bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Emission Effect of Utilization of Gasoline, Ethanol, and Methanol (GEM) Blend in Passenger Car
Authors: Agama Askar Adika , Sugiarto Bambang , Mokhtar  , Sukmono Anis , Igorio Kevan Jeremy , Fathoni Muhammad Her Perwito , Muhammad Fadhel 
Published: May 2023

Abstract
The energy demand for fuel is increasing as the vehicle population increases. Until 2035, it is projected that energy use will still be dominated by fossil energy. This needs to be addressed to diversify the use of energy which should be renewable energy (RE) to get a larger portion than at this time, RE has only utilized 5% of all fossil energy used. The higher the use of fuel, the emission of combustion gases is one of the impacts that will accompany it. So, it is necessary to look for renewable and environmentally friendly energy. Ethanol and Methanol are alcoholic chemical compounds, which contain hydrocarbons that can be used as fuel. This study looks for the fuel consumption, emissions, and performance resulting from the use of Ethanol and Methanol as a gasoline fuel mixture in the passenger car. The method used is UN-ECE R101 for fuel consumption and UN-ECE R83 for emission tests. The based and comparison fuel used is gasoline with RON 90 produced by PT. Pertamina and the mixture used is GEM. The car used is Toyota Kijang LGX. As a result, fuel consumption is still better using based fuel with a difference of 18% decreased from M1010. Meanwhile, for cleaner emissions, GEM has reduced CO and HC levels, respectively 43% in E15M5 blend, than 18% in E10M10, while NOx increased by 30% in E10M10 and E15M5 blend. The performance result is GEM better than based.
Generation of a spatially multiplexed perfect vortex beam for digital data transmission
Authors: Gangwar Swati , Jaiswal V K , Mehrotra Ranjana , Saha Shibu , Sharma Parag 
Published: May 2023

Abstract
A perfect vortex beam (PVB) has emerged as a potential candidate for optical communication as its intensity profile remains invariant for different orbital angular momenta being carried by the beam. The present study proposes a simple technique to enhance the data handling capacity of the beam through its spatial multiplexing. The variation of the ring radius of the PVB under Fourier transformation, with different focal lengths, has been used to generate multiple PVBs of different annular radii, and then combined into a single beam to produce a concentric PVB (CPVB). Each ring may represent one bit of digital data. A simple experimental setup was used to generate the beam, which has also been validated through theoretical simulation. Further studies confirm that each ring of the CPVB has the same phase profile, with impeccable spatial alignment. Spatial multiplexing of the PVB adds an additional degree which can multiply the data handling capacity of the PVB, as a result of which it can prove to be a potential candidate for optical data transmission.
Effect of Al2TiO5 powder coating on the tribological, corrosion and mechanical properties of AISI 316L stainless steel
Authors: Kumar J Jagadesh , Satyanarayana Vaddi Venkata , Reddy Gundeti Sreeram , Reddy B Ravinder , Dharmavarapu Pratibha 
Published: May 2023

Abstract
The aim of the current research is to examine the tribological, corrosion and mechanical properties of AISI 316L stainless steel without and with Al2TiO5 surface coating. AISI 316L is selected for the study, owing to its extensive usage in power plant and marine members that are usually subjected to wear, fatigue and corrosion either separately or in a combinatorial mode from mild to severe intensities. Al2TiO5 coating is provided to components to improve their tribological, corrosion and mechanical properties. Being a ceramic material, Al2TiO5 coating is expected to improve the properties under consideration. The coated specimens are analyzed by considering two factors viz. ‘speed of rotation of job’ and ‘axial speed of the spray gun’, applying Taguchi L4 array. Coating of Al2TiO5 on AISI 316L substrate increased the corrosion resistance, coefficient of friction and micro-hardness, however the wear rate and fatigue life decreased. Twenty times reduction in wear rate is recorded with the coating of Al2TiO5 on the base material when compared to the uncoated counterpart. The wear rate has also decreased by 16% with the increase in coating thickness from 300 to 375 μm. The fatigue life of the coated specimens reduced by around 12% while their corrosion resistance increased by 20% when compared to the uncoated specimens.
Photocatalytic degradation of ethylene by mesoporous nano silica loaded chlorin e6
Authors: Jia Liangyu , Jin Weihao , Zhang Weiwei , Wang Jun , Li Wanzhen , Tao Yugui 
Published: May 2023

Abstract
Green foods are usually harvested and stored before ripening, but excessive ethylene generated during storage can lead to the loss of vegetables and fruits. Photocatalytic oxidation provides a promising method to remove ethylene and extend the shelf life of green foods. In this study, a mesoporous nanosilica-loaded Ce6 composite nanomaterial (Ce6@SiO2) was designed and synthesized to adsorb ethylene into its pores and then degrade it under visible light illumination. The photosensitizer Ce6 in the mesopore produced large amounts of reactive oxygen species that degraded ethylene. The specific surface area was greatly increased by coating the material on the surface of a sodium alginate (SA) hydrogel membrane. This improved its degradation efficiency and facilitated the adsorption and degradation of ethylene during fruit storage. The ethylene removal capacity was studied by measuring the ethylene concentration and increases in the humidity in Tedlar gas bags. Practical applications were tested by observing color changes and surface decay of tomatoes stored with and without SA/Ce6@SiO2. The results showed the system may be used in practical commercial preservation systems.
Shellac as dielectric materials in organic field-effect transistors: from silicon to paper substrates
Authors: Skaf Daniella , Gomes Tiago Carneiro , Majidzadeh Robabeh , Hussein Rahaf Nafez , Carmichael Tricia Breen , Rondeau-Gagné Simon 
Published: May 2023

Abstract
Recent advances in the design and preparation of electroactive materials, particularly semiconducting and conductive polymers, have resulted in the creation of novel organic electronics with advanced functionality and performance competitive with that of devices made of silicon. With an increasing number of organic and printed electronics being engineered and produced at a larger scale, the environmental cost of the final organic electronic devices (life cycle, environmental impact, etc) needs to be considered. While e-waste is already a growing global problem, improving the sustainability of emerging electronics through a careful materials selection is highly desirable. In this work, we explore the use of shellac as a sustainable greener dielectric material in organic field-effect transistors. A careful examination of shellac in combination with diketopyrrolopyrrole-based semiconducting polymers was performed on rigid substrates through atomic force microscopy (AFM) and the fabrication of thin film transistors. All devices made from this green dielectric showed good performance and device characteristics. Building from this investigation, shellac was further integrated with paper substrates to fabricate paper-based thin film transistors. Thin film samples based on shellac on both silicon wafer and paper substrates were characterized by AFM to investigate solid-state morphology of shellac and selected semiconducting materials. Through careful optimization of the device architecture and processing time, device characteristics and performances on paper substrates (average charge mobilities and on/off current ratios) were comparable to those of devices prepared on silicon wafers, confirming that shellac, in combination with organic semiconducting polymers, can be an advantageous dielectric material to be used for the fabrication of greener and sustainable thin film electronics from renewable feedstocks and components.
Valuation of pollination services from habitat management: a case study of utility scale solar energy facilities in the United States
Authors: Mishra Shruti Khadka , Zhu Minjia , Bernknopf Richard L , Walston Leeroy J 
Published: May 2023

Abstract
Creating and maintaining pollinator habitats following the ecological infrastructure concept in degraded or unutilized land, such as solar energy facilities, is a practical way to synergistically advance the food, energy, and ecology nexus. Given the large land-use requirements for solar farming—the fastest growing renewable energy technology–considerable attention has been focused on strategies to maximize multiple ecosystem services. In this study, we coupled the principles of agronomy and ecology with economics and integrated national-scale data on crops, pollinators, and solar facilities to identify locations for creating pollinator habitats and estimating the economic value of pollination from the habitats. We examined opportunities for pollination services from pollinator-friendly utility-scale solar facilities adjacent to 42 million hectares of pollination-dependent crops in the conterminous United States at high resolution of 1 ha. We used the net income method to estimate the potential economic value of creating habitat in the land adjacent to solar facilities in the eight states with the greatest number of solar installations. Creating pollinator habitats at the 217 utility-scale solar facilities in these states could support adjacent 80,000 hectares of high pollinator dependent crops, which could potentially generate a pollination value of $120 to $264 million USD. The location-specific information and high-resolution maps generated for the United States demonstrate integration of grey and green infrastructure to support the food, energy, and environment nexus.
Improved the heterodimer protein complex prediction with protein language models
Authors: Chen Bo , Xie Ziwei , Qiu Jiezhong , Ye Zhaofeng , Xu Jinbo , Tang Jie 
Published: May 2023

Abstract
AlphaFold-Multimer has greatly improved the protein complex structure prediction, but its accuracy also depends on the quality of the multiple sequence alignment (MSA) formed by the interacting homologs (i.e. interologs) of the complex under prediction. Here we propose a novel method, ESMPair, that can identify interologs of a complex using protein language models. We show that ESMPair can generate better interologs than the default MSA generation method in AlphaFold-Multimer. Our method results in better complex structure prediction than AlphaFold-Multimer by a large margin (+10.7% in terms of the Top-5 best DockQ), especially when the predicted complex structures have low confidence. We further show that by combining several MSA generation methods, we may yield even better complex structure prediction accuracy than Alphafold-Multimer (+22% in terms of the Top-5 best DockQ). By systematically analyzing the impact factors of our algorithm we find that the diversity of MSA of interologs significantly affects the prediction accuracy. Moreover, we show that ESMPair performs particularly well on complexes in eucaryotes.
C-RCPred: a multi-objective algorithm for interactive secondary structure prediction of RNA complexes integrating user knowledge and SHAPE data
Authors: Ibéné Mandy , Legendre Audrey , Postic Guillaume , Angel Eric , Tahi Fariza 
Published: May 2023

Abstract
RNAs can interact with other molecules in their environment, such as ions, proteins or other RNAs, to form complexes with important biological roles. The prediction of the structure of these complexes is therefore an important issue and a difficult task. We are interested in RNA complexes composed of several (more than two) interacting RNAs. We show how available knowledge on the considered RNAs can help predict their secondary structure. We propose an interactive tool for the prediction of RNA complexes, called C-RCPRed, that considers user knowledge and probing data (which can be generated experimentally or artificially). C-RCPred is based on a multi-objective optimization algorithm. Through an extensive benchmarking procedure, which includes state-of-the-art methods, we show the efficiency of the multi-objective approach and the positive impact of considering user knowledge and probing data on the prediction results. C-RCPred is freely available as an open-source program and web server on the EvryRNA website (https://evryrna.ibisc.univ-evry.fr).
Similarity measures-based graph co-contrastive learning for drug–disease association prediction
Authors: Gao Zihao , Ma Huifang , Zhang Xiaohui , Wang Yike , Wu Zheyu 
Published: May 2023

Abstract

Motivation
An imperative step in drug discovery is the prediction of drug–disease associations (DDAs), which tries to uncover potential therapeutic possibilities for already validated drugs. It is costly and time-consuming to predict DDAs using wet experiments. Graph Neural Networks as an emerging technique have shown superior capacity of dealing with DDA prediction. However, existing Graph Neural Networks-based DDA prediction methods suffer from sparse supervised signals. As graph contrastive learning has shined in mitigating sparse supervised signals, we seek to leverage graph contrastive learning to enhance the prediction of DDAs. Unfortunately, most conventional graph contrastive learning-based models corrupt the raw data graph to augment data, which are unsuitable for DDA prediction. Meanwhile, these methods could not model the interactions between nodes effectively, thereby reducing the accuracy of association predictions.

Results
A model is proposed to tap potential drug candidates for diseases, which is called Similarity Measures-based Graph Co-contrastive Learning (SMGCL). For learning embeddings from complicated network topologies, SMGCL includes three essential processes: (i) constructs three views based on similarities between drugs and diseases and DDA information; (ii) two graph encoders are performed over the three views, so as to model both local and global topologies simultaneously; and (iii) a graph co-contrastive learning method is introduced, which co-trains the representations of nodes to maximize the agreement between them, thus generating high-quality prediction results. Contrastive learning serves as an auxiliary task for improving DDA predictions. Evaluated by cross-validations, SMGCL achieves pleasing comprehensive performances. Further proof of the SMGCL’s practicality is provided by case study of Alzheimer’s disease.

Availability and implementation
https://github.com/Jcmorz/SMGCL.
Consistency enhancement of model prediction on document-level named entity recognition
Authors: Jeong Minbyul , Kang Jaewoo 
Published: May 2023

Abstract

Summary
Biomedical named entity recognition (NER) plays a crucial role in extracting information from documents in biomedical applications. However, many of these applications require NER models to operate at a document level, rather than just a sentence level. This presents a challenge, as the extension from a sentence model to a document model is not always straightforward. Despite the existence of document NER models that are able to make consistent predictions, they still fall short of meeting the expectations of researchers and practitioners in the field. To address this issue, we have undertaken an investigation into the underlying causes of inconsistent predictions. Our research has led us to believe that the use of adjectives and prepositions within entities may be contributing to low label consistency. In this article, we present our method, ConNER, to enhance a label consistency of modifiers such as adjectives and prepositions. By refining the labels of these modifiers, ConNER is able to improve representations of biomedical entities. The effectiveness of our method is demonstrated on four popular biomedical NER datasets. On three datasets, we achieve a higher F1 score than the previous state-of-the-art model. Our method shows its efficacy on two datasets, resulting in 7.5%–8.6% absolute improvements in the F1 score. Our findings suggest that our ConNER method is effective on datasets with intrinsically low label consistency. Through qualitative analysis, we demonstrate how our approach helps the NER model generate more consistent predictions.

Availability and implementation
Our code and resources are available at https://github.com/dmis-lab/ConNER/.
Quantification and statistical modeling of droplet-based single-nucleus RNA-sequencing data
Authors: Kuo Albert , Hansen Kasper D , Hicks Stephanie C 
Published: May 2023

Summary
In complex tissues containing cells that are difficult to dissociate, single-nucleus RNA-sequencing (snRNA-seq) has become the preferred experimental technology over single-cell RNA-sequencing (scRNA-seq) to measure gene expression. To accurately model these data in downstream analyses, previous work has shown that droplet-based scRNA-seq data are not zero-inflated, but whether droplet-based snRNA-seq data follow the same probability distributions has not been systematically evaluated. Using pseudonegative control data from nuclei in mouse cortex sequenced with the 10x Genomics Chromium system and mouse kidney sequenced with the DropSeq system, we found that droplet-based snRNA-seq data follow a negative binomial distribution, suggesting that parametric statistical models applied to scRNA-seq are transferable to snRNA-seq. Furthermore, we found that the quantification choices in adapting quantification mapping strategies from scRNA-seq to snRNA-seq can play a significant role in downstream analyses and biological interpretation. In particular, reference transcriptomes that do not include intronic regions result in significantly smaller library sizes and incongruous cell type classifications. We also confirmed the presence of a gene length bias in snRNA-seq data, which we show is present in both exonic and intronic reads, and investigate potential causes for the bias.
Porous Penality and the Myth of Liberal Punishment: Lessons from South Africa
Authors: Super Gail 
Published: May 2023

Abstract
Drawing on Walter Benjamin, this paper discusses the relationships between law, violence, and punishment. The main argument I make is that state punishment is BOTH a violent and logically contradictory practice and that the state’s legal right to punish often spills over into extralegal penal violence, perpetrated by a range of actors against the racialized poor. I use the term penal violence to refer to all forms of violence which are aimed at enforcing law or punishing a perceived transgression of law or norms. The paper focuses on the infliction of penal violence in South Africa on/in three different scales and jurisdictions: Makwanyane and violence in prisons; police and prosecutorial violence; and extralegal civilian violence.
Mechanical bowel preparation and antibiotics in elective colorectal surgery: network meta-analysis
Authors: Tan Jonavan , Ryan Éanna J , Davey Matthew G , McHugh Fiachra T , Creavin Ben , Whelan Maria C , Kelly Michael E , Neary Paul C , Kavanagh Dara O , O’Riordan James M 
Published: May 2023

Abstract

Background
The use of intravenous antibiotics at anaesthetic induction in colorectal surgery is the standard of care. However, the role of mechanical bowel preparation, enemas, and oral antibiotics in surgical site infection, anastomotic leak, and other perioperative outcomes remains controversial. The aim of this study was to determine the optimal preoperative bowel preparation strategy in elective colorectal surgery.

Methods
A systematic review and network meta-analysis of RCTs was performed with searches from PubMed/MEDLINE, Scopus, Embase, and the Cochrane Central Register of Controlled Trials from inception to December 2022. Primary outcomes included surgical site infection and anastomotic leak. Secondary outcomes included 30-day mortality rate, ileus, length of stay, return to theatre, other infections, and side effects of antibiotic therapy or bowel preparation.

Results
Sixty RCTs involving 16 314 patients were included in the final analysis: 3465 (21.2 per cent) had intravenous antibiotics alone, 5268 (32.3 per cent) had intravenous antibiotics + mechanical bowel preparation, 1710 (10.5 per cent) had intravenous antibiotics + oral antibiotics, 4183 (25.6 per cent) had intravenous antibiotics + oral antibiotics + mechanical bowel preparation, 262 (1.6 per cent) had intravenous antibiotics + enemas, and 1426 (8.7 per cent) had oral antibiotics + mechanical bowel preparation. With intravenous antibiotics as a baseline comparator, network meta-analysis demonstrated a significant reduction in total surgical site infection risk with intravenous antibiotics + oral antibiotics (OR 0.47 (95 per cent c.i. 0.32 to 0.68)) and intravenous antibiotics + oral antibiotics + mechanical bowel preparation (OR 0.55 (95 per cent c.i. 0.40 to 0.76)), whereas oral antibiotics + mechanical bowel preparation resulted in a higher surgical site infection rate compared with intravenous antibiotics alone (OR 1.84 (95 per cent c.i. 1.20 to 2.81)). Anastomotic leak rates were lower with intravenous antibiotics + oral antibiotics (OR 0.63 (95 per cent c.i. 0.44 to 0.90)) and intravenous antibiotics + oral antibiotics + mechanical bowel preparation (OR 0.62 (95 per cent c.i. 0.41 to 0.94)) compared with intravenous antibiotics alone. There was no significant difference in outcomes with mechanical bowel preparation in the absence of intravenous antibiotics and oral antibiotics in the main analysis.

Conclusion
A bowel preparation strategy with intravenous antibiotics + oral antibiotics, with or without mechanical bowel preparation, should represent the standard of care for patients undergoing elective colorectal surgery.
A structural magnetic resonance imaging review of clinical motor outcomes from deep brain stimulation in movement disorders
Authors: Andrews Luke , Keller Simon S , Osman-Farah Jibril , Macerollo Antonella 
Published: May 2023

AbstractPatients with movement disorders treated by deep brain stimulation do not always achieve successful therapeutic alleviation of motor symptoms, even in cases where surgery is without complications. Magnetic resonance imaging (MRI) offers methods to investigate structural brain-related factors that may be predictive of clinical motor outcomes. This review aimed to identify features which have been associated with variability in clinical post-operative motor outcomes in patients with Parkinson’s disease, dystonia, and essential tremor from structural MRI modalities. We performed a literature search for articles published between 1 January 2000 and 1 April 2022 and identified 5197 articles. Following screening through our inclusion criteria, we identified 60 total studies (39 = Parkinson’s disease, 11 = dystonia syndromes and 10 = essential tremor). The review captured a range of structural MRI methods and analysis techniques used to identify factors related to clinical post-operative motor outcomes from deep brain stimulation. Morphometric markers, including volume and cortical thickness were commonly identified in studies focused on patients with Parkinson’s disease and dystonia syndromes. Reduced metrics in basal ganglia, sensorimotor and frontal regions showed frequent associations with reduced motor outcomes. Increased structural connectivity to subcortical nuclei, sensorimotor and frontal regions was also associated with greater motor outcomes. In patients with tremor, increased structural connectivity to the cerebellum and cortical motor regions showed high prevalence across studies for greater clinical motor outcomes. In addition, we highlight conceptual issues for studies assessing clinical response with structural MRI and discuss future approaches towards optimizing individualized therapeutic benefits. Although quantitative MRI markers are in their infancy for clinical purposes in movement disorder treatments, structural features obtained from MRI offer the powerful potential to identify candidates who are more likely to benefit from deep brain stimulation and provide insight into the complexity of disorder pathophysiology.