bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pickering Emulsions based on Sustainable MIL-101(Fe)/CNC Hybrid Nanoparticles for Effective Photocatalytic Degradation of Aqueous Dyes
Authors: Yu Dehai , Wang Zhile , Ji Dandan , Jing Changshui , Song Zhaoping , Wang Huili , Liu Wenxia 
Published: November 2022

Abstract

Photocatalytic degradation of organic pollutants is an efficient, energy-saving, renewable technology. MIL-101(Fe) is an effective photocatalyst; however, its degradation efficiency for dye molecules is reduced by its hydrophobicity and sedimentation in aqueous solutions. In this study, MIL-101(Fe)/cellulose-nanocrystal (CNC) hybrid nanoparticles (MCs) with high photocatalytic activity were prepared by in situ growth. Owing to their unique amphiphilicity and interfacial adsorption, the MC-stabilised Pickering emulsions exhibited uniform particle size and stability. The MCs were uniformly adsorbed at the oil–water interface of the emulsion and dispersed in the water phase through a three-dimensional (3D) network with clusters. This facilitated complete contact of the MCs with pollutants, thereby significantly increasing the photocatalytic efficiency. The tuneable wettability of MIL-101(Fe) induced by CNCs considerably affected the stabilisation and photocatalytic degradation performance. At 0.5 wt% CNC, the MCs had a three-phase contact angle of 92°, resulting in the highest photocatalytic degradation rate and emulsion stability of the samples studied herein. An emulsion with a constant emulsification index of 100% was obtained at 0.4 wt% MCs and an oil-phase volume fraction of ~ 0.2. The MC-stabilised emulsion system exhibited high photocatalytic degradation efficiency for Rhodamine B. Moreover, the MCs could be recycled several times while ensuring a degradation efficiency within an acceptable range. Herein we introduce a new approach for improving and fabricating green, sustainable photocatalysts for degradation of organic pollutants in aqueous systems.
Harm Estimation from psychoactive drug use under MCDA principles and community perceptions in Colombia, 2021.
Authors: CASTAÑO GUILLERMO , GASCA ELLS NATALIA GALEANO , SANDOVAL JUAN DE JESUS 
Published: November 2022

Abstract

Background
Public policies related to drug use have been formulated based on the guidelines given by international organizations that have classified them based on more socio-legal motivations, lacking a scientific basis.
Methods
To achieve the hierarchy of harm associated with the consumption of psychoactive drugs through a consensus of experts, complemented with the social representations that communities have in this regard. Principles of Multcriteria Decision Analysis (MCDA) were used and through the Delphi method, 15 expert scientists in psychoactive drugs were consulted, who weighted on a scale the construction of consensus on damages related to 15 psychoactive drugs at the individual level and third parties. In addition, focus groups and individual interviews were conducted with social actors to inquire about their perceptions regarding the harm associated with drug use, both in consumers and in others. Finally, Bayesian elicitation was applied to the qualitative information of the substances, where medians and 95% credibility intervals were estimated.
Results
The consensus showed that smokeable cocaine (basuco), heroin, alcohol and cocaine were, in order, the most harmful substances for individual users, with medians of 40.3; CRI95% (39.3–41.3); 40; CRI95% (38.9–40.9), 39.7; CRI95% (38.9–40.5) and 39; CRI95% (38.4–39.7), respectively, while cocaine, alcohol and basuco were the most harmful to another 43.4; CRI95% (42.8–44), 42.7; CRI95% (42.2–43.3) and 42.7; CRI95% (42.3–43.1), respectively. For their part, the community actors considered alcohol to be the most harmful substance both for the individual who consumes it and for third parties, followed by cocaine and marijuana.
Conclusion
The disagreement in the management given to drugs by public policies regarding the problem of both legal and illegal drug use is corroborated, in relation to the international discussion the variables that had more weight in the context Colombia were those related to violence, displacement and crime associated with the production and trafficking of substances.
Case Study-Based Predictive Linear Regression Model to Measure Anxiety and Depression as the Impact of COVID- 19 among Students in Higher Education
Authors: Krishnan Reshmy , Jeba Shermina , James Menila , Badi Ali Al , Kumari Shantha , Anajemba Joseph Henry , Iwendi Celestine , Harinda Eugene 
Published: November 2022

Abstract
The authors have requested that this preprint be removed from Research Square.
Alkaline Ethanol Oxidation on Porous Fe/Pd-Fe Nanostructured Bimetallic Electrodes
Authors: Hosseini Javad , Abdolmaleki Mehdi , Allahgholipourb Gholam Reza , Hanifehpour Younes 
Published: November 2022

Abstract

In this work, to prepare nanostructured and porous Fe/Pd-Fe bimetallic catalysts, the iron coating is applied firstly onto the copper substrate by the electrochemical deposition method. Subsequently, iron-zinc alloy coating is deposited on the underlayer iron. Eventually, by immersing this alloy coating in an alkaline solution containing palladium ions, the palladium will replace the zinc, resulting in porous Fe/Pd-Fe catalysts. The X-ray diffraction (XRD) technique was used for the characterization of the physical properties of the as-prepared electrocatalysts. Their electrocatalytic activity was studied by electrochemical methods such as cyclic voltammetry (CV), chronoamperometry (CA), and electrochemical impedance spectroscopy (EIS). The XRD results showed that the zinc element was the main component of the Fe/Zn-Fe alloy and was replaced by palladium as a result of leaching-galvanic replacement. The electrochemical investigations showed that a new porous Fe/Pd-Fe bimetallic catalyst had higher electro-catalytic activity and stability than pure Pd and Fe electrodes for ethanol electro-oxidation in alkaline media. The superiority of the Fe/Pd-Fe catalyst is related to the high surface area and a synergistic effect between Fe and Pd in Fe/Pd-Fe catalysts. Therefore, the nanostructured Fe/Pd-Fe catalysts can be proposed as potential anode materials for alkaline ethanol fuel cells.
Clinical impact of weight loss on mortality in patients with idiopathic pulmonary fibrosis: a retrospective cohort study
Authors: Lee Ju Kwang , Chung Chiwook , Kim Jiwon , Cho Hyo Sin , Kim Ho Cheol 
Published: November 2022

Abstract
Background
Patients with idiopathic pulmonary fibrosis (IPF) often experience weight loss during the follow-up period. However, the prevalence and clinical impact of weight loss in these patients still need to be elucidated.
Methods
This retrospective single-center study reviewed 134 consecutive patients diagnosed with IPF. Clinical data of patients were compared according to the significant weight loss. We analyzed whether the clinical impact of significant weight loss differed regarding the pirfenidone dose.
Results
The median follow-up period was 22.1 months. Of the 134 patients, 42 (31.3%) experienced significant weight loss. Significant weight loss was independently associated with mortality (hazard ratio [HR]; 2.650; 95% confidence interval [CI], 1.108–6.341; p = 0.029) after adjusting for lung function and other significant risk factor (6-minute walk test distance: HR, 0.992; 95% CI, 0.992–0.987; p = 0.003) in multivariate analysis. The median survival of patients with significant weight loss (n = 26) was shorter than that of those without significant weight loss (n = 50) in the reduced dose pirfenidone group (28.3 ± 3.1 vs 41.8 ± 3.1 months, p = 0.013). Compared with patients without significant weight loss (n = 38), patients with significant weight loss (n = 15) also showed a marginally-significant shorter survival in the full-dose pirfenidone group (28.9 ± 3.1 vs 39.8 ± 2.6 months, p = 0.085).
Conclusions
Significant weight loss is a prognostic factor in patients with IPF regardless of pirfenidone dose. Vigilant monitoring might be necessary to detect weight loss during the clinical course in these patients.
Chronic subdural electrocorticography in nonhuman primates by an implantable wireless device for brain-machine interfaces
Authors: Yan Tianfang , Suzuki Katsuyoshi , Kameda Seiji , Maeda Masashi , Mihara Takuma , Hirata Masayuki 
Published: November 2022

Abstract
Background

Subdural electrocorticography (ECoG) signals have been proposed as a stable, good-quality source for brain-machine interfaces (BMIs), with a higher spatial and temporal resolution than electroencephalogram (EEG). However, long-term implantation may lead to chronic inflammatory reactions and connective tissue encapsulation, resulting in a decline in the signal recording quality. However, no study has reported the effects of the surrounding tissue on signal recording and device functionality thus far.

Methods

In this study, we implanted a wireless recording device with a customized 32-electrode-ECoG array subdurally in two nonhuman primates for 15 months. We evaluated the neural activities recorded and wirelessly transmitted to the devices and the chronic tissue reactions around the electrodes.

Results

Time-frequency analyses of the acute and chronic phases showed similar signal features. The average root mean square voltage and power spectral density remained relatively stable after chronic implantation. Histological examination revealed thickening of the reactive tissue around the electrode array; however, no evident inflammation in the cortex. In addition, we measured the gain factor of the newly formed ventral fibrous tissue in vivo.

Conclusions

This study suggests that subdural ECoG may provide stable chronic signal recordings for future clinical applications and neuroscience research and highlights the role of reducing the thickness of ventral tissue proliferation.
Construction of Feature Analysis Model for Demeanor Evidence Investigation Based on Data Mining Algorithm
Authors: Zhang Mengxing , Qi Lin , Guo Yulong 
Published: November 2022

Abstract

Demeanor evidence refers to the authoritative opinions of experts using analytical instruments to collect and interpret the formation of external physiological symptoms of the entire human body.The use of man-made reasoning in the field of legal settling has become increasingly broad.Specialists are giving increasingly more consideration to the securing of modular proof. This paper proposes the development of a component examination model for modular proof examination applications. The technique for this paper is to apply the guileless Bayes strategy, propose a superior information mining calculation, and lay out a model for proof observation and examination. The function of these methods is to systematically explore the basic theoretical issues of demeanor evidence based on the status quo of judicial application of demeanor evidence. Through the prediction of individual demeanor based on data mining algorithm, the evidence analysis model is designed, the neurobiological experiment is carried out, and the demeanor evidence animal stress model is constructed to verify the scientific basis of multidemeanor evidence observation. The experimental results showed that after repeated stimulation of SD rats, the maximum changes in the expression of HSP70 gene and SAA gene were 10.77 and 14.1 respectively, reflecting the high reliability of demeanor evidence biological experiments.
In silico designing of a novel polyvalent multi-subunit peptide vaccine leveraging cross- immunity against human visceral &amp; cutaneous leishmaniasis: An Immunoinformatics-based approach
Authors: Bhattacharjee Mainak , Banerjee Monojit , Mukherjee Arun 
Published: November 2022

Abstract
Leishmaniasis necessitates grave medical concern due to emergence of drug resistant strains & adverse side effects of the drugs. Already set foot in the endemic disease to tropical & subtropical countries in the world. Presently no promising & apposite vaccination strategy exists as curative therapy. In this study, we have designed for the first time a multi-subunit peptide vaccine that may confer cross-immunity against both visceral leishmaniasis (VL) & cutaneous leishmaniasis (CL) in humans. It is based on twelve experimentally validated leishmania-specific antigenic proteins that stem from multiple pathogenic species of Leishmania. Immuno-dominant B/T-cell epitopes were identified, amalgamated with proper linker & appropriate adjuvant (IL-12) to enhance the immunogenicity. Further, various physicochemical parameters, allergenicity, antigenicity and toxicity of the vaccine were also predicted to ensure the safety of the final vaccine construct. Homology modeling was performed to predict the structure of the proposed vaccine peptide & interactions with the TLR receptors were studied by molecular docking approach. Stability of the vaccine-TLR complex was also studied by implementing molecular dynamics simulation. Again mRNA structure prediction, codon optimization and in silico cloning of the corresponding gene sequence were carried out in order to anticipate the amenability of the gene construct to get expressed under in vitro system. Finally, computational immune simulation findings reveal promising cellular & humoral immune responses. Thereupon our engineered chimeric peptide appears to be a potential vaccine candidate against VL & CL.
Early Multi-Class Ensemble Based Fake News Detection Using Content Features
Authors: Rezaei Sajjad , Kahani Mohsen , Behkamal Behshid , Jalayer Rahman 
Published: November 2022

Abstract
Nowadays, social media play an essential role in spreading the news with low cost and high speed in publishing, and easy availability. As anybody can publish some news, many fake materials are also published in this media. Multi-class detection of fake news (more than just false or true) have gained more attention, as some news are half-true. In addition, early detection of fake news has an important role, before their impacts on the society become great. This paper investigates an early detection of fake news using multi-class classification. This is achieved by extracting the content features from the news, such as sentiment and semantic features. The proposed model employs five classifiers (Random Forest, Support Vector Machine, Decision Tree, LightGBM, and XGBoost) as primary classifiers. Furthermore, the AdaBoost is used for the meta-learning algorithm to develop a stacking generalization model concerning fake news detection. Stacking generalization is an ensemble learning method that uses all data produced by the first-level algorithms. We trained our model with Politifact data in the evaluation, and the model performance was evaluated by their Accuracy, Precision, Recall, and F1 score. Performance improvements are visible over previous models, in both binary and multi-class classifications.
Relationship between Early-onset stroke and Triglyceride-glucose index among young Chinese adults
Authors: 许 文奇 , Zhao Haiyan , Han Xu , Liu jianrong , Li haixia , Sun Junyan , Xing Aijun , Chen Shuohua , Wu Shouling , wu yuntao 
Published: November 2022

Abstract
Background: The triglyceride–glucose index (TyG-index), an alternative indicator of peripheral insulin resistance (IR), is associated with cardiovascular disease (CVD) in the general population. The aim of this researchwas to determine the correlation between early-onset stroke and the TyG-index among young Chinese adults.
Methods: Participants (age ≤40 years) who attended their first physical examination in Kailuan General Hospital or its 11 subsidiary hospitals between 2006 and 2012 were enrolled. A Cox proportional hazard models wasemployed to assess the correlation between early-onset stroke incident and the TyG-index. Restricted cubic splineanalysis was further conducted to examine nonlinear associations. TyG-index was calculated as Ln [Triglyceride (TG, mg/dl) × Fasting Blood Glucose (FBG, mg/dl)/2].
Results: Overall, 35999 subjects met the inclusion criteria. Their mean age was 30.82±5.66 years, and 77.1% of subjects were males. During a median observation period of 11 years, 281 stroke events occurred (62 hemorrhagic strokes and 219 ischemic strokes). Compared to the Q1 group, subjects in groups Q2-Q4 had significantly higher risks of early-onset stroke (P<0.05) after adjustment for relevant confounders in the Cox proportional hazards model. The restricted cubic splines revealed that the risk of stroke progressively increased with a high TyG-index≥ 8.41.
Conclusions: The TyG-index may be a major risk factor for early-onset stroke among young Chinese adults. TyG-index ≥8.41 can be used as an indicator for screening high-risk stroke groups.
A 6.6-nA 3.6-nW CMOS Current Reference with 0.4-V Supply Voltage
Authors: Mahmoudi Azad 
Published: November 2022

Abstract

This paper presents an all-MOSFET nano-ampere (nA) current reference with a wide operating temperature range able to operate at a supply voltage as low as 0.4 V for ultra-low power applications. The temperature-compensated current is generated by combining two currents with opposite temperature coefficients (TCs) which are derived from two NMOS transistors biased with different complementary-to-absolute-temperature (CTAT) gate voltages. Self-biased self-cascode MOSFET (SBSCM) stages operating in the subthreshold region are utilized as the slope-adjustable linear CTAT voltage generators to achieve both low-power and low-voltage operation. Designed in a 130 nm CMOS process with an active area of 0.0021 mm2, the proposed current reference achieves an average untrimmed TC of 308 ppm/°C over − 40°C to 120°C while generating an output reference current of 6.6 nA. Post simulation results show that the power consumption is only 3.68 nW at the minimum supply voltage at room temperature.
WITHDRAWN: Role of mesoporous Zr based KIT-6 silicas for catalytic decomposition application at mild conditions
Authors: Gunduz-Meric Gamze 
Published: November 2022

Abstract
KIT-6 silicas were synthesized using pluronic P123 as surfactant and n-butanol as co-solvent in mild acidic condition. Different amounts of Zr (zirconium) were loaded to la3d mesoporous silica KIT-6. The syntheses were performed by hydrothermal method and the effect of metal on KIT-6 was investigated by SEM, TEM, FT-IR, NH3-TPD, H2-TPR, ICP-OES, N2 adsorption-desorption and XRD methods. The results showed that Zr ions were well incorporated into the KIT-6 silica. Zr has also effective and excellent performance for H2O2 decomposition reaction. The results also showed that Zr based KIT-6 catalysts were stable and reusability materials over H2O2 decomposition tests. The all catalysts presented mesoporous structure with BET results (~ 768 m2/g surface area) and high activity (~ 0.91) at mild conditions. H2O2 is an effective and environmentally friendly material and many studies were carried out to develop new and efficient materials in recent years. Mesoporous silicas especially KIT-6 is one of them but there are few studies about it and its derivatives.
Role of Autonomous Adaptation to Climate Change in Agriculture: A Survey-Based Analysis of Bihar, India
Authors: Das Saheli , Mehra Meeta Keswani , Paulson Jones 
Published: November 2022

Abstract
Climate change is having adverse implications for agricultural yields in India, especially in Bihar. Bihar is also less resilient to climate change requiring climate adaptation. Within adaptation, it is important to understand the channels of autonomous adaptation besides planned adaptation. So far, most studies have had the focus on technology-based planned adaptation, while autonomous adaptation has gained eminence for sustainability in agriculture. This research fills the gap to understand the role of autonomous adaptation – through channels of inherent ability and community networks – on agricultural productivity in case of paddy and wheat crops. We use a mix of quantitative perception analysis and survey econometric methods to delineate the role of these two channels. The survey of farmers is based on stratified random sampling for two districts of Bihar, belonging to different agro-climatic zones: Gaya (drought-prone) and Purnia (flood-prone). Our research finds that farming communities are discerning about climate change and rely on adaptation, through planned and autonomous channels. The effectiveness of planned adaptation is rather weak in both districts. The main empirical finding is that ancestral knowledge and community networks have positive impacts on paddy and wheat yields. However, results differ for types of community networks. Formal community networks boost crop yields, while informal community networks dampen paddy yields, but improve wheat yields in both districts.
Inexact Floating-Point Multiplication Using Vedic Algorithm
Authors: Priya S. Roobini , Ranjan P. Vanaja 
Published: November 2022

Abstract
This paper discusses on Vedic based floating point multiplication. The computations with integers representing very big or tiny numbers is cumbersome due to power aware designs required in IC designs. A good way to express integer values is using the IEEE 754 floating point encoding. DSP applications that use floating point numbers have a wide dynamic range of sophisticated computational processes that call for imprecise calculation techniques. This article describes the implementation and design of the Vedic multiplication and exact floating-point adder-based IEEE 754 channelized floating-point multiplier. The concepts in Vedic mathematics can be meritoriously used to improve logical sequences for implementing computational blocks in system on chip designs. Specific Vedic Sutras are discussed in this paper to implement and create the channelized floating-point multiplier according to IEEE 754. The multiplier's inputs are presented in IEEE 754 32-bit format. For mantissa multiplication, it uses the Urdhva Triyakbhyam sutra. Cases of underflow and overflow are handled through effective designs, to support design of pSoCs with power awareness.
Intraseasonal variations in winter surface air temperature over China and its prediction skill in ECMWF System5
Authors: Zheng Ting , Zheng Zhihai , Feng Guolin , Zhi Rong , Zhao Yuheng 
Published: November 2022

Abstract
Based on the daily average temperature data of 2374 stations in China from 1993 to 2019 and the monthly average temperature data of ECMWF System5 in winter, this paper analyses the intraseasonal variations of winter surface air temperature (SAT) over China from 1993 to 2019 by using seasonal empirical orthogonal function decomposition (S-EOF) and the prediction skill differences of the first three dominant modes in ECMWF System5. The first mode (S-EOF1) is characterized by out-of-phase changes in SAT anomalies (SATA) between December and January. Both the Siberian high (SH) and the central Pacific El Niño-Southern Oscillation (CP ENSO) can impact S-EOF1. The second mode (S-EOF2) is characterized by consistency in warm anomalies over the Northeast and Qinghai-Tibet Plateau’s variation, while the other areas are characterized by the out-of-phase change in SATA. S-EOF2 is closely related to Arctic Oscillation (AO). The third mode is characterized by alternating changes over three months in winter, which is related to the Pacific meridional mode (PMM) and the North Atlantic SST tripole (NAT). Comparing the ECMWF System5 prediction skill for the first three leading modes for the intraseasonal variations in winter SAT, the prediction skill is not high. The model did not predict the extent of warm anomalies and the intraseasonal fluctuation in S-EOF1, which may be due to the poor prediction skills for the range and intensity of the CP ENSO and the SH. The second mode has the highest prediction skill among the three modes. For the prediction skill of AO is good. The model can capture the intraseasonal reverse SATA between December and January but did not capture the reverse SATAs between January and February. Because the model does not predict obvious PMM and NAT well.