bluuebunny/crossref_metadata_2025 huggingface dataset:
Crop raiding pattern by Elephant in Nepal: A Case study of Khata Corridor
Authors: Pahari Sagar , Paudel Umesh , K.C. Rabin Bahadur
Published: August 2023

Human – Elephant Conflict (HEC) causes the socio-economic distress in the settlement around Bardiya National Park (BNP). The transboundary migration of Asian elephant from Katarniyaghat Wildlife Sanctuary (KWS) in India to BNP and vice versa through Khata corridor exposes the vulnerable settlement in the corridor to frequent elephant encounters. The distance to conflict sites from features such as the forest frontage, the river edge, and the boundary of protected areas influences the severity of the crop damage. Municipalities within Khata corridor i.e., Thakurbaba and Madhuban were the study area that were further divided into three sample clusters i.e., MB, MBBZ and TBBZ. Hundred sample households (HHs) were surveyed and the annual stored crop damage and the crop damage on the agricultural field were quantified. ArcGis and R – studio were used to map and analyse the raid pattern. The result showed that small to medium landholding was possessed by most sample HHs and paddy was the most grown crop. Paddy was also the most depredated crop. The most severe crop damage in the agricultural field was sustained by MB cluster whereas the most severe stored crop damage was experienced by MBBZ cluster. A moderately strong correlation between the crop damage and the distance to BNP, the distance to KWS, and the number of elephants in a raiding herd was identified. The crop raid from larger herds was suffered by settlements closer to KWS. However, more study on the question raised regarding the elephant behaviour on transboundary movement through Khata corridor is required.
Evaluating a shared care pathway intervention to reduce chemotherapy outpatients’ unplanned presentations to hospital: a randomised controlled trial
Authors: Fethney Judith , Kim Bora , Boustany Chantale , McKenzie Heather , Hayes Lillian , Cox Keith , Simpson Judy M , Horvath Lisa G , Vardy Janette L , McLeod Jodi , Willcock Simon , Cook Natalie , Acret Louise , White Kate
Published: August 2023

Purpose
The aim of this randomised controlled trial (RCT) was to explore whether a community nursing intervention for outpatients receiving systemic therapy reduced unplanned hospital presentations and improved physical and psychosocial health outcomes over the first three cycles of treatment compared to a control group receiving standard care.
Methods
The number of and reasons for unplanned presentations were obtained for 170 intervention and 176 control group patients. Patients self-completed the Hospital Anxiety and Depression Scale, the Cancer Behavior Inventory and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire core 30 (EORTC QLQ-C30) at the start of the first four cycles. Poisson regression was used to compare the number of presentations between the intervention and control groups. Linear regression techniques were used to compare quality of life outcomes.
Results
The relative difference in unplanned presentations between the control and intervention groups was 12% (95% CI: −25%, 37%; P = 0.48) in favour of the intervention. At the start of cycle 4, there was no difference in anxiety (difference = 0.47 (95%CI: −0.28 to 1.22; P = 0.22)), depression (difference = 0.57 (95%CI:−0.18 to 1.31; P = 0.13)) or EORTC QLQ-C30 summary score (difference = 0.16 (95%CI:−2.67, 3.00; P = 0.90)). Scores for self-efficacy as measured by the Cancer Behavior Inventory were higher in the intervention group (difference = 4.3 (95%CI:0.7 to 7.9; P = 0.02).
Conclusion
This RCT did not demonstrate a benefit in reducing unplanned presentations to hospital. The trial identified improved cancer-based self-efficacy in patients receiving the intervention.
What is the actual relationship between neutrophil extracellular traps and COVID-19 severity? A longitudinal study
Authors: de Diego Cristina , Lasierra Ana Belen , Lopez-Vergara Lucia , Torralba Laura , de Gopegui Pablo Ruiz , Lahoz Raquel , Abadía Claudia , Godino Javier , Cebollada Alberto , Jimeno Beatriz , Bello Carlota , Tejada Antonio , Bello Salvador
Published: August 2023

Background Neutrophil extracellular traps (NETs), have repeatedly been related to COVID-19 severity and mortality. However, there is no consensus on their quantification, and there are scarce data on their evolution during the disease. We studied circulating NET markers in patients with COVID-19 throughout their hospitalization.Methods We prospectively included 93 patients (201 blood samples), evaluating the disease severity in 3 evolutionary phases (viral, early, and late inflammation). Of these, 72 had 180 samples in various phases. We also evaluated 55 controls with similar age, sex and comorbidities. We measured 4 NET markers: cfDNA, CitH3, and MPO-DNA and NE-DNA complexes; as well as neutrophil-related cytokines IL-8 and G-CSF.Results The COVID-19 group had higher CitH3 (p = 0.022), and cfDNA, MPO-DNA, and NE-DNA (p < 0.001) than the controls throughout hospitalisation. cfDNA was the only NET marker clearly related to severity, and it remained higher in non-survivors during the 3 phases. Only cfDNA was an independent risk factor for mortality and need for intensive care. Neutrophil count, IL-8, and G-CSF were significantly related to severity. MPO-DNA and NE-DNA showed significant correlations in all 3 phases and across all severity grades, and they only remained significantly higher on days 10–16 of evolution in those who died. Correlations among the other NET markers were lower than expected.Conclusions Although NETs were present in patients with COVID-19 throughout hospitalization, their markers, except cfDNA, showed little or no association with severity and mortality. Neutrophil activity and neutrophil count were also associated with severity. MPO-DNA and NE-DNA better reflected NET formation. cfDNA appeared to be more associated with overall tissue damage; previous widespread use of this marker could have overestimated the relationship between NETs and severity. Currently, there are limitations to accurate NET markers measurement that make it difficult to assess its true role in COVID-19 pathogenesis.
First Report of Ageratum yellow vein virus infecting Papaya in Lampung, Indonesia
Authors: Helina Selvi , Akin Hasriadi Mat , Pramono Sudi , Lestari Puji , Nurdin Muhammad , Afandi Auliana , Dewi Lionita
Published: August 2023

Begomovirus attacks on papaya plants have been identified in several regions in Indonesia. Detection based on viral nucleic acid in each category of papaya plants in Tanggamus, Pringsewu, West Lampung, Pesawaran, Central Lampung, and Bandar Lampung using universal primers SPG1/SPG2 showed positive results for Begomovirus infection with visible DNA bands measuring ± 900 bp. Direct nucleotide sequencing using SPG1/SPG2 primers for the AC2 dan AC1 genes of the Begomovirus and confirmed by the BLAST program showed that papaya samples were infected with Ageratum yellow vein virus (AYVV). The phylogenetic results show that AYVV from papaya samples has a close relationship with the AYVV group from several other countries with 98% homology. Overall, our study further indicated Ageratum acts as an alternative host and reservoir for Begomovirus.
Role of imidacloprid and chlorfenapyr nanocapsule in control of hunting billbug, Sphenophorus venatus and white grub, Phyllophaga crinita as principal pests of golf courses
Authors: Hussein Mona Ahmed , Sabry Al-Kazafy Hassan
Published: August 2023

Both hunting billbug, Sphenophorus venatus and white grub, Phyllophaga crinita are considered the most destructive pest for golf courses not only in Egypt but worldwide. This work is considered the first record of P. crinita in Egypt. So, many efforts were carried out to control these pests. In this work nanopesticides formulations were used against these pests; imidacloprid nanocapsule and chlorfenapyr nanocapsule under laboratory conditions. Three concentrations were used for each nanocapsule. Two stages for each pest were treated; the third and sixth instar larvae of S. venatus and the first and third instar larvae of P. crinita. The obtained results showed that the nanocapsule formulations of both imidacloprid and chlorfenapyr were very effective against S. venatus and P. crinita larvae. The LC50 of imidacloprid and chlorfenapyr nanocapsule against the sixth instar larvae of S. venatus were 10.3 and 11.8 ppm, respectively, and 8.8 and 9.1 ppm, respectively, against the third instar larvae. The corresponding results with the third and first instar larvae of P. crinita were 11.2 and 17.2 ppm, and 8.7 and 11.6 ppm, respectively. The results showed also imidacloprid nanocapsule was more effective than chlorfenapyr nanocapsule especially, with the first concentration; the percentages of mortality ranged between 91.7 to 96.7% with imidacloprid nanocapsule compared with 75 to 88.3% with chlorfenapyr nanocapsule. The obtained results found that the nanocapsule formulations were very promising in the control of S. venatus and P. crinita larvae. These formulations can be reduced soil contamination compared with the traditional formulations.
Landscape connectivity for predicting the spread of ASF in the European wild boar population
Authors: Goicolea Teresa , Cisneros-Araújo Pablo , Vega Cecilia Aguilar , Sánchez-Vizcaíno Jose Manuel , Bosch Jaime , Mateo-Sánchez MCruz
Published: August 2023

African swine fever (ASF) is an infectious and highly fatal disease affecting wild and domestic swine, which is unstoppably spreading worldwide. In Europe, wild boars are the main driver of local spread, transmission, and maintenance of the disease in endemic areas, and introduction into ASF-free countries. Landscape connectivity studies are the main discipline to analyze wild-species dispersal networks, and it can be an essential tool to predict dispersal-wild boars’ movement routes and probabilities and therefore the associated potential ASF-spread. We aimed to integrate wild boar connectivity predictions with their occurrence, population abundance, and ASF notifications to calculate the impact (capacity of a landscape feature to favor ASF-spread) and the risk (likelihood of a habitat patch becoming infected) of wild boars’ infection across Europe. Furthermore, we tested the accuracy of the risk of infection by comparing the results with the temporal distribution of ASF cases. Our findings highlighted that the impact and risk factors were generally higher in Europe's central and Eastern regions in the current ASF-situation. Additionally, the impact factor was 31 times higher on habitat patches that were infected the next year, proving the utility of the proposed approach and the key role of wild boars’ movements in ASF-spread.
A Statistical Model for Identifying Collusive Bidding Based on Historical Quotations
Authors: Li Jieni , Guo Hongchao
Published: August 2023

Open bidding is widely used in Sinopec's material procurement due to its advantages of openness, transparency, and cost savings. However, there have been many complaints about collusive bidding in recent years. Collusive bidding will pose significant challenges to fair competition and increase project costs for material procurement. Therefore, there is an urgent need for an analysis model to identify and prevent the collusive bidding. In this paper, we propose a statistical model for identifying collusive bidding using the coefficient of variation cv, the ratio of high to low average quotation rP and the deviation rate of quotation δ as indicators, based on the statistics of the historical quotations for material A. Through a case study of the framework agreement bidding for material B, we identified the collusion behavior of bidders effectively, thereby verifying the applicability of this statistical model.
Association of Time in Range with Cognitive Impairment in Type 2 Diabetic Patients
Authors: Liu Yanting , Liu Yanlan , Qiu Huina , Haghbin Nahal , Li Jingbo , Li Yaoshuang , Jiang Weiran , Xia Longfei , Wu Fan , Lin Chenying , Lin Jingna , Li Chunjun
Published: August 2023

OBJECTIVE
This study investigated the association of Time In Range (TIR) obtained from Blood Glucose Monitoring (BGM) with Cognitive Impairment (CI) inpatients with Type 2 Diabetes Mellitus (T2DM) and further explored whether a TIR goal for T2DM in adults with > 70% possess a protective effect on cognitive function.
RESEARCH DESIGN AND METHODS
A total of 274 inpatients with T2DM aged 40–64 years, who underwent seven-point BGM (120 mins pre and post meals and at bedtime) were recruited in this cross-sectional study. TIR was defined as the percentage of blood glucose within the target range of 3.9-10.0mmol/L. Subjects were divided into Normal Cognitive Function (NCF) (n = 160) and CI (n = 114) groups according to the results of the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE). The association of TIR and other glycemic metrics, calculated from seven-point BGM data, with cognitive dysfunction was analyzed.
RESULTS
The prevalence of CI was 41.6% in patients with T2DM (median age 58 years). TIR was lower in CI group than in NCF group (28.6% vs. 42.9%, P = 0.004). The prevalence of CI decreased with ascending tertiles of TIR (p for trend < 0.05). Binary logistic regression analysis showed a significant association between TIR and CI (odds ratio [OR] = 0.84, p < 0.001) after adjusting for confounders (age, education, marital status, age at Diabetes Mellitus (DM) onset, cerebrovascular disease). Further adjustment of Standard Deviation (SD)(OR = 0.84, p = 0.001) or Coefficient of Variation (CV)(OR = 0.83, p < 0.001), TIR was still associated with CI. While a TIR goal of > 70% probably possessed independent protective effect on cognitive function (OR = 0.25, p = 0.001) after controlling for confounders above.
CONCLUSIONS
TIR obtained from BGM was related to CI in T2DM patients and a TIR goal of > 70% probably possessed a protective effect on cognitive function for T2DM adults.
Spray dried VCrO/SiO2 micro-spheroidal catalyst for the ammoxidation of p-chlorotoluene
Authors: Du Chuanqing , Huang Yeyin , Tang Wanjun , Sun Lang , You Qingliang , Li Tingcheng , Xie Guangyong
Published: August 2023

Formulation and shaping of heterogeneous catalysts are vital in the successful industrial application. Here micro-sized vanadium chromium composite oxides catalysts with the spherical shape were prepared via spray drying with colloidal silica as a binder material. The structural properties of catalysts with different Cr/V molar ratios were characterized by XRD, XPS, FT-IR, TPR, and particle size distribution analysis. It was revealed that the addition of Cr inhibited the formation of the crystalline phase V2O5 and decreased the reduction temperature of pentavalent vanadium species, and also resulted in the formation of monoclinic CrVO4 and a highly dispersed state of vanadia species. VCrO/SiO2 particles with various Cr/V atomic ratio were studied as catalysts for p-chlorotoluene ammoxidation to p-chlorobenzonitrile, in which the catalyst with Cr/V ratio of 1 exhibited the best catalytic performance. When the Cr/V ratio was less than 1, mixed phases of orthorhombic CrVO4 and monoclinic Cr2V4O13 were formed and resulted in a low catalytic activity. With the increase of Cr/V ratio, the ratio of V4+/V5+ in the catalysts increased, thus the activation energy required for V reduction decreased, resulting in the catalytic activity of the catalysts improved. However, too large an amount of Cr led to the formation of highly oxidizing hexagonal-Cr2O3 phase, which reduced the selectivity of the catalytic reaction.
Stochastic Variational Inference for GARCH Models
Authors: Xuan Hanwen , Maestrini Luca , Chen Feng , Grazian Clara
Published: August 2023

Stochastic variational inference algorithms are derived for fitting various heteroskedastic time series models. We examine Gaussian, t, and skew-t response GARCH models and fit these using Gaussian variational approximating densities. We implement efficient stochastic gradient ascent procedures based on the use of control variates or the reparameterization trick and demonstrate that the proposed implementations provide a fast and accurate alternative to Markov chain Monte Carlo sampling. Additionally, we present sequential updating versions of our variational algorithms, which are suitable for efficient portfolio construction and dynamic asset allocation.
3D Quantum-Corrected Monte Carlo Device Simulator of n-FinFETs
Authors: Soares Caroline dos Santos , Furtado Gabriela , Rossetto Alan Carlos Junior , Wirth Gilson Inácio , Vasileska Dragica
Published: August 2023

The Effective Potential approach was successfully incorporated as a quantum correction to a Monte Carlo simulator of n-FinFETs to take into account the electron confinement in nanoscale n-FinFETs. The electron line density calculated by the Effective Potential approach agrees very well with the one calculated by a Schrödinger-Poisson solver. We compared the results obtained by the semiclassical and quantum-corrected Monte Carlo simulator. The quantum-corrected Monte Carlo device simulator can predict volume inversion, which reduces the impact of surface roughness scattering, improving the electron velocity. Additionally, the quantum corrections allow the modelling of the reduction of electron density in the n-FinFETs channel. This is a result of the reduced density of states in two-dimensional confined transistors and degrades the on-current in comparison with the one predicted by a semiclassical Monte Carlo simulator.
Direct in Situ Determination and Modulation of Asymmetric Surface Reconstruction on Rutile TiO2
Authors: Wang Yong , Yuan Wentao , Chen Bingwei , Han Zhongkang , You Ruiyang , Jiang Ying , Qi Rui , Li Guanxing , Wu Hanglong , Ganduglia-Pirovano Maria Veronica
Published: August 2023

Rutile TiO2 (110)-(1×2) reconstructions have triggered long time debate due to the conflicts among different experimental observations. Here, by combining state-of-the-art in situ spherical aberration corrected scanning transmission electron microscopy and density functional theory calculations, we unravel the asymmetric surface reconstruction of rutile TiO2 (110)-(1×2). We conclude that the repulsive electrostatic interaction among the small polarons, formed by the excess electrons after removal of neutral oxygen, and the consequent small polaron induced surface relaxations play critical roles in the asymmetric surface reconstruction. Importantly, the emergence and dissolution of these asymmetric structures can be modulated by tuning the oxygen partial pressure. This research offers an innovative approach to better understand, predict, and control surface reconstructions, holding significant implications for various applications and technological advancements.
DPHFM: A Deep Parallel Hybrid Fusion Model for Disaster Tweet Classification on Twitter Data
Authors: DASARI SIVA KRISHNA , Gorla Srinivas , D Prasad Reddy P V G
Published: August 2023

In recent years, disaster tweet classification has garnered significant attention in natural language processing (NLP) due to its potential to aid disaster response and emergency management. The goal of disaster tweet classification is to automate the identification of informative tweets containing information related to various types of disasters, such as floods, earthquakes, wildfires, and more. This classification task plays a crucial role in real-time monitoring, situational awareness, and timely response coordination during emergency situations. In this context, we propose a deep parallel hybrid fusion model (DPHFM) that combines features extracted from Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM) as base learners. The extracted features from the base learners are combined using a fusion mechanism, and the resulting features are then reconstructed and supplied to a meta-learner as input for making predictions. The DPHFM is trained on disaster datasets, such as crisisMMD, which consists of seven natural disaster events. The model was thoroughly evaluated using various metrics, demonstrating an average performance improvement of 90–96%. Furthermore, the proposed model's performance surpassed that of other state-of-the-art models, showcasing its potential for disaster tweet classification using deep learning techniques.
In silico secretome prediction and expression analysis of potential effector candidates of fall armyworm (Spodoptera frugiperda)
Authors: Shilpi Sundaram , Choudhary Chhavi , Meghwanshi Keshav Kumar , Verma Vivek , Shukla Jayendra Nath
Published: August 2023

Effector proteins, one of the major insect salivary gland components, alter host defence mechanism(s) and facilitate pests for successful infestation of host plant. Fall armyworm, Spodoptera frugiperdais a polyphagous lepidopteran insect infesting a wide range of agricultural crops. Despite being one of the world’s deadliest pests, no information about the effector proteins of S. frugiperda is available, till date. Therefore, in this study, we have performed an in silico analysis of interproscan-annotated protein sequences of S. frugiperda (derived from its transcriptome) using established secretome prediction pipelines. Out of 21,779protein sequences of S. frugiperda, 821 proteins were predicted to be secretory in nature, leading to the generation of an in silico secretome database of S. frugiperda. The proteins of S. frugiperda secretome were categorised into different functional groups as per their annotated functions. The expression of 40 selected candidates was analysed in different tissues (head, gut, salivary gland and fat body) of S. frugiperda, which revealed 14 candidates to be exclusive to a single tissue. In addition, expression of 13 candidates were found to be exclusive to gut or salivary glands or to both the tissues indicating that they may be secreted out from the insect's body and serve as potential effector proteins. The study described here is the first report of S. frugiperda's secretome prediction which will help in the identification of candidates which are secretory in nature, ultimately opening the door to the identification of possible effector proteins of S. frugiperda.
Topic extraction and categorization using a glove based technique for representing topics
Authors: Scholar Farha Naznin Research , Hazarika Irani , Mahanta Anjana K.
Published: August 2023

Topic extraction and categorization is an important task because by doing that it is easy to find out which are the topics most discussed by the users in their tweets or opinions and need to be analyzed. In this work, topics are extracted from positive and negative opinions and then categorized into different groups. For performing this, first a collection of opinions are divided into two sets- positive opinions and negative opinions by using a sentiment analyzer. Then a method is proposed to find out the most discussed topics in the set of positive opinions and negative opinions. For extracting the topics from a set of opinions the noun words are extracted from the set of the opinions. After extracting the topics, the similar topics have been combined by using synonymy relation. Then the frequent topic words are represented with the help of GloVe embedding technique. Finally, the topics are categorized by using a clustering algorithm by applying it on the frequent topic words. For the evaluation of the proposed method, tweets from a Twitter User dataset are used. The results obtained from the experiments by applying the proposed method on the dataset give promising result and provide interesting and meaningful clusters of topics. Moreover an analysis of the result obtained for both positive and negative opinions is also presented.
Revealing Delay Propagation Patterns and Interactions in the Chinese Air Traffic System Using Bayesian Network Analysis
Authors: Li Qiang , Guan Xinjia , Liu Jinpeng
Published: August 2023

Flight delays in the air traffic system exhibit strong correlations among different airports, making it crucial to comprehend the dynamics of delay propagation for optimal structural design and prevention of cascading delays. In this study, we adopt a Bayesian network (BN) approach, assuming that flight delays in individual airports are influenced by various potential airports. Through the application of complex network analysis, we enhance our understanding of delay propagation characteristics, while employing a BN-based analytical framework to uncover spatial and temporal patterns of delay propagation. Using Chinese air traffic data from 2015 to 2017 as a case study, our findings reveal that each airport's flight delays have an impact on approximately eight external airports, indicating a significant degree of interconnectedness within the air traffic system. Larger airports tend to be affected by delays originating from upstream airports, whereas smaller airports typically influence downstream airports. Additionally, economically developed regions experience broader delay propagation, impacting a larger number of airports compared to less developed regions. These insights provide valuable guidance for the development of delay propagation damping strategies and the implementation of efficient delay management techniques. By understanding the patterns of delay propagation and their spatial-temporal dynamics, stakeholders can take proactive measures to mitigate the impact of delays and optimize overall system performance.
Experimental Analysis of Multiwalled CNT-incorporated Self-compacting Mortar (SCM)
Authors: Raza Ali , Ndiaye Mamadou , Myler Prof. Peter
Published: August 2023

The placement of reinforcement and mortar in narrow sections of a framework is a significant challenge in the construction industry. Self-compacting mortar (SCM), despite its ability to flow under its own weight, exhibits reduced durability, flexure, and compressive strength due to its porous and brittle nature. As the demands for high-rise structures, coastal tunnels, pass bridges, and sustainability increase, the development of high-performance SCM is crucial. Carbon nanotubes (CNTs) are indeed the strongest known materials to date and could be utilized to enhance the functionality and strength of traditional SCM. Therefore, it is imperative to investigate the effect of multiwalled carbon nanotubes (MWCNTs) on the fundamental properties of SCM. In this study, the fresh and mechanical properties of conventional SCM were evaluated with the addition of MWCNTs. Specifically, 0.05%, 0.1%, 0.15%, and 0.5% MWCNTs by mass of cement were incorporated into SCM. The results indicate that the incorporation of MWCNTs has a retarding effect on the workability of fresh SCMs, yet the flow spread values still comply with the EFNARC standard for SCMs without segregation or bleeding. Additionally, the optimal content of MWCNTs was 0.1%, which led to a significant enhancement in flexural and compressive strength of SCMs at 28 days, 54.6% and 61.2%, respectively, compared to the control specimen of SCMs, a breakthrough that could have important implications in the construction industry.
Incidence of Inequality of Opportunity on Income Inequality in Côte d’Ivoire: An analysis through parents’ access to education
Authors: Kouakou Auguste K. , Kouadio Augustin Abogni , N'Da Christian K.
Published: August 2023

In this paper, we analyze the inequalities of opportunity in Côte d'Ivoire applying the method of Checchi and Peragine (2010) with the household standard of living survey of 2015. The education level of the head of household is considered as an exogenous characteristic of the individuals. For an in-depth analysis, the individuals are grouped according to the gender of the head of household and the stratum. Then, we calculate income inequalities within a group and between groups. Within a group, inequalities are attributed to differences in effort between individuals and inequalities between groups are attributed to differences in circumstances and are called inequalities of opportunity. Findings show that the incidence of inequality of opportunity in income inequality is 11.85% when considering the head of household, when looking at the sex of the head of household, it is 9.86% for women and 11.85% for men. Ensuring equal access to education for everyone in society would be an important public policy measure to reduce inequality of opportunity.
JEL Classification Codes: D63 ; D31 ; J62
Hall Coefficient and Resistivity in the Doped Bilayer Hubbard Model
Authors: Shi Yin , Schirmer Jonathan , Chen Long-Qing
Published: August 2023

Finding and understanding non-Fermi liquid transport behaviors are at the core of condensed matter physics. Most of the existing studies were devoted to the monolayer Hubbard model, which is the simplest model that captures essential features of high-temperature superconductivity. Here we discover a new type of non-Fermi liquid behavior emergent in the hole-doped bilayer Hubbard model, using dynamical mean-field theory with a full consideration of the short-range interlayer electron correlation. We find that at low temperatures, the Hall coefficient has a strong nonmonotonic dependence on temperature, leading to a double or quadruple reversal of its sign depending on the doping level. At the same time, the resistivity exhibits two plateaus rather than linearity in its temperature dependence. We show that these intriguing transport behaviors stem from the formation of coherent interlayer singlets, which scatter off gapped collective modes arising from short-range interlayer antiferromagnetic fluctuations.
Continuous lifelong learning for modeling of gene regulation from single cell multiome data by leveraging atlas-scale external data
Authors: Duren Zhana
Published: August 2023

Accurate context-specific Gene Regulatory Networks (GRNs) inference from genomics data is a crucial task in computational biology. However, existing methods face limitations, such as reliance on gene expression data alone, lower resolution from bulk data, and data scarcity for specific cellular systems. Despite recent technological advancements, including single-cell sequencing and the integration of ATAC-seq and RNA-seq data, learning such complex mechanisms from limited independent data points still presents a daunting challenge, impeding GRN inference accuracy. To overcome this challenge, we present LINGER (LIfelong neural Network for GEne Regulation), a novel deep learning-based method to infer GRNs from single-cell multiome data with paired gene expression and chromatin accessibility data from the same cell. LINGER incorporates both 1) atlas-scale external bulk data across diverse cellular contexts and 2) the knowledge of transcription factor (TF) motif matching to cis-regulatory elements as a manifold regularization to address the challenge of limited data and extensive parameter space in GRN inference. Our results demonstrate that LINGER achieves 4-10 fold relative increase in accuracy over existing methods. LINGER reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Additionally, following the GRN inference from a reference sc-multiome data, LINGER allows for the estimation of TF activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies. Overall, LINGER provides a comprehensive tool for robust gene regulation inference from single-cell multiome data, empowering deeper insights into cellular mechanisms.
SEM-Assisted Mechanistic Study: pH-Driven Compressive Strength and Setting Time Behavior in Geopolymer Concrete
Authors: Haq Md Zia ul , Sood Hemant , Kumar Rajesh
Published: August 2023

This research study investigates the influence of pH on the setting time, compressive strength, and surface roughness of different waste materials, namely Fly Ash, Rice Husk Ash, Red Mud, and Ground Granulated Blast Furnace Slag (GGBS). The average pH values for these waste materials were found to be 8.77, 9.3, 8.3, and 11, respectively. The Initial setting time (IST) and Final setting time (FST) varied among the materials, with red mud having maximum IST and FST with a value of 180min and 24hrs. Compressive strength (CS-(Mpa)) measurements revealed that GGBS exhibited the highest value of 36 Mpa, followed by Rice Husk Ash (28 Mpa), Red Mud (22 Mpa), and Fly Ash (24 Mpa). Surface roughness analysis showed that red mud had the highest roughness value of 69.70788986, followed by Fly ash (62.81751473), Rice Husk Ash (53.14515068), and GGBS (49.08805681). The findings indicate a positive correlation between pH levels and compressive strength of 97% , setting time surface roughness has negative correlation with compressive strength when analyzed with heatmap . Higher pH values were associated with increased compressive strength, particularly after 28 days of curing which is shown by the heatmap and sub-plots. Additionally, microscopic analysis provided insights into particle size, orientation, and clustering, aiding in understanding the bonding and reactivity patterns contributing to the observed variations in compressive strength.
Infections in patients with systemic lupus erythematosus: microbiological, clinical characteristics and risk factors
Authors: Li Guanhua , Liu Xiaojing , Zhao Jiakang , Li Mengdi , Wu Ge
Published: August 2023

Background Infections are the leading cause of morbidity and mortality in patients with SLE. We aimed to investigate infections and analyze the risk factors; clinical and microbiological characteristics in a single-center systemic lupus erythematosus (SLE) cohort.
Method All hospital patients in The First Affiliated Hospital of Zhengzhou University from 2019-2021 who met ≥4 ACR-97 SLE criteria were identified. Patients with and without infection were included with a ratio of 1:2. Then they were compared in terms of sex, age, comorbidities, severity, and laboratory data. A logistic regression was employed to evaluate risk factors.
Results A total of 1687 SLE patients were identified, and 224 patients with infection and 448 patients without infection were included. In the infection group, the average diagnosed and included age was 33.13±13.02 and 33.14±12.94 years old; 87.06％ of the patients were females; SLEDAI score: 12.33±7.44. A total of 6(2.68％) patients suffered≥2 infection. The infections were divided into bacterial infections(65.63％),viral infections(9.82％), fungal infection(27.23％) groups, and respiratory tract infections were most common, with an incidence rate 62.59％. The most common microorganisms in each group were Escherichia coli, Herpes zoster and Aspergillus, respectively. In the binary logistic regression analysis, diabetes(OR 6.135; 95% CI: 2.519-14.942), CD4+ T cell (OR 1.003; 95% CI: 1.002-1.005), prednisone＞10mg/day (OR 1.971; 95% CI: 1.146-3.391), renal disorder(OR 3.148; 95% CI: 1.828-5.422)were risk factors associate with infection.
Conclusions In this retrospective study, bacteria was most common in SLE patients with infection, and the respiratory tract was the most common infection site. Diabetes, prednisone＞10mg/day and renal disorder were associated with infection. Monitoring CD4+ T cells can predict the infection incidence. A lower dosage of prednisone reduced the occurrence of infection.
Short-Depth QAOA circuits and Quantum Annealing on Higher-Order Ising Models
Authors: Pelofske Elijah , Bärtschi Andreas , Eidenbenz Stephan
Published: August 2023

The Quantum Alternating Operator Ansatz (QAOA) and Quantum Annealing (QA) are quantum algorithms that are both based on the adiabatic theorem and both have the goal of sampling the optimal solution(s) of combinatorial optimization problems. Quantum annealing has been physically instantiated on D-Wave devices using superconducting flux qubits, and QAOA can be programmed on digital gate-model quantum computers such as the programmable superconducting transmon qubit devices manufactured by IBM Quantum, for instance ibm_washington. QAOA and QA address the same types of problems, but it is unclear how they will scale to large problem sizes and to larger and higher-fidelity quantum computers. In this article, we present a direct comparison between QAOA, one and two rounds, run on all 127 qubits of ibm_washington and QA run on D-Wave Advantage_system4.1 and Advantage_system6.1. The problems which allow for this comparison are random Ising model problems whose connectivity matches the heavy hexagonal lattice topology of ibm_washington and the Pegasus graph connectivity of the two D-Wave devices. We create two classes of problem instances for this comparison: one with higher order terms (ZZZ variable interactions), linear terms, and quadratic terms, and a separate problem type with only linear and quadratic terms. The QAOA circuits are novel and extremely short depth, with a CNOT depth of 6 per round, which allows whole chip usage of ibm_washington's heavy hexagonal lattice and can be applied to future heavy-hex chips. We also test the effectiveness of the error suppression technique digital dynamical decoupling on the QAOA circuits, allowing one of the largest experimental evaluations of dynamical decoupling to date. The QAOA circuits compiled to ibm_washington are composed of approximately 3,000 gate instructions making these some the largest quantum circuits ever executed on a digital quantum processor. QAOA and QA are compared against the classical heuristic algorithm of simulated annealing and all problem instances are exactly solved using CPLEX in order to evaluate ground state success sampling probability. We find that (i) QA outperforms QAOA on all problem instances, (ii) QAOA samples the problems better than random sampling, and (iii) QAOA parameters exhibit clear parameter concentration across the ensemble of Ising models.
Sustainable Land Use Planning in Developing Countries Using GIS and Multi-criteria Analysis: A Case Study of Lalitpur District, Nepal
Authors: Thapa Narayan , Pant Pragya , Parsai Ritika , Mahata Aayush , Dulal Sujan
Published: August 2023

Nepal is facing rapid urbanization leading to unplanned land development/use. This research paper focuses on conducting a comprehensive land use suitability analysis for urban development in Lalitpur District, Nepal. By employing Geographic Information System (GIS) and Multi-criteria Analysis (MCA) techniques, this study aims to provide valuable insights into identifying suitable area for urban development and facilitating valuable insights into identifying suitable area for urban expansion and opening doors for informed decision making by planner and policy makers. By studying factors such as existing infrastructure which include land cover, population density, flood sensitivity, slope, digital elevation model (DEM), road network, distance to brick factory and land fill site, the study identified suitable areas for urban development in Lalitpur District. We divided the study area into four suitability classes: highly suitable, moderately suitable, marginally suitable, and not suitable. From our case study we found out that the highly suitable regions are typically flat, have low population densities, areas close to infrastructure, far away from landfill site, flood region and brick factory. Moderate suitable areas are typically hilly, have higher population densities, and areas farther from infrastructure. Marginal suitable areas are in floodplains, have steep slopes, and are near to industrial areas. Not suitable areas are too steep, highly prone to flood, too close to industrial areas, and have high population density. The findings of the analysis emphasize the importance of considering environmental factors in land use planning and urban development. It highlights the need to preserve green spaces, protect water bodies, and ensure the long-term well-being of residents. The results provide valuable insights for decision-makers and urban planners, enabling them to make informed decisions and allocate resources effectively.
Self-efficacy and Optimism in frail elderly men without functional disability attending geriatric outpatient clinic in Mansoura university: a case-control study.
Authors: Ageez Doaa
Published: August 2023

Background Psychological frailty including cognitive, mood, and motivational components has a major impact on the well- being of frail elderly. Therefore, there is an increasing scientific interest in studying different domains of psychological frailty. Unfortunately, the psychological components of frailty are not completely evaluated and currently there is no consensus regarding appropriate intervention. This study aimed to evaluate self-efficacy and optimism in frail elderly without functional disability. This case-control study was conducted between August 2022 and March 2023, and included 70 elderly male community dwelling participants aged ≥ 60 years. The participants were divided into 2 groups: frail and non-frail defined by AVILA modification of Fried criteria. Self-efficacy scale (SES) and Revised-Life orientation test (R-LOT) were applied for all participants.Results Those with physical frailty had lower self-efficacy and optimism scores. R-LOT and SES had moderate diagnostic accuracy in predicting frailty; AUC for both tools were 0.75 and 0.71, respectively.Conclusions Psychological determinants of frailty are as equally important to be addressed as factors affecting physical frailty. self-efficacy and optimism are both affected in frail elderly. The Self-efficacy scale (SES) and the Revised-Life orientation test (R-LOT) should be applied as a measure of psychological resilience during comprehensive assessment of physical frailty to improve overall well-being and resilience among frail elderly.
Directly converting waste PET into closed-loop biodegradable plastics
Authors: Cai Qiuquan , Qin Lidong , Li Xiaoxu , Ren Geng , Yuan Rongyan , Wang Xinyu , Hu Zexu , Ye Chenwu , Zou Yangyang , Ding Peiqing , Zhang Hongjie
Published: August 2023

Polyethylene terephthalate (PET), a prevalent commodity polymer for manufacturing bottles, films, fibers, and engineering plastics, has an annual production of over 74 million tons worldwide1-3. Unfortunately, averaging 70% of PET products are single-use, leading to substantial non-degradable waste that contributes to environmental pollution and the proliferation of microplastics detectable in the human body1,4-8. Efforts to chemically depolymerize PET into its starting monomers using amines, water, and alcohols9, however, have encountered challenges in efficiently extracting the monomers from multimers, hindering its full utilization10-14. Here we demonstrate a closed-loop strategy to directly convert waste PET into biodegradable plastics named PEXT, involving forward and reverse transesterifications between ester groups and hydroxyl, carboxyl, and ester groups. By utilizing commercially available aliphatic X-monomers, e.g., diacids and diols, or their polymers, and leveraging existing industrial equipment, we avoid the inefficient PET depolymerization back to monomers and achieve its complete utilization within a one-off framework. Through precisely controlling the molecular structure, PEXT exhibits superior mechanical performance compared to conventional elastic materials including low-density polyethylene, as well as programmable thermal and biodegradable properties, enabling its extensive applications, e.g., shape memory materials, flexible films, and rigid plastic parts. Additionally, incorporating of PET or X-monomers into PEXT facilitates its closed-loop recycling between different components. This feature, coupled with its biodegradability, effectively balances economic viability with environmental concerns.
The impact of investment and social factors on pension savings in Kazakhstan
Authors: Bekbossinova Assel , Kireyeva Anel , Kenzhegulova Gaukhar , Bekturganova Makpal , Imangali Zhansaya
Published: August 2023

In the current social conditions, pension systems have become the most important topic on the agenda for many countries. Therefore, governments have started paying attention and should reform their pension systems to guarantee an adequate contribution to pensions. Thus, this study analyzes the impact of investments and social factors on pension savings using Kazakhstan as an example. The paper is based on secondary data from the annual reports of the Unified Accumulative Pension Fund and annual statistical reports of the Bureau of National Statistics of the Republic of Kazakhstan from 2014 to 2022. SPSS software was used to analyze the collected data, specifically through correlation and regression analysis, to determine the impact and relationships between selected indicators (i.e., inflation rate, number of contributors, pension contribution, investment income and average wage). To check the reliability of the models, Fisher’s F-test and Student’s t-test were conducted. Therefore, a VIF diagnosis was conducted. The correlation analysis results showed that in the group of investment factors, pension savings are more dependent on pension contributions (,900**), and in social factors, on average wages (1,000**). Based on the results obtained, all factors have a positive impact on pension savings, except inflation. Inflation growth by 1% on average reduces the amount of pension savings by 23% over the nine-year period between 2014 to 2022, which is reflected in the results of Model 2. The study’s results can be applied to managing pension funds and reforms related to the pension system.
AcknowledgmentsThis research was funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant “Exploring the impact of economic, social, and environmental factors on the relationship between urbanization and greenhouse gas emissions” No. AP19576071).
Ecological restoration process of watershed land space with intelligent IoT technology
Authors: Wang Li , Wang Kai , Xu Jianhui , Liu Jingjie
Published: August 2023

Watershed ecological protection and restoration is the key link to restore the ecological service function of damaged watersheds and promote the harmonious coexistence between humans and nature. In practice, it is difficult to balance the interests of different parties in the process of watershed ecological protection and restoration in terms of water resource utilization, water environment protection, and biodiversity conservation, which makes it difficult to achieve the watershed ecological protection goals. Therefore, this study takes the Jianghuai watershed area as the research object and explores the core points of the ecological restoration of land space in the relevant experimental area in the form of algorithmic optimization frequency. The experimental data of the algorithm model show that the land types in the Jianghuai watershed are mainly woodlands and watersheds, with relatively uneven spatial distribution; a total of 85 ecological corridors were identified that showed a pattern of more in the middle and less around; and a total of 267 radiation channels were identified that showed irregular tree distribution. The empirical results show that the model has a fitting accuracy of 89%.