bluuebunny/crossref_metadata_2025_split huggingface dataset:
An online experimental test of the compensatory process in hoarding disorder: reducing loneliness and its effects on object attachment
Authors: Yap Keong , Timpano Kiara R. , Schmidt Norman B. , Grisham Jessica R. 
Published: March 2024

AbstractAttachment theory suggests that strong object attachment in hoarding disorder (HD) may be due to an attempt at compensating for unmet relatedness needs. We tested this compensatory process with an online experiment and hypothesized that reducing loneliness among participants with high hoarding symptoms would result in lower object attachment, and that change in loneliness would mediate the impact of an online loneliness intervention on object attachment. A pretest-posttest control group design was used. Participants were 298 MTurk workers pre-screened for high hoarding symptoms recruited via cloudresearch.com. At Time 1, participants completed measures of hoarding severity, loneliness, and four aspects of object attachment: overall object attachment to possessions, insecure object attachment, attachment to an old cherished item, and attachment to a novel item. We randomly assigned participants to either a loneliness intervention (n = 142) or an active control (a health education program; n = 156). All participants completed follow-up questionnaires after two weeks. We conducted ANCOVAs to assess for group differences at Time 2 whilst controlling for Time 1 variables. Results showed small but significant improvements in loneliness, thwarted belongingness, and object attachment for the novel item for participants who received the loneliness intervention relative to control participants. Mediational analyses revealed that the change in loneliness mediated the effect of the intervention on insecure object attachment. Consistent with attachment theory, these results indicate that reducing loneliness might lead to lower object attachment in hoarding disorder. Trials with clinical participants using more intensive loneliness interventions are warranted.
Cyclone detection with end-to-end super resolution and faster R-CNN
Authors: Moustafa Marwa S. , Metwalli Mohamed R. , Samshitha Roy , Mohamed Sayed A. , Shovan Barma 
Published: March 2024

AbstractThe accurate identification of extreme weather events (EWEs), particularly cyclones, has become increasingly crucial due to the intensifying impacts of climate change. In the Indian subcontinent, the frequency and severity of cyclones have demonstrably risen, highlighting the need for reliable detection methods to minimize casualties and economic losses. However, the inherent limitations of low-resolution data pose significant challenges to traditional detection methods. Deep learning models offer a promising solution, enabling the precise identification of cyclone boundaries crucial for assessing regional impacts using global climate models data. By leveraging the power of deep learning, we can significantly enhance our capabilities for cyclone detection and contribute to improved risk mitigation strategies in the vulnerable Indian subcontinent. Therefore, this paper introduces an edge-enhanced super-resolution GAN (EESRGAN) leveraging an end-to-end detector network. The proposed approach comprised of a generator network equipped by residual-in-residual dense block (RRDB) and discriminator containing Faster RCNN detector. The precise patterns of cyclone had been effectively extracted to help boundary detection. Extensive experiments have been conducted on Community Atmospheric Model (CAM5.1) data taken into account only seven variables. Four matrices including precision, recall, intersection over union, and mean average precision have been considered to assess the proposed approach. The results have been found very effective while achieving accuracy up to 86.3% and average precision (AP) of 88.63%. Moreover, the proposed method demonstrates its superiority while compared with benchmarks object detectors methods. Thus, the proposed method can be employed in the area of extreme climate detection and could enrich the climate research domain.
The Psychological Process Underlying Attitudes Toward Human-Animal Chimeric Brain Research: An Empirical Investigation
Authors: Tanibe Tetsushi , Watanabe Takumi , Oguchi Mineki , Iijima Kazuki , Ota Koji 
Published: March 2024

AbstractThis study adopted an empirical method to investigate lay people’s attitudes toward the bioethical issues of human-animal chimeric brains. The results of online surveys showed that (1) people did not entirely reject chimeric brain research, but showed slightly more negative responses than ordinary animal testing; and that (2) their ethical concerns arose in connection with the perception that chimerism in the brain would humanize the animal. This means that people’s psychology was consistent with the ethical argument that crossing the human-animal boundary would bring moral confusion to our society. Meanwhile, it was not in line with another argument that moral status depended on having high capacities, and that chimerism would cause a problem if it enhanced animals’ capacities. Furthermore, this study analyzed additional psychological factors related to people’s moral judgment and the relationship among those factors. Several psychological factors, such as the perception that chimeric brain research is unnatural, were identified as mediating the relationship between perception of animal humanization and ethical concerns about creating and using chimeric brains. Introducing an empirical approach to the ethics of human-animal chimeric brains brought two findings: (1) this study informed us of socially shared intuition regarding this novel technology; and (2) it unveiled the psychological processes behind people’s ethical concerns in more detail than they spontaneously mentioned. These findings will help to build normative arguments and future policies that are understandable and acceptable to society.
An image-based metal artifact reduction technique utilizing forward projection in computed tomography
Authors: Ichikawa Katsuhiro , Kawashima Hiroki , Takata Tadanori 
Published: March 2024

AbstractThe projection data generated via the forward projection of a computed tomography (CT) image (FP-data) have useful potentials in cases where only image data are available. However, there is a question of whether the FP-data generated from an image severely corrupted by metal artifacts can be used for the metal artifact reduction (MAR). The aim of this study was to investigate the feasibility of a MAR technique using FP-data by comparing its performance with that of a conventional robust MAR using projection data normalization (NMARconv). The NMARconv was modified to make use of FP-data (FPNMAR). A graphics processing unit was used to reduce the time required to generate FP-data and subsequent processes. The performances of FPNMAR and NMARconv were quantitatively compared using a normalized artifact index (AIn) for two cases each of hip prosthesis and dental fillings. Several clinical CT images with metal artifacts were processed by FPNMAR. The AIn values of FPNMAR and NMARconv were not significantly different from each other, showing almost the same performance between these two techniques. For all the clinical cases tested, FPNMAR significantly reduced the metal artifacts; thereby, the images of the soft tissues and bones obscured by the artifacts were notably recovered. The computation time per image was ~ 56 ms. FPNMAR, which can be applied to CT images without accessing the projection data, exhibited almost the same performance as that of NMARconv, while consuming significantly shorter processing time. This capability testifies the potential of FPNMAR for wider use in clinical settings.
Short-term system imbalance forecast using linear and non-linear methods
Authors: Balázs István , Fodor Attila , Magyar Attila 
Published: March 2024

AbstractThe imbalance between supply and demand is a critical factor in the operation of the power system, as it leads to a change in the system frequency. Therefore, it is essential to be able to predict its value from historical, measured and forecast data. Based on the assumption that system imbalance is correlated with measured values of system variables as well as predictions of exogenous variables, this work proposes a multi-step version of the autoregressive distributed lag model for the short-term forecast of system imbalance. The proposed forecasting model has been compared with a long short-term memory network-based procedure as well as with an extratree regression model using real data. The results show that the proposed multi-step autoregressive forecasting model outperforms the others in all three evaluation metrics. Since, in many cases, it is sufficient to specify the sign of the imbalance, this paper introduces the concept of sign accuracy as a function of the predicted imbalance and evaluates it for the investigated solutions.
Hermite-Hadamard type inequalities for new conditions on h-convex functions via $$\psi $$-Hilfer integral operators
Authors: Benaissa Bouharket , Azzouz Noureddine , Budak Hüseyin 
Published: March 2024

AbstractWe employ a new function class called B-function to create a new version of fractional Hermite–Hadamard and trapezoid type inequalities on the right-hand side that involves h-convex and $$\psi $$
ψ
-Hilfer operators. We also provide new midpoint-type inequalities using h-convex functions.
Monitoring the changes in the dynamic properties of an RC building experiencing column loss
Authors: Aras Fuat , Çatbaş F. Necati 
Published: March 2024

AbstractIn this study, an existing six-storey reinforced concrete building with an asymmetric structural plan and soft storey irregularity was used as a test specimen and subjected to three-step progressive structural damages to detect the variations in its dynamic properties. Mode shapes and dominant frequencies of the undamaged building were determined by the ambient vibration survey (AVS) and it was seen that its first three modes were torsion coupled. Besides, soft storey irregularity was evident due to the lack of masonry infill walls on its ground floor. Later on, three-step progressive damages were applied to the building. The first step targeted three columns and one beam of the building, located on a corner region of its ground floor to peel off their clear covers. The second step razed two adjacent corner columns which were already moderately damaged in the first step, while the third step knocked the third moderately damaged column down. After each damage step, AVS was repeated with the same details as applied for the undamaged building. The obtained dynamic properties for the four phases of the building were evaluated with the sustained damage. Numerical analyses with the finite element model of the building representing its four different phases were also performed and the unique responses due to damage effects on the structure were investigated numerically. As a result of induced damage, the quantified amount of frequency change in modes and the new mode observed after particularly column loss scenarios can be utilized for efficient structural health-monitoring strategies of plan-asymmetric buildings and post-earthquake assessment of partially damaged buildings where timely objective assessment is important.
Explainable machine learning models with privacy
Authors: Bozorgpanah Aso , Torra Vicenç 
Published: March 2024

AbstractThe importance of explainable machine learning models is increasing because users want to understand the reasons behind decisions in data-driven models. Interpretability and explainability emerge from this need to design comprehensible systems. This paper focuses on privacy-preserving explainable machine learning. We study two data masking techniques: maximum distance to average vector (MDAV) and additive noise. The former is for achievingk-anonymity, and the second uses Laplacian noise to avoid record leakage and provide a level of differential privacy. We are interested in the process of developing data-driven models that, at the same time, make explainable decisions and are privacy-preserving. That is, we want to avoid the decision-making process leading to disclosure. To that end, we propose building models from anonymized data. More particularly, data that arek-anonymous or that have been anonymized add an appropriate level of noise to satisfy some differential privacy requirements. In this paper, we study how explainability has been affected by these data protection procedures. We use TreeSHAP as our technique for explainability. The experiments show that we can keep up to a certain degree both accuracy and explainability. So, our results show that some trade-off between privacy and explainability is possible for data protection usingk-anonymity and noise addition.
Consideration on the Intergenerational Ethics on Uranium Waste Disposal
Authors: Yasuda Hiroshi , Fumoto Hiromichi , Saito Tatsuo , Sugawara Shin-etsu , Tsuchida Shoji 
Published: March 2024

Abstract
Purpose of Review
This review provides insights into resolving intergenerational issues related to the disposal of waste containing high amounts of uranium (uranium waste), from which distant future generations will have higher health risks than the current generation.

Recent Findings
Uranium (half-life: 4.5 billion years) produces various progeny radionuclides through radioactive decay over the long term, and its radioactivity, as the sum of its contributions, continues to increase for more than 100,000 years. In contrast to high-level radioactive wastes, protective measures, such as attenuation of radiation and confinement of radionuclides from the disposal facility, cannot work effectively for uranium waste. Thus, additional considerations from the perspective of intergenerational ethics are needed in the strategy for uranium waste disposal.

Summary
The current generation, which has benefited from the use and disposal of uranium waste, is responsible for protecting future generations from the potential risk of buried uranium beyond the lifetime of a disposal facility. Fulfilling this responsibility means making more creative efforts to convey critical information on buried materials to the distant future to ensure that future generations can properly take measures to reduce the harm by themselves in response to changing circumstances including people’s values.
High frequency of transient congenital hypothyroidism among infants referred for suspected congenital hypothyroidism from the Turkish National screening program: thyroxine dose may guide the prediction of transients
Authors: Özer Y. , Anık A. , Sayılı U. , Tercan U. , Deveci Sevim R. , Güneş S. , Buhur Pirimoğlu M. , Elmaoğulları S. , Dündar İ. , Ökdemir D. , Besci Ö. , Jalilova A. , Çiçek D. , Singin B. , Ulu Ş. E. , Turan H. , Albayrak S. , Kocabey Sütçü Z. , Eklioğlu B. S. , Eren E. , Çetinkaya S. , Savaş-Erdeve Ş. , Esen İ. , Demir K. , Darcan Ş. , Hatipoğlu N. , Parlak M. , Dursun F. , Şıklar Z. , Berberoğlu M. , Keskin M. , Orbak Z. , Tezel B. , Yürüker E. , Keskinkılıç B. , Kara F. , Erginöz E. , Darendeliler F. , Evliyaoğlu O. 
Published: March 2024

Abstract
Purpose
We aimed to determine the frequency of transient congenital hypothyroidism (TCH) in 17 participating centers in Türkiye, evaluate the etiological distribution in permanent congenital hypothyroidism (PCH) cases, and investigate the role of laboratory and clinical findings in predicting TCH.

Methods
This retrospective observational multicenter study included patients from 17 pediatric endocrinology centers identified by “National Newborn Screening Program” (NNSP) who were born in 2015 and followed for 6 years. Demographic, clinical, and laboratory information of the cases were compiled through the database http://cedd.saglik-network.org (CEDD-NET).

Results
Of the 239 cases initially treated for CH, 128 (53.6%) were determined as transient in whom a trial of levothyroxine (LT4) withdrawal was performed at a median age of 36 (34–38) months. Among the patients with PCH (n = 111), thyroid dysgenesis was diagnosed in 39.6% (n = 44). The predictive factors for TCH were: LT4 dose at the withdrawal of treatment, and initial newborn blood screening (NBS)-TSH level. Based on the receiver operating characteristic (ROC) curve analysis to predict optimal cut-offs for TCH predictors, LT4 dose < 2.0 µg/kg/day at treatment discontinuation was predictive for TCH and was associated with 94.5% specificity and 55.7% sensitivity, with an area under the curve (AUC) of 0.802. The initial NBS-TSH level value < 45 µIU/mL was predictive for TCH with 93.1% specificity and 45.5% sensitivity, with an AUC of 0.641. In patients with eutopic thyroid gland only LT4 dose < 1.1 µg/kg/day at withdrawal time was predictive for TCH with 84.7% sensitivity and 40.4% specificity, with an AUC of 0.750.

Conclusion
According to our national follow-up data, the frequency of TCH was 53.6%. We determined the LT4 dose < 2.0 µg/kg/day at discontinuation of treatment and the initial NBS-TSH level < 45 µIU/mL as the best cut-off limits to predict TCH.
Amphipathic Phenylalanine-Induced Nucleophilic–Hydrophobic Interface Toward Highly Reversible Zn Anode
Authors: Zhou Anbin , Wang Huirong , Zhang Fengling , Hu Xin , Song Zhihang , Chen Yi , Huang Yongxin , Cui Yanhua , Cui Yixiu , Li Li , Wu Feng , Chen Renjie 
Published: March 2024

AbstractAqueous Zn2+-ion batteries (AZIBs), recognized for their high security, reliability, and cost efficiency, have garnered considerable attention. However, the prevalent issues of dendrite growth and parasitic reactions at the Zn electrode interface significantly impede their practical application. In this study, we introduced a ubiquitous biomolecule of phenylalanine (Phe) into the electrolyte as a multifunctional additive to improve the reversibility of the Zn anode. Leveraging its exceptional nucleophilic characteristics, Phe molecules tend to coordinate with Zn2+ ions for optimizing the solvation environment. Simultaneously, the distinctive lipophilicity of aromatic amino acids empowers Phe with a higher adsorption energy, enabling the construction of a multifunctional protective interphase. The hydrophobic benzene ring ligands act as cleaners for repelling H2O molecules, while the hydrophilic hydroxyl and carboxyl groups attract Zn2+ ions for homogenizing Zn2+ flux. Moreover, the preferential reduction of Phe molecules prior to H2O facilitates the in situ formation of an organic–inorganic hybrid solid electrolyte interphase, enhancing the interfacial stability of the Zn anode. Consequently, Zn||Zn cells display improved reversibility, achieving an extended cycle life of 5250 h. Additionally, Zn||LMO full cells exhibit enhanced cyclability of retaining 77.3% capacity after 300 cycles, demonstrating substantial potential in advancing the commercialization of AZIBs.
Alternative feature selection with user control
Authors: Bach Jakob , Böhm Klemens 
Published: March 2024

AbstractFeature selection is popular for obtaining small, interpretable, yet highly accurate prediction models. Conventional feature-selection methods typically yield one feature set only, which does not suffice in certain scenarios. For example, users might be interested in finding alternative feature sets with similar prediction quality, offering different explanations of the data. In this article, we introduce alternative feature selection and formalize it as an optimization problem. In particular, we define alternatives via constraints and enable users to control the number and dissimilarity of alternatives. Next, we analyze the complexity of this optimization problem and show $$\mathcal{N}\mathcal{P}$$

N
P

-hardness. Further, we discuss how to integrate conventional feature-selection methods as objectives. Finally, we evaluate alternative feature selection in comprehensive experiments with 30 datasets representing binary-classification problems. We observe that alternative feature sets may indeed have high prediction quality, and we analyze factors influencing this outcome.
Advancements in deep learning for accurate classification of grape leaves and diagnosis of grape diseases
Authors: Kunduracioglu Ismail , Pacal Ishak 
Published: March 2024

AbstractPlant diseases cause significant agricultural losses, demanding accurate detection methods. Traditional approaches relying on expert knowledge may be biased, but advancements in computing, particularly deep learning, offer non-experts effective tools. This study focuses on fine-tuning cutting-edge pre-trained CNN and vision transformer models to classify grape leaves and diagnose grape leaf diseases through digital images. Our research examined a PlantVillage dataset, which comprises 4062 leaf images distributed across four categories. Additionally, we utilized the Grapevine dataset, consisting of 500 leaf images. This dataset is organized into five distinct groups, with each group containing 100 images corresponding to one of the five grape types. The PlantVillage dataset focuses on four classes related to grape diseases, namely Black Rot, Leaf Blight, Healthy, and Esca leaves. On the other hand, the Grapevine dataset includes five classes for leaf recognition, specifically Ak, Alaidris, Buzgulu, Dimnit, and Nazli. In experiments with 14 CNN and 17 vision transformer models, deep learning demonstrated high accuracy in distinguishing grape diseases and recognizing leaves. Notably, four models achieved 100% accuracy on PlantVillage and Grapevine datasets, with Swinv2-Base standing out. This approach holds promise for enhancing crop productivity through early disease detection and providing insights into grape variety characterization in agriculture.
Laser photostability of chitosan coated gold-GO nanocomposite and its role as a nano-therapeutic agent for control breast cancer growth
Authors: Ramadan Marwa A. , Gad Sara , Sharaky Marwa , Faid Amna H. 
Published: March 2024

Abstract
Background
As the global cancer burden increases, it is critical to develop alternative treatments that produce minimal side effects, owing to its optical, thermal, and biological anticancer and biomedical applications of graphene and its nanocomposites. researchers have devoted the past few years to developing graphene/polymer and graphene/metal nanocomposites.

Materials and methods
Here a simple, highly stable, non-toxic, eco- friendly chitosan gold nanoparticles (Cs-AuNPs) and its nanocomposites with graphene oxide nanosheets (GO) to form novel photostable (GO/AuNPs) nanocomposites. The prepared nanomaterials were characterized by UV–Vis, FTIRs, TEM and Raman as shown in graphical abstract. In addition, the anticancer efficiency of the prepared nanomaterials in breast cell lines (MCF7, T47D, MDA-MB-468 and MDA-MB-231) using SRP assay were evaluated.

Results
Results revealed that Cs-AuNPs bound with the GO sheets via electrostatic interaction with high stability with uniform decoration. A new FTIRs peak of high intensity was found in the GO/Au nanocomposite confirming the decoration of Cs-AuNPs on the surface of the GO layers. GO/Au nanocomposite has a significant cytotoxic effect on breast cell lines as compared to GO only.

Conclusion
This work opens perceptions for translational applications of GO/Au nanocomposite on cancer cell lines, future work is to use the prepared nanocomposite in photothermal chemotherapy combined treatment.

Graphical abstract