bluuebunny/crossref_metadata_2025_split huggingface dataset:
Youths’ Investigations of Critical Urban Forestry Through Multimodal Sensemaking
Authors: Smith Blaine E. , Carlone Heidi B. , Ziegler Hannah , Janumyan Yelena , Conley Zachary , Chen Jingyi , Jen Tessaly 
Published: June 2024

AbstractA growing body of research suggests that digital multimodal composing can provide students multiple points of entry for making sense of local climate change issues and sharing their voices through digital activism. Building upon this scholarship, this study examined the processes of 32 small groups (n = 55) of 7th- and 8th-grade students as they co-created a wide range of multimodal projects (e.g., videos, podcasts, infographics, posters, and cartoons) that explored the environmental, ecological, and sociopolitical impacts of inequitable access to urban tree canopy and greenspace in their city. In particular, scholarship on onto-epistemic heterogeneity, critical place-based learning, and multimodality were integrated to gain an interdisciplinary understanding of how digital multimodal composing mediated students’ sensemaking about urban forestry impacts on community health and ecological well-being. Data sources consisted of field notes, audio and video recordings, survey data, student interviews, and students’ final multimodal projects. Through qualitative and multimodal data analysis, five main themes emerged for how multiple modes mediated students’ sensemaking about critical urban forestry: (1) embracing tree equity for compelling stories, (2) engaging authentic audiences through storytelling, (3) perspective-taking through multiple modes, (4) exploring affective dimensions of urban heat islands, and (5) developing solutions for critical urban forestry issues. These findings contribute new insights into how digital multimodal storytelling can provide a productive way for students to make sense of climate justice issues and gain agency by experiencing multiple ways of knowing.
Microstructure, martensitic transformation kinetics, and magnetic properties of (Ni50Mn40In10)100−xCox melt-spun ribbons
Authors: Bekhouche Ahlem , Alleg Safia , Dadda Karima , Daoudi Mourad Ibrahim , Saurina Joan , Suñol Joan-Josep 
Published: June 2024

AbstractThe effect of Co-doping on the structure, microstructure, martensitic phase transformation kinetics, and magnetic properties of the melt-spun (Ni50Mn40In10)1−xCox (x = 1, 2, and 3) Heusler ribbons, named hereafter Co1 (x = 1), Co2 (x = 2), and Co3 (x = 3), was assessed using X-ray diffraction, scanning electron microscope, energy-dispersive spectroscopy, X-ray fluorescence, differential scanning calorimetry, and vibrating sample magnetometer. The XRD results reveal the formation of a 14M martensite structure alongside the face-centered-cubic (fcc) γ phase. The crystallite size ranges between 50 and 98 nm for the 14M martensite and from 9 to 16 nm for the γ phase. The mass fraction of the γ phase lies between 36.4 and 44.2%. Co-doping affects the lattice parameters and the characteristic temperatures (martensite start, martensite finish, austenite start, and austenite finish). The calculated activation energy values for the non-isothermal martensitic transformation kinetics are 257 kJ mol−1 and 135.6 kJ mol−1 for the Co1 and Co2, respectively. The produced ribbons show a paramagnetic behavior. The variation in the coercivity can be related to the crystallite size and mass fraction of the γ phase. The produced ribbons exhibit an exchange bias at room temperature that decreases with increasing the Co content.
Research on the supply point mechanism of Shanghai during the closure of COVID-19 in 2022 from the perspective of urban resilience
Authors: Wang Jun , Ran Wei 
Published: June 2024

AbstractIn the context of significant public emergencies, the capacity of cities to withstand shocks assumes paramount importance, serving as a key indicator of urban resilience. This study focuses on the supply points established during the lockdown period of Shanghai in 2022 in response to the COVID-19 pandemic. In this investigation, the actual distribution of the population is depicted using a thermodynamic map generated from cell phone usage data, while the per capita supply capacity and the intensity of supply coverage are assessed using an official list. Furthermore, the efficacy of the supply point is evaluated through a public survey. Through these combined analyses, the level of "Supply Resilience" is assessed and categorized for each district of Shanghai. The findings indicate a high level of supply resilience in the central city, with varying degrees of divergence observed in the near suburban districts and generally lower resilience in the remote suburban districts. Based on these observations, this research identifies and discusses the supply challenges exposed during the epidemic lockdown in Shanghai and proposes strategies to enhance the urban resilience in the future. Leveraging both data analysis and public survey, this study aims to offer approaches and methodologies for evaluating urban resilience under major public emergencies.
Understanding user intent modeling for conversational recommender systems: a systematic literature review
Authors: Farshidi Siamak , Rezaee Kiyan , Mazaheri Sara , Rahimi Amir Hossein , Dadashzadeh Ali , Ziabakhsh Morteza , Eskandari Sadegh , Jansen Slinger 
Published: June 2024

AbstractUser intent modeling in natural language processing deciphers user requests to allow for personalized responses. The substantial volume of research (exceeding 13,000 publications in the last decade) underscores the significance of understanding prevalent models in AI systems, with a focus on conversational recommender systems. We conducted a systematic literature review to identify models frequently employed for intent modeling in conversational recommender systems. From the collected data, we developed a decision model to assist researchers in selecting the most suitable models for their systems. Furthermore, we conducted two case studies to assess the utility of our proposed decision model in guiding research modelers in selecting user intent modeling models for developing their conversational recommender systems. Our study analyzed 59 distinct models and identified 74 commonly used features. We provided insights into potential model combinations, trends in model selection, quality concerns, evaluation measures, and frequently used datasets for training and evaluating these models. The study offers practical insights into the domain of user intent modeling, specifically enhancing the development of conversational recommender systems. The introduced decision model provides a structured framework, enabling researchers to navigate the selection of the most apt intent modeling methods for conversational recommender systems.
The application of machine learning for demand prediction under macroeconomic volatility: a systematic literature review
Authors: Muth Manuel , Lingenfelder Michael , Nufer Gerd 
Published: June 2024

AbstractIn a contemporary context characterised by shifts in macroeconomic conditions and global uncertainty, predicting the future behaviour of demanders is critical for management science disciplines such as marketing. Despite the recognised potential of Machine Learning, there is a lack of reviews of the literature on the application of Machine Learning in predicting demanders’ behaviour in a volatile environment. To fill this gap, the following systematic literature review provides an interdisciplinary overview of the research question: “How can Machine Learning be effectively applied to predict demand patterns under macroeconomic volatility?” Following a rigorous review protocol, a literature sample of studies (n = 64) is identified and analysed based on a hybrid methodological approach. The findings of this systematic literature review yield novel insights into the conceptual structure of the field, recent publication trends, geographic centres of scientific activity, as well as leading sources. The research also discusses whether and in which ways Machine Learning can be used for demand prediction under dynamic market conditions. The review outlines various implementation strategies, such as the integration of forward-looking data with economic indicators, demand modelling using the Coefficient of Variation, or the application of combined algorithms and specific Artificial Neural Networks for accurate demand predictions.
Development of a trash classification system to map potential Aedes aegypti breeding grounds using unmanned aerial vehicle imaging
Authors: Rosser Joelle I. , Tarpenning Morgan S. , Bramante Juliet T. , Tamhane Anoushka , Chamberlin Andrew J. , Mutuku Paul S. , De Leo Giulio A. , Ndenga Bryson , Mutuku Francis , LaBeaud Angelle Desiree 
Published: June 2024

AbstractAedes aegypti mosquitos are the primary vector for dengue, chikungunya, and Zika viruses and tend to breed in small containers of water, with a propensity to breed in small piles of trash and abandoned tires. This study piloted the use of aerial imaging to map and classify potential Ae. aegypti breeding sites with a specific focus on trash, including discarded tires. Aerial images of coastal and inland sites in Kenya were obtained using an unmanned aerial vehicle. Aerial images were reviewed for identification of trash and suspected trash mimics, followed by extensive community walk-throughs to identify trash types and mimics by description and ground photography. An expert panel reviewed aerial images and ground photos to develop a classification scheme and evaluate the advantages and disadvantages of aerial imaging versus walk-through trash mapping. A trash classification scheme was created based on trash density, surface area, potential for frequent disturbance, and overall likelihood of being a productive Ae. aegypti breeding site. Aerial imaging offers a novel strategy to characterize, map, and quantify trash at risk of promoting Ae. aegypti proliferation, generating opportunities for further research on trash associations with disease and trash interventions.
Mathematical vs. machine learning models for particle size distribution in fragile soils of North-Western Himalayas
Authors: Bashir Owais , Bangroo Shabir Ahmad , Shafai Shahid Shuja , Shah Tajamul Islam , Kader Shuraik , Jaufer Lizny , Senesi Nicola , Kuriqi Alban , Omidvar Negar , Naresh Kumar Soora , Arunachalam Ayyanadar , Michael Ruby , Ksibi Mohamed , Spalevic Velibor , Sestras Paul , Marković Slobodan B. , Billi Paolo , Ercişli Sezai , Hysa Artan 
Published: June 2024

Abstract
Purpose
Particle size distribution (PSD) assessment, which affects all physical, chemical, biological, mineralogical, and geological properties of soil, is crucial for maintaining soil sustainability. It plays a vital role in ensuring appropriate land use, fertilizer management, crop selection, and conservation practices, especially in fragile soils such as those of the North-Western Himalayas.

Materials and methods
In this study, the performance of eleven mathematical and three Machine Learning (ML) models used in the past was compared to investigate PSD modeling of different soils from the North-Western Himalayan region, considering that an appropriate model must fit all PSD data.

Results and discussion
Our study focuses on the significance of evaluating the goodness of fit in particle size distribution modeling using the coefficient of determination (R2adj = 0.79 to 0.45), the Akaike information criterion (AIC = 67 to 184), and the root mean square error (RMSE = 0.01 to 0.09). The Fredlund, Weibull, and Rosin Rammler models exhibited the best fit for all samples, while the performance of the Gompertz, S-Curve, and Van Genutchen models was poor. Of the three ML models tested, the Random Forest model performed the best (R2 = 0.99), and the SVM model was the lowest (R2 = 0.95). Thus, the PSD of the soil can be best predicted by ML approaches, especially by the Random Forest model.

Conclusion
The Fredlund model exhibited the best fit among mathematical models while random forest performed best among the machine learning models. As the number of parameters in the model increased better was the accuracy.
Integrating subject-generic and subject-specific teaching frameworks: searching for stages of teaching in mathematics
Authors: Kyriakides Leonidas , Charalambous Charalambos Y. , Antoniou Panayiotis 
Published: June 2024

AbstractCurrently there is an attempt to combine subject-generic and subject-specific teaching frameworks to comprehensively capture teaching quality. This study explores the possibility of integrating two widely used and validated frameworks, the subject-generic Dynamic Model of Educational Effectiveness (DMEE) and the subject-specific Mathematical Quality of Instruction (MQI). Toward this end, we drew on data from 38 upper-grade primary school teachers, each observed in six mathematics lessons, which were coded using both frameworks. Data were analyzed using the Extended Logistic model of Rasch to explore whether a common scale of teaching quality with good psychometric properties could be developed. Saltus was then utilized to investigate the possibility of forming levels of effective teaching in mathematics. A common scale encompassing both subject-generic and subject-specific teaching aspects, which had good psychometric properties, was developed. The subject-generic and subject-specific teaching aspects of these frameworks were clustered in five distinct levels. With the exception of the top level that included only subject-generic aspects, all other levels included teaching aspects from both frameworks, thus providing support to the assumption that it is possible to develop levels of effective teaching that combine related subject-generic and subject-specific aspects. In discussing the study findings, we consider their implications for developing an integrated framework of teaching quality and for developing professional development programs that combine subject-generic and subject-specific teaching aspects.
Primary Intranodal Epithelioid Haemangioendothelioma in the Submandibular Region: A Case Report
Authors: Shukla Khilan , Taheri Touraj , Baghaie Hooman , Breik Omar 
Published: June 2024

AbstractEpithelioid haemangioendothelioma (EHE) is a rare vascular tumour that primarily affects the liver, lungs and bone. It is very rarely described in the head and neck region, and is exceptionally uncommon within the submandibular region. We report a very rare case of EHE originating in a lymph node within the submandibular salivary gland of a 54-year-old female patient. The tumour was resected and the patient was regularly followed up, with no recurrence of disease at 24 months postoperatively. A review of existing literature, clinical and immunohistopathological features are discussed, which highlight the diagnostic dilemma, absence of consensus for management and appropriate surveillance method associated with EHE.
Towards a personalized prediction, prevention and therapy of insomnia: gut microbiota profile can discriminate between paradoxical and objective insomnia in post-menopausal women
Authors: Barone Monica , Martucci Morena , Sciara Giuseppe , Conte Maria , Medina Laura Smeldy Jurado , Iattoni Lorenzo , Miele Filomena , Fonti Cristina , Franceschi Claudio , Brigidi Patrizia , Salvioli Stefano , Provini Federica , Turroni Silvia , Santoro Aurelia 
Published: June 2024

Abstract
Background
Insomnia persists as a prevalent sleep disorder among middle-aged and older adults, significantly impacting quality of life and increasing susceptibility to age-related diseases. It is classified into objective insomnia (O-IN) and paradoxical insomnia (P-IN), where subjective and objective sleep assessments diverge. Current treatment regimens for both patient groups yield unsatisfactory outcomes. Consequently, investigating the neurophysiological distinctions between P-IN and O-IN is imperative for devising novel precision interventions aligned with primary prediction, targeted prevention, and personalized medicine (PPPM) principles.
Working hypothesis and methodology.
Given the emerging influence of gut microbiota (GM) on sleep physiology via the gut-brain axis, our study focused on characterizing the GM profiles of a well-characterized cohort of 96 Italian postmenopausal women, comprising 54 insomniac patients (18 O-IN and 36 P-IN) and 42 controls, through 16S rRNA amplicon sequencing. Associations were explored with general and clinical history, sleep patterns, stress, hematobiochemical parameters, and nutritional patterns.

Results
Distinctive GM profiles were unveiled between O-IN and P-IN patients. O-IN patients exhibited prominence in the Coriobacteriaceae family, including Collinsella and Adlercreutzia, along with Erysipelotrichaceae, Clostridium, and Pediococcus. Conversely, P-IN patients were mainly discriminated by Bacteroides, Staphylococcus, Carnobacterium, Pseudomonas, and respective families, along with Odoribacter.

Conclusions
These findings provide valuable insights into the microbiota-mediated mechanism of O-IN versus P-IN onset. GM profiling may thus serve as a tailored stratification criterion, enabling the identification of women at risk for specific insomnia subtypes and facilitating the development of integrated microbiota-based predictive diagnostics, targeted prevention, and personalized therapies, ultimately enhancing clinical effectiveness.
Explicit expression for water permeation flux in forward osmosis desalination process
Authors: Al-Mutaz Ibrahim S. , Alalawi Abdulrahman , Bin Darwish Nawaf 
Published: June 2024

AbstractForward osmosis (FO) is a membrane process of water separation and purification. FO uses the osmotic pressure difference across a semipermeable membrane. The effective osmotic pressure at the membrane–solution interface on both the feed and permeate sides of the membrane is the main driving force for the generation of the water flux. The major hindrance to the permeation of the water flux is the prevalence of concentration polarization on both sides of the membrane. Concentration polarization inhibits permeate flux by increasing the osmotic pressure at the membrane active layer interface on the feed side of the membrane. This work focused on the development of a mathematical model for water flux in the FO process. Combined film theory model and diffusion transport through the membrane were utilized. The effect of internal concentration polarization and external concentration polarization on the flux was studied. Both internal and external concentration polarization were taken into consideration in both membrane orientations, i.e., active layer facing the feed solution and active layer facing the draw solution. The obtained explicit expression for water permeation flux in forward osmosis desalination process shows excellent agreement with the literature data.
MXene Key Composites: A New Arena for Gas Sensors
Authors: Wang Yitong , Wang Yuhua , Jian Min , Jiang Qinting , Li Xifei 
Published: June 2024

AbstractWith the development of science and technology, the scale of industrial production continues to grow, and the types and quantities of gas raw materials used in industrial production and produced during the production process are also constantly increasing. These gases include flammable and explosive gases, and even contain toxic gases. Therefore, it is very important and necessary for gas sensors to detect and monitor these gases quickly and accurately. In recent years, a new two-dimensional material called MXene has attracted widespread attention in various applications. Their abundant surface functional groups and sites, excellent current conductivity, tunable surface chemistry, and outstanding stability make them promising for gas sensor applications. Since the birth of MXene materials, researchers have utilized the efficient and convenient solution etching preparation, high flexibility, and easily functionalize MXene with other materials to prepare composites for gas sensing. This has opened a new chapter in high-performance gas sensing materials and provided a new approach for advanced sensor research. However, previous reviews on MXene-based composite materials in gas sensing only focused on the performance of gas sensing, without systematically explaining the gas sensing mechanisms generated by different gases, as well as summarizing and predicting the advantages and disadvantages of MXene-based composite materials. This article reviews the latest progress in the application of MXene-based composite materials in gas sensing. Firstly, a brief summary was given of the commonly used methods for preparing gas sensing device structures, followed by an introduction to the key attributes of MXene related to gas sensing performance. This article focuses on the performance of MXene-based composite materials used for gas sensing, such as MXene/graphene, MXene/Metal oxide, MXene/Transition metal sulfides (TMDs), MXene/Metal–organic framework (MOF), MXene/Polymer. It summarizes the advantages and disadvantages of MXene composite materials with different composites and discusses the possible gas sensing mechanisms of MXene-based composite materials for different gases. Finally, future directions and inroads of MXenes-based composites in gas sensing are presented and discussed.
Willingness to experience unpleasant thoughts, emotions, and bodily sensations at admission does not predict treatment outcome in inpatients with obsessive–compulsive disorder
Authors: Zisler Eva M. , Meule Adrian , Koch Stefan , Voderholzer Ulrich 
Published: June 2024

Abstract
Background
Some persons with obsessive–compulsive disorder (OCD) refuse or drop out of treatment because of the aversive nature of exposure and response prevention therapy when they have to face and tolerate unpleasant thoughts, emotions, and bodily sensations. Indeed, one study suggested that a higher willingness to experience unpleasant thoughts, emotions, and bodily sensations (WTE) predicts a better treatment outcome, but this finding has not been replicated yet.

Methods
We examined whether WTE at admission predicted treatment outcome in a sample of 324 inpatients with OCD who received a multimodal treatment that included cognitive-behavioral therapy with exposure and response prevention sessions.

Results
Obsessive–compulsive symptoms (based on OCD-specific self-report questionnaires) decreased with medium-to-large effect sizes (all ps < 0.001) and global functioning (based on therapist ratings) increased with a large effect size (d = 1.3, p < 0.001) from admission to discharge. In contrast to previous findings, however, WTE did not predict treatment outcome (all ps > 0.005). The effect of WTE on treatment outcome remained non-significant when controlling for any comorbidity, age, sex, length of stay, and antidepressant medication and was not moderated by these variables.

Conclusions
Results indicate that higher WTE at the beginning of inpatient treatment does not facilitate improvements in obsessive–compulsive symptoms from admission to discharge. However, they also indicate that lower WTE at the beginning of inpatient treatment does not adversely affect treatment outcome, that is, even patients who indicate that they are unwilling to face the negative experiences associated with exposure and response prevention can still achieve considerable symptom reductions.