bluuebunny/crossref_metadata_2025_split huggingface dataset:
nHi-SEGA: n-Hierarchy SEmantic Guided Attention for few-shot learning
Authors: Yuan Xinpan , Xie Shaojun , Zeng Zhigao , Li Changyun , Wang Luda 
Published: July 2024

AbstractHumans excel at learning and recognizing objects, swiftly adapting to new concepts with just a few samples. However, current studies in computer vision on few-shot learning have not yet achieved human performance in integrating prior knowledge during the learning process. Humans utilize a hierarchical structure of object categories based on past experiences to facilitate learning and classification. Therefore, we propose a method named n-Hierarchy SEmantic Guided Attention (nHi-SEGA) that acquires abstract superclasses. This allows the model to associate with and pay attention to different levels of objects utilizing semantics and visual features embedded in the class hierarchy (e.g., house finch-bird-animal, goldfish-fish-animal, rose-flower-plant), resembling human cognition. We constructed an nHi-Tree using WordNet and Glove tools and devised two methods to extract hierarchical semantic features, which were then fused with visual features to improve sample feature prototypes.
Leveraging hybrid 1D-CNN and RNN approach for classification of brain cancer gene expression
Authors: Afify Heba M. , Mohammed Kamel K. , Hassanien Aboul Ella 
Published: July 2024

AbstractLeveraging deep learning (DL) approaches in genomics data has led to significant advances in cancer prediction. The continuous availability of gene expression datasets over the preceding years has made them one of the most accessible sources of genome-wide data, advancing cancer bioinformatics research and advanced prediction of cancer genomic data. To contribute to this topic, the proposed work is based on DL prediction in both convolutional neural network (CNN) and recurrent neural network (RNN) for five classes in brain cancer using gene expression data obtained from Curated Microarray Database (CuMiDa). This database is used for cancer classification and is publicly accessible on the official CuMiDa website. This paper implemented DL approaches using a One Dimensional-Convolutional Neural Network (1D-CNN) followed by an RNN classifier with and without Bayesian hyperparameter optimization (BO). The accuracy of this hybrid model combination of (BO + 1D-CNN + RNN) produced the highest classification accuracy of 100% instead of the 95% for the ML model in prior work and 90% for the (1D-CNN + RNN) algorithm considered in the paper. Therefore, the classification of brain cancer gene expression according to the hybrid model (BO + 1D-CNN + RNN) provides more accurate and useful assessments for patients with different types of brain cancers. Thus, gene expression data are used to create a DL classification-based- hybrid model that will hold senior promise in the treatment of brain cancer.
Fluctuations in Cortisol and Testosterone Map to Fluctuations in Opinion Strength in Healthy Men
Authors: Serpell Benjamin G. , Crewther Blair T. , Fourie Phillip J. , Cook Christian J. 
Published: July 2024

Abstract
Purpose
Opinions are often linked to emotions and stress. It is well established that testosterone and cortisol are useful biomarkers of stress and can predict human emotion and behavior. Therefore, the purpose of this work was to explore whether changes in testosterone and cortisol concentration map to shifts in opinions.

Methods
We present three studies with healthy men, where we (1) monitored diurnal changes in testosterone and cortisol alongside political opinion change using a descriptive longitudinal design, (2) assessed testosterone, cortisol and political opinion change in an experimental trial using a physical exercise intervention designed to decrease cortisol concentration and/or increase testosterone concentration, and (3) monitored testosterone and cortisol change with political opinion in a trial with psychosocial intervention designed to increase cortisol concentration/decrease testosterone concentration.

Results
Testosterone concentration and opinion changes were observed across the day (p < 0.05). Furthermore, opinions changed similarly to exercise-induced increases in testosterone (8.6–11.5%), but inversely to exercise-induced reductions in cortisol (p < 0.05). Opinion changes also occurred in similar fashion to testosterone changes following psychosocial intervention (-5.6% to -10.0%), and inversely to cortisol changes (p < 0.05). Finally, opinion change and testosterone change correlated at the within person level (r = 0.17 to 0.33; p < 0.05).

Conclusion
We showed natural fluctuations in testosterone and cortisol covaried in parallel with shifts in opinion. Further, given the emergence of relationships for opinion change with hormone change, we contend that physiological stress response (i.e., testosterone and cortisol change) may predict, or even cause, opinion change.
A Review of Anode Materials for Dual-Ion Batteries
Authors: Wu Hongzheng , Luo Shenghao , Wang Hubing , Li Li , Fang Yaobing , Zhang Fan , Gao Xuenong , Zhang Zhengguo , Yuan Wenhui 
Published: July 2024

AbstractDistinct from "rocking-chair" lithium-ion batteries (LIBs), the unique anionic intercalation chemistry on the cathode side of dual-ion batteries (DIBs) endows them with intrinsic advantages of low cost, high voltage, and eco-friendly, which is attracting widespread attention, and is expected to achieve the next generation of large-scale energy storage applications. Although the electrochemical reactions on the anode side of DIBs are similar to that of LIBs, in fact, to match the rapid insertion kinetics of anions on the cathode side and consider the compatibility with electrolyte system which also serves as an active material, the anode materials play a very important role, and there is an urgent demand for rational structural design and performance optimization. A review and summarization of previous studies will facilitate the exploration and optimization of DIBs in the future. Here, we summarize the development process and working mechanism of DIBs and exhaustively categorize the latest research of DIBs anode materials and their applications in different battery systems. Moreover, the structural design, reaction mechanism and electrochemical performance of anode materials are briefly discussed. Finally, the fundamental challenges, potential strategies and perspectives are also put forward. It is hoped that this review could shed some light for researchers to explore more superior anode materials and advanced systems to further promote the development of DIBs.
On The Diffusivity of Boron in Slag During Silicon Refining
Authors: Putera A. D. P. , Avarmaa K. , Petrus H. T. B. M. , Brooks G. A. , Rhamdhani M. A. 
Published: July 2024

AbstractSlag treatment is one of the pyrometallurgical routes to refine and remove impurities (such as boron) from silicon. Many studies have demonstrated that the rate-controlling step in the process is the mass transfer of boron (B) in the slag phase. Hence, information regarding the B diffusivity is vital. This paper discusses the diffusivity of B in the slag from secondary data collated from previous kinetics studies and compares it with semi-empirical diffusivity equations.
Graphical Abstract
Relation between edge stress, bending strength, surface stress and fracture pattern of thermally toughened glass
Authors: Efferz Lena , Thiele Kerstin , Schuster Miriam , Schuler Christian , Siebert Geralt , Schneider Jens 
Published: July 2024

AbstractThermally toughened safety glass must meet safety requirements in the building industry. Here, destructive tests are defined in the product standards, which must be carried out on small, standardized format (360 mm × 1100 mm) glass elements to determine the fracture pattern and bending strength. This is costly and not in the interests of sustainability. As part of the quality control of optical anisotropy effects in thermally toughened glass, isochromatic scans that can provide information on the edge stress are acquired. The evaluation of the isochromatics and retardations at the edge with deduction of the edge stress and transfer to bending strength and fracture pattern could provide essential findings for assuring the safety requirements of thermally toughened glass. In this experimental investigation, the surface and edge stress were measured on standardized format thermally toughened safety glass, with different edge processing and glass thicknesses from three different suppliers. Afterwards, the fracture pattern is controlled, or the bending strength is analyzed in a destructive four-point bending test. Conclusively, the results from the photoelastic and destructive tests are compared to determine whether the photoelastic measurement methods used to measure surface and edge stress can be employed as quality control.
The Effectiveness of Savouring Interventions in Adult Clinical Populations: A Systematic Review
Authors: Cullen Katie , Murphy Mike , Di Blasi Zelda , Bryant Fred B. 
Published: July 2024

AbstractA narrative systematic review evaluating the evidence base of savouring interventions was conducted. Savouring interventions were defined as interventions in which participants were instructed to engage in processes to regulate, intensify or strengthen positive emotions and experiences, whether they be past, present or future focused. Relevant studies were identified via PsycINFO, Pubmed, CINAHL, Scopus, Google Scholar and through a manual search of the literature. Only studies which adopted a randomised controlled design and evaluated the efficacy of savouring interventions in adult clinical populations were included in the review. Of the 1,054 articles identified through searches, nine studies were included in the final systematic review. Data were extracted from these nine studies involving over 900 participants from mental and physical health clinical populations from seven countries. Four different type of savouring interventions are described: life review, reminiscence, Three-Good-Things and one multicomponent savouring intervention. These specific interventions promoted some positive changes amongst participants, such as increasing levels of well-being and reducing levels of depression and anxiety. Both individuals with mental and physical health difficulties appeared to benefit. However, when comparing the results of these interventions against outcomes gained by participants in no intervention or active control groups, the efficacy of these interventions appears relatively weak. The low quality of included studies and the disparity of the format, delivery mode and methodological characteristics of studies may limit the generalisability of these findings. However, results from this systematic review do highlight the therapeutic potential of savouring interventions amongst clinical samples.
Dance2MIDI: Dance-driven multi-instrument music generation
Authors: Han Bo , Li Yuheng , Shen Yixuan , Ren Yi , Han Feilin 
Published: July 2024

AbstractDance-driven music generation aims to generate musical pieces conditioned on dance videos. Previous works focus on monophonic or raw audio generation, while the multi-instrument scenario is under-explored. The challenges associated with dance-driven multi-instrument music (MIDI) generation are twofold: (i) lack of a publicly available multi-instrument MIDI and video paired dataset and (ii) the weak correlation between music and video. To tackle these challenges, we have built the first multi-instrument MIDI and dance paired dataset (D2MIDI). Based on this dataset, we introduce a multi-instrument MIDI generation framework (Dance2MIDI) conditioned on dance video. Firstly, to capture the relationship between dance and music, we employ a graph convolutional network to encode the dance motion. This allows us to extract features related to dance movement and dance style. Secondly, to generate a harmonious rhythm, we utilize a transformer model to decode the drum track sequence, leveraging a cross-attention mechanism. Thirdly, we model the task of generating the remaining tracks based on the drum track as a sequence understanding and completion task. A BERT-like model is employed to comprehend the context of the entire music piece through self-supervised learning. We evaluate the music generated by our framework trained on the D2MIDI dataset and demonstrate that our method achieves state-of-the-art performance.
»Wissenschaft hat ein eiskaltes Händchen«: Zu einigen Dimensionen der Identitätskonstruktion von Wissenschaft im NDR-Podcast Coronavirus-Update
Authors: Bührig Kristin 
Published: July 2024

ZusammenfassungIn dem Beitrag geht es um Identitätskonstruktionen von Wissenschaft im Rahmen des NDR-Podcasts Coronavirus-Update. Ausgehend von einer Zählung der Konstruktion zu die Wissenschaft in den als PDF veröffentlichten Folgen des Podcasts als Kategorienlabel, werden ausgewählte Ausschnitte aus zwei retranskribierten Folgen des Podcasts untersucht. Der Idee der Prinzipien von Identität folgend, werden Erkenntnisse unterschiedlicher Ansätze der Gesprächsforschung genutzt, um das Emergieren von Identitätskonstruktionen und ihre Funktion zu rekonstruieren.
„To treat or not to treat?“ – Wann ist ein Harnwegsinfekt ein Harnwegsinfekt und sollte behandelt werden?
Authors: Bausch Kathrin , Seifert Hans Helge 
Published: July 2024

ZusammenfassungHarnwegsinfekte zählen mit zu den häufigsten Infektionen weltweit. Ihre suffiziente Therapie wird durch steigende antimikrobielle Resistenzen zunehmend erschwert. Daher sind Massnahmen des „antimicrobial stewardship“ essenziell, um auch noch langfristig in der Lage zu sein, Infektionen antibiotisch behandeln zu können. In erster Linie spielt hier die korrekte Diagnosestellung eine wichtige Rolle: Bei einem Harnwegsinfekt handelt es sich in der Regel um ein Syndrom aus Symptomen und einem Keimnachweis. Liegen nur Symptome vor, kann als Ursache eine andere Erkrankung zugrunde liegen. Insbesondere bei katheterisierten Patienten ist eine asymptomatische Bakteriurie häufig und sollte ebenfalls nichtantibiotisch therapiert werden.
Within-Person Fluctuations in Objective Smartphone Use and Emotional Processes During Adolescence: An Intensive Longitudinal Study
Authors: Rodman Alexandra M. , Burns Jason A. , Cotter Grace K. , Ohashi Yuri-Grace B. , Rich Rachael K. , McLaughlin Katie A. 
Published: July 2024

AbstractSince the advent of smartphones, peer interactions over digital platforms have become a primary mode of socializing among adolescents. Despite the rapid rise in digital social activity, it remains unclear how this dramatic shift has impacted adolescent social and emotional experiences. In an intensive, longitudinal design (N = 26, n = 206 monthly observations for up to 12 months, 12–17 years), we used digital phenotyping methods to objectively measure within-person fluctuations in smartphone use (screen time, pickups, notifications) across different categories (social media, communication, entertainment, games) and examined their prospective, bidirectional associations with positive and negative mood. Bayesian hierarchical models showed that when adolescents reported better mood than usual, they subsequently spent more time on communication apps and launched social media and communication apps upon pickup less often. Meanwhile, when adolescents used entertainment apps more than usual, they subsequently reported improved mood. These preliminary findings suggest a pattern where fluctuations in mood relate to subsequent changes in smartphone use that are primarily social, whereas the fluctuations in smartphone use relating to subsequent changes in mood were primarily entertainment-related. We found little evidence that within-person fluctuations in screen time or social media use were associated with increases in negative mood, as frequently theorized. These findings highlight the importance of disentangling the distinct components of smartphone use that relate to affective processes and examining their bidirectional, prospective relationships over time, due to the possibility of differential outcomes. This work is a necessary first step in identifying targets for intervention efforts promoting resilience and wellbeing during adolescence.
Segmental deformity markers offer novel indicators of deformity progression risk in deformity-matched adolescent idiopathic scoliosis patients
Authors: Labrom Fraser R. , Izatt Maree T. , Askin Geoffrey N. , Labrom Robert D. , Claus Andrew P. , Little J. Paige 
Published: July 2024

Abstract
Purpose
Identification of adolescent idiopathic scoliosis (AIS) patients with mild curvatures who pose significant risk of progressing to severe levels of curvatures is of paramount importance for clinical care. This study aimed to compare segmental deformity changes in AIS sub-cohorts that are dichotomised by progression status.

Methods
Thirty-six female participants with Lenke 1 AIS curves were investigated with sequential MRIs during growth. Scans were reformatted to measure orthogonal segmental parameters, including sagittal/coronal wedging angles and axial rotation angles. Participants were dichotomised by progression. Two-tailed, independent sample t-tests were used to compare sub-cohort multi-segmental and segmental deformity parameters. Measurements were compared at each scan number and variable rates of change were determined using actual time between measures.

Results
AIS progression status sub-cohorts were comparable at scan 1 for multi-segmental deformity parameters (e.g. major thoracic curve angle, rib hump, kyphosis) (P > 0.05). However, apical measures of coronal IVD wedging, axial IVD rotation and axial vertebral rotation were segmental parameters at scan 1 which were larger for participants whose AIS would later go on to clinically progress (all P < 0.05). Measures of segmental hypokyphosis were comparable between groups. As development was tracked at each subsequent scan, coronal and axial plane differences between groups increased in both magnitude and number of differences.

Conclusion
Initial disparity and then subsequent increasing magnitude of change of axial rotation may indicate a higher propensity to clinically progress in the future. This knowledge hopes to provide useful management information for AIS care providers and prognostic education for patients alike.

Level of evidence
II.
Operationalizing responsible AI principles through responsible AI capabilities
Authors: Akbarighatar Pouria 
Published: July 2024

AbstractResponsible artificial intelligence (RAI) has emerged in response to growing concerns about the impact of AI. While high-level principles have been provided, operationalizing these principles poses challenges. This study, grounded in recent RAI literature in organizational contexts and dynamic capability theory, and informed by literature on RAI principles and expert interviews in organizations deploying AI systems, (1) problematizes the high-level principles and low-level requirements and underscores the need for mid-level norms by adopting dynamic capability as a theoretical lens, and (2) develops five themes to capture firms’ RAI capability, including (i) understandable AI model, (ii) bias remediation, (iii) responsiveness, (iv) harmless, and vi) common good. As our contribution to the field of information systems (IS), this study extends the emerging literature on operationalizing RAI and dynamic capabilities, empirically elucidating the capabilities needed by firms. For IS practice, we provide organizations deploying AI with novel insights to aid in the responsible implementation of AI.
A Novel Hierarchical High-Dimensional Unsupervised Active Learning Method
Authors: Haghzad Klidbary Sajad , Javadian Mohammad 
Published: July 2024

AbstractThis paper processes a novel hierarchical high-dimensional clustering algorithm based on the Active Learning Method (ALM), which is a fuzzy-learning algorithm. The hierarchical part of the algorithm is composed of two phases: divisible and agglomerative. The divisible phase, a zooming-in-process, searches for sub-clusters in already-found clusters hierarchically. At each level of the hierarchy, the clusters are found by an ensemble clustering method based on the density of data. This part of the algorithm blurs each data point as multiple one-dimensional fuzzy membership functions called ink-drop patterns; then, it accumulates the ink-drop patterns of all data points on every dimension separately. Next, it performs one-dimensional density partitioning to produce an ensemble of clustering solutions; after that, combining the results is done based on a novel consensus method with the aid of prime numbers. An agglomerative phase is a bottom-up approach that merges clusters based on a novel distance metric, named$${K}^{2}$$K2-nearest neighbor. The algorithm is named as the Hierarchical High-Dimensional Unsupervised Active Learning Method (HiDUALM) and is explained in more detail throughout this paper. Although the classical clustering methods are not suitable for high-dimensional data clustering, the proposed method solves the problems related to speed and memory using ensemble learning, while due to its hierarchy and the use of different distance criteria, different levels of the cluster provide the clause. Experiments on synthetic and real-world datasets are presented to show the effectiveness of the proposed-clustering algorithm.
Association Between Osteoporosis and Adiposity Index Reveals Nonlinearity Among Postmenopausal Women and Linearity Among Men Aged over 50 Years
Authors: Chen Po-Ju , Lu Yueh-Chien , Lu Sheng-Nan , Liang Fu-Wen , Chuang Hung-Yi 
Published: July 2024

Abstract
Purpose
Previous research shows conflicting views on the relationship between obesity and osteoporosis, partly due to variations in obesity classification and the nonlinear nature of these relationships. This study investigated the association between adiposity indices and osteoporosis, diagnosed using dual-energy X-ray absorptiometry (DXA), employing nonlinear models and offering optimal thresholds to prevent further bone mineral density decline.

Methods
In 2019, a prospective study enrolled males over 50 years and postmenopausal women. Anthropometric measurements, blood biochemistry, and osteoporosis measured by DXA were collected. Associations between adiposity indices and osteoporosis were analyzed using a generalized additive model and segmented regression model.

Results
The study included 872 women and 1321 men. Indices such as abdominal volume index (AVI), visceral adiposity index (VAI), waist circumference (WC), hip circumference, body mass index (BMI), waist-to-hip ratio, and waist-to-height ratio (WHtR) were inversely associated with osteoporosis. In women, the relationship between the risk of osteoporosis and the adiposity indices was U-shaped, with thresholds of WC = 94 cm, AVI = 17.67 cm2, BMI = 25.74 kg/m2, VAI = 4.29, and WHtR = 0.61, considering changes in bone mineral density. Conversely, men exhibited a linear patterns for the inverse association.

Conclusion
The impact of obesity and adiposity on osteoporosis varies significantly between women and men. In postmenopausal women, the relationship is nonlinear (U-shaped), with both very low and very high adiposity linked to higher osteoporosis risk. In men over 50, the relationship is linear, with higher adiposity associated with lower osteoporosis risk. The study suggests that maintaining specific levels of adiposity could help prevent osteoporosis in postmenopausal women.