bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rethinking parameters of “success” in breaking bad news conversations from patient’s perspective: the successful delivery process model
Authors: Koch Martin , Seifart Carola 
Published: February 2024

Abstract
Purpose
Studies that focus on improving the difficult process of breaking bad news in oncology should include the patient perspective and be guided by appropriate outcome measures. Endpoints such as “patient satisfaction” fall short to capture the complex nature of breaking bad news (BBN) conversations. However, this is true of many studies. The present study attempts to develop a framework model from a new, patient-centered perspective, which can be applied equally in clinical practice and in education.

Methods
Semi-structured in-depth interviews with twelve cancer patients were conducted. Transcripts were analyzed by “qualitative content analysis” following Mayring. Outcomes were further extrapolated in interpretational steps, and a model of “success” from patients view in BBN was developed.

Results
Two central needs of the patients could be identified: understanding and feelings. Their fulfillment depends on two groups of variables: first, structural characteristics, such as the inevitable shock, individuality, and processability; second, strongly influenceable variables, such as relationship, transfer of knowledge, and atmosphere. From these results, a framework model for successful breaking of bad news from the patient’s perspective was developed: The successful delivery process model (SDP model).

Conclusion
As a basic model for the framework for breaking bad news from the patient’s perspective, the SDP model can be applied to many different situations in oncology and help to frame the difficult conversations by tailoring the BBN conversations on determinants that favorably influence the process in a patient centered manner. In this sense, the model can be useful in clinical practice as well as in education.
Quantum mayfly optimization based feature subset selection with hybrid CNN for biomedical Parkinson’s disease diagnosis
Authors: Mansour Romany F. 
Published: February 2024

AbstractParkinson's disease (PD) arises from brain cell damage and necessitates early detection for effective treatment and symptom management. While various methods such as voice, speech, and written exams have been explored, utilizing automated tools is crucial to enhance accuracy. Recent advancements in artificial intelligence (AI) and deep learning (DL) provide an opportunity for precise early-stage PD identification. This study introduces a novel approach known as Quantum Mayfly Optimization-based feature subset selection with hybrid convolutional neural network (QMFOFS-HCNN) to improve PD detection and classification. QMFOFS-HCNN is designed to identify optimal feature subsets and overcome the dimensionality challenge. It combines a quantum mayfly optimization approach for feature selection with a convolutional neural network with attention-based long short-term memory for PD detection and classification. Additionally, hyperparameter selection is optimized using the Nadam optimizer. Experimental validation using benchmark datasets yielded compelling results. The QMFOFS-HCNN technique achieved accuracy rates: 96.35% for HandPD Spiral, 96.7% for HandPD Meander, 98.5% for Speech PD, and a perfect 100% for Voice PD datasets. These quantitative findings underscore the potential of AI and DL to enhance early PD detection accuracy significantly. These results offer promising prospects for improving healthcare outcomes in managing PD and related neurological disorders.
COVID-19 drug repurposing model based on pigeon-inspired optimizer and rough sets theory
Authors: Gad Ibrahim , Torky Mohamed , Elshaier Yaseen A. M. M. , Darwish Ashraf , Hassanien Aboul Ella 
Published: February 2024

AbstractDiscovering the most effective anti-SARS-CoV-2 drugs is the optimal solution to get back to a normal life without COVID-19. Drug repurposing, also known as drug repositioning, has become one of the most important solutions for developing new COVID-19 drugs. However, this alternative requires long-term laboratory experiments to reach the optimal drug that involves the best combination of drug features to resist the COVID-19 virus. In response to this challenge, the COVID-19 drug repurposing (C19-DR) model based on pigeon-inspired optimizer (PIO) and rough sets theory (RST) is proposed. The proposed model presents a new rough set-based feature selection technique that uses a pigeon-inspired optimizer algorithm to find and validate the optimal reduct of drug features to design an effective COVID-19 drug. Moreover, the proposed model can investigate the efficiency of multiple medications against the COVID-19 virus based on the half-maximal inhibitory concentration (IC50) threshold. The effectiveness of the proposed COVID-19 drug repurposing model has been validated using a laboratory drug dataset consisting of 60 medications. The practical results show that the optimized rough set reduct of {hydrogen bonding acceptor (HBA) and number of chiral centers} is the most significant reduct that can be used to design an effective COVID-19 drug. Moreover, the proposed drug design model could verify the efficiency of a selected dataset of drug models based on evaluating the IC50 metric. The verification results proved the high effectiveness of the proposed model in evaluating the predicted IC50 with an accuracy of 91.4% and MSE of 0.034. These findings might be a promising solution that can assist researchers in developing and repurposing novel medications to treat COVID-19 and its new viral mutants.
Hybrid deep learning models for time series forecasting of solar power
Authors: Salman Diaa , Direkoglu Cem , Kusaf Mehmet , Fahrioglu Murat 
Published: February 2024

AbstractForecasting solar power production accurately is critical for effectively planning and managing renewable energy systems. This paper introduces and investigates novel hybrid deep learning models for solar power forecasting using time series data. The research analyzes the efficacy of various models for capturing the complex patterns present in solar power data. In this study, all of the possible combinations of convolutional neural network (CNN), long short-term memory (LSTM), and transformer (TF) models are experimented. These hybrid models also compared with the single CNN, LSTM and TF models with respect to different kinds of optimizers. Three different evaluation metrics are also employed for performance analysis. Results show that the CNN–LSTM–TF hybrid model outperforms the other models, with a mean absolute error (MAE) of 0.551% when using the Nadam optimizer. However, the TF–LSTM model has relatively low performance, with an MAE of 16.17%, highlighting the difficulties in making reliable predictions of solar power. This result provides valuable insights for optimizing and planning renewable energy systems, highlighting the significance of selecting appropriate models and optimizers for accurate solar power forecasting. This is the first time such a comprehensive work presented that also involves transformer networks in hybrid models for solar power forecasting.
Comorbidity in myasthenia gravis: multicentric, hospital-based, and controlled study of 178 Italian patients
Authors: Di Stefano Vincenzo , Iacono Salvatore , Militello Massimiliano , Leone Olga , Rispoli Marianna Gabriella , Ferri Laura , Ajdinaj Paola , Lanza Placido , Lupica Antonino , Crescimanno Grazia , Monastero Roberto , Di Muzio Antonio , Brighina Filippo 
Published: February 2024

Abstract
Background
Myasthenia gravis (MG) is an autoimmune disorder with fluctuating weakness that causes significant disability and morbidity. Comorbidities may influence the course of MG, particularly in specific subgroups. The aim of this study is to investigate the frequency of comorbidities in MG patients compared to healthy controls (HC) and to evaluate their distribution according to age at disease onset, sex, and disease severity.

Methods
MG patients attending the University Hospital “Paolo Giaccone” in Palermo and “SS Annunziata” Hospital in Chieti were enrolled; HC were enrolled from the general population. Non-parametric statistics and logistic regression were used to assess the association of specific comorbidities according to age at disease onset, sex, disease subtypes, and severity of the disease.

Results
A total of 356 subjects were included in the study: 178 MG patients (46% F; median age 60 years [51–71]) and 178 sex- and age-matched HC (46% F, median age 59 years [50–66]). Overall, 86% of MG patients and 76% of HC suffered from comorbidities, and MG patients had a higher number of comorbidities compared to HC. Patients with late-onset suffered from more comorbidities than those with early-onset MG. Hypertension was more common in male patients with MG, while thymic hyperplasia, osteoporosis, and autoimmune diseases were more common in females. Respiratory disorders and thymoma were more common in patients with more severe disease (p < 0.05 for all comparisons).

Conclusion
MG patients, particularly those with late onset, showed a higher prevalence of comorbidities than HC. Assessment of comorbidities in MG is an essential issue to identify the appropriate treatment and achieve the best management.
Immunomodulatory effects of iTr35 cell subpopulation and its research progress
Authors: Yang Chenxi , Dong Lingli , Zhong Jixin 
Published: February 2024

AbstractThe spotlight in recent years has increasingly focused on inducible regulatory T cells 35 (iTr35), a novel subpopulation of regulatory T cells characterized by phenotypic stability, heightened reactivity, and potent immunosuppressive function through the production of IL-35. Despite being in the exploratory phase, research on iTr35 has garnered significant interest. In this review, we aim to consolidate our understanding of the biological characteristics and immunomodulatory mechanisms of iTr35, offering fresh perspectives that may pave the way for its potential applications in disease diagnosis and treatment.
A dynamic game approach for optimal consumption, investment and life insurance problem
Authors: Maggistro Rosario , Marino Mario , Martire Antonio 
Published: February 2024

AbstractIn this paper, we consider a multi-agent portfolio optimization model with life insurance for two players with random lifetime under a dynamic game approach. Each player is a price-taker and invests in the market to maximize her own utility for consumption and bequest. The market is complete and consists of n different assets, of which $$n-1$$

n
-
1

are risky with prices driven by Geometric Brownian motion, while one is risk-free. We analyze both the non-cooperative and cooperative scenarios, and by considering the family of CRRA utility functions, we determine the closed-form expressions of the optimal consumption, investment, and life insurance for both players. A sensitivity analysis is provided both to illustrate the impact of the biometric and risk aversion parameters on the optimal controls and to compare the non-cooperative strategies with the cooperative ones. As a result, we suggest that cooperation favors the consumption optimality, while non-cooperation promotes the coverage of the risk of death.
From Industry 4.0 Digital Manufacturing to Industry 5.0 Digital Society: a Roadmap Toward Human-Centric, Sustainable, and Resilient Production
Authors: Ghobakhloo Morteza , Mahdiraji Hannan Amoozad , Iranmanesh Mohammad , Jafari-Sadeghi Vahid 
Published: February 2024

AbstractThe present study addresses two critical controversies surrounding the emerging Industry 5.0 agenda. Firstly, it seeks to elucidate the driving forces behind the accelerated momentum of the Industry 5.0 agenda amidst the ongoing digital industrial transformation. Secondly, it explores how the agenda’s sustainability values can be effectively realised. The study conducted a comprehensive content-centric literature synthesis and identified how Industry 4.0 shortcomings adversely impacted sustainability values. Furthermore, the study implements a novel approach that determines how and in what order the sustainability functions of Industry 4.0 should be leveraged to promote the sustainability objectives of Industry 5.0. Results reveal that Industry 4.0 has benefited economic and environmental sustainability values most at the organisational and supply chain levels. Nonetheless, most micro and meso-social sustainability values have been adversely impacted by Industry 4.0. Similarly, Industry 4.0 has been worryingly detrimental to macro sustainability values like social or economic growth equality. These contradictory implications of Industry 4.0 have pulled the Industry 5.0 agenda. However, the results identified nine sustainability functions of Industry 4.0 that, when leveraged appropriately and in the correct order, can offer important implications for realising the economic and socio-environmental goals of Industry 5.0. For example, under extreme unpredictability of business world uncertainties, the business should first leverage the automation and integration capabilities of Industry 4.0 to gain the necessary cost-saving, resource efficiency, risk management capability, and business antifragility that allow them to introduce sustainable innovation into their business model without jeopardising their survival. Various scenarios for empowering Industry 5.0 sustainability values identified in the present study offer important implications for knowledge and practice.
Evolution of teachers’ and researchers’ praxeologies for designing inquiry mathematics tasks: the role of teachers’ beliefs
Authors: Pocalana Gabriella , Robutti Ornella 
Published: February 2024

AbstractThis paper is focused on the collaborative work of two communities, one of teachers and one of researchers, during a teacher professional development program on the inquiry-based learning approach in mathematics, addressed to lower secondary school in-service mathematics teachers. We conceptualize the design of inquiry mathematics task as the boundary object on which the two communities work collaboratively. We aim to study the evolution of teachers’ and researchers’ meta-didactical praxeologies for designing inquiry mathematics tasks, with the Meta-Didactical Transposition framework, to understand if and how their collaboration favors their convergence toward shared components of final meta-didactical praxeologies. In the results, we show that this convergence is reached thanks to internalization processes of praxeological components for designing inquiry mathematics tasks, as a consequence of the learning mechanisms activated by the common work on the boundary object. In this paper, we address also the issue of understanding the complex relationship between teachers’ beliefs on inquiry mathematics tasks and the meta-didactical praxeologies of teachers and researchers. As a result, we propose a model in which the evolution of teachers’ beliefs is taken into account both as an agent and a consequence of the evolution of the meta-didactical praxeologies of the two communities involved in the teacher professional development program.
Principal Accompaniment in Australian Faith-Based Schools: A Salutogenesis Approach
Authors: Sultmann W. , Hall D. , Lamb J. 
Published: February 2024

AbstractA program designed to provide accompaniment reflects a salutogenesis emphasis aimed at sustaining the professional well-being of experienced principals. A mixed methods pilot study focused on participant (N = 12) orientation, principles of accompaniment, mission-aligned processes, leadership agency, structured conversations, and nominated outcomes. Data were collected over twelve months at three stages using online survey. Australian Catholic principals reported a positive orientation experience, professional well-being, the comprehensiveness of the program, and the manageability of its implementation. Discussion confirmed design elements contributed to the wholistic nature of accompaniment linking body, mind, and spirit in conversational processes and the transformative effects of these exchanges on professional practice and well-being.
Hypofractionated stereotactic re-irradiation for progressive glioblastoma: twelve years’ experience of a single center
Authors: Yilmaz Melek Tugce , Kahvecioglu Alper , Yazici Gozde , Mohammadipour Sepideh , Kertmen Neyran , Cifci Gokcen Coban , Zorlu Faruk 
Published: February 2024

Abstract
Purpose
We aimed to evaluate the prognostic factors and the role of stereotactic radiotherapy (SRT) as a re-irradiation technique in the management of progressive glioblastoma.

Methods
The records of 77 previously irradiated glioblastoma patients who progressed and received second course hypofractionated SRT (1–5 fractions) between 2009 and 2022 in our department were evaluated retrospectively. Statistical Package for the Social Sciences (SPSS) version 23.0 (IBM, Armonk, NY, USA) was utilized for all statistical analyses.

Results
The median time to progression from the end of initial radiotherapy was 14 months (range, 6–68 months). The most common SRT schedule was 30 Gy (range, 18–50 Gy) in 5 fractions (range, 1–5 fractions). The median follow-up after SRT was 9 months (range, 3–80 months). One-year overall (OS) and progression-free survival (PFS) rates after SRT were 46% and 35%, respectively. Re-irradiation dose and the presence of pseudoprogression were both significant independent positive prognostic factors for both OS (p = 0.009 and p = 0.04, respectively) and PFS (p = 0.008 and p = 0.04, respectively). For PFS, progression-free interval > 14 months was also a prognostic factor (p = 0.04). The treatment was well tolerated without significant acute toxicity. During follow-up, radiation necrosis was observed in 17 patients (22%), and 14 (82%) of them were asymptomatic.

Conclusion
Hypofractionated SRT is an effective treatment approach for patients with progressive glioblastoma. Younger patients who progressed later than 14 months, received higher SRT doses, and experienced pseudoprogression following SRT had improved survival rates.
Residual Contextual Hourglass Network for Single-Image Deraining
Authors: Zhou Weina , Ye Linhui , Wang Xiu 
Published: February 2024

AbstractRain streaks could blur and distort images, significantly impacting further image processing. Single-image deraining is a hotspot and has practical application value, while most existing methods still have problems such as residual rain streaks and inadequate recovery of detail textures. To address these issues, we propose a Residual Contextual Hourglass Network (RCHNet) for single-image deraining, which could adapt to remove rain streaks in complex environments. Firstly, a contextual distillation block is presented to obtain local and global features across different scales. Further, residual downsampling block and residual upsampling block are used to maintain the residual nature of our architecture and better restore the details of the image. Finally, a dual attention mechanism is introduced to compensate for the spatial information lost by the downsampling. Extensive experiments on five synthetic datasets and a real-world dataset demonstrate that our proposed RCHNet outperforms existing state-of-the-art deraining approaches. On average across all synthetic datasets, the PSNR score of RCHNet is as high as 33.31 dB.
Beyond evidence-based policymaking? Exploring knowledge formation and source effects in US migration policymaking
Authors: Pettrachin Andrea , Hadj Abdou Leila 
Published: February 2024

AbstractSeveral scholars have observed persistent gaps between policy responses to complex, ambiguous and politicized problems (such as migration, climate change and the recent Covid-19 pandemic) and evidence or ‘facts’. While most existing explanations for this ‘evidence-policy gap’ in the migration policy field focus on knowledge availability and knowledge use by policymakers, this article shifts the focus to processes of knowledge formation, exploring the questions of what counts as ‘evidence’ for migration policymakers and what are the sources of information that shape their understandings of migration policy issues. It does so, by developing a network-centred approach and focusing on elite US policy-makers in the field of irregular and asylum-seeking migration. This ‘heuristic case’ is used to challenge existing explanations of the ‘evidence-policy gap’ and to generate new explanations to be tested in future research. Our findings—based on qualitative and quantitative data collected in 2015–2018 through 57 elite interviews analysed applying social network analysis and qualitative content analysis—challenge scholarly claims about policymakers’ lack of access to evidence about migration. We also challenge claims that migration-related decision-making processes are irrational or merely driven by political interests, showing that policymakers rationally collect information, select sources and attribute different relevance to ‘evidence’ acquired. We instead highlight that knowledge acquisition processes by elite policymakers are decisively shaped by dynamics of trust and perceptions of political and organizational like-mindedness among actors, and that political and ideological factors determine what qualifies as 'evidence' in the first place.
Why Is Oppression Wrong?
Authors: Khader Serene J. 
Published: February 2024

AbstractIt is often argued that oppression reduces freedom. I argue against the view that oppression is wrong because it reduces freedom. Conceiving oppression as wrong because it reduces freedom is at odds with recognizing structural cases of oppression, because (a) many cases of oppression, including many structural ones, do not reduce agents’ freedom, and (b) the type of freedom reduction involved in many structural instances of oppression is not morally objectionable. If the mechanisms of oppression are sometimes indistinguishable from benign, or even ineluctable, processes of social shaping, arguments that oppression is objectionably freedom-reducing risk implausibly suggesting that socialization itself is objectionably freedom-reducing. I show how three strategies for explaining how oppression reduces freedom either fall into the trap of overgenerality, or end up appealing to values other than freedom to avoid it. I conclude by suggesting that oppression might be better thought of as an affront to equality.
Sugarcane yield estimation in Thailand at multiple scales using the integration of UAV and Sentinel-2 imagery
Authors: Som-ard Jaturong , Immitzer Markus , Vuolo Francesco , Atzberger Clement 
Published: February 2024

AbstractTimely and accurate estimates of sugarcane yield provide valuable information for food management, bio-energy production, (inter)national trade, industry planning and government policy. Remote sensing and machine learning approaches can improve sugarcane yield estimation. Previous attempts have however often suffered from too few training samples due to the fact that field data collection is expensive and time-consuming. Our study demonstrates that unmanned aerial vehicle (UAV) data can be used to generate field-level yield data using only a limited number of field measurements. Plant height obtained from RGB UAV-images was used to train a model to derive intra-field yield maps based on 41 field sample plots spread over 20 sugarcane fields in the Udon Thani Province, Thailand. The yield maps were subsequently used as reference data to train another model to estimate yield from multi-spectral Sentinel-2 (S2) imagery. The integrated UAV yield and S2 data was found efficient with RMSE of 6.88 t/ha (per 10 m × 10 m pixel), for average yields of about 58 t/ha. The expansion of the sugarcane yield mapping across the entire region of 11,730 km2 was in line with the official statistical yield data and highlighted the high spatial variability of yields, both between and within fields. The presented method is a cost-effective and high-quality yield mapping approach which provides useful information for sustainable sugarcane yield management and decision-making.