bluuebunny/crossref_metadata_2025_split huggingface dataset:
Advancing Anaerobic Digestion of Biodiesel Byproducts: A Comprehensive Review
Authors: Gebreegziabher Blen W. , Dubale Amare A. , Adaramola Muyiwa S. , Morken John 
Published: January 2025

Abstract
The energy crisis, climate change, and insufficient waste management practices are compelling factors driving research into sustainable waste-to-resource technologies. Anaerobic digestion, aiming to recover energy and nutrients from organic waste, aligns with the circular economy's principles. This paper provides a comprehensive overview of utilizing biodiesel byproducts for biogas production, exploring techniques for enhancing biogas yield and addressing associated challenges. Assessing the potential of biodiesel byproducts highlights their environmental sustainability and economic viability for biogas production. Non-edible seed cake, rich in nutrients, shows promise for significant biogas yield. Additionally, crude glycerol, easily biodegradable, is identified as a promising co-digester, aiding in digesting recalcitrant substrates. Empirical data reveals remarkable methane yield boosts, ranging from 14 to 226% when co-digesting with crude glycerol. Moreover, the resulting digestate enhances soil fertility, promoting healthier plant growth and productivity. Challenges in anaerobic digestion, such as substrate C/N ratio imbalance and recalcitrance, necessitate strategies like substrate pretreatment and co-digestion with compatible materials to optimize biogas yield. Furthermore, advancements in anaerobic digestion technologies are crucial for effectively converting biodiesel wastes into biogas. Additionally, interdisciplinary investigations, including techno-economic analysis, lifecycle assessment, and sensitivity analysis, are vital to enhance and validate the feasibility of anaerobic digestion for biodiesel byproducts. This review serves as a valuable resource for future utilization of biodiesel byproducts for biogas production.
Alternate wetting and drying irrigation with field aged biochar may enhance water and rice productivity
Authors: Vicente Luis , Peña David , Fernández Damián , Albarrán Ángel , Rato-Nunes José Manuel , López-Piñeiro Antonio 
Published: January 2025

Abstract
Rice is traditionally cultivated worldwide under continuous flooding irrigation. However, in Mediterranean environments, there has recently been a decline in the area of rice cultivation in several producing regions where water supplies for this crop cannot always be guaranteed. Therefore, it is necessary to identify alternative crop management strategies that improve water-use efficiency in order to ensure the sustainability of rice production. It has been postulated that rice production under alternate wetting and drying (AWD) irrigation requires less water than flooding. However, the effects of the AWD system on rice yield components remain unclear, with different trends observed. It has been suggested that the soil properties are a crucial factor in this regard. In fact, drops in rice yields under AWD have been attributed to the low soil organic matter content. Consequently, the incorporation of organic amendments could offset this organic matter deficit, and the subsequent enhancement in rice productivity might also ensure its sustainability in areas where water availability is scarce. This study is the first to analyse how the soils properties, rice yields components, and water productivity were influenced by fresh and field aged biochar applied to rice soils under conventional flooding and AWD using two-threshold (mild and severe). The results showed that the transition from flood management to AWD management has had a significant impact on soil properties and rice yields, though this was dependent on the threshold. Consequently, yield losses occurred under severe AWD conditions in comparison to the flooded systems. Nevertheless, the use of holm oak biochar was found to enhance rice yields under AWD systems, particularly under severe conditions and following the field ageing process. Thus, the combined use of biochar and AWD may be a sustainable strategy to enhance water productivity, which is one of the main objectives in the rice crop.
Anesthetic Considerations and Management of Spine Surgery Performed Under Neuraxial Anesthesia
Authors: Buren Marc A. , Cil Hemra 
Published: January 2025

Abstract

Purpose of Review
This paper aims to review the evidence supporting the use of spinal anesthesia (SA) for spinal surgery, highlighting its benefits and addressing the technical challenges and considerations. The goal is to provide practical guidelines to help anesthesia providers feel more comfortable offering SA as an option for appropriate cases.

Recent Findings
Recent research indicates that SA may offer several benefits for spinal surgery, including better immediate postoperative analgesia, decreased intraoperative blood loss, and faster operating room egress. However, the evidence on hemodynamic stability, postoperative nausea and vomiting, and overall hospital length of stay is mixed.

Summary
SA presents a viable alternative to general anesthesia (GA) for specific spinal surgeries, offering distinct benefits. Patient selection is crucial, especially for those with cardiovascular risks or airway management challenges. Proper choice of spinal medication and adjuncts, along with meticulous planning for airway management, is essential. Further research will continue to refine the use of SA in spinal surgery, enhancing patient outcomes and expanding anesthetic practice.
Fe-doped SnO2 nanoparticles: enhancing the photocatalytic hydrogen efficiency, Rhodamine-B dye degradation and visible light absorption
Authors: Moses Aashish K , Tripathy Srinath Ranjan , Baral Saroj Sundar 
Published: January 2025

Abstract
The existing energy-wastewater nexus may be resolved using metal oxide semiconductor photocatalysts in photocatalytic hydrogen production and pollutant degradation, which is a clean and sustainable process. SnO2 is one such well-researched and proven photocatalyst that is now in use, although it only works with ultraviolet light, which only makes up 4% of the total solar energy received. The present research aims to use iron as a dopant to make SnO2 active under visible light, enhancing reactions like water splitting and dye degradation. The sol-gel method was used to synthesize the photocatalysts. XRD, BET, UV diffuse reflectance spectra, PL spectra, XPS, and SEM micrographs were used to characterize the synthesized photocatalysts. For 7.5 wt% Fe-doped SnO2, a remarkable hydrogen generation rate of 18.81 µmol/hr under sunlight was achieved, nearly three times that of pure SnO2 (5.71 µmol/h). The nanocomposites display excellent photoreactivity towards RhB dye degradation with an optimal concentration of 7.5 wt% Fe-doped SnO2. This optimal composite photocatalyst removes 93% of RhB dye on 0.1 g/L photocatalysts in only 60 min under sunlight. Pristine SnO2 removes 36% of the dye under similar reaction conditions. The photoluminescence spectra of Fe-doped SnO2 had lower peak locations than the pristine SnO2, indicating a decreased rate of charge recombination and increased life duration of the active species. As a result, hydrogen generation rates and dye degradation efficiencies have increased significantly. The photocatalyst’s recyclability study revealed that the photocatalysts can be used efficiently for four cycles without significant reduction in the yield.
Tailoring durable MnOx-based electrodes for high-performance electrocatalytic function for next-generation electrocatalysis applications
Authors: Tayeba Hashem , Kiani-Anbouhi Roya , Neda Royaei  
Published: January 2025

Abstract
This study introduces a high-performance electrode coated with MnOx compounds to enhance the HER reaction. The active and precipitated MnOx species facilitate interconnected electron transport throughout the Ti electrodes. The tailored MnOx electrodes exhibited a significant reduction in Rct (69.7%), superior Cdl (31.6%), and a notably lower Nyquist ring compared to traditional Ti electrodes, confirming their excellent electrocatalytic performance in Cl− and NaCl production. Additionally, LSV and PDP analysis demonstrated that the MnOx electrodes achieved a 53.9% decrease in Tafel slopes (from 139 mV/decade to 64 mV/decade), lower activity potentials, and robust corrosion resistance (99.4%), indicating faster kinetics and higher efficiency. High-resolution FESEM and contact angle images revealed that the MnOx electrodes possess uniform porous networks and semi-super hydrophilic function, optimizing H2 release and expanding the interfacial area for electron transfer. Finally, the Ti electrodes with advanced MnOx coatings can serve as reliable, cost-effective, and efficient candidates for use as regenerating electrodes in electrocatalytic industries. Moreover, the novel MnOx/rGO composites are versatile materials used as catalysts in chemical reactions, effective electrodes in energy storage devices, sensitive gas sensors, and for water treatment to remove contaminants.
Hospitalizations of the older adults with and without dementia during the last two years of life: the impact of comorbidity and changes from 2002 to 2017
Authors: Vargese Saritha Susan , Jylhä Marja , Raitanen Jani , Forma Leena , Aaltonen Mari 
Published: January 2025

Abstract

Background
Multimorbidity creates challenges for care and increases health care utilization and costs. People with dementia often have multiple comorbidities, but little is known about the role of these comorbidities in hospitalizations.

Aims
This study examines the frequency of hospitalizations during the last two years of life in older adults with and without dementia, the impact of comorbidities on hospitalizations, and their time trends.

Methods
The data came from national registers and covered all persons 70 and above who died in Finland in 2002–2017. The effect of dementia and comorbidities on hospitalizations in the last two years of life was determined using binary logistic regression and negative binomial regression.

Results
At all levels of comorbidity, people with dementia were less likely to be hospitalized and had a lower number of hospitalizations than people at the same level of comorbidity but no dementia. Hospitalizations were strongly associated with multimorbidity. During the study period, the overall hospitalization rates from home and LTC have declined.

Discussion
The declining trend of hospitalization during the 15-year study period should be interpreted in the context of the health and long-term care system.

Conclusion
Among people with dementia, comorbidities were the main driver for hospitalizations. Regardless of the number of comorbidities, people with dementia were hospitalized less often than people without dementia in last two years of life. It remains unclear whether the lower hospitalization rate is due to the improved ability to care for people with dementia outside the hospital or to the lack of sufficient medical care for them.
Viral infections of the central nervous system increase the risk of knee osteoarthritis: a two-sample mendelian randomization study
Authors: Yang Mingyi , Su Yani , Xu Ke , Wen Pengfei , Xie Jiale , Wan Xianjie , Jing Wensen , Yang Zhi , Liu Lin , Xu Peng 
Published: January 2025

Abstract

Objective
Osteoarthritis (OA) represents a condition under the influence of central nervous system (CNS) regulatory mechanisms. This investigation aims to examine the causal association between viral infections of the central nervous system (VICNS) and inflammatory diseases of the central nervous system (IDCNS) and knee osteoarthritis (KOA) at the genetic level.

Methods
In this investigation, VICNS and IDCNS were considered as primary exposure variables, while KOA served as the primary outcome. Employing a two-sample mendelian randomization (MR) approach, we conducted an analysis utilizing summary data derived from genome-wide association studies (GWAS). The GWAS summary data pertaining to VICNS and IDCNS were procured from the Finnish consortium, whereas the IEU OpenGWAS database furnished the requisite data for KOA. To ensure the robustness of our genetic causal assessment, a comprehensive array of sensitivity analyses was undertaken, encompassing evaluations of heterogeneity, horizontal pleiotropy, outlier identification, leave-one-out analyses, and assessment of the normal distribution.

Results
The results of the MR analyses revealed a suggestive positive genetic causal relationship between VICNS and KOA (P = 0.012, odds ratio [OR] with a 95% confidence interval [CI] of 1.033 [1.007–1.059]). Conversely, the MR analyses did not indicate any evidence of genetic causation between IDCNS and KOA (P = 0.575, OR 95% CI = 0.986 [0.940–1.035]). Importantly, the genetic causal assessment of the exposure and outcome variables did not demonstrate any indications of heterogeneity, horizontal pleiotropy, or outliers. Furthermore, this assessment remained robust against the influence of individual single nucleotide polymorphisms (SNPs) and exhibited adherence to a normal distribution.

Conclusion
The result of this study has elucidated a suggestive positive genetic causal link between the VICNS and KOA. However, no such genetic causal relationship was observed between the IDCNS and KOA. These findings substantiate the genetic underpinnings supporting the association between the CNS and OA.
Probabilistic Property Modeling for Reliable Casting Design and Production
Authors: Bramann Horst , Olofsson Jakob , Sturm Jörg C. 
Published: January 2025

Abstract
A new methodology leverages virtual casting process simulations to predict the statistical distribution of local mechanical properties, based on computed local microstructures and defects. The extensive result sets generated from each simulation can be statistically evaluated in a manner similar to numerous individual tensile tests. By incorporating process variability into the simulation, this innovative approach enables probabilistic modeling of expected local properties. Consequently, this method supports designers during the design phase for reliable component development and assists casters in achieving robust process designs.
An empirical evaluation of deep semi-supervised learning
Authors: Fredriksson Teodor , Bosch Jan , Olsson Helena H. 
Published: January 2025

Abstract
Obtaining labels for supervised learning is time-consuming, and practitioners seek to minimize manual labeling. Semi-supervised learning allows practitioners to eliminate manual labeling by including unlabeled data in the training process. With many deep semi-supervised algorithms and applications available, practitioners need guidelines to select the optimal labeling algorithm for their problem. The performance of new algorithms is rarely compared against existing algorithms on real-world data. This study empirically evaluates 16 deep semi-supervised learning algorithms to fill the research gap. To investigate whether the algorithms perform differently in different scenarios, the algorithms are run on 15 commonly known datasets of three datatypes (image, text and sound). Since manual data labeling is expensive, practitioners must know how many manually labeled instances are needed to achieve the lowest error rates. Therefore, this study utilizes different configurations for the number of available labels to study the manual effort required for optimal error rate. Additionally, to study how different algorithms perform on real-world datasets, the researchers add noise to the datasets to mirror real-world datasets. The study utilizes the Bradley–Terry model to rank the algorithms based on error rates and the Binomial model to investigate the probability of achieving an error rate lower than 10%. The results demonstrate that utilizing unlabeled data with semi-supervised learning may improve classification accuracy over supervised learning. Based on the results, the authors recommend FreeMatch, SimMatch, and SoftMatch since they provide the lowest error rate and have a high probability of achieving an error rate below 10% on noisy datasets.
Supervised and unsupervised mapping of binary variables: a proximity perspective
Authors: de Rooij Mark , Woestenburg Dion , Busing Frank 
Published: January 2025

Abstract
We propose a new mapping tool for supervised and unsupervised analysis of multivariate binary data with multiple items, questions, or response variables. The mapping assumes an underlying proximity response function, where participants can have multiple reasons to disagree or say “no” to a question. The probability to endorse, or to agree with an item depends on an item specific parameter and the distance in a joint space between a point representing the item and a point representing the participant. The item specific parameter defines a circle in the joint space around the location of the item such that for participants positioned within the circle the endorsement probability is larger than 0.5. For map estimation, we develop and test an MM-algorithm in which the negative log-likelihood function is majorized with a weighted least squares function. The weighted least squares function can be minimized with standard algorithms for multidimensional unfolding. To illustrate the new mapping, two empirical data sets are analyzed. The mappings are interpreted in detail and the unsupervised map is compared to a visualization based on correspondence analysis. In a Monte Carlo study, we test the performance of the algorithm in terms of recovery of population parameters and conclude that this recovery is adequate. A second Monte Carlo study investigates the predictive performance of the new mapping compared to a similar mapping with a monotone response function.
drSMALL: Database for disease resistance-shaping small molecules derived from the plant microbiome
Authors: Cheng Rui , Ke Tingli , Gui Fangze , Li Junnan , Zhang Xiaoyan , Vílchez Juan Ignacio , Matsumoto Haruna 
Published: January 2025

Abstract
Recent evidence highlights the potential of the plant microbiota to increase host plant disease resistance through the production of bioactive small molecules. However, the absence of comprehensive platforms for rapid access to this information hampers progress in the field. To address this gap, we developed the Disease Resistance-Shaping Small Molecules Database (drSMALL), a freely accessible and continuously updated resource that compiles profiles of microbial small molecules, which were experimentally evidenced to be associated with host disease resistance. drSMALL interlinks detailed information on microbial species, the small molecules they produce, host plants, and targeted pathogens, facilitating streamlined access to essential data. This initiative aims to advance the understanding of small molecules in disease resistance, filling a critical gap in data accessibility while fostering deeper exploration of sustainable agricultural practices. By leveraging the natural capabilities of plant microbiomes, drSMALL seeks to support innovative strategies for enhancing crop health and resilience against diseases.
A NOTE ON CONTINUOUS FUNCTIONS ON METRIC SPACES
Authors: SANDERS SAM 
Published: January 2025

AbstractContinuous functions on the unit interval are relatively tame from the logical and computational point of view. A similar behaviour is exhibited by continuous functions on compact metric spaces equipped with a countable dense subset. It is then a natural question what happens if we omit the latter ‘extra data’, i.e., work with ‘unrepresented’ compact metric spaces. In this paper, we study basic third-order statements about continuous functions on such unrepresented compact metric spaces in Kohlenbach’s higher-order Reverse Mathematics. We establish that some (very specific) statements are classified in the (second-order) Big Five of Reverse Mathematics, while most variations/generalisations are not provable from the latter, and much stronger systems. Thus, continuous functions on unrepresented metric spaces are ‘wild’, though ‘more tame’ than (slightly) discontinuous functions on the reals.
Evaluating translational science knowledge gains following an online short course for a general scientific audience
Authors: Vogel Amanda L. , Hussain Shadab F. , Faupel-Badger Jessica M. 
Published: January 2025

Abstract

Purpose:
The translational science workforce requires preparation in both core skills for biomedical research and competencies for advancing progress along the translational pipeline. Delivering this content in a highly accessible manner will help expand and diversify the workforce.

Methods:
The NCATS Education Branch offers online case study-based courses in translational science for a general scientific audience. The branch updated its course in preclinical translational science with additional content aligned with the NCATS Translational Science Principles, which characterize effective approaches to advance translation. The updated course was offered in 2021 and 2022. The branch also revised the course evaluation to capture knowledge change aligned with the NCATS Translational Science Principles.

Results:
Of 106 students, 88 completed baseline or endpoint surveys, with 48 completing both. Most found the online format (n = 48; 91%) and case study approach (n = 48; 91%) effective. There was a statistically significant increase in knowledge related to the Translational Science Principles (p < 0.001). Survey items with the highest endpoint scores reflected the principles on creativity and innovation, efficiency, cross-disciplinary team science, and boundary-crossing collaborations. Findings highlighted the effectiveness of pairing a case study with lectures that offer generalizable strategies aligned with the translational science principles. Students reported the course helped them learn about the trajectory of a drug discovery and development initiative, where their own work fit in, and scientific and operational approaches to apply in their own work.

Conclusions:
This online case study-based course was effective in teaching generalizable principles for translational science to students with varied scientific backgrounds.
The party politics of diplomatic engagements: evidence from Italy
Authors: Corda Tiziana , Casiraghi Matteo C.M. 
Published: January 2025

Abstract
Leaders decide to engage diplomatically with their foreign peers for various reasons but, given their limited time and resources, they have to choose which peers to prioritize. As such, the study of international diplomatic visits helps shed light on a government's foreign policy approach and better understand its priorities in how it conceives and builds foreign relations. While the literature on diplomatic engagements has largely debated its drivers and effects, the role of domestic influences, in particular of party politics, has remained understudied. We address this gap and investigate the party politics of diplomatic engagements leveraging a new dataset on Italy's high-level international bilateral diplomatic visits in 2000–2023. Our findings show that partisan differences influence not only the overall frequency of such engagements, following curvilinear left–right patterns, but also the political regimes that left- and right-wing governments prioritize in such endeavours, exposing the lower importance right-wing parties assign to democratic principles when managing their countries' foreign relations, as these governments are systematically more likely to interact with authoritarian regimes than with democracies.