bluuebunny/crossref_metadata_2025 huggingface dataset:
Single-Atom Platinum Sites with Asymmetric Coordination Environment on Fully Conjugated Covalent Organic Framework for Efficient Electrocatalysis
Authors: Shi Zhan , Zhang Ziqi , Zhang Zhe , Chen Cailing , Wang Rui , Xie Minggang , Wan Sheng , Zhang Ruige , Cong Linchuan , Lu Haiyan , Han Yu , Xing Wei , Feng Shouhua
Published: December 2023

Two-dimensional (2D) covalent organic frameworks (COFs) and their derivatives have been applied as electrocatalysts towards a wide range of reactions owing to their unique nanoscale pore configurations, stable periodic structures, abundant coordination sites and high surface area. This work aims to construct the first non-thermodynamically stable Pt-N2 coordination active site through electrochemically modifying platinum (Pt) single atoms into fully conjugated 2D COF as conductive agent-free and pyrolysis-free electrocatalyst for hydrogen evolution reaction (HER). In addition to maximizing atomic utilization, single-atom catalysts with definite structures can be used to investigate catalytic mechanisms and structure-activity relationships. A series of physical characterizations, theoretical calculations and in-situ experiments demonstrate that NGA-COF not only provide favorable metal-support effect for Pt, but also enhance electron transport ability and structural stability. The former can adjust the binding energy between active sites to H* intermediate by forming unique Pt-N2 instead of the mainstream Pt-N4 coordination environment. The latter is ascribed to its unique stability in acidic environments.
Event-Triggered Optimal Tracking Control for Strict-Feedback Nonlinear Systems With Non-Affine Nonlinear Faults
Authors: Wang Ling , Wang Xin , Wang Ziming
Published: December 2023

This article studies the control ideas of the optimal backstepping technique, proposing an event-triggered optimal tracking control scheme for a class of strict-feedback nonlinear systems with non-affine and nonlinear faults. A simplified identifier-critic-actor structure of the reinforcement learning algorithm is utilized to achieve optimal control. The identifier estimates the unknown dynamic functions, the critic evaluates the system performance, and the actor implements the control actions, which allow for modeling and control of anonymous systems to achieve optimal control performance. In this paper, a simplified reinforcement learning algorithm is designed by deriving update rules from the negative gradient of a simple positive function related to the Hamilton-Jacobi-Bellman equation, and it also releases the stringent persistent excitation condition. Then, a fault-tolerant control method is developed by applying filtered signals for controller design. Moreover, considering communication resource reduction, an event-triggered mechanism is adopted to design the actual controller. Finally, the feasibility of the proposed scheme is validated through theoretical analysis and simulation.
Performance Analysis of Distance Measures for Mixed-Variables Data
Authors: Sumbherwal Nisha , Hooda B.K. , Vinit Parmeet Kumar
Published: December 2023

Mixed variables data occurs frequently in most of the fields including medical, industrial, agriculture, marketing and remote sensing. In traditional clustering algorithms, Euclidean distance function is used to compute similarity between two data elements. It gives better and reliable results if numeric attributes are present in the data set. Although, when attributes are non-numeric i.e., categorical or mixed, this measure is not suitable for capturing the similarity of data elements. This study, includes five distance measures viz. Gower, Podani, Huang, Ahmad and Harikumar which is used for mixed variables data. Further, they have been fused into the agglomerative hierarchical clustering algorithms. The performance of these distance measures has been compared by cluster validation methods: Cophenetic correlation coefficient, Connectivity, Average Silhouette Width and Dunn index. The empirical analysis has been carried on pearl millet data with quantitative and qualitative variables using R software package. The experimental results showed that using three cluster validation measures; Cophenetic correlation coefficient, Connectivity, and Average Silhouette width, the Ahmad distance performed better than the Gower, Huang, Ahmad, and Harikumar distances.
Investigating the psychometric properties of the Persian version of the quality of life of women infected with HPV questionnaire ( EQUALI-HPV)
Authors: Arashlouei Azam Zare , Maghsoudi Saiedeh Haji , Irani Morvarid , Honarvar Zahra , Ahmadi Atefeh , Pereira-Caldeira Natália Maria Vieira
Published: December 2023

Introduction People with human papiloma virus (HPV) struggle with numerous challenges and needs in physical, psychological aspects of quality of life in relation to this virus. Various studies have recommended counseling interventions to improve these people’s quality of life. Also, there is a need for standardized and normalized instruments in Iran to evaluate the effect of educational and therapeutic interventions on these patients’ quality of life. Therefore, this study was conducted in gynecologic cancer clinics in Kerman to investigate the psychometric properties of the Persian version of the “quality of life of women infected with HPV questionnaire”. Methods This study was a cross-sectional study including 580 women infected with HPV referred to Kerman’s gynecologic cancer clinics. The samples were selected using convenience sampling in 2023. The content validity, face validity, and construct validity using explanatory factor analysis (EFA) and confirmatory factor analysis (CFA) were evaluated. Results The results of the content validity assessment, which included the Content Validity Index (CVI) and Content Validity Ratio (CVR), indicated strong content validity. Additionally, face validity, confirmed qualitatively, further supports these findings. The results of the EFA revealed that the Persian version of the quality of life questionnaire, with 34 items remaining from the 54 items, has a 6-factor structure. These 6 factors showed a 56.7% variance and were confirmed using confirmatory factor analysis using robust root mean square error of approximation (RMSEA = 0.054), comparative fit index (CFI = 0.928), and Tucker-Lewis index (TLI = 0.920). The content validity results showed that all the items had a good validity ratio and index. The Cronbach’s alpha and Raykov’s rho were above 0.7 for all the dimensions. Conclusion The findings depicted that the Persian version of the “quality of life of women infected with HPV questionnaire ” had acceptable validity and reliability and can be utilized to measure these people’s quality of life and conduct psychological studies.
Chromosome regions linked to peanut black pod revealed by QTL-seq
Authors: Altamirano Marco David Goyzueta , Tillman Barry , Wang Jianping
Published: December 2023

The peanut black pod (BP) trait is controlled by a single dominant gene as revealed by a previous genetic study. However, the gene controlling BP is still unknown. In this study, a bulk segregant analysis aided by QTL-seq was utilized to identify genomic regions controlling the BP trait. A population of peanut segregating for the BP trait was developed by crossing a BP line and FloRun™ ‘331’ without BP trait. DNA samples of homozygous F2 lines of normal pod coloration and homozygous F2 lines of BP (homozygosity was validated by F3 plots) were equally pooled for deep sequencing together with the parental lines. A total of 220,714 single nucleotide polymorphisms (SNP) were found between the plants of BP and normally colored pods. Following the QTLseq pipeline, the ΔSNP-index and G′ statistics were calculated by comparing the alignments of the bulks to each parental reference genome, identifying a region of 5.36Mb on Chromosome B10 harboring the genes controlling the BP trait. SNPs within this region were further annotated, and three genes were identified as candidates for the BP trait. KASP markers were successfully developed for these SNPs and two of them were validated. The study of BP trait gives us more understanding of its genetic background, potential use in breeding programs, and potential benefits for growers in the southeast US.
Cost-effectiveness of Lung Cancer Screening Combining with CVD and COPD Screening: A Microsimulation Study
Authors: Yuan Jingmin , Xu Fenglin , Sun Yan , Fan Meng , Ren Xiaoxiao , Chen Mingwei , Ren Hui
Published: December 2023

Objective
This study aims to estimate the cost-effectiveness of lung cancer (LC), cardiovascular disease (CVD), and chronic obstructive pulmonary disease (COPD), the so-called “big-3 diseases”, combining screening and identify the optimal target screening population in China.
Methods
A stage shift microsimulation model constructed and different screening strategies were set. Cost, quality-adjusted life years (QALYs), incremental cost-effectiveness ratios (ICER) and net health benefits (NHB) under different screening strategies were calculated. Strategies with a mean ICER less than $38,223 (3 times as much as China’s GDP per capita in 2022) were deemed to be cost-effective, and the optimal one in this case is the strategy with the largest NHB obtained at the same willingness to pay (WTP). One-way and probabilistic sensitivity analysis was conducted to estimate results’ stability.
Results
ICERs in all strategies ranged from $2,186.5 to 11,227.6 per QALY, which was less than China’s GDP per capita in 2022. This value was basically lower in combined screening for “big-3 diseases” than in screening for LC alone. The largest NHB and probability of cost-effectiveness were both obtained in the strategy with “big-3 diseases” screening for people over 45 years old with a smoking history of 20 pack-year at least.
Conclusion
The optimal target screening population should be current smokers or smoking quitters in the past 15 years, aged over 45 years old, with a smoking history of 20 pack-year at least. These findings may provide data support for the revision of lung cancer screening guidelines.
Application of different doses of fospropofol in the induction of general anesthesia: a pilot randomized controlled study
Authors: Guo Jiaxin , Liu Jiaqi , Tang Weijie , Qian Mei , Tang Lili , Shen Qiying , Liu Xuesheng
Published: December 2023

Background
Fospropofol is a propofol prodrug with good water solubility, which is less likely to cause injection pain and intraoperative hypotension than propofol. The purpose of this study was to investigate the application of different doses of fospropofol in the induction of general anesthesia.
Methods
This single-center randomized clinical trial enrolled 125 patients, aged 18 to 65 years, scheduled for elective surgery under general anesthesia. Patients were randomly assigned to receive propofol 2 mg/kg (P2 group) or fospropofol 10 mg/kg (F10 group), 12.5 mg/kg (F12.5 group), 15 mg/kg (F15 group), or 20 mg/kg (F20 group). The efficacy and safety end points were the induction success rates of single administration of different doses of fospropofol, the incidences of adverse event, and changes in hemodynamic index and BIS during the 5 min following the first administration.
Results
A total of 125 patients completed this study. The induction success rates of single administration were 100%, 92%, 96%, 100% and 100% (P = 0.508) in the P2 group, F10 group, F12.5 group, F15 group and F20 group, respectively. Our results indicated success rates of F12.5, F15 and F20 groups were not inferior to propofol in general anesthesia induction for ASA I-III adult patients undergoing elective surgeries when the non-inferiority value was − 5% using a one-sided test. Hypotension during induction was the most common side effect. The incidence of hypotension was significantly lower in the F10 group than in the P2 group (28% vs. 76%; P = 0.006). Compared with the P2 group, the incidence of injection pain was significantly lower in all the fospropofol groups (P༜0.001). In fospropofol groups, the rate of bispectral index (BIS) reaching trough and rising is slower, and the incidence of deep anesthesia is lower (P༜0.001).
Conclusion
Fospropofol has comparable efficacy to propofol when used for the induction of general anesthesia in healthy adults. Considering the success rate and the incidence of adverse reactions, 12.5 ~ 15 mg/kg of fospropofol may be a potential option for general anesthesia induction.
Trial registration
The trial was retrospectively registered with Chinese Clinical Trial Registry on Dec 13, 2022 (ChiCTR2200066678).
Added value of ECOG-Performance Status to qSOFA for predicting 30-day mortality in older patients admitted with suspected infections
Authors: Kudo Masataka , Sasaki Sho , Takada Toshihiko , Fujii Kotaro , Yagi Yu , Yano Tetsuhiro , Sada Ken-ei , Fukuhara Shunichi , Suganuma Narufumi
Published: December 2023

Quick Sequential Organ Failure Assessment (qSOFA) is a simple and easy tool for identifying patients with suspected infection, who are at a high risk of poor outcome. However, its predictive performance is still insufficient. The Eastern Cooperative Oncology Group performance status (ECOG-PS) score, a tool to evaluate physical function, has been recently reported to be useful in predicting the prognosis of patients with pneumonia. We aimed to evaluate the added value of ECOG-PS to qSOFA in predicting 30-day mortality in older patients admitted with suspected infections. Of the 1536 enrolled patients, 135 (8.8%) died within 30 days. The area under the curve of the extended model was significantly higher than that of the qSOFA model (0.68 vs. 0.64, p = 0.008). When the risk groups were categorized as follows: low (< 5%), intermediate (5–10%), and high (≥ 10%), 5.0% of those who died and 2.1% of those survived were correctly reclassified by the extended model, with an overall categorized net reclassification improvement of 0.03 (95% confidence interval: -0.06 to 0.30). In conclusion, our results suggest that adding the ECOG-PS score could improve the performance of qSOFA, in predicting the 30-day mortality in older patients admitted with suspected infection.
Application of protection motivation theory to identify factors associated with breast self-examination behavior among female students at the University of Gondar: Structural equation modeling approach
Authors: Semagn Birhan Ewunu , Bisetegn Telake Azale , Wolde Maereg
Published: December 2023

Background Most Ethiopian women with breast cancer present late-stage disease, and the majority of breast cancer cases occur at an early age. Promoting breast self-examination is one of the high priorities of health education and behavioral change communication interventions in Ethiopia. This study aimed to assess the magnitude and factors associated with breast self-examination behavior among undergraduate female students at the University of Gondar using protection motivation theory. Methods An institution-based cross-sectional study was conducted from May 20-June 15/2022. After the study participants were selected through a stratified simple random sampling technique, a self-administered questionnaire was used to collect data from 849 students. The data were entered into Epi Data version 4.6 and exported to STATA 14 and SPSS AMOS 26 for further data management and analysis. A descriptive analysis was performed. The path coefficient and the relationship between the variables were tested by structural equation modeling (SEM). A P value less than 0.05 was considered to indicate statistical significance. Results Only 6.77% (95% CI 5.20%, 8.78%) of female university students reported engaging in regular breast self-examination. Among the protection motivation theory constructs, response cost (β= -0.34; 95% CI, -0.84,0) and protection motivation (β = 0.34; 95% CI, 0.13,0.58) were found to have statistically significant direct associations with BSE behavior, whereas self-efficacy (β = 0.15; 95% CI, 0.05, 0.28), response efficacy (β = 0.05; 95% CI, 0.01, 0.13), and fear of breast cancer (β = 0.02; 95% CI,0, 0.07) had statistically significant indirect associations. Among the other covariates ever discussed about breast self-examination (β = 0.83; 95% CI = 0.44, 1.26), family history of breast cancer (β = 0.61; 95% CI = 0.07, 1.37), ever noticing a lump in a breast (β = 0.81; 95% CI = 0.38, 1.35), father educational status (β=-0.19; 95% CI=-0.38, -0.04), and knowledge about breast self-examination (β = 0.12; 95% CI = 0.05, 0.19) were found to have a statistically significant direct association with breast self-examination behavior. Conclusion Regular breast self-examination is very infrequent among undergraduate female university students. The coping appraisal component of protection motivation theory was significantly associated with breast self-examination behavior.
VQAutism: Leveraging Visual Question Answering to Analyse Autism Spectrum Disorder
Authors: Bannur Charvi , Bhat Chaitra , Goutham Gagan , Kakarla Sada , HR Mamatha , Kumar Shankar , M Abhishek
Published: December 2023

Background: Diagnosing Autism Spectrum Disorder is an open-ended critical problem and has significant implications for the field of medicine and healthcare, and it can play a crucial role in addressing pressing medical causes and improving healthcare outcomes. The current diagnostic interviews, screening tools, clinical assessments, imaging studies and questionnaires used for the detection of Autism Spectrum Disorder are time-consuming and expensive. There is no single method ideal for all children and experiments that involve an unfamiliar environment make it difficult for children to exhibit their typical behaviour resulting in inaccurate diagnosis.
Results: The proposed VQAutism model was developed to accurately predict answers to multiple questions specifically designed to assist the diagnosis of Autism Spectrum Disorder (ASD). The model was trained on Action Units (AUs)extracted from the video frames from two datasets: YouTube Dataset and Clinical Sources. The test results, questions and facial features were validated by medical practitioners. VQAutism outperforms baseline models like Artificial Neural Networks (ANN) and transformers by an accuracy margin of 20% and a final test (AUC-ROC) score of 0.9862.
Conclusion: The developed VQAutism model aims to simplify the diagnosis of Autism Spectrum Disorder. Extraction of Action Units and Facial Coordinatesreduces computational resources required to train models with high dimensional features. The results of this study demonstrate that VQAutism outperforms other baseline models. The YouTube dataset and pipeline code are freely available as open source with MIT licence on GitHub.
Reappearances of the SARINAH heritage mall building: an “urban forest” theme and restore the historical artwork
Authors: Ardhiati Yuke , Hasan Asikin , Rushintadevi Fena , Alfiano Rafli
Published: December 2023

The Modern Architecture Movement’s stance against ornamentation, however, the architectural characteristics of the International Style in Indonesia in 1960s are often combined with iconography in the building façade or structural wall. The existence of these artworks inside or outside of various buildings are considered to colour the triumph of Modern Architectural Style. This Design-Build project of SARINAH heritage mall building in Jakarta is organized in a multidisciplinary manner by accommodating an adaptive reuse method. Due to spatial changes on the ground floor, 15 figures of the relief-sculptures as a part of the structural wall, which had been ‘buried’ for 30 years, were rediscovered and they needed to be restored. This case study seeks to reveal the uniqueness of the ongoing transformation project, especially how to treat the heritage building to meet to the contemporaneous. By accommodated “urban forest” theme and restore the historical artwork, the reappearances of the SARINAH heritage mall can meet to the sustainable city goals, enriched by the 15 relief-sculptures reinforces the cultural aesthetics for the shopping centre’s urban destination. Meanwhile, to avoid the high risk to casting the historical artwork then the digitally record held by digital sculpting.
Pan-Cancer transcriptomics reveals diverse R-loop events based on multiple machine learning algorithms
Authors: Jiang Bincan , Zhang Yuhan , Chen Ziyang
Published: December 2023

Background:
R-loops are three-stranded RNA-DNA hybrids which play an important role in various cellular and chromosomal function including transcriptional regulation and genome instability. Due to the absence of approach characterizing R-Loops events at a larger scale, we developed a computational metrics as R-Loops score to decipher the landscape of R-Loops events at a pan-cancer level and within LUAD patients.
Methods:
We developed a computational metric, R-Loops Score (RS), to provide a quantified approach of evaluating the R-Loop events. Then, Unsupervised clustering of diverse R-Loops pattern with LUAD patients revealed intratumoral heterogeneity, on the basis of which did we construct a prognostic model and corresponding R-Loops Related Score (RRS) via the multi-machine learning framework for the prediction of clinical outcome.
Results:
Comparing with patients with low RS, the high RS group had significantly lower survival, higher detectable chromosomal instability (CIN), alongside various oncogenic pathway activities. LUAD patients with lower RS exhibited distinct immune infiltration pattern, better clinical outcomes, and different mutation landscapes.
Conclusion:
The RS could function as a quantified method to evaluate R-Loops events across individual cancer types. The RRS provided the LUAD patients with a R-Loops-based prognostic model indicating how CIN involves cancerous peculiarities and immune patterns.
Time series analysis of malaria cases to assess the impact of various interventions over the last three decades and forecasting India’s malaria towards 2030 elimination goals
Authors: Singh Mrigendra P. , Rajvanshi Harsh , Bharti Praveen K. , Anvikar Anup R. , Lal Altaf A.
Published: December 2023

Background
Despite the progress made in this decade towards malaria elimination, it remains a significant public health concern in India and many other countries in South Asia and Asia Pacific region. Understanding the historical trends of malaria incidence in relation to various commodity and policy interventions and identifying the factors associated with its occurrence can inform future intervention strategies for malaria elimination goals.
Methods
This study analysed historical malaria cases in India from 1990 to 2022 to assess the annual trends and the impact of key anti-malarial interventions on malaria incidence. Factors associated with malaria incidence were identified using univariate and multivariate linear regression analyses. Generalized linear, smoothing, autoregressive integrated moving averages (ARIMA) and Holt’s models were used to forecast malaria cases from 2023 to 2030.
Results
The reported annual malaria cases in India during 1990–2000 were 2.38 million, which dropped to 0.73 million cases annually during 2011–2022. The overall reduction from 1990 (2,018,783) to 2022 (176,522) was 91%. The key interventions of the Enhanced Malaria Control Project (EMCP), Intensified Malaria Control Project (IMCP), use of bivalent Rapid Diagnostic Tests (RDT-Pf/Pv), Artemisinin-based Combination Therapies (ACT), and involvement of the Accredited Social Health Activists (ASHAs) as front line workers were found to result in the decline of malaria significantly. The ARIMA and Holt’s models projected a continued decline in cases with the potential for reaching zero indigenous cases by 2027–2028. Important factors influencing malaria incidence included tribal population density, literacy rate, health infrastructure, and forested and hard-to-reach areas.
Conclusions
Studies aimed at assessing the impact of major commodity and policy interventions on the incidence of disease and studies of disease forecasting will inform programs and policymakers of steps needed during the last mile phase to achieve malaria elimination. It is proposed that these time series and disease forecasting studies should be performed periodically using granular (monthly) and meteorological data to validate predictions of prior studies and suggest any changes needed for elimination efforts at national and sub-national levels.
MASC: A Dataset for the Development and Classification of Mobile Applications Screens
Authors: ahmed ali , Zaki Alaa , elgeldawi enas , Abdallah Mohamed , girgis moheb
Published: December 2023

Mobile applications have become an integral part of our daily lives, offering a wide range of functionalities and services. Understanding the diversity of mobile application screens is crucial for optimizing user experience and delivering personalized content. This paper presents a novel dataset, calledMASC(Mobile App Screens Classification) consisting of 7065 images, representing various types of mobile apps screens. MASC dataset is collected from the well-known Rico dataset. These screens were carefully manually classified into ten unique classes to capture the diverse nature of app interfaces. By employing advanced feature extraction techniques, we extracted key characteristics from each screenshot image of app screens related to visual elements, text, and keywords. Based on this dataset, this paper presents a proposed framework for applying machine learning algorithms to the classification of mobile apps screens. Using this framework, the paper also presents a comprehensive study of the classification of mobile apps screens using machine learning algorithms. Several classification algorithms including XGBoost, Gradient Boosting, Random Forest, SVM, Logistic Regression, and others were trained and evaluated on MASC. Results showed high accuracy rates above 93% for top models like Gradient Boosting, indicating that machine learning provides an effective approach to mobile app screen classification. This study contributes to the field of mobile application analysis and user interface understanding. In addition, the proposed mobile app screens classification framework is a promising development that can enhance the accuracy and efficiency of mobile app screens classification.
Green synthesis of Russian knapweed aqueous extract iron oxide nanoparticles and their applications for weed management in wheat
Authors: Souha Leyli Nabati , Alebrahim Mohammad Taghi , Feizpoor Solmaz , Afrouz Mehdi , Zangoueinejad Rouzbeh , Kalkhoran Elham Samadi
Published: December 2023

Plant-derived substances are becoming increasingly popular as eco-friendly alternatives to conventional chemical herbicides in crop production. A promising weed control strategy for achieving sustainable agriculture is integrated weed management using bioherbicides, which inhibit the germination and growth of weed seeds. The properties of such bioherbicides have only rarely been studied in vitro. An extract from Russian knapweed (Acroptilon repense L.) is used here to synthesize iron oxide nanoparticles. Biodegradable plant components and other biodegradable waste materials can be utilized in green synthesis to produce nanoparticles. The resulting nanostructures were characterized in terms of magnetic properties, chemical structure, morphology, thermal stability, and crystal structure using a vibrating sample magnetometer (VSM), Fourier-transform infrared spectroscopy (FT-IR), energy dispersive spectroscopy (EDS), transmission electron microscopy (TEM), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and X-ray diffraction (XRD), respectively. Wild mustard, wild oats, and wheat seedlings were exposed to different concentrations of aerial parts extract of Russian knapweed formulated with iron oxide. Germination percentage, seedling length, and dry weight of both the pure and formulated Russian knapweed extract treated seedlings were decreased trend. Reduced germination of wild mustard (zero), wild oats (zero), and wheat (65%) was achieved after treatment with 20% aqueous extract of Russian knapweed formulated as iron oxide nanoparticles. The results suggest that Russian knapweed extracts formulated with iron oxide can be used as a bioherbicide.
Heteroatoms (-B-N-) doped graphene nanoflakes for the sensor mechanism of Carbon and Nitrogen dioxide adsorption: Approach from Theoretical Calculation
Authors: Ashishie Providence , Louis Hitler , Edet Henry , Offiong Offiong E. , Adeyinke Adepapo
Published: December 2023

Density functional theory (DFT) calculations were carried out to study the adsorption of selected greenhouse gases (CO2 and NO2). The graphene doped with Heteroatoms (Boron and Nitrogen) atoms induces a surface character of electron delocalization arising from the sp2 carbon of graphene to oxygen bond which aids high surface specificity. The orbital analysis, such as the density of states as well as frontier molecular orbital, has been studied indicating the kind of interaction (physisorption or chemisorption). It can be seen clearly that electrical conductivity is significantly enhanced by a decrease in energy gap (E.g.) which makes proposed surfaces suitable for the adsorption of CO2 and NO2. A negative value for adsorption energy indicates that the process of adsorption is thermodynamically favoured. The B3LYP and PBE0 functional were employed for a benchmark study on adsorption energies. The adsorption energies (Eads) for the B3LYP functional ranged from -6.42 eV to -20.03 whereas -7.20eV to -30.90eV. the obtained adsorption energies (Eads) forPBE0 functional are more negative than that of the B3LYP functional, which reveals that, PBE0 functional shows better performance in the estimation of such weak interactions. The highest Eads of -30.90 eV was observed for GPQD_B for CO2 adsorption. Thus, CO2 is better adsorbed than NO2 on the studied surface. Non-covalent interactions were observed for interaction between surfaces and probed molecules. The calculated results indicate that the graphene-doped surface is sensitive to CO2 and NO2 gas molecules.
Traditional craniotomy versus current minimally invasive surgery for spontaneous supratentorial intracerebral haemorrhage: a propensity-matched analysis
Authors: Xiao Zhen Kun , Duan Yong Hong , Mao XinYu , Liang RiChu , Zhou Min , Liu Ai-Hua , Yang Yong-Mei
Published: December 2023

Background
Minimally invasive surgery (MIS) and craniotomy are the current treatments for spontaneous supratentorial cerebral haemorrhage (SSTICH). In this study, we aimed to compare the efficacy and safety of MIS and craniotomy for the treatment of SSTICH.
Methods
Clinical and imaging data of 557 consecutive patients with SSTICH who underwent MIS or craniotomy between January 2017 and December 2022 were retrospectively analysed. The patients were divided into two subgroups: the MIS group (MI) and craniotomy group (CI). Propensity score matching was performed to minimise case selection bias. The primary outcome was a dichotomous prognostic (favourable or unfavourable) outcome based on the modified Rankin Scale (mRS) scores at 3 months; an mRS score of 0–2 was considered favourable.
Results
In both conventional statistical and binary logistic regression analyses, the MI group had a better outcome. The outcome of propensity score matching was unexpected (odds ratio = 0.582; 95% confidence interval = 0.281–1.204; P = 0.144), which indicated that after excluding the interference of each confounder, different surgical modalities were more effective, and there was no significant difference in their prognosis.
Conclusions
The decision to choose MIS or craniotomy should be made based on the individual patient, considering the hematoma size, the degree of midline shift, cerebral swelling, and the preoperative Glasgow Coma Scale score.
Climate change mitigation: Which strategies work?
Authors: de Uribarri Pilar Monsalvete Alvarez , Menon Ramanunni , Eicker Ursula
Published: December 2023

Many key studies in recent times have proposed integration of renewable energy and transition to 100% renewable energy as a solution to tackling climate change. However, there is little consensus and a wide range of assumptions made across the various studies. This study attempts to draw the most reliable set of parameters identified and numbers available from reputed sources and use them to provide incisive insights on the impact of Renewable Energy (RE) integration towards climate change mitigation with special attention paid to the speed of implementation, EROI of the sources utilized, associated carbon emissions and the resulting temperature increase. These are studied across pathways suggested within literature and contrast them for pessimistic, neutral and optimistic transformation scenarios. The temporal impacts of the transition over time, from today to 2100 have been elaborated upon. Results across the board show the difficulty in achieving 100% renewable energy integration and their consequent impact on the temperature increases, a large majority of them failing to stop at the oft-quoted 1.5 °C mark. These results underline the difficulty of the task at-hand, especially if decarbonisation and temperature increase goals are to be successfully met.
Fundamental Regions for Non-Isometric Group Actions
Authors: Leistner Thomas , Teisseire Stuart
Published: December 2023

We generalise results about isometric group actions on metric spaces and their fundamental regions to the context of merely continuous group actions. In particular, we obtain results that yield the relative compactness of a funda- mental region for a cocompact group action.
Perinatal anxiety disorders screening study: A study protocol
Authors: Fairbrother Nichole , Stagg Bryn , Scoten Olivia , Keeney Cora , Cargnelli Claudia
Published: December 2023

Background:The anxiety and their related disorders (AD) are the most common of all mental health conditions, and affect approximately 20% of pregnant and postpartum people. They are associated with significant distress and life interference for sufferers, as well as negative consequences for fetal and infant development. At present, little if any routine screening for prenatal AD is being conducted and data regarding the most effective tools to screen for these disorders is lacking. The majority of screening studies suffer from methodological difficulties which undermine the confidence needed to recommend measures for population distribution. The primary purpose of this research is to identify the most accurate self-report tool(s) to screen for perinatal AD.Methods:A large, prospective cohort of pregnant people (N=1,000) is being recruited proportionally across health service delivery regions in British Columbia (BC). The screening accuracy of a broad range of perinatal AD self-report measures are being assessed using gold standard methodology. Consenting individuals are administered online questionnaires followed by a semi-structured diagnostic interview between 16- and 36-weeks’ gestation, and again between 6 and 20 weeks postpartum. Questionnaires include all screening measures, measures of sleep and unpaid family work, and questions pertaining to demographic and reproductive history, COVID-19, gender role burden, and mental health treatment utilization. Interviews assess all current anxiety disorders, as well as obsessive-compulsive disorder, and posttraumatic stress disorder.Discussion:This research is in response to an urgent demand for accurate perinatal AD screening tools based on high quality evidence. AD among perinatal people often go unidentified and untreated, resulting in continued suffering and life impairment. Findings from this research will inform healthcare providers, policymakers, and scientists, about the most effective approach to screening for anxiety and related disorders in pregnancy in the postpartum period.
Medical image compression and restoration using a hybrid neural network combining Huffman and meta-heuristic techniques
Authors: Khalifeh Mohammad Hossein , Taghizadeh Mehdi , Ghanbarian Mohammad Mehdi , Jamali Jasem
Published: December 2023

In order to compress medical pictures for long-term storage, two methods are used in this work. The first step is to use a neural network–based categorization system to simplify images using a hierarchical modeling technique. The Huffman cipher is then used to compress the reduced images. In the second method, a deep neural network is trained to make predictions. This method can potentially reduce the amount of data needed to describe a picture by using a trained neural network to make intelligent guesses about the location of individual pixels. Huffman compression is used to encrypt the remaining data. By using an improved spatial filtering method to the picture data, we can decode it and then use meta-heuristic algorithms like gray wolf optimization (GWO) and wild horse optimization (WHO) to rebuild the image. Without sacrificing data compression efficacy, this paves the way for a more practical implementation of the proposed techniques in cases when outcomes are uncertain. Images can be simplified using the suggested approaches, leading to faster decoding. Afterwards, performance metrics were taken and evaluated following predetermined daily procedures. The suggested approaches outperformed state-of-the-art deep learning-based systems in compressing medical images while maintaining an exceptionally high quality level.
Blackspot Analysis and Treatment measures at Penamaluru and Gosala intersections on NH-65 – A Case Study Vijayawada
Authors: Narra Malathi , Ch Srinivas , Mummaneni Shobana , Ventrapragada Hari Chandana , Kammila Naga Priyanka
Published: December 2023

The complex flow pattern of vehicle traffic and the presence of pedestrians and mixed traffic, the transportation industry is plagued by accidents. Property and lives are lost in traffic accidents. Most road accidents are caused by four interconnected conditions, human behavior, vehicle efficiency, and infrastructure characteristics. The aim of the project is to collect the accident data from the respective police station, analyze the data obtained, identify the blackspot regions, conduct field studies and proposal of respective measures which reduce the accident severity. The analysis of traffic accidents is based on vehicle type, location (urban or rural), age, gender, driver behavior, alcohol consumption, overloading, and other factors. We employ statistical procedures like frequency analysis, clustering with a two-step algorithm, and the ASI to identify the blackspot areas from collected data in accident analysis. The individual field examination is performed. Inventory investigations, traffic studies like volume studies and speed studies, and geometric investigations are all part of the field investigations. Lastly, the analysis and field research lead to the proposal of Highway safety measures to reduce the severity of accidents in blackspot areas by providing physical and non-physical engineering measures there. These measures are intended to improve road traffic safety and smoothness in the near future.
Vi-DiSC: A novel dataset and framework for extracting information from Vietnamese signposts images
Authors: Nguyen Minh Tam , Tran Hoang Anh , Nguyen Truong Thinh , Tran Gia Phu Pham , Quach Co Thai , Do Trong-Hop
Published: December 2023

With the development of autonomous vehicle technologies, a vision-based vehicle guidance system should be able to understand traffic signs, especially directional signposts. Directional signposts are not as specific as the warning and compulsory signs since they include many different variations. More specifically, they have special arrangements, but still follow some principles. It is difficult for captioning models to tell precisely what information the sign road includes. From the above difficulty, we propose to build a framework that simulates the process of reading a human traffic sign, which we named Vi-DiSC. The framework includes a directional sign recognition model, an optical character recognition system in parallel with a directional arrow recognition model, and finally a rule-based model based on the information extracted from the above models. We also built two Deep Learning models for the Image Captioning task to compare the generated results with the rule-based system. Our team also collects, filters, labels, and augments the directional signposts images to create the dataset for the image captioning task. In addition, we also propose TRID, a suitable metric for evaluating the directional signposts image captioning task. The framework is trained and evaluated on our dataset using various metrics, including our proposed metric TRID, and achieved promising resutls. The dataset is available at \url{https://github.com/thinhnt19393/ViDirectionSignpostCaptioning}.
Recognition of trimeric anion-water clusters by peptide-based supramolecular capsules
Authors: Granja Juan , Lopez-Corbalán Victoria , Fuertes Aberto , Llamas-Saiz Antonio , Amorín Manuel
Published: December 2023

The biological and technological importance of anion-mediated processes has made the development of improved methods for the selective recognition of anions one of the most relevant research topics today. The hydration sphere of anions plays an important role in the functions performed by anions by forming a variety of cluster complexes. Here we describe a supramolecular capsule that recognizes new hydrated anion clusters. These clusters are composed of three ions forming hydrated C3 symmetry complexes that are entrapped within the supramolecular capsule of the same symmetry. The capsule is made of self-assembled Acp-based cyclic peptide equipped with a tris(triazolylethyl)amine cap. To recognise the hydrated anion clusters, the hexapeptide capsule must disassemble to entrap them between its two subunits.
Cross-continental shifts of ecological strategy in a global plant invader
Authors: Irimia Ramona E. , Zhao Weihan , Cao Peipei , Parepa Madalin , Liao Zhiyong , Wang Shengyu , Mounger Jeannie M. , Richardson Conner , Elkott Fatima , Zhuang Xin , Bi Jingwen , Zhao Yujie , Kuglar Elodie , Rafalski Julia , Schloter Eva , Wu Jihua , Ju Ruiting , Yang Ji , Li Bo , Bossdorf Oliver , Richards Christina L.
Published: December 2023

Plant invasions are a global problem that requires studying plants and their environmental associations across native and introduced ranges. We surveyed 150 populations of Japanese knotweed, a key invader of the temperate zone, along 2000 km transects in native China and the introduced ranges of Europe and North America. We found that larger plants and denser populations in the introduced ranges were associated with shifts in leaf economy and chemical defenses. Introduced knotweed populations had higher SLA but reduced leaf chlorophyll, lignin, C:N and leaf toughness along with altered leaf tannins, flavonoids and alkaloids. We found three distinct multivariate knotweed phenotypes primarily in the introduced ranges, and two phenotype clusters mainly in native populations. Decreased herbivore and pathogen impacts in invasive populations and changes in environmental associations indicate that enemy release and novel habitat conditions might have driven the emergence of novel ecological strategies in this global plant invader.
Dynamic Modelling of Mixed Crop-Livestock Systems: A Case Study of Climate Change Impacts in sub-Saharan Africa
Authors: Srivast Amit Kumar , Rahimi Jaber , Alsafadi Karam , Vianna Murilo , Enders Andreas , Zheng Wenzhi , Demircan Alparslan , Dieng Mame Diarra Bousso , Salack Seyni , Faye Babacar , Singh Manmeet , Ewert Frank , Gaiser Thomas
Published: December 2023

Climate change significantly challenges smallholder mixed crop-livestock (MCL) systems in sub-Saharan Africa (SSA), affecting food and feed production. This study enhances the SIMPLACE modeling framework by incorporating crop-vegetation-livestock models, which contribute to the development of sustainable agricultural practices in response to climate change. Applying such a framework in a domain in West Africa (786,500 km2) allowed us to estimate the changes in crop (Maize, Millet, and Sorghum) yield, grass biomass, livestock numbers, and greenhouse gas emission in response to future climate scenarios. We demonstrate that this framework accurately estimated the key components of the domain for the past (1981-2005) and enables us to project their future changes using dynamically downscaled Global Circulation Model (GCM) projections (2020-2050). The results demonstrate that in the future, northern part of the study area will experience a significant decline in crop biomass (upto -56%) and grass biomass (upto -57%) production leading to a decrease in livestock numbers (upto -43%). Consequently, this will impact total emissions (upto -47% CH4) and decrease of -41% in milk production, -47% in meat production concentrated in the Sahelian zone. Whereas, in pockets of the Sudanian zone, an increase in livestock population and CH4 emission of about +24% has been estimated.
Dual hollow-core anti-resonant fiber polarization beam splitter with ultra-short length and ultra-wide bandwidth
Authors: Zou Hui , Du Yuhang , Zhou Jingkai , Zhou Dinghao , Zhang Ruizhe
Published: December 2023

Apolarization beam splitter based on a dual hollow-core anti-resonant fiber is proposed. In the horizontal direction, elliptical shapes are introduced to form two hollow cores, with an air gap between the ellipses serving as a mode-coupling channel. Using the finite element method, a specific analysis is conducted on the influence of various structural parameters of the dual hollow-core anti-resonant fiber on the coupling length of the x-polarized and y-polarized modes, as well as the extinction ratio of higher-order modes.Optimizing the structural parameters of the fiber has led to the design of a dual hollow-core anti-resonant fiber polarization beam splitter that is exceptionally compact, measuring just 1.306 cm in length. This device achieves a broad bandwidth of 420 nm ranging from 1.53 µm to 1.95 µm and maintains superior single-mode characteristics within this wavelength spectrum. The novel dual hollow-core anti-resonant fiber is poised to play a significant role in the optical communication systems and optical network technologies.
Seasonal Price Variation Analysis of Palm oil: Case Study of Nigeria (2016-2022)
Authors: Tobi Adewale Elizabeth , Thomas Alabi Ayodeji , Eyitayo Ayinde Opeyemi , Kafayat Belewu Yemisi
Published: December 2023

In order to determine price at the appropriate time of the season, the study looked at the seasonal price variation analysis of palm oil and the relationship between processors and specific buyer types. This was done by understanding buyer requirements, producer effort, measurements taken, and contracting with buyers credit from customers. A multiphase sampling strategy was used. Organized interviews and questionnaires were utilized to collect primary data, while the National Bureau of Statistics provided secondary time series data on the average monthly retail price of palm oil from 2016 to 2022. Among the analysis techniques employed are multiple regression models, descriptive statistics, and time series decomposition. The findings showed that women made up the majority of producers. The majority of respondents stated that growing palm oil was their main source of income, indicating a variety of revenue streams. Over half of the participants derived their funding from their savings. The monthly seasonal price increase of palm oil in Naira per liter was obtained by regressing the price difference at time t (Pt) against trend (T). The need for biofuel, production growth, governmental regulations, market dynamics, and the seasonality of palm oil products are then identified by the study as important factors influencing palm oil prices. Therefore, it is advised that in order to maximize the benefits that farmers receive from processing their palm oil, producers and dealers should be aware of seasonal price variations. This will help them make well-informed decisions about when to buy and sell the product.