bluuebunny/crossref_metadata_2025_split huggingface dataset:
Efficient Matching with Memoization for Regexes with Look-around and Atomic Grouping
Authors: Fujinami Hiroya , Hasuo Ichiro 
Published: April 2024

AbstractRegular expression (regex) matching is fundamental in many applications, especially in web services. However, matching by backtracking—preferred by most real-world implementations for its practical performance and backward compatibility—can suffer from so-called catastrophic backtracking, which makes the number of backtracking super-linear and leads to the well-known ReDoS vulnerability. Inspired by a recent algorithm by Davis et al. that runs in linear time for (non-extended) regexes, we study efficient backtracking matching for regexes with two common extensions, namely look-around and atomic grouping. We present linear-time backtracking matching algorithms for these extended regexes. Their efficiency relies on memoization, much like the one by Davis et al.; we also strive for smaller memoization tables by carefully trimming their range. Our experiments—we used some real-world regexes with the aforementioned extensions—confirm the performance advantage of our algorithms.
Intel PMDK Transactions: Specification, Validation and Concurrency
Authors: Raad Azalea , Lahav Ori , Wickerson John , Balcer Piotr , Dongol Brijesh 
Published: April 2024

AbstractSoftware Transactional Memory (STM) is an extensively studied paradigm that provides an easy-to-use mechanism for thread safety and concurrency control. With the recent advent of byte-addressable persistent memory, a natural question to ask is whether STM systems can be adapted to support failure atomicity. In this paper, we answer this question by showing how STM can be easily integrated with Intel’s Persistent Memory Development Kit (PMDK) transactional library (which we refer to as txPMDK) to obtain STM systems that are both concurrent and persistent. We demonstrate this approach using known STM systems, TML and NOrec, which when combined with txPMDK result in persistent STM systems, referred to as PMDK-TML and PMDK-NORec, respectively. However, it turns out that existing correctness criteria are insufficient for specifying the behaviour of txPMDK and our concurrent extensions. We therefore develop a new correctness criterion, dynamic durable opacity, that extends the previously defined notion of durable opacity with dynamic memory allocation. We provide a model of txPMDK, then show that this model satisfies dynamic durable opacity. Moreover, dynamic durable opacity supports concurrent transactions, thus we also use it to show correctness of both PMDK-TML and PMDK-NORec.
Matrix Representation of Magnetic Pseudo-Differential Operators via Tight Gabor Frames
Authors: Cornean Horia D. , Helffer Bernard , Purice Radu 
Published: April 2024

AbstractIn this paper we use some ideas from [12, 13] and consider the description of Hörmander type pseudo-differential operators on $$\mathbb {R}^d$$

R

d

($$d\ge 1$$

d
≥
1

), including the case of the magnetic pseudo-differential operators introduced in [15, 16], with respect to a tight Gabor frame. We show that all these operators can be identified with some infinitely dimensional matrices whose elements are strongly localized near the diagonal. Using this matrix representation, one can give short and elegant proofs to classical results like the Calderón-Vaillancourt theorem and Beals’ commutator criterion, and also establish local trace-class criteria.
Network effects on information acquisition by DeGroot updaters
Authors: Risco Miguel 
Published: April 2024

Abstract
In today’s world, social networks have a significant impact on information processes, shaping individuals’ beliefs and influencing their decisions. This paper proposes a model to understand how boundedly rational (DeGroot) individuals behave when seeking information to make decisions in situations where both social communication and private learning take place. The model assumes that information is a local public good, and individuals must decide how much effort to invest in costly information sources to improve their knowledge of the state of the world. Depending on the network structure and agents’ positions, some individuals will invest in private learning, while others will free-ride on the social supply of information. The model shows that multiple equilibria can arise, and uniqueness is controlled by the lowest eigenvalue of a matrix determined by the network. The lowest eigenvalue roughly captures how two-sided a network is. Two-sided networks feature multiple equilibria. Under a utilitarian perspective, agents would be more informed than they are in equilibrium. Social welfare would be improved if influential agents increased their information acquisition levels.
Determination of tomato leafminer: Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) damage on tomato using deep learning instance segmentation method
Authors: Uygun Tahsin , Ozguven Mehmet Metin 
Published: April 2024

AbstractPests significantly negatively affect product yield and quality in agricultural production. Agricultural producers may not accurately identify pests and signs of pest damage. Thus, incorrect or excessive insecticides may be used. Excessive use of insecticides not only causes human health and environmental pollution, but also increases input costs. Therefore, early detection and diagnosis of pests is extremely important. In this study, the effectiveness of the instance segmentation method, a deep learning-based method, was investigated for the early detection of the damage caused by the T. absoluta pest in the leaf part of the tomato plant under greenhouse conditions. An original dataset was created by acquiring 800 healthy and damaged images under greenhouse conditions. The acquired images were labelled as bounding box and automatically converted to a mask label with the Segment Anything Model (SAM) model. The created dataset was trained with YOLOv8(n/s/m/l/x)-Seg models. As a result of the training, the box performance of the proposed YOLOv8l-Seg model was measured as 0.924 in the mAP0.5 metric. The YOLOv8l-Seg model mask values are, respectively: mAP0.5, mAP0.5–0.95, Precision, Recall showed the best performance with values of 0.935, 0.806, 0.956 and 0.859. Then, the YOLOv8l-Seg model, trained with different data input sizes, showed the best performance at 640 × 640 size and the lowest performance with a value of 0.699 in the mAP0.5 metric in the 80 × 80 size. The same dataset was trained with YOLOv7, YOLOv5l, YOLACT and Mask R-CNN instance segmentation models and performance comparisons were made with the YOLOv8l-Seg model. As a result, it was determined that the model that best detected T. absoluta damage in tomato plants was the YOLOv8l-Seg model. The Mask R-CNN model showed the lowest performance with a metric of 0.806 mAP0.5. The results obtained from this study revealed that the proposed model and method can be used effectively in detecting the damage caused by the T. absoluta pest.
Extremes of summer Arctic sea ice reduction investigated with a rare event algorithm
Authors: Sauer Jerome , Demaeyer Jonathan , Zappa Giuseppe , Massonnet François , Ragone Francesco 
Published: April 2024

AbstractVarious studies identified possible drivers of extremes of Arctic sea ice reduction, such as observed in the summers of 2007 and 2012, including preconditioning, local feedback mechanisms, oceanic heat transport and the synoptic- and large-scale atmospheric circulations. However, a robust quantitative statistical analysis of extremes of sea ice reduction is hindered by the small number of events that can be sampled in observations and numerical simulations with computationally expensive climate models. Recent studies tackled the problem of sampling climate extremes by using rare event algorithms, i.e., computational techniques developed in statistical physics to reduce the computational cost required to sample rare events in numerical simulations. Here we apply a rare event algorithm to ensemble simulations with the intermediate complexity coupled climate model PlaSim-LSG to investigate extreme negative summer pan-Arctic sea ice area anomalies under pre-industrial greenhouse gas conditions. Owing to the algorithm, we estimate return times of extremes orders of magnitude larger than feasible with direct sampling, and we compute statistically significant composite maps of dynamical quantities conditional on the occurrence of these extremes. We find that extremely low sea ice summers in PlaSim-LSG are associated with preconditioning through the winter sea ice-ocean state, with enhanced downward longwave radiation due to an anomalously moist and warm spring Arctic atmosphere and with enhanced downward sensible heat fluxes during the spring-summer transition. As a consequence of these three processes, the sea ice-albedo feedback becomes active in spring and leads to an amplification of pre-existing sea ice area anomalies during summer.
Elastic medium and torsional spring effects on the nonlocal dynamic of functionally graded porous nanotubes
Authors: Uzun Büşra , Yaylı Mustafa Özgür , Civalek Ömer 
Published: April 2024

AbstractIn this study, Eringen’s nonlocal elasticity theory that applies the small size effects in functionally graded porous nanotubes embedded in an elastic matrix is discussed. The material properties of functionally graded porous nanotubes are taken into account to vary over the radius direction with a rule of mixture. The free torsional vibration relation according to nonlocal elasticity theory, via Hamilton’s principle, is obtained and an eigenvalue solution is constructed for the free torsional vibration response of the presented work. The presented analytical model is validated by comparing the calculated mathematical results for
homogeneous nanotubes with rigid and non-rigid boundary conditions. Special attention is given to deformable boundary conditions, porosity coefficient, material grading coefficient and also to the influence of elastic medium on the free torsional vibration frequencies. In this paper, it has been proven that the influence of length, elastic medium, elastic torsional spring rigidities, material grading and porosity coefficients on the vary in the torsional vibration frequency of the functionally graded nanotube is not small.
A hyperelastic extended Kirchhoff–Love shell model with out-of-plane normal stress: II. An isogeometric discretization method for incompressible materials
Authors: Taniguchi Yasutoshi , Takizawa Kenji , Otoguro Yuto , Tezduyar Tayfun E. 
Published: April 2024

AbstractThis is Part II of a multipart article on a hyperelastic extended Kirchhoff–Love shell model with out-of-plane normal stress. We introduce an isogeometric discretization method for incompressible materials and present test computations. Accounting for the out-of-plane normal stress distribution in the out-of-plane direction affects the accuracy in calculating the deformed-configuration out-of-plane position, and consequently the nonlinear response of the shell. The return is more than what we get from accounting for the out-of-plane deformation mapping. The traction acting on the shell can be specified on the upper and lower surfaces separately. With that, the model is now free from the “midsurface’ location in terms of specifying the traction. In dealing with incompressible materials, we start with an augmented formulation that includes the pressure as a Lagrange multiplier and then eliminate it by using the geometrical representation of the incompressibility constraint. The resulting model is an extended one, in the Kirchhoff–Love category in the degree-of-freedom count, and encompassing all other extensions in the isogeometric subcategory. We include ordered details as a recipe for making the implementation practical. The implementation has two components that will not be obvious but might be critical in boundary integration. The first one is related to the edge-surface moment created by the Kirchhoff–Love assumption. The second one is related to the pressure/traction integrations over all the surfaces of the finite-thickness geometry. The test computations are for dome-shaped inflation of a flat circular shell, rolling of a rectangular plate, pinching of a cylindrical shell, and uniform hydrostatic pressurization of the pinched cylindrical shell. We compute with neo-Hookean and Mooney–Rivlin material models. To understand the effect of the terms added in the extended model, we compare with models that exclude some of those terms.
The concept of privacy in the digital world according to teenagers
Authors: Vespoli Giovanni , Taddei Benedetta , Imbimbo Enrico , De Luca Lisa , Nocentini Annalaura 
Published: April 2024

Abstract
Aim
This study aims first to explore how adolescents define the idea of online privacy and how well they understand all the implications of using their data online, using emergent themes modeling. A co-occurrence analysis comparing narratives was performed to better understand how the concept of online privacy differed according to participants’ age.

Subject and methods
Five hundred eighty-eight adolescents (64.8% female) completed a narrative task about their definition of online privacy. “Reflection of self-online,” “digital safe,” “human right,” and “ability to surf the web and emotional consequences” are the four emergent themes closely related to the adolescents’ definition and meaning of online privacy.

Results
The results did not show differences between the two age groups in the interpretation of the construct, except for the concept of safety. Adolescents older than 15 years used more emotional content.

Conclusion
The findings are discussed in terms of how understanding young people’s online privacy can help to design a safer Internet.
Sludge from sea-based Atlantic salmon (Salmo salar L.) production: quantification, composition, and potential application in integrated multi-trophic aquaculture
Authors: Anglade Inka , Krogli Trygve M. , Reitan Kjell Inge 
Published: April 2024

AbstractFuture growth of Atlantic salmon (Salmo salar L.) in Norway is tied to finding solutions for major ecological challenges connected to salmon lice, escapees, and nutrient emissions from sea cages. At the same time, nutrient-rich sludge from salmon production comprises a valuable resource for the cultivation of lower trophic species using an integrated multi-trophic aquaculture (IMTA) approach. This study aimed to quantify the sedimentation of aquaculture sludge under sea cages of an Atlantic salmon aquaculture site and to qualify the composition of this sludge. Additionally, the study evaluated the hypothetical use of sludge from sea-based aquaculture as a feed source for polychaetes Hediste diversicolor. Using sediment traps, sludge samples were collected under two different Atlantic salmon sea cages, at two different depths, and three different sampling dates. Subsequently, they were quantified, and their composition was assessed with regards to carbon (C), nitrogen (N), phosphorus (P), lipid, fatty acid (FA), protein, amino acid (AA), and ash content as well as elemental ratios and composition of FAs and AAs. The quantity of collected sludge was significantly different between sea cages, with a strong positive correlation between feed input and collected sludge (R2 = 0.98, p < 0.05). Sampling depth did not affect the quantity of collected sludge in the sediment traps (2215 ± 480 mg DW day−1), and no significant difference in sedimented sludge as a proportion of theoretically produced sludge (12.94 ± 2.16%) was found when comparing the different cages and sampling depths. Furthermore, the composition of collected sludge was similar at all sampling points. The overall nutritional value was lower compared to sludge from land-based aquaculture; regardless, sludge from sea-based salmon production can in theory be considered a potential feed resource to be used for the production of polychaetes H. diversicolor.
Depression symptoms modelling from social media text: an LLM driven semi-supervised learning approach
Authors: Farruque Nawshad , Goebel Randy , Sivapalan Sudhakar , Zaïane Osmar R. 
Published: April 2024

AbstractA fundamental component of user-level social media language based clinical depression modelling is depression symptoms detection (DSD). Unfortunately, there does not exist any DSD dataset that reflects both the clinical insights and the distribution of depression symptoms from the samples of self-disclosed depressed population. In our work, we describe a semi-supervised learning (SSL) framework which uses an initial supervised learning model that leverages (1) a state-of-the-art large mental health forum text pre-trained language model further fine-tuned on a clinician annotated DSD dataset, (2) a Zero-Shot learning model for DSD, and couples them together to harvest depression symptoms related samples from our large self-curated depressive tweets repository (DTR). Our clinician annotated dataset is the largest of its kind. Furthermore, DTR is created from the samples of tweets in self-disclosed depressed users Twitter timeline from two datasets, including one of the largest benchmark datasets for user-level depression detection from Twitter. This further helps preserve the depression symptoms distribution of self-disclosed tweets. Subsequently, we iteratively retrain our initial DSD model with the harvested data. We discuss the stopping criteria and limitations of this SSL process, and elaborate the underlying constructs which play a vital role in the overall SSL process. We show that we can produce a final dataset which is the largest of its kind. Furthermore, a DSD and a Depression Post Detection model trained on it achieves significantly better accuracy than their initial version.
IoT-based eHealth using blockchain technology: a survey
Authors: Allam Aya H. , Gomaa Ibrahim , Zayed Hala H. , Taha Mohamed 
Published: April 2024

AbstractThe eHealth sector has witnessed significant growth due to technological advancements, facilitating care delivery in patients' homes and moving away from traditional hospital settings. Blockchain and the Internet of Things (IoT) play pivotal roles in enhancing healthcare services, offering features such as remote patient monitoring, streamlined electronic medical record (EMR) management, drug traceability, and effective disease control, particularly during events like the COVID-19 pandemic. The growing utilization of IoT devices brings about security challenges, including concerns related to data integrity and device authentication. This paper proposes the integration of blockchain technology as a robust solution. Leveraging its decentralized and tamper-resistant features, blockchain establishes trust among diverse IoT devices, ensuring the integrity of IoT data. Additionally, smart contracts enhance device authentication, fortifying overall security by addressing vulnerabilities associated with centralization. Regarding the management of eHealth, this survey begins with an overview of the industry, highlighting IoT-related challenges in healthcare. It explores various IoT applications in eHealth and discusses how blockchain can effectively address obstacles in healthcare management through IoT. Notably, the paper provides insights into examining consensus algorithm parameters within blockchain systems, clarifying the methodology used to assess and optimize these critical components. The survey extends to a thorough review of existing research on integrating blockchain-based IoT in eHealth. Finally, it presents an overview of challenges and potential solutions for implementing blockchain-based IoT in the eHealth sector. This comprehensive survey aims to empower stakeholders by providing insights to enhance patient care in this dynamic and evolving field.
How using a response system in blended synchronous seminars encourages online and onsite student participation
Authors: Diaz Patricia , Hrastinski Stefan , Norström Per 
Published: April 2024

AbstractThe interest in blended synchronous learning environments has increased dramatically since the COVID-19 pandemic. However, a key challenge is how to simultaneously encourage online and onsite student participation. Response systems have been found to stimulate student participation in classroom and online education settings. This study investigates how online and onsite students participate in blended synchronous seminars where a response system is being used. The data comprises observations of blended synchronous seminars, students’ written reflections, and student interviews, all of which were thematically analyzed. It was found that using a response system encouraged online and onsite students to participate in various ways. Although online students mostly remained quiet, they perceived to engage through listening and thinking, participating in the seminars by absorbing information, and interacting with the content displayed via the response system. The onsite students participated vocally and more spontaneously. All students participated in written, anonymous, and game-based modes, suggesting that there were different and complementary ways for students to participate when using a response system, which extended beyond merely talking or chatting. Notably, most students perceived the response system crucial to their participation in the blended synchronous seminars. The findings underscore the importance of encouraging student participation in blended synchronous learning environments, highlighting response systems as effective tools to encourage onsite and, particularly, online student participation.
On the relevance of query definition in the performance of 3D ligand-based virtual screening
Authors: Vázquez Javier , García Ricardo , Llinares Paula , Luque F. Javier , Herrero Enric 
Published: April 2024

AbstractLigand-based virtual screening (LBVS) methods are widely used to explore the vast chemical space in the search of novel compounds resorting to a variety of properties encoded in 1D, 2D or 3D descriptors. The success of 3D-LBVS is affected by the overlay of molecular pairs, thus making selection of the template compound, search of accessible conformational space and choice of the query conformation to be potential factors that modulate the successful retrieval of actives. This study examines the impact of adopting different choices for the query conformation of the template, paying also attention to the influence exerted by the structural similarity between templates and actives. The analysis is performed using PharmScreen, a 3D LBVS tool that relies on similarity measurements of the hydrophobic/philic pattern of molecules, and Phase Shape, which is based on the alignment of atom triplets followed by refinement of the volume overlap. The study is performed for the original DUD-E+ database and a Morgan Fingerprint filtered version (denoted DUD-E+-Diverse; available in https://github.com/Pharmacelera/Query-models-to-3DLBVS), which was prepared to minimize the 2D resemblance between template and actives. Although in most cases the query conformation exhibits a mild influence on the overall performance, a critical analysis is made to disclose factors, such as the content of structural features between template and actives and the induction of conformational strain in the template, that underlie the drastic impact of the query definition in the recovery of actives for certain targets. The findings of this research also provide valuable guidance for assisting the selection of the query definition in 3D LBVS campaigns.
Graphical Abstract