bluuebunny/crossref_metadata_2025_split huggingface dataset:
Publicly expressed climate scepticism is greatest in regions with high CO2 emissions
Authors: Pearson Samuel , Hornsey Matthew J. , Rekker Saphira , Wade Belinda , Greig Chris 
Published: May 2024

AbstractWe analysed a recently released corpus of climate-related tweets to examine the macro-level factors associated with public declarations of climate change scepticism. Analyses of over 2 million geo-located tweets in the U.S. showed that climate scepticism – and the aggressiveness of climate-related tweets – was greater in states with higher per capita carbon emissions. This pattern remained significant after controlling for political conservatism, GDP per capita, education, and gender, and was replicated across 126 nations from around the world. The findings are consistent with a vested interest hypothesis—misinformation around climate change is most likely to be distributed in regions where there is high fossil fuel reliance, and where the economic stakes of acknowledging climate change are high. Understanding the macro-level patterns that are implicated in climate scepticism can help inform structural interventions for those seeking a low-carbon future.
The Effectiveness of Modular Transdiagnostic Cognitive Behavioral Therapy Versus Treatment as Usual for Youths Displaying School Attendance Problems: A Randomized Controlled Trial
Authors: Johnsen Daniel B. , Lomholt Johanne J. , Heyne David , Jensen Morten B. , Jeppesen Pia , Silverman Wendy K. , Thastum Mikael 
Published: May 2024

AbstractSchool attendance problems (SAPs) are associated with negative short- and long-term outcomes. Despite high prevalence of SAPs, there is a shortage of evidence-based interventions. Existing approaches often target either school refusal or truancy, leaving a gap in effective interventions addressing both types of SAPs. This randomized controlled trial (RCT) assessed the effectiveness of Back2School (B2S), a modular transdiagnostic cognitive behavioral therapy (CBT) for SAPs, compared to treatment as usual (TAU). Outcomes included youths’ school attendance and mental health. A group (B2S, TAU) × time (Pre, Post, 3-Month Follow-Up) design involving 152 youths (B2S; n = 74, TAU; n = 78) with SAPs (i.e., ≥ 10% absence in the past three months), aged 6–16 years (M = 12.2, SD = 2.2, males = 60%) were used. B2S comprised three months of CBT with youths, parents, and school involvement, while TAU comprised public and/or private intervention services. On average, youths in B2S received 15.0, (SD = 3.9) hours of intervention, while those in TAU received 13.4, (SD = 21.6). Intervention effects were investigated using mixed linear models. Both B2S and TAU exhibited significant within-group improvements in school attendance, with no significant differences between them. However, the B2S group significantly outperformed TAU in reducing youths’ emotional problems, conduct problems, problems with peers, the overall impact of problems, and increasing youths self-efficacy for attending school and parent self-efficacy for dealing with a SAP. This RCT represents the first evaluation of a modular transdiagnostic CBT for youths displaying SAPs, showing significant mental health and self-efficacy benefits. (Clinical trial registration: ClinicalTrials.gov: NCT03459677).
Comparison of p-n and p-i-n vertical diodes based on p-PMItz/n-Si, p-PMItz/n-4HSiC and p-PMItz/i-SiO2/n-Si heterojunctions
Authors: Alarabi Ahmed Ali , Çiçek Osman , Makara Hasan , Ünal Fatih , Zurnacı Merve , Altındal Şemsettin 
Published: May 2024

AbstractIn this paper, we present a comprehensive comparison study between p-n and p-i-n vertical diodes employing diverse heterojunction configurations under dark conditions. The diodes are fabricated utilizing p-PMItz as the organic semiconductor layer interfacing with different inorganic substrates, including n-Si (n-type silicon), n-4HSiC (n-type 4H silicon carbide), and incorporating an intrinsic SiO2 (silicon dioxide) layer in the p-PMItz/i-SiO2/n++-Si configuration. The current–voltage and dielectric characteristics are analyzed to discern the performance discrepancies among these diode configurations. The influence of heterojunction interfaces and band alignments on device behavior is investigated, shedding light on the charge transport mechanisms within these structures. Our findings reveal distinct trends in device characteristics for p-n and p-i-n diodes, highlighting the significance of heterojunction design in optimizing device performance. This comparative analysis offers valuable insights for the development of efficient organic–inorganic hybrid diodes tailored for various optoelectronic applications.
Latency Evaluation in the Image Acquisition System Based on MTCA.4 Architecture for Plasma Diagnostics
Authors: Pietrzak P. , Perek P. , Makowski D. 
Published: May 2024

AbstractITER diagnostic systems provide measurements to the Plasma Control System (PCS) in real-time. These measurements are used for plasma control and machine protection. Latency is an important parameter in the assessment of such systems. It is a time gap between capturing an external event by hardware and finishing the processing of acquired data. PCS requires the diagnostic systems to introduce a maximum total latency of 10 to 100 ms, therefore, the systems need to be tested if they meet the requirements. The system evaluated in this paper is a reference real-time image acquisition system developed as a base for ITER diagnostic systems. It consists of hardware based on the Micro Telecommunications Computing Architecture (MicroTCA) standard, developed firmware, and software. It supports cameras with various interfaces. In the paper, two cameras, with a Camera Link and 1 GigE Vision interfaces were selected to perform latency evaluation. The paper presents two methods of measuring the latency of image acquisition. The first one is based on precise time stamping consecutive stages of acquisition. This approach allows for determining which step of acquisition takes more or less time. In consequence, the software or hardware can be optimized. The other one uses LED to evaluate a particular camera, by checking the time of camera reaction to the trigger. A dedicated testing framework is developed to perform automated tests to evaluate latency. It supports collecting and analyzing the results of measurements. Besides that, a dedicated hardware is used to perform the latency tests using LED. The results and discussion of the measurements are presented in the manuscript. They show the latency evaluated using earlier proposed methods, comparing the cameras used in the image acquisition system.
Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review
Authors: Darsha Jayamini Widana Kankanamge , Mirza Farhaan , Asif Naeem M. , Chan Amy Hai Yan 
Published: May 2024

Abstract
Asthma, a common chronic respiratory disease among children and adults, affects more than 200 million people worldwide and causes about 450,000 deaths each year. Machine learning is increasingly applied in healthcare to assist health practitioners in decision-making. In asthma management, machine learning excels in performing well-defined tasks, such as diagnosis, prediction, medication, and management. However, there remain uncertainties about how machine learning can be applied to predict asthma exacerbation. This study aimed to systematically review recent applications of machine learning techniques in predicting the risk of asthma attacks to assist asthma control and management. A total of 860 studies were initially identified from five databases. After the screening and full-text review, 20 studies were selected for inclusion in this review. The review considered recent studies published from January 2010 to February 2023. The 20 studies used machine learning techniques to support future asthma risk prediction by using various data sources such as clinical, medical, biological, and socio-demographic data sources, as well as environmental and meteorological data. While some studies considered prediction as a category, other studies predicted the probability of exacerbation. Only a group of studies applied prediction windows. The paper proposes a conceptual model to summarise how machine learning and available data sources can be leveraged to produce effective models for the early detection of asthma attacks. The review also generated a list of data sources that other researchers may use in similar work. Furthermore, we present opportunities for further research and the limitations of the preceding studies.
Generalizability of a Musculoskeletal Therapist Electronic Health Record for Modelling Outcomes to Work-Related Musculoskeletal Disorders
Authors: Wassell M. , Vitiello A. , Butler-Henderson K. , Verspoor K. , Pollard H. 
Published: May 2024

Abstract

Purpose
Electronic Health Records (EHRs) can contain vast amounts of clinical information that could be reused in modelling outcomes of work-related musculoskeletal disorders (WMSDs). Determining the generalizability of an EHR dataset is an important step in determining the appropriateness of its reuse. The study aims to describe the EHR dataset used by occupational musculoskeletal therapists and determine whether the EHR dataset is generalizable to the Australian workers’ population and injury characteristics seen in workers’ compensation claims.

Methods
Variables were considered if they were associated with outcomes of WMSDs and variables data were available. Completeness and external validity assessment analysed frequency distributions, percentage of records and confidence intervals.

Results
There were 48,434 patient care plans across 10 industries from 2014 to 2021. The EHR collects information related to clinical interventions, health and psychosocial factors, job demands, work accommodations as well as workplace culture, which have all been shown to be valuable variables in determining outcomes to WMSDs. Distributions of age, duration of employment, gender and region of birth were mostly similar to the Australian workforce. Upper limb WMSDs were higher in the EHR compared to workers’ compensation claims and diagnoses were similar.

Conclusion
The study shows the EHR has strong potential to be used for further research into WMSDs as it has a similar population to the Australian workforce, manufacturing industry and workers’ compensation claims. It contains many variables that may be relevant in modelling outcomes to WMSDs that are not typically available in existing datasets.
Different receptor models show differences in ligand binding strength and location: a computational drug screening for the tick-borne encephalitis virus
Authors: Finke Felicitas , Hungerland Jonathan , Solov’yov Ilia A. , Schuhmann Fabian 
Published: May 2024

Abstract
The tick-borne encephalitis virus (TBE) is a neurotrophic disease that has spread more rapidly throughout Europe and Asia in the past few years. At the same time, no cure or specific therapy is known to battle the illness apart from vaccination. To find a pharmacologically relevant drug, a computer-aided drug screening was initiated. Such a procedure probes a possible binding of a drug to the RNA Polymerase of TBE. The crystal structure of the receptor, however, includes missing and partially modeled regions, which rendered the structure incomplete and of questionable use for a thorough drug screening procedure. The quality of the receptor model was addressed by studying three putative structures created. We show that the choice of receptor models greatly influences the binding affinity of potential drug molecules and that the binding location could also be significantly impacted. We demonstrate that some drug candidates are unsuitable for one model but show decent results for another. Without any prejudice on the three employed receptor models, the study reveals the imperative need to investigate the receptor structure before drug binding is probed whether experimentally or computationally.
An efficient methodological approach for synthesis of selenopyridines: generation, reactions, anticancer activity, EGFR inhibitory activity and molecular docking studies
Authors: Hussein Bahgat R. M. , El-Saghier Sham M. M. , Allam Rasha M. , Mohamed Mamdouh F. A. , Amer Amer A. 
Published: May 2024

Abstract
In the present work, we successfully synthesized Se-alkyl selenopyridines 1 and 3, selenopheno[2,3-b]pyridine 2, and bis-selenopyridine 4 derivatives using an eco-friendly method by utilizing NaHSe instead of toxic hydrogen selenide. The effect of the temperature on the reaction was screening at various temperatures. The regiospecific reaction of selenopyridine 1 with bromine afforded an unexpected product 4,6-diamino-5-bromo-2-[(cyanomethyl)selenyl]-pyridine-3-carbonitrile (5), which was cyclized to selenopheno[2,3-b]pyridine (7) by refluxing in the presence of TEA. While its treatment with thiophenol and/or p-chlorothiophenol gave 8a, b. On the other hand, its reaction with aminothiophenol afforded 2-(benzo[d]-thiazol-2-yl)-5-bromoselenopheno[2,3-b]pyridine-3,4,6-triamine (9). Also, N-(2-cyano-4-methyl-5H-1-seleno-3,5,8-triazaacenaphthylen-7-yl)acetamide (11) and a novel series of selenoazo dyes 12a–d were synthesized by treatment of selenopheno[2,3-b]pyridine 2 with acetic anhydride and/or diazonium chlorides of aromatic amines, respectively. Then, we ascertained the potential activity of synthesized compounds against highly metastatic prostate cancer cells (PC-3) and osteosarcoma cells (MG-63) and found that 12a, 12b, 12c, and 12d were more cytotoxic than doxorubicin in both tested cell lines, showing nearly the same anticancer activity with IC50 values ranging from 2.59 ± 0.02 µM to 3.93 ± 0.23 µM. Mechanistically, the most potent compounds 12a and 12b proved to be potent EGFR inhibitors with IC50 values of 0.301 and 0.123 µM, respectively, compared to lapatinib as a positive reference (IC50 = 0.049 µM). Moreover, the docking results are in good agreement with the anticancer activity as well as the EGFR inhibitory activity, suggesting these two compounds as promising EGFR anticancer candidates.

Graphical abstract
Determination of promising inhibitors for N-SH2 domain of SHP2 tyrosine phosphatase: an in silico study
Authors: Gencer Akçok Emel Başak , Güner Hüseyin , Akçok İsmail 
Published: May 2024

AbstractThere are many genes that produce proteins related to diseases and these proteins can be targeted with drugs as a potential therapeutic approach. Recent advancement in drug discovery techniques have created new opportunities for treating variety of diseases by targeting disease-related proteins. Structure-based drug discovery is a faster and more cost-effective approach than traditional methods. SHP2 phosphatase, encoded by the PTPN11 gene, has been the focus of much attention due to its involvement in many types of diseases. The biological function of SHP2 is enabled mostly by protein–protein interaction through its SH2 domains. In this study, we report the identification of a potential small molecule inhibitor for the N-SH2 domain of SHP2 by structure-based drug discovery approach. We utilized molecular docking studies, followed by molecular dynamics simulations and MM/PBSA calculations, to analyze compounds retrieved from the Broad's Drug Repurposing Hub and ZINC15 databases. We selected 10 hit compounds with the best docking scores from the libraries and examined their binding properties in the N-SH2 domain. We found that compound CID 60838 (Irinotecan) was the most suitable compound with a binding free energy value of − 64.45 kcal/mol and significant interactions with the target residues in the domain.
Graphical abstract
Hyperbolic Tangent-Type Variant-Parameter and Robust ZNN Solutions for Resolving Time-Variant Sylvester Equation in Preassigned-Time
Authors: Luo Jiawei , Yu Lei , Xiong Bangshu 
Published: May 2024

AbstractTo solve a general time-variant Sylvester equation, two novel zeroing neural networks (ZNNs) solutions are designed and analyzed. In the foregoing ZNN solutions, the design convergent parameters (CPs) before the nonlinear stimulated functions are very pivotal because CPs basically decide the convergent speeds. Nonetheless, the CPs are generally set to be constants, which is not feasible because CPs are generally time-variant in practical hardware conditions particularly when the external noises invade. So, a lot of variant-parameter ZNNs (VP-ZNNs) with time-variant CPs have been come up with. Comparing with fixed-parameter ZNNs, the foregoing VP-ZNNs have been illustrated to own better convergence, the downside is that the CPs generally increases over time, and will be probably infinite at last. Obviously, infinite large CPs would lead to be non-robustness of the ZNN schemes, which are not permitted in reality when the exterior noises inject. Moreover, even though VP-ZNNs are convergent over time, the growth of CPs will waste tremendous computing resources. Based on these factors, 2 hyperbolic tangent-type variant-parameter robust ZNNs (HTVPR-ZNNs) have been proposed in this paper. Both the convergent preassigned-time of the HTVPR-ZNN and top-time boundary of CPs are theoretically investigated. Many numerical simulations substantiated the admirable validity of the HTVPR-ZNN solutions.
Sub-One Quasi-Norm-Based k-Means Clustering Algorithm and Analyses
Authors: An Qi , Jiang Shan 
Published: May 2024

AbstractRecognizing the pivotal role of choosing an appropriate distance metric in designing the clustering algorithm, our focus is on innovating the k-means method by redefining the distance metric in its distortion. In this study, we introduce a novel k-means clustering algorithm utilizing a distance metric derived from the $$\ell _p$$

ℓ
p

quasi-norm with $$p\in (0,1)$$

p
∈
(
0
,
1
)

. Through an illustrative example, we showcase the advantageous properties of the proposed distance metric compared to commonly used alternatives for revealing natural groupings in data. Subsequently, we present a novel k-means type heuristic by integrating this sub-one quasi-norm-based distance, offer a step-by-step iterative relocation scheme, and prove the convergence to the Kuhn-Tucker point. Finally, we empirically validate the effectiveness of our clustering method through experiments on synthetic and real-life datasets, both in their original form and with additional noise introduced. We also investigate the performance of the proposed method as a subroutine in a deep learning clustering algorithm. Our results demonstrate the efficacy of the proposed k-means algorithm in capturing distinctive patterns exhibited by certain data types.
Turning Into Disengaged Public Servants: Examining the Effects of Discrimination on Work Engagement
Authors: Palumbo Rocco 
Published: May 2024

AbstractDiscrimination disempowers public servants, curtailing their vigor, dedication, and absorption at work. It breaches the psychological contract between public servants and public sector entities, provoking work disengagement. To the best of the author’s knowledge, there is limited agreement about how work discrimination disengages public servants. A mediation analysis was conducted to examine the effects of discrimination on disengagement through work meaningfulness and job satisfaction. Discrimination did not directly alter work engagement. It disengaged public servants by disrupting meaningfulness and satisfaction. Alongside preventing discrimination, public managers should enhance the work climate, restoring meaningfulness and satisfaction to keep public servants engaged.
Algorithmic Management and the Social Order of Digital Markets
Authors: Rilinger Georg 
Published: May 2024

AbstractPlatform companies use techniques of algorithmic management to control their users. Though digital marketplaces vary in their use of these techniques, few studies have asked why. This question is theoretically consequential. Economic sociology has traditionally focused on the embedded activities of market actors to explain competitive and valuation dynamics in markets. But restrictive platforms can leave little autonomy to market actors. Whether or not the analytical focus on their interactions makes sense thus depends on how restrictive the platform is, turning the question into a first order analytical concern. The paper argues that we can explain why platforms adopt more and less restrictive architectures by focusing on the design logic that informs their construction. Platforms treat markets as search algorithms that blend software computation with human interactions. If the algorithm requires actors to follow narrow scripts of behavior, the platform should become more restrictive. This depends on the need for centralized computation, the degree to which required inputs can be standardized, and the misalignment of interests between users. The paper discusses how these criteria can be mobilized to explain the architectures of four illustrative cases.
A credence-based theory-heavy approach to non-human consciousness
Authors: de Weerd C. R. 
Published: May 2024

AbstractMany different methodological approaches have been proposed to infer the presence of consciousness in non-human systems. In this paper, a version of the theory-heavy approach is defended. Theory-heavy approaches rely heavily on considerations from theories of consciousness to make inferences about non-human consciousness. Recently, the theory-heavy approach has been critiqued in the form of Birch’s (Noûs 56(1):133–153, 2022) dilemma of demandingness and Shevlin’s (Mind Lang 36(2):297–314, 2021) specificity problem. However, both challenges implicitly assume an inapt characterization of the theory-heavy approach. I argue that an alternative characterization of the approach, what I call a credence-based theory-heavy approach, avoids these challenges. Theorists can generate interpretations of their theory, at different levels of generality, and operationalize these into theory-informed markers. These theory-informed markers are assigned a likelihood and are used to assess the probability that a target system is conscious. In providing this characterization, and mapping out the possible ways in which a credence-based theory-heavy approach can be fleshed out, the aim is to situate the theory-heavy approach as a more compelling approach than it is currently being perceived as. Our attention, then, needs to shift towards remaining challenges such as the consensus problem and the problem of calibrating the likelihoods associated with theory-informed markers. I also explore methodological pluralism and assess how the credence-based theory-heavy approach can benefit from other methodological approaches.