eleni , Kaltsonoudis Christos , Androulakis Silas , Apostolopoulos Ioannis , Simonati Andrea , Pandis Spyros N.
Published: December 2024

The levels of gas and particulate pollutants were measured inside and outside of a primary school classroom located in a suburban area in the city of Patras, Greece, during wintertime to evaluate the indoor air quality, identify potential problems, and determine the effectiveness of ventilation. The Foundation for Research and Technology–Hellas (FORTH) mobile laboratory was deployed, and a switching valve system was used to obtain both indoor and outdoor measurements. The outdoor air was the main source of in‐classroom pollutants, as the windows remained partially open. Ventilation of the classroom was achieved naturally through the windows, and it was continuous during school hours, maintaining the pollutant concentrations at low levels, with average fine particulate matter equal to 2.6 μg m−3 and total aromatic and oxygenated volatile organic compound (VOC) levels in the order of 10 ppb. The levels of all measured pollutants were lower than the World Health Organization (WHO) safety limits, and they are not expected to affect the health of the students. Good air quality was observed within the classroom, showing the effectiveness of natural ventilation in this setting. Cleaning activities were a source of VOCs outside school hours, resulting in increases in the levels of VOCs of a few parts per billion. However, these concentrations were gradually reduced, and they did not reduce the indoor air quality the next school day. A box model was used to estimate an effective air exchange rate of 3.5 h−1 during school hours.
Antioxidant, Antibacteria, and Anti‐Inflammatory Effects of Cordyceps militaris Extracts and Their Bioactive Compounds
Authors: Kaewkod Thida , Ngamsaoad Pornpimon , Mayer Kanok-orn , Cheepchirasuk Nitsanat , Promputtha Itthayakorn , Tragoolpua Yingmanee
Published: December 2024

This study presents the bioactive compounds, antioxidant, antibacteria, and anti‐inflammation of C. militaris fruiting body (FB) and FB with substrate (FBS). C. militaris FB and FBS were extracted by water, ethanol, and methanol. The chemical composition analysis of C. militaris extracts from FB and FBS showed bioactive compounds including adenosine, cordycepin, and carotenoids using HPLC. Moreover, the aqueous extract of FB and FBS showed the highest antioxidant activity against DPPH and ABTS radicals, supported by the presence of adenosine, cordycepin, and carotenoid compounds. Moreover, the ethanolic and methanolic extracts of C. militaris and the bioactive compounds, cordycepin and carotenoids, exhibited the greatest bactericidal activity against enteric pathogenic bacteria. In addition, C. militaris extracts and bioactive compounds were confirmed as new agents to prevent the adhesion and invasion of enteric pathogenic bacteria on Caco‐2 colon cells. This finding also demonstrated the anti‐inflammatory activity found in the aqueous extract of C. militaris and bioactive compounds on the LPS‐stimulated Caco‐2 cell model, which had the efficacy to suppress inflammatory moderators including iNos, Cox‐2, NF‐κB, TNF‐α, AP‐1, TLR‐4, IL‐1ß, and IL‐6. Therefore, C. militaris extract and its bioactive compounds, cordycepin and carotenoids, impeded the adhesion and invasion of enteric pathogenic bacteria on colonic epithelial cells and also promoted anti‐inflammation mechanisms. This study attests to C. militaris as an alternative therapeutic agent to prevent enteric pathogenic bacterial infection and inflammation due to its proven health benefits and high level of antioxidants.
Blocking Feedback Immunosuppression of Antigen Presentation in Brain Tumor During Oncolytic Virotherapy with oHSV-mshPKR
Authors: Tsuboi Nobushige , Rivera-Caraballo Kimberly A. , Sahu Upasana , Pacholczyk Rafal , Douglass Eugene , Johnson Theodore S. , Wang Qin , Kolhe Ravindra , Hedrick Catherine C. , Munn David H. , Hong Bangxing
Published: December 2024

Glioblastoma (GBM) is the most frequent malignant brain tumor. We recently discovered that oncolytic herpes simplex virus engineered to disable tumor-intrinsic protein kinase R (PKR) signaling (oHSV-shPKR) could increase oHSV oncolysis and antitumor immune response. However, in this study, we show that disabling tumor-intrinsic PKR signaling can also induce the activation of the indoleamine 2,3-dioxygenase (IDO) signaling pathway. Both GBM tumor progression and oHSV intratumoral therapy increased infiltration of IDO+CD11c+ dendritic cells (DC) into the tumor. The coculture of oHSV-infected human GBM neurospheres with monocyte-derived DCs (MoDC) dramatically increased IDO signaling activation in MoDCs through type-I IFN signaling. Addition of IDO inhibitor (indoximod) in the coculture significantly increased MoDC activation and reduced the consumption of tryptophan. Combining indoximod and oHSV significantly inhibited tumor growth and induced antigen-specific CD8+ T-cell activation. These results suggest that inhibition of the IDO pathway could significantly block feedback immunosuppression during oncolytic virotherapy of GBM.
Upadacitinib Was Administered as a Sequential Salvage Therapy for Acute Severe Ulcerative Colitis: A Case Report
Authors: Huynh David , Khaing Myat Myat , Fernandes Richard Gareth , Malloy Reuben , Lin Lei , Gilmore Robert , Walker Nicole , Khoo Emi , Begun Jakob
Published: December 2024

Introduction: Acute severe ulcerative colitis (ASUC) represents a medical emergency associated with high mortality and morbidity. While corticosteroids are the primary treatment, cases that are unresponsive often require rescue therapy with either infliximab or cyclosporine to reduce the rate of colectomy. Janus kinase inhibitors, such as tofacitinib and upadacitinib, are a highly efficacious therapy with rapid induction of clinical response in moderate to severe ulcerative colitis (UC). Limited data are available on its use on ASUC. We present the first case utilizing upadacitinib as sequential medical rescue therapy in ASUC as well as intestinal ultrasound as a useful tool for disease and response monitoring. Case Presentation: A 69-year-old female who presented with corticosteroid-refractory ASUC partially responded to dose-intensified infliximab and finally achieved clinical remission with upadacitinib. This resulted in swift clinical remission and significant improvement in her mucosal inflammation on intestinal ultrasound. Conclusion: This successful intervention not only avoided colectomy but demonstrated sustained clinical and sonographic remission 16 weeks of post-treatment. Upadacitinib, with its rapid action and efficacy, shows promise in ASUC and should be supported by registration trials and real-world studies. Despite successful outcomes in this case, further validation and long-term data are necessary.
Comparing the Randomized Trial Outcomes of 3D Low-Light Intensity-Assisted and Traditional Eyepiece-Assisted Pars Plana Vitrectomy for Rhegmatogenous Retinal Detachment
Authors: Ge Xing , Liu Dandan , Liu Yalu , Fan Fangfang , Wang Yue , Zhang Zhengpei , Liu Haiyang , Li Suyan
Published: December 2024

Introduction: Pars plana vitrectomy (PPV) is a primary surgical method for rhegmatogenous retinal detachment (RRD). The introduction of the 3D head-up system has provided ophthalmologists with a new surgical experience. This study aimed to compare the surgical outcomes between 3D low-light intensity-assisted and traditional eyepiece-assisted PPV for RRD. Methods: A prospective randomized controlled design was employed to compare the surgical outcomes of 3D low-light intensity-assisted and traditional eyepiece-assisted PPV for RRD. The optical intensity parameters of optical fibers and chandeliers were set according to the minimum lighting standard for 25-G PPV. Surgery duration and intraoperative conditions were documented. Post-surgery, the light intensity of the optical fiber and chandeliers during surgery was measured using a photometer. Patients were followed up for six months to assess their postoperative recovery. Statistical analysis was performed using SPSS 26.0 software, with p < 0.05 indicating statistically significant differences. Results: There was no statistically significant difference between the two groups in baseline data (p > 0.05). PPV was completed in all patients, and there was no statistically significant difference in surgery time between the two groups (p > 0.05). The optical fiber and chandelier light intensity in the 3D group were significantly lower than those in the eyepiece group and the difference being statistically significant (p < 0.001). Six months after surgery, the retinal attachment rate was 100%. Post-surgery, both best corrected visual acuity (BCVA) and intraocular pressure (IOP) were significantly higher than presurgery levels. There were no significant differences between the two groups in terms of retinal attachment rate, BCVA, IOP, and flash electroretinogram (p > 0.05). Conclusion: Compared to the traditional eyepiece, the 3D head-up system can effectively complete surgery under lower illumination intensity. The anatomical restoration and functional success of the retina after surgery are equivalent.
Role of Infiltration on Land–Atmosphere Feedbacks in Central Europe: Fully Coupled WRF-Hydro Simulations Evaluated with Cosmic-Ray Neutron Soil Moisture Measurements
Authors: Arnault Joël , Fersch Benjamin , Schrön Martin , Bogena Heye Reemt , Hendricks Franssen Harrie-Jan , Kunstmann Harald
Published: December 2024

The skill of regional climate models partly relies on their ability to represent land–atmosphere feedbacks in a realistic manner, through coupling with a land surface model. However, these models often suffer from insufficient or erroneous information on soil hydraulic parameters. In this study, the fully coupled land–atmosphere model WRF-Hydro driven with ERA5 reanalysis is employed to reproduce the regional climate over central Europe with a horizontal resolution of 4 km for the period 2017–20. Simulated soil moisture is compared with data from cosmic-ray neutron sensors (CRNSs) at three terrestrial environmental observatories. Soil hydraulic parameters from continental and global digital soil datasets (SoilGrids and EU-SoilHydroGrids), together with Campbell and van Genuchten–Mualem retention curve equations, are used to assess the role of infiltration on modeled land–atmosphere feedbacks. The percolation parameter is calibrated to better capture observed discharge amounts in the observatories. WRF-Hydro with Campbell and SoilGrids gives the lowest mean annual temperature and mean annual precipitation differences compared to the E-OBS product from European Climate Assessment and Dataset, which is achieved by reducing soil moisture in the rootzone, increasing air temperature, and decreasing precipitation through a positive soil moisture–precipitation feedback process. WRF-Hydro with van Genuchten–Mualem and EU-SoilHydroGrids best reproduces CRNS soil moisture daily variations, despite enhanced positive biases that generate a larger proportion of convective precipitation favored over wet soils and spurious discharge peaks. This study demonstrates the importance of infiltration processes to realistically reproduce land–atmosphere feedbacks.

Significance Statement

The ability to correctly reproduce the feedbacks between land and atmosphere is crucial for a regional climate model. This study shows that the nature of these land–atmosphere feedbacks is partly related to the description of soil infiltration, which potentially helps to improve regional climate modeling results. An updated method to disentangle the proportion of convective precipitation being favored over wet, dry, and mixed soils is provided, which sheds new light on the soil moisture–precipitation feedback mechanism.
Comparing “incomparable” schools? Extended case method and reflexive comparative study of teaching practices in Brussels and São Paulo
Authors: Antoine Sébastien
Published: December 2024

Comparing dissimilar social realities inexorably raises the question of the way to establish a basis for comparison that respects the specificities of the cases studied while not at the same time considering the context as negligible. Seeking to highlight the contributions of Burawoy’s extended case method to addressing these challenges of comparative research, this article retraces the iterative design process of a multicase socio-ethnographic study of teaching practices carried out in Brussels and São Paulo public high schools. Revealing a sharp contrast between a teaching to transgress kind of critical pedagogy in the Brazilian case and a more conventional form of status quo education for democratic citizenship in the Belgian one, the research thus sheds light on the role of contextual dynamics in the emergence of these ideological divergences between two instances of public education. Unlike the inductive approach and its logic of generalization based on the search for lowest common denominators—relegating context and case specificities to the background—this article thus argues that the least intuitive comparisons are, in fact, the most likely to stimulate sociological imagination, questioning the obvious in the studied societies and opening up to a deeper understanding of each case.
Crises and Negotiations in Mutual Interventions
Authors: Duursma Allard , Tamm Henning
Published: December 2024

Why do states that simultaneously support rebel groups in each other’s intrastate conflicts enter negotiations to resolve such mutual interventions? This is an important question, as negotiations between interveners typically lead to negotiated settlements, which in turn tend to make their intrastate conflicts far less deadly. We argue that international crises make negotiations more likely. Crises raise the costs of intervention and often lead potential mediators to put pressure on states to negotiate. Using new monthly data on mutual interventions in Africa, the article shows that crises are indeed significantly associated with the likelihood that negotiations will occur. This finding is robust to using fixed effects and matching. The article contributes to the literature by investigating a widespread though little-studied type of conflict, as well as by studying the impact of state leaders’ crisis perception using a quantitative research design, which helps overcome a methodological limitation seen in previous studies.
Reusing zebrafish facility equipment: Planning, investment and what we have learned
Authors: Fernández-Rodriguez Olga M , Cortés Emilio , Pelegrín Pablo , Maldonado Laura , Fernández Jesus , Peñalver Jose , Ostrovska Khrystyna , Fernández Maria T , Ramírez Pablo , Cayuela Maria L
Published: December 2024

The purpose of this study is to provide a detailed account of our successful experience in establishing a functional zebrafish holding facility by repurposing materials from a previous installation. On the eve of the start-up of our new animal facility we were notified that a research centre was putting part of its zebrafish holding facility (29 racks, accessories, water treatment unit) up for sale. Although the originally planned room was designed for six double racks, but encouraged by the increasing use of the zebrafish model, we decided to seize the opportunity, purchase the equipment and utilize it to create a larger configuration and an independent quarantine to protect the main facility. This decision inevitably led to extra expenses and situations, such as reinforcing the floor in the new location for the main facility, constructing an adjacent shelter for quarantine and adapting the racks. In spite of the additional costs, we consider the purchase and adaptation of this second-hand equipment for our facility to have been a success, and it has proven to be much more advantageous than acquiring brand new equipment. We trust that our experience will be of benefit to colleagues undertaking similar initiatives.
‘Hiding in plain sight’ – mass disasters and murder
Authors: Byard Roger W.
Published: December 2024

The majority of homicides do not occur in mass disasters but are often solitary events. This enables medicolegal investigations to be targeted around the features of a specific case. Mass disasters may, however, result in a large numbers of bodies being brought in over relatively short periods of time. Such disasters may also occur in isolated locations with limited resources and facilities resulting in full autopsies not being undertaken, with faster processing of cases than is usual. For this reason it is possible that injuries due to inflicted trauma may not always be identified. Given the varied circumstances of mass disasters, ranging from the London Blitz of WWII to the 2004 South East Asian tsunami, the possibility of concealed homicides should always be considered.
Can I Trust GenAI to Plan My Next Trip? A Multi-Method Approach to Optimizing Media Mix
Authors: Kang Sung-Eun , Kim Myung Ja , Kim Jinok Susanna , Olya Hossein
Published: December 2024

Although tourists can now book trips directly using generative artificial intelligence (GenAI), it remains unclear whether the real-time travel information it provides is comprehensive and sufficiently trustworthy enough to make booking decisions. The present research addresses this gap by integrating media richness, trust transfer, and the value-based adoption model (VAM) to investigate the impact of varying levels of travel information richness (text-only, text-image, and text-image-audio) on the booking behaviors of tourists using GenAI such as ChatGPT. With data from 578 participants, we tested the proposed structural and configurational models using a multi-analytical approach. Our findings revealed that the three media richness levels yield both analogous and distinctive effects on tourist perceptions regarding benefits, costs, trust formation, and intentions in ChatGPT online travel booking. Specifically, the text-image group demonstrated the strongest links from media richness to trust in ChatGPT, perceived benefit to value, and ultimately value to increased booking intention. Our findings from configurational modeling confirm a significant opportunity to harness the power of AI-empowered platforms for online travel booking.
Using Administrative Data to Better Understand the Reliability of ICMA’s Alternative Service Delivery Survey
Authors: Lamothe Scott
Published: December 2024

The International City/County Management Association’s Alternative Service Delivery (ASD) Survey has been highly influential in the study of make-or-buy and sector choice decisions at the local level. Recently, the reliability of the ASD has been called into question. This paper builds on Lamothe, Lamothe, and Bell’s findings of inconsistencies in these data by comparing 2007 ASD survey results with those found in 2007–2008 administrative data from California. The findings lend further credence to the idea that ASD data should be used with caution and an understanding of its limits and weaknesses, as well as the need to collect more reliable data on local service delivery decisions.
An exploration of individuals with sight impairment and their experiences with employment in the United Kingdom
Authors: Limbachia Vaishali , Mistry Usha
Published: December 2024

There is a significant disparity between the percentage employed, of the general population, and those registered with sight impairment. In the United Kingdom, it is illegal for companies to discriminate against employees who have disabilities, and reasonable adjustments must be made by employers to prevent this from happening. This study examines factors that affect employment and explores participants’ experiences in the workforce who have sight impairments that cannot be corrected by glasses. Participants who were unregistered with sight impairment, registered with partial sight impairment, or registered with severe sight impairment completed a pre-tested online questionnaire. The aim of the questionnaire was to delve into participants experiences with employment. Results were analysed quantitatively, using descriptive statistics and correlation tests, and qualitatively, using thematic analysis. Eighty-eight participants were included, 53 of whom had severe sight impairments. The following factors were found to be associated with employment: a higher level of qualification (correlation coefficient 0.410, 95% confidence interval [CI: 0.213, 0.574]) and the ability to travel independently (correlation coefficient 0.321, 95% CI [0.113, 0.502]). Two types of support for employees were found: (1) adjustments in the workplace and (2) empathy from employers. Two types of barriers were found for unemployed individuals who are seeking employment: (1) employer discrimination and (2) a lack of self-confidence. This study highlights the factors influencing employment outcomes for individuals with sight impairment. In addition seen is the broadly positive impact that the perception of supportive employers has on people with sight impairment and supports solutions that require reducing employment-related barriers.
Hot compression behavior and 3D processing maps of Mg-Gd-Y-Zn alloy formed by cold metal transfer wire arc additive manufacturing
Authors: Li Jiyu , Hao Yunbo , Zhao Kai , Liang Hongyu , Zhang Bo , Deng Ze , Wang Fenghua
Published: December 2024

Based on hot compression experiments, the hot deformation behavior and workability of the Mg-Gd-Y-Zn alloy fabricated by cold metal transfer wire arc additive manufacturing (WAAM-CMT) were studied. The compression experiments were performed at temperatures ranging from 350 to 500 °C, with strain rates between 0.001 and 1 s−1. The results indicate that the peak stress of the alloy decreases significantly with lower strain rates and higher deformation temperatures. Combined with the analysis of the Arrhenius type equation (Eq.), the average value of the recrystallization activation energy (Q) was 308.4 kJ/mol. Utilizing the Zener-Hollomon parameter, we derived the constitutive Eq. for the deposited Mg-Gd-Y-Zn alloy during hot deformation: [Formula: see text]. Using the dynamic material model, we established a three-dimensional (3D) processing maps for the deposited Mg-Gd-Y-Zn alloy. The main instability domain were temperatures from 350 to 385 °C with strain rates of 0.1 to 1.0 s−1, and temperatures from 435 to 500 °C with strain rates of 0.4 to 1.0 s−1. Considering the power dissipation map, the stable and power-efficient forming domains is in the temperature range from 435 to 500 °C and strain rate range from 0.001 to 0.04 s−1. Due to dynamic recrystallisation (DRX), the grain of the deposited Mg-Gd-Y-Zn alloy is significantly refined after hot deformation. These findings provide valuable guidance for selecting integrated process equipment and optimizing deformation parameters for additive and on-line micro-forged magnesium rare earth alloys.
Generative AI in Higher Education Assessments: Examining Risk and Tech-Savviness on Student’s Adoption
Authors: Oc Yusuf , Gonsalves Chahna , Quamina La Toya
Published: December 2024

The integration of generative artificial intelligence (AI) tools is a paradigm shift in enhanced learning methodologies and assessment techniques. This study explores the adoption of generative AI tools in higher education assessments by examining the perceptions of 353 students through a survey and 17 in-depth interviews. Anchored in the Unified Theory of Acceptance and Use of Technology (UTAUT), this study investigates the roles of perceived risk and tech-savviness in the use of AI tools. Perceived risk emerged as a significant deterrent, while trust and tech-savviness were pivotal in shaping student engagement with AI. Tech-savviness not only influenced adoption but also moderated the effect of performance expectancy on AI use. These insights extend UTAUT’s application, highlighting the importance of considering perceived risks and individual proficiency with technology. The findings suggest educators and policymakers need to tailor AI integration strategies to accommodate students’ personal characteristics and diverse needs, harnessing generative AI’s opportunities and mitigating its challenges.
Cerebral infarction presenting with bilateral sudden deafness as the primary symptom: a case report
Authors: Zhang Jie , Zhou Lin , Liu Tao , Cheng Yuhan , Dou Yanling
Published: December 2024

The current case report presents a rare occurrence of cerebral infarction with bilateral sudden deafness as the primary symptom. The patient was a 59-year-old man with hypertension who tested positive for new coronary antibodies and had a long history of smoking and alcohol consumption. Despite receiving treatment for sudden deafness, the patient’s condition rapidly deteriorated and he was diagnosed with basilar artery occlusion and stenosis. The patient died 5 days after mechanical recanalization of the artery. Sudden binaural deafness is a rare clinical condition that may be a prodromal symptom of brainstem infarction. Early diagnosis and prompt treatment are essential for reducing mortality and disability.