bluuebunny/crossref_metadata_2025_split huggingface dataset:
Developing problem-solving efficacy and job performance: Moderation of knowledge-oriented leadership
Authors: Lin Chieh-Peng 
Published: September 2024

AbstractDrawing upon social cognitive career theory, this study presents the formation of job performance by concurrently justifying problem-solving efficacy as a mediator and knowledge-oriented leadership as a moderator. In the proposed model, job performance is influenced by the learning value of the job, perceived job insecurity, and learning goal orientation (i.e., three predictors) indirectly through problem-solving efficacy. Moreover, knowledge-oriented leadership hypothetically moderates the effects of the three predictors on problem-solving efficacy. Empirical evaluations are demonstrated on the basis of the analyzed data of high-tech workers in Taiwan. Finally, theoretical and practical implications about workplace learning and job performance are discussed.
Monoamine alterations in Alzheimer’s disease and their implications in comorbid neuropsychiatric symptoms
Authors: Saggu Shalini , Bai Ava , Aida Mae , Rehman Hasibur , Pless Andrew , Ware Destany , Deak Ferenc , Jiao Kai , Wang Qin 
Published: September 2024

AbstractAlzheimer’s disease (AD) is a devastating neurodegenerative disorder characterized by relentless cognitive decline and the emergence of profoundly disruptive neuropsychiatric symptoms. As the disease progresses, it unveils a formidable array of neuropsychiatric manifestations, including debilitating depression, anxiety, agitation, and distressing episodes of psychosis. The intricate web of the monoaminergic system, governed by serotonin, dopamine, and norepinephrine, significantly influences our mood, cognition, and behavior. Emerging evidence suggests that dysregulation and degeneration of this system occur early in AD, leading to notable alterations in these critical neurotransmitters’ levels, metabolism, and receptor function. However, how the degeneration of monoaminergic neurons and subsequent compensatory changes contribute to the presentation of neuropsychiatric symptoms observed in Alzheimer’s disease remains elusive. This review synthesizes current findings on monoamine alterations in AD and explores how these changes contribute to the neuropsychiatric symptomatology of the disease. By elucidating the biological underpinnings of AD-related psychiatric symptoms, we aim to underscore the complexity and inform innovative approaches for treating neuropsychiatric symptoms in AD.
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Trending and emerging prospects of physics-based and ML-based wildfire spread models: a comprehensive review
Authors: Singh Harikesh , Ang Li-Minn , Lewis Tom , Paudyal Dipak , Acuna Mauricio , Srivastava Prashant Kumar , Srivastava Sanjeev Kumar 
Published: September 2024

AbstractThe significant threat of wildfires to forest ecology and biodiversity, particularly in tropical and subtropical regions, underscores the necessity for advanced predictive models amidst shifting climate patterns. There is a need to evaluate and enhance wildfire prediction methods, focusing on their application during extended periods of intense heat and drought. This study reviews various wildfire modelling approaches, including traditional physical, semi-empirical, numerical, and emerging machine learning (ML)-based models. We critically assess these models’ capabilities in predicting fire susceptibility and post-ignition spread, highlighting their strengths and limitations. Our findings indicate that while traditional models provide foundational insights, they often fall short in dynamically estimating parameters and predicting ignition events. Cellular automata models, despite their potential, face challenges in data integration and computational demands. Conversely, ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets, though they encounter interpretability issues. This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths. By incorporating data assimilation techniques with dynamic forecasting models, the predictive capabilities of ML-based predictions can be significantly enhanced. This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications, ultimately contributing to more effective wildfire mitigation and management strategies. Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities.
Development of brain metastases in patients managed with non-curative thoracic radiotherapy for stage II/III non-small cell lung cancer
Authors: Nieder Carsten , Aanes Siv Gyda , Stanisavljevic Luka , Mannsåker Bård 
Published: September 2024

Abstract
Background
This retrospective study analyzed the incidence of subsequent brain metastases after palliative radiotherapy or chemoradiation in patients with stage II/III non-small cell lung cancer (NSCLC). Risk factors for brain metastases development and survival after diagnosis were evaluated.

Methods
Different baseline parameters including but not limited to age, stage and target volume size were assessed. Outcomes were abstracted from electronic health records. Uni- and multivariate tests were performed.

Results
The study included 102 patients and found an actuarial risk of brain metastases of 15% (standard error ± 4) at one year and 20% (± 5) at two years. The maximum time interval was 15 months from start of radiation treatment. A non-significant survival difference was observed (median 12 months without versus 8.3 months with brain metastases, p = 0.21). Incidence was higher in patients with N2/3 stage, larger planning target volume size, and younger age (univariately significant factors). Trends were seen for stage III and adenocarcinoma histology. The multivariate analysis confirmed age as the most important risk factor.

Conclusion
The risk of brain metastases development was comparable to that reported in studies of curative chemoradiation. All events occurred within 15 months of follow-up, suggesting that long-term surveillance imaging may not be warranted. Patients younger than 60 years had a very high risk of brain metastases development.
Random forest regression kriging modeling for soil organic carbon density estimation using multi-source environmental data in central Vietnamese forests
Authors: Ho Viet Hoang , Morita Hidenori , Bachofer Felix , Ho Thanh Ha 
Published: September 2024

AbstractForest soil organic carbon plays a vital role in the terrestrial carbon cycle. Accurately analyzing the spatial distribution of soil organic carbon density (SOCD) is therefore necessary for sustainable forest management and climate change mitigation. Previous studies explored the potential of random forest (RF) in modeling forest SOCD using various environmental data sources. However, how forest SOCD prediction would be affected by using random forest regression kriging (RFRK), which integrates the predictive power of RF in generating deterministic trends and the capability of the ordinary kriging (OK) in handling spatial autocorrelation structure of residuals, based on the environmental data sources and their combinations remains elusive and deserves further exploration. For this purpose, 104 soil samples were collected at a depth of 30 cm in forest ecosystems of Central Vietnam, and 33 environmental covariates were derived from Sentinel-2 (S2) imagery, Advanced Land Observing Satellite-2 Phased Array L-band Synthetic Aperture Radar-2 (AL2) imagery, digital elevation model (DEM), and climatic data. Using a leave-one-out cross-validation procedure to evaluate and compare the model performances, four metrics, including coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and relative improvement (RI), were calculated. The results showed that enhanced RFRK performance for forest SOCD estimation was found with the inclusion of additional environmental data sources, with RFRK based on all data sources achieving a high accuracy (R2 = 0.78, MAE = 8.28 t·ha−1, and RMSE = 10.54 t·ha−1). The comparison of the RF and RFRK models exhibited that additionally interpolated residuals by OK were more accurate than only considering the influences of predictor covariates. The relative improvement of the RFRK models over the RF models in forest SOCD estimation was notable, with $$\:{RI}_{{R}^{2}}$$

RI

R

2

ranging from 8.20 to 65.00%, $$\:{RI}_{MAE}$$

RI

MAE

ranging from 8.18 to 21.07%, and $$\:{RI}_{RMSE}$$

RI

RMSE

ranging from 6.76 to 18.18%. The result from our case study emphasizes the robustness of RFRK using S2, AL2, DEM, and climatic data in accurately predicting forest SOCD.
Molecule-Level Multiscale Design of Nonflammable Gel Polymer Electrolyte to Build Stable SEI/CEI for Lithium Metal Battery
Authors: Sun Qiqi , Gong Zelong , Zhang Tao , Li Jiafeng , Zhu Xianli , Zhu Ruixiao , Wang Lingxu , Ma Leyuan , Li Xuehui , Yuan Miaofa , Zhang Zhiwei , Zhang Luyuan , Qian Zhao , Yin Longwei , Ahuja Rajeev , Wang Chengxiang 
Published: September 2024

AbstractThe risk of flammability is an unavoidable issue for gel polymer electrolytes (GPEs). Usually, flame-retardant solvents are necessary to be used, but most of them would react with anode/cathode easily and cause serious interfacial instability, which is a big challenge for design and application of nonflammable GPEs. Here, a nonflammable GPE (SGPE) is developed by in situ polymerizing trifluoroethyl methacrylate (TFMA) monomers with flame-retardant triethyl phosphate (TEP) solvents and LiTFSI–LiDFOB dual lithium salts. TEP is strongly anchored to PTFMA matrix via polarity interaction between -P = O and -CH2CF3. It reduces free TEP molecules, which obviously mitigates interfacial reactions, and enhances flame-retardant performance of TEP surprisingly. Anchored TEP molecules are also inhibited in solvation of Li+, leading to anion-dominated solvation sheath, which creates inorganic-rich solid electrolyte interface/cathode electrolyte interface layers. Such coordination structure changes Li+ transport from sluggish vehicular to fast structural transport, raising ionic conductivity to 1.03 mS cm−1 and transfer number to 0.41 at 30 °C. The Li|SGPE|Li cell presents highly reversible Li stripping/plating performance for over 1000 h at 0.1 mA cm−2, and 4.2 V LiCoO2|SGPE|Li battery delivers high average specific capacity > 120 mAh g−1 over 200 cycles. This study paves a new way to make nonflammable GPE that is compatible with Li metal anode.
Locally Enhanced Flow and Electric Fields Through a Tip Effect for Efficient Flow-Electrode Capacitive Deionization
Authors: Wang Ziquan , Chen Xiangfeng , Zhang Yuan , Ma Jie , Lin Zhiqun , Abdelkader Amor , Titirici Maria-Magdalena , Deng Libo 
Published: September 2024

AbstractLow-electrode capacitive deionization (FCDI) is an emerging desalination technology with great potential for removal and/or recycling ions from a range of waters. However, it still suffers from inefficient charge transfer and ion transport kinetics due to weak turbulence and low electric intensity in flow electrodes, both restricted by the current collectors. Herein, a new tip-array current collector (designated as T-CC) was developed to replace the conventional planar current collectors, which intensifies both the charge transfer and ion transport significantly. The effects of tip arrays on flow and electric fields were studied by both computational simulations and electrochemical impedance spectroscopy, which revealed the reduction of ion transport barrier, charge transport barrier and internal resistance. With the voltage increased from 1.0 to 1.5 and 2.0 V, the T-CC-based FCDI system (T-FCDI) exhibited average salt removal rates (ASRR) of 0.18, 0.50, and 0.89 μmol cm−2 min−1, respectively, which are 1.82, 2.65, and 2.48 folds higher than that of the conventional serpentine current collectors, and 1.48, 1.67, and 1.49 folds higher than that of the planar current collectors. Meanwhile, with the solid content in flow electrodes increased from 1 to 5 wt%, the ASRR for T-FCDI increased from 0.29 to 0.50 μmol cm−2 min−1, which are 1.70 and 1.67 folds higher than that of the planar current collectors. Additionally, a salt removal efficiency of 99.89% was achieved with T-FCDI and the charge efficiency remained above 95% after 24 h of operation, thus showing its superior long-term stability. "Image missing"
Comprehensive geriatric assessment, and related interventions, to improve outcomes for older patients undergoing transcatheter aortic valve implantation (TAVI): a systematic review
Authors: Chin Katherine , Jones Rosalind , Lester Eleni , Hegarty Alice , Thielemans Lieze , Schiff Rebekah 
Published: September 2024

Abstract
Introduction
Transcatheter aortic valve implantation (TAVI) is a treatment for people with severe symptomatic aortic stenosis, particularly those living with frailty. Increasing frailty is associated with poorer outcomes post-TAVI. Comprehensive Geriatric Assessment (CGA) has been shown in other settings to improve outcomes in those with frailty, including perioperatively. This systematic review aims to determine whether CGA, or interventions targeting its components, improves outcomes for older people undergoing TAVI.

Methods
EMBASE, MEDLINE, CINAHL and Cochrane CENTRAL were searched on 09/01/23 and then the search was rerun on the 16/04/24. The review was registered on PROSPERO (CRD42022299955). Included studies had to evaluate either CGA, or a single- or multi-domain intervention targeting components of CGA, in those aged ≥ 65.

Results
From 4091 papers, 24 met the inclusion criteria. Two studies assessed CGA pre-TAVI and reported mixed improvements in functional independence but no change in length of stay or post-operative delirium, although both studies had a serious risk of bias. Fifteen papers described an exercise-based intervention, and 1 paper detailed a Cognitive Behavioural Therapy-based intervention. Seven studies evaluated a multi-component intervention. There were conflicting results reported for the multi-component and single-component interventions. All studies had at least a moderate risk of bias.

Conclusion(s)
There is a lack of evidence to determine whether CGA, or related interventions, improve outcomes for older adults undergoing-TAVI. The evidence for perioperative CGA, and the results of this review, support the need for well-designed trials evaluating whether CGA improves outcomes post-TAVI for older adults living with frailty.
Five days of inpatient scoliosis-specific exercises improve preoperative spinal flexibility and facilitate curve correction of patients with rigid idiopathic scoliosis
Authors: Fan Yunli , To Michael K. T. , Kuang Guan-Ming , Lou Nan , Zhu Feng , Tao Huiren , Li Guangshuo , Yeung Eric H. K. , Cheung Kenneth M. C. , Cheung Jason P. Y. 
Published: September 2024

Abstract
Preoperative spine flexibility plays a key role in the intraoperative treatment course of severe scoliosis. In this cohort study, we examined the effects of 5 day inpatient scoliosis-specific exercise (SSE) on the spinal flexibility of patients with adolescent idiopathic scoliosis before surgery. A total of 65 patients were analyzed. These patients were divided into a prospective cohort (n = 43, age: 15 ± 1.6 years, 36 girls and 7 boys, Lenke class 1 and 2, Cobb angle: 64 ± 11°) who underwent spinal fusion in 2020, and a retrospective cohort (n = 22, age: 15 ± 1.5 years, 17 girls and 5 boys, Lenke class 1 or 2, Cobb angle: 63 ± 10°), who underwent surgery between 2018 and 2019 and did not receive preoperative SSE. Rigid scoliosis was defined as a reduction of less than 50% in Cobb angle between the preoperative fulcrum bending and initial standing curve magnitude. In the prospective cohort, 21 patients (Cobb angle: 65 ± 11°) presented with rigid thoracic scoliosis (pre-SSE fulcrum bending: 40 ± 9°, 39% reduction), and therefore received 5-day SSE to improve their preoperative spinal flexibility (SSE group), whereas 22 patients (Cobb angle: 63 ± 12°) presented with flexible thoracic scoliosis (pre-SSE fulcrum bending: 27 ± 8°, 58% reduction), and therefore underwent surgery without preoperative SSE (non-SSE group). For patients who received 5-day preoperative SSE for 4 h every day, the International Schroth Three-Dimensional Scoliosis Therapy technique was implemented with an inpatient model. After 5 days of SSE, improvements in Cobb angle with post-SSE fulcrum-bending radiography (23 ± 7°, 66% reduction) and pulmonary function (forced expiratory volume in 1 s/forced expiratory volume: 87% before SSE and 92% after SSE, p < 0.01) were observed. At the postoperative day 5, the degree of scoliosis had reduced from 44 ± 6.6° to 22 ± 6° in the SSE group, which is 1° less than the Cobb angle obtained on post-SSE fulcrum-bending radiography. In the non-SSE group, the degree of scoliosis decreased to 26 ± 5.7°. In the retrospective cohort, the degree of scoliosis decreased to 35 ± 5°, with the group also having higher postoperative pain (Visual Analog Scale score = 7, range = 5–10) and an extended hospitalization duration (11 ± 3 days). At 2-year follow-up, curve correction was found to be maintained without adding-on or proximal junctional kyphosis. Compared with the non-SSE group, the SSE group exhibited a greater curve correction (66%) with a shorter hospitalization duration (5 ± 1 days) and a lower degree of postoperative pain (Visual Analog Scale score = 4, range = 3–8). Taken together, our findings indicate that 5 day SSE improves preoperative spinal flexibility and facilitates curve correction.
Hiding in the shadows: a qualitative exploration of women gamers identities and perceptions of the video gaming context
Authors: Scidone Emelia , Phillips Matthew J. , Pantaleo Ashleigh M. 
Published: September 2024

AbstractVideo gaming has evolved into a complex, digital landscape where gender dynamics are increasingly scrutinised. While women now constitute a significant portion of the gaming population, they face unique challenges in a traditionally male-dominated space. Despite the growing presence of women in gaming, persistent gender inequalities continue to shape their experiences and identities within gaming cultures. This qualitative study explored how women gamers conceptualise their identities and navigate their experiences in the video gaming context, focusing on motivations, well-being, and representation. Utilising a feminist theoretical perspective and social constructionist epistemology, we conducted semi-structured interviews with 12 international women gamers (aged 23–43, M = 28, SD = 5.51). Findings constructed through our reflexive thematic analysis highlighted how women negotiate their identities within often hostile gaming environments, revealing both challenges and sources of resilience. Participants reported using strategies such as concealing their gender and creating private gaming communities to mitigate harassment. However, they also described gaming as a source of empowerment, offering opportunities for identity exploration and stress relief. Our research contributes to understanding gender dynamics in gaming by providing nuanced insights into women’s lived experiences, challenging simplistic narratives about their identities, preferences, and behaviours. Implications for game developers, community managers, and policymakers are discussed, offering guidance for creating more inclusive gaming environments that cater to diverse players and combat gender-based discrimination.
Enhancing environmental decision-making: a systematic review of data analytics applications in monitoring and management
Authors: Ncube Mthokozisi Masumbika , Ngulube Patrick 
Published: September 2024

AbstractEscalating environmental challenges necessitate paramount decision-making to safeguard ecosystems and resources. However, the burgeoning volume and intricate nature of environmental data often present a formidable challenge in gleaning actionable insights. In this context, integrating data analytics tools within environmental monitoring and management frameworks offers a compelling avenue for progress. These tools facilitate efficient data processing, uncover hidden patterns, and enable predictive modelling, leading to more informed decisions. Despite growing research, a comprehensive understanding of specific data analytics applications, methodologies, and demonstrably effective implementations remains elusive. This systematic review aimed to address this gap. Following PRISMA guidelines, a meticulous search across five databases was conducted using predefined inclusion/exclusion criteria. Rigorous data extraction captured salient study characteristics, methodologies, data analysis techniques, key findings, and acknowledged limitations. The review revealed that data analytics offers a powerful toolkit for environmental management, transforming decision-making across all stages. Big data and advanced techniques enable proactive strategies through earlier issue detection and improved predictive models. However, maximising this potential requires a multifaceted approach, including standardised data collection, data literacy, ethical frameworks, and stakeholder engagement.
The influence mechanism of urban street environment on juvenile delinquency based on multi-source data fusion: a case study of Manhattan, New York
Authors: Li Bingcheng , Li Gang , Lan Li , Jin Annan , Lin Zhe , Wang Yatong , Chen Xiliang 
Published: September 2024

AbstractStreets are an important component of urban public spaces and also a high-incidence area for urban crime. However, current research mainly involves adult crime, or fails to distinguish between adult and juvenile crime, which poses a severe challenge to the prevention of juvenile delinquency. Juveniles have lower self-control abilities and are more likely to be influenced by external environmental factors to trigger criminal behavior compared to adults. Therefore, this study uses New York’s Manhattan district as an example, based on CPTED and social disorganization theories, and utilizes street view data and deep learning techniques to extract street environment indicators. The GWR model is used to explore the influence mechanism of urban street environment on juvenile crime. The results of this study, considering spatial heterogeneity, demonstrate the impact of various physical environmental indicators of urban streets on juvenile delinquency, and reveal that some street indicators have differentiated effects on crime in different areas of the city. Overall, our research helps to uncover the relationship between juvenile delinquency and the built environment of streets in complex urban settings, providing important references for future urban street design and juvenile delinquency prevention.