bluuebunny/crossref_metadata_2025_split huggingface dataset:
PARADIGM-PV: a randomized, multicenter phase 4 study to assess the efficacy and safety of ropeginterferon alfa-2b in patients with low- or high-risk polycythemia vera
Authors: Yacoub Abdulraheem , Abu-Zeinah Ghaith , Qin Albert , Tashi Tsewang , Da’na Waleed , Shih Weichung Joe , Zagrijtschuk Oleh , Tsai Chan-Yen , Geller Robert , Komatsu Norio , Mesa Ruben , Gill Harinder 
Published: January 2025

Abstract
Polycythemia vera (PV) is characterized by clonal hematopoietic stem or progenitor cells with constitutively active somatic mutation(s) in the Janus kinase 2 gene. Phlebotomy (Phl) and aspirin are often used alone for low-risk PV patients. However, data from the Low-PV study demonstrated that Phl and aspirin may not be adequate for patients. Therapeutic intervention with disease-modifying treatment appears to be beneficial for patients with PV regardless of the risk category. Ropeginterferon alfa-2b (ropeg) is a novel interferon-based therapy with favorable dosing schedules. A higher starting-dose (250 µg) regimen with simpler dose titrations was found to have a potent disease-modifying effect with respect to inducing a molecular response. PARADIGM-PV is a randomized, phase 4 study with the primary goal of assessing the efficacy of ropeg at this dosing regimen in alleviating Phl-dependence in both low- and high-risk patients with PV. The secondary endpoints include complete hematologic response, molecular response, symptom improvement, maintenance of median hematocrit (Hct) values < 45% without disease progression, and safety. Patients will be randomized equally to receive either ropeg every two weeks or to continue their current treatment with Phl or other cytoreductive agents (e.g., hydroxyurea, other interferons, or ruxolitinib) as applicable. All patients will receive Phl if their Hct values are elevated to ≥45% according to the National Comprehensive Cancer Network guidelines. The study will enroll approximately 70 patients internationally, including patients in the US. This study will provide new efficacy data, measured as the ability of ropeg to reduce Phl eligibility and modify the disease.
Basal forebrain innervation of the amygdala: an anatomical and computational exploration
Authors: Tuna Tuğçe , Banks Tyler , Glickert Gregory , Sevinc Cem , Nair Satish S. , Unal Gunes 
Published: January 2025

Abstract
Theta oscillations of the mammalian amygdala are associated with processing, encoding and retrieval of aversive memories. In the hippocampus, the power of the network theta oscillation is modulated by basal forebrain (BF) GABAergic projections. Here, we combine anatomical and computational approaches to investigate if similar BF projections to the amygdaloid complex provide an analogous modulation of local network activity. We used retrograde tracing with fluorescent immunohistochemistry to identify cholinergic and non-cholinergic parvalbumin- or calbindin-immunoreactive BF neuronal subgroups targeting the input (lateral and basolateral nuclei) and output (central nucleus and the central bed nucleus of the stria terminalis) regions of the amygdaloid complex. We observed a dense non-cholinergic, putative GABAergic projection from the ventral pallidum (VP) and the substantia innominata (SI) to the basolateral amygdala (BLA). The VP/SI axonal projections to the BLA were confirmed using viral anterograde tracing and transsynaptic labeling. We tested the potential function of this VP/SI-BLA pathway in a 1000-cell biophysically realistic network model, which incorporated principal neurons and three major interneuron groups of the BLA, together with extrinsic glutamatergic, cholinergic, and VP/SI GABAergic inputs. We observed in silico that theta-modulation of VP/SI GABAergic projections enhanced theta oscillations in the BLA via their selective innervation of the parvalbumin-expressing local interneurons. Ablation of parvalbumin-, but not somatostatin- or calretinin-expressing, interneurons reduced theta power in the BLA model. These results suggest that long-range BF GABAergic projections may modulate network activity at their target regions through the formation of a common interneuron-type and oscillatory phase-specific disinhibitory motif.
Collision avoidance and routing based on location access (CARLA) of mobile robots
Authors: ElSayyad Shimaa Ezzat , Saleh Ahmed I. , Ali Hesham A. , Saraya M. S. , Rabie Asmaa H. , Abdelsalam Mohamed M. 
Published: January 2025

Abstract
The paper introduces a new path-planning robotic system methodology called Collision Avoidance and Routing based on Location Access (CARLA) for use in critical environments such as hospitals and crises where quick action and saving human lives are vital. The main focus of our framework is on accuracy and fast responses, such as delivering tools or items in a specific area while avoiding collisions with other robots and obstacles. CARLA is designed to provide quick responses during emergencies, unlike most existing algorithms that are integrated into site control units or distributed among mobile robots on-site. By being loaded onto a remote server node rather than individual robots, CARLA helps to conserve the robots' capabilities, hardware resources, and power consumption. Additionally, our system utilizes cloud computing and Fog servers technology to improve data transmission times between the cloud and smart devices, especially for applications with strict timing requirements like emergency response. The Fog platform is also leveraged to enhance on-site access to real-time interaction and location-based services by bringing processing power closer to the robots from far-off Cloud servers. CARLA has various applications, such as in factories and warehouses, where mobile robots need to be selected and directed by a central control system remotely. The proposed framework consists of three main modules: Robot Knowledge Module, Robot Selection Module, and Route Reservation Module, which will all be discussed in detail in this paper. The results of simulations using this framework show that the robots have improved flexibility and efficiency in terms of computing paths and successfully fulfiling requests without colliding, compared to traditional methods used in similar scenarios.
Assessment of hard tissue changes after horizontal guided bone regeneration with the aid of deep learning CBCT segmentation
Authors: Palkovics Daniel , Hegyi Alexandra , Molnar Balint , Frater Mark , Pinter Csaba , García-Mato David , Diaz-Pinto Andres , Windisch Peter 
Published: January 2025

Abstract

Objectives
To investigate the performance of a deep learning (DL) model for segmenting cone-beam computed tomography (CBCT) scans taken before and after mandibular horizontal guided bone regeneration (GBR) to evaluate hard tissue changes.

Materials and methods
The proposed SegResNet-based DL model was trained on 70 CBCT scans. It was tested on 10 pairs of pre- and post-operative CBCT scans of patients who underwent mandibular horizontal GBR. DL segmentations were compared to semi-automated (SA) segmentations of the same scans. Augmented hard tissue segmentation performance was evaluated by spatially aligning pre- and post-operative CBCT scans and subtracting preoperative segmentations obtained by DL and SA segmentations from the respective postoperative segmentations. The performance of DL compared to SA segmentation was evaluated based on the Dice similarity coefficient (DSC), intersection over the union (IoU), Hausdorff distance (HD95), and volume comparison.

Results
The mean DSC and IoU between DL and SA segmentations were 0.96 ± 0.01 and 0.92 ± 0.02 in both pre- and post-operative CBCT scans. While HD95 values between DL and SA segmentations were 0.62 mm ± 0.16 mm and 0.77 mm ± 0.31 mm for pre- and post-operative CBCTs respectively. The DSC, IoU and HD95 averaged 0.85 ± 0.08; 0.78 ± 0.07 and 0.91 ± 0.92 mm for augmented hard tissue models respectively. Volumes mandible- and augmented hard tissue segmentations did not differ significantly between the DL and SA methods.

Conclusions
The SegResNet-based DL model accurately segmented CBCT scans acquired before and after mandibular horizontal GBR. However, the training database must be further increased to increase the model’s robustness.

Clinical relevance
Automated DL segmentation could aid treatment planning for GBR and subsequent implant placement procedures and in evaluating hard tissue changes.
ESG rating and ambiguity: an informative and distorted signal-based approach
Authors: Bongermino Giorgio , Romagnoli Silvia 
Published: January 2025

Abstract
In an increasingly uncertain, complex socio-economic, and geopolitical environment, the significance of information signals and their perception becomes more crucial, especially in assessing sustainability and environmental impact. The challenges arise from businesses’ lack of transparency and reported data, making it difficult for investors and rating agencies to evaluate and manage risks, costs, sustainability, risk-adjusted performance, greenwashing, fiduciary duty clarification, and scoring. This emphasizes the substantial impact of a high level of ambiguity on the market. Considering the three pillars of ESG parameters, we propose a novel model for assessing an ESG Rating based on (i) the level of disclosure, representing the quality of the signal and released information, and (ii) the subjective perception of the signal itself. This perception can be influenced by factors such as personal risk aversion and ESG disagreement arising from controversies in the rating process. Recognizing the identified distortion in the ESG rating as having predictive power, where ambiguity can been seen as a way to represent the market’s sentiment, the distortion turns out to play the role of a policy driver capable of identifying sectors where ESG is under/overestimated and testing the robustness of a scoring method
“It’s Like Doctor Jekyll and Mister Hyde”: The Construction of Moral Identity by Israeli Men Who Pay Women for Sex
Authors: Prior Ayelet , Peled Einat 
Published: January 2025

Abstract
This study examined how Israeli men who pay women for sex (MPWS) construct and sustain a moral identity within the social context that often portrays them as deviants, perpetrators, and abusers, thereby challenging their ability to maintain a respectful and dignified image. Twenty-three Israeli MPWS participated in in-depth semi-structured interviews, which were then analyzed using constructivist grounded theory method. Using the theoretical framework moral reflexivity, we conceptualize three central dynamics of constructing and sustaining a moral identity in the context of paying for sex: Maintaining an intact moral self by resisting the moral conflict; presenting a reflexive agonizing moral self; and constructing a moral self through identity fragmentation. The findings reveal that these facets of the moral reflexivity process can overlap, coexist, and circulate in a messy manner, thus promoting an ecological understanding of how morality is shaped by various societal forces, rather than discovering what a moral identity is. We therefore call for a morally sensitive approach in studies in the field of MPWS and sex work. Such an approach encourages researchers to be aware of moral issues, moral questions, and moral processes, and to treat morality as a socially context-dependent trait that is highly relevant to the study of the sex industry.
Time-Scaling, Ergodicity, and Covariance Decay of Interacting Particle Systems
Authors: Głuchowski Maciej , Menz Georg 
Published: January 2025

Abstract
The main focus of this article is the study of ergodicity of Interacting Particle Systems (IPS). We present a simple lemma showing that scaling time is equivalent to taking the convex combination of the transition matrix of the IPS with the identity. As a consequence, the ergodic properties of IPS are invariant under this transformation. Surprisingly, this simple observation has non-trivial implications: It allows to extend any result that does not respect this invariance, which we demonstrate with examples. Additionally, we develop a recursive method to deduce decay of correlations for IPS with alphabets of arbitrary (finite) size, and apply the Time-Scaling Lemma to that as well. As an application of this new criterion we show that certain one-dimensional IPS are ergodic answering an open question of Toom et al.
Fast online feature selection in streaming data
Authors: Hochma Yael , Last Mark 
Published: January 2025

Abstract
The challenge of getting big amounts of high-quality labeled data is compounded by the fact that data labeling is often subjective and requires significant human effort. In many cases, the quality of the labeled data depends entirely on the expertise and experience of human annotators, making it challenging to ensure labeling accuracy in large and dynamic datasets. Moreover, there may be a significant delay between the arrival of a new instance and its manual labeling. This paper explores the use of fully unsupervised feature selection algorithms in non-stationary data streams, where the importance of features may change over time. We introduce a novel feature selection algorithm called Online Fast FEa-ture SELection-OFFESEL, which calculates the feature importance scores in each incoming window based on their mean normalized values and without using any class labels. We evaluate OFFESEL on 17 benchmark data streams, both stationary and non-stationary, using popular online classifiers like PerceptronMask, VFDT, Online Boosting, and Linear SVM. We compare OFFESEL to several other feature selection algorithms, including state-of-the-art supervised ones like FIRES and ABFS, as well as popular unsupervised ones like MCFS, LS, and Max Variance, which we adapted to data streams. Our results indicate that OFFESEL outperforms all supervised and unsupervised feature selection algorithms in terms of classification accuracy. Specifically, OFFESEL preserves the accuracy level of the supervised FIRES algorithm, which proved more accurate than ABFS in our experiments, while maintaining the accuracy level achieved by the unsupervised Max Variance algorithm. Moreover, OFFESEL requires even less computation time than Max Variance and shows high stability on stationary datasets. Overall, our study demonstrates the potential benefits of using unlabeled data for feature ranking and selection in dynamic data streams.
Self explainable graph convolutional recurrent network for spatio-temporal forecasting
Authors: García-Sigüenza Javier , Curado Manuel , Llorens-Largo Faraon , Vicent Jose F. 
Published: January 2025

Abstract
Artificial intelligence (AI) is transforming industries and decision-making processes, but concerns about transparency and fairness have increased. Explainable artificial intelligence (XAI) is crucial to address these concerns, providing transparency in AI decision making, alleviating the effect of biases and fostering trust. However, the application of XAI in conjunction with problems with spatio-temporal components represents a challenge due to the small number of options, which when implemented penalize performance in exchange for the explainability obtained. This paper proposes self explainable graph convolutional recurrent network (SEGCRN), a model that seeks to integrate explainability into the architecture itself, seeking to increase the ability to infer the relationship and dependence between the different nodes, proposing an alternative to explainability techniques, which are applied as a second layer. The proposed model has been able to show in different data sets the ability to reduce the amount of information needed to make a prediction, while reducing the impact on the prediction caused by applying an explainability technique, having managed to reduce the use of information without loss of accuracy. Thus, SEGCRN is proposed as a gray box, which allows a better understanding of its behavior than black box models, having validated the model with traffic data, combining both spatial and temporal components, achieving promising results.
Unveiling Students’ Mental Models and Learning Demands: an Empirical Validation of Secondary Students’ Model Progression on Plant Nutrition
Authors: Pedrera Oier , Barrutia Oihana , Díez José Ramón 
Published: January 2025

Abstract
Identifying the mental models held by students has been widely emphasized as being a pivotal aspect of effective science education. In fact, it allows us to understand students’ conceptions, detect teaching-learning difficulties and tailor instruction accordingly. Hence, in this study, the plant nutrition mental models held by upper secondary students were investigated and empirically validated with the aim of detecting the most pressing learning demands and providing instructional guidelines to improve the teaching-learning of the topic. In order to unveil students’ mental models a 5-question open-ended questionnaire was administered to 122 Spanish upper secondary students. Their responses were analyzed through an innovative approach that merged phenomenography and Item Response Theory. Three distinct models emerge from the analysis investigating the sequential development of students’ reasoning. The first and most basic comprises students with heterotrophic explanatory ideas based on intuitive and naïve conceptions. The intermediate model consists of participants who incorporate photosynthesis and plants’ gas exchanges to their models without fully grasping some key underlying concepts probably due to previous misconception inducing instruction. The upper model encompasses the few students who have autotrophic mental models and ideas which resemble the consensus Scientific Model of Plant Nutrition. The findings also reveal that the overall conceptualization level of the participants is relatively low and that several teaching-learning difficulties are strictly linked to each of the different mental models. Finally, the learning demands of the topic are described, and the implications for the teaching-learning designs aimed at overcoming those difficulties are discussed.
An analysis of the modality and flexibility of the inverse stereographic normal distribution
Authors: Hinz Florian B. , Mahmoud Amr H. , Lill Markus A. 
Published: January 2025

Abstract
Circular data arises in various fields including robotics, biology, geology and material sciences. Modelling such data requires flexible distribution families on the hypertorus. Common choices are the von Mises and the wrapped normal distributions. In this work we investigate the inverse stereographic normal distribution as an alternative distribution family both from a theoretical and applied perspective. We first generalize unimodality results to arbitrary dimensions by proving that the inverse stereographic normal distribution is unimodal if and only if all eigenvalues of the covariance matrix are less than or equal to 0.5. We then analyze the flexibility by fitting mixtures of shifted inverse stereographic normal distributions via gradient descent to several examples of toroidal data.
An examination of daily CO2 emissions prediction through a comparative analysis of machine learning, deep learning, and statistical models
Authors: Ajala Adewole Adetoro , Adeoye Oluwatosin Lawrence , Salami Olawale Moshood , Jimoh Ayoola Yusuf 
Published: January 2025

Abstract
Human-induced global warming, primarily attributed to the rise in atmospheric CO2, poses a substantial risk to the survival of humanity. While most research focuses on predicting annual CO2 emissions, which are crucial for setting long-term emission mitigation targets, the precise prediction of daily CO2 emissions is equally vital for setting short-term targets. This study examines the performance of 14 models in predicting daily CO2 emissions data from 1/1/2022 to 30/9/2023 across the top four polluting regions (China, India, the USA, and the EU27&UK). The 14 models used in the study include four statistical models (ARMA, ARIMA, SARMA, and SARIMA), three machine learning models (support vector machine (SVM), random forest (RF), and gradient boosting (GB)), and seven deep learning models (artificial neural network (ANN), recurrent neural network variations such as gated recurrent unit (GRU), long short-term memory (LSTM), bidirectional-LSTM (BILSTM), and three hybrid combinations of CNN-RNN). Performance evaluation employs four metrics (R
2, MAE, RMSE, and MAPE). The results show that the machine learning (ML) and deep learning (DL) models, with higher R
2 (0.714–0.932) and lower RMSE (0.480–0.247) values, respectively, outperformed the statistical model, which had R
2 (− 0.060–0.719) and RMSE (1.695–0.537) values, in predicting daily CO2 emissions across all four regions. The performance of the ML and DL models was further enhanced by differencing, a technique that improves accuracy by ensuring stationarity and creating additional features and patterns from which the model can learn. Additionally, applying ensemble techniques such as bagging and voting improved the performance of the ML models by approximately 9.6%, whereas hybrid combinations of CNN-RNN enhanced the performance of the RNN models. In summary, the performance of both the ML and DL models was relatively similar. However, due to the high computational requirements associated with DL models, the recommended models for daily CO2 emission prediction are ML models using the ensemble technique of voting and bagging. This model can assist in accurately forecasting daily emissions, aiding authorities in setting targets for CO2 emission reduction.
Direct measurement of brake wear particles from a light-duty vehicle under real-world driving conditions
Authors: Al Wasif-Ruiz Tawfiq , Suárez-Bertoa Ricardo , Sánchez-Martín José Alberto , Barrios-Sánchez Carmen Cecilia 
Published: January 2025

Abstract
As tailpipe emissions have decreased, there is a growing focus on the relative contribution of non-exhaust sources of vehicle emissions. Addressing these emissions is key to better evaluating and reducing vehicles’ impact on air quality and public health. Tailoring solutions for different non-exhaust sources, including brake emissions, is essential for achieving sustainable mobility. Studying emissions from vehicles in real-world scenarios provides a better understanding of their environmental impact compared to laboratory testing alone. This study presents findings on the direct measurement of brake particles and the characterization of this source of particulate matter in real-world conditions using a mobile laboratory. In situ measurements of particle concentration and size distribution showed good agreement with previous laboratory studies, indicating the suitability of the approach to investigate break particle emissions during real-world operation. The study demonstrates that particle size distributions can vary based on the temperature of the brake disk, which is influenced by the initial braking speed, with significant variations observed between speeds of 60, 80, 100, and 120 km/h. Particles with sizes between 6 and 523 nm were released into the air from the brake system, although it is likely that larger particles were also emitted but not captured due to the upper detection limit of the Engine Exhaust Particle Sizer. During harsh braking events, such as decelerations of 4.2 m/s2 from 120 km/h, a concentration of up 106 (#/cm3) was measured for particles under 8 nm. Moreover, scanning electron microscope analysis revealed that nanoparticles are present in the form of agglomerates, whose shape can change depending on the formation process. Elements present in the particles comprised mainly iron, copper, and aluminium, indicating wear of the brake pad materials and disk components.