bluuebunny/crossref_metadata_2025_split huggingface dataset:
RibMR – A Mixed Reality Visualization System for Rib Fracture Localization in Surgical Stabilization of Rib Fractures: Phantom, Preclinical, and Clinical Studies
Authors: Jung Hoijoon , Raythatha Jineel , Moghadam Alireza , Jin Ge , Mao Jiawei , Hsu Jeremy , Kim Jinman 
Published: December 2024

Abstract
In surgical stabilization of rib fractures (SSRF), the current standard relies on preoperative CT imaging and often incorporates ultrasound (US) imaging. As an alternative, mixed reality (MR) technology holds promise for improving rib fracture localization. This study presents an MR-based visualization system designed for SSRF in a clinical setting. We developed RibMR – a visualization system using an MR head-mounted display that projects a patient-specific 3D hologram onto the patient. RibMR enables the localization of rib fractures in relation to the patient’s anatomy. We conducted phantom study using a human mannequin, a preclinical study with two healthy patients, and clinical study with two patients to evaluate RibMR and compared it to US practice. RibMR localized rib fractures with an average accuracy of 0.38 ± 0.21 cm in phantom, 3.75 ± 2.45 cm in preclinical, and 1.47 ± 1.33 cm in clinical studies. RibMR took an average time (minutes) of 4.42 ± 0.98 for the phantom, 8.03 ± 3.67 for the preclinical, and 8.76 ± 0.65 for the clinical studies. Compared to US, RibMR located more fractures, including fractures occluded by other structures, with higher accuracy, faster speed, and improved localization rate. All participating surgeons provided positive feedback regarding accuracy, visualization quality, and usability. RibMR enabled accurate and time-efficient localization of rib fractures and showed better performance compared to US. RibMR is a promising alternative to US for localizing rib fractures in SSRF.
Advancing explainable MOOC recommendation systems: a morphological operations-based framework on partially ordered neutrosophic fuzzy hypergraphs
Authors: Shareef Mehbooba , Jose Babita Roslind , Mathew Jimson , Pruthviraja Dayananda 
Published: December 2024

Abstract
Recommendation systems constitute an integral part of nearly all digital service platforms. However, the common assumption in most recommendation systems in the literature is that similar users will be interested in similar items. This assumption holds only sometimes due to the inherent inhomogeneity of user-item interactions. To address this challenge, we introduce a novel recommendation system that leverages partially ordered neutrosophic hypergraphs to model higher-order relationships among users and items. The partial ordering of nodes enables the system to develop efficient top-N recommendations with very high Normalized Discounted Cumulative Gain (NDCG). Our approach incorporates the morphological operation of dilation, applied to user clusters obtained through fuzzy spectral clustering of the hypergraph, to generate the requisite number of recommendations. Explanations for recommendations are obtained through morphological erosion applied on the dual of the embedded hypergraph. Through rigorous testing in educational and e-commerce domains, it has been proved that our method outperforms state-of-the-art techniques and demonstrates excellent performance for various evaluation parameters. The NDCG value, a measure of ranking quality, surpasses 0.10, and the Hit Ratio (HR) consistently falls within the range of 0.25 to 0.30. The Root Mean Square Error (RMSE) values are minimal, reaching as low as 0.4. These results collectively position our algorithm as a good choice for generating recommendations with proper explanations, making it a promising solution for real-world applications.
Evidence-based policy or beauty contest? An LLM-based meta-analysis of EU cohesion policy evaluations
Authors: Asatryan Zareh , Birkholz Carlo , Heinemann Friedrich 
Published: December 2024

AbstractIndependent and high-quality evaluations of government policies are an important input for designing evidence-based policy. Institutional frictions and lack of incentives to write such evaluations, on the other hand, carry the risk of turning the system into a costly beauty contest. We study one of the most advanced markets of policy evaluations in the world, the evaluations of EU Cohesion Policy interventions by the EU Member States. We use a large language model to quantify the findings of about 2,300 evaluations, and complement this data with our own survey of the evaluation authors. We show that the findings of evaluations are inconsistent with those of the academic literature on the output impacts of Cohesion Policy. Using further variation across Member States, our analysis suggests that the market of evaluations is rather oligopolistic within Member States, that it is very fragmented across the EU, and that there is often a strong involvement of managing authorities in the work of formally independent evaluators. These factors contribute to making the findings of the evaluations overly optimistic (beautiful) risking their relevance for (evidence-based) policy. We conclude by discussing reform options to make the evaluations of EU Cohesion Policy more unbiased and effective.
Social Class and Well-being Trajectories during COVID-19: A 4-year Longitudinal Study Revealed a Steeper Decline among Higher-class Individuals in South Korea
Authors: Kwon Yuri , Zong Sooyoun Kristina , Kim Namhee , Choi Yuhyun , Choi Incheol 
Published: December 2024

Abstract
Little research has examined the role of social class in the long-term impact of the COVID-19 pandemic on mental well-being. This 4-year longitudinal study in South Korea (N = 86,872; 875,967 responses) assessed well-being before (January 2019 to January 2020) and during (January 2020 to January 2023) the COVID-19 pandemic to determine whether change in well-being during this period differed by social class. We expanded the investigation to consider transitions in social class, social distancing measures, and the mechanisms underlying well-being changes according to social class during the pandemic. In particular, we assessed the moderating effects of social class on within-person changes in well-being using conducting multilevel modeling-based analyses in four approaches. First, we found that, while higher-class individuals maintained an average well-being that was higher than that of lower-class individuals, they also experienced a steeper decline in well-being over the course of the pandemic. Relative to their pre-COVID-19 levels, they experienced a significant decrease in well-being in the first, second, and third years of COVID-19, showing no sign of recovery until the pandemic neared its end. Second, this pattern persisted without regard for critical social class transitions following the pandemic: individuals remaining in the higher class during both the pre-COVID-19 and COVID-19 periods experienced significant declines in well-being relative to baseline. Third, higher-class individuals faced larger difficulties in maintaining their well-being, particularly with respect to social distancing measures, while the well-being of lower-class individuals was less affected. Fourth, perceived changes in daily life mediated observed class difference in well-being declines, showing that higher-class individuals experienced greater changes in their daily lives due to COVID-19 than lower-class individuals did, resulting in greater declines in well-being. Taken together, these findings indicate that COVID-19 transformed components of life that are essential for the psychological health of the well-off, providing novel insights into the significant power of social class in the experiences of changes in well-being, going beyond the absolute gap that has been well-established by cross-sectional studies. This indicates the need for class-targeted interventions and policies to support well-being across all socioeconomic strata in future crises.
Bringing a path to life: the integration of narrative therapy and playback theater in individual therapy
Authors: Keisari Shoshi 
Published: December 2024

Abstract
Processing the life story is vital to a human’s development, providing individuals with resources and promoting adaptation to changes throughout the life course. Narrative therapy is a main approach in psychotherapy that is found to positively affect individuals’ psychological well-being and mental health. This paper in the Zeitschrift für Psychodrama und Soziometrie (ZPS) presents the ways narrative therapy is integrated with role play and playback theatre in individual therapy sessions. Dramatic roles from different periods of one’s life are explored simultaneously as part of the therapeutic process to create a concrete and integrative representation of one’s life story. A case study illustrates the integration of narrative therapy, role reversal, and playback theater techniques in one-on-one sessions.
Next-Generation Desalination Membranes Empowered by Novel Materials: Where Are We Now?
Authors: Wu Siqi , Peng Lu Elfa , Yang Zhe , Sarkar Pulak , Barboiu Mihail , Tang Chuyang Y. , Fane Anthony G. 
Published: December 2024

AbstractMembrane desalination is an economical and energy-efficient method to meet the current worldwide water scarcity. However, state-of-the-art reverse osmosis membranes are gradually being replaced by novel membrane materials as a result of ongoing technological advancements. These novel materials possess intrinsic pore structures or can be assembled to form lamellar membrane channels for selective transport of water or solutes (e.g., NaCl). Still, in real applications, the results fall below the theoretical predictions, and a few properties, including large-scale fabrication, mechanical strength, and chemical stability, also have an impact on the overall effectiveness of those materials. In view of this, we develop a new evaluation framework in the form of radar charts with five dimensions (i.e., water permeance, water/NaCl selectivity, membrane cost, scale of development, and stability) to assess the advantages, disadvantages, and potential of state-of-the-art and newly developed desalination membranes. In this framework, the reported thin film nanocomposite membranes and membranes developed from novel materials were compared with the state-of-the-art thin film composite membranes. This review will demonstrate the current advancements in novel membrane materials and bridge the gap between different desalination membranes. In this review, we also point out the prospects and challenges of next-generation membranes for desalination applications. We believe that this comprehensive framework may be used as a future reference for designing next-generation desalination membranes and will encourage further research and development in the field of membrane technology, leading to new insights and advancements.
Life Cycle Assessment of Metallurgical Grade Silicon Comparing Charge Mixtures and Yields
Authors: Nøstvold Cathrine , Pastor-Vallés Elisa , Andersen Vegar , Tranell Gabriella , Pettersen Johan Berg 
Published: December 2024

AbstractMetallurgical-grade silicon is considered a critical raw material in Europe due to its supply risk and wide range of applications in technologies needed to transition to a low-carbon society. It is produced by reducing SiO2 in quartz using carbon, traditionally a mix of fossil and biocarbon. As part of the transition to a low-carbon future, fossil carbon is being replaced by biocarbon. While several studies have investigated the environmental impact of silicon production concerning the conventional carbon mix, little information is found on the consequences of switching to a purely biobased mix. A parametric life cycle assessment has been conducted to investigate the impact of going from a traditional reductant charge mix to a purely biobased carbon mix, and through mass and energy balance, the consequences for the cradle-to-gate life cycle inventory. The inventory analysis was performed as a mass and energy balance of metallurgical grade silicon production. The study was conducted for two charge mixes: one representing the traditional charge mix, and one based on biocarbon only. Three different yields for each mix were examined to investigate how silicon yield affects performance, giving six scenarios in total. The results showed that a completely biobased carbon mix reduced the impact for 10 of 18 midpoint impact indicators and all three endpoint indicators, but only when the biocarbon considered is sustainably sourced. The contribution analysis showed that in addition to carefully sourcing biocarbon raw materials of sustainable origin, reducing direct emissions through off-gas treatment is another option to decrease the environmental impact of biobased production further.
Graphical Abstract
Manufacturability of A20X printed lattice heat sinks
Authors: Chouhan Ganesh , Bidare Prveen 
Published: December 2024

AbstractLaser powder bed fusion (LPBF) is a well-established technique for manufacturing compact and intricate lattice structures; however, surface roughness on curved surfaces remains a notable limitation. Triple periodic minimal surface lattices are beneficial for their lightweight, high-strength components and increased surface area for heat transfer, making them highly desirable in aerospace applications. This study designs five TPMS lattice-based heat sinks (Gyroid, Diamond, Lidinoid, Schwarz P, and Split P) utilizing two unit cell sizes (5 mm and 10 mm), with a consistent thickness of 1 mm and a base thickness of 2 mm, all within a specified volume of 15 × 15 × 15 mm3. Additionally, two cylindrical designs featuring varying periodicity for the gyroid and diamond lattices have been developed, utilizing unit cell sizes of 5 mm and 10 mm. The laser powder bed fusion technique was employed to fabricate A20x aluminium-based heat sinks, achieving excellent surface quality. Surface texture characterization of metal heat sinks was conducted using surface topography analysis with an optical profilometer and microstructural examination via scanning electron microscopy. Additionally, the relative density of the LPBF-printed heat sinks was measured to be over 99.5%.
Association of Vitamin D level and periodontitis: a comprehensive review
Authors: Crnić Tin , Kašaj Adrian 
Published: December 2024

AbstractPeriodontitis is a chronic inflammatory disease affecting approximately 40% of the human population, resulting from the immune response to microbial infections in periodontal tissues. While inflammation serves to protect against pathogens, it also leads to tissue damage and is associated with various systemic diseases. Risk factors include diabetes, smoking, and stress, with increasing evidence linking periodontitis to Vitamin D deficiency, which impacts over one billion people worldwide. Vitamin D is essential for maintaining bone health and supporting immune function, and its deficiency has been shown to adversely affect periodontal health. Adequate Vitamin D levels are crucial for effective management of periodontitis, as inflammation can lead to decreased 25(OH)D levels. Recent advancements in point-of-care technology now allows for rapid chairside testing of Vitamin D, significantly enhancing diagnostic efficiency in dental practices. Research indicates that Vitamin D plays a significant role in modulating immune responses, particularly by suppressing pro-inflammatory cytokines. Patients with periodontitis often exhibit lower Vitamin D levels, and supplementation has been shown to improve clinical outcomes, such as clinical attachment level (CAL) and bleeding index (BI). Thus, the use of adjunctive Vitamin D during non-surgical periodontal therapy (NSPT) may enhance treatment effectiveness, although further longitudinal studies are needed to determine optimal dosages and long-term effects. In conclusion, maintaining adequate Vitamin D levels may offer protective benefits against the progression of periodontitis.
Asymptotics for the conditional higher moment coherent risk measure with weak contagion
Authors: Liu Jiajun , Yi Qingxin 
Published: December 2024

AbstractVarious measures have been introduced in the existing literature to evaluate extreme risk exposure under the effect of an observable factor. Due to the nice properties of the higher-moment (HM) coherent risk measure, we propose a conditional version of the HM (CoHM) risk measure by incorporating the information of an observable factor. We conduct an asymptotic analysis of this measure in the presence of extreme risks under the weak contagion at a high confidence level, which is further applied to the special case of the conditional Haezendonck–Goovaerts risk measure (CoHG). Numerical illustrations are also provided to examine the accuracy of the asymptotic formulas and to analyze the sensitivity of the risk contribution of the CoHG. Based on the asymptotic result in the Fréchet case, we propose an estimator for the CoHM via an extrapolation, supported by a simulation study.
Prevalence, distribution, and antibiotic resistance of bacterial isolates in daycare centers in Ile-Ife, Nigeria
Authors: Wilkie Eunice Damilola , Alao Jude Oluwapelumi , Thonda Oluwakemi Abike , Oluduro Anthonia Olufunke 
Published: December 2024

Abstract
This study investigates bacterial pathogen prevalence in daycare centers in Ile-Ife, Nigeria, highlighting significant contamination and high antibiotic resistance rates, particularly among Staphylococcus aureus. Findings underscore the need for enhanced infection control measures, sanitation practices, and public health interventions to protect children’s health in resource-limited settings.
A bibliographic outlook: machine learning on biofilm
Authors: Ding Yuanzhao , Chen Shan 
Published: December 2024

Abstract
A biofilm refers to an intricate community of microorganisms firmly attached to surfaces and enveloped within a self-generated extracellular matrix. Machine learning (ML) methodologies have been harnessed across diverse facets of biofilm research, encompassing predictions of biofilm formation, identification of pivotal genes and the formulation of novel therapeutic approaches. This investigation undertook a bibliographic analysis focused on ML applications in biofilm research, aiming to present a comprehensive overview of the field’s current status. Our exploration involved searching the Web of Science database for articles incorporating the term “machine learning biofilm,” leading to the identification and analysis of 126 pertinent articles. Our findings indicate a substantial upswing in the publication count concerning ML in biofilm over the last decade, underscoring an escalating interest in deploying ML techniques for biofilm investigations. The analysis further disclosed prevalent research themes, predominantly revolving around biofilm formation, prediction and control. Notably, artificial neural networks and support vector machines emerged as the most frequently employed ML techniques in biofilm research. Overall, our study furnishes valuable insights into prevailing trends and future trajectories within the realm of ML applied to biofilm research. It underscores the significance of collaborative efforts between biofilm researchers and ML experts, advocating for interdisciplinary synergy to propel innovation in this domain.
Data-driven supervision to optimize the effectiveness of proactive case detection for mental health care among children: a proof-of-concept study
Authors: van den Broek Myrthe , Greene M. Claire , Guevara Anthony F. , Agondeze Sandra , Kyanjo Erimiah , Irakoze Olivier , Kasujja Rosco , Kohrt Brandon A. , Jordans Mark J. D. 
Published: December 2024

Abstract
This proof-of-concept study evaluated an optimization strategy for the Community Case Detection Tool (CCDT) aimed at improving community-level mental health detection and help-seeking among children aged 6–18 years. The optimization strategy, CCDT+, combined data-driven supervision with motivational interviewing techniques and behavioural nudges for community gatekeepers using the CCDT. This mixed-methods study was conducted from January to May 2023 in Palorinya refugee settlement in Uganda. We evaluated (1) the added value of the CCDT+ in improving the accuracy of detection and mental health service utilization compared to standard CCDT, and (2) implementation outcomes of the CCDT+. Of the 1026 children detected, 801 (78%) sought help, with 656 needing mental health care (PPV = 0.82; 95% CI: 0.79, 0.84). The CCDT+ significantly increased detection accuracy, with 2.34 times higher odds compared to standard CCDT (95% CI: 1.41, 3.83). Additionally, areas using the CCDT+ had a 2.05-fold increase in mental health service utilization (95% CI: 1.09, 3.83). The CCDT+ shows promise as an embedded quality-optimization process for the detection of mental health problems among children and enhance help-seeking, potentially leading to more efficient use of mental health care resources.
Responses of Ventenata dubia and other species seven years after herbicide applications to an improved pasture in Montana
Authors: Mangold Jane M. , Sencenbaugh Lilly , Harvey Audrey J. , Rebis Matthew E. , Rew Lisa J. 
Published: December 2024

AbstractWe evaluated herbicides for controlling the annual grass ventenata [Ventenata dubia (Leers) Coss.], with particular interest in indaziflam, a preemergence cellulose biosynthesis inhibitor. In 2016, indaziflam was applied postemergence alone and in mixture with glyphosate, imazapic, propoxycarbazone-sodium, or rimsulfuron to an improved pasture in southwestern Montana. A non-sprayed control was included for comparison purposes. Canopy cover of each species was assessed annually for 7 yr; cover was grouped by life-form and longevity, and species richness was calculated. Five years (2021) after treatment, the seedbank was assessed. Our results indicated that treatments including indaziflam reduced V. dubia cover 1 to 3 yr and even up to 6 yr after application, with V. dubia cover being zero or close to zero. However, at 7 yr (2023) after treatment, V. dubia was low across all treatments, including the non-sprayed control. Perennial grasses and forbs and annual forbs were generally unaffected by any treatment and did not increase in cover over the 7 yr, even though V. dubia decreased. Two years after treatment, species richness was lowest in treatments that included indaziflam, but at 7 yr, species richness was similar across all treatments. Indaziflam depleted the monocot and dicot seedbank, with fewer than 5 seedlings of any species emerging from treatments that included indaziflam, while other treatments resulted in 60 to 165 seedlings per sample (40 cm3 of soil). In summary, at our study site, a single application of indaziflam controlled V. dubia for 6 yr, appeared to deplete the seedbank at 5 yr, and cover of perennial and annual vegetation and species richness was unaffected. By the end of the study, though, V. dubia cover appeared to be influenced by factors other than herbicide treatments, possibly variable precipitation over time, an exclusion of grazing, and competitive perennial grasses dominating the site.